diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/ai4bio-schema-check.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/ai4bio-schema-check.yml index a53e3f9..e2880c3 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/ai4bio-schema-check.yml +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/ai4bio-schema-check.yml @@ -2,9 +2,9 @@ title: "Ai4Bio Schema Check" task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/ai4bio-schema-check.yml -upstream_sha: 7a064bf0 -imported_at: 2026-08-08 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/.github/workflows/ai4bio-schema-check.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: unknown upstream_changes: accepted author: upstream @@ -50,6 +50,7 @@ jobs: - name: Build enriched artifacts run: uv run --with pyyaml python scripts/build_resources_v2.py - name: Verify enriched artifacts are committed + if: github.event_name == 'pull_request' run: | if git diff --quiet; then echo "AI4Bio artifacts are in sync." diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/README.md b/upstream/inoue0426-awesome-computational-biology/catalogue/README.md index 94e95c4..69d9c23 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/README.md +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/README.md @@ -2,9 +2,9 @@ title: "Awesome Computational Biology [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)" task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/c6f07d90/README.md -upstream_sha: c6f07d90 -imported_at: 2026-08-31 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/README.md +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -32,6 +32,22 @@ A curated collection of databases, software, and papers related to computational Browse and search the resources via the [GitHub Pages UI](https://inoue0426.github.io/awesome-computational-biology/). +For treatment-response work, the [Dataset Explorer](https://inoue0426.github.io/awesome-computational-biology/dataset-explorer.html) compares curated cell-line, patient, and PDX datasets by year, sample type, perturbation metadata, matched pre/post availability, SMILES coverage, and clinical outcomes. + +The [Method Explorer](https://inoue0426.github.io/awesome-computational-biology/method-explorer.html) compares drug-response and perturbation methods by task, molecular/context representation, unseen-drug support, dose/time conditioning, and patient transfer. + +The [Foundation Model Explorer](https://inoue0426.github.io/awesome-computational-biology/foundation-explorer.html) compares foundation models by fine-grained modality, parameter count, pretraining scale, species, zero-shot support, weights/code availability, perturbation support, and spatial support. + +The [Agent Explorer](https://inoue0426.github.io/awesome-computational-biology/agent-explorer.html) compares agentic AI systems by scientific domain, single- vs multi-agent architecture, tool/code execution, literature and web retrieval, omics and wet-lab support, autonomy, and human-in-the-loop design. The Agent Explorer table is sortable and supports domain, architecture, omics, code-execution, and year filters. + +All explorer tables support sortable columns and focused filters for quick comparison, including a Recent (≥2025) toggle. + +The Pages home screen also summarizes total resources, profiled datasets, methods, foundation models, agents, and the most common foundation-model modalities. + +Chemical and genetic perturbation datasets can be filtered separately in the Dataset Explorer. Genetic screens can also be filtered by CRISPRi, CRISPRa, knockout, enhancer-targeting, combinatorial, or mixed perturbation modes, including mixed-resource collections. + +The explorer also includes the eight perturbation datasets used in the Bison unseen-compound benchmark. + - Search matches `name`, `description`, `tasks`, `modalities`, and `tags`. - The **Task**, **Modality**, and **Type** filters map directly to `tasks`, `modalities`, and `type` in `docs/data/resources.json`. - Clicking badges on cards applies the corresponding filter. @@ -236,44 +252,82 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith ## Benchmarks & Datasets -- [1000 Genomes Project](https://www.internationalgenome.org/) — Reference panel of human genetic variation from 2,504 individuals across 26 populations. -- [BACE](https://www.kaggle.com/datasets/gokturkkoch/bace) — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity. +### Drug Response & Perturbation + - [BEAT AML](https://biodev.github.io/BeatAML2/) — Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia. +- [Cancer Therapeutics Response Portal (CTRP)](https://portals.broadinstitute.org/ctrp/) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds. +- [Chem-PerturBridge](https://github.com/theislab/Chem-PerturBridge) — Harmonized compendium and processing pipelines for small-molecule perturbation transcriptomics across heterogeneous assays and gene panels. +- [GSE191127 Breast Cancer Pre/Post Chemotherapy](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127) — Bulk RNA-seq and genomics from matched pre- and post-neoadjuvant chemotherapy breast tumors with treatment outcomes. +- [Genomics of Drug Sensitivity in Cancer (GDSC)](https://www.cancerrxgene.org/) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds. +- [JUMP Cell Painting Datasets](https://github.com/jump-cellpainting/datasets) — Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research. +- [LINCS L1000](https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data) — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines. +- [LINCS L1000 Phase 1](https://github.com/theislab/Chem-PerturBridge) — Phase-1 L1000 benchmark view used by Bison with 692,787 profiles, 9,233 molecules, 70 contexts, and 978 landmark genes. +- [LINCS L1000 Phase 2](https://github.com/theislab/Chem-PerturBridge) — Phase-2 L1000 benchmark view used by Bison with 333,263 profiles, 1,760 molecules, 30 contexts, and 978 landmark genes. +- [MIX-Seq](https://www.nature.com/articles/s41467-020-17440-w) — Multiplexed single-cell transcriptional profiling of chemical and genetic perturbation responses across pools of cancer cell lines. +- [NCI60](https://dtp.cancer.gov/discovery_development/nci-60/) — Drug sensitivity benchmark across 60 diverse human cancer cell lines. +- [Novartis Perturbation Dataset](https://github.com/theislab/Chem-PerturBridge) — Large chemical perturbation transcriptomic screen used in Chem-PerturBridge; the Bison benchmark view contains 46,748 profiles and 3,770 molecules. +- [OP3](https://github.com/theislab/Chem-PerturBridge) — Chemical perturbation dataset used by Chem-PerturBridge and the Bison unseen-compound benchmark; the Bison benchmark view contains 1,813 profiles, 138 molecules, and 4 cellular contexts. +- [PDX-Atlas](https://muralportal.pdxatlas.org/) — Portal integrating clinical, genomic, transcriptomic, and drug-response data from patient-derived xenograft models. +- [PharmGKB](https://www.pharmgkb.org/) — Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs. +- [PRISM](https://depmap.org/portal/prism/) — Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding. +- [sci-Plex](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944) — Single-cell chemical perturbation screen of ~650,000 transcriptomes across three cancer cell lines, 188 compounds, and four doses. +- [scPerturb](https://github.com/sanderlab/scPerturb) — Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies. +- [Tahoe-100M](https://huggingface.co/datasets/tahoebio/Tahoe-100M) — Giga-scale single-cell perturbation atlas with >100 million profiles from 50 cancer cell lines exposed to ~1,100 small molecules. +- [VCPI-0001](https://github.com/theislab/Chem-PerturBridge) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 27,517 profiles and 2,272 molecules in one cellular context. +- [VCPI-0002](https://github.com/theislab/Chem-PerturBridge) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 18,139 profiles and 1,488 molecules in one cellular context. + +### Genetic Perturbation & Perturb-seq + +- [Dixit et al. 2016 Perturb-seq](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063) — Pooled CRISPR knockout with single-cell RNA-seq across K562 and dendritic-cell screens, including stimulated conditions and combinatorial perturbations. +- [Adamson et al. 2016 Perturb-seq](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546) — CRISPRi Perturb-seq in K562 cells for systematic dissection of the unfolded protein response. +- [Datlinger et al. 2017 CROP-seq](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872) — Pooled CRISPR knockout screen with single-cell transcriptomic readout in Jurkat cells under T-cell receptor stimulation. +- [Norman et al. 2019 CRISPRa Perturb-seq](https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323) — Large-scale CRISPR activation Perturb-seq in K562 cells spanning single-gene and combinatorial perturbations. +- [Gasperini et al. 2019 CRISPRi enhancer screen](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861) — High-MOI single-cell CRISPRi screen in K562 cells targeting thousands of candidate enhancers to map enhancer-gene regulation. +- [Replogle et al. 2022 K562 Genome-wide Perturb-seq](https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387) — Genome-scale CRISPRi Perturb-seq targeting nearly all expressed genes in K562 cells. +- [Replogle et al. 2022 K562 Essential Perturb-seq](https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387) — CRISPRi Perturb-seq focused on essential genes in K562 cells. +- [Replogle et al. 2022 RPE1 Essential Perturb-seq](https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387) — CRISPRi Perturb-seq focused on essential genes in RPE1 cells. +- [Frangieh et al. 2021 Perturb-CITE-seq](https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion) — Multimodal CRISPR knockout screen with RNA and surface-protein readouts in patient-derived melanoma models under immune-related conditions. + +### Single-Cell & Spatial + +- [HEST Xenium virtual spatial transcriptomics](https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics) — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1). +- [scIB (Single-cell Integration Benchmarks)](https://github.com/theislab/scib) — Comprehensive benchmarking framework for single-cell data integration methods. +- [Tabula Muris](https://tabula-muris.ds.czbiohub.org/) — Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons. +- [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors. +- [TCGA virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas) — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1). + +### Molecular, Protein & Drug Discovery + +- [BACE](https://www.kaggle.com/datasets/gokturkkoch/bace) — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity. - [Bento](https://github.com/LigandPro/Bento) — Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks. - [BindingDB Curated Sets](https://www.bindingdb.org/rwd/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes) — Curated binding affinity datasets for protein–ligand interaction benchmarking. -- [Cancer Therapeutics Response Portal (CTRP)](https://portals.broadinstitute.org/ctrp/) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds. - [ClinTox](https://tdcommons.ai/single_pred_tasks/tox/#clintox) — Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity. -- [CPTAC (Clinical Proteomic Tumor Analysis Consortium)](https://proteomics.cancer.gov/programs/cptac) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics. - [CrossDocked2020](https://arxiv.org/abs/2001.01037) — Large-scale dataset for structure-based virtual screening. - [DUD-E (Directory of Useful Decoys, Enhanced)](http://dude.docking.org/) — Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets. - [FLIP (Fitness Landscape Inference for Proteins)](https://github.com/J-SNACKKB/FLIP) — Benchmark collection of protein fitness landscape datasets for evaluating protein ML models. -- [Genomics of Drug Sensitivity in Cancer (GDSC)](https://www.cancerrxgene.org/) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds. - [GuacaMol](https://github.com/BenevolentAI/guacamol) — Benchmark suite for generative molecular design models. -- [JUMP Cell Painting Datasets](https://github.com/jump-cellpainting/datasets) — Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research. -- [LINCS L1000](https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data) — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines. - [MoleculeNet](http://moleculenet.ai/) — Benchmark datasets for molecular machine learning. - [MOSES](https://github.com/molecularsets/moses) — Benchmarking platform for molecular generation models. -- [NCI60](https://dtp.cancer.gov/discovery_development/nci-60/) — Drug sensitivity benchmark across 60 diverse human cancer cell lines. -- [OGB (Open Graph Benchmark)](https://ogb.stanford.edu/) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv. -- [OpenBioLink](https://github.com/OpenBioLink/OpenBioLink) — Benchmark datasets for biological knowledge graph completion. -- [PharmGKB](https://www.pharmgkb.org/) — Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs. - [PK-DB](https://pk-db.com/) — Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies. -- [PRISM](https://depmap.org/portal/prism/) — Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding. - [ProteinGym](https://github.com/OATML-Markslab/ProteinGym) — Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models. - [QM9](https://figshare.com/collections/Quantum_chemistry_structures_and_properties_of_134_kilo_molecules/978904) — Quantum chemistry properties for 134K stable small organic molecules computed at DFT level. -- [scIB (Single-cell Integration Benchmarks)](https://github.com/theislab/scib) — Comprehensive benchmarking framework for single-cell data integration methods. -- [scPerturb](https://github.com/sanderlab/scPerturb) — Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies. - [SIDER (Side Effect Resource)](http://sideeffects.embl.de/) — Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes. -- [Tabula Muris](https://tabula-muris.ds.czbiohub.org/) — Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons. -- [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors. - [TAPE (Tasks Assessing Protein Embeddings)](https://github.com/songlab-cal/tape) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations. -- [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients. -- [TCGA virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas) — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1). -- [HEST Xenium virtual spatial transcriptomics](https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics) — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1). - [Therapeutics Data Commons (TDC)](https://tdcommons.ai/) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more. - [Tox21](https://tripod.nih.gov/tox21/challenge/) — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction. + +### Genomics, Cancer & Biomedical Cohorts + +- [1000 Genomes Project](https://www.internationalgenome.org/) — Reference panel of human genetic variation from 2,504 individuals across 26 populations. +- [CPTAC (Clinical Proteomic Tumor Analysis Consortium)](https://proteomics.cancer.gov/programs/cptac) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics. +- [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients. - [UK Biobank](https://www.ukbiobank.ac.uk/) — Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies. +### General Biological ML & Knowledge Graph Benchmarks + +- [OGB (Open Graph Benchmark)](https://ogb.stanford.edu/) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv. +- [OpenBioLink](https://github.com/OpenBioLink/OpenBioLink) — Benchmark datasets for biological knowledge graph completion. + --- ## API @@ -344,6 +398,11 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [TGSA](https://github.com/violet-sto/TGSA) — Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction. - [HiDRA](https://github.com/bsml320/HiDRA) — Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction. - [DRUML](https://github.com/CutillasLab/DRUMLR) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data. +- [PASO](https://github.com/queryang/PASO) — Pathway-aware multi-omics drug response model combining pathway-difference features, multi-scale convolutions, Transformer encoding, and drug SMILES. +- [DTLCDR](https://doi.org/10.1016/j.jpha.2025.101315) — Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization. +- [THERAPI](https://github.com/Sunginyoung/THERAPI) — Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction. +- [EXPRESSO](https://doi.org/10.1158/0008-5472.CAN-25-5220) — Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies. +- [PerturbRx](https://arxiv.org/abs/2608.21349) — Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction. #### Drug Perturbation @@ -352,6 +411,16 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [chemCPA](https://github.com/theislab/chemCPA) — Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations. - [cycleCDR](https://github.com/hliulab/cycleCDR) — Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations. - [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery. +- [Bison](https://arxiv.org/abs/2609.32467) — Cross-dataset model for globally unseen-compound response prediction using a shared gene representation, discrete diffusion models, and matched drug-contrast supervision. +- [biolord](https://github.com/nitzanlab/biolord) — Deep generative model that disentangles known and unknown attributes for conditional generation of single-cell states. +- [PerturbNet](https://github.com/welch-lab/PerturbNet) — Conditional generative model for predicting distributions of single-cell states under unseen chemical and genetic perturbations. +- [CellFlow](https://github.com/theislab/CellFlow) — Conditional flow-matching framework for modeling and predicting cellular phenotypes under chemical, genetic, and other perturbations. +- [Prophet](https://github.com/theislab/prophet) — Transformer model for predicting cellular phenotypes under unseen chemical or genetic perturbations across heterogeneous assays and contexts. +- [PrePR-CT](https://github.com/reem12345/Cell-Type-Specific-Graphs) — Graph-based model using cell-type-specific co-expression networks as inductive priors for small-data chemical perturbation response prediction. +- [XPert](https://github.com/GSanShui/XPert) — Knowledge-informed dual-branch Transformer for drug-induced transcriptional perturbation prediction across dose, time, and cellular context. +- [State](https://github.com/ArcInstitute/state) — Transition model for predicting cellular perturbation responses across diverse contexts and sets of cells. +- [TxPert](https://github.com/valence-labs/TxPert) — Knowledge-graph-informed latent-transfer model for transcriptomic perturbation prediction across unseen single perturbations, combinations, and cross-context settings. +- [LPM](https://github.com/perturblib/perturblib) — Large perturbation model that jointly learns heterogeneous perturbation experiments by disentangling perturbation, readout, and context representations. #### Drug Repurposing @@ -396,11 +465,45 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [GeneGPT](https://github.com/ncbi/GeneGPT) — LLM for biomedical information, integrated with various APIs. - [GenePT](https://github.com/yiqunchen/GenePT) — Foundation LLM for single-cell data. - [scPRINT](https://github.com/cantinilab/scPRINT) — Pretrained on 50M cells for scRNA-seq denoising & zero imputation. -- [ClawBio](https://github.com/ClawBio/ClawBio) — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills. - [BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM) — 2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical question answering and text generation. - [MolT5](https://github.com/blender-nlp/MolT5) — Language model for molecular tasks bridging text and SMILES, enabling molecule captioning and text-driven molecule generation. - [ChatDrug](https://github.com/chao1224/ChatDrug) — LLM-based conversational pipeline for drug discovery, using natural language prompts for iterative drug editing and optimization. -- [CASSIA](https://github.com/ElliotXie/CASSIA) — Multi-agent LLM for reference-free, interpretable cell-type annotation of single-cell RNA-seq data, with dedicated annotation, validation, scoring, and reporting agents. + +### Agentic AI for Biology + +#### General Biomedical Agents + +- [Biomni](https://github.com/snap-stanford/Biomni) — General-purpose biomedical AI agent integrating planning, code execution, specialized tools, databases, and software across diverse biomedical research tasks. +- [ToolUniverse](https://github.com/mims-harvard/ToolUniverse) — Unified scientific tool ecosystem for building AI scientists that can discover, select, and execute biomedical tools and databases. +- [ClawBio](https://github.com/ClawBio/ClawBio) — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills. +- [BioMedAgent](https://github.com/BOBQWERA/BioMedAgent) — Self-evolving multi-agent framework for autonomous biomedical data analysis with tool discovery, workflow planning, code generation, execution, correction, and cross-omics analysis. +- [BioMaster](https://github.com/ai4nucleome/BioMaster) — Multi-agent system for automated and auditable bioinformatics workflows spanning RNA-seq, ChIP-seq, single-cell, spatial omics, Hi-C, long reads, metagenomics, and proteomics. +- [BRAD](https://github.com/Jpickard1/BRAD) — Retrieval-augmented bioinformatics assistant integrating scientific literature, databases, external tools, and executable workflows. + +#### Therapeutics & Drug Discovery Agents + +- [TxAgent](https://github.com/mims-harvard/TxAgent) — Therapeutic reasoning agent using multi-step reasoning and a large scientific tool universe for drug interactions, contraindications, and personalized treatment analysis. +- [Medea](https://github.com/mims-harvard/Medea) — Multi-agent therapeutic discovery system combining research planning, biological data analysis, literature reasoning, and multi-LLM deliberation across single-cell, cell-line, and patient contexts. +- [DrugAgent](https://github.com/inoue0426/DrugAgent) — Multi-agent biomedical evidence synthesis framework for computational drug discovery with reliability-aware aggregation. + +#### Bioinformatics & Omics Agents + +- [CASSIA](https://github.com/ElliotXie/CASSIA) — Multi-agent LLM framework for reference-free and interpretable single-cell cell-type annotation with dedicated annotation, validation, scoring, and reporting agents. +- [STELLA](https://github.com/zaixizhang/STELLA) — Self-evolving biomedical research agent that expands its tool repertoire and supports literature reasoning, computational analysis, and laboratory-oriented scientific workflows. +- [AutoBA](https://github.com/JoshuaChou2018/AutoBA) — Automated multi-omics analysis agent that plans, executes, and repairs bioinformatics workflows from natural-language objectives. +- [GenoMAS](https://github.com/Liu-Hy/GenoMAS) — Multi-agent framework for code-driven gene-expression analysis with planning, execution, debugging, backtracking, and GEO/TCGA-based scientific discovery. +- [BIA](https://github.com/biagent-dev/bia) — Bioinformatics agent for GEO search, sample metadata extraction, count-matrix processing, and pipeline extraction from papers. +- [BioAgents](https://github.com/microsoft/bioinformagus) — Multi-agent bioinformatics assistant using specialized language models and retrieval for genomics workflow development and troubleshooting. +- [Genomi](https://github.com/exon-research/genomi) — Local-first genomics agent runtime that indexes personal variants, queries evidence, and generates evidence-grounded reports while keeping raw genome data on-device. + +#### Multi-Agent Scientific Labs + +- [Virtual Lab](https://github.com/zou-group/virtual-lab) — Human–AI collaborative research environment in which an LLM principal investigator coordinates specialized scientist agents for scientific discovery. +- [Agent Laboratory](https://github.com/SamuelSchmidgall/AgentLaboratory) — End-to-end multi-agent research workflow for literature review, experimentation, implementation, analysis, and report generation. + +#### Paper & Workflow Agents + +- [Paper2Agent](https://github.com/jmiao24/Paper2Agent) — Multi-agent system that transforms research papers and associated code into interactive, testable scientific agents and MCP tools. ### Foundation Models @@ -418,6 +521,8 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [GEARS](https://github.com/snap-stanford/GEARS) — Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using biological priors. - [SATURN](https://github.com/snap-stanford/SATURN) — Transformer-based model integrating gene expression and protein sequences via a protein language model to learn unified multi-species cell embeddings. - [CancerFoundation](https://github.com/BoevaLab/CancerFoundation) — Single-cell RNA-seq foundation model trained exclusively on a curated dataset of malignant cells to learn cancer-specific embeddings. +- [CellFM](https://github.com/biomed-AI/CellFM) — 800M-parameter single-cell foundation model pretrained on transcriptomics from 100 million human cells for annotation, integration, gene-function, and perturbation tasks. +- [scPRINT-2](https://github.com/cantinilab/scPRINT-2) — Next-generation single-cell foundation model pretrained on 350M+ cells across 22K+ datasets and 16 species for embeddings, denoising, annotation, gene-network inference, and cross-species transfer. ##### Spatial Foundation Models @@ -432,6 +537,16 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [DeepSpot-M](https://github.com/ratschlab/DeepSpotM) — Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology. - [AESTETIK](https://github.com/ratschlab/aestetik) — Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge. +##### Pathology Foundation Models + +- [TITAN](https://github.com/mahmoodlab/TITAN) — Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis. +- [Virchow2](https://huggingface.co/paige-ai/Virchow2) — 632M-parameter pathology vision transformer pretrained on 3.1M whole-slide images with mixed-magnification self-supervision. +- [H-Optimus-0](https://huggingface.co/bioptimus/H-optimus-0) — 1.1B-parameter histopathology foundation model trained with self-supervised learning on a large multi-center slide corpus. +- [H-Optimus-1](https://huggingface.co/bioptimus/H-optimus-1) — 1.1B-parameter pathology foundation model trained on billions of histology images from more than one million slides and 800K+ patients. +- [UNI2-h](https://huggingface.co/MahmoodLab/UNI2-h) — Billion-parameter histopathology vision foundation model for tile-level feature extraction and downstream computational pathology tasks. +- [Phikon-v2](https://huggingface.co/owkin/phikon-v2) — Updated pathology foundation model for general-purpose histology feature extraction and transfer learning. +- [GenBio-PathFM](https://github.com/genbio-ai/genbio-pathfm) — 1.1B-parameter histopathology foundation model trained on public data using morphology-aware curation and dual-stage JEPA+DINO learning. + ##### Multi-Omics Foundation Models - [scMulan](https://github.com/SuperBianC/scMulan) — Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics for cross-omics transfer tasks. @@ -465,6 +580,8 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [Mol2Vec](https://github.com/samoturk/mol2vec) — Unsupervised molecular embedding method inspired by Word2Vec for learning vector representations of chemical substructures. - [MolFormer](https://github.com/IBM/molformer) — Linear attention transformer pretrained on millions of SMILES strings for efficient molecular embeddings. - [Uni-Mol](https://github.com/deepmodeling/Uni-Mol) — 3D molecular pretraining framework for universal representation learning on molecules and protein pockets. +- [Uni-Mol2](https://github.com/deepmodeling/Uni-Mol/tree/main/unimol2) — Scaled molecular pretraining model using atomic, graph, and 3D geometry features, with models up to 1.1B parameters pretrained on 800M conformations. +- [ChemFM](https://github.com/TheLuoFengLab/ChemFM) — 1B/3B-parameter chemical language model pretrained on 178M molecules for molecular representation, property prediction, generation, and synthesis tasks. #### Protein Foundation Models @@ -474,11 +591,14 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [ProtTrans](https://github.com/agemagician/ProtTrans) — Suite of protein language models (ProtBERT, ProtT5, ProtXLNet) trained on billions of protein sequences from UniRef and BFD. - [ProGen2](https://github.com/salesforce/progen) — Protein language model trained on diverse protein families for sequence generation and fitness prediction. - [Ankh](https://github.com/agemagician/Ankh) — Efficient protein language model optimized for downstream prediction tasks including secondary structure, localization, and function annotation. +- [ESM Cambrian (ESM C)](https://github.com/Biohub/esm) — Protein representation foundation-model family designed as an efficient next-generation successor to ESM2, spanning 300M to multi-billion-parameter models. ##### Protein Structure Prediction and Design - [AlphaFold3](https://github.com/google-deepmind/alphafold3) — Predicts structures of proteins, nucleic acids, small molecules, and their complexes. - [Boltz-1](https://github.com/jwohlwend/boltz) — Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their complexes achieving AlphaFold3-level accuracy. +- [Boltz-2](https://github.com/jwohlwend/boltz) — Biomolecular foundation model jointly predicting complex structures and binding affinities for protein–ligand interaction modeling and virtual screening. +- [Protenix](https://github.com/bytedance/Protenix) — Trainable biomolecular structure-prediction framework for proteins, nucleic acids, ligands, and complexes with open training and inference pipelines. - [Chai-1](https://github.com/chaidiscovery/chai-lab) — Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes. - [ESM3](https://github.com/evolutionaryscale/esm) — Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering. - [ESMFold](https://github.com/facebookresearch/esm) — Fast protein structure prediction using language model embeddings. @@ -501,6 +621,36 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [PLIP](https://github.com/PathologyFoundation/plip) — Vision-language foundation model for pathology trained with contrastive learning on pathology image–text pairs for image classification and text-to-image retrieval. - [MUSK](https://github.com/lilab-stanford/MUSK) — Vision-language foundation model for precision oncology analyzing multimodal paired text and pathology image data for biomarker prediction and retrieval. +#### Cancer Genome Foundation Models + +- [TESSERA](https://github.com/JW-Sidhom-Lab/tessera) — Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning. +- [MutationProjector](https://doi.org/10.1101/2025.09.08.674723) — Pan-cancer genotype foundation model trained on mutations and copy-number alterations from >30K tumors for clinical representation learning. + +#### Single-Cell Epigenomics Foundation Models + +- [EpiAgent](https://github.com/xy-chen16/EpiAgent) — scATAC-seq foundation model pretrained on ~5M cells and >35B tokens for representation learning, annotation, imputation, perturbation prediction, and in-silico cCRE knockout. +- [SCARF](https://doi.org/10.1101/2025.04.07.647689) — Single-cell RNA+ATAC foundation model pretrained on >2.7M cells for multimodal representation, matching, cross-omics translation, and few-shot annotation. +- [scDNAm-GPT](https://github.com/ChaoqiLiang/scDNAm-GPM) — Foundation model for single-cell whole-genome bisulfite sequencing with whole-genome context modeling at single-CpG resolution. +- [EpiFoundation](https://doi.org/10.1101/2025.02.05.636688) — Foundation model for scATAC-seq using peak-to-gene aligned pretraining for cell representation, annotation, batch correction, and gene-expression prediction. +- [ChromFound](https://github.com/SAIS-LifeScience/ChromFound) — Genome-aware scATAC-seq foundation model pretrained on 1.97M cells across tissues and disease contexts for zero-shot cell representations, annotation, and cross-omics prediction. +- [Atacformer](https://doi.org/10.1101/2025.11.03.685753) — Transformer foundation model for scATAC-seq that learns embeddings of cis-regulatory elements for clustering, annotation, and reference mapping. +- [CLM-X](https://doi.org/10.64898/2026.02.17.704943) — Multi-way Transformer foundation model jointly handling RNA-only, ATAC-only, and paired RNA–ATAC single-cell inputs for integration, translation, annotation, and perturbation prediction. + +#### Other Omics Foundation Models + +- [CAPTAIN](https://doi.org/10.1038/s41467-026-72882-y) — Multimodal foundation model pretrained on co-assayed single-cell RNA and protein for joint representation learning and cross-modal downstream tasks. +- [HiCFoundation](https://doi.org/10.1038/s41592-026-03097-8) — Hi-C foundation model pretrained on large-scale chromatin-contact maps for 3D-genome analysis, epigenomic prediction, and single-cell Hi-C adaptation. +- [OmicsFM](https://github.com/CompOmics/OmicsFM) — Modality-agnostic molecular-expression foundation model with matched proteomics, bulk-transcriptomics, and single-cell-transcriptomics checkpoints. +- [VirTues](https://github.com/bunnelab/virtues) — Spatial-proteomics foundation model learning marker-aware representations across proteins, cells, niches, and tissues from multiplexed imaging. +- [MethylGPT](https://github.com/albert-ying/MethylGPT) — Transformer foundation model for DNA methylation pretrained on >150K human methylomes across thousands of datasets, with 3M/7M/15M parameter variants. +- [CpGPT](https://github.com/lucascamillomd/CpGPT) — DNA methylation foundation model pretrained on >150K samples for zero-shot imputation, array conversion, reference mapping, and downstream phenotype prediction. +- [Casanovo Foundation](https://github.com/Noble-Lab/casanovo-tl) — Tandem mass-spectrometry proteomics foundation model that reuses a pretrained Casanovo spectrum encoder for spectrum quality, chimericity, and post-translational-modification prediction. + +#### RNA Foundation Models + +- [RNA-FM](https://github.com/ml4bio/RNA-FM) — General-purpose RNA foundation model pretrained on large-scale RNA sequences for structural and functional representation learning. +- [RiNALMo](https://github.com/lbcb-sci/RiNALMo) — RNA language-model family pretrained on tens of millions of RNA sequences for secondary-structure and functional prediction tasks. + #### Genomics Foundation Models - [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer) — Foundation model for genomic sequences across multiple species. @@ -510,6 +660,9 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith - [Basenji](https://github.com/calico/basenji) — Sequential regulatory activity prediction from DNA sequences. - [Caduceus](https://github.com/kuleshov-group/caduceus) — Bidirectional equivariant long-range DNA sequence model based on Mamba. - [Evo](https://github.com/evo-design/evo) — Long-context genomic foundation model (up to 1M tokens). +- [Evo 2](https://github.com/arcinstitute/evo2) — Genome foundation model trained on 9 trillion DNA base pairs across all domains of life with a 1M-token context window and single-nucleotide resolution. +- [AlphaGenome](https://github.com/google-deepmind/alphagenome) — Long-context DNA model predicting multimodal regulatory outputs including expression, splicing, chromatin features, and contact maps at near base-pair resolution. +- [modernGENA](https://github.com/AIRI-Institute/GENA_LM) — ModernBERT-style DNA foundation-model family pretrained on hundreds of vertebrate genome assemblies for efficient long-sequence regulatory modeling. - [HyenaDNA](https://github.com/HazyResearch/hyena-dna) — Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention. - [Borzoi](https://github.com/calico/borzoi) — Extended successor to Enformer for predicting RNA-seq coverage from long genomic sequence windows (524 kb) with improved resolution. - [DeepSEA](http://deepsea.princeton.edu/) — Deep learning framework for predicting chromatin effects of sequence alterations with single-nucleotide sensitivity across thousands of chromatin features. diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/cspell.json b/upstream/inoue0426-awesome-computational-biology/catalogue/cspell.json index fc803bd..b37f980 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/cspell.json +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/cspell.json @@ -2,9 +2,9 @@ title: "Cspell" task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/c6f07d90/cspell.json -upstream_sha: c6f07d90 -imported_at: 2026-08-31 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/cspell.json +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: unknown upstream_changes: accepted author: upstream @@ -15,166 +15,249 @@ validated: false "language": "en", "allowCompoundWords": true, "words": [ - "behavioural", - "KEGG", - "NCBI", - "UCSC", - "EMBL", - "RCSB", - "CASP", - "Uniclust", - "Reactome", - "Bioactive", - "biopolymers", - "proteomics", - "transcriptomics", - "metagenomic", - "metatranscriptomic", - "CRISPR", - "JASPAR", - "druggable", - "Toxicogenomics", - "GDSC", - "biomolecular", - "DRKG", - "Hetionet", - "Eudra", - "esearch", - "efetch", - "Ensembl", - "Cheminformatics", - "Deconv", - "Scanpy", - "Squidpy", - "explainability", - "MOFGCN", - "Autoencoder", - "DGDRP", - "MCPINN", - "Pretrained", - "pretrained", - "denoising", - "transcriptomic", - "CELLxGENE", - "eukaryotic", - "metabolites", - "OMIM", - "OncoKB", - "Mendelian", - "DisGeNET", - "GWAS", - "IntAct", - "Biopython", - "MDAnalysis", - "trajectories", - "Geneformer", - "equivariant", - "HyenaDNA", - "Hyena", - "Caduceus", - "Mamba", - "retrosynthesis", - "TargetDiff", - "Chai", - "Zuckerberg", - "HMDB", - "CTRP", + "Adamson", + "ADME", "ADMET", - "Omics", - "omics", - "omic", - "OADB", + "AESTETIK", + "AlphaGenome", + "Ankh", + "ATAC", + "Atacformer", + "AutoBA", + "Autoencoder", + "BACE", + "behavioural", + "Bento", + "Bioactive", + "BioAgents", + "biolord", + "BioMaster", + "BioMedAgent", + "Biomni", + "biomolecular", + "biopolymers", + "Biopython", + "Bison", + "Boltz", + "bowang", + "Caduceus", + "Cambrian", + "Casanovo", + "CASP", + "CASSIA", + "CellFlow", + "CellFM", + "CellOT", + "CELLxGENE", + "Chai", + "ChatDrug", + "Chem", + "ChemFM", + "Cheminformatics", + "chimericity", + "Chrom", + "ChromFound", + "CITE-seq", + "ClawBio", + "Clin", + "CMonge", + "convolutional", + "CpGPT", + "CPTAC", + "CRISPR", + "CRISPRa", + "CRISPRi", + "CROP", + "ctheodoris", + "CTRP", + "Datlinger", + "Deconv", + "deconvolution", + "DeepSEA", + "denoising", + "DGDRP", + "DisGeNET", + "Dixit", + "DoubletFinder", + "DRKG", + "druggable", + "DRUML", + "DTLCDR", + "efetch", + "EMBL", + "Enformer", + "Ensembl", + "EpiFoundation", + "epigenome", + "epigenomic", + "epigenomics", + "equivariant", + "esearch", + "Eudra", + "eukaryotic", + "explainability", + "EXPRESSO", + "FANTOM", + "FCFP4", + "FFPE", + "finetunable", + "Frangieh", + "Gasperini", + "GDSC", + "GenBio", + "Geneformer", + "Geno", + "GenoMAS", + "Genomi", + "Giga", "Gnify", "gnom", - "Rfam", + "GPN", + "GPT", "Guaca", - "deconvolution", - "scvi", - "pharmacogenomics", - "nutrigenomics", - "Giga", - "Phikon", - "TCGA", - "Mulan", - "epigenomics", - "methylation", - "ATAC", - "MOFA", - "TOSICA", - "Boltz", - "MPNN", - "Enformer", - "Velo", - "BACE", - "secretase", - "Clin", - "CPTAC", - "Proteomic", - "proteogenomic", - "LINCS", - "ogbl", - "ogbg", - "SIDER", - "Muris", - "Pfam", - "PROSITE", - "epigenome", - "TRRUST", - "kallisto", - "pseudoalignment", - "multiplet", - "TGSA", + "GWAS", + "H-Optimus", + "Haplotype", + "Hetionet", + "HiDRA", + "HMDB", + "Hyena", + "HyenaDNA", + "Imageomics", + "IntAct", + "JASPAR", + "JEPA", "JTVAE", + "Jurkat", + "kallisto", + "KEGG", + "KIBA", + "L1000", + "ligand", + "LINCS", + "Mamba", + "MCPINN", + "MDAnalysis", + "Medea", + "Mendelian", + "metabolites", + "metagenomic", + "metatranscriptomic", + "methylation", + "MethylGPT", + "methylomes", "miRBase", "miRNA", - "ProtTrans", - "ProtBERT", - "ProGen", - "Ankh", - "DeepSEA", - "RegNetwork", - "ROADMAP", - "FANTOM", - "NeXtProt", - "HiDRA", - "MolT", - "ChatDrug", - "DoubletFinder", - "pseudotime", - "ligand", - "SCENIC", - "GPN", - "Sei", - "KIBA", - "pharmacokinetics", - "ADME", - "Haplotype", - "NCEM", - "multiome", + "modernGENA", + "MOFA", + "MOFGCN", "MOGONET", - "convolutional", - "DRUML", - "SBDD", - "Pacc", + "MolT", + "Morgan", + "MPNN", + "Mulan", + "multiome", "multiomics", - "Pathomic", - "PLIP", - "Omni", - "Bento", - "FFPE", - "Xenium", - "Zyme", - "Neur", - "Imageomics", - "AESTETIK", - "CellOT", - "CMonge", - "bowang", - "ctheodoris", - "OpenAI", - "GPT", + "multiplet", + "Muris", + "MutationProjector", "nanobody", - "thermostability" + "NCBI", + "NCEM", + "Neur", + "NeXtProt", + "Novartis", + "nutrigenomics", + "OADB", + "ogbg", + "ogbl", + "omic", + "omics", + "Omics", + "OmicsFM", + "OMIM", + "Omni", + "OncoKB", + "OP3", + "OpenAI", + "Pacc", + "Paper2Agent", + "Pathomic", + "Perturb", + "Perturb-seq", + "PerturbNet", + "PerturBridge", + "PerturbRx", + "Pfam", + "pharmacogenomics", + "pharmacokinetics", + "Phikon", + "PLIP", + "PrePR", + "pretrained", + "Pretrained", + "ProGen", + "PROSITE", + "ProtBERT", + "Protenix", + "proteogenomic", + "Proteomic", + "proteomics", + "ProtTrans", + "pseudoalignment", + "pseudobulk", + "pseudotime", + "RCSB", + "RDKit", + "Reactome", + "RegNetwork", + "Replogle", + "retrosynthesis", + "Rfam", + "RiNALMo", + "ROADMAP", + "SBDD", + "Scanpy", + "SCENIC", + "SciPlex", + "scPRINT", + "scrna", + "scvi", + "secretase", + "Sei", + "SIDER", + "silico", + "Squidpy", + "State", + "STELLA", + "TargetDiff", + "TCGA", + "TGSA", + "THERAPI", + "thermostability", + "TITAN", + "ToolUniverse", + "TOSICA", + "Toxicogenomics", + "trajectories", + "transcriptomic", + "transcriptomics", + "TRRUST", + "TxAgent", + "TxPert", + "UCSC", + "Uni-Mol2", + "UNI2", + "Uniclust", + "UniMol", + "VCPI", + "Velo", + "Virchow2", + "VirTues", + "Xenium", + "XPert", + "Zuckerberg", + "Zyme" ], "ignorePaths": [ "node_modules/**" diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.agent-explorer-v1.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.agent-explorer-v1.yml new file mode 100644 index 0000000..ec171dc --- /dev/null +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.agent-explorer-v1.yml @@ -0,0 +1,298 @@ +--- +title: "Agent Explorer v1 metadata." +task: "" +lineage_type: import +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.agent-explorer-v1.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +# Agent Explorer v1 metadata. +resources: + biomni: + agent_profile: + year: 2025 + domains: [general-biomedicine, bioinformatics, therapeutics, omics] + architecture: single-agent + tool_use: true + code_execution: true + web_retrieval: true + literature: true + omics: true + wet_lab: limited + autonomous_experiment: limited + human_in_loop: true + open_source: true + + tooluniverse: + agent_profile: + year: 2025 + domains: [general-biomedicine, tool-ecosystem, therapeutics, bioinformatics] + architecture: agent-ecosystem + tool_use: true + code_execution: limited + web_retrieval: true + literature: true + omics: true + wet_lab: limited + autonomous_experiment: limited + human_in_loop: true + open_source: true + + clawbio: + agent_profile: + year: 2026 + domains: [bioinformatics, genomics, pharmacogenomics, omics] + architecture: agent-ecosystem + tool_use: true + code_execution: true + web_retrieval: true + literature: limited + omics: true + wet_lab: false + autonomous_experiment: false + human_in_loop: true + open_source: true + + txagent: + agent_profile: + year: 2025 + domains: [therapeutics, drug-discovery, precision-medicine] + architecture: single-agent + tool_use: true + code_execution: limited + web_retrieval: true + literature: true + omics: limited + wet_lab: false + autonomous_experiment: limited + human_in_loop: true + open_source: true + + medea: + agent_profile: + year: 2025 + domains: [therapeutics, drug-discovery, single-cell, omics] + architecture: multi-agent + tool_use: true + code_execution: true + web_retrieval: true + literature: true + omics: true + wet_lab: limited + autonomous_experiment: limited + human_in_loop: true + open_source: true + + drugagent: + agent_profile: + year: 2026 + domains: [drug-discovery, therapeutics, evidence-synthesis] + architecture: multi-agent + tool_use: true + code_execution: limited + web_retrieval: true + literature: true + omics: limited + wet_lab: false + autonomous_experiment: limited + human_in_loop: true + open_source: true + + cassia: + agent_profile: + year: 2024 + domains: [single-cell, bioinformatics, omics] + architecture: multi-agent + tool_use: limited + code_execution: limited + web_retrieval: limited + literature: true + omics: true + wet_lab: false + autonomous_experiment: false + human_in_loop: true + open_source: true + + stella: + agent_profile: + year: 2025 + domains: [general-biomedicine, bioinformatics, scientific-workflow] + architecture: single-agent + tool_use: true + code_execution: true + web_retrieval: true + literature: true + omics: limited + wet_lab: limited + autonomous_experiment: true + human_in_loop: true + open_source: true + + virtual_lab: + agent_profile: + year: 2024 + domains: [scientific-workflow, protein-design, experimental-design] + architecture: multi-agent + tool_use: true + code_execution: true + web_retrieval: limited + literature: true + omics: limited + wet_lab: true + autonomous_experiment: limited + human_in_loop: true + open_source: true + + agent_laboratory: + agent_profile: + year: 2025 + domains: [scientific-workflow, research-automation] + architecture: multi-agent + tool_use: true + code_execution: true + web_retrieval: true + literature: true + omics: false + wet_lab: false + autonomous_experiment: true + human_in_loop: true + open_source: true + + paper2agent: + agent_profile: + year: 2025 + domains: [paper-to-agent, scientific-workflow, tool-generation] + architecture: multi-agent + tool_use: true + code_execution: true + web_retrieval: true + literature: true + omics: limited + wet_lab: false + autonomous_experiment: limited + human_in_loop: true + open_source: true + + biomedagent: + agent_profile: + year: 2026 + domains: [general-biomedicine, bioinformatics, omics] + architecture: multi-agent + tool_use: true + code_execution: true + web_retrieval: limited + literature: limited + omics: true + wet_lab: false + autonomous_experiment: true + human_in_loop: true + open_source: true + + biomaster: + agent_profile: + year: 2026 + domains: [bioinformatics, omics, scientific-workflow] + architecture: multi-agent + tool_use: true + code_execution: true + web_retrieval: limited + literature: limited + omics: true + wet_lab: false + autonomous_experiment: true + human_in_loop: true + open_source: true + + brad: + agent_profile: + year: 2025 + domains: [bioinformatics, literature, scientific-workflow] + architecture: single-agent + tool_use: true + code_execution: true + web_retrieval: true + literature: true + omics: limited + wet_lab: false + autonomous_experiment: limited + human_in_loop: true + open_source: true + + autoba: + agent_profile: + year: 2024 + domains: [bioinformatics, omics, scientific-workflow] + architecture: single-agent + tool_use: true + code_execution: true + web_retrieval: limited + literature: limited + omics: true + wet_lab: false + autonomous_experiment: true + human_in_loop: true + open_source: true + + genomas: + agent_profile: + year: 2025 + domains: [bioinformatics, gene-expression, scientific-workflow] + architecture: multi-agent + tool_use: true + code_execution: true + web_retrieval: limited + literature: true + omics: true + wet_lab: false + autonomous_experiment: true + human_in_loop: true + open_source: true + + bia: + agent_profile: + domains: [bioinformatics, genomics, literature] + architecture: single-agent + tool_use: true + code_execution: true + web_retrieval: true + literature: true + omics: true + wet_lab: false + autonomous_experiment: limited + human_in_loop: true + open_source: true + + bioagents: + agent_profile: + year: 2025 + domains: [bioinformatics, genomics, scientific-workflow] + architecture: multi-agent + tool_use: true + code_execution: limited + web_retrieval: limited + literature: limited + omics: true + wet_lab: false + autonomous_experiment: limited + human_in_loop: true + open_source: true + + genomi: + agent_profile: + year: 2026 + domains: [genomics, precision-medicine, evidence-synthesis] + architecture: agent-ecosystem + tool_use: true + code_execution: limited + web_retrieval: limited + literature: true + omics: limited + wet_lab: false + autonomous_experiment: false + human_in_loop: true + open_source: true diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.benchmark-v2.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.benchmark-v2.yml index 1a3bd3d..135b436 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.benchmark-v2.yml +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.benchmark-v2.yml @@ -2,9 +2,9 @@ title: "Provenance-backed single-cell and biomedical benchmark enrichment batch." task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.benchmark-v2.yml -upstream_sha: 7a064bf0 -imported_at: 2026-08-08 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.benchmark-v2.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -39,13 +39,55 @@ resources: last_checked: 2026-08-08 metadata_sources: - https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data + dataset_profile: + year: 2017 + readout: l1000 + gene_panel: "978 landmark genes" + time: true + view_scope: full-resource + species: [human] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical, genetic] + genetic_modes: [mixed] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: "large-scale perturbational profiles" + n_cells: null + access: open prism: entities: [cell, drug] tasks: [drug-response-prediction] - last_checked: 2026-08-08 + last_checked: 2026-10-02 metadata_sources: - https://depmap.org/portal/prism/ + dataset_profile: + year: 2020 + readout: viability + n_compounds: ">4,500" + n_contexts: ">900 cell lines" + time: false + view_scope: full-resource + species: [human] + sample_type: cell-line + biological_context: cancer + perturbation_type: [chemical] + paired: true + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: "PRISM pooled viability screens" + n_cells: null + access: open pharmgkb: entities: [drug, gene, phenotype, variant] diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.cancer-databases-v1.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.cancer-databases-v1.yml new file mode 100644 index 0000000..e9dfff4 --- /dev/null +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.cancer-databases-v1.yml @@ -0,0 +1,125 @@ +--- +title: "Provenance-backed cancer and pharmacogenomics database enrichment batch." +task: "" +lineage_type: import +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.cancer-databases-v1.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +# Provenance-backed cancer and pharmacogenomics database enrichment batch. +resources: + cancer_cell_line_encyclopedia: + entities: [cell, gene, disease, drug] + modalities: [genomics, transcriptomics] + documentation: https://sites.broadinstitute.org/ccle/ + last_checked: 2026-08-08 + metadata_sources: + - https://sites.broadinstitute.org/ccle/ + + catalogue_of_somatic_mutations_in_cancer_cosmic: + entities: [disease, gene, variant] + modalities: [genomics] + documentation: https://cancer.sanger.ac.uk/cosmic + last_checked: 2026-08-08 + metadata_sources: + - https://cancer.sanger.ac.uk/cosmic + + cbioportal: + entities: [disease, gene, variant] + modalities: [genomics, clinical] + github: https://github.com/cBioPortal/cbioportal + documentation: https://www.cbioportal.org/ + last_checked: 2026-08-08 + metadata_sources: + - https://www.cbioportal.org/ + - https://github.com/cBioPortal/cbioportal + + dependency_map_depmap: + entities: [cell, gene, disease, drug] + modalities: [genomics] + documentation: https://depmap.org/portal/ + last_checked: 2026-08-08 + metadata_sources: + - https://depmap.org/portal/ + + dgidb: + entities: [drug, gene] + modalities: [knowledge-graph] + documentation: https://www.dgidb.org/ + last_checked: 2026-08-08 + metadata_sources: + - https://www.dgidb.org/ + + drug_mechanism_database_drugmechdb: + entities: [drug, disease, gene, pathway] + modalities: [knowledge-graph] + github: https://github.com/SuLab/DrugMechDB + last_checked: 2026-08-08 + metadata_sources: + - https://github.com/SuLab/DrugMechDB + + drug_repurposing_hub: + entities: [drug, protein, disease] + modalities: [chemical-structure] + documentation: https://repo-hub.broadinstitute.org/repurposing + last_checked: 2026-08-08 + metadata_sources: + - https://repo-hub.broadinstitute.org/repurposing + + drugcentral: + entities: [drug, protein, disease] + modalities: [chemical-structure] + documentation: https://drugcentral.org/ + last_checked: 2026-08-08 + metadata_sources: + - https://drugcentral.org/ + + drugtargetcommons: + entities: [drug, protein] + modalities: [chemical-structure] + documentation: https://drugtargetcommons.fimm.fi/ + last_checked: 2026-08-08 + metadata_sources: + - https://drugtargetcommons.fimm.fi/ + + comparative_toxicogenomics_database: + entities: [compound, gene, disease] + modalities: [knowledge-graph] + documentation: https://ctdbase.org/ + last_checked: 2026-08-08 + metadata_sources: + - https://ctdbase.org/ + + the_cancer_genome_atlas_tcga: + entities: [disease, gene] + modalities: [clinical, genomics] + last_checked: 2026-10-02 + metadata_sources: + - https://www.cancer.gov/ccg/research/genome-sequencing/tcga + dataset_profile: + year: 2018 + readout: multi-omics + n_profiles: ">11,000 patients" + n_contexts: 33 + gene_panel: "transcriptome-wide plus multi-omics" + time: false + view_scope: full-resource + species: [human] + sample_type: patient + biological_context: cancer + perturbation_type: [] + paired: false + pre_post: false + longitudinal: false + drug_identity: limited + dose: false + smiles: false + clinical_outcome: true + n_samples: ">11,000 patients across 33 cancer types" + n_cells: null + access: mixed diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-explorer-v1.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-explorer-v1.yml new file mode 100644 index 0000000..262913f --- /dev/null +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-explorer-v1.yml @@ -0,0 +1,785 @@ +--- +title: "Foundation Model Explorer v1 metadata." +task: "" +lineage_type: import +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.foundation-explorer-v1.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +# Foundation Model Explorer v1 metadata. +# Fine-grained modalities intentionally distinguish assay/data types for navigation. +resources: + scfoundation: + foundation_profile: + year: 2024 + modalities: [scrna] + params: "100M" + params_millions: 100 + pretraining_scale: ">50M human single-cell transcriptomes" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: limited + spatial: false + + scgpt: + foundation_profile: + year: 2024 + modalities: [scrna, multi-omics] + pretraining_scale: ">33M single-cell profiles" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: true + spatial: limited + + geneformer: + foundation_profile: + year: 2024 + modalities: [scrna] + params: "10M / 104M / 316M" + params_millions: 316 + pretraining_scale: "~104M human single-cell transcriptomes in V2; original corpus ~30M" + species: [human] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: true + spatial: false + + bulkformer: + foundation_profile: + year: 2025 + modalities: [bulk-rna] + pretraining_scale: "bulk RNA-seq compendium" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + scbert: + foundation_profile: + year: 2022 + modalities: [scrna] + pretraining_scale: "large-scale scRNA-seq corpus" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + cellplm: + foundation_profile: + year: 2023 + modalities: [scrna] + pretraining_scale: "multi-dataset single-cell pretraining" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: limited + spatial: false + + uce: + foundation_profile: + year: 2026 + modalities: [scrna] + params: ">650M" + params_millions: 650 + pretraining_scale: ">36M cells across >300 datasets" + species: [multi-species] + zero_shot: true + finetunable: limited + weights: true + code: true + perturbation: false + spatial: false + + saturn: + foundation_profile: + year: 2024 + modalities: [scrna, protein] + pretraining_scale: "cross-species single-cell atlases with protein-language-model embeddings" + species: [multi-species] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + cancerfoundation: + foundation_profile: + year: 2025 + modalities: [scrna] + pretraining_scale: "curated malignant-cell single-cell corpus" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: limited + spatial: false + + cellfm: + foundation_profile: + year: 2025 + modalities: [scrna] + params: "800M" + params_millions: 800 + pretraining_scale: "100M human cells" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: true + spatial: false + + scprint: + foundation_profile: + year: 2025 + modalities: [scrna] + pretraining_scale: "large multi-dataset single-cell corpus" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: limited + spatial: false + + scprint_2: + foundation_profile: + year: 2026 + modalities: [scrna] + pretraining_scale: "350M+ cells; 22K+ datasets; 16 species" + species: [multi-species] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: limited + spatial: false + + nicheformer: + foundation_profile: + year: 2025 + modalities: [scrna, spatial] + params: "49.3M" + params_millions: 49.3 + pretraining_scale: "SpatialCorpus-110M" + species: [human, mouse] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: true + + scgpt_spatial: + foundation_profile: + year: 2025 + modalities: [scrna, spatial, multi-omics] + pretraining_scale: "continued pretraining from scGPT with spatial transcriptomics" + species: [human, mouse] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: true + + scmulan: + foundation_profile: + year: 2024 + modalities: [scrna, atac, proteomics, multi-omics] + pretraining_scale: "~10M cells across transcriptomics, epigenomics, and proteomics" + species: [human, mouse] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + genecompass: + foundation_profile: + year: 2024 + modalities: [scrna, dna, multi-omics] + pretraining_scale: "120M+ cells across multiple species" + species: [multi-species] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: limited + spatial: false + + titan: + foundation_profile: + year: 2025 + modalities: [pathology] + pretraining_scale: "whole-slide pathology with multimodal image-language supervision" + species: [human] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + virchow2: + foundation_profile: + year: 2024 + modalities: [pathology] + params: "632M" + params_millions: 632 + pretraining_scale: "3.1M whole-slide images" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: limited + perturbation: false + spatial: false + + h_optimus_0: + foundation_profile: + year: 2024 + modalities: [pathology] + params: "1.1B" + params_millions: 1100 + pretraining_scale: "large multi-center histopathology corpus" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: limited + perturbation: false + spatial: false + + h_optimus_1: + foundation_profile: + year: 2025 + modalities: [pathology] + params: "1.1B" + params_millions: 1100 + pretraining_scale: ">1M slides; >800K patients" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: limited + perturbation: false + spatial: false + + uni2_h: + foundation_profile: + year: 2025 + modalities: [pathology] + params: "~1B" + params_millions: 1000 + pretraining_scale: "large-scale histopathology pretraining" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: limited + perturbation: false + spatial: false + + phikon_v2: + foundation_profile: + year: 2025 + modalities: [pathology] + pretraining_scale: "large-scale histopathology pretraining" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: limited + perturbation: false + spatial: false + + genbio_pathfm: + foundation_profile: + year: 2026 + modalities: [pathology] + params: "1.1B" + params_millions: 1100 + pretraining_scale: "public histopathology corpus with morphology-aware curation" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + uni_mol: + foundation_profile: + year: 2023 + modalities: [chemical, protein] + pretraining_scale: "large-scale 3D molecular and pocket pretraining" + species: [multi-species] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + uni_mol2: + foundation_profile: + year: 2024 + modalities: [chemical] + params: "up to 1.1B" + params_millions: 1100 + pretraining_scale: "800M molecular conformations" + species: [] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + chemfm: + foundation_profile: + year: 2024 + modalities: [chemical] + params: "1B / 3B" + params_millions: 3000 + pretraining_scale: "178M molecules" + species: [] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + esm_cambrian_esm_c: + foundation_profile: + year: 2024 + modalities: [protein] + params: "300M / 600M / 6B" + params_millions: 6000 + pretraining_scale: "evolutionary-scale protein sequence corpus" + species: [multi-species] + zero_shot: true + finetunable: true + weights: limited + code: true + perturbation: false + spatial: false + + esm3: + foundation_profile: + year: 2024 + modalities: [protein] + params: "1.4B / 7B / 98B" + params_millions: 98000 + pretraining_scale: "multimodal protein sequence, structure, and function corpus" + species: [multi-species] + zero_shot: true + finetunable: limited + weights: limited + code: true + perturbation: false + spatial: false + + boltz_2: + foundation_profile: + year: 2025 + modalities: [protein, chemical] + pretraining_scale: "biomolecular complex structure and affinity data" + species: [multi-species] + zero_shot: true + finetunable: limited + weights: true + code: true + perturbation: false + spatial: false + + protenix: + foundation_profile: + year: 2025 + modalities: [protein, chemical] + pretraining_scale: "biomolecular structure pretraining" + species: [multi-species] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + rna_fm: + foundation_profile: + year: 2022 + modalities: [rna] + params: "99M" + params_millions: 99 + pretraining_scale: "23.7M non-coding RNA sequences" + species: [multi-species] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + rinalmo: + foundation_profile: + year: 2025 + modalities: [rna] + params: "33.5M / 148M / 650M" + params_millions: 650 + pretraining_scale: "36M non-coding RNA sequences" + species: [multi-species] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + evo_2: + foundation_profile: + year: 2026 + modalities: [dna] + params: "1B / 7B / 40B" + params_millions: 40000 + pretraining_scale: "9T DNA base pairs" + species: [multi-species] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + alphagenome: + foundation_profile: + year: 2025 + modalities: [dna] + pretraining_scale: "long-context regulatory genomics corpus" + species: [human, mouse] + zero_shot: true + finetunable: unknown + weights: no + code: limited + perturbation: false + spatial: false + + moderngena: + foundation_profile: + year: 2026 + modalities: [dna] + pretraining_scale: "hundreds of vertebrate genome assemblies" + species: [multi-species] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + nucleotide_transformer: + foundation_profile: + year: 2025 + modalities: [dna] + params: "50M – 2.5B" + params_millions: 2500 + pretraining_scale: "3,202 human genomes and up to 850 species" + species: [multi-species] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + dnabert_2: + foundation_profile: + year: 2023 + modalities: [dna] + params: "117M" + params_millions: 117 + pretraining_scale: "multi-species GenBank genome corpus" + species: [multi-species] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + caduceus: + foundation_profile: + year: 2024 + modalities: [dna] + pretraining_scale: "long-range genomic sequence pretraining" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + hyenadna: + foundation_profile: + year: 2023 + modalities: [dna] + pretraining_scale: "long-context human genome sequence" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + gpn_genomic_pre_trained_network: + foundation_profile: + year: 2023 + modalities: [dna] + pretraining_scale: "genomic sequence pretraining for variant-effect prediction" + species: [multi-species] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + methylgpt: + foundation_profile: + year: 2024 + modalities: [methylation] + params: "3M / 7M / 15M" + params_millions: 15 + pretraining_scale: "154,063 human methylomes after QC; 5,281 datasets; 49,156 CpG sites" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + cpgpt: + foundation_profile: + year: 2025 + modalities: [methylation] + pretraining_scale: "150K+ methylation samples across >2,000 datasets" + species: [human] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + mutationprojector: + foundation_profile: + year: 2026 + modalities: [mutation, copy-number] + pretraining_scale: ">30K tumors with somatic genomic alterations including copy-number changes" + species: [human] + zero_shot: limited + finetunable: unknown + weights: unknown + code: unknown + perturbation: false + spatial: false + + casanovo_foundation: + foundation_profile: + year: 2025 + modalities: [proteomics] + pretraining_scale: "30M labeled tandem mass spectra" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + tessera: + foundation_profile: + year: 2026 + modalities: [mutation, copy-number] + pretraining_scale: "TCGA pan-cancer somatic SNVs and copy-number segments" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + epiagent: + foundation_profile: + year: 2025 + modalities: [atac] + pretraining_scale: ">5.4M scATAC-seq cells across 37 tissues or cell lines" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: true + spatial: false + + scarf: + foundation_profile: + year: 2025 + modalities: [scrna, atac, multi-omics] + pretraining_scale: ">2.7M paired and unpaired single cells across tissues and species" + species: [multi-species] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: limited + spatial: false + + scdnam_gpt: + foundation_profile: + year: 2025 + modalities: [methylation] + pretraining_scale: ">1M single-cell methylomes from human and mouse tissues" + species: [human, mouse] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + epifoundation: + foundation_profile: + year: 2025 + modalities: [atac] + pretraining_scale: "large-scale scATAC-seq pretraining with peak-to-gene alignment" + species: [human] + zero_shot: limited + finetunable: true + weights: limited + code: limited + perturbation: false + spatial: false + + chromfound: + foundation_profile: + year: 2025 + modalities: [atac] + pretraining_scale: "1.97M scATAC-seq cells; 30 tissues; 6 disease contexts" + species: [human] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + atacformer: + foundation_profile: + year: 2025 + modalities: [atac] + pretraining_scale: "large atlas of scATAC-seq experiments" + species: [human] + zero_shot: limited + finetunable: true + weights: unknown + code: unknown + perturbation: false + spatial: false + + clm_x: + foundation_profile: + year: 2026 + modalities: [scrna, atac, multi-omics] + pretraining_scale: "million-scale RNA-only, ATAC-only, and paired RNA-ATAC data" + species: [human] + zero_shot: limited + finetunable: true + weights: unknown + code: unknown + perturbation: true + spatial: false + + captain: + foundation_profile: + year: 2026 + modalities: [scrna, proteomics, multi-omics] + pretraining_scale: "co-assayed single-cell RNA and protein data" + species: [human] + zero_shot: limited + finetunable: true + weights: unknown + code: unknown + perturbation: false + spatial: false + + hicfoundation: + foundation_profile: + year: 2026 + modalities: [hic] + pretraining_scale: "hundreds of Hi-C assays; 118M contact-matrix submatrices" + species: [multi-species] + zero_shot: limited + finetunable: true + weights: unknown + code: unknown + perturbation: false + spatial: false + + omicsfm: + foundation_profile: + year: 2026 + modalities: [proteomics, bulk-rna, scrna] + params: "4.9M" + params_millions: 4.9 + pretraining_scale: "48,837 proteomics profiles; 680,216 bulk RNA profiles; 4.55M single cells" + species: [human] + zero_shot: limited + finetunable: true + weights: true + code: true + perturbation: false + spatial: false + + virtues: + foundation_profile: + year: 2026 + modalities: [proteomics, spatial] + pretraining_scale: "31-32 harmonized spatial-proteomics datasets across technologies" + species: [human] + zero_shot: true + finetunable: true + weights: true + code: true + perturbation: false + spatial: true diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-models-v3.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-models-v3.yml new file mode 100644 index 0000000..e203c85 --- /dev/null +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-models-v3.yml @@ -0,0 +1,232 @@ +--- +title: "Provenance-backed foundation model enrichment batch 3." +task: "" +lineage_type: import +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.foundation-models-v3.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +# Provenance-backed foundation model enrichment batch 3. +resources: + cellfm: + entities: [cell, gene] + methods: [self-supervised-learning, transformer] + modalities: [single-cell-rna-seq, transcriptomics] + tasks: [cell-type-annotation, foundation-model-pretraining, perturbation-prediction, representation-learning] + year: 2025 + github: https://github.com/biomed-AI/CellFM + paper: https://doi.org/10.1038/s41467-025-59926-5 + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/biomed-AI/CellFM + - https://doi.org/10.1038/s41467-025-59926-5 + + scprint_2: + entities: [cell, gene] + methods: [self-supervised-learning, transformer] + modalities: [single-cell-rna-seq, transcriptomics] + tasks: [batch-correction, cell-type-annotation, foundation-model-pretraining, gene-regulatory-network-inference, imputation, representation-learning] + year: 2026 + github: https://github.com/cantinilab/scPRINT-2 + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/cantinilab/scPRINT-2 + + titan: + entities: [disease, tissue] + methods: [contrastive-learning, self-supervised-learning, transformer] + modalities: [histopathology, imaging] + tasks: [classification, foundation-model-pretraining, representation-learning] + year: 2025 + github: https://github.com/mahmoodlab/TITAN + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/mahmoodlab/TITAN + + virchow2: + entities: [disease, tissue] + methods: [self-supervised-learning, transformer] + modalities: [histopathology, imaging] + tasks: [classification, foundation-model-pretraining, representation-learning] + year: 2024 + documentation: https://huggingface.co/paige-ai/Virchow2 + last_checked: 2026-10-02 + metadata_sources: + - https://huggingface.co/paige-ai/Virchow2 + + h_optimus_0: + entities: [disease, tissue] + methods: [self-supervised-learning, transformer] + modalities: [histopathology, imaging] + tasks: [classification, foundation-model-pretraining, representation-learning] + year: 2024 + documentation: https://huggingface.co/bioptimus/H-optimus-0 + last_checked: 2026-10-02 + metadata_sources: + - https://huggingface.co/bioptimus/H-optimus-0 + + h_optimus_1: + entities: [disease, tissue] + methods: [self-supervised-learning, transformer] + modalities: [histopathology, imaging] + tasks: [classification, foundation-model-pretraining, representation-learning] + year: 2025 + documentation: https://huggingface.co/bioptimus/H-optimus-1 + last_checked: 2026-10-02 + metadata_sources: + - https://huggingface.co/bioptimus/H-optimus-1 + + uni2_h: + entities: [disease, tissue] + methods: [self-supervised-learning, transformer] + modalities: [histopathology, imaging] + tasks: [classification, foundation-model-pretraining, representation-learning] + year: 2025 + documentation: https://huggingface.co/MahmoodLab/UNI2-h + last_checked: 2026-10-02 + metadata_sources: + - https://huggingface.co/MahmoodLab/UNI2-h + + phikon_v2: + entities: [disease, tissue] + methods: [self-supervised-learning, transformer] + modalities: [histopathology, imaging] + tasks: [classification, foundation-model-pretraining, representation-learning] + year: 2025 + documentation: https://huggingface.co/owkin/phikon-v2 + last_checked: 2026-10-02 + metadata_sources: + - https://huggingface.co/owkin/phikon-v2 + + genbio_pathfm: + entities: [disease, tissue] + methods: [self-supervised-learning, transformer] + modalities: [histopathology, imaging] + tasks: [classification, foundation-model-pretraining, representation-learning] + year: 2026 + github: https://github.com/genbio-ai/genbio-pathfm + paper: https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1 + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/genbio-ai/genbio-pathfm + - https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1 + + uni_mol2: + entities: [molecule] + methods: [self-supervised-learning, transformer] + modalities: [chemical-structure, molecular-structure] + tasks: [foundation-model-pretraining, representation-learning] + year: 2024 + github: https://github.com/deepmodeling/Uni-Mol + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/deepmodeling/Uni-Mol + + chemfm: + entities: [molecule] + methods: [generative-model, language-model, transformer] + modalities: [chemical-structure] + tasks: [foundation-model-pretraining, molecular-generation, representation-learning] + year: 2024 + github: https://github.com/TheLuoFengLab/ChemFM + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/TheLuoFengLab/ChemFM + + esm_cambrian_esm_c: + entities: [protein] + methods: [language-model, self-supervised-learning, transformer] + modalities: [protein-sequence] + tasks: [foundation-model-pretraining, protein-function-prediction, representation-learning] + year: 2024 + github: https://github.com/Biohub/esm + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/Biohub/esm + - https://www.evolutionaryscale.ai/blog/esm-cambrian + + boltz_2: + entities: [molecule, protein, protein-complex] + methods: [diffusion, generative-model, transformer] + modalities: [molecular-structure, protein-sequence] + tasks: [drug-target-interaction, structure-prediction, virtual-screening] + year: 2025 + github: https://github.com/jwohlwend/boltz + paper: https://doi.org/10.1101/2025.06.14.659707 + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/jwohlwend/boltz + - https://doi.org/10.1101/2025.06.14.659707 + + protenix: + entities: [molecule, protein, protein-complex] + methods: [diffusion, generative-model, transformer] + modalities: [molecular-structure, protein-sequence] + tasks: [drug-target-interaction, structure-prediction] + year: 2025 + github: https://github.com/bytedance/Protenix + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/bytedance/Protenix + + rna_fm: + entities: [gene, transcript] + methods: [language-model, self-supervised-learning, transformer] + modalities: [rna-sequence] + tasks: [foundation-model-pretraining, representation-learning, structure-prediction] + year: 2022 + github: https://github.com/ml4bio/RNA-FM + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/ml4bio/RNA-FM + + rinalmo: + entities: [gene, transcript] + methods: [language-model, self-supervised-learning, transformer] + modalities: [rna-sequence] + tasks: [foundation-model-pretraining, representation-learning, structure-prediction] + year: 2024 + github: https://github.com/lbcb-sci/RiNALMo + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/lbcb-sci/RiNALMo + + evo_2: + entities: [gene, genome, organism] + methods: [generative-model, language-model] + modalities: [dna-sequence, genomics] + tasks: [foundation-model-pretraining, representation-learning] + year: 2026 + github: https://github.com/arcinstitute/evo2 + paper: https://doi.org/10.1038/s41586-026-10176-5 + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/arcinstitute/evo2 + - https://doi.org/10.1038/s41586-026-10176-5 + + alphagenome: + entities: [gene, genome, regulatory-element, variant] + methods: [supervised-learning, transformer] + modalities: [dna-sequence, epigenomics, genomics] + tasks: [regression, representation-learning] + year: 2025 + github: https://github.com/google-deepmind/alphagenome + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/google-deepmind/alphagenome + + moderngena: + entities: [gene, genome, regulatory-element] + methods: [language-model, self-supervised-learning, transformer] + modalities: [dna-sequence, genomics] + tasks: [classification, foundation-model-pretraining, regression, representation-learning] + year: 2026 + github: https://github.com/AIRI-Institute/GENA_LM + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/AIRI-Institute/GENA_LM diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.genetic-perturbation-v1.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.genetic-perturbation-v1.yml new file mode 100644 index 0000000..d9ef672 --- /dev/null +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.genetic-perturbation-v1.yml @@ -0,0 +1,292 @@ +--- +title: "Genetic perturbation Dataset Explorer metadata; artifact sync verified." +task: "" +lineage_type: import +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.genetic-perturbation-v1.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +# Genetic perturbation Dataset Explorer metadata; artifact sync verified. +# Curated genetic perturbation datasets for Dataset Explorer. +resources: + dixit_et_al_2016_perturb_seq: + paper: https://doi.org/10.1016/j.cell.2016.11.038 + last_checked: 2026-10-02 + metadata_sources: + - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063 + - https://doi.org/10.1016/j.cell.2016.11.038 + dataset_profile: + year: 2016 + view_scope: full-study + species: [human, mouse] + sample_type: cell-line+primary + biological_context: immune-and-cell-line + perturbation_type: [genetic] + genetic_modes: [crispr-ko, combinatorial] + readout: single-cell-rna + n_profiles: "~200,000 cells" + n_perturbations: "pooled CRISPR-KO across six screens" + n_contexts: 6 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: true + smiles: false + clinical_outcome: false + n_samples: "six pooled screens across K562 and dendritic-cell conditions" + n_cells: "~200,000" + access: open + + adamson_et_al_2016_perturb_seq: + paper: https://doi.org/10.1016/j.cell.2016.11.048 + last_checked: 2026-10-02 + metadata_sources: + - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546 + dataset_profile: + year: 2016 + view_scope: full-study + species: [human] + sample_type: cell-line + biological_context: unfolded-protein-response + perturbation_type: [genetic] + genetic_modes: [crispri, combinatorial] + readout: single-cell-rna + n_profiles: "~86,000 cells" + n_perturbations: "~100 UPR hits plus combinatorial CRISPRi perturbations" + n_contexts: 3 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: false + smiles: false + clinical_outcome: false + n_samples: "three pooled CRISPRi Perturb-seq experiments in K562" + n_cells: "~86,000" + access: open + + datlinger_et_al_2017_crop_seq: + paper: https://doi.org/10.1038/nmeth.4177 + last_checked: 2026-10-02 + metadata_sources: + - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872 + - https://doi.org/10.1038/nmeth.4177 + dataset_profile: + year: 2017 + view_scope: full-study + species: [human] + sample_type: cell-line + biological_context: t-cell-receptor-signaling + perturbation_type: [genetic] + genetic_modes: [crispr-ko] + readout: single-cell-rna + n_profiles: 5905 + n_perturbations: "TCR regulators and transcription factors" + n_contexts: 2 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: false + smiles: false + clinical_outcome: false + n_samples: "stimulated and unstimulated Jurkat CROP-seq" + n_cells: 5905 + access: open + + norman_et_al_2019_crispra_perturb_seq: + paper: https://doi.org/10.1126/science.aax4438 + last_checked: 2026-10-02 + metadata_sources: + - https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323 + - https://doi.org/10.1126/science.aax4438 + dataset_profile: + year: 2019 + view_scope: full-study + species: [human] + sample_type: cell-line + biological_context: genetic-interactions + perturbation_type: [genetic] + genetic_modes: [crispra, combinatorial] + readout: single-cell-rna + n_profiles: "~110,000 cells" + n_perturbations: 287 + n_contexts: 1 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: false + smiles: false + clinical_outcome: false + n_samples: "single-gene and pairwise CRISPRa perturbations in K562" + n_cells: "~110,000" + access: open + + gasperini_et_al_2019_crispri_enhancer_screen: + paper: https://doi.org/10.1016/j.cell.2018.11.029 + last_checked: 2026-10-02 + metadata_sources: + - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861 + dataset_profile: + year: 2019 + view_scope: full-study + species: [human] + sample_type: cell-line + biological_context: enhancer-gene-regulation + perturbation_type: [genetic] + genetic_modes: [crispri, enhancer-targeting] + readout: single-cell-rna + n_profiles: 254974 + n_perturbations: 5920 + n_contexts: 3 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: false + smiles: false + clinical_outcome: false + n_samples: "three CRISPRi screens in K562, including an at-scale enhancer screen" + n_cells: 254974 + access: open + + replogle_et_al_2022_k562_genome_wide_perturb_seq: + paper: https://doi.org/10.1016/j.cell.2022.05.013 + last_checked: 2026-10-02 + metadata_sources: + - https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387 + - https://gwps.wi.mit.edu/ + dataset_profile: + year: 2022 + view_scope: full-study + species: [human] + sample_type: cell-line + biological_context: genome-wide-functional-genomics + perturbation_type: [genetic] + genetic_modes: [crispri] + readout: single-cell-rna + n_profiles: "genome-scale; mean ~183 cells per detected perturbation" + n_perturbations: "9,866 target genes; 11,258 detected sgRNA-pair perturbations" + n_contexts: 1 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: true + smiles: false + clinical_outcome: false + n_samples: "K562 CRISPRi genome-wide screen sampled at day 8" + n_cells: "mean ~183 cells per detected perturbation" + access: open + + replogle_et_al_2022_k562_essential_perturb_seq: + paper: https://doi.org/10.1016/j.cell.2022.05.013 + last_checked: 2026-10-02 + metadata_sources: + - https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387 + - https://gwps.wi.mit.edu/ + dataset_profile: + year: 2022 + view_scope: full-study + species: [human] + sample_type: cell-line + biological_context: essential-gene-screen + perturbation_type: [genetic] + genetic_modes: [crispri] + readout: single-cell-rna + n_profiles: 310385 + n_perturbations: 2057 + n_contexts: 1 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: true + smiles: false + clinical_outcome: false + n_samples: "K562 CRISPRi essential-gene screen sampled at day 6" + n_cells: 310385 + access: open + + replogle_et_al_2022_rpe1_essential_perturb_seq: + paper: https://doi.org/10.1016/j.cell.2022.05.013 + last_checked: 2026-10-02 + metadata_sources: + - https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387 + - https://gwps.wi.mit.edu/ + dataset_profile: + year: 2022 + view_scope: full-study + species: [human] + sample_type: cell-line + biological_context: essential-gene-screen + perturbation_type: [genetic] + genetic_modes: [crispri] + readout: single-cell-rna + n_profiles: 247914 + n_perturbations: 2393 + n_contexts: 1 + gene_panel: transcriptome-wide + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: true + smiles: false + clinical_outcome: false + n_samples: "RPE1 CRISPRi essential-gene screen sampled at day 7" + n_cells: 247914 + access: open + + frangieh_et_al_2021_perturb_cite_seq: + paper: https://doi.org/10.1038/s41588-021-00779-1 + last_checked: 2026-10-02 + metadata_sources: + - https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion + - https://doi.org/10.1038/s41588-021-00779-1 + dataset_profile: + year: 2021 + view_scope: full-study + species: [human] + sample_type: patient-derived-cell-line + biological_context: melanoma-immunotherapy + perturbation_type: [genetic] + genetic_modes: [crispr-ko] + readout: cite-seq + n_profiles: ">200,000 cells" + n_perturbations: 248 + n_contexts: 3 + gene_panel: "transcriptome + surface proteins" + paired: true + pre_post: false + longitudinal: false + drug_identity: false + dose: false + time: false + smiles: false + clinical_outcome: false + n_samples: "patient-derived melanoma under control, IFN-gamma, and TIL co-culture conditions" + n_cells: ">200,000" + access: mixed diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.method-explorer-v1.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.method-explorer-v1.yml new file mode 100644 index 0000000..6a47832 --- /dev/null +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.method-explorer-v1.yml @@ -0,0 +1,285 @@ +--- +title: "Method Explorer v1; changes to this file trigger artifact regeneration." +task: "" +lineage_type: import +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.method-explorer-v1.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 +prompt_class: unknown +upstream_changes: accepted +author: upstream +validated: false +--- + +# Method Explorer v1; changes to this file trigger artifact regeneration. +# Structured comparison metadata for Method Explorer v1. +resources: + bison: + method_profile: + task_family: drug-perturbation + year: 2026 + input: bulk/pseudobulk transcriptomics + drug_representation: Morgan fingerprints + chemical tokens + context_representation: control-derived context features + embeddings + output: transcriptomic response + unseen_drug: yes + unseen_context: limited + dose: true + time: true + patient_transfer: false + code: false + + txpert: + method_profile: + task_family: drug-perturbation + year: 2026 + input: transcriptomics + biological knowledge + drug_representation: knowledge-graph-informed perturbation representation + context_representation: cellular context representation + output: transcriptomic response + unseen_drug: yes + unseen_context: yes + dose: false + time: false + patient_transfer: false + code: true + + lpm: + method_profile: + task_family: drug-perturbation + year: 2025 + input: heterogeneous perturbation experiments + drug_representation: learned perturbation representation + context_representation: learned context representation + output: molecular state + unseen_drug: yes + unseen_context: yes + dose: limited + time: limited + patient_transfer: false + code: true + + state: + method_profile: + task_family: drug-perturbation + year: 2026 + input: sets of cellular expression profiles + drug_representation: perturbation embedding + context_representation: cellular state + output: transcriptomic response + unseen_drug: limited + unseen_context: yes + dose: limited + time: limited + patient_transfer: false + code: true + + chemcpa: + method_profile: + task_family: drug-perturbation + year: 2022 + input: single-cell expression + covariates + drug_representation: RDKit2D descriptors + context_representation: cell covariates + basal state + output: transcriptomic response + unseen_drug: yes + unseen_context: limited + dose: true + time: limited + patient_transfer: false + code: true + + cmonge: + method_profile: + task_family: drug-perturbation + year: 2026 + input: source/target expression distributions + drug_representation: RDKit2D descriptors + context_representation: matched control distribution + output: transported expression distribution + unseen_drug: yes + unseen_context: yes + dose: true + time: limited + patient_transfer: false + code: true + + prnet: + method_profile: + task_family: drug-perturbation + year: 2024 + input: basal expression + perturbation + drug_representation: FCFP4 fingerprint + context_representation: matched basal expression + output: transcriptomic response + unseen_drug: yes + unseen_context: limited + dose: true + time: limited + patient_transfer: false + code: true + + biolord: + method_profile: + task_family: drug-perturbation + year: 2024 + input: expression + known attributes + drug_representation: chemical descriptors + context_representation: unknown latent attributes + output: transcriptomic response + unseen_drug: yes + unseen_context: yes + dose: true + time: true + patient_transfer: false + code: true + + perturbnet: + method_profile: + task_family: drug-perturbation + year: 2025 + input: single-cell expression distributions + drug_representation: molecular representation + context_representation: conditional latent state + output: cell-state distribution + unseen_drug: yes + unseen_context: limited + dose: limited + time: limited + patient_transfer: false + code: true + + cellflow: + method_profile: + task_family: drug-perturbation + year: 2025 + input: control/treated expression distributions + drug_representation: molecular features + context_representation: matched control distribution + output: transported cell-state distribution + unseen_drug: yes + unseen_context: yes + dose: true + time: true + patient_transfer: false + code: true + + prophet: + method_profile: + task_family: drug-perturbation + year: 2024 + input: control expression + perturbation + drug_representation: learned chemical representation + context_representation: control-derived context + output: gene-expression response + unseen_drug: yes + unseen_context: yes + dose: true + time: true + patient_transfer: false + code: true + + prepr_ct: + method_profile: + task_family: drug-perturbation + year: 2026 + input: aggregate expression + context graph + drug_representation: RDKit descriptors + context_representation: context-specific gene graph + output: transcriptomic response + unseen_drug: yes + unseen_context: limited + dose: true + time: true + patient_transfer: false + code: true + + xpert: + method_profile: + task_family: drug-perturbation + year: 2026 + input: transcriptomics + heterogeneous graph + drug_representation: Uni-Mol + knowledge graph + context_representation: basal expression + gene embeddings + output: transcriptomic response + unseen_drug: yes + unseen_context: yes + dose: true + time: true + patient_transfer: false + code: true + + paso: + method_profile: + task_family: drug-response-prediction + year: 2025 + input: pathway-aware multi-omics + drug_representation: SMILES-derived representation + context_representation: pathway-difference representation + output: cell-line drug response + unseen_drug: limited + unseen_context: limited + dose: false + time: false + patient_transfer: false + code: true + + dtlcdr: + method_profile: + task_family: drug-response-prediction + year: 2025 + input: multimodal tumor/cell-line features + targets + drug_representation: target-aware drug representation + context_representation: biological sample representation + output: preclinical/clinical response + unseen_drug: yes + unseen_context: limited + dose: false + time: false + patient_transfer: true + code: false + + therapi: + method_profile: + task_family: drug-response-prediction + year: 2026 + input: tumor transcriptomics + cell-line knowledge + drug_representation: perturbation/gene-level representation + context_representation: aligned tumor/cell-line representation + output: patient drug response + unseen_drug: limited + unseen_context: yes + dose: false + time: false + patient_transfer: true + code: true + + expresso: + method_profile: + task_family: drug-response-prediction + year: 2026 + input: pretreatment tumor transcriptomics + drug_representation: drug targets + biomarkers + context_representation: tumor transcriptomic context + output: patient treatment response + unseen_drug: limited + unseen_context: yes + dose: false + time: false + patient_transfer: true + code: false + + perturbrx: + method_profile: + task_family: drug-response-prediction + year: 2026 + input: pretreatment tumor RNA-seq + perturbation pretraining + drug_representation: drug embedding + treatment-conditioned transition + context_representation: patient latent state + output: patient treatment response + unseen_drug: limited + unseen_context: yes + dose: true + time: false + patient_transfer: true + code: false diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.perturbation-datasets-v1.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.perturbation-datasets-v1.yml new file mode 100644 index 0000000..eb8552f --- /dev/null +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.perturbation-datasets-v1.yml @@ -0,0 +1,321 @@ +--- +title: "Includes Bison benchmark coverage metadata." +task: "" +lineage_type: import +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.perturbation-datasets-v1.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 +prompt_class: catalogue +upstream_changes: accepted +author: upstream +validated: false +--- + +# Includes Bison benchmark coverage metadata. +# Curated perturbation, patient, and PDX dataset profiles for Dataset Explorer. +resources: + tahoe_100m: + dataset_profile: + year: 2025 + view_scope: bison-benchmark-view + readout: single-cell-rna + n_profiles: 67018 + n_compounds: 376 + n_contexts: 50 + gene_panel: "20,573 full / 2,000 HVG (Bison benchmark view)" + time: true + species: [human] + sample_type: cell-line + biological_context: cancer + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: ">100 million profiles; 50 cancer cell lines; ~1,100 compounds" + n_cells: ">100 million" + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://huggingface.co/datasets/tahoebio/Tahoe-100M + + sci_plex: + dataset_profile: + year: 2020 + view_scope: bison-benchmark-view + readout: pseudobulk-rna + n_profiles: 4974 + n_compounds: 186 + n_contexts: 3 + gene_panel: "20,251 full / 2,000 HVG (Bison benchmark view)" + time: true + species: [human, mouse] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: "~5,000 conditions; 188 compounds across 3 human cancer cell lines" + n_cells: 649340 + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944 + - https://pubmed.ncbi.nlm.nih.gov/31806696/ + + mix_seq: + dataset_profile: + year: 2020 + view_scope: full-resource + readout: single-cell-rna + n_compounds: 13 + n_contexts: "100+ cell lines" + gene_panel: "transcriptome-wide" + time: true + species: [human] + sample_type: cell-line + biological_context: cancer + perturbation_type: [chemical, genetic] + genetic_modes: [mixed] + paired: false + pre_post: false + longitudinal: true + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: "pools of up to 100+ cancer cell lines; 13-drug demonstration" + n_cells: "single-cell RNA-seq" + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://www.nature.com/articles/s41467-020-17440-w + + pdx_atlas: + dataset_profile: + year: 2026 + view_scope: current-portal + readout: multi-omics + n_profiles: ">50 PDXs" + n_contexts: "30 patients" + gene_panel: "multi-omics" + time: false + species: [human, mouse] + sample_type: pdx + biological_context: cancer + perturbation_type: [chemical] + paired: true + pre_post: false + longitudinal: false + drug_identity: true + dose: false + smiles: partial + clinical_outcome: false + n_samples: ">50 PDXs from 30 prostate cancer patients in the current portal" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://muralportal.pdxatlas.org/ + + gse191127_breast_cancer_pre_post_chemotherapy: + dataset_profile: + year: 2022 + view_scope: study-cohort + readout: bulk-rna + n_profiles: "317 pretreatment; 22 matched pre/post deeply profiled" + n_contexts: 1 + gene_panel: "transcriptome-wide" + time: true + species: [human] + sample_type: patient + biological_context: breast-cancer + perturbation_type: [chemical] + paired: true + pre_post: true + longitudinal: true + drug_identity: limited + dose: false + smiles: partial + clinical_outcome: true + n_samples: "317 pretreatment biopsies; 22 matched pre/post tumors with deep profiling" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127 + + + op3: + dataset_profile: + year: 2024 + view_scope: bison-benchmark-view + readout: bulk-rna + n_profiles: 1813 + n_compounds: 138 + n_contexts: 4 + gene_panel: "5,198 full / 2,000 HVG" + time: true + species: [human] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: true + clinical_outcome: false + n_samples: "1,813 profiles; 138 molecules; 4 contexts" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/theislab/Chem-PerturBridge + + vcpi_0001: + dataset_profile: + view_scope: bison-benchmark-view + readout: bulk-rna + n_profiles: 27517 + n_compounds: 2272 + n_contexts: 1 + gene_panel: "20,629 full / 2,000 HVG" + time: true + species: [human] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: true + clinical_outcome: false + n_samples: "27,517 profiles; 2,272 molecules; 1 context" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/theislab/Chem-PerturBridge + + vcpi_0002: + dataset_profile: + view_scope: bison-benchmark-view + readout: bulk-rna + n_profiles: 18139 + n_compounds: 1488 + n_contexts: 1 + gene_panel: "20,629 full / 2,000 HVG" + time: true + species: [human] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: true + clinical_outcome: false + n_samples: "18,139 profiles; 1,488 molecules; 1 context" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/theislab/Chem-PerturBridge + + novartis_perturbation_dataset: + dataset_profile: + year: 2025 + view_scope: bison-benchmark-view + readout: bulk-rna + n_profiles: 46748 + n_compounds: 3770 + n_contexts: 1 + gene_panel: "20,038 full / 2,000 HVG" + time: true + species: [human] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: true + clinical_outcome: false + n_samples: "46,748 profiles; 3,770 molecules; 1 context" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/theislab/Chem-PerturBridge + + lincs_l1000_phase_1: + dataset_profile: + year: 2017 + view_scope: bison-benchmark-view + readout: l1000 + n_profiles: 692787 + n_compounds: 9233 + n_contexts: 70 + gene_panel: "978 landmark genes" + time: true + species: [human] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: true + clinical_outcome: false + n_samples: "692,787 profiles; 9,233 molecules; 70 contexts; 978 landmark genes" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/theislab/Chem-PerturBridge + + lincs_l1000_phase_2: + dataset_profile: + year: 2017 + view_scope: bison-benchmark-view + readout: l1000 + n_profiles: 333263 + n_compounds: 1760 + n_contexts: 30 + gene_panel: "978 landmark genes" + time: true + species: [human] + sample_type: cell-line + biological_context: perturbation-screen + perturbation_type: [chemical] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: true + clinical_outcome: false + n_samples: "333,263 profiles; 1,760 molecules; 30 contexts; 978 landmark genes" + n_cells: null + access: open + last_checked: 2026-10-02 + metadata_sources: + - https://github.com/theislab/Chem-PerturBridge diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.pharmacogenomics-v1.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.pharmacogenomics-v1.yml index a9fdacb..866d3a1 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.pharmacogenomics-v1.yml +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.pharmacogenomics-v1.yml @@ -2,9 +2,9 @@ title: "Provenance-backed drug-response and pharmacogenomics enrichment batch." task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.pharmacogenomics-v1.yml -upstream_sha: 7a064bf0 -imported_at: 2026-08-08 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.pharmacogenomics-v1.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -20,6 +20,25 @@ resources: last_checked: 2026-08-08 metadata_sources: - https://biodev.github.io/BeatAML2/ + dataset_profile: + year: 2018 + readout: ex-vivo-viability + time: false + view_scope: full-resource + species: [human] + sample_type: patient + biological_context: cancer + perturbation_type: [chemical] + paired: true + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: true + n_samples: "primary AML patient samples" + n_cells: null + access: open cancer_therapeutics_response_portal_ctrp: entities: [cell, drug] @@ -27,6 +46,27 @@ resources: last_checked: 2026-08-08 metadata_sources: - https://portals.broadinstitute.org/ctrp/ + dataset_profile: + year: 2013 + readout: viability + n_compounds: ">400" + n_contexts: "~900 cell lines" + time: false + view_scope: full-resource + species: [human] + sample_type: cell-line + biological_context: cancer + perturbation_type: [chemical] + paired: true + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: "~900 cancer cell lines" + n_cells: null + access: open bindingdb_curated_sets: entities: [molecule, protein] @@ -71,9 +111,30 @@ resources: modalities: [single-cell-rna-seq] tasks: [perturbation-prediction] github: https://github.com/sanderlab/scPerturb - last_checked: 2026-08-08 + last_checked: 2026-10-02 metadata_sources: - https://github.com/sanderlab/scPerturb + dataset_profile: + year: 2024 + readout: single-cell-rna + gene_panel: "varies by source study" + time: true + view_scope: harmonized-collection + species: [human, mouse] + sample_type: cell-line + biological_context: perturbation-collection + perturbation_type: [chemical, genetic] + genetic_modes: [mixed] + paired: false + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: "scPerturb integrated perturbation datasets" + n_cells: "multi-study single-cell compendium" + access: open genomics_of_drug_sensitivity_in_cancer_gdsc: entities: [cell, drug, gene] @@ -82,6 +143,26 @@ resources: last_checked: 2026-08-08 metadata_sources: - https://www.cancerrxgene.org/ + dataset_profile: + year: 2012 + readout: viability + n_contexts: "~1,000 cell lines" + time: false + view_scope: full-resource + species: [human] + sample_type: cell-line + biological_context: cancer + perturbation_type: [chemical] + paired: true + pre_post: false + longitudinal: false + drug_identity: true + dose: true + smiles: partial + clinical_outcome: false + n_samples: "~1,000 cancer cell lines" + n_cells: null + access: open cellminer_cross_database_cellminercdb: entities: [cell, drug, gene] diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.json b/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.json index adc387b..75a075d 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.json +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.json @@ -2,9 +2,9 @@ title: "Resources" task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/c6f07d90/data/resources.json -upstream_sha: c6f07d90 -imported_at: 2026-08-31 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/resources.json +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -221,13 +221,64 @@ validated: false "url": "https://www.internationalgenome.org/", "description": "Reference panel of human genetic variation from 2,504 individuals across 26 populations.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "adamson_et_al_2016_perturb_seq", + "name": "Adamson et al. 2016 Perturb-seq", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546", + "description": "CRISPRi Perturb-seq in K562 cells for systematic dissection of the unfolded protein response.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2016.11.048", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546" + ], + "dataset_profile": { + "year": 2016, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "unfolded-protein-response", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri", + "combinatorial" + ], + "readout": "single-cell-rna", + "n_profiles": "~86,000 cells", + "n_perturbations": "~100 UPR hits plus combinatorial CRISPRi perturbations", + "n_contexts": 3, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "three pooled CRISPRi Perturb-seq experiments in K562", + "n_cells": "~86,000", + "access": "open" + } + }, { "id": "bace", "name": "BACE", @@ -235,7 +286,7 @@ validated: false "url": "https://www.kaggle.com/datasets/gokturkkoch/bace", "description": "Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "classification", @@ -262,7 +313,7 @@ validated: false "url": "https://biodev.github.io/BeatAML2/", "description": "Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -281,7 +332,31 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://biodev.github.io/BeatAML2/" - ] + ], + "dataset_profile": { + "year": 2018, + "readout": "ex-vivo-viability", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "patient", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": true, + "n_samples": "primary AML patient samples", + "n_cells": null, + "access": "open" + } }, { "id": "bento", @@ -290,7 +365,7 @@ validated: false "url": "https://github.com/LigandPro/Bento", "description": "Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -304,7 +379,7 @@ validated: false "url": "https://www.bindingdb.org/rwd/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes", "description": "Curated binding affinity datasets for protein–ligand interaction benchmarking.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "drug-target-interaction" @@ -330,7 +405,7 @@ validated: false "url": "https://portals.broadinstitute.org/ctrp/", "description": "Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -345,7 +420,47 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://portals.broadinstitute.org/ctrp/" - ] + ], + "dataset_profile": { + "year": 2013, + "readout": "viability", + "n_compounds": ">400", + "n_contexts": "~900 cell lines", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "~900 cancer cell lines", + "n_cells": null, + "access": "open" + } + }, + { + "id": "chem_perturbridge", + "name": "Chem-PerturBridge", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Harmonized compendium and processing pipelines for small-molecule perturbation transcriptomics across heterogeneous assays and gene panels.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false }, { "id": "clintox", @@ -354,7 +469,7 @@ validated: false "url": "https://tdcommons.ai/single_pred_tasks/tox/#clintox", "description": "Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "classification" @@ -379,7 +494,7 @@ validated: false "url": "https://proteomics.cancer.gov/programs/cptac", "description": "Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], "modalities": [], @@ -393,13 +508,117 @@ validated: false "url": "https://arxiv.org/abs/2001.01037", "description": "Large-scale dataset for structure-based virtual screening.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "datlinger_et_al_2017_crop_seq", + "name": "Datlinger et al. 2017 CROP-seq", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872", + "description": "Pooled CRISPR knockout screen with single-cell transcriptomic readout in Jurkat cells under T-cell receptor stimulation.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1038/nmeth.4177", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872", + "https://doi.org/10.1038/nmeth.4177" + ], + "dataset_profile": { + "year": 2017, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "t-cell-receptor-signaling", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispr-ko" + ], + "readout": "single-cell-rna", + "n_profiles": 5905, + "n_perturbations": "TCR regulators and transcription factors", + "n_contexts": 2, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "stimulated and unstimulated Jurkat CROP-seq", + "n_cells": 5905, + "access": "open" + } + }, + { + "id": "dixit_et_al_2016_perturb_seq", + "name": "Dixit et al. 2016 Perturb-seq", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063", + "description": "Pooled CRISPR knockout with single-cell RNA-seq across K562 and dendritic-cell screens, including stimulated conditions and combinatorial perturbations.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2016.11.038", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063", + "https://doi.org/10.1016/j.cell.2016.11.038" + ], + "dataset_profile": { + "year": 2016, + "view_scope": "full-study", + "species": [ + "human", + "mouse" + ], + "sample_type": "cell-line+primary", + "biological_context": "immune-and-cell-line", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispr-ko", + "combinatorial" + ], + "readout": "single-cell-rna", + "n_profiles": "~200,000 cells", + "n_perturbations": "pooled CRISPR-KO across six screens", + "n_contexts": 6, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "six pooled screens across K562 and dendritic-cell conditions", + "n_cells": "~200,000", + "access": "open" + } + }, { "id": "dud_e_directory_of_useful_decoys_enhanced", "name": "DUD-E (Directory of Useful Decoys, Enhanced)", @@ -407,7 +626,7 @@ validated: false "url": "http://dude.docking.org/", "description": "Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -421,13 +640,160 @@ validated: false "url": "https://github.com/J-SNACKKB/FLIP", "description": "Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "frangieh_et_al_2021_perturb_cite_seq", + "name": "Frangieh et al. 2021 Perturb-CITE-seq", + "type": "benchmark", + "url": "https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion", + "description": "Multimodal CRISPR knockout screen with RNA and surface-protein readouts in patient-derived melanoma models under immune-related conditions.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1038/s41588-021-00779-1", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion", + "https://doi.org/10.1038/s41588-021-00779-1" + ], + "dataset_profile": { + "year": 2021, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "patient-derived-cell-line", + "biological_context": "melanoma-immunotherapy", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispr-ko" + ], + "readout": "cite-seq", + "n_profiles": ">200,000 cells", + "n_perturbations": 248, + "n_contexts": 3, + "gene_panel": "transcriptome + surface proteins", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "patient-derived melanoma under control, IFN-gamma, and TIL co-culture conditions", + "n_cells": ">200,000", + "access": "mixed" + } + }, + { + "id": "gasperini_et_al_2019_crispri_enhancer_screen", + "name": "Gasperini et al. 2019 CRISPRi enhancer screen", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861", + "description": "High-MOI single-cell CRISPRi screen in K562 cells targeting thousands of candidate enhancers to map enhancer-gene regulation.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2018.11.029", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861" + ], + "dataset_profile": { + "year": 2019, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "enhancer-gene-regulation", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri", + "enhancer-targeting" + ], + "readout": "single-cell-rna", + "n_profiles": 254974, + "n_perturbations": 5920, + "n_contexts": 3, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "three CRISPRi screens in K562, including an at-scale enhancer screen", + "n_cells": 254974, + "access": "open" + } + }, + { + "id": "gse191127_breast_cancer_pre_post_chemotherapy", + "name": "GSE191127 Breast Cancer Pre/Post Chemotherapy", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127", + "description": "Bulk RNA-seq and genomics from matched pre- and post-neoadjuvant chemotherapy breast tumors with treatment outcomes.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2022, + "view_scope": "study-cohort", + "readout": "bulk-rna", + "n_profiles": "317 pretreatment; 22 matched pre/post deeply profiled", + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "time": true, + "species": [ + "human" + ], + "sample_type": "patient", + "biological_context": "breast-cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": true, + "longitudinal": true, + "drug_identity": "limited", + "dose": false, + "smiles": "partial", + "clinical_outcome": true, + "n_samples": "317 pretreatment biopsies; 22 matched pre/post tumors with deep profiling", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127" + ] + }, { "id": "guacamol", "name": "GuacaMol", @@ -435,7 +801,7 @@ validated: false "url": "https://github.com/BenevolentAI/guacamol", "description": "Benchmark suite for generative molecular design models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "molecular-generation" @@ -461,7 +827,7 @@ validated: false "url": "https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics", "description": "DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [ "regression" @@ -491,7 +857,7 @@ validated: false "url": "https://github.com/jump-cellpainting/datasets", "description": "Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [], "modalities": [], @@ -505,7 +871,7 @@ validated: false "url": "https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data", "description": "Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "perturbation-prediction" @@ -523,6 +889,176 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data" + ], + "dataset_profile": { + "year": 2017, + "readout": "l1000", + "gene_panel": "978 landmark genes", + "time": true, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical", + "genetic" + ], + "genetic_modes": [ + "mixed" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "large-scale perturbational profiles", + "n_cells": null, + "access": "open" + } + }, + { + "id": "lincs_l1000_phase_1", + "name": "LINCS L1000 Phase 1", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Phase-1 L1000 benchmark view used by Bison with 692,787 profiles, 9,233 molecules, 70 contexts, and 978 landmark genes.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2017, + "view_scope": "bison-benchmark-view", + "readout": "l1000", + "n_profiles": 692787, + "n_compounds": 9233, + "n_contexts": 70, + "gene_panel": "978 landmark genes", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "692,787 profiles; 9,233 molecules; 70 contexts; 978 landmark genes", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, + { + "id": "lincs_l1000_phase_2", + "name": "LINCS L1000 Phase 2", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Phase-2 L1000 benchmark view used by Bison with 333,263 profiles, 1,760 molecules, 30 contexts, and 978 landmark genes.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2017, + "view_scope": "bison-benchmark-view", + "readout": "l1000", + "n_profiles": 333263, + "n_compounds": 1760, + "n_contexts": 30, + "gene_panel": "978 landmark genes", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "333,263 profiles; 1,760 molecules; 30 contexts; 978 landmark genes", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, + { + "id": "mix_seq", + "name": "MIX-Seq", + "type": "benchmark", + "url": "https://www.nature.com/articles/s41467-020-17440-w", + "description": "Multiplexed single-cell transcriptional profiling of chemical and genetic perturbation responses across pools of cancer cell lines.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2020, + "view_scope": "full-resource", + "readout": "single-cell-rna", + "n_compounds": 13, + "n_contexts": "100+ cell lines", + "gene_panel": "transcriptome-wide", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical", + "genetic" + ], + "genetic_modes": [ + "mixed" + ], + "paired": false, + "pre_post": false, + "longitudinal": true, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "pools of up to 100+ cancer cell lines; 13-drug demonstration", + "n_cells": "single-cell RNA-seq", + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.nature.com/articles/s41467-020-17440-w" ] }, { @@ -532,7 +1068,7 @@ validated: false "url": "http://moleculenet.ai/", "description": "Benchmark datasets for molecular machine learning.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "classification", @@ -559,13 +1095,111 @@ validated: false "url": "https://github.com/molecularsets/moses", "description": "Benchmarking platform for molecular generation models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "norman_et_al_2019_crispra_perturb_seq", + "name": "Norman et al. 2019 CRISPRa Perturb-seq", + "type": "benchmark", + "url": "https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323", + "description": "Large-scale CRISPR activation Perturb-seq in K562 cells spanning single-gene and combinatorial perturbations.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1126/science.aax4438", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323", + "https://doi.org/10.1126/science.aax4438" + ], + "dataset_profile": { + "year": 2019, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "genetic-interactions", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispra", + "combinatorial" + ], + "readout": "single-cell-rna", + "n_profiles": "~110,000 cells", + "n_perturbations": 287, + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "single-gene and pairwise CRISPRa perturbations in K562", + "n_cells": "~110,000", + "access": "open" + } + }, + { + "id": "novartis_perturbation_dataset", + "name": "Novartis Perturbation Dataset", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Large chemical perturbation transcriptomic screen used in Chem-PerturBridge; the Bison benchmark view contains 46,748 profiles and 3,770 molecules.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2025, + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 46748, + "n_compounds": 3770, + "n_contexts": 1, + "gene_panel": "20,038 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "46,748 profiles; 3,770 molecules; 1 context", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, { "id": "ogb_open_graph_benchmark", "name": "OGB (Open Graph Benchmark)", @@ -573,13 +1207,59 @@ validated: false "url": "https://ogb.stanford.edu/", "description": "Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.", "tags": [ - "benchmarks-and-datasets" + "general-biological-ml-and-knowledge-graph-benchmarks" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "op3", + "name": "OP3", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Chemical perturbation dataset used by Chem-PerturBridge and the Bison unseen-compound benchmark; the Bison benchmark view contains 1,813 profiles, 138 molecules, and 4 cellular contexts.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2024, + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 1813, + "n_compounds": 138, + "n_contexts": 4, + "gene_panel": "5,198 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "1,813 profiles; 138 molecules; 4 contexts", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, { "id": "openbiolink", "name": "OpenBioLink", @@ -587,13 +1267,59 @@ validated: false "url": "https://github.com/OpenBioLink/OpenBioLink", "description": "Benchmark datasets for biological knowledge graph completion.", "tags": [ - "benchmarks-and-datasets" + "general-biological-ml-and-knowledge-graph-benchmarks" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "pdx_atlas", + "name": "PDX-Atlas", + "type": "benchmark", + "url": "https://muralportal.pdxatlas.org/", + "description": "Portal integrating clinical, genomic, transcriptomic, and drug-response data from patient-derived xenograft models.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2026, + "view_scope": "current-portal", + "readout": "multi-omics", + "n_profiles": ">50 PDXs", + "n_contexts": "30 patients", + "gene_panel": "multi-omics", + "time": false, + "species": [ + "human", + "mouse" + ], + "sample_type": "pdx", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": false, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": ">50 PDXs from 30 prostate cancer patients in the current portal", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://muralportal.pdxatlas.org/" + ] + }, { "id": "pharmgkb", "name": "PharmGKB", @@ -601,7 +1327,7 @@ validated: false "url": "https://www.pharmgkb.org/", "description": "Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -630,7 +1356,7 @@ validated: false "url": "https://pk-db.com/", "description": "Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [ @@ -653,7 +1379,7 @@ validated: false "url": "https://depmap.org/portal/prism/", "description": "Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -665,10 +1391,36 @@ validated: false "cell", "drug" ], - "last_checked": "2026-08-08", + "last_checked": "2026-10-02", "metadata_sources": [ "https://depmap.org/portal/prism/" - ] + ], + "dataset_profile": { + "year": 2020, + "readout": "viability", + "n_compounds": ">4,500", + "n_contexts": ">900 cell lines", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "PRISM pooled viability screens", + "n_cells": null, + "access": "open" + } }, { "id": "proteingym", @@ -677,7 +1429,7 @@ validated: false "url": "https://github.com/OATML-Markslab/ProteinGym", "description": "Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "regression" @@ -703,13 +1455,214 @@ validated: false "url": "https://figshare.com/collections/Quantum_chemistry_structures_and_properties_of_134_kilo_molecules/978904", "description": "Quantum chemistry properties for 134K stable small organic molecules computed at DFT level.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "replogle_et_al_2022_k562_essential_perturb_seq", + "name": "Replogle et al. 2022 K562 Essential Perturb-seq", + "type": "benchmark", + "url": "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "description": "CRISPRi Perturb-seq focused on essential genes in K562 cells.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2022.05.013", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "https://gwps.wi.mit.edu/" + ], + "dataset_profile": { + "year": 2022, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "essential-gene-screen", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri" + ], + "readout": "single-cell-rna", + "n_profiles": 310385, + "n_perturbations": 2057, + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "K562 CRISPRi essential-gene screen sampled at day 6", + "n_cells": 310385, + "access": "open" + } + }, + { + "id": "replogle_et_al_2022_k562_genome_wide_perturb_seq", + "name": "Replogle et al. 2022 K562 Genome-wide Perturb-seq", + "type": "benchmark", + "url": "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "description": "Genome-scale CRISPRi Perturb-seq targeting nearly all expressed genes in K562 cells.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2022.05.013", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "https://gwps.wi.mit.edu/" + ], + "dataset_profile": { + "year": 2022, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "genome-wide-functional-genomics", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri" + ], + "readout": "single-cell-rna", + "n_profiles": "genome-scale; mean ~183 cells per detected perturbation", + "n_perturbations": "9,866 target genes; 11,258 detected sgRNA-pair perturbations", + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "K562 CRISPRi genome-wide screen sampled at day 8", + "n_cells": "mean ~183 cells per detected perturbation", + "access": "open" + } + }, + { + "id": "replogle_et_al_2022_rpe1_essential_perturb_seq", + "name": "Replogle et al. 2022 RPE1 Essential Perturb-seq", + "type": "benchmark", + "url": "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "description": "CRISPRi Perturb-seq focused on essential genes in RPE1 cells.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2022.05.013", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "https://gwps.wi.mit.edu/" + ], + "dataset_profile": { + "year": 2022, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "essential-gene-screen", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri" + ], + "readout": "single-cell-rna", + "n_profiles": 247914, + "n_perturbations": 2393, + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "RPE1 CRISPRi essential-gene screen sampled at day 7", + "n_cells": 247914, + "access": "open" + } + }, + { + "id": "sci_plex", + "name": "sci-Plex", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944", + "description": "Single-cell chemical perturbation screen of ~650,000 transcriptomes across three cancer cell lines, 188 compounds, and four doses.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2020, + "view_scope": "bison-benchmark-view", + "readout": "pseudobulk-rna", + "n_profiles": 4974, + "n_compounds": 186, + "n_contexts": 3, + "gene_panel": "20,251 full / 2,000 HVG (Bison benchmark view)", + "time": true, + "species": [ + "human", + "mouse" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "~5,000 conditions; 188 compounds across 3 human cancer cell lines", + "n_cells": 649340, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944", + "https://pubmed.ncbi.nlm.nih.gov/31806696/" + ] + }, { "id": "scib_single_cell_integration_benchmarks", "name": "scIB (Single-cell Integration Benchmarks)", @@ -717,7 +1670,7 @@ validated: false "url": "https://github.com/theislab/scib", "description": "Comprehensive benchmarking framework for single-cell data integration methods.", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], @@ -731,7 +1684,7 @@ validated: false "url": "https://github.com/sanderlab/scPerturb", "description": "Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "perturbation-prediction" @@ -747,10 +1700,40 @@ validated: false "gene" ], "github": "https://github.com/sanderlab/scPerturb", - "last_checked": "2026-08-08", + "last_checked": "2026-10-02", "metadata_sources": [ "https://github.com/sanderlab/scPerturb" - ] + ], + "dataset_profile": { + "year": 2024, + "readout": "single-cell-rna", + "gene_panel": "varies by source study", + "time": true, + "view_scope": "harmonized-collection", + "species": [ + "human", + "mouse" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-collection", + "perturbation_type": [ + "chemical", + "genetic" + ], + "genetic_modes": [ + "mixed" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "scPerturb integrated perturbation datasets", + "n_cells": "multi-study single-cell compendium", + "access": "open" + } }, { "id": "sider_side_effect_resource", @@ -759,7 +1742,7 @@ validated: false "url": "http://sideeffects.embl.de/", "description": "Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [ @@ -783,7 +1766,7 @@ validated: false "url": "https://tabula-muris.ds.czbiohub.org/", "description": "Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], @@ -797,13 +1780,59 @@ validated: false "url": "https://tabula-sapiens-portal.ds.czbiohub.org/", "description": "Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "tahoe_100m", + "name": "Tahoe-100M", + "type": "benchmark", + "url": "https://huggingface.co/datasets/tahoebio/Tahoe-100M", + "description": "Giga-scale single-cell perturbation atlas with >100 million profiles from 50 cancer cell lines exposed to ~1,100 small molecules.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2025, + "view_scope": "bison-benchmark-view", + "readout": "single-cell-rna", + "n_profiles": 67018, + "n_compounds": 376, + "n_contexts": 50, + "gene_panel": "20,573 full / 2,000 HVG (Bison benchmark view)", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": ">100 million profiles; 50 cancer cell lines; ~1,100 compounds", + "n_cells": ">100 million", + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/datasets/tahoebio/Tahoe-100M" + ] + }, { "id": "tape_tasks_assessing_protein_embeddings", "name": "TAPE (Tasks Assessing Protein Embeddings)", @@ -811,7 +1840,7 @@ validated: false "url": "https://github.com/songlab-cal/tape", "description": "Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -825,7 +1854,7 @@ validated: false "url": "https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas", "description": "DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], @@ -839,12 +1868,48 @@ validated: false "url": "https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga", "description": "Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], - "modalities": [], + "modalities": [ + "clinical", + "genomics" + ], "organism": [], - "api": false + "api": false, + "entities": [ + "disease", + "gene" + ], + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.cancer.gov/ccg/research/genome-sequencing/tcga" + ], + "dataset_profile": { + "year": 2018, + "readout": "multi-omics", + "n_profiles": ">11,000 patients", + "n_contexts": 33, + "gene_panel": "transcriptome-wide plus multi-omics", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "patient", + "biological_context": "cancer", + "perturbation_type": [], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": "limited", + "dose": false, + "smiles": false, + "clinical_outcome": true, + "n_samples": ">11,000 patients across 33 cancer types", + "n_cells": null, + "access": "mixed" + } }, { "id": "therapeutics_data_commons_tdc", @@ -853,7 +1918,7 @@ validated: false "url": "https://tdcommons.ai/", "description": "Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -867,7 +1932,7 @@ validated: false "url": "https://tripod.nih.gov/tox21/challenge/", "description": "12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -881,13 +1946,103 @@ validated: false "url": "https://www.ukbiobank.ac.uk/", "description": "Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "vcpi_0001", + "name": "VCPI-0001", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 27,517 profiles and 2,272 molecules in one cellular context.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 27517, + "n_compounds": 2272, + "n_contexts": 1, + "gene_panel": "20,629 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "27,517 profiles; 2,272 molecules; 1 context", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, + { + "id": "vcpi_0002", + "name": "VCPI-0002", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 18,139 profiles and 1,488 molecules in one cellular context.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 18139, + "n_compounds": 1488, + "n_contexts": 1, + "gene_panel": "20,629 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "18,139 profiles; 1,488 molecules; 1 context", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, { "id": "10x_genomics_dataset", "name": "10x Genomics Dataset", @@ -1662,8 +2817,8 @@ validated: false "url": "https://www.cancerrxgene.org/", "description": "Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.", "tags": [ - "benchmarks-and-datasets", "drug-cell-line-response", + "drug-response-and-perturbation", "interaction" ], "tasks": [ @@ -1682,7 +2837,32 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://www.cancerrxgene.org/" - ] + ], + "dataset_profile": { + "year": 2012, + "readout": "viability", + "n_contexts": "~1,000 cell lines", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "~1,000 cancer cell lines", + "n_cells": null, + "access": "open" + } }, { "id": "gnomad", @@ -2049,8 +3229,8 @@ validated: false "url": "https://dtp.cancer.gov/discovery_development/nci-60/", "description": "Focuses on 60 cancer cell lines and many drugs.", "tags": [ - "benchmarks-and-datasets", "drug-cell-line-response", + "drug-response-and-perturbation", "interaction" ], "tasks": [], @@ -2683,6 +3863,38 @@ validated: false "https://github.com/ratschlab/aestetik" ] }, + { + "id": "agent_laboratory", + "name": "Agent Laboratory", + "type": "model", + "url": "https://github.com/SamuelSchmidgall/AgentLaboratory", + "description": "End-to-end multi-agent research workflow for literature review, experimentation, implementation, analysis, and report generation.", + "tags": [ + "agentic-ai-for-biology", + "multi-agent-scientific-labs" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "scientific-workflow", + "research-automation" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": false, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "ai4chem_chemllm_7b_chat", "name": "AI4Chem/ChemLLM-7B-Chat", @@ -2735,6 +3947,61 @@ validated: false "https://github.com/google-deepmind/alphafold3" ] }, + { + "id": "alphagenome", + "name": "AlphaGenome", + "type": "model", + "url": "https://github.com/google-deepmind/alphagenome", + "description": "Long-context DNA model predicting multimodal regulatory outputs including expression, splicing, chromatin features, and contact maps at near base-pair resolution.", + "tags": [ + "foundation-models", + "genomics-foundation-models" + ], + "tasks": [ + "regression", + "representation-learning" + ], + "modalities": [ + "dna-sequence", + "epigenomics", + "genomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "dna" + ], + "pretraining_scale": "long-context regulatory genomics corpus", + "species": [ + "human", + "mouse" + ], + "zero_shot": true, + "finetunable": "unknown", + "weights": false, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "genome", + "regulatory-element", + "variant" + ], + "methods": [ + "supervised-learning", + "transformer" + ], + "year": 2025, + "github": "https://github.com/google-deepmind/alphagenome", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/google-deepmind/alphagenome" + ] + }, { "id": "ankh", "name": "Ankh", @@ -2755,6 +4022,72 @@ validated: false "organism": [], "api": false }, + { + "id": "atacformer", + "name": "Atacformer", + "type": "model", + "url": "https://doi.org/10.1101/2025.11.03.685753", + "description": "Transformer foundation model for scATAC-seq that learns embeddings of cis-regulatory elements for clustering, annotation, and reference mapping.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": "large atlas of scATAC-seq experiments", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, + { + "id": "autoba", + "name": "AutoBA", + "type": "model", + "url": "https://github.com/JoshuaChou2018/AutoBA", + "description": "Automated multi-omics analysis agent that plans, executes, and repairs bioinformatics workflows from natural-language objectives.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2024, + "domains": [ + "bioinformatics", + "omics", + "scientific-workflow" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "babel", "name": "BABEL", @@ -2795,6 +4128,71 @@ validated: false "organism": [], "api": false }, + { + "id": "bia", + "name": "BIA", + "type": "model", + "url": "https://github.com/biagent-dev/bia", + "description": "Bioinformatics agent for GEO search, sample metadata extraction, count-matrix processing, and pipeline extraction from papers.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "domains": [ + "bioinformatics", + "genomics", + "literature" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "bioagents", + "name": "BioAgents", + "type": "model", + "url": "https://github.com/microsoft/bioinformagus", + "description": "Multi-agent bioinformatics assistant using specialized language models and retrieval for genomics workflow development and troubleshooting.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "bioinformatics", + "genomics", + "scientific-workflow" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "biogpt", "name": "BioGPT", @@ -2813,6 +4211,106 @@ validated: false "organism": [], "api": false }, + { + "id": "biolord", + "name": "biolord", + "type": "model", + "url": "https://github.com/nitzanlab/biolord", + "description": "Deep generative model that disentangles known and unknown attributes for conditional generation of single-cell states.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2024, + "input": "expression + known attributes", + "drug_representation": "chemical descriptors", + "context_representation": "unknown latent attributes", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, + { + "id": "biomaster", + "name": "BioMaster", + "type": "model", + "url": "https://github.com/ai4nucleome/BioMaster", + "description": "Multi-agent system for automated and auditable bioinformatics workflows spanning RNA-seq, ChIP-seq, single-cell, spatial omics, Hi-C, long reads, metagenomics, and proteomics.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "bioinformatics", + "omics", + "scientific-workflow" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "biomedagent", + "name": "BioMedAgent", + "type": "model", + "url": "https://github.com/BOBQWERA/BioMedAgent", + "description": "Self-evolving multi-agent framework for autonomous biomedical data analysis with tool discovery, workflow planning, code generation, execution, correction, and cross-omics analysis.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "general-biomedicine", + "bioinformatics", + "omics" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "biomedclip", "name": "BiomedCLIP", @@ -2850,6 +4348,74 @@ validated: false "organism": [], "api": false }, + { + "id": "biomni", + "name": "Biomni", + "type": "model", + "url": "https://github.com/snap-stanford/Biomni", + "description": "General-purpose biomedical AI agent integrating planning, code execution, specialized tools, databases, and software across diverse biomedical research tasks.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "general-biomedicine", + "bioinformatics", + "therapeutics", + "omics" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": "limited", + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "bison", + "name": "Bison", + "type": "model", + "url": "https://arxiv.org/abs/2609.32467", + "description": "Cross-dataset model for globally unseen-compound response prediction using a shared gene representation, discrete diffusion models, and matched drug-contrast supervision.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "bulk/pseudobulk transcriptomics", + "drug_representation": "Morgan fingerprints + chemical tokens", + "context_representation": "control-derived context features + embeddings", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": true, + "patient_transfer": false, + "code": false + } + }, { "id": "boltz_1", "name": "Boltz-1", @@ -2871,6 +4437,64 @@ validated: false "organism": [], "api": false }, + { + "id": "boltz_2", + "name": "Boltz-2", + "type": "model", + "url": "https://github.com/jwohlwend/boltz", + "description": "Biomolecular foundation model jointly predicting complex structures and binding affinities for protein–ligand interaction modeling and virtual screening.", + "tags": [ + "foundation-models", + "protein-foundation-models", + "protein-structure-prediction-and-design" + ], + "tasks": [ + "drug-target-interaction", + "structure-prediction", + "virtual-screening" + ], + "modalities": [ + "molecular-structure", + "protein-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "protein", + "chemical" + ], + "pretraining_scale": "biomolecular complex structure and affinity data", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": "limited", + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule", + "protein", + "protein-complex" + ], + "methods": [ + "diffusion", + "generative-model", + "transformer" + ], + "year": 2025, + "github": "https://github.com/jwohlwend/boltz", + "paper": "https://doi.org/10.1101/2025.06.14.659707", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/jwohlwend/boltz", + "https://doi.org/10.1101/2025.06.14.659707" + ] + }, { "id": "borzoi", "name": "Borzoi", @@ -2890,6 +4514,39 @@ validated: false "organism": [], "api": false }, + { + "id": "brad", + "name": "BRAD", + "type": "model", + "url": "https://github.com/Jpickard1/BRAD", + "description": "Retrieval-augmented bioinformatics assistant integrating scientific literature, databases, external tools, and executable workflows.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "bioinformatics", + "literature", + "scientific-workflow" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "bulkformer", "name": "BulkFormer", @@ -2909,7 +4566,23 @@ validated: false "Transcriptomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "bulk-rna" + ], + "pretraining_scale": "bulk RNA-seq compendium", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "caduceus", @@ -2928,7 +4601,23 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "dna" + ], + "pretraining_scale": "long-range genomic sequence pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "cancerfoundation", @@ -2949,25 +4638,216 @@ validated: false "Transcriptomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna" + ], + "pretraining_scale": "curated malignant-cell single-cell corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } + }, + { + "id": "captain", + "name": "CAPTAIN", + "type": "model", + "url": "https://doi.org/10.1038/s41467-026-72882-y", + "description": "Multimodal foundation model pretrained on co-assayed single-cell RNA and protein for joint representation learning and cross-modal downstream tasks.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna", + "proteomics", + "multi-omics" + ], + "pretraining_scale": "co-assayed single-cell RNA and protein data", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, + { + "id": "casanovo_foundation", + "name": "Casanovo Foundation", + "type": "model", + "url": "https://github.com/Noble-Lab/casanovo-tl", + "description": "Tandem mass-spectrometry proteomics foundation model that reuses a pretrained Casanovo spectrum encoder for spectrum quality, chimericity, and post-translational-modification prediction.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "proteomics" + ], + "pretraining_scale": "30M labeled tandem mass spectra", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "cassia", "name": "CASSIA", "type": "model", "url": "https://github.com/ElliotXie/CASSIA", - "description": "Multi-agent LLM for reference-free, interpretable cell-type annotation of single-cell RNA-seq data, with dedicated annotation, validation, scoring, and reporting agents.", + "description": "Multi-agent LLM framework for reference-free and interpretable single-cell cell-type annotation with dedicated annotation, validation, scoring, and reporting agents.", "tags": [ - "llm-for-biology" + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2024, + "domains": [ + "single-cell", + "bioinformatics", + "omics" + ], + "architecture": "multi-agent", + "tool_use": "limited", + "code_execution": "limited", + "web_retrieval": "limited", + "literature": true, + "omics": true, + "wet_lab": false, + "autonomous_experiment": false, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "cellflow", + "name": "CellFlow", + "type": "model", + "url": "https://github.com/theislab/CellFlow", + "description": "Conditional flow-matching framework for modeling and predicting cellular phenotypes under chemical, genetic, and other perturbations.", + "tags": [ + "drug-discovery", + "drug-perturbation" ], "tasks": [ - "Language Modeling" + "Drug Discovery", + "Drug Perturbation" ], "modalities": [ - "Text" + "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2025, + "input": "control/treated expression distributions", + "drug_representation": "molecular features", + "context_representation": "matched control distribution", + "output": "transported cell-state distribution", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, + { + "id": "cellfm", + "name": "CellFM", + "type": "model", + "url": "https://github.com/biomed-AI/CellFM", + "description": "800M-parameter single-cell foundation model pretrained on transcriptomics from 100 million human cells for annotation, integration, gene-function, and perturbation tasks.", + "tags": [ + "foundation-models", + "single-cell-foundation-models", + "transcriptomics-foundation-models" + ], + "tasks": [ + "cell-type-annotation", + "foundation-model-pretraining", + "perturbation-prediction", + "representation-learning" + ], + "modalities": [ + "single-cell-rna-seq", + "transcriptomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna" + ], + "params": "800M", + "params_millions": 800, + "pretraining_scale": "100M human cells", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": false + }, + "entities": [ + "cell", + "gene" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "github": "https://github.com/biomed-AI/CellFM", + "paper": "https://doi.org/10.1038/s41467-025-59926-5", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/biomed-AI/CellFM", + "https://doi.org/10.1038/s41467-025-59926-5" + ] }, { "id": "cellot", @@ -3025,7 +4905,23 @@ validated: false "metadata_sources": [ "https://github.com/OmicsML/CellPLM", "https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1" - ] + ], + "foundation_profile": { + "year": 2023, + "modalities": [ + "scrna" + ], + "pretraining_scale": "multi-dataset single-cell pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } }, { "id": "chai_1", @@ -3117,7 +5013,73 @@ validated: false "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2022, + "input": "single-cell expression + covariates", + "drug_representation": "RDKit2D descriptors", + "context_representation": "cell covariates + basal state", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": "limited", + "patient_transfer": false, + "code": true + } + }, + { + "id": "chemfm", + "name": "ChemFM", + "type": "model", + "url": "https://github.com/TheLuoFengLab/ChemFM", + "description": "1B/3B-parameter chemical language model pretrained on 178M molecules for molecular representation, property prediction, generation, and synthesis tasks.", + "tags": [ + "compound-embedding", + "compound-foundation-models", + "foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "molecular-generation", + "representation-learning" + ], + "modalities": [ + "chemical-structure" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "chemical" + ], + "params": "1B / 3B", + "params_millions": 3000, + "pretraining_scale": "178M molecules", + "species": [], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule" + ], + "methods": [ + "generative-model", + "language-model", + "transformer" + ], + "year": 2024, + "github": "https://github.com/TheLuoFengLab/ChemFM", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/TheLuoFengLab/ChemFM" + ] }, { "id": "chief", @@ -3138,6 +5100,39 @@ validated: false "organism": [], "api": false }, + { + "id": "chromfound", + "name": "ChromFound", + "type": "model", + "url": "https://github.com/SAIS-LifeScience/ChromFound", + "description": "Genome-aware scATAC-seq foundation model pretrained on 1.97M cells across tissues and disease contexts for zero-shot cell representations, annotation, and cross-omics prediction.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": "1.97M scATAC-seq cells; 30 tissues; 6 disease contexts", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "clawbio", "name": "ClawBio", @@ -3145,16 +5140,67 @@ validated: false "url": "https://github.com/ClawBio/ClawBio", "description": "Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.", "tags": [ - "llm-for-biology" + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "bioinformatics", + "genomics", + "pharmacogenomics", + "omics" + ], + "architecture": "agent-ecosystem", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": false, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "clm_x", + "name": "CLM-X", + "type": "model", + "url": "https://doi.org/10.64898/2026.02.17.704943", + "description": "Multi-way Transformer foundation model jointly handling RNA-only, ATAC-only, and paired RNA–ATAC single-cell inputs for integration, translation, annotation, and perturbation prediction.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" ], "tasks": [ - "Language Modeling" - ], - "modalities": [ - "Text" + "Foundation Model" ], + "modalities": [], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna", + "atac", + "multi-omics" + ], + "pretraining_scale": "million-scale RNA-only, ATAC-only, and paired RNA-ATAC data", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": true, + "spatial": false + } }, { "id": "cmonge", @@ -3174,7 +5220,21 @@ validated: false "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "source/target expression distributions", + "drug_representation": "RDKit2D descriptors", + "context_representation": "matched control distribution", + "output": "transported expression distribution", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": "limited", + "patient_transfer": false, + "code": true + } }, { "id": "concerto", @@ -3231,6 +5291,39 @@ validated: false "https://github.com/mahmoodlab/CONCH" ] }, + { + "id": "cpgpt", + "name": "CpGPT", + "type": "model", + "url": "https://github.com/lucascamillomd/CpGPT", + "description": "DNA methylation foundation model pretrained on >150K samples for zero-shot imputation, array conversion, reference mapping, and downstream phenotype prediction.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "methylation" + ], + "pretraining_scale": "150K+ methylation samples across >2,000 datasets", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "cyclecdr", "name": "cycleCDR", @@ -3555,7 +5648,25 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "dna" + ], + "params": "117M", + "params_millions": 117, + "pretraining_scale": "multi-species GenBank genome corpus", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "drgat", @@ -3577,6 +5688,39 @@ validated: false "organism": [], "api": false }, + { + "id": "drugagent", + "name": "DrugAgent", + "type": "model", + "url": "https://github.com/inoue0426/DrugAgent", + "description": "Multi-agent biomedical evidence synthesis framework for computational drug discovery with reliability-aware aggregation.", + "tags": [ + "agentic-ai-for-biology", + "therapeutics-and-drug-discovery-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "drug-discovery", + "therapeutics", + "evidence-synthesis" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "drugban", "name": "DrugBAN", @@ -3639,6 +5783,40 @@ validated: false "organism": [], "api": false }, + { + "id": "dtlcdr", + "name": "DTLCDR", + "type": "model", + "url": "https://doi.org/10.1016/j.jpha.2025.101315", + "description": "Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2025, + "input": "multimodal tumor/cell-line features + targets", + "drug_representation": "target-aware drug representation", + "context_representation": "biological sample representation", + "output": "preclinical/clinical response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": false, + "time": false, + "patient_transfer": true, + "code": false + } + }, { "id": "enformer", "name": "Enformer", @@ -3658,6 +5836,127 @@ validated: false "organism": [], "api": false }, + { + "id": "epiagent", + "name": "EpiAgent", + "type": "model", + "url": "https://github.com/xy-chen16/EpiAgent", + "description": "scATAC-seq foundation model pretrained on ~5M cells and >35B tokens for representation learning, annotation, imputation, perturbation prediction, and in-silico cCRE knockout.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": ">5.4M scATAC-seq cells across 37 tissues or cell lines", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": false + } + }, + { + "id": "epifoundation", + "name": "EpiFoundation", + "type": "model", + "url": "https://doi.org/10.1101/2025.02.05.636688", + "description": "Foundation model for scATAC-seq using peak-to-gene aligned pretraining for cell representation, annotation, batch correction, and gene-expression prediction.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": "large-scale scATAC-seq pretraining with peak-to-gene alignment", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "limited", + "code": "limited", + "perturbation": false, + "spatial": false + } + }, + { + "id": "esm_cambrian_esm_c", + "name": "ESM Cambrian (ESM C)", + "type": "model", + "url": "https://github.com/Biohub/esm", + "description": "Protein representation foundation-model family designed as an efficient next-generation successor to ESM2, spanning 300M to multi-billion-parameter models.", + "tags": [ + "foundation-models", + "pre-trained-embedding", + "protein-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "protein-function-prediction", + "representation-learning" + ], + "modalities": [ + "protein-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "protein" + ], + "params": "300M / 600M / 6B", + "params_millions": 6000, + "pretraining_scale": "evolutionary-scale protein sequence corpus", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": "limited", + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "protein" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "github": "https://github.com/Biohub/esm", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/Biohub/esm", + "https://www.evolutionaryscale.ai/blog/esm-cambrian" + ] + }, { "id": "esm3", "name": "ESM3", @@ -3679,6 +5978,24 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "protein" + ], + "params": "1.4B / 7B / 98B", + "params_millions": 98000, + "pretraining_scale": "multimodal protein sequence, structure, and function corpus", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": "limited", + "weights": "limited", + "code": true, + "perturbation": false, + "spatial": false + }, "entities": [ "protein" ], @@ -3747,6 +6064,62 @@ validated: false "organism": [], "api": false }, + { + "id": "evo_2", + "name": "Evo 2", + "type": "model", + "url": "https://github.com/arcinstitute/evo2", + "description": "Genome foundation model trained on 9 trillion DNA base pairs across all domains of life with a 1M-token context window and single-nucleotide resolution.", + "tags": [ + "foundation-models", + "genomics-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "dna-sequence", + "genomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "dna" + ], + "params": "1B / 7B / 40B", + "params_millions": 40000, + "pretraining_scale": "9T DNA base pairs", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "genome", + "organism" + ], + "methods": [ + "generative-model", + "language-model" + ], + "year": 2026, + "github": "https://github.com/arcinstitute/evo2", + "paper": "https://doi.org/10.1038/s41586-026-10176-5", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/arcinstitute/evo2", + "https://doi.org/10.1038/s41586-026-10176-5" + ] + }, { "id": "evodiff", "name": "EvoDiff", @@ -3801,6 +6174,40 @@ validated: false "https://github.com/facebookresearch/esm" ] }, + { + "id": "expresso", + "name": "EXPRESSO", + "type": "model", + "url": "https://doi.org/10.1158/0008-5472.CAN-25-5220", + "description": "Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2026, + "input": "pretreatment tumor transcriptomics", + "drug_representation": "drug targets + biomarkers", + "context_representation": "tumor transcriptomic context", + "output": "patient treatment response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": false, + "time": false, + "patient_transfer": true, + "code": false + } + }, { "id": "gears", "name": "GEARS", @@ -3822,6 +6229,63 @@ validated: false "organism": [], "api": false }, + { + "id": "genbio_pathfm", + "name": "GenBio-PathFM", + "type": "model", + "url": "https://github.com/genbio-ai/genbio-pathfm", + "description": "1.1B-parameter histopathology foundation model trained on public data using morphology-aware curation and dual-stage JEPA+DINO learning.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "pathology" + ], + "params": "1.1B", + "params_millions": 1100, + "pretraining_scale": "public histopathology corpus with morphology-aware curation", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2026, + "github": "https://github.com/genbio-ai/genbio-pathfm", + "paper": "https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/genbio-ai/genbio-pathfm", + "https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1" + ] + }, { "id": "genecompass", "name": "GeneCompass", @@ -3858,7 +6322,25 @@ validated: false "metadata_sources": [ "https://github.com/xCompass-AI/GeneCompass", "https://www.nature.com/articles/s41422-024-01034-y" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "dna", + "multi-omics" + ], + "pretraining_scale": "120M+ cells across multiple species", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } }, { "id": "geneformer", @@ -3898,7 +6380,25 @@ validated: false "metadata_sources": [ "https://huggingface.co/ctheodoris/Geneformer", "https://www.nature.com/articles/s41586-023-06139-9" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna" + ], + "params": "10M / 104M / 316M", + "params_millions": 316, + "pretraining_scale": "~104M human single-cell transcriptomes in V2; original corpus ~30M", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": false + } }, { "id": "genegpt", @@ -3954,6 +6454,72 @@ validated: false "https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2" ] }, + { + "id": "genomas", + "name": "GenoMAS", + "type": "model", + "url": "https://github.com/Liu-Hy/GenoMAS", + "description": "Multi-agent framework for code-driven gene-expression analysis with planning, execution, debugging, backtracking, and GEO/TCGA-based scientific discovery.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "bioinformatics", + "gene-expression", + "scientific-workflow" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": true, + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "genomi", + "name": "Genomi", + "type": "model", + "url": "https://github.com/exon-research/genomi", + "description": "Local-first genomics agent runtime that indexes personal variants, queries evidence, and generates evidence-grounded reports while keeping raw genome data on-device.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "genomics", + "precision-medicine", + "evidence-synthesis" + ], + "architecture": "agent-ecosystem", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": "limited", + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": false, + "human_in_loop": true, + "open_source": true + } + }, { "id": "gigapath", "name": "GigaPath", @@ -4026,7 +6592,23 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "dna" + ], + "pretraining_scale": "genomic sequence pretraining for variant-effect prediction", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "graphdta", @@ -4082,6 +6664,149 @@ validated: false "https://github.com/tencent-ailab/grover" ] }, + { + "id": "h_optimus_0", + "name": "H-Optimus-0", + "type": "model", + "url": "https://huggingface.co/bioptimus/H-optimus-0", + "description": "1.1B-parameter histopathology foundation model trained with self-supervised learning on a large multi-center slide corpus.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "pathology" + ], + "params": "1.1B", + "params_millions": 1100, + "pretraining_scale": "large multi-center histopathology corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "documentation": "https://huggingface.co/bioptimus/H-optimus-0", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/bioptimus/H-optimus-0" + ] + }, + { + "id": "h_optimus_1", + "name": "H-Optimus-1", + "type": "model", + "url": "https://huggingface.co/bioptimus/H-optimus-1", + "description": "1.1B-parameter pathology foundation model trained on billions of histology images from more than one million slides and 800K+ patients.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "params": "1.1B", + "params_millions": 1100, + "pretraining_scale": ">1M slides; >800K patients", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "documentation": "https://huggingface.co/bioptimus/H-optimus-1", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/bioptimus/H-optimus-1" + ] + }, + { + "id": "hicfoundation", + "name": "HiCFoundation", + "type": "model", + "url": "https://doi.org/10.1038/s41592-026-03097-8", + "description": "Hi-C foundation model pretrained on large-scale chromatin-contact maps for 3D-genome analysis, epigenomic prediction, and single-cell Hi-C adaptation.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "hic" + ], + "pretraining_scale": "hundreds of Hi-C assays; 118M contact-matrix submatrices", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, { "id": "hidra", "name": "HiDRA", @@ -4119,7 +6844,23 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "dna" + ], + "pretraining_scale": "long-context human genome sequence", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "jamie", @@ -4162,6 +6903,40 @@ validated: false "organism": [], "api": false }, + { + "id": "lpm", + "name": "LPM", + "type": "model", + "url": "https://github.com/perturblib/perturblib", + "description": "Large perturbation model that jointly learns heterogeneous perturbation experiments by disentangling perturbation, readout, and context representations.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2025, + "input": "heterogeneous perturbation experiments", + "drug_representation": "learned perturbation representation", + "context_representation": "learned context representation", + "output": "molecular state", + "unseen_drug": true, + "unseen_context": true, + "dose": "limited", + "time": "limited", + "patient_transfer": false, + "code": true + } + }, { "id": "matcha", "name": "Matcha", @@ -4203,6 +6978,75 @@ validated: false "organism": [], "api": false }, + { + "id": "medea", + "name": "Medea", + "type": "model", + "url": "https://github.com/mims-harvard/Medea", + "description": "Multi-agent therapeutic discovery system combining research planning, biological data analysis, literature reasoning, and multi-LLM deliberation across single-cell, cell-line, and patient contexts.", + "tags": [ + "agentic-ai-for-biology", + "therapeutics-and-drug-discovery-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "therapeutics", + "drug-discovery", + "single-cell", + "omics" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": "limited", + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "methylgpt", + "name": "MethylGPT", + "type": "model", + "url": "https://github.com/albert-ying/MethylGPT", + "description": "Transformer foundation model for DNA methylation pretrained on >150K human methylomes across thousands of datasets, with 3M/7M/15M parameter variants.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "methylation" + ], + "params": "3M / 7M / 15M", + "params_millions": 15, + "pretraining_scale": "154,063 human methylomes after QC; 5,281 datasets; 49,156 CpG sites", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "midas", "name": "MIDAS", @@ -4245,6 +7089,61 @@ validated: false "organism": [], "api": false }, + { + "id": "moderngena", + "name": "modernGENA", + "type": "model", + "url": "https://github.com/AIRI-Institute/GENA_LM", + "description": "ModernBERT-style DNA foundation-model family pretrained on hundreds of vertebrate genome assemblies for efficient long-sequence regulatory modeling.", + "tags": [ + "foundation-models", + "genomics-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "regression", + "representation-learning" + ], + "modalities": [ + "dna-sequence", + "genomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "dna" + ], + "pretraining_scale": "hundreds of vertebrate genome assemblies", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "genome", + "regulatory-element" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2026, + "github": "https://github.com/AIRI-Institute/GENA_LM", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/AIRI-Institute/GENA_LM" + ] + }, { "id": "mofa", "name": "MOFA+", @@ -4479,6 +7378,40 @@ validated: false "organism": [], "api": false }, + { + "id": "mutationprojector", + "name": "MutationProjector", + "type": "model", + "url": "https://doi.org/10.1101/2025.09.08.674723", + "description": "Pan-cancer genotype foundation model trained on mutations and copy-number alterations from >30K tumors for clinical representation learning.", + "tags": [ + "cancer-genome-foundation-models", + "foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "mutation", + "copy-number" + ], + "pretraining_scale": ">30K tumors with somatic genomic alterations including copy-number changes", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": "unknown", + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, { "id": "nbbayeslm", "name": "NbBayesLM", @@ -4540,6 +7473,26 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna", + "spatial" + ], + "params": "49.3M", + "params_millions": 49.3, + "pretraining_scale": "SpatialCorpus-110M", + "species": [ + "human", + "mouse" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": true + }, "entities": [ "cell", "gene", @@ -4575,7 +7528,25 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "dna" + ], + "params": "50M – 2.5B", + "params_millions": 2500, + "pretraining_scale": "3,202 human genomes and up to 850 species", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "omegafold", @@ -4598,6 +7569,43 @@ validated: false "organism": [], "api": false }, + { + "id": "omicsfm", + "name": "OmicsFM", + "type": "model", + "url": "https://github.com/CompOmics/OmicsFM", + "description": "Modality-agnostic molecular-expression foundation model with matched proteomics, bulk-transcriptomics, and single-cell-transcriptomics checkpoints.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "proteomics", + "bulk-rna", + "scrna" + ], + "params": "4.9M", + "params_millions": 4.9, + "pretraining_scale": "48,837 proteomics profiles; 680,216 bulk RNA profiles; 4.55M single cells", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "openfold", "name": "OpenFold", @@ -4639,6 +7647,73 @@ validated: false "organism": [], "api": false }, + { + "id": "paper2agent", + "name": "Paper2Agent", + "type": "model", + "url": "https://github.com/jmiao24/Paper2Agent", + "description": "Multi-agent system that transforms research papers and associated code into interactive, testable scientific agents and MCP tools.", + "tags": [ + "agentic-ai-for-biology", + "paper-and-workflow-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "paper-to-agent", + "scientific-workflow", + "tool-generation" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "paso", + "name": "PASO", + "type": "model", + "url": "https://github.com/queryang/PASO", + "description": "Pathway-aware multi-omics drug response model combining pathway-difference features, multi-scale convolutions, Transformer encoding, and drug SMILES.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2025, + "input": "pathway-aware multi-omics", + "drug_representation": "SMILES-derived representation", + "context_representation": "pathway-difference representation", + "output": "cell-line drug response", + "unseen_drug": "limited", + "unseen_context": "limited", + "dose": false, + "time": false, + "patient_transfer": false, + "code": true + } + }, { "id": "pathomicfusion", "name": "PathomicFusion", @@ -4658,6 +7733,74 @@ validated: false "organism": [], "api": false }, + { + "id": "perturbnet", + "name": "PerturbNet", + "type": "model", + "url": "https://github.com/welch-lab/PerturbNet", + "description": "Conditional generative model for predicting distributions of single-cell states under unseen chemical and genetic perturbations.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2025, + "input": "single-cell expression distributions", + "drug_representation": "molecular representation", + "context_representation": "conditional latent state", + "output": "cell-state distribution", + "unseen_drug": true, + "unseen_context": "limited", + "dose": "limited", + "time": "limited", + "patient_transfer": false, + "code": true + } + }, + { + "id": "perturbrx", + "name": "PerturbRx", + "type": "model", + "url": "https://arxiv.org/abs/2608.21349", + "description": "Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2026, + "input": "pretreatment tumor RNA-seq + perturbation pretraining", + "drug_representation": "drug embedding + treatment-conditioned transition", + "context_representation": "patient latent state", + "output": "patient treatment response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": true, + "time": false, + "patient_transfer": true, + "code": false + } + }, { "id": "phikon", "name": "Phikon", @@ -4692,6 +7835,59 @@ validated: false "https://huggingface.co/owkin/phikon" ] }, + { + "id": "phikon_v2", + "name": "Phikon-v2", + "type": "model", + "url": "https://huggingface.co/owkin/phikon-v2", + "description": "Updated pathology foundation model for general-purpose histology feature extraction and transfer learning.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "pretraining_scale": "large-scale histopathology pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "documentation": "https://huggingface.co/owkin/phikon-v2", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/owkin/phikon-v2" + ] + }, { "id": "plip", "name": "PLIP", @@ -4743,6 +7939,40 @@ validated: false "organism": [], "api": false }, + { + "id": "prepr_ct", + "name": "PrePR-CT", + "type": "model", + "url": "https://github.com/reem12345/Cell-Type-Specific-Graphs", + "description": "Graph-based model using cell-type-specific co-expression networks as inductive priors for small-data chemical perturbation response prediction.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "aggregate expression + context graph", + "drug_representation": "RDKit descriptors", + "context_representation": "context-specific gene graph", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, { "id": "prnet", "name": "PRNet", @@ -4761,7 +7991,21 @@ validated: false "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2024, + "input": "basal expression + perturbation", + "drug_representation": "FCFP4 fingerprint", + "context_representation": "matched basal expression", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": "limited", + "patient_transfer": false, + "code": true + } }, { "id": "progen2", @@ -4797,6 +8041,40 @@ validated: false "https://github.com/salesforce/progen" ] }, + { + "id": "prophet", + "name": "Prophet", + "type": "model", + "url": "https://github.com/theislab/prophet", + "description": "Transformer model for predicting cellular phenotypes under unseen chemical or genetic perturbations across heterogeneous assays and contexts.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2024, + "input": "control expression + perturbation", + "drug_representation": "learned chemical representation", + "context_representation": "control-derived context", + "output": "gene-expression response", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, { "id": "proteinmpnn", "name": "ProteinMPNN", @@ -4831,6 +8109,61 @@ validated: false "https://github.com/dauparas/ProteinMPNN" ] }, + { + "id": "protenix", + "name": "Protenix", + "type": "model", + "url": "https://github.com/bytedance/Protenix", + "description": "Trainable biomolecular structure-prediction framework for proteins, nucleic acids, ligands, and complexes with open training and inference pipelines.", + "tags": [ + "foundation-models", + "protein-foundation-models", + "protein-structure-prediction-and-design" + ], + "tasks": [ + "drug-target-interaction", + "structure-prediction" + ], + "modalities": [ + "molecular-structure", + "protein-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "protein", + "chemical" + ], + "pretraining_scale": "biomolecular structure pretraining", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule", + "protein", + "protein-complex" + ], + "methods": [ + "diffusion", + "generative-model", + "transformer" + ], + "year": 2025, + "github": "https://github.com/bytedance/Protenix", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/bytedance/Protenix" + ] + }, { "id": "prottrans", "name": "ProtTrans", @@ -4945,6 +8278,114 @@ validated: false "organism": [], "api": false }, + { + "id": "rinalmo", + "name": "RiNALMo", + "type": "model", + "url": "https://github.com/lbcb-sci/RiNALMo", + "description": "RNA language-model family pretrained on tens of millions of RNA sequences for secondary-structure and functional prediction tasks.", + "tags": [ + "foundation-models", + "rna-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning", + "structure-prediction" + ], + "modalities": [ + "rna-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "rna" + ], + "params": "33.5M / 148M / 650M", + "params_millions": 650, + "pretraining_scale": "36M non-coding RNA sequences", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "transcript" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "github": "https://github.com/lbcb-sci/RiNALMo", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/lbcb-sci/RiNALMo" + ] + }, + { + "id": "rna_fm", + "name": "RNA-FM", + "type": "model", + "url": "https://github.com/ml4bio/RNA-FM", + "description": "General-purpose RNA foundation model pretrained on large-scale RNA sequences for structural and functional representation learning.", + "tags": [ + "foundation-models", + "rna-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning", + "structure-prediction" + ], + "modalities": [ + "rna-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2022, + "modalities": [ + "rna" + ], + "params": "99M", + "params_millions": 99, + "pretraining_scale": "23.7M non-coding RNA sequences", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "transcript" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2022, + "github": "https://github.com/ml4bio/RNA-FM", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/ml4bio/RNA-FM" + ] + }, { "id": "rosettafold", "name": "RoseTTAFold", @@ -5006,7 +8447,24 @@ validated: false "Transcriptomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "protein" + ], + "pretraining_scale": "cross-species single-cell atlases with protein-language-model embeddings", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "scarches", @@ -5029,6 +8487,41 @@ validated: false "organism": [], "api": false }, + { + "id": "scarf", + "name": "SCARF", + "type": "model", + "url": "https://doi.org/10.1101/2025.04.07.647689", + "description": "Single-cell RNA+ATAC foundation model pretrained on >2.7M cells for multimodal representation, matching, cross-omics translation, and few-shot annotation.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna", + "atac", + "multi-omics" + ], + "pretraining_scale": ">2.7M paired and unpaired single cells across tissues and species", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } + }, { "id": "scbert", "name": "scBERT", @@ -5067,7 +8560,23 @@ validated: false "metadata_sources": [ "https://github.com/TencentAILabHealthcare/scBERT", "https://www.nature.com/articles/s42256-022-00534-z" - ] + ], + "foundation_profile": { + "year": 2022, + "modalities": [ + "scrna" + ], + "pretraining_scale": "large-scale scRNA-seq corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "scbutterfly", @@ -5090,6 +8599,40 @@ validated: false "organism": [], "api": false }, + { + "id": "scdnam_gpt", + "name": "scDNAm-GPT", + "type": "model", + "url": "https://github.com/ChaoqiLiang/scDNAm-GPM", + "description": "Foundation model for single-cell whole-genome bisulfite sequencing with whole-genome context modeling at single-CpG resolution.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "methylation" + ], + "pretraining_scale": ">1M single-cell methylomes from human and mouse tissues", + "species": [ + "human", + "mouse" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "scfoundation", "name": "scFoundation", @@ -5129,7 +8672,25 @@ validated: false "metadata_sources": [ "https://github.com/biomap-research/scFoundation", "https://www.nature.com/articles/s41592-024-02305-7" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna" + ], + "params": "100M", + "params_millions": 100, + "pretraining_scale": ">50M human single-cell transcriptomes", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } }, { "id": "scgpt", @@ -5173,7 +8734,24 @@ validated: false "metadata_sources": [ "https://github.com/bowang-lab/scGPT", "https://www.nature.com/articles/s41592-024-02201-0" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "multi-omics" + ], + "pretraining_scale": ">33M single-cell profiles", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": "limited" + } }, { "id": "scgpt_spatial", @@ -5198,6 +8776,25 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna", + "spatial", + "multi-omics" + ], + "pretraining_scale": "continued pretraining from scGPT with spatial transcriptomics", + "species": [ + "human", + "mouse" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": true + }, "entities": [ "cell", "gene", @@ -5253,7 +8850,27 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://github.com/SuperBianC/scMulan" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "atac", + "proteomics", + "multi-omics" + ], + "pretraining_scale": "~10M cells across transcriptomics, epigenomics, and proteomics", + "species": [ + "human", + "mouse" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "scpair", @@ -5299,6 +8916,22 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna" + ], + "pretraining_scale": "large multi-dataset single-cell corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + }, "entities": [ "cell", "gene" @@ -5317,6 +8950,62 @@ validated: false "https://www.nature.com/articles/s41467-025-58699-1" ] }, + { + "id": "scprint_2", + "name": "scPRINT-2", + "type": "model", + "url": "https://github.com/cantinilab/scPRINT-2", + "description": "Next-generation single-cell foundation model pretrained on 350M+ cells across 22K+ datasets and 16 species for embeddings, denoising, annotation, gene-network inference, and cross-species transfer.", + "tags": [ + "foundation-models", + "single-cell-foundation-models", + "transcriptomics-foundation-models" + ], + "tasks": [ + "batch-correction", + "cell-type-annotation", + "foundation-model-pretraining", + "gene-regulatory-network-inference", + "imputation", + "representation-learning" + ], + "modalities": [ + "single-cell-rna-seq", + "transcriptomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna" + ], + "pretraining_scale": "350M+ cells; 22K+ datasets; 16 species", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + }, + "entities": [ + "cell", + "gene" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2026, + "github": "https://github.com/cantinilab/scPRINT-2", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/cantinilab/scPRINT-2" + ] + }, { "id": "sei", "name": "Sei", @@ -5357,6 +9046,73 @@ validated: false "organism": [], "api": false }, + { + "id": "state", + "name": "State", + "type": "model", + "url": "https://github.com/ArcInstitute/state", + "description": "Transition model for predicting cellular perturbation responses across diverse contexts and sets of cells.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "sets of cellular expression profiles", + "drug_representation": "perturbation embedding", + "context_representation": "cellular state", + "output": "transcriptomic response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": "limited", + "time": "limited", + "patient_transfer": false, + "code": true + } + }, + { + "id": "stella", + "name": "STELLA", + "type": "model", + "url": "https://github.com/zaixizhang/STELLA", + "description": "Self-evolving biomedical research agent that expands its tool repertoire and supports literature reasoning, computational analysis, and laboratory-oriented scientific workflows.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "general-biomedicine", + "bioinformatics", + "scientific-workflow" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": "limited", + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "targetdiff", "name": "TargetDiff", @@ -5377,6 +9133,40 @@ validated: false "organism": [], "api": false }, + { + "id": "tessera", + "name": "TESSERA", + "type": "model", + "url": "https://github.com/JW-Sidhom-Lab/tessera", + "description": "Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning.", + "tags": [ + "cancer-genome-foundation-models", + "foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "mutation", + "copy-number" + ], + "pretraining_scale": "TCGA pan-cancer somatic SNVs and copy-number segments", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "tgsa", "name": "TGSA", @@ -5397,6 +9187,94 @@ validated: false "organism": [], "api": false }, + { + "id": "therapi", + "name": "THERAPI", + "type": "model", + "url": "https://github.com/Sunginyoung/THERAPI", + "description": "Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2026, + "input": "tumor transcriptomics + cell-line knowledge", + "drug_representation": "perturbation/gene-level representation", + "context_representation": "aligned tumor/cell-line representation", + "output": "patient drug response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": false, + "time": false, + "patient_transfer": true, + "code": true + } + }, + { + "id": "titan", + "name": "TITAN", + "type": "model", + "url": "https://github.com/mahmoodlab/TITAN", + "description": "Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "pretraining_scale": "whole-slide pathology with multimodal image-language supervision", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "contrastive-learning", + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "github": "https://github.com/mahmoodlab/TITAN", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/mahmoodlab/TITAN" + ] + }, { "id": "toad", "name": "TOAD", @@ -5416,6 +9294,40 @@ validated: false "organism": [], "api": false }, + { + "id": "tooluniverse", + "name": "ToolUniverse", + "type": "model", + "url": "https://github.com/mims-harvard/ToolUniverse", + "description": "Unified scientific tool ecosystem for building AI scientists that can discover, select, and execute biomedical tools and databases.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "general-biomedicine", + "tool-ecosystem", + "therapeutics", + "bioinformatics" + ], + "architecture": "agent-ecosystem", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": "limited", + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "tosica", "name": "TOSICA", @@ -5499,6 +9411,73 @@ validated: false "organism": [], "api": false }, + { + "id": "txagent", + "name": "TxAgent", + "type": "model", + "url": "https://github.com/mims-harvard/TxAgent", + "description": "Therapeutic reasoning agent using multi-step reasoning and a large scientific tool universe for drug interactions, contraindications, and personalized treatment analysis.", + "tags": [ + "agentic-ai-for-biology", + "therapeutics-and-drug-discovery-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "therapeutics", + "drug-discovery", + "precision-medicine" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "txpert", + "name": "TxPert", + "type": "model", + "url": "https://github.com/valence-labs/TxPert", + "description": "Knowledge-graph-informed latent-transfer model for transcriptomic perturbation prediction across unseen single perturbations, combinations, and cross-context settings.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "transcriptomics + biological knowledge", + "drug_representation": "knowledge-graph-informed perturbation representation", + "context_representation": "cellular context representation", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": true, + "dose": false, + "time": false, + "patient_transfer": false, + "code": true + } + }, { "id": "uce", "name": "UCE", @@ -5533,7 +9512,25 @@ validated: false "metadata_sources": [ "https://github.com/snap-stanford/UCE", "https://www.nature.com/articles/s41586-026-10689-z" - ] + ], + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna" + ], + "params": ">650M", + "params_millions": 650, + "pretraining_scale": ">36M cells across >300 datasets", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": "limited", + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "uni", @@ -5590,6 +9587,23 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "chemical", + "protein" + ], + "pretraining_scale": "large-scale 3D molecular and pocket pretraining", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, "entities": [ "molecule", "protein" @@ -5607,6 +9621,112 @@ validated: false "https://openreview.net/forum?id=6K2RM6wVqKu" ] }, + { + "id": "uni_mol2", + "name": "Uni-Mol2", + "type": "model", + "url": "https://github.com/deepmodeling/Uni-Mol/tree/main/unimol2", + "description": "Scaled molecular pretraining model using atomic, graph, and 3D geometry features, with models up to 1.1B parameters pretrained on 800M conformations.", + "tags": [ + "compound-embedding", + "compound-foundation-models", + "foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "chemical-structure", + "molecular-structure" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "chemical" + ], + "params": "up to 1.1B", + "params_millions": 1100, + "pretraining_scale": "800M molecular conformations", + "species": [], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "github": "https://github.com/deepmodeling/Uni-Mol", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/deepmodeling/Uni-Mol" + ] + }, + { + "id": "uni2_h", + "name": "UNI2-h", + "type": "model", + "url": "https://huggingface.co/MahmoodLab/UNI2-h", + "description": "Billion-parameter histopathology vision foundation model for tile-level feature extraction and downstream computational pathology tasks.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "params": "~1B", + "params_millions": 1000, + "pretraining_scale": "large-scale histopathology pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "documentation": "https://huggingface.co/MahmoodLab/UNI2-h", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/MahmoodLab/UNI2-h" + ] + }, { "id": "unitednet", "name": "UnitedNet", @@ -5647,6 +9767,162 @@ validated: false "organism": [], "api": false }, + { + "id": "virchow2", + "name": "Virchow2", + "type": "model", + "url": "https://huggingface.co/paige-ai/Virchow2", + "description": "632M-parameter pathology vision transformer pretrained on 3.1M whole-slide images with mixed-magnification self-supervision.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "pathology" + ], + "params": "632M", + "params_millions": 632, + "pretraining_scale": "3.1M whole-slide images", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "documentation": "https://huggingface.co/paige-ai/Virchow2", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/paige-ai/Virchow2" + ] + }, + { + "id": "virtual_lab", + "name": "Virtual Lab", + "type": "model", + "url": "https://github.com/zou-group/virtual-lab", + "description": "Human–AI collaborative research environment in which an LLM principal investigator coordinates specialized scientist agents for scientific discovery.", + "tags": [ + "agentic-ai-for-biology", + "multi-agent-scientific-labs" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2024, + "domains": [ + "scientific-workflow", + "protein-design", + "experimental-design" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": true, + "omics": "limited", + "wet_lab": true, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "virtues", + "name": "VirTues", + "type": "model", + "url": "https://github.com/bunnelab/virtues", + "description": "Spatial-proteomics foundation model learning marker-aware representations across proteins, cells, niches, and tissues from multiplexed imaging.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "proteomics", + "spatial" + ], + "pretraining_scale": "31-32 harmonized spatial-proteomics datasets across technologies", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": true + } + }, + { + "id": "xpert", + "name": "XPert", + "type": "model", + "url": "https://github.com/GSanShui/XPert", + "description": "Knowledge-informed dual-branch Transformer for drug-induced transcriptional perturbation prediction across dose, time, and cellular context.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "transcriptomics + heterogeneous graph", + "drug_representation": "Uni-Mol + knowledge graph", + "context_representation": "basal expression + gene embeddings", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, { "id": "autozyme", "name": "AutoZyme", diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.yml b/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.yml index bfcb7c7..40cc846 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.yml +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.yml @@ -2,9 +2,9 @@ title: "Awesome Computational Biology - machine-readable resource list" task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/c6f07d90/data/resources.yml -upstream_sha: c6f07d90 -imported_at: 2026-08-31 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/resources.yml +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -121,7 +121,18 @@ resources: type: benchmark url: https://www.internationalgenome.org/ description: "Reference panel of human genetic variation from 2,504 individuals across 26 populations." - tags: [benchmarks-and-datasets] + tags: [genomics-cancer-and-biomedical-cohorts] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: adamson_et_al_2016_perturb_seq + name: "Adamson et al. 2016 Perturb-seq" + type: benchmark + url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546 + description: "CRISPRi Perturb-seq in K562 cells for systematic dissection of the unfolded protein response." + tags: [genetic-perturbation-and-perturb-seq] tasks: [] modalities: [] organism: [] @@ -132,7 +143,7 @@ resources: type: benchmark url: https://www.kaggle.com/datasets/gokturkkoch/bace description: "Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -143,7 +154,7 @@ resources: type: benchmark url: https://biodev.github.io/BeatAML2/ description: "Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia." - tags: [benchmarks-and-datasets] + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -154,7 +165,7 @@ resources: type: benchmark url: https://github.com/LigandPro/Bento description: "Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -165,7 +176,7 @@ resources: type: benchmark url: https://www.bindingdb.org/rwd/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes description: "Curated binding affinity datasets for protein–ligand interaction benchmarking." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -176,7 +187,18 @@ resources: type: benchmark url: https://portals.broadinstitute.org/ctrp/ description: "Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds." - tags: [benchmarks-and-datasets] + tags: [drug-response-and-perturbation] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: chem_perturbridge + name: "Chem-PerturBridge" + type: benchmark + url: https://github.com/theislab/Chem-PerturBridge + description: "Harmonized compendium and processing pipelines for small-molecule perturbation transcriptomics across heterogeneous assays and gene panels." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -187,7 +209,7 @@ resources: type: benchmark url: https://tdcommons.ai/single_pred_tasks/tox/#clintox description: "Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -198,7 +220,7 @@ resources: type: benchmark url: https://proteomics.cancer.gov/programs/cptac description: "Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics." - tags: [benchmarks-and-datasets] + tags: [genomics-cancer-and-biomedical-cohorts] tasks: [] modalities: [] organism: [] @@ -209,7 +231,29 @@ resources: type: benchmark url: https://arxiv.org/abs/2001.01037 description: "Large-scale dataset for structure-based virtual screening." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: datlinger_et_al_2017_crop_seq + name: "Datlinger et al. 2017 CROP-seq" + type: benchmark + url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872 + description: "Pooled CRISPR knockout screen with single-cell transcriptomic readout in Jurkat cells under T-cell receptor stimulation." + tags: [genetic-perturbation-and-perturb-seq] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: dixit_et_al_2016_perturb_seq + name: "Dixit et al. 2016 Perturb-seq" + type: benchmark + url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063 + description: "Pooled CRISPR knockout with single-cell RNA-seq across K562 and dendritic-cell screens, including stimulated conditions and combinatorial perturbations." + tags: [genetic-perturbation-and-perturb-seq] tasks: [] modalities: [] organism: [] @@ -220,7 +264,7 @@ resources: type: benchmark url: http://dude.docking.org/ description: "Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -231,7 +275,40 @@ resources: type: benchmark url: https://github.com/J-SNACKKB/FLIP description: "Benchmark collection of protein fitness landscape datasets for evaluating protein ML models." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: frangieh_et_al_2021_perturb_cite_seq + name: "Frangieh et al. 2021 Perturb-CITE-seq" + type: benchmark + url: https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion + description: "Multimodal CRISPR knockout screen with RNA and surface-protein readouts in patient-derived melanoma models under immune-related conditions." + tags: [genetic-perturbation-and-perturb-seq] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: gasperini_et_al_2019_crispri_enhancer_screen + name: "Gasperini et al. 2019 CRISPRi enhancer screen" + type: benchmark + url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861 + description: "High-MOI single-cell CRISPRi screen in K562 cells targeting thousands of candidate enhancers to map enhancer-gene regulation." + tags: [genetic-perturbation-and-perturb-seq] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: gse191127_breast_cancer_pre_post_chemotherapy + name: "GSE191127 Breast Cancer Pre/Post Chemotherapy" + type: benchmark + url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127 + description: "Bulk RNA-seq and genomics from matched pre- and post-neoadjuvant chemotherapy breast tumors with treatment outcomes." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -242,7 +319,7 @@ resources: type: benchmark url: https://github.com/BenevolentAI/guacamol description: "Benchmark suite for generative molecular design models." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -253,7 +330,7 @@ resources: type: benchmark url: https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics description: "DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1)." - tags: [benchmarks-and-datasets] + tags: [single-cell-and-spatial] tasks: [] modalities: [] organism: [] @@ -264,7 +341,7 @@ resources: type: benchmark url: https://github.com/jump-cellpainting/datasets description: "Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research." - tags: [benchmarks-and-datasets] + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -275,7 +352,40 @@ resources: type: benchmark url: https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data description: "Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines." - tags: [benchmarks-and-datasets] + tags: [drug-response-and-perturbation] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: lincs_l1000_phase_1 + name: "LINCS L1000 Phase 1" + type: benchmark + url: https://github.com/theislab/Chem-PerturBridge + description: "Phase-1 L1000 benchmark view used by Bison with 692,787 profiles, 9,233 molecules, 70 contexts, and 978 landmark genes." + tags: [drug-response-and-perturbation] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: lincs_l1000_phase_2 + name: "LINCS L1000 Phase 2" + type: benchmark + url: https://github.com/theislab/Chem-PerturBridge + description: "Phase-2 L1000 benchmark view used by Bison with 333,263 profiles, 1,760 molecules, 30 contexts, and 978 landmark genes." + tags: [drug-response-and-perturbation] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: mix_seq + name: "MIX-Seq" + type: benchmark + url: https://www.nature.com/articles/s41467-020-17440-w + description: "Multiplexed single-cell transcriptional profiling of chemical and genetic perturbation responses across pools of cancer cell lines." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -286,7 +396,7 @@ resources: type: benchmark url: http://moleculenet.ai/ description: "Benchmark datasets for molecular machine learning." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -297,7 +407,29 @@ resources: type: benchmark url: https://github.com/molecularsets/moses description: "Benchmarking platform for molecular generation models." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: norman_et_al_2019_crispra_perturb_seq + name: "Norman et al. 2019 CRISPRa Perturb-seq" + type: benchmark + url: https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323 + description: "Large-scale CRISPR activation Perturb-seq in K562 cells spanning single-gene and combinatorial perturbations." + tags: [genetic-perturbation-and-perturb-seq] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: novartis_perturbation_dataset + name: "Novartis Perturbation Dataset" + type: benchmark + url: https://github.com/theislab/Chem-PerturBridge + description: "Large chemical perturbation transcriptomic screen used in Chem-PerturBridge; the Bison benchmark view contains 46,748 profiles and 3,770 molecules." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -308,7 +440,18 @@ resources: type: benchmark url: https://ogb.stanford.edu/ description: "Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv." - tags: [benchmarks-and-datasets] + tags: [general-biological-ml-and-knowledge-graph-benchmarks] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: op3 + name: "OP3" + type: benchmark + url: https://github.com/theislab/Chem-PerturBridge + description: "Chemical perturbation dataset used by Chem-PerturBridge and the Bison unseen-compound benchmark; the Bison benchmark view contains 1,813 profiles, 138 molecules, and 4 cellular contexts." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -319,7 +462,18 @@ resources: type: benchmark url: https://github.com/OpenBioLink/OpenBioLink description: "Benchmark datasets for biological knowledge graph completion." - tags: [benchmarks-and-datasets] + tags: [general-biological-ml-and-knowledge-graph-benchmarks] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: pdx_atlas + name: "PDX-Atlas" + type: benchmark + url: https://muralportal.pdxatlas.org/ + description: "Portal integrating clinical, genomic, transcriptomic, and drug-response data from patient-derived xenograft models." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -330,7 +484,7 @@ resources: type: benchmark url: https://www.pharmgkb.org/ description: "Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs." - tags: [benchmarks-and-datasets] + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -341,7 +495,7 @@ resources: type: benchmark url: https://pk-db.com/ description: "Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -352,7 +506,7 @@ resources: type: benchmark url: https://depmap.org/portal/prism/ description: "Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding." - tags: [benchmarks-and-datasets] + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -363,7 +517,7 @@ resources: type: benchmark url: https://github.com/OATML-Markslab/ProteinGym description: "Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -374,7 +528,51 @@ resources: type: benchmark url: https://figshare.com/collections/Quantum_chemistry_structures_and_properties_of_134_kilo_molecules/978904 description: "Quantum chemistry properties for 134K stable small organic molecules computed at DFT level." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: replogle_et_al_2022_k562_essential_perturb_seq + name: "Replogle et al. 2022 K562 Essential Perturb-seq" + type: benchmark + url: https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387 + description: "CRISPRi Perturb-seq focused on essential genes in K562 cells." + tags: [genetic-perturbation-and-perturb-seq] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: replogle_et_al_2022_k562_genome_wide_perturb_seq + name: "Replogle et al. 2022 K562 Genome-wide Perturb-seq" + type: benchmark + url: https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387 + description: "Genome-scale CRISPRi Perturb-seq targeting nearly all expressed genes in K562 cells." + tags: [genetic-perturbation-and-perturb-seq] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: replogle_et_al_2022_rpe1_essential_perturb_seq + name: "Replogle et al. 2022 RPE1 Essential Perturb-seq" + type: benchmark + url: https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387 + description: "CRISPRi Perturb-seq focused on essential genes in RPE1 cells." + tags: [genetic-perturbation-and-perturb-seq] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: sci_plex + name: "sci-Plex" + type: benchmark + url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944 + description: "Single-cell chemical perturbation screen of ~650,000 transcriptomes across three cancer cell lines, 188 compounds, and four doses." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -385,7 +583,7 @@ resources: type: benchmark url: https://github.com/theislab/scib description: "Comprehensive benchmarking framework for single-cell data integration methods." - tags: [benchmarks-and-datasets] + tags: [single-cell-and-spatial] tasks: [] modalities: [] organism: [] @@ -396,7 +594,7 @@ resources: type: benchmark url: https://github.com/sanderlab/scPerturb description: "Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies." - tags: [benchmarks-and-datasets] + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -407,7 +605,7 @@ resources: type: benchmark url: http://sideeffects.embl.de/ description: "Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -418,7 +616,7 @@ resources: type: benchmark url: https://tabula-muris.ds.czbiohub.org/ description: "Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons." - tags: [benchmarks-and-datasets] + tags: [single-cell-and-spatial] tasks: [] modalities: [] organism: [] @@ -429,7 +627,18 @@ resources: type: benchmark url: https://tabula-sapiens-portal.ds.czbiohub.org/ description: "Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors." - tags: [benchmarks-and-datasets] + tags: [single-cell-and-spatial] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: tahoe_100m + name: "Tahoe-100M" + type: benchmark + url: https://huggingface.co/datasets/tahoebio/Tahoe-100M + description: "Giga-scale single-cell perturbation atlas with >100 million profiles from 50 cancer cell lines exposed to ~1,100 small molecules." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -440,7 +649,7 @@ resources: type: benchmark url: https://github.com/songlab-cal/tape description: "Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -451,7 +660,7 @@ resources: type: benchmark url: https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas description: "DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1)." - tags: [benchmarks-and-datasets] + tags: [single-cell-and-spatial] tasks: [] modalities: [] organism: [] @@ -462,7 +671,7 @@ resources: type: benchmark url: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga description: "Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients." - tags: [benchmarks-and-datasets] + tags: [genomics-cancer-and-biomedical-cohorts] tasks: [] modalities: [] organism: [] @@ -473,7 +682,7 @@ resources: type: benchmark url: https://tdcommons.ai/ description: "Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -484,7 +693,7 @@ resources: type: benchmark url: https://tripod.nih.gov/tox21/challenge/ description: "12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction." - tags: [benchmarks-and-datasets] + tags: [molecular-protein-and-drug-discovery] tasks: [] modalities: [] organism: [] @@ -495,7 +704,29 @@ resources: type: benchmark url: https://www.ukbiobank.ac.uk/ description: "Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies." - tags: [benchmarks-and-datasets] + tags: [genomics-cancer-and-biomedical-cohorts] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: vcpi_0001 + name: "VCPI-0001" + type: benchmark + url: https://github.com/theislab/Chem-PerturBridge + description: "Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 27,517 profiles and 2,272 molecules in one cellular context." + tags: [drug-response-and-perturbation] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: vcpi_0002 + name: "VCPI-0002" + type: benchmark + url: https://github.com/theislab/Chem-PerturBridge + description: "Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 18,139 profiles and 1,488 molecules in one cellular context." + tags: [drug-response-and-perturbation] tasks: [] modalities: [] organism: [] @@ -880,7 +1111,7 @@ resources: type: database url: https://www.cancerrxgene.org/ description: "Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds." - tags: [benchmarks-and-datasets, drug-cell-line-response, interaction] + tags: [drug-cell-line-response, drug-response-and-perturbation, interaction] tasks: [] modalities: [Gene Expression, Small Molecule] organism: [] @@ -1133,7 +1364,7 @@ resources: type: database url: https://dtp.cancer.gov/discovery_development/nci-60/ description: "Focuses on 60 cancer cell lines and many drugs." - tags: [benchmarks-and-datasets, drug-cell-line-response, interaction] + tags: [drug-cell-line-response, drug-response-and-perturbation, interaction] tasks: [] modalities: [Gene Expression, Small Molecule] organism: [] @@ -1535,6 +1766,17 @@ resources: organism: [] api: false + - id: agent_laboratory + name: "Agent Laboratory" + type: model + url: https://github.com/SamuelSchmidgall/AgentLaboratory + description: "End-to-end multi-agent research workflow for literature review, experimentation, implementation, analysis, and report generation." + tags: [agentic-ai-for-biology, multi-agent-scientific-labs] + tasks: [] + modalities: [] + organism: [] + api: false + - id: ai4chem_chemllm_7b_chat name: "AI4Chem/ChemLLM-7B-Chat" type: model @@ -1557,6 +1799,17 @@ resources: organism: [] api: false + - id: alphagenome + name: "AlphaGenome" + type: model + url: https://github.com/google-deepmind/alphagenome + description: "Long-context DNA model predicting multimodal regulatory outputs including expression, splicing, chromatin features, and contact maps at near base-pair resolution." + tags: [foundation-models, genomics-foundation-models] + tasks: [Foundation Model] + modalities: [Genomics] + organism: [] + api: false + - id: ankh name: "Ankh" type: model @@ -1568,6 +1821,28 @@ resources: organism: [] api: false + - id: atacformer + name: "Atacformer" + type: model + url: https://doi.org/10.1101/2025.11.03.685753 + description: "Transformer foundation model for scATAC-seq that learns embeddings of cis-regulatory elements for clustering, annotation, and reference mapping." + tags: [foundation-models, single-cell-epigenomics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + + - id: autoba + name: "AutoBA" + type: model + url: https://github.com/JoshuaChou2018/AutoBA + description: "Automated multi-omics analysis agent that plans, executes, and repairs bioinformatics workflows from natural-language objectives." + tags: [agentic-ai-for-biology, bioinformatics-and-omics-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: babel name: "BABEL" type: model @@ -1590,6 +1865,28 @@ resources: organism: [] api: false + - id: bia + name: "BIA" + type: model + url: https://github.com/biagent-dev/bia + description: "Bioinformatics agent for GEO search, sample metadata extraction, count-matrix processing, and pipeline extraction from papers." + tags: [agentic-ai-for-biology, bioinformatics-and-omics-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: bioagents + name: "BioAgents" + type: model + url: https://github.com/microsoft/bioinformagus + description: "Multi-agent bioinformatics assistant using specialized language models and retrieval for genomics workflow development and troubleshooting." + tags: [agentic-ai-for-biology, bioinformatics-and-omics-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: biogpt name: "BioGPT" type: model @@ -1601,6 +1898,39 @@ resources: organism: [] api: false + - id: biolord + name: "biolord" + type: model + url: https://github.com/nitzanlab/biolord + description: "Deep generative model that disentangles known and unknown attributes for conditional generation of single-cell states." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + + - id: biomaster + name: "BioMaster" + type: model + url: https://github.com/ai4nucleome/BioMaster + description: "Multi-agent system for automated and auditable bioinformatics workflows spanning RNA-seq, ChIP-seq, single-cell, spatial omics, Hi-C, long reads, metagenomics, and proteomics." + tags: [agentic-ai-for-biology, general-biomedical-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: biomedagent + name: "BioMedAgent" + type: model + url: https://github.com/BOBQWERA/BioMedAgent + description: "Self-evolving multi-agent framework for autonomous biomedical data analysis with tool discovery, workflow planning, code generation, execution, correction, and cross-omics analysis." + tags: [agentic-ai-for-biology, general-biomedical-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: biomedclip name: "BiomedCLIP" type: model @@ -1623,6 +1953,28 @@ resources: organism: [] api: false + - id: biomni + name: "Biomni" + type: model + url: https://github.com/snap-stanford/Biomni + description: "General-purpose biomedical AI agent integrating planning, code execution, specialized tools, databases, and software across diverse biomedical research tasks." + tags: [agentic-ai-for-biology, general-biomedical-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: bison + name: "Bison" + type: model + url: https://arxiv.org/abs/2609.32467 + description: "Cross-dataset model for globally unseen-compound response prediction using a shared gene representation, discrete diffusion models, and matched drug-contrast supervision." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + - id: boltz_1 name: "Boltz-1" type: model @@ -1634,6 +1986,17 @@ resources: organism: [] api: false + - id: boltz_2 + name: "Boltz-2" + type: model + url: https://github.com/jwohlwend/boltz + description: "Biomolecular foundation model jointly predicting complex structures and binding affinities for protein–ligand interaction modeling and virtual screening." + tags: [foundation-models, protein-foundation-models, protein-structure-prediction-and-design] + tasks: [Foundation Model, Protein Structure Prediction] + modalities: [Protein] + organism: [] + api: false + - id: borzoi name: "Borzoi" type: model @@ -1645,6 +2008,17 @@ resources: organism: [] api: false + - id: brad + name: "BRAD" + type: model + url: https://github.com/Jpickard1/BRAD + description: "Retrieval-augmented bioinformatics assistant integrating scientific literature, databases, external tools, and executable workflows." + tags: [agentic-ai-for-biology, general-biomedical-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: bulkformer name: "BulkFormer" type: model @@ -1678,14 +2052,58 @@ resources: organism: [] api: false + - id: captain + name: "CAPTAIN" + type: model + url: https://doi.org/10.1038/s41467-026-72882-y + description: "Multimodal foundation model pretrained on co-assayed single-cell RNA and protein for joint representation learning and cross-modal downstream tasks." + tags: [foundation-models, other-omics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + + - id: casanovo_foundation + name: "Casanovo Foundation" + type: model + url: https://github.com/Noble-Lab/casanovo-tl + description: "Tandem mass-spectrometry proteomics foundation model that reuses a pretrained Casanovo spectrum encoder for spectrum quality, chimericity, and post-translational-modification prediction." + tags: [foundation-models, other-omics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: cassia name: "CASSIA" type: model url: https://github.com/ElliotXie/CASSIA - description: "Multi-agent LLM for reference-free, interpretable cell-type annotation of single-cell RNA-seq data, with dedicated annotation, validation, scoring, and reporting agents." - tags: [llm-for-biology] - tasks: [Language Modeling] - modalities: [Text] + description: "Multi-agent LLM framework for reference-free and interpretable single-cell cell-type annotation with dedicated annotation, validation, scoring, and reporting agents." + tags: [agentic-ai-for-biology, bioinformatics-and-omics-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: cellflow + name: "CellFlow" + type: model + url: https://github.com/theislab/CellFlow + description: "Conditional flow-matching framework for modeling and predicting cellular phenotypes under chemical, genetic, and other perturbations." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + + - id: cellfm + name: "CellFM" + type: model + url: https://github.com/biomed-AI/CellFM + description: "800M-parameter single-cell foundation model pretrained on transcriptomics from 100 million human cells for annotation, integration, gene-function, and perturbation tasks." + tags: [foundation-models, single-cell-foundation-models, transcriptomics-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell, Transcriptomics] organism: [] api: false @@ -1755,6 +2173,17 @@ resources: organism: [] api: false + - id: chemfm + name: "ChemFM" + type: model + url: https://github.com/TheLuoFengLab/ChemFM + description: "1B/3B-parameter chemical language model pretrained on 178M molecules for molecular representation, property prediction, generation, and synthesis tasks." + tags: [compound-embedding, compound-foundation-models, foundation-models] + tasks: [Foundation Model] + modalities: [Small Molecule] + organism: [] + api: false + - id: chief name: "CHIEF" type: model @@ -1766,14 +2195,36 @@ resources: organism: [] api: false + - id: chromfound + name: "ChromFound" + type: model + url: https://github.com/SAIS-LifeScience/ChromFound + description: "Genome-aware scATAC-seq foundation model pretrained on 1.97M cells across tissues and disease contexts for zero-shot cell representations, annotation, and cross-omics prediction." + tags: [foundation-models, single-cell-epigenomics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: clawbio name: "ClawBio" type: model url: https://github.com/ClawBio/ClawBio description: "Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills." - tags: [llm-for-biology] - tasks: [Language Modeling] - modalities: [Text] + tags: [agentic-ai-for-biology, general-biomedical-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: clm_x + name: "CLM-X" + type: model + url: https://doi.org/10.64898/2026.02.17.704943 + description: "Multi-way Transformer foundation model jointly handling RNA-only, ATAC-only, and paired RNA–ATAC single-cell inputs for integration, translation, annotation, and perturbation prediction." + tags: [foundation-models, single-cell-epigenomics-foundation-models] + tasks: [Foundation Model] + modalities: [] organism: [] api: false @@ -1810,6 +2261,17 @@ resources: organism: [] api: false + - id: cpgpt + name: "CpGPT" + type: model + url: https://github.com/lucascamillomd/CpGPT + description: "DNA methylation foundation model pretrained on >150K samples for zero-shot imputation, array conversion, reference mapping, and downstream phenotype prediction." + tags: [foundation-models, other-omics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: cyclecdr name: "cycleCDR" type: model @@ -1975,6 +2437,17 @@ resources: organism: [] api: false + - id: drugagent + name: "DrugAgent" + type: model + url: https://github.com/inoue0426/DrugAgent + description: "Multi-agent biomedical evidence synthesis framework for computational drug discovery with reliability-aware aggregation." + tags: [agentic-ai-for-biology, therapeutics-and-drug-discovery-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: drugban name: "DrugBAN" type: model @@ -2008,6 +2481,17 @@ resources: organism: [] api: false + - id: dtlcdr + name: "DTLCDR" + type: model + url: https://doi.org/10.1016/j.jpha.2025.101315 + description: "Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization." + tags: [drug-discovery, drug-response-prediction] + tasks: [Drug Discovery, Drug Response Prediction] + modalities: [Small Molecule] + organism: [] + api: false + - id: enformer name: "Enformer" type: model @@ -2019,6 +2503,39 @@ resources: organism: [] api: false + - id: epiagent + name: "EpiAgent" + type: model + url: https://github.com/xy-chen16/EpiAgent + description: "scATAC-seq foundation model pretrained on ~5M cells and >35B tokens for representation learning, annotation, imputation, perturbation prediction, and in-silico cCRE knockout." + tags: [foundation-models, single-cell-epigenomics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + + - id: epifoundation + name: "EpiFoundation" + type: model + url: https://doi.org/10.1101/2025.02.05.636688 + description: "Foundation model for scATAC-seq using peak-to-gene aligned pretraining for cell representation, annotation, batch correction, and gene-expression prediction." + tags: [foundation-models, single-cell-epigenomics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + + - id: esm_cambrian_esm_c + name: "ESM Cambrian (ESM C)" + type: model + url: https://github.com/Biohub/esm + description: "Protein representation foundation-model family designed as an efficient next-generation successor to ESM2, spanning 300M to multi-billion-parameter models." + tags: [foundation-models, pre-trained-embedding, protein-foundation-models] + tasks: [Foundation Model] + modalities: [Protein] + organism: [] + api: false + - id: esm3 name: "ESM3" type: model @@ -2052,6 +2569,17 @@ resources: organism: [] api: false + - id: evo_2 + name: "Evo 2" + type: model + url: https://github.com/arcinstitute/evo2 + description: "Genome foundation model trained on 9 trillion DNA base pairs across all domains of life with a 1M-token context window and single-nucleotide resolution." + tags: [foundation-models, genomics-foundation-models] + tasks: [Foundation Model] + modalities: [Genomics] + organism: [] + api: false + - id: evodiff name: "EvoDiff" type: model @@ -2074,6 +2602,17 @@ resources: organism: [] api: false + - id: expresso + name: "EXPRESSO" + type: model + url: https://doi.org/10.1158/0008-5472.CAN-25-5220 + description: "Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies." + tags: [drug-discovery, drug-response-prediction] + tasks: [Drug Discovery, Drug Response Prediction] + modalities: [Small Molecule] + organism: [] + api: false + - id: gears name: "GEARS" type: model @@ -2085,6 +2624,17 @@ resources: organism: [] api: false + - id: genbio_pathfm + name: "GenBio-PathFM" + type: model + url: https://github.com/genbio-ai/genbio-pathfm + description: "1.1B-parameter histopathology foundation model trained on public data using morphology-aware curation and dual-stage JEPA+DINO learning." + tags: [foundation-models, pathology-foundation-models, single-cell-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell] + organism: [] + api: false + - id: genecompass name: "GeneCompass" type: model @@ -2129,6 +2679,28 @@ resources: organism: [] api: false + - id: genomas + name: "GenoMAS" + type: model + url: https://github.com/Liu-Hy/GenoMAS + description: "Multi-agent framework for code-driven gene-expression analysis with planning, execution, debugging, backtracking, and GEO/TCGA-based scientific discovery." + tags: [agentic-ai-for-biology, bioinformatics-and-omics-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: genomi + name: "Genomi" + type: model + url: https://github.com/exon-research/genomi + description: "Local-first genomics agent runtime that indexes personal variants, queries evidence, and generates evidence-grounded reports while keeping raw genome data on-device." + tags: [agentic-ai-for-biology, bioinformatics-and-omics-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: gigapath name: "GigaPath" type: model @@ -2184,6 +2756,39 @@ resources: organism: [] api: false + - id: h_optimus_0 + name: "H-Optimus-0" + type: model + url: https://huggingface.co/bioptimus/H-optimus-0 + description: "1.1B-parameter histopathology foundation model trained with self-supervised learning on a large multi-center slide corpus." + tags: [foundation-models, pathology-foundation-models, single-cell-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell] + organism: [] + api: false + + - id: h_optimus_1 + name: "H-Optimus-1" + type: model + url: https://huggingface.co/bioptimus/H-optimus-1 + description: "1.1B-parameter pathology foundation model trained on billions of histology images from more than one million slides and 800K+ patients." + tags: [foundation-models, pathology-foundation-models, single-cell-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell] + organism: [] + api: false + + - id: hicfoundation + name: "HiCFoundation" + type: model + url: https://doi.org/10.1038/s41592-026-03097-8 + description: "Hi-C foundation model pretrained on large-scale chromatin-contact maps for 3D-genome analysis, epigenomic prediction, and single-cell Hi-C adaptation." + tags: [foundation-models, other-omics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: hidra name: "HiDRA" type: model @@ -2228,6 +2833,17 @@ resources: organism: [] api: false + - id: lpm + name: "LPM" + type: model + url: https://github.com/perturblib/perturblib + description: "Large perturbation model that jointly learns heterogeneous perturbation experiments by disentangling perturbation, readout, and context representations." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + - id: matcha name: "Matcha" type: model @@ -2250,6 +2866,28 @@ resources: organism: [] api: false + - id: medea + name: "Medea" + type: model + url: https://github.com/mims-harvard/Medea + description: "Multi-agent therapeutic discovery system combining research planning, biological data analysis, literature reasoning, and multi-LLM deliberation across single-cell, cell-line, and patient contexts." + tags: [agentic-ai-for-biology, therapeutics-and-drug-discovery-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: methylgpt + name: "MethylGPT" + type: model + url: https://github.com/albert-ying/MethylGPT + description: "Transformer foundation model for DNA methylation pretrained on >150K human methylomes across thousands of datasets, with 3M/7M/15M parameter variants." + tags: [foundation-models, other-omics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: midas name: "MIDAS" type: model @@ -2272,6 +2910,17 @@ resources: organism: [] api: false + - id: moderngena + name: "modernGENA" + type: model + url: https://github.com/AIRI-Institute/GENA_LM + description: "ModernBERT-style DNA foundation-model family pretrained on hundreds of vertebrate genome assemblies for efficient long-sequence regulatory modeling." + tags: [foundation-models, genomics-foundation-models] + tasks: [Foundation Model] + modalities: [Genomics] + organism: [] + api: false + - id: mofa name: "MOFA+" type: model @@ -2393,6 +3042,17 @@ resources: organism: [] api: false + - id: mutationprojector + name: "MutationProjector" + type: model + url: https://doi.org/10.1101/2025.09.08.674723 + description: "Pan-cancer genotype foundation model trained on mutations and copy-number alterations from >30K tumors for clinical representation learning." + tags: [cancer-genome-foundation-models, foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: nbbayeslm name: "NbBayesLM" type: model @@ -2448,6 +3108,17 @@ resources: organism: [] api: false + - id: omicsfm + name: "OmicsFM" + type: model + url: https://github.com/CompOmics/OmicsFM + description: "Modality-agnostic molecular-expression foundation model with matched proteomics, bulk-transcriptomics, and single-cell-transcriptomics checkpoints." + tags: [foundation-models, other-omics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: openfold name: "OpenFold" type: model @@ -2470,6 +3141,28 @@ resources: organism: [] api: false + - id: paper2agent + name: "Paper2Agent" + type: model + url: https://github.com/jmiao24/Paper2Agent + description: "Multi-agent system that transforms research papers and associated code into interactive, testable scientific agents and MCP tools." + tags: [agentic-ai-for-biology, paper-and-workflow-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: paso + name: "PASO" + type: model + url: https://github.com/queryang/PASO + description: "Pathway-aware multi-omics drug response model combining pathway-difference features, multi-scale convolutions, Transformer encoding, and drug SMILES." + tags: [drug-discovery, drug-response-prediction] + tasks: [Drug Discovery, Drug Response Prediction] + modalities: [Small Molecule] + organism: [] + api: false + - id: pathomicfusion name: "PathomicFusion" type: model @@ -2481,6 +3174,28 @@ resources: organism: [] api: false + - id: perturbnet + name: "PerturbNet" + type: model + url: https://github.com/welch-lab/PerturbNet + description: "Conditional generative model for predicting distributions of single-cell states under unseen chemical and genetic perturbations." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + + - id: perturbrx + name: "PerturbRx" + type: model + url: https://arxiv.org/abs/2608.21349 + description: "Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction." + tags: [drug-discovery, drug-response-prediction] + tasks: [Drug Discovery, Drug Response Prediction] + modalities: [Small Molecule] + organism: [] + api: false + - id: phikon name: "Phikon" type: model @@ -2492,6 +3207,17 @@ resources: organism: [] api: false + - id: phikon_v2 + name: "Phikon-v2" + type: model + url: https://huggingface.co/owkin/phikon-v2 + description: "Updated pathology foundation model for general-purpose histology feature extraction and transfer learning." + tags: [foundation-models, pathology-foundation-models, single-cell-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell] + organism: [] + api: false + - id: plip name: "PLIP" type: model @@ -2514,6 +3240,17 @@ resources: organism: [] api: false + - id: prepr_ct + name: "PrePR-CT" + type: model + url: https://github.com/reem12345/Cell-Type-Specific-Graphs + description: "Graph-based model using cell-type-specific co-expression networks as inductive priors for small-data chemical perturbation response prediction." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + - id: prnet name: "PRNet" type: model @@ -2536,6 +3273,17 @@ resources: organism: [] api: false + - id: prophet + name: "Prophet" + type: model + url: https://github.com/theislab/prophet + description: "Transformer model for predicting cellular phenotypes under unseen chemical or genetic perturbations across heterogeneous assays and contexts." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + - id: proteinmpnn name: "ProteinMPNN" type: model @@ -2547,6 +3295,17 @@ resources: organism: [] api: false + - id: protenix + name: "Protenix" + type: model + url: https://github.com/bytedance/Protenix + description: "Trainable biomolecular structure-prediction framework for proteins, nucleic acids, ligands, and complexes with open training and inference pipelines." + tags: [foundation-models, protein-foundation-models, protein-structure-prediction-and-design] + tasks: [Foundation Model, Protein Structure Prediction] + modalities: [Protein] + organism: [] + api: false + - id: prottrans name: "ProtTrans" type: model @@ -2602,6 +3361,28 @@ resources: organism: [] api: false + - id: rinalmo + name: "RiNALMo" + type: model + url: https://github.com/lbcb-sci/RiNALMo + description: "RNA language-model family pretrained on tens of millions of RNA sequences for secondary-structure and functional prediction tasks." + tags: [foundation-models, rna-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + + - id: rna_fm + name: "RNA-FM" + type: model + url: https://github.com/ml4bio/RNA-FM + description: "General-purpose RNA foundation model pretrained on large-scale RNA sequences for structural and functional representation learning." + tags: [foundation-models, rna-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: rosettafold name: "RoseTTAFold" type: model @@ -2646,6 +3427,17 @@ resources: organism: [] api: false + - id: scarf + name: "SCARF" + type: model + url: https://doi.org/10.1101/2025.04.07.647689 + description: "Single-cell RNA+ATAC foundation model pretrained on >2.7M cells for multimodal representation, matching, cross-omics translation, and few-shot annotation." + tags: [foundation-models, single-cell-epigenomics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: scbert name: "scBERT" type: model @@ -2668,6 +3460,17 @@ resources: organism: [] api: false + - id: scdnam_gpt + name: "scDNAm-GPT" + type: model + url: https://github.com/ChaoqiLiang/scDNAm-GPM + description: "Foundation model for single-cell whole-genome bisulfite sequencing with whole-genome context modeling at single-CpG resolution." + tags: [foundation-models, single-cell-epigenomics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: scfoundation name: "scFoundation" type: model @@ -2734,6 +3537,17 @@ resources: organism: [] api: false + - id: scprint_2 + name: "scPRINT-2" + type: model + url: https://github.com/cantinilab/scPRINT-2 + description: "Next-generation single-cell foundation model pretrained on 350M+ cells across 22K+ datasets and 16 species for embeddings, denoising, annotation, gene-network inference, and cross-species transfer." + tags: [foundation-models, single-cell-foundation-models, transcriptomics-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell, Transcriptomics] + organism: [] + api: false + - id: sei name: "Sei" type: model @@ -2756,6 +3570,28 @@ resources: organism: [] api: false + - id: state + name: "State" + type: model + url: https://github.com/ArcInstitute/state + description: "Transition model for predicting cellular perturbation responses across diverse contexts and sets of cells." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + + - id: stella + name: "STELLA" + type: model + url: https://github.com/zaixizhang/STELLA + description: "Self-evolving biomedical research agent that expands its tool repertoire and supports literature reasoning, computational analysis, and laboratory-oriented scientific workflows." + tags: [agentic-ai-for-biology, bioinformatics-and-omics-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: targetdiff name: "TargetDiff" type: model @@ -2767,6 +3603,17 @@ resources: organism: [] api: false + - id: tessera + name: "TESSERA" + type: model + url: https://github.com/JW-Sidhom-Lab/tessera + description: "Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning." + tags: [cancer-genome-foundation-models, foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + - id: tgsa name: "TGSA" type: model @@ -2778,6 +3625,28 @@ resources: organism: [] api: false + - id: therapi + name: "THERAPI" + type: model + url: https://github.com/Sunginyoung/THERAPI + description: "Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction." + tags: [drug-discovery, drug-response-prediction] + tasks: [Drug Discovery, Drug Response Prediction] + modalities: [Small Molecule] + organism: [] + api: false + + - id: titan + name: "TITAN" + type: model + url: https://github.com/mahmoodlab/TITAN + description: "Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis." + tags: [foundation-models, pathology-foundation-models, single-cell-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell] + organism: [] + api: false + - id: toad name: "TOAD" type: model @@ -2789,6 +3658,17 @@ resources: organism: [] api: false + - id: tooluniverse + name: "ToolUniverse" + type: model + url: https://github.com/mims-harvard/ToolUniverse + description: "Unified scientific tool ecosystem for building AI scientists that can discover, select, and execute biomedical tools and databases." + tags: [agentic-ai-for-biology, general-biomedical-agents] + tasks: [] + modalities: [] + organism: [] + api: false + - id: tosica name: "TOSICA" type: model @@ -2833,6 +3713,28 @@ resources: organism: [] api: false + - id: txagent + name: "TxAgent" + type: model + url: https://github.com/mims-harvard/TxAgent + description: "Therapeutic reasoning agent using multi-step reasoning and a large scientific tool universe for drug interactions, contraindications, and personalized treatment analysis." + tags: [agentic-ai-for-biology, therapeutics-and-drug-discovery-agents] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: txpert + name: "TxPert" + type: model + url: https://github.com/valence-labs/TxPert + description: "Knowledge-graph-informed latent-transfer model for transcriptomic perturbation prediction across unseen single perturbations, combinations, and cross-context settings." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + - id: uce name: "UCE" type: model @@ -2866,6 +3768,28 @@ resources: organism: [] api: false + - id: uni_mol2 + name: "Uni-Mol2" + type: model + url: https://github.com/deepmodeling/Uni-Mol/tree/main/unimol2 + description: "Scaled molecular pretraining model using atomic, graph, and 3D geometry features, with models up to 1.1B parameters pretrained on 800M conformations." + tags: [compound-embedding, compound-foundation-models, foundation-models] + tasks: [Foundation Model] + modalities: [Small Molecule] + organism: [] + api: false + + - id: uni2_h + name: "UNI2-h" + type: model + url: https://huggingface.co/MahmoodLab/UNI2-h + description: "Billion-parameter histopathology vision foundation model for tile-level feature extraction and downstream computational pathology tasks." + tags: [foundation-models, pathology-foundation-models, single-cell-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell] + organism: [] + api: false + - id: unitednet name: "UnitedNet" type: model @@ -2888,6 +3812,50 @@ resources: organism: [] api: false + - id: virchow2 + name: "Virchow2" + type: model + url: https://huggingface.co/paige-ai/Virchow2 + description: "632M-parameter pathology vision transformer pretrained on 3.1M whole-slide images with mixed-magnification self-supervision." + tags: [foundation-models, pathology-foundation-models, single-cell-foundation-models] + tasks: [Foundation Model] + modalities: [Single Cell] + organism: [] + api: false + + - id: virtual_lab + name: "Virtual Lab" + type: model + url: https://github.com/zou-group/virtual-lab + description: "Human–AI collaborative research environment in which an LLM principal investigator coordinates specialized scientist agents for scientific discovery." + tags: [agentic-ai-for-biology, multi-agent-scientific-labs] + tasks: [] + modalities: [] + organism: [] + api: false + + - id: virtues + name: "VirTues" + type: model + url: https://github.com/bunnelab/virtues + description: "Spatial-proteomics foundation model learning marker-aware representations across proteins, cells, niches, and tissues from multiplexed imaging." + tags: [foundation-models, other-omics-foundation-models] + tasks: [Foundation Model] + modalities: [] + organism: [] + api: false + + - id: xpert + name: "XPert" + type: model + url: https://github.com/GSanShui/XPert + description: "Knowledge-informed dual-branch Transformer for drug-induced transcriptional perturbation prediction across dose, time, and cellular context." + tags: [drug-discovery, drug-perturbation] + tasks: [Drug Discovery, Drug Perturbation] + modalities: [Small Molecule] + organism: [] + api: false + - id: autozyme name: "AutoZyme" type: toolkit diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/SCHEMA.md b/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/SCHEMA.md index 170de65..26ef89c 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/SCHEMA.md +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/SCHEMA.md @@ -2,9 +2,9 @@ title: "Resource Data Schema (`docs/data/resources.json`)" task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/docs/data/SCHEMA.md -upstream_sha: 12d87583 -imported_at: 2026-06-26 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/docs/data/SCHEMA.md +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -52,6 +52,10 @@ When contributing new resources, update `README.md` first, then regenerate artif | `api` | boolean | Whether programmatic API access is available. Defaults to `false`. | | `paper` | string | DOI or URL to preprint/peer-reviewed publication. | | `updated` | string | Last-known update date, recommended `YYYY-MM-DD`. | +| `dataset_profile` | object | Optional Dataset Explorer metadata for structured comparison of datasets/benchmarks. | +| `method_profile` | object | Optional Method Explorer metadata for structured comparison of response and perturbation methods. | +| `foundation_profile` | object | Optional Foundation Model Explorer metadata for modality, model scale, size, and availability. | +| `agent_profile` | object | Optional Agent Explorer metadata for agent architecture, capabilities, and scientific workflow support. | ## Naming and consistency guidance @@ -79,3 +83,101 @@ When contributing new resources, update `README.md` first, then regenerate artif "updated": "2026-01-15" } ``` + +## Dataset Explorer profile + +Resources that participate in the Dataset Explorer may define a `dataset_profile` object in a modular `data/enrichment.*.yml` file. This keeps README-derived identity fields separate from deeper dataset metadata. + +Supported v1 fields: + +| Field | Type | Meaning | +|---|---|---| +| `year` | integer | Representative primary publication or public release year. | +| `species` | array of strings | Species represented in the dataset. | +| `sample_type` | string | Primary unit, e.g. `patient` or `cell-line`. | +| `biological_context` | string | High-level context such as `cancer` or `perturbation-screen`. | +| `perturbation_type` | array of strings | Chemical, genetic, or other interventions. | +| `genetic_modes` | array of strings | Genetic perturbation mechanisms, e.g. `crispri`, `crispra`, `crispr-ko`, `enhancer-targeting`, `combinatorial`, or `mixed`. | +| `paired` | boolean | Whether molecular/sample identity can be linked to the measured response or perturbation condition. | +| `pre_post` | boolean | Whether matched pre-treatment and post-treatment samples are available. | +| `longitudinal` | boolean | Whether repeated measurements over time are available as a core design feature. | +| `drug_identity` | boolean or string | Whether treatment identity is available; strings such as `limited` may encode partial availability. | +| `dose` | boolean | Whether dose/concentration metadata are available. | +| `smiles` | boolean or `partial`/`limited` | Whether compound structures are directly available or can only be partially mapped. | +| `clinical_outcome` | boolean | Whether patient-level clinical outcomes are available. | +| `n_samples` | string | Human-readable scale summary. | +| `n_cells` | integer, string, or null | Cell count for single-cell datasets when meaningful. | +| `n_profiles` | integer, string, or null | Number of profiles in the curated comparison view. | +| `n_perturbations` | integer, string, or null | Number of perturbation targets/conditions, especially useful for genetic screens. | +| `n_compounds` | integer, string, or null | Number of compounds/molecules in the curated comparison view. | +| `n_contexts` | integer, string, or null | Number of cellular or experimental contexts. | +| `readout` | string | Primary readout, e.g. `bulk-rna`, `pseudobulk-rna`, `single-cell-rna`, or `viability`. | +| `gene_panel` | string | Gene/readout panel summary, e.g. `20,251 full / 2,000 HVG` or `978 landmark genes`. | +| `time` | boolean | Whether treatment time metadata are available. | +| `view_scope` | string | Scope of the displayed scale fields, e.g. `full-resource`, `study-cohort`, or `bison-benchmark-view`. | +| `access` | string | Dataset-level access summary. | + +The v1 profiles are colocated with their existing modular `data/enrichment.*.yml` owners and rendered at `docs/dataset-explorer.html`. + +## Method Explorer profile + +Methods shown in the Method Explorer may define a `method_profile` object in a modular enrichment fragment. + +| Field | Type | Meaning | +|---|---|---| +| `task_family` | string | High-level task, currently `drug-response-prediction` or `drug-perturbation`. | +| `year` | integer | Publication/release year used for navigation. | +| `input` | string | Main biological/model input. | +| `drug_representation` | string | Chemical or treatment representation. | +| `context_representation` | string | Cellular, patient, or experimental context representation. | +| `output` | string | Main prediction target. | +| `unseen_drug` | boolean/string | Support for unseen-drug generalization; `limited` denotes partial or task-dependent support. | +| `unseen_context` | boolean/string | Support for new cellular/patient contexts. | +| `dose` | boolean/string | Whether dose is explicitly represented. | +| `time` | boolean/string | Whether treatment time is explicitly represented. | +| `patient_transfer` | boolean/string | Whether the method explicitly transfers to or predicts patient response. | +| `code` | boolean/string | Whether an implementation is linked from the curated resource entry. | + +The Method Explorer is rendered at `docs/method-explorer.html`. + +## Foundation Model Explorer profile + +Foundation models shown in the Foundation Model Explorer may define a `foundation_profile` object in a modular enrichment fragment. + +| Field | Type | Meaning | +|---|---|---| +| `year` | integer | Publication or release year used for navigation. | +| `modalities` | array of strings | Fine-grained modalities such as `scrna`, `bulk-rna`, `proteomics`, `mutation`, `methylation`, `atac`, `spatial`, `pathology`, `dna`, `rna`, `chemical`, or `protein`. | +| `params` | string or null | Human-readable parameter count or family range, e.g. `100M` or `1B / 7B / 40B`. | +| `params_millions` | number | Largest representative parameter count in millions, used only for sorting. | +| `pretraining_scale` | string or null | Human-readable summary of the pretraining corpus size. | +| `species` | array of strings | Species represented in pretraining or primary use. | +| `zero_shot` | boolean/string | Whether zero-shot use is explicitly supported; `limited` denotes task-dependent support. | +| `finetunable` | boolean/string | Whether fine-tuning or parameter-efficient adaptation is supported. | +| `weights` | boolean/string | Whether pretrained weights are publicly available. | +| `code` | boolean/string | Whether implementation code is publicly available. | +| `perturbation` | boolean/string | Whether perturbation prediction or in-silico perturbation is a supported use case. | +| `spatial` | boolean/string | Whether spatial data are directly modeled or supported. | + +Unknown or unverified values should be omitted rather than inferred. The Foundation Model Explorer is rendered at `docs/foundation-explorer.html`. + +## Agent Explorer profile + +Agentic AI systems shown in the Agent Explorer may define an `agent_profile` object in a modular enrichment fragment. + +| Field | Type | Meaning | +|---|---|---| +| `year` | integer | Initial public release or publication year. | +| `domains` | array of strings | Main scientific domains such as `general-biomedicine`, `therapeutics`, `bioinformatics`, `single-cell`, `scientific-workflow`, or `paper-to-agent`. | +| `architecture` | string | `single-agent`, `multi-agent`, or `agent-ecosystem`. | +| `tool_use` | boolean/string | Whether external scientific tools/APIs are used. | +| `code_execution` | boolean/string | Whether the system executes code as part of its workflow. | +| `web_retrieval` | boolean/string | Whether web or online database retrieval is supported. | +| `literature` | boolean/string | Whether literature search/reasoning is explicitly supported. | +| `omics` | boolean/string | Whether omics analysis is a first-class supported workflow. | +| `wet_lab` | boolean/string | Whether wet-lab planning, experimental design, or laboratory-facing workflows are supported. | +| `autonomous_experiment` | boolean/string | Whether the system can carry out iterative computational/experimental research loops. | +| `human_in_loop` | boolean/string | Whether human collaboration or approval is an explicit part of the design. | +| `open_source` | boolean/string | Whether implementation code is publicly available. | + +Capability fields may use `limited` or `unknown` when support is workflow-dependent or not clearly documented. The Agent Explorer is rendered at `docs/agent-explorer.html`. diff --git a/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/resources.json b/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/resources.json index f47086f..c5be846 100644 --- a/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/resources.json +++ b/upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/resources.json @@ -2,9 +2,9 @@ title: "Resources" task: "" lineage_type: import -upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/c6f07d90/docs/data/resources.json -upstream_sha: c6f07d90 -imported_at: 2026-08-31 +upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/docs/data/resources.json +upstream_sha: 4ed7cfef +imported_at: 2026-10-26 prompt_class: catalogue upstream_changes: accepted author: upstream @@ -221,13 +221,64 @@ validated: false "url": "https://www.internationalgenome.org/", "description": "Reference panel of human genetic variation from 2,504 individuals across 26 populations.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "adamson_et_al_2016_perturb_seq", + "name": "Adamson et al. 2016 Perturb-seq", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546", + "description": "CRISPRi Perturb-seq in K562 cells for systematic dissection of the unfolded protein response.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2016.11.048", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546" + ], + "dataset_profile": { + "year": 2016, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "unfolded-protein-response", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri", + "combinatorial" + ], + "readout": "single-cell-rna", + "n_profiles": "~86,000 cells", + "n_perturbations": "~100 UPR hits plus combinatorial CRISPRi perturbations", + "n_contexts": 3, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "three pooled CRISPRi Perturb-seq experiments in K562", + "n_cells": "~86,000", + "access": "open" + } + }, { "id": "bace", "name": "BACE", @@ -235,7 +286,7 @@ validated: false "url": "https://www.kaggle.com/datasets/gokturkkoch/bace", "description": "Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "classification", @@ -262,7 +313,7 @@ validated: false "url": "https://biodev.github.io/BeatAML2/", "description": "Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -281,7 +332,31 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://biodev.github.io/BeatAML2/" - ] + ], + "dataset_profile": { + "year": 2018, + "readout": "ex-vivo-viability", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "patient", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": true, + "n_samples": "primary AML patient samples", + "n_cells": null, + "access": "open" + } }, { "id": "bento", @@ -290,7 +365,7 @@ validated: false "url": "https://github.com/LigandPro/Bento", "description": "Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -304,7 +379,7 @@ validated: false "url": "https://www.bindingdb.org/rwd/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes", "description": "Curated binding affinity datasets for protein–ligand interaction benchmarking.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "drug-target-interaction" @@ -330,7 +405,7 @@ validated: false "url": "https://portals.broadinstitute.org/ctrp/", "description": "Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -345,7 +420,47 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://portals.broadinstitute.org/ctrp/" - ] + ], + "dataset_profile": { + "year": 2013, + "readout": "viability", + "n_compounds": ">400", + "n_contexts": "~900 cell lines", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "~900 cancer cell lines", + "n_cells": null, + "access": "open" + } + }, + { + "id": "chem_perturbridge", + "name": "Chem-PerturBridge", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Harmonized compendium and processing pipelines for small-molecule perturbation transcriptomics across heterogeneous assays and gene panels.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false }, { "id": "clintox", @@ -354,7 +469,7 @@ validated: false "url": "https://tdcommons.ai/single_pred_tasks/tox/#clintox", "description": "Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "classification" @@ -379,7 +494,7 @@ validated: false "url": "https://proteomics.cancer.gov/programs/cptac", "description": "Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], "modalities": [], @@ -393,13 +508,117 @@ validated: false "url": "https://arxiv.org/abs/2001.01037", "description": "Large-scale dataset for structure-based virtual screening.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "datlinger_et_al_2017_crop_seq", + "name": "Datlinger et al. 2017 CROP-seq", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872", + "description": "Pooled CRISPR knockout screen with single-cell transcriptomic readout in Jurkat cells under T-cell receptor stimulation.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1038/nmeth.4177", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872", + "https://doi.org/10.1038/nmeth.4177" + ], + "dataset_profile": { + "year": 2017, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "t-cell-receptor-signaling", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispr-ko" + ], + "readout": "single-cell-rna", + "n_profiles": 5905, + "n_perturbations": "TCR regulators and transcription factors", + "n_contexts": 2, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "stimulated and unstimulated Jurkat CROP-seq", + "n_cells": 5905, + "access": "open" + } + }, + { + "id": "dixit_et_al_2016_perturb_seq", + "name": "Dixit et al. 2016 Perturb-seq", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063", + "description": "Pooled CRISPR knockout with single-cell RNA-seq across K562 and dendritic-cell screens, including stimulated conditions and combinatorial perturbations.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2016.11.038", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063", + "https://doi.org/10.1016/j.cell.2016.11.038" + ], + "dataset_profile": { + "year": 2016, + "view_scope": "full-study", + "species": [ + "human", + "mouse" + ], + "sample_type": "cell-line+primary", + "biological_context": "immune-and-cell-line", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispr-ko", + "combinatorial" + ], + "readout": "single-cell-rna", + "n_profiles": "~200,000 cells", + "n_perturbations": "pooled CRISPR-KO across six screens", + "n_contexts": 6, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "six pooled screens across K562 and dendritic-cell conditions", + "n_cells": "~200,000", + "access": "open" + } + }, { "id": "dud_e_directory_of_useful_decoys_enhanced", "name": "DUD-E (Directory of Useful Decoys, Enhanced)", @@ -407,7 +626,7 @@ validated: false "url": "http://dude.docking.org/", "description": "Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -421,13 +640,160 @@ validated: false "url": "https://github.com/J-SNACKKB/FLIP", "description": "Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "frangieh_et_al_2021_perturb_cite_seq", + "name": "Frangieh et al. 2021 Perturb-CITE-seq", + "type": "benchmark", + "url": "https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion", + "description": "Multimodal CRISPR knockout screen with RNA and surface-protein readouts in patient-derived melanoma models under immune-related conditions.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1038/s41588-021-00779-1", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion", + "https://doi.org/10.1038/s41588-021-00779-1" + ], + "dataset_profile": { + "year": 2021, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "patient-derived-cell-line", + "biological_context": "melanoma-immunotherapy", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispr-ko" + ], + "readout": "cite-seq", + "n_profiles": ">200,000 cells", + "n_perturbations": 248, + "n_contexts": 3, + "gene_panel": "transcriptome + surface proteins", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "patient-derived melanoma under control, IFN-gamma, and TIL co-culture conditions", + "n_cells": ">200,000", + "access": "mixed" + } + }, + { + "id": "gasperini_et_al_2019_crispri_enhancer_screen", + "name": "Gasperini et al. 2019 CRISPRi enhancer screen", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861", + "description": "High-MOI single-cell CRISPRi screen in K562 cells targeting thousands of candidate enhancers to map enhancer-gene regulation.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2018.11.029", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861" + ], + "dataset_profile": { + "year": 2019, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "enhancer-gene-regulation", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri", + "enhancer-targeting" + ], + "readout": "single-cell-rna", + "n_profiles": 254974, + "n_perturbations": 5920, + "n_contexts": 3, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "three CRISPRi screens in K562, including an at-scale enhancer screen", + "n_cells": 254974, + "access": "open" + } + }, + { + "id": "gse191127_breast_cancer_pre_post_chemotherapy", + "name": "GSE191127 Breast Cancer Pre/Post Chemotherapy", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127", + "description": "Bulk RNA-seq and genomics from matched pre- and post-neoadjuvant chemotherapy breast tumors with treatment outcomes.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2022, + "view_scope": "study-cohort", + "readout": "bulk-rna", + "n_profiles": "317 pretreatment; 22 matched pre/post deeply profiled", + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "time": true, + "species": [ + "human" + ], + "sample_type": "patient", + "biological_context": "breast-cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": true, + "longitudinal": true, + "drug_identity": "limited", + "dose": false, + "smiles": "partial", + "clinical_outcome": true, + "n_samples": "317 pretreatment biopsies; 22 matched pre/post tumors with deep profiling", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127" + ] + }, { "id": "guacamol", "name": "GuacaMol", @@ -435,7 +801,7 @@ validated: false "url": "https://github.com/BenevolentAI/guacamol", "description": "Benchmark suite for generative molecular design models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "molecular-generation" @@ -461,7 +827,7 @@ validated: false "url": "https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics", "description": "DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [ "regression" @@ -491,7 +857,7 @@ validated: false "url": "https://github.com/jump-cellpainting/datasets", "description": "Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [], "modalities": [], @@ -505,7 +871,7 @@ validated: false "url": "https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data", "description": "Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "perturbation-prediction" @@ -523,6 +889,176 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data" + ], + "dataset_profile": { + "year": 2017, + "readout": "l1000", + "gene_panel": "978 landmark genes", + "time": true, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical", + "genetic" + ], + "genetic_modes": [ + "mixed" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "large-scale perturbational profiles", + "n_cells": null, + "access": "open" + } + }, + { + "id": "lincs_l1000_phase_1", + "name": "LINCS L1000 Phase 1", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Phase-1 L1000 benchmark view used by Bison with 692,787 profiles, 9,233 molecules, 70 contexts, and 978 landmark genes.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2017, + "view_scope": "bison-benchmark-view", + "readout": "l1000", + "n_profiles": 692787, + "n_compounds": 9233, + "n_contexts": 70, + "gene_panel": "978 landmark genes", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "692,787 profiles; 9,233 molecules; 70 contexts; 978 landmark genes", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, + { + "id": "lincs_l1000_phase_2", + "name": "LINCS L1000 Phase 2", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Phase-2 L1000 benchmark view used by Bison with 333,263 profiles, 1,760 molecules, 30 contexts, and 978 landmark genes.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2017, + "view_scope": "bison-benchmark-view", + "readout": "l1000", + "n_profiles": 333263, + "n_compounds": 1760, + "n_contexts": 30, + "gene_panel": "978 landmark genes", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "333,263 profiles; 1,760 molecules; 30 contexts; 978 landmark genes", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, + { + "id": "mix_seq", + "name": "MIX-Seq", + "type": "benchmark", + "url": "https://www.nature.com/articles/s41467-020-17440-w", + "description": "Multiplexed single-cell transcriptional profiling of chemical and genetic perturbation responses across pools of cancer cell lines.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2020, + "view_scope": "full-resource", + "readout": "single-cell-rna", + "n_compounds": 13, + "n_contexts": "100+ cell lines", + "gene_panel": "transcriptome-wide", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical", + "genetic" + ], + "genetic_modes": [ + "mixed" + ], + "paired": false, + "pre_post": false, + "longitudinal": true, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "pools of up to 100+ cancer cell lines; 13-drug demonstration", + "n_cells": "single-cell RNA-seq", + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.nature.com/articles/s41467-020-17440-w" ] }, { @@ -532,7 +1068,7 @@ validated: false "url": "http://moleculenet.ai/", "description": "Benchmark datasets for molecular machine learning.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "classification", @@ -559,13 +1095,111 @@ validated: false "url": "https://github.com/molecularsets/moses", "description": "Benchmarking platform for molecular generation models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "norman_et_al_2019_crispra_perturb_seq", + "name": "Norman et al. 2019 CRISPRa Perturb-seq", + "type": "benchmark", + "url": "https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323", + "description": "Large-scale CRISPR activation Perturb-seq in K562 cells spanning single-gene and combinatorial perturbations.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1126/science.aax4438", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323", + "https://doi.org/10.1126/science.aax4438" + ], + "dataset_profile": { + "year": 2019, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "genetic-interactions", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispra", + "combinatorial" + ], + "readout": "single-cell-rna", + "n_profiles": "~110,000 cells", + "n_perturbations": 287, + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": false, + "smiles": false, + "clinical_outcome": false, + "n_samples": "single-gene and pairwise CRISPRa perturbations in K562", + "n_cells": "~110,000", + "access": "open" + } + }, + { + "id": "novartis_perturbation_dataset", + "name": "Novartis Perturbation Dataset", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Large chemical perturbation transcriptomic screen used in Chem-PerturBridge; the Bison benchmark view contains 46,748 profiles and 3,770 molecules.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2025, + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 46748, + "n_compounds": 3770, + "n_contexts": 1, + "gene_panel": "20,038 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "46,748 profiles; 3,770 molecules; 1 context", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, { "id": "ogb_open_graph_benchmark", "name": "OGB (Open Graph Benchmark)", @@ -573,13 +1207,59 @@ validated: false "url": "https://ogb.stanford.edu/", "description": "Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.", "tags": [ - "benchmarks-and-datasets" + "general-biological-ml-and-knowledge-graph-benchmarks" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "op3", + "name": "OP3", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Chemical perturbation dataset used by Chem-PerturBridge and the Bison unseen-compound benchmark; the Bison benchmark view contains 1,813 profiles, 138 molecules, and 4 cellular contexts.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2024, + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 1813, + "n_compounds": 138, + "n_contexts": 4, + "gene_panel": "5,198 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "1,813 profiles; 138 molecules; 4 contexts", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, { "id": "openbiolink", "name": "OpenBioLink", @@ -587,13 +1267,59 @@ validated: false "url": "https://github.com/OpenBioLink/OpenBioLink", "description": "Benchmark datasets for biological knowledge graph completion.", "tags": [ - "benchmarks-and-datasets" + "general-biological-ml-and-knowledge-graph-benchmarks" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "pdx_atlas", + "name": "PDX-Atlas", + "type": "benchmark", + "url": "https://muralportal.pdxatlas.org/", + "description": "Portal integrating clinical, genomic, transcriptomic, and drug-response data from patient-derived xenograft models.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2026, + "view_scope": "current-portal", + "readout": "multi-omics", + "n_profiles": ">50 PDXs", + "n_contexts": "30 patients", + "gene_panel": "multi-omics", + "time": false, + "species": [ + "human", + "mouse" + ], + "sample_type": "pdx", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": false, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": ">50 PDXs from 30 prostate cancer patients in the current portal", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://muralportal.pdxatlas.org/" + ] + }, { "id": "pharmgkb", "name": "PharmGKB", @@ -601,7 +1327,7 @@ validated: false "url": "https://www.pharmgkb.org/", "description": "Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -630,7 +1356,7 @@ validated: false "url": "https://pk-db.com/", "description": "Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [ @@ -653,7 +1379,7 @@ validated: false "url": "https://depmap.org/portal/prism/", "description": "Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "drug-response-prediction" @@ -665,10 +1391,36 @@ validated: false "cell", "drug" ], - "last_checked": "2026-08-08", + "last_checked": "2026-10-02", "metadata_sources": [ "https://depmap.org/portal/prism/" - ] + ], + "dataset_profile": { + "year": 2020, + "readout": "viability", + "n_compounds": ">4,500", + "n_contexts": ">900 cell lines", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "PRISM pooled viability screens", + "n_cells": null, + "access": "open" + } }, { "id": "proteingym", @@ -677,7 +1429,7 @@ validated: false "url": "https://github.com/OATML-Markslab/ProteinGym", "description": "Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [ "regression" @@ -703,13 +1455,214 @@ validated: false "url": "https://figshare.com/collections/Quantum_chemistry_structures_and_properties_of_134_kilo_molecules/978904", "description": "Quantum chemistry properties for 134K stable small organic molecules computed at DFT level.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "replogle_et_al_2022_k562_essential_perturb_seq", + "name": "Replogle et al. 2022 K562 Essential Perturb-seq", + "type": "benchmark", + "url": "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "description": "CRISPRi Perturb-seq focused on essential genes in K562 cells.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2022.05.013", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "https://gwps.wi.mit.edu/" + ], + "dataset_profile": { + "year": 2022, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "essential-gene-screen", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri" + ], + "readout": "single-cell-rna", + "n_profiles": 310385, + "n_perturbations": 2057, + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "K562 CRISPRi essential-gene screen sampled at day 6", + "n_cells": 310385, + "access": "open" + } + }, + { + "id": "replogle_et_al_2022_k562_genome_wide_perturb_seq", + "name": "Replogle et al. 2022 K562 Genome-wide Perturb-seq", + "type": "benchmark", + "url": "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "description": "Genome-scale CRISPRi Perturb-seq targeting nearly all expressed genes in K562 cells.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2022.05.013", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "https://gwps.wi.mit.edu/" + ], + "dataset_profile": { + "year": 2022, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "genome-wide-functional-genomics", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri" + ], + "readout": "single-cell-rna", + "n_profiles": "genome-scale; mean ~183 cells per detected perturbation", + "n_perturbations": "9,866 target genes; 11,258 detected sgRNA-pair perturbations", + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "K562 CRISPRi genome-wide screen sampled at day 8", + "n_cells": "mean ~183 cells per detected perturbation", + "access": "open" + } + }, + { + "id": "replogle_et_al_2022_rpe1_essential_perturb_seq", + "name": "Replogle et al. 2022 RPE1 Essential Perturb-seq", + "type": "benchmark", + "url": "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "description": "CRISPRi Perturb-seq focused on essential genes in RPE1 cells.", + "tags": [ + "genetic-perturbation-and-perturb-seq" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "paper": "https://doi.org/10.1016/j.cell.2022.05.013", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387", + "https://gwps.wi.mit.edu/" + ], + "dataset_profile": { + "year": 2022, + "view_scope": "full-study", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "essential-gene-screen", + "perturbation_type": [ + "genetic" + ], + "genetic_modes": [ + "crispri" + ], + "readout": "single-cell-rna", + "n_profiles": 247914, + "n_perturbations": 2393, + "n_contexts": 1, + "gene_panel": "transcriptome-wide", + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": false, + "dose": false, + "time": true, + "smiles": false, + "clinical_outcome": false, + "n_samples": "RPE1 CRISPRi essential-gene screen sampled at day 7", + "n_cells": 247914, + "access": "open" + } + }, + { + "id": "sci_plex", + "name": "sci-Plex", + "type": "benchmark", + "url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944", + "description": "Single-cell chemical perturbation screen of ~650,000 transcriptomes across three cancer cell lines, 188 compounds, and four doses.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2020, + "view_scope": "bison-benchmark-view", + "readout": "pseudobulk-rna", + "n_profiles": 4974, + "n_compounds": 186, + "n_contexts": 3, + "gene_panel": "20,251 full / 2,000 HVG (Bison benchmark view)", + "time": true, + "species": [ + "human", + "mouse" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "~5,000 conditions; 188 compounds across 3 human cancer cell lines", + "n_cells": 649340, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944", + "https://pubmed.ncbi.nlm.nih.gov/31806696/" + ] + }, { "id": "scib_single_cell_integration_benchmarks", "name": "scIB (Single-cell Integration Benchmarks)", @@ -717,7 +1670,7 @@ validated: false "url": "https://github.com/theislab/scib", "description": "Comprehensive benchmarking framework for single-cell data integration methods.", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], @@ -731,7 +1684,7 @@ validated: false "url": "https://github.com/sanderlab/scPerturb", "description": "Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.", "tags": [ - "benchmarks-and-datasets" + "drug-response-and-perturbation" ], "tasks": [ "perturbation-prediction" @@ -747,10 +1700,40 @@ validated: false "gene" ], "github": "https://github.com/sanderlab/scPerturb", - "last_checked": "2026-08-08", + "last_checked": "2026-10-02", "metadata_sources": [ "https://github.com/sanderlab/scPerturb" - ] + ], + "dataset_profile": { + "year": 2024, + "readout": "single-cell-rna", + "gene_panel": "varies by source study", + "time": true, + "view_scope": "harmonized-collection", + "species": [ + "human", + "mouse" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-collection", + "perturbation_type": [ + "chemical", + "genetic" + ], + "genetic_modes": [ + "mixed" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "scPerturb integrated perturbation datasets", + "n_cells": "multi-study single-cell compendium", + "access": "open" + } }, { "id": "sider_side_effect_resource", @@ -759,7 +1742,7 @@ validated: false "url": "http://sideeffects.embl.de/", "description": "Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [ @@ -783,7 +1766,7 @@ validated: false "url": "https://tabula-muris.ds.czbiohub.org/", "description": "Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], @@ -797,13 +1780,59 @@ validated: false "url": "https://tabula-sapiens-portal.ds.czbiohub.org/", "description": "Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "tahoe_100m", + "name": "Tahoe-100M", + "type": "benchmark", + "url": "https://huggingface.co/datasets/tahoebio/Tahoe-100M", + "description": "Giga-scale single-cell perturbation atlas with >100 million profiles from 50 cancer cell lines exposed to ~1,100 small molecules.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "year": 2025, + "view_scope": "bison-benchmark-view", + "readout": "single-cell-rna", + "n_profiles": 67018, + "n_compounds": 376, + "n_contexts": 50, + "gene_panel": "20,573 full / 2,000 HVG (Bison benchmark view)", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": ">100 million profiles; 50 cancer cell lines; ~1,100 compounds", + "n_cells": ">100 million", + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/datasets/tahoebio/Tahoe-100M" + ] + }, { "id": "tape_tasks_assessing_protein_embeddings", "name": "TAPE (Tasks Assessing Protein Embeddings)", @@ -811,7 +1840,7 @@ validated: false "url": "https://github.com/songlab-cal/tape", "description": "Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -825,7 +1854,7 @@ validated: false "url": "https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas", "description": "DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).", "tags": [ - "benchmarks-and-datasets" + "single-cell-and-spatial" ], "tasks": [], "modalities": [], @@ -839,12 +1868,48 @@ validated: false "url": "https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga", "description": "Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], - "modalities": [], + "modalities": [ + "clinical", + "genomics" + ], "organism": [], - "api": false + "api": false, + "entities": [ + "disease", + "gene" + ], + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://www.cancer.gov/ccg/research/genome-sequencing/tcga" + ], + "dataset_profile": { + "year": 2018, + "readout": "multi-omics", + "n_profiles": ">11,000 patients", + "n_contexts": 33, + "gene_panel": "transcriptome-wide plus multi-omics", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "patient", + "biological_context": "cancer", + "perturbation_type": [], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": "limited", + "dose": false, + "smiles": false, + "clinical_outcome": true, + "n_samples": ">11,000 patients across 33 cancer types", + "n_cells": null, + "access": "mixed" + } }, { "id": "therapeutics_data_commons_tdc", @@ -853,7 +1918,7 @@ validated: false "url": "https://tdcommons.ai/", "description": "Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -867,7 +1932,7 @@ validated: false "url": "https://tripod.nih.gov/tox21/challenge/", "description": "12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.", "tags": [ - "benchmarks-and-datasets" + "molecular-protein-and-drug-discovery" ], "tasks": [], "modalities": [], @@ -881,13 +1946,103 @@ validated: false "url": "https://www.ukbiobank.ac.uk/", "description": "Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.", "tags": [ - "benchmarks-and-datasets" + "genomics-cancer-and-biomedical-cohorts" ], "tasks": [], "modalities": [], "organism": [], "api": false }, + { + "id": "vcpi_0001", + "name": "VCPI-0001", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 27,517 profiles and 2,272 molecules in one cellular context.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 27517, + "n_compounds": 2272, + "n_contexts": 1, + "gene_panel": "20,629 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "27,517 profiles; 2,272 molecules; 1 context", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, + { + "id": "vcpi_0002", + "name": "VCPI-0002", + "type": "benchmark", + "url": "https://github.com/theislab/Chem-PerturBridge", + "description": "Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 18,139 profiles and 1,488 molecules in one cellular context.", + "tags": [ + "drug-response-and-perturbation" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "dataset_profile": { + "view_scope": "bison-benchmark-view", + "readout": "bulk-rna", + "n_profiles": 18139, + "n_compounds": 1488, + "n_contexts": 1, + "gene_panel": "20,629 full / 2,000 HVG", + "time": true, + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "perturbation-screen", + "perturbation_type": [ + "chemical" + ], + "paired": false, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": true, + "clinical_outcome": false, + "n_samples": "18,139 profiles; 1,488 molecules; 1 context", + "n_cells": null, + "access": "open" + }, + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/theislab/Chem-PerturBridge" + ] + }, { "id": "10x_genomics_dataset", "name": "10x Genomics Dataset", @@ -1662,8 +2817,8 @@ validated: false "url": "https://www.cancerrxgene.org/", "description": "Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.", "tags": [ - "benchmarks-and-datasets", "drug-cell-line-response", + "drug-response-and-perturbation", "interaction" ], "tasks": [ @@ -1682,7 +2837,32 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://www.cancerrxgene.org/" - ] + ], + "dataset_profile": { + "year": 2012, + "readout": "viability", + "n_contexts": "~1,000 cell lines", + "time": false, + "view_scope": "full-resource", + "species": [ + "human" + ], + "sample_type": "cell-line", + "biological_context": "cancer", + "perturbation_type": [ + "chemical" + ], + "paired": true, + "pre_post": false, + "longitudinal": false, + "drug_identity": true, + "dose": true, + "smiles": "partial", + "clinical_outcome": false, + "n_samples": "~1,000 cancer cell lines", + "n_cells": null, + "access": "open" + } }, { "id": "gnomad", @@ -2049,8 +3229,8 @@ validated: false "url": "https://dtp.cancer.gov/discovery_development/nci-60/", "description": "Focuses on 60 cancer cell lines and many drugs.", "tags": [ - "benchmarks-and-datasets", "drug-cell-line-response", + "drug-response-and-perturbation", "interaction" ], "tasks": [], @@ -2683,6 +3863,38 @@ validated: false "https://github.com/ratschlab/aestetik" ] }, + { + "id": "agent_laboratory", + "name": "Agent Laboratory", + "type": "model", + "url": "https://github.com/SamuelSchmidgall/AgentLaboratory", + "description": "End-to-end multi-agent research workflow for literature review, experimentation, implementation, analysis, and report generation.", + "tags": [ + "agentic-ai-for-biology", + "multi-agent-scientific-labs" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "scientific-workflow", + "research-automation" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": false, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "ai4chem_chemllm_7b_chat", "name": "AI4Chem/ChemLLM-7B-Chat", @@ -2735,6 +3947,61 @@ validated: false "https://github.com/google-deepmind/alphafold3" ] }, + { + "id": "alphagenome", + "name": "AlphaGenome", + "type": "model", + "url": "https://github.com/google-deepmind/alphagenome", + "description": "Long-context DNA model predicting multimodal regulatory outputs including expression, splicing, chromatin features, and contact maps at near base-pair resolution.", + "tags": [ + "foundation-models", + "genomics-foundation-models" + ], + "tasks": [ + "regression", + "representation-learning" + ], + "modalities": [ + "dna-sequence", + "epigenomics", + "genomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "dna" + ], + "pretraining_scale": "long-context regulatory genomics corpus", + "species": [ + "human", + "mouse" + ], + "zero_shot": true, + "finetunable": "unknown", + "weights": false, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "genome", + "regulatory-element", + "variant" + ], + "methods": [ + "supervised-learning", + "transformer" + ], + "year": 2025, + "github": "https://github.com/google-deepmind/alphagenome", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/google-deepmind/alphagenome" + ] + }, { "id": "ankh", "name": "Ankh", @@ -2755,6 +4022,72 @@ validated: false "organism": [], "api": false }, + { + "id": "atacformer", + "name": "Atacformer", + "type": "model", + "url": "https://doi.org/10.1101/2025.11.03.685753", + "description": "Transformer foundation model for scATAC-seq that learns embeddings of cis-regulatory elements for clustering, annotation, and reference mapping.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": "large atlas of scATAC-seq experiments", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, + { + "id": "autoba", + "name": "AutoBA", + "type": "model", + "url": "https://github.com/JoshuaChou2018/AutoBA", + "description": "Automated multi-omics analysis agent that plans, executes, and repairs bioinformatics workflows from natural-language objectives.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2024, + "domains": [ + "bioinformatics", + "omics", + "scientific-workflow" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "babel", "name": "BABEL", @@ -2795,6 +4128,71 @@ validated: false "organism": [], "api": false }, + { + "id": "bia", + "name": "BIA", + "type": "model", + "url": "https://github.com/biagent-dev/bia", + "description": "Bioinformatics agent for GEO search, sample metadata extraction, count-matrix processing, and pipeline extraction from papers.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "domains": [ + "bioinformatics", + "genomics", + "literature" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "bioagents", + "name": "BioAgents", + "type": "model", + "url": "https://github.com/microsoft/bioinformagus", + "description": "Multi-agent bioinformatics assistant using specialized language models and retrieval for genomics workflow development and troubleshooting.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "bioinformatics", + "genomics", + "scientific-workflow" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "biogpt", "name": "BioGPT", @@ -2813,6 +4211,106 @@ validated: false "organism": [], "api": false }, + { + "id": "biolord", + "name": "biolord", + "type": "model", + "url": "https://github.com/nitzanlab/biolord", + "description": "Deep generative model that disentangles known and unknown attributes for conditional generation of single-cell states.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2024, + "input": "expression + known attributes", + "drug_representation": "chemical descriptors", + "context_representation": "unknown latent attributes", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, + { + "id": "biomaster", + "name": "BioMaster", + "type": "model", + "url": "https://github.com/ai4nucleome/BioMaster", + "description": "Multi-agent system for automated and auditable bioinformatics workflows spanning RNA-seq, ChIP-seq, single-cell, spatial omics, Hi-C, long reads, metagenomics, and proteomics.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "bioinformatics", + "omics", + "scientific-workflow" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "biomedagent", + "name": "BioMedAgent", + "type": "model", + "url": "https://github.com/BOBQWERA/BioMedAgent", + "description": "Self-evolving multi-agent framework for autonomous biomedical data analysis with tool discovery, workflow planning, code generation, execution, correction, and cross-omics analysis.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "general-biomedicine", + "bioinformatics", + "omics" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "biomedclip", "name": "BiomedCLIP", @@ -2850,6 +4348,74 @@ validated: false "organism": [], "api": false }, + { + "id": "biomni", + "name": "Biomni", + "type": "model", + "url": "https://github.com/snap-stanford/Biomni", + "description": "General-purpose biomedical AI agent integrating planning, code execution, specialized tools, databases, and software across diverse biomedical research tasks.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "general-biomedicine", + "bioinformatics", + "therapeutics", + "omics" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": "limited", + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "bison", + "name": "Bison", + "type": "model", + "url": "https://arxiv.org/abs/2609.32467", + "description": "Cross-dataset model for globally unseen-compound response prediction using a shared gene representation, discrete diffusion models, and matched drug-contrast supervision.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "bulk/pseudobulk transcriptomics", + "drug_representation": "Morgan fingerprints + chemical tokens", + "context_representation": "control-derived context features + embeddings", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": true, + "patient_transfer": false, + "code": false + } + }, { "id": "boltz_1", "name": "Boltz-1", @@ -2871,6 +4437,64 @@ validated: false "organism": [], "api": false }, + { + "id": "boltz_2", + "name": "Boltz-2", + "type": "model", + "url": "https://github.com/jwohlwend/boltz", + "description": "Biomolecular foundation model jointly predicting complex structures and binding affinities for protein–ligand interaction modeling and virtual screening.", + "tags": [ + "foundation-models", + "protein-foundation-models", + "protein-structure-prediction-and-design" + ], + "tasks": [ + "drug-target-interaction", + "structure-prediction", + "virtual-screening" + ], + "modalities": [ + "molecular-structure", + "protein-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "protein", + "chemical" + ], + "pretraining_scale": "biomolecular complex structure and affinity data", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": "limited", + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule", + "protein", + "protein-complex" + ], + "methods": [ + "diffusion", + "generative-model", + "transformer" + ], + "year": 2025, + "github": "https://github.com/jwohlwend/boltz", + "paper": "https://doi.org/10.1101/2025.06.14.659707", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/jwohlwend/boltz", + "https://doi.org/10.1101/2025.06.14.659707" + ] + }, { "id": "borzoi", "name": "Borzoi", @@ -2890,6 +4514,39 @@ validated: false "organism": [], "api": false }, + { + "id": "brad", + "name": "BRAD", + "type": "model", + "url": "https://github.com/Jpickard1/BRAD", + "description": "Retrieval-augmented bioinformatics assistant integrating scientific literature, databases, external tools, and executable workflows.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "bioinformatics", + "literature", + "scientific-workflow" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "bulkformer", "name": "BulkFormer", @@ -2909,7 +4566,23 @@ validated: false "Transcriptomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "bulk-rna" + ], + "pretraining_scale": "bulk RNA-seq compendium", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "caduceus", @@ -2928,7 +4601,23 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "dna" + ], + "pretraining_scale": "long-range genomic sequence pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "cancerfoundation", @@ -2949,25 +4638,216 @@ validated: false "Transcriptomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna" + ], + "pretraining_scale": "curated malignant-cell single-cell corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } + }, + { + "id": "captain", + "name": "CAPTAIN", + "type": "model", + "url": "https://doi.org/10.1038/s41467-026-72882-y", + "description": "Multimodal foundation model pretrained on co-assayed single-cell RNA and protein for joint representation learning and cross-modal downstream tasks.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna", + "proteomics", + "multi-omics" + ], + "pretraining_scale": "co-assayed single-cell RNA and protein data", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, + { + "id": "casanovo_foundation", + "name": "Casanovo Foundation", + "type": "model", + "url": "https://github.com/Noble-Lab/casanovo-tl", + "description": "Tandem mass-spectrometry proteomics foundation model that reuses a pretrained Casanovo spectrum encoder for spectrum quality, chimericity, and post-translational-modification prediction.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "proteomics" + ], + "pretraining_scale": "30M labeled tandem mass spectra", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "cassia", "name": "CASSIA", "type": "model", "url": "https://github.com/ElliotXie/CASSIA", - "description": "Multi-agent LLM for reference-free, interpretable cell-type annotation of single-cell RNA-seq data, with dedicated annotation, validation, scoring, and reporting agents.", + "description": "Multi-agent LLM framework for reference-free and interpretable single-cell cell-type annotation with dedicated annotation, validation, scoring, and reporting agents.", "tags": [ - "llm-for-biology" + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2024, + "domains": [ + "single-cell", + "bioinformatics", + "omics" + ], + "architecture": "multi-agent", + "tool_use": "limited", + "code_execution": "limited", + "web_retrieval": "limited", + "literature": true, + "omics": true, + "wet_lab": false, + "autonomous_experiment": false, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "cellflow", + "name": "CellFlow", + "type": "model", + "url": "https://github.com/theislab/CellFlow", + "description": "Conditional flow-matching framework for modeling and predicting cellular phenotypes under chemical, genetic, and other perturbations.", + "tags": [ + "drug-discovery", + "drug-perturbation" ], "tasks": [ - "Language Modeling" + "Drug Discovery", + "Drug Perturbation" ], "modalities": [ - "Text" + "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2025, + "input": "control/treated expression distributions", + "drug_representation": "molecular features", + "context_representation": "matched control distribution", + "output": "transported cell-state distribution", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, + { + "id": "cellfm", + "name": "CellFM", + "type": "model", + "url": "https://github.com/biomed-AI/CellFM", + "description": "800M-parameter single-cell foundation model pretrained on transcriptomics from 100 million human cells for annotation, integration, gene-function, and perturbation tasks.", + "tags": [ + "foundation-models", + "single-cell-foundation-models", + "transcriptomics-foundation-models" + ], + "tasks": [ + "cell-type-annotation", + "foundation-model-pretraining", + "perturbation-prediction", + "representation-learning" + ], + "modalities": [ + "single-cell-rna-seq", + "transcriptomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna" + ], + "params": "800M", + "params_millions": 800, + "pretraining_scale": "100M human cells", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": false + }, + "entities": [ + "cell", + "gene" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "github": "https://github.com/biomed-AI/CellFM", + "paper": "https://doi.org/10.1038/s41467-025-59926-5", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/biomed-AI/CellFM", + "https://doi.org/10.1038/s41467-025-59926-5" + ] }, { "id": "cellot", @@ -3025,7 +4905,23 @@ validated: false "metadata_sources": [ "https://github.com/OmicsML/CellPLM", "https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1" - ] + ], + "foundation_profile": { + "year": 2023, + "modalities": [ + "scrna" + ], + "pretraining_scale": "multi-dataset single-cell pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } }, { "id": "chai_1", @@ -3117,7 +5013,73 @@ validated: false "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2022, + "input": "single-cell expression + covariates", + "drug_representation": "RDKit2D descriptors", + "context_representation": "cell covariates + basal state", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": "limited", + "patient_transfer": false, + "code": true + } + }, + { + "id": "chemfm", + "name": "ChemFM", + "type": "model", + "url": "https://github.com/TheLuoFengLab/ChemFM", + "description": "1B/3B-parameter chemical language model pretrained on 178M molecules for molecular representation, property prediction, generation, and synthesis tasks.", + "tags": [ + "compound-embedding", + "compound-foundation-models", + "foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "molecular-generation", + "representation-learning" + ], + "modalities": [ + "chemical-structure" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "chemical" + ], + "params": "1B / 3B", + "params_millions": 3000, + "pretraining_scale": "178M molecules", + "species": [], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule" + ], + "methods": [ + "generative-model", + "language-model", + "transformer" + ], + "year": 2024, + "github": "https://github.com/TheLuoFengLab/ChemFM", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/TheLuoFengLab/ChemFM" + ] }, { "id": "chief", @@ -3138,6 +5100,39 @@ validated: false "organism": [], "api": false }, + { + "id": "chromfound", + "name": "ChromFound", + "type": "model", + "url": "https://github.com/SAIS-LifeScience/ChromFound", + "description": "Genome-aware scATAC-seq foundation model pretrained on 1.97M cells across tissues and disease contexts for zero-shot cell representations, annotation, and cross-omics prediction.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": "1.97M scATAC-seq cells; 30 tissues; 6 disease contexts", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "clawbio", "name": "ClawBio", @@ -3145,16 +5140,67 @@ validated: false "url": "https://github.com/ClawBio/ClawBio", "description": "Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.", "tags": [ - "llm-for-biology" + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "bioinformatics", + "genomics", + "pharmacogenomics", + "omics" + ], + "architecture": "agent-ecosystem", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": "limited", + "omics": true, + "wet_lab": false, + "autonomous_experiment": false, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "clm_x", + "name": "CLM-X", + "type": "model", + "url": "https://doi.org/10.64898/2026.02.17.704943", + "description": "Multi-way Transformer foundation model jointly handling RNA-only, ATAC-only, and paired RNA–ATAC single-cell inputs for integration, translation, annotation, and perturbation prediction.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" ], "tasks": [ - "Language Modeling" - ], - "modalities": [ - "Text" + "Foundation Model" ], + "modalities": [], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna", + "atac", + "multi-omics" + ], + "pretraining_scale": "million-scale RNA-only, ATAC-only, and paired RNA-ATAC data", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": true, + "spatial": false + } }, { "id": "cmonge", @@ -3174,7 +5220,21 @@ validated: false "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "source/target expression distributions", + "drug_representation": "RDKit2D descriptors", + "context_representation": "matched control distribution", + "output": "transported expression distribution", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": "limited", + "patient_transfer": false, + "code": true + } }, { "id": "concerto", @@ -3231,6 +5291,39 @@ validated: false "https://github.com/mahmoodlab/CONCH" ] }, + { + "id": "cpgpt", + "name": "CpGPT", + "type": "model", + "url": "https://github.com/lucascamillomd/CpGPT", + "description": "DNA methylation foundation model pretrained on >150K samples for zero-shot imputation, array conversion, reference mapping, and downstream phenotype prediction.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "methylation" + ], + "pretraining_scale": "150K+ methylation samples across >2,000 datasets", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "cyclecdr", "name": "cycleCDR", @@ -3555,7 +5648,25 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "dna" + ], + "params": "117M", + "params_millions": 117, + "pretraining_scale": "multi-species GenBank genome corpus", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "drgat", @@ -3577,6 +5688,39 @@ validated: false "organism": [], "api": false }, + { + "id": "drugagent", + "name": "DrugAgent", + "type": "model", + "url": "https://github.com/inoue0426/DrugAgent", + "description": "Multi-agent biomedical evidence synthesis framework for computational drug discovery with reliability-aware aggregation.", + "tags": [ + "agentic-ai-for-biology", + "therapeutics-and-drug-discovery-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "drug-discovery", + "therapeutics", + "evidence-synthesis" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "drugban", "name": "DrugBAN", @@ -3639,6 +5783,40 @@ validated: false "organism": [], "api": false }, + { + "id": "dtlcdr", + "name": "DTLCDR", + "type": "model", + "url": "https://doi.org/10.1016/j.jpha.2025.101315", + "description": "Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2025, + "input": "multimodal tumor/cell-line features + targets", + "drug_representation": "target-aware drug representation", + "context_representation": "biological sample representation", + "output": "preclinical/clinical response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": false, + "time": false, + "patient_transfer": true, + "code": false + } + }, { "id": "enformer", "name": "Enformer", @@ -3658,6 +5836,127 @@ validated: false "organism": [], "api": false }, + { + "id": "epiagent", + "name": "EpiAgent", + "type": "model", + "url": "https://github.com/xy-chen16/EpiAgent", + "description": "scATAC-seq foundation model pretrained on ~5M cells and >35B tokens for representation learning, annotation, imputation, perturbation prediction, and in-silico cCRE knockout.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": ">5.4M scATAC-seq cells across 37 tissues or cell lines", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": false + } + }, + { + "id": "epifoundation", + "name": "EpiFoundation", + "type": "model", + "url": "https://doi.org/10.1101/2025.02.05.636688", + "description": "Foundation model for scATAC-seq using peak-to-gene aligned pretraining for cell representation, annotation, batch correction, and gene-expression prediction.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "atac" + ], + "pretraining_scale": "large-scale scATAC-seq pretraining with peak-to-gene alignment", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "limited", + "code": "limited", + "perturbation": false, + "spatial": false + } + }, + { + "id": "esm_cambrian_esm_c", + "name": "ESM Cambrian (ESM C)", + "type": "model", + "url": "https://github.com/Biohub/esm", + "description": "Protein representation foundation-model family designed as an efficient next-generation successor to ESM2, spanning 300M to multi-billion-parameter models.", + "tags": [ + "foundation-models", + "pre-trained-embedding", + "protein-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "protein-function-prediction", + "representation-learning" + ], + "modalities": [ + "protein-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "protein" + ], + "params": "300M / 600M / 6B", + "params_millions": 6000, + "pretraining_scale": "evolutionary-scale protein sequence corpus", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": "limited", + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "protein" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "github": "https://github.com/Biohub/esm", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/Biohub/esm", + "https://www.evolutionaryscale.ai/blog/esm-cambrian" + ] + }, { "id": "esm3", "name": "ESM3", @@ -3679,6 +5978,24 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "protein" + ], + "params": "1.4B / 7B / 98B", + "params_millions": 98000, + "pretraining_scale": "multimodal protein sequence, structure, and function corpus", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": "limited", + "weights": "limited", + "code": true, + "perturbation": false, + "spatial": false + }, "entities": [ "protein" ], @@ -3747,6 +6064,62 @@ validated: false "organism": [], "api": false }, + { + "id": "evo_2", + "name": "Evo 2", + "type": "model", + "url": "https://github.com/arcinstitute/evo2", + "description": "Genome foundation model trained on 9 trillion DNA base pairs across all domains of life with a 1M-token context window and single-nucleotide resolution.", + "tags": [ + "foundation-models", + "genomics-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "dna-sequence", + "genomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "dna" + ], + "params": "1B / 7B / 40B", + "params_millions": 40000, + "pretraining_scale": "9T DNA base pairs", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "genome", + "organism" + ], + "methods": [ + "generative-model", + "language-model" + ], + "year": 2026, + "github": "https://github.com/arcinstitute/evo2", + "paper": "https://doi.org/10.1038/s41586-026-10176-5", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/arcinstitute/evo2", + "https://doi.org/10.1038/s41586-026-10176-5" + ] + }, { "id": "evodiff", "name": "EvoDiff", @@ -3801,6 +6174,40 @@ validated: false "https://github.com/facebookresearch/esm" ] }, + { + "id": "expresso", + "name": "EXPRESSO", + "type": "model", + "url": "https://doi.org/10.1158/0008-5472.CAN-25-5220", + "description": "Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2026, + "input": "pretreatment tumor transcriptomics", + "drug_representation": "drug targets + biomarkers", + "context_representation": "tumor transcriptomic context", + "output": "patient treatment response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": false, + "time": false, + "patient_transfer": true, + "code": false + } + }, { "id": "gears", "name": "GEARS", @@ -3822,6 +6229,63 @@ validated: false "organism": [], "api": false }, + { + "id": "genbio_pathfm", + "name": "GenBio-PathFM", + "type": "model", + "url": "https://github.com/genbio-ai/genbio-pathfm", + "description": "1.1B-parameter histopathology foundation model trained on public data using morphology-aware curation and dual-stage JEPA+DINO learning.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "pathology" + ], + "params": "1.1B", + "params_millions": 1100, + "pretraining_scale": "public histopathology corpus with morphology-aware curation", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2026, + "github": "https://github.com/genbio-ai/genbio-pathfm", + "paper": "https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/genbio-ai/genbio-pathfm", + "https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1" + ] + }, { "id": "genecompass", "name": "GeneCompass", @@ -3858,7 +6322,25 @@ validated: false "metadata_sources": [ "https://github.com/xCompass-AI/GeneCompass", "https://www.nature.com/articles/s41422-024-01034-y" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "dna", + "multi-omics" + ], + "pretraining_scale": "120M+ cells across multiple species", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } }, { "id": "geneformer", @@ -3898,7 +6380,25 @@ validated: false "metadata_sources": [ "https://huggingface.co/ctheodoris/Geneformer", "https://www.nature.com/articles/s41586-023-06139-9" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna" + ], + "params": "10M / 104M / 316M", + "params_millions": 316, + "pretraining_scale": "~104M human single-cell transcriptomes in V2; original corpus ~30M", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": false + } }, { "id": "genegpt", @@ -3954,6 +6454,72 @@ validated: false "https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2" ] }, + { + "id": "genomas", + "name": "GenoMAS", + "type": "model", + "url": "https://github.com/Liu-Hy/GenoMAS", + "description": "Multi-agent framework for code-driven gene-expression analysis with planning, execution, debugging, backtracking, and GEO/TCGA-based scientific discovery.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "bioinformatics", + "gene-expression", + "scientific-workflow" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": true, + "omics": true, + "wet_lab": false, + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "genomi", + "name": "Genomi", + "type": "model", + "url": "https://github.com/exon-research/genomi", + "description": "Local-first genomics agent runtime that indexes personal variants, queries evidence, and generates evidence-grounded reports while keeping raw genome data on-device.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2026, + "domains": [ + "genomics", + "precision-medicine", + "evidence-synthesis" + ], + "architecture": "agent-ecosystem", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": "limited", + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": false, + "human_in_loop": true, + "open_source": true + } + }, { "id": "gigapath", "name": "GigaPath", @@ -4026,7 +6592,23 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "dna" + ], + "pretraining_scale": "genomic sequence pretraining for variant-effect prediction", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "graphdta", @@ -4082,6 +6664,149 @@ validated: false "https://github.com/tencent-ailab/grover" ] }, + { + "id": "h_optimus_0", + "name": "H-Optimus-0", + "type": "model", + "url": "https://huggingface.co/bioptimus/H-optimus-0", + "description": "1.1B-parameter histopathology foundation model trained with self-supervised learning on a large multi-center slide corpus.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "pathology" + ], + "params": "1.1B", + "params_millions": 1100, + "pretraining_scale": "large multi-center histopathology corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "documentation": "https://huggingface.co/bioptimus/H-optimus-0", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/bioptimus/H-optimus-0" + ] + }, + { + "id": "h_optimus_1", + "name": "H-Optimus-1", + "type": "model", + "url": "https://huggingface.co/bioptimus/H-optimus-1", + "description": "1.1B-parameter pathology foundation model trained on billions of histology images from more than one million slides and 800K+ patients.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "params": "1.1B", + "params_millions": 1100, + "pretraining_scale": ">1M slides; >800K patients", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "documentation": "https://huggingface.co/bioptimus/H-optimus-1", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/bioptimus/H-optimus-1" + ] + }, + { + "id": "hicfoundation", + "name": "HiCFoundation", + "type": "model", + "url": "https://doi.org/10.1038/s41592-026-03097-8", + "description": "Hi-C foundation model pretrained on large-scale chromatin-contact maps for 3D-genome analysis, epigenomic prediction, and single-cell Hi-C adaptation.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "hic" + ], + "pretraining_scale": "hundreds of Hi-C assays; 118M contact-matrix submatrices", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, { "id": "hidra", "name": "HiDRA", @@ -4119,7 +6844,23 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "dna" + ], + "pretraining_scale": "long-context human genome sequence", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "jamie", @@ -4162,6 +6903,40 @@ validated: false "organism": [], "api": false }, + { + "id": "lpm", + "name": "LPM", + "type": "model", + "url": "https://github.com/perturblib/perturblib", + "description": "Large perturbation model that jointly learns heterogeneous perturbation experiments by disentangling perturbation, readout, and context representations.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2025, + "input": "heterogeneous perturbation experiments", + "drug_representation": "learned perturbation representation", + "context_representation": "learned context representation", + "output": "molecular state", + "unseen_drug": true, + "unseen_context": true, + "dose": "limited", + "time": "limited", + "patient_transfer": false, + "code": true + } + }, { "id": "matcha", "name": "Matcha", @@ -4203,6 +6978,75 @@ validated: false "organism": [], "api": false }, + { + "id": "medea", + "name": "Medea", + "type": "model", + "url": "https://github.com/mims-harvard/Medea", + "description": "Multi-agent therapeutic discovery system combining research planning, biological data analysis, literature reasoning, and multi-LLM deliberation across single-cell, cell-line, and patient contexts.", + "tags": [ + "agentic-ai-for-biology", + "therapeutics-and-drug-discovery-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "therapeutics", + "drug-discovery", + "single-cell", + "omics" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": "limited", + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "methylgpt", + "name": "MethylGPT", + "type": "model", + "url": "https://github.com/albert-ying/MethylGPT", + "description": "Transformer foundation model for DNA methylation pretrained on >150K human methylomes across thousands of datasets, with 3M/7M/15M parameter variants.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "methylation" + ], + "params": "3M / 7M / 15M", + "params_millions": 15, + "pretraining_scale": "154,063 human methylomes after QC; 5,281 datasets; 49,156 CpG sites", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "midas", "name": "MIDAS", @@ -4245,6 +7089,61 @@ validated: false "organism": [], "api": false }, + { + "id": "moderngena", + "name": "modernGENA", + "type": "model", + "url": "https://github.com/AIRI-Institute/GENA_LM", + "description": "ModernBERT-style DNA foundation-model family pretrained on hundreds of vertebrate genome assemblies for efficient long-sequence regulatory modeling.", + "tags": [ + "foundation-models", + "genomics-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "regression", + "representation-learning" + ], + "modalities": [ + "dna-sequence", + "genomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "dna" + ], + "pretraining_scale": "hundreds of vertebrate genome assemblies", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "genome", + "regulatory-element" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2026, + "github": "https://github.com/AIRI-Institute/GENA_LM", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/AIRI-Institute/GENA_LM" + ] + }, { "id": "mofa", "name": "MOFA+", @@ -4479,6 +7378,40 @@ validated: false "organism": [], "api": false }, + { + "id": "mutationprojector", + "name": "MutationProjector", + "type": "model", + "url": "https://doi.org/10.1101/2025.09.08.674723", + "description": "Pan-cancer genotype foundation model trained on mutations and copy-number alterations from >30K tumors for clinical representation learning.", + "tags": [ + "cancer-genome-foundation-models", + "foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "mutation", + "copy-number" + ], + "pretraining_scale": ">30K tumors with somatic genomic alterations including copy-number changes", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": "unknown", + "weights": "unknown", + "code": "unknown", + "perturbation": false, + "spatial": false + } + }, { "id": "nbbayeslm", "name": "NbBayesLM", @@ -4540,6 +7473,26 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna", + "spatial" + ], + "params": "49.3M", + "params_millions": 49.3, + "pretraining_scale": "SpatialCorpus-110M", + "species": [ + "human", + "mouse" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": true + }, "entities": [ "cell", "gene", @@ -4575,7 +7528,25 @@ validated: false "Genomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "dna" + ], + "params": "50M – 2.5B", + "params_millions": 2500, + "pretraining_scale": "3,202 human genomes and up to 850 species", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "omegafold", @@ -4598,6 +7569,43 @@ validated: false "organism": [], "api": false }, + { + "id": "omicsfm", + "name": "OmicsFM", + "type": "model", + "url": "https://github.com/CompOmics/OmicsFM", + "description": "Modality-agnostic molecular-expression foundation model with matched proteomics, bulk-transcriptomics, and single-cell-transcriptomics checkpoints.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "proteomics", + "bulk-rna", + "scrna" + ], + "params": "4.9M", + "params_millions": 4.9, + "pretraining_scale": "48,837 proteomics profiles; 680,216 bulk RNA profiles; 4.55M single cells", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "openfold", "name": "OpenFold", @@ -4639,6 +7647,73 @@ validated: false "organism": [], "api": false }, + { + "id": "paper2agent", + "name": "Paper2Agent", + "type": "model", + "url": "https://github.com/jmiao24/Paper2Agent", + "description": "Multi-agent system that transforms research papers and associated code into interactive, testable scientific agents and MCP tools.", + "tags": [ + "agentic-ai-for-biology", + "paper-and-workflow-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "paper-to-agent", + "scientific-workflow", + "tool-generation" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "paso", + "name": "PASO", + "type": "model", + "url": "https://github.com/queryang/PASO", + "description": "Pathway-aware multi-omics drug response model combining pathway-difference features, multi-scale convolutions, Transformer encoding, and drug SMILES.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2025, + "input": "pathway-aware multi-omics", + "drug_representation": "SMILES-derived representation", + "context_representation": "pathway-difference representation", + "output": "cell-line drug response", + "unseen_drug": "limited", + "unseen_context": "limited", + "dose": false, + "time": false, + "patient_transfer": false, + "code": true + } + }, { "id": "pathomicfusion", "name": "PathomicFusion", @@ -4658,6 +7733,74 @@ validated: false "organism": [], "api": false }, + { + "id": "perturbnet", + "name": "PerturbNet", + "type": "model", + "url": "https://github.com/welch-lab/PerturbNet", + "description": "Conditional generative model for predicting distributions of single-cell states under unseen chemical and genetic perturbations.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2025, + "input": "single-cell expression distributions", + "drug_representation": "molecular representation", + "context_representation": "conditional latent state", + "output": "cell-state distribution", + "unseen_drug": true, + "unseen_context": "limited", + "dose": "limited", + "time": "limited", + "patient_transfer": false, + "code": true + } + }, + { + "id": "perturbrx", + "name": "PerturbRx", + "type": "model", + "url": "https://arxiv.org/abs/2608.21349", + "description": "Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2026, + "input": "pretreatment tumor RNA-seq + perturbation pretraining", + "drug_representation": "drug embedding + treatment-conditioned transition", + "context_representation": "patient latent state", + "output": "patient treatment response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": true, + "time": false, + "patient_transfer": true, + "code": false + } + }, { "id": "phikon", "name": "Phikon", @@ -4692,6 +7835,59 @@ validated: false "https://huggingface.co/owkin/phikon" ] }, + { + "id": "phikon_v2", + "name": "Phikon-v2", + "type": "model", + "url": "https://huggingface.co/owkin/phikon-v2", + "description": "Updated pathology foundation model for general-purpose histology feature extraction and transfer learning.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "pretraining_scale": "large-scale histopathology pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "documentation": "https://huggingface.co/owkin/phikon-v2", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/owkin/phikon-v2" + ] + }, { "id": "plip", "name": "PLIP", @@ -4743,6 +7939,40 @@ validated: false "organism": [], "api": false }, + { + "id": "prepr_ct", + "name": "PrePR-CT", + "type": "model", + "url": "https://github.com/reem12345/Cell-Type-Specific-Graphs", + "description": "Graph-based model using cell-type-specific co-expression networks as inductive priors for small-data chemical perturbation response prediction.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "aggregate expression + context graph", + "drug_representation": "RDKit descriptors", + "context_representation": "context-specific gene graph", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, { "id": "prnet", "name": "PRNet", @@ -4761,7 +7991,21 @@ validated: false "Small Molecule" ], "organism": [], - "api": false + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2024, + "input": "basal expression + perturbation", + "drug_representation": "FCFP4 fingerprint", + "context_representation": "matched basal expression", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": "limited", + "dose": true, + "time": "limited", + "patient_transfer": false, + "code": true + } }, { "id": "progen2", @@ -4797,6 +8041,40 @@ validated: false "https://github.com/salesforce/progen" ] }, + { + "id": "prophet", + "name": "Prophet", + "type": "model", + "url": "https://github.com/theislab/prophet", + "description": "Transformer model for predicting cellular phenotypes under unseen chemical or genetic perturbations across heterogeneous assays and contexts.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2024, + "input": "control expression + perturbation", + "drug_representation": "learned chemical representation", + "context_representation": "control-derived context", + "output": "gene-expression response", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, { "id": "proteinmpnn", "name": "ProteinMPNN", @@ -4831,6 +8109,61 @@ validated: false "https://github.com/dauparas/ProteinMPNN" ] }, + { + "id": "protenix", + "name": "Protenix", + "type": "model", + "url": "https://github.com/bytedance/Protenix", + "description": "Trainable biomolecular structure-prediction framework for proteins, nucleic acids, ligands, and complexes with open training and inference pipelines.", + "tags": [ + "foundation-models", + "protein-foundation-models", + "protein-structure-prediction-and-design" + ], + "tasks": [ + "drug-target-interaction", + "structure-prediction" + ], + "modalities": [ + "molecular-structure", + "protein-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "protein", + "chemical" + ], + "pretraining_scale": "biomolecular structure pretraining", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule", + "protein", + "protein-complex" + ], + "methods": [ + "diffusion", + "generative-model", + "transformer" + ], + "year": 2025, + "github": "https://github.com/bytedance/Protenix", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/bytedance/Protenix" + ] + }, { "id": "prottrans", "name": "ProtTrans", @@ -4945,6 +8278,114 @@ validated: false "organism": [], "api": false }, + { + "id": "rinalmo", + "name": "RiNALMo", + "type": "model", + "url": "https://github.com/lbcb-sci/RiNALMo", + "description": "RNA language-model family pretrained on tens of millions of RNA sequences for secondary-structure and functional prediction tasks.", + "tags": [ + "foundation-models", + "rna-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning", + "structure-prediction" + ], + "modalities": [ + "rna-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "rna" + ], + "params": "33.5M / 148M / 650M", + "params_millions": 650, + "pretraining_scale": "36M non-coding RNA sequences", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "transcript" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "github": "https://github.com/lbcb-sci/RiNALMo", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/lbcb-sci/RiNALMo" + ] + }, + { + "id": "rna_fm", + "name": "RNA-FM", + "type": "model", + "url": "https://github.com/ml4bio/RNA-FM", + "description": "General-purpose RNA foundation model pretrained on large-scale RNA sequences for structural and functional representation learning.", + "tags": [ + "foundation-models", + "rna-foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning", + "structure-prediction" + ], + "modalities": [ + "rna-sequence" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2022, + "modalities": [ + "rna" + ], + "params": "99M", + "params_millions": 99, + "pretraining_scale": "23.7M non-coding RNA sequences", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "gene", + "transcript" + ], + "methods": [ + "language-model", + "self-supervised-learning", + "transformer" + ], + "year": 2022, + "github": "https://github.com/ml4bio/RNA-FM", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/ml4bio/RNA-FM" + ] + }, { "id": "rosettafold", "name": "RoseTTAFold", @@ -5006,7 +8447,24 @@ validated: false "Transcriptomics" ], "organism": [], - "api": false + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "protein" + ], + "pretraining_scale": "cross-species single-cell atlases with protein-language-model embeddings", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "scarches", @@ -5029,6 +8487,41 @@ validated: false "organism": [], "api": false }, + { + "id": "scarf", + "name": "SCARF", + "type": "model", + "url": "https://doi.org/10.1101/2025.04.07.647689", + "description": "Single-cell RNA+ATAC foundation model pretrained on >2.7M cells for multimodal representation, matching, cross-omics translation, and few-shot annotation.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna", + "atac", + "multi-omics" + ], + "pretraining_scale": ">2.7M paired and unpaired single cells across tissues and species", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } + }, { "id": "scbert", "name": "scBERT", @@ -5067,7 +8560,23 @@ validated: false "metadata_sources": [ "https://github.com/TencentAILabHealthcare/scBERT", "https://www.nature.com/articles/s42256-022-00534-z" - ] + ], + "foundation_profile": { + "year": 2022, + "modalities": [ + "scrna" + ], + "pretraining_scale": "large-scale scRNA-seq corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "scbutterfly", @@ -5090,6 +8599,40 @@ validated: false "organism": [], "api": false }, + { + "id": "scdnam_gpt", + "name": "scDNAm-GPT", + "type": "model", + "url": "https://github.com/ChaoqiLiang/scDNAm-GPM", + "description": "Foundation model for single-cell whole-genome bisulfite sequencing with whole-genome context modeling at single-CpG resolution.", + "tags": [ + "foundation-models", + "single-cell-epigenomics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "methylation" + ], + "pretraining_scale": ">1M single-cell methylomes from human and mouse tissues", + "species": [ + "human", + "mouse" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "scfoundation", "name": "scFoundation", @@ -5129,7 +8672,25 @@ validated: false "metadata_sources": [ "https://github.com/biomap-research/scFoundation", "https://www.nature.com/articles/s41592-024-02305-7" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna" + ], + "params": "100M", + "params_millions": 100, + "pretraining_scale": ">50M human single-cell transcriptomes", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + } }, { "id": "scgpt", @@ -5173,7 +8734,24 @@ validated: false "metadata_sources": [ "https://github.com/bowang-lab/scGPT", "https://www.nature.com/articles/s41592-024-02201-0" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "multi-omics" + ], + "pretraining_scale": ">33M single-cell profiles", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": true, + "spatial": "limited" + } }, { "id": "scgpt_spatial", @@ -5198,6 +8776,25 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna", + "spatial", + "multi-omics" + ], + "pretraining_scale": "continued pretraining from scGPT with spatial transcriptomics", + "species": [ + "human", + "mouse" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": true + }, "entities": [ "cell", "gene", @@ -5253,7 +8850,27 @@ validated: false "last_checked": "2026-08-08", "metadata_sources": [ "https://github.com/SuperBianC/scMulan" - ] + ], + "foundation_profile": { + "year": 2024, + "modalities": [ + "scrna", + "atac", + "proteomics", + "multi-omics" + ], + "pretraining_scale": "~10M cells across transcriptomics, epigenomics, and proteomics", + "species": [ + "human", + "mouse" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "scpair", @@ -5299,6 +8916,22 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "scrna" + ], + "pretraining_scale": "large multi-dataset single-cell corpus", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + }, "entities": [ "cell", "gene" @@ -5317,6 +8950,62 @@ validated: false "https://www.nature.com/articles/s41467-025-58699-1" ] }, + { + "id": "scprint_2", + "name": "scPRINT-2", + "type": "model", + "url": "https://github.com/cantinilab/scPRINT-2", + "description": "Next-generation single-cell foundation model pretrained on 350M+ cells across 22K+ datasets and 16 species for embeddings, denoising, annotation, gene-network inference, and cross-species transfer.", + "tags": [ + "foundation-models", + "single-cell-foundation-models", + "transcriptomics-foundation-models" + ], + "tasks": [ + "batch-correction", + "cell-type-annotation", + "foundation-model-pretraining", + "gene-regulatory-network-inference", + "imputation", + "representation-learning" + ], + "modalities": [ + "single-cell-rna-seq", + "transcriptomics" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna" + ], + "pretraining_scale": "350M+ cells; 22K+ datasets; 16 species", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": "limited", + "spatial": false + }, + "entities": [ + "cell", + "gene" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2026, + "github": "https://github.com/cantinilab/scPRINT-2", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/cantinilab/scPRINT-2" + ] + }, { "id": "sei", "name": "Sei", @@ -5357,6 +9046,73 @@ validated: false "organism": [], "api": false }, + { + "id": "state", + "name": "State", + "type": "model", + "url": "https://github.com/ArcInstitute/state", + "description": "Transition model for predicting cellular perturbation responses across diverse contexts and sets of cells.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "sets of cellular expression profiles", + "drug_representation": "perturbation embedding", + "context_representation": "cellular state", + "output": "transcriptomic response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": "limited", + "time": "limited", + "patient_transfer": false, + "code": true + } + }, + { + "id": "stella", + "name": "STELLA", + "type": "model", + "url": "https://github.com/zaixizhang/STELLA", + "description": "Self-evolving biomedical research agent that expands its tool repertoire and supports literature reasoning, computational analysis, and laboratory-oriented scientific workflows.", + "tags": [ + "agentic-ai-for-biology", + "bioinformatics-and-omics-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "general-biomedicine", + "bioinformatics", + "scientific-workflow" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": "limited", + "autonomous_experiment": true, + "human_in_loop": true, + "open_source": true + } + }, { "id": "targetdiff", "name": "TargetDiff", @@ -5377,6 +9133,40 @@ validated: false "organism": [], "api": false }, + { + "id": "tessera", + "name": "TESSERA", + "type": "model", + "url": "https://github.com/JW-Sidhom-Lab/tessera", + "description": "Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning.", + "tags": [ + "cancer-genome-foundation-models", + "foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "mutation", + "copy-number" + ], + "pretraining_scale": "TCGA pan-cancer somatic SNVs and copy-number segments", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } + }, { "id": "tgsa", "name": "TGSA", @@ -5397,6 +9187,94 @@ validated: false "organism": [], "api": false }, + { + "id": "therapi", + "name": "THERAPI", + "type": "model", + "url": "https://github.com/Sunginyoung/THERAPI", + "description": "Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction.", + "tags": [ + "drug-discovery", + "drug-response-prediction" + ], + "tasks": [ + "Drug Discovery", + "Drug Response Prediction" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-response-prediction", + "year": 2026, + "input": "tumor transcriptomics + cell-line knowledge", + "drug_representation": "perturbation/gene-level representation", + "context_representation": "aligned tumor/cell-line representation", + "output": "patient drug response", + "unseen_drug": "limited", + "unseen_context": true, + "dose": false, + "time": false, + "patient_transfer": true, + "code": true + } + }, + { + "id": "titan", + "name": "TITAN", + "type": "model", + "url": "https://github.com/mahmoodlab/TITAN", + "description": "Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "pretraining_scale": "whole-slide pathology with multimodal image-language supervision", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "contrastive-learning", + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "github": "https://github.com/mahmoodlab/TITAN", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/mahmoodlab/TITAN" + ] + }, { "id": "toad", "name": "TOAD", @@ -5416,6 +9294,40 @@ validated: false "organism": [], "api": false }, + { + "id": "tooluniverse", + "name": "ToolUniverse", + "type": "model", + "url": "https://github.com/mims-harvard/ToolUniverse", + "description": "Unified scientific tool ecosystem for building AI scientists that can discover, select, and execute biomedical tools and databases.", + "tags": [ + "agentic-ai-for-biology", + "general-biomedical-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "general-biomedicine", + "tool-ecosystem", + "therapeutics", + "bioinformatics" + ], + "architecture": "agent-ecosystem", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": true, + "literature": true, + "omics": true, + "wet_lab": "limited", + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, { "id": "tosica", "name": "TOSICA", @@ -5499,6 +9411,73 @@ validated: false "organism": [], "api": false }, + { + "id": "txagent", + "name": "TxAgent", + "type": "model", + "url": "https://github.com/mims-harvard/TxAgent", + "description": "Therapeutic reasoning agent using multi-step reasoning and a large scientific tool universe for drug interactions, contraindications, and personalized treatment analysis.", + "tags": [ + "agentic-ai-for-biology", + "therapeutics-and-drug-discovery-agents" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2025, + "domains": [ + "therapeutics", + "drug-discovery", + "precision-medicine" + ], + "architecture": "single-agent", + "tool_use": true, + "code_execution": "limited", + "web_retrieval": true, + "literature": true, + "omics": "limited", + "wet_lab": false, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "txpert", + "name": "TxPert", + "type": "model", + "url": "https://github.com/valence-labs/TxPert", + "description": "Knowledge-graph-informed latent-transfer model for transcriptomic perturbation prediction across unseen single perturbations, combinations, and cross-context settings.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "transcriptomics + biological knowledge", + "drug_representation": "knowledge-graph-informed perturbation representation", + "context_representation": "cellular context representation", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": true, + "dose": false, + "time": false, + "patient_transfer": false, + "code": true + } + }, { "id": "uce", "name": "UCE", @@ -5533,7 +9512,25 @@ validated: false "metadata_sources": [ "https://github.com/snap-stanford/UCE", "https://www.nature.com/articles/s41586-026-10689-z" - ] + ], + "foundation_profile": { + "year": 2026, + "modalities": [ + "scrna" + ], + "params": ">650M", + "params_millions": 650, + "pretraining_scale": ">36M cells across >300 datasets", + "species": [ + "multi-species" + ], + "zero_shot": true, + "finetunable": "limited", + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + } }, { "id": "uni", @@ -5590,6 +9587,23 @@ validated: false ], "organism": [], "api": false, + "foundation_profile": { + "year": 2023, + "modalities": [ + "chemical", + "protein" + ], + "pretraining_scale": "large-scale 3D molecular and pocket pretraining", + "species": [ + "multi-species" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, "entities": [ "molecule", "protein" @@ -5607,6 +9621,112 @@ validated: false "https://openreview.net/forum?id=6K2RM6wVqKu" ] }, + { + "id": "uni_mol2", + "name": "Uni-Mol2", + "type": "model", + "url": "https://github.com/deepmodeling/Uni-Mol/tree/main/unimol2", + "description": "Scaled molecular pretraining model using atomic, graph, and 3D geometry features, with models up to 1.1B parameters pretrained on 800M conformations.", + "tags": [ + "compound-embedding", + "compound-foundation-models", + "foundation-models" + ], + "tasks": [ + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "chemical-structure", + "molecular-structure" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "chemical" + ], + "params": "up to 1.1B", + "params_millions": 1100, + "pretraining_scale": "800M molecular conformations", + "species": [], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": false + }, + "entities": [ + "molecule" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "github": "https://github.com/deepmodeling/Uni-Mol", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://github.com/deepmodeling/Uni-Mol" + ] + }, + { + "id": "uni2_h", + "name": "UNI2-h", + "type": "model", + "url": "https://huggingface.co/MahmoodLab/UNI2-h", + "description": "Billion-parameter histopathology vision foundation model for tile-level feature extraction and downstream computational pathology tasks.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2025, + "modalities": [ + "pathology" + ], + "params": "~1B", + "params_millions": 1000, + "pretraining_scale": "large-scale histopathology pretraining", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2025, + "documentation": "https://huggingface.co/MahmoodLab/UNI2-h", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/MahmoodLab/UNI2-h" + ] + }, { "id": "unitednet", "name": "UnitedNet", @@ -5647,6 +9767,162 @@ validated: false "organism": [], "api": false }, + { + "id": "virchow2", + "name": "Virchow2", + "type": "model", + "url": "https://huggingface.co/paige-ai/Virchow2", + "description": "632M-parameter pathology vision transformer pretrained on 3.1M whole-slide images with mixed-magnification self-supervision.", + "tags": [ + "foundation-models", + "pathology-foundation-models", + "single-cell-foundation-models" + ], + "tasks": [ + "classification", + "foundation-model-pretraining", + "representation-learning" + ], + "modalities": [ + "histopathology", + "imaging" + ], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2024, + "modalities": [ + "pathology" + ], + "params": "632M", + "params_millions": 632, + "pretraining_scale": "3.1M whole-slide images", + "species": [ + "human" + ], + "zero_shot": "limited", + "finetunable": true, + "weights": true, + "code": "limited", + "perturbation": false, + "spatial": false + }, + "entities": [ + "disease", + "tissue" + ], + "methods": [ + "self-supervised-learning", + "transformer" + ], + "year": 2024, + "documentation": "https://huggingface.co/paige-ai/Virchow2", + "last_checked": "2026-10-02", + "metadata_sources": [ + "https://huggingface.co/paige-ai/Virchow2" + ] + }, + { + "id": "virtual_lab", + "name": "Virtual Lab", + "type": "model", + "url": "https://github.com/zou-group/virtual-lab", + "description": "Human–AI collaborative research environment in which an LLM principal investigator coordinates specialized scientist agents for scientific discovery.", + "tags": [ + "agentic-ai-for-biology", + "multi-agent-scientific-labs" + ], + "tasks": [], + "modalities": [], + "organism": [], + "api": false, + "agent_profile": { + "year": 2024, + "domains": [ + "scientific-workflow", + "protein-design", + "experimental-design" + ], + "architecture": "multi-agent", + "tool_use": true, + "code_execution": true, + "web_retrieval": "limited", + "literature": true, + "omics": "limited", + "wet_lab": true, + "autonomous_experiment": "limited", + "human_in_loop": true, + "open_source": true + } + }, + { + "id": "virtues", + "name": "VirTues", + "type": "model", + "url": "https://github.com/bunnelab/virtues", + "description": "Spatial-proteomics foundation model learning marker-aware representations across proteins, cells, niches, and tissues from multiplexed imaging.", + "tags": [ + "foundation-models", + "other-omics-foundation-models" + ], + "tasks": [ + "Foundation Model" + ], + "modalities": [], + "organism": [], + "api": false, + "foundation_profile": { + "year": 2026, + "modalities": [ + "proteomics", + "spatial" + ], + "pretraining_scale": "31-32 harmonized spatial-proteomics datasets across technologies", + "species": [ + "human" + ], + "zero_shot": true, + "finetunable": true, + "weights": true, + "code": true, + "perturbation": false, + "spatial": true + } + }, + { + "id": "xpert", + "name": "XPert", + "type": "model", + "url": "https://github.com/GSanShui/XPert", + "description": "Knowledge-informed dual-branch Transformer for drug-induced transcriptional perturbation prediction across dose, time, and cellular context.", + "tags": [ + "drug-discovery", + "drug-perturbation" + ], + "tasks": [ + "Drug Discovery", + "Drug Perturbation" + ], + "modalities": [ + "Small Molecule" + ], + "organism": [], + "api": false, + "method_profile": { + "task_family": "drug-perturbation", + "year": 2026, + "input": "transcriptomics + heterogeneous graph", + "drug_representation": "Uni-Mol + knowledge graph", + "context_representation": "basal expression + gene embeddings", + "output": "transcriptomic response", + "unseen_drug": true, + "unseen_context": true, + "dose": true, + "time": true, + "patient_transfer": false, + "code": true + } + }, { "id": "autozyme", "name": "AutoZyme",