[upstream-sync] README.md from ai-boost/awesome-ai-for-science@0888b5b1 [catalogue]

2026-07-26 10:04:52 +00:00
26 changed files with 387 additions and 4156 deletions
@@ -2,9 +2,9 @@
 title: "Awesome Genomic Skills [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
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-upstream_sha: 8cf42e1d
-imported_at: 2026-08-08
+upstream_source: https://github.com/GoekeLab/awesome-genomic-skills/blob/f88d9494/README.md
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 prompt_class: catalogue
 upstream_changes: accepted
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@@ -50,7 +50,7 @@ Skill libraries and tool collections specifically targeting genomics, bioinforma
  - **Description:** AWS sample bundle for the Kiro IDE: 24 MCP servers wrapping 100+ databases/tools across genomics, proteomics, structural biology, and clinical/pharma (NCBI, Ensembl, ClinVar, gnomAD, UniProt, STRING, PDB, AlphaFold, ChEMBL, Open Targets, etc.), plus 10 domain skills and 16 workflows, with cross-database search and AWS HealthOmics pipeline execution. MIT-0; the MCP servers use standard MCP and are portable, though the skills and workflows are built for Kiro. [Blog post](https://aws.amazon.com/blogs/publicsector/accelerating-life-sciences-research-with-kiro-a-unified-ai-interface-to-100-open-source-databases/).
  - **Developers:** AWS (AWS Samples).
 - [ClawBio](https://github.com/ClawBio/ClawBio)
-  - **Description:** Bioinformatics-native AI agent skill library; 95 reproducible, local-first skills for genomics tasks (variant calling, RNA-seq, population genetics) that work with Claude Code, Copilot, Codex, and other agents. Since v0.6.1 the whole library is also callable as an MCP server (`uvx --from 'clawbio[mcp]' clawbio mcp`) from Cursor, Claude Desktop, VS Code, or Zed (see the MCP section below). Skills are actively [benchmarked](https://clawbio.ai/benchmarks.html).
+  - **Description:** The first bioinformatics-native AI agent skill library; provides reproducible, local-first skills for genomics tasks (variant calling, RNA-seq, population genetics) that work with Claude Code, Copilot, Codex, and other agents.
  - **Developers:** Independent open-source project built on [OpenClaw](https://openclaw.ai).
 - [SciAgent-skills](https://github.com/jaechang-hits/SciAgent-Skills)
  - **Description:** 197 open-source skills for Claude Code, Cursor, Codex, and Windsurf, covering genomics-bioinformatics, proteomics-protein engineering, structural biology, drug discovery, systems biology, biostatistics, and scientific writing; achieves 92% accuracy on BixBench-Verified-50 (+26.7 pts over Claude Code baseline). The hosted OmicsHorizon web platform runs these skills in-browser.
@@ -83,9 +83,6 @@ Model Context Protocol (MCP) servers that give AI agents direct access to bioinf
 - [gget-mcp](https://github.com/longevity-genie/gget-mcp) -  MCP server wrapping the Pachter Lab [gget](https://github.com/pachterlab/gget) bioinformatics toolkit. Exposes 13 tools covering gene search and metadata (Ensembl), sequence retrieval, BLAST/BLAT/MUSCLE alignment, expression data (ARCHS4), functional enrichment (Enrichr), protein structure (PDB, AlphaFold), cancer mutations (COSMIC), and single-cell queries (CellxGene).
 - [Seqera MCP](https://docs.seqera.io/platform-cloud/seqera-mcp/overview) - Hosted MCP server from Seqera Labs (the developers of Nextflow) exposing the Seqera Platform (workflow launch/management), Wave (container provisioning), nf-core modules, and SRA/ENA/GEO retrieval. 
 - [knowledgebase-mcp](https://github.com/biocontext-ai/knowledgebase-mcp) - BioContextAI Knowledgebase MCP server, included in the BioContextAI registry (below), one of the most comprehensive single MCP packages (wraps STRINGDb, Open Targets, Reactome, UniProt, HPA, KEGG, AlphaFold, Ensembl, ClinicalTrials.gov, bioRxiv, etc.)
- [ClawBio MCP](https://github.com/ClawBio/ClawBio) - MCP mode of the [ClawBio](#bioinformatics-and-genomics-agent-skills) skill library (0.6.1): exposes all 95 genomics skills as callable tools over local stdio, letting different agents (Cursor, Zed, etc.) run analyses (variant calling, RNA-seq, population genetics). 
- [roda-mcp](https://github.com/awslabs/mcp/tree/main/src/roda-mcp-server) - AWS Labs MCP server for discovering and exploring datasets in the Registry of Open Data on AWS (RODA), covering 1,100+ public datasets across life sciences, climate, geospatial, and satellite imagery. Search/filter by keyword, organization, or license; inspect dataset details; and browse or sample public S3 bucket contents directly (no AWS account required) without downloading full files. Not genomics-specific, but useful for locating and previewing life-sciences datasets (e.g. SG-NEx) hosted on AWS.
- [plant-genomics-mcp](https://github.com/musharna/plant-genomics-mcp) - Plant-genomics MCP server exposing 50 tools across 23 backends, keyed on TAIR-style loci with organism resolution across 12 crop and model species. Covers plant-specific resources that general bioinformatics servers do not (Ensembl Plants, Phytozome, Gramene, Planteome PO/TO, PlantCyc/PMN, AraGWAS, 1001 Genomes, ThaleMine, BAR, JASPAR) alongside the usual UniProt/KEGG/STRING/AlphaFold/PDBe/InterPro/Europe PMC, plus cross-source synthesis tools that compose several backends into a single gene report.

 Existing registries and lists of MCP servers: 
 - [BioContextAI Registry](https://github.com/biocontext-ai/registry/) - Community-curated catalogue of biomedical MCP servers, with submission criteria requiring biomedical focus, free academic access, OSI-approved open-source licenses, and MCP specification compliance. Ships a [cookiecutter template](https://github.com/biocontext-ai/mcp-server-cookiecutter) for new servers and follows Schema.org ontologies for metadata. [A community hub for agentic biomedical systems](https://www.nature.com/articles/s41587-025-02900-9)
@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/e52565a5/README.md
-upstream_sha: e52565a5
-imported_at: 2026-08-11
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/0888b5b1/README.md
+upstream_sha: 0888b5b1
+imported_at: 2026-07-26
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -216,14 +216,7 @@ validated: false
 - [OpenScience (Synthetic Sciences)](https://github.com/synthetic-sciences/openscience) - Open-source AI workbench for scientific research that automates the full research loop — literature review, hypothesis generation, code writing, experiment execution, database querying, and report writing — with 290+ skills, specialized research agents, and a browser-based workspace (1453+ stars, Apache 2.0, 2026)
 - [Claude Scholar](https://github.com/Galaxy-Dawn/claude-scholar) - Semi-automated research assistant for academic research and software development, supporting Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication (Galaxy-Dawn, 4.5K+ stars, MIT License, 2026)
 - [K-Dense BYOK](https://github.com/K-Dense-AI/k-dense-byok) - Free, open-source desktop AI research assistant that runs locally and turns natural-language requests into real data analysis, literature search, figure generation, and manuscript review; ships with 149 scientific skills, 326 workflow templates, and 229 databases across genomics, proteomics, drug discovery, and materials science, plus a living lab notebook, 60+ scientific file previews, and LaTeX editing (K-Dense-AI, 908+ stars, MIT License, 2026)
- [Science Superpowers (K-Dense-AI)](https://github.com/K-Dense-AI/science-superpowers) - Composable computational-science methodology skills for AI research agents emphasizing pre-registration, reproducible workspaces, and red-team review to guard against p-hacking and HARKing; zero third-party dependencies and runs with any agent harness plus a POSIX shell (281+ stars, MIT License, 2026)
- [Wisp Science](https://github.com/xuzhougeng/wisp-science) - Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models (857+ stars, 2026)
 - [Academic Research Skills (ARS)](https://github.com/Imbad0202/academic-research-skills) - Comprehensive Claude Code skill suite covering the full academic pipeline from deep research and paper writing to multi-perspective peer review, revision, and finalization; features multi-agent teams, PRISMA systematic review, style calibration, claim-level citation audits, integrity gates, and human-in-the-loop safeguards (38K+ stars, CC BY-NC 4.0, 2026)
- [Qinyan Academic Skills](https://github.com/LeonChaoX/qinyan-academic-skills) - Curated, multilingual library of 182 installable AI agent skills for end-to-end academic research spanning literature discovery, scientific writing, grant development, bioinformatics, drug discovery, clinical research, machine learning, and data analysis (779+ stars, MIT License, 2026)
- [SkillOpt (Microsoft, 2026)](https://github.com/microsoft/SkillOpt) - Text-space optimizer that treats agent skill documents as trainable parameters for frozen LLMs, using scored rollouts and held-out validation gates to iteratively improve reusable natural-language skills; includes SkillOpt-Sleep for nightly self-evolution and improves accuracy across Claude Code, Codex, Copilot, and direct-chat harnesses, making it a meta-tool for evolving scientific agent skill workflows (15.5K+ stars, MIT License, PyPI)
- [Open Science (AIPOCH)](https://github.com/aipoch/open-science) - Open-source, local-first, model-agnostic AI research workbench for reproducible scientific discovery; runs Python/R notebooks, searches the web, calls scientific data connectors, and produces inspectable reports, tables, and figures in a self-hosted desktop workspace (1.5K+ stars, Apache 2.0, 2026)
- [OmicsClaw](https://github.com/TianGzlab/OmicsClaw) - Local-first, conversational AI research partner for multi-omics analysis with CLI, desktop app, and 95+ reproducible skills; keeps raw data local while routing natural-language requests to Python/R/CLI tools with persistent memory, autonomous analysis paths, and multi-method consensus workflows (TianGzlab, 155+ stars, Apache 2.0, 2026)
- [MedgeClaw](https://github.com/xjtulyc/MedgeClaw) - Open-source AI research assistant for biomedicine — chat to run RNA-seq, drug discovery, clinical analysis, and more; built on OpenClaw and Claude Code with 140 K-Dense scientific skills, real-time dashboard, and RStudio/JupyterLab integration (xjtulyc, 669+ stars, 2026)

 ### Literature Management Plugins
 - [llm-for-zotero](https://github.com/yilewang/llm-for-zotero) - Research agent system deeply integrated with Zotero supporting Agent Mode, skills, multi-model backends (OpenAI-compatible, Claude Code, WebChat, Codex), and MinerU PDF parsing for literature Q&A, summarization, figure inspection, and source comparison (1.3K+ stars, 2026)
@@ -247,7 +240,6 @@ validated: false
 - [GraphGen](https://github.com/open-sciencelab/GraphGen) - Knowledge graph-guided synthetic data generation for LLM fine-tuning, achieving strong performance on scientific QA (GPQA-Diamond) and math reasoning (AIME)
 - [KoPA](https://github.com/zjukg/KoPA) - Structure-aware prefix adaptation for integrating LLMs with knowledge graphs (ACM MM 2024)
 - [Scholarly KGQA](https://arxiv.org/abs/2311.09841) - LLM-powered question answering over scholarly knowledge graphs (ArXiv paper)
- [SciAtlas](https://github.com/zjunlp/SciAtlas) - Large-scale knowledge graph and pip-installable client for literature-grounded automated scientific research, connecting papers, authors, institutions, venues, keywords, citations, and a four-level research taxonomy across medicine, social sciences, engineering, computer science, materials science, and more (ZJU NLP, arXiv 2026, 136+ stars, MIT License)

 ### Knowledge Graph Resources
 - [Awesome-LLM-KG](https://github.com/RManLuo/Awesome-LLM-KG) - Comprehensive collection of papers on unifying LLMs and knowledge graphs
@@ -262,8 +254,6 @@ validated: false
 - [SkyDiscover](https://github.com/skydiscover-ai/skydiscover) - Modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks; introduces AdaEvolve and EvoX adaptive/evolutionary algorithms and natively supports OpenEvolve, GEPA, and Harbor-format benchmarks (skydiscover-ai, 568+ stars, Apache 2.0, 2026)
 - [EvoMaster (SJTU SAI, arXiv 2026)](https://github.com/sjtu-sai-agents/EvoMaster) - Foundational auto-research agent framework for agentic science at scale, providing modular agent construction, run-level self-evolution, and multiple SciMaster domain agents (ML-Master, X-Master, Browse-Master); outperforms general-purpose agents across authoritative benchmarks including the OpenAI Frontier Science Benchmark (206+ stars, Apache 2.0, 2026)
 - [Virtual Lab (Stanford Zou Group, Nature 2025)](https://github.com/zou-group/virtual-lab) - AI-human collaborative research platform where a human researcher works with a team of LLM agents via team and individual meetings to perform scientific research; demonstrated by designing new SARS-CoV-2 nanobodies with wet-lab validation
- [AI Co-Scientist (Google DeepMind, Nature Medicine 2026)](https://deepmind.google/blog/co-scientist-a-multi-agent-ai-partner-to-accelerate-research/) - Multi-agent AI research partner that generates, reviews, ranks, and evolves research hypotheses alongside scientists, with experimental validation in biomedicine and other domains (2026)
- [Hyra (Tencent Hunyuan, 2026)](https://hy.tencent.com/research/hyra) - Hunyuan Research Agent for autonomous open-ended discovery across AI4Science, mathematics, and engineering, releasing reproducible solution artifacts for autocorrelation constants, Erdős problems, PARP1 docking, qubit routing, and record-breaking packing problems ([Hyra-results](https://github.com/Tencent-Hunyuan/Hyra-results), 112+ stars, Apache 2.0)
 - [The AI Scientist (SakanaAI)](https://github.com/SakanaAI/AI-Scientist) - First fully autonomous open-ended scientific discovery system with official implementation: hypothesis→experiment→writing→review simulation (13.8K+ stars, 2024)
 - [The AI Scientist v2 (SakanaAI)](https://github.com/SakanaAI/AI-Scientist-v2) - Official implementation of the second-generation fully autonomous scientific discovery system, extending the original with agentic tree search and reduced template dependency to achieve workshop-level accepted papers (6.7K+ stars, 2025)
 - [The AI Scientist v1 (2024)](https://arxiv.org/abs/2408.06292) - First fully autonomous research system: hypothesis→experiment→writing→review simulation
@@ -310,7 +300,6 @@ validated: false
 - [ScienceAgentBench (ICLR 2025)](https://github.com/OSU-NLP-Group/ScienceAgentBench) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
 - [AIRS-Bench (Meta, 2026)](https://github.com/facebookresearch/airs-bench) - Benchmark quantifying end-to-end autonomous AI research abilities of LLM agents across 20 tasks from SOTA machine learning papers spanning NLP, code, math, biochemical modelling, and time series forecasting, with normalized score metrics against human SOTA and HuggingFace dataset
 - [PaperBench (OpenAI, 2025)](https://github.com/openai/preparedness/tree/main/project/paperbench) - Benchmark evaluating AI agents' ability to replicate 20 ICML 2024 Spotlight/Oral papers from scratch, with 8,316 gradable tasks and author-co-developed rubrics
- [PaperGuru (AutoTrustAI, 2026)](https://github.com/AutoTrustAI/PaperGuru-Benchmark) - Lifecycle-Aware Memory (LAM) primitive and benchmark for long-horizon research agents, achieving 65.95% mean reproduction on PaperBench and 94.66% on SurveyBench through Capital Chunk Memory (CCM) with versioned content, structural multi-hop relevance, and provenance-grounded composition; 10 peer-reviewed acceptances at FSE/ICML/TOSEM/AEI/ICoGB (1.3K+ stars)
 - [MLE-Bench (OpenAI, 2024)](https://github.com/openai/mle-bench) - Benchmark evaluating AI agents on 75 curated Kaggle-style ML engineering competitions with reproducible Docker-based grading harness, human baselines, and end-to-end task lifecycle, used as a primary benchmark for autonomous ML research agents (e.g., InternAgent #1 at 36.44%)
 - [ScienceBoard (ICLR 2026)](https://github.com/OS-Copilot/ScienceBoard) - Evaluating multimodal autonomous agents in realistic scientific workflows across real scientific software environments (KAlgebra, Celestia, Grass GIS, Lean 4, etc.) with VM-based evaluation infrastructure and agent trajectories
 - [BuildArena](https://github.com/AI4Science-WestlakeU/BuildArena) - First physics-aligned interactive benchmark for LLM agents in engineering construction, designing rockets/cars/bridges in physics simulator with 3D spatial geometry library
@@ -327,13 +316,11 @@ validated: false

 ### Domain-Specific Research Agents
 - [Aletheia](https://arxiv.org/abs/2602.10177) - Google DeepMind's autonomous mathematics research agent powered by Gemini Deep Think, autonomously solving 4 open problems from 700 Erdős conjectures and generating complete research papers without human intervention (February 2026)
- [AlphaProof Nexus (Google DeepMind, arXiv 2026)](https://github.com/google-deepmind/alphaproof-nexus-results) - LLM-driven formal proof search system that pairs large language models with Lean verification to solve open mathematics problems; autonomously resolved 9 of 353 Erdős problems and 44 of 492 OEIS conjectures, with proofs and natural-language prose released for combinatorics, optimization, graph theory, algebraic geometry, and quantum optics collaborations (282+ stars, Apache 2.0)
 - [AlphaGeometry](https://github.com/google-deepmind/alphageometry) - DeepMind's Olympiad-level geometry theorem prover combining neural language model with symbolic deduction engine, AlphaGeometry2 solves 84% of IMO geometry problems (42/50) at gold-medalist level (Nature 2024)
 - [Goedel-Prover-V2](https://github.com/Goedel-LM/Goedel-Prover-V2) - Strongest open-source automated theorem prover in Lean 4, 8B model matches DeepSeek-Prover-V2-671B at 84.6% MiniF2F, 32B model achieves 90.4% with self-correction, using scaffolded data synthesis and verifier-guided proof refinement (Princeton, 2025)
 - [DeepSeek-Prover-V2](https://github.com/deepseek-ai/DeepSeek-Prover-V2) - DeepSeek's open-source large language model for formal theorem proving in Lean 4, integrating informal and formal mathematical reasoning through recursive subgoal decomposition and reinforcement learning powered by DeepSeek-V3, with open weights and ProverBench evaluation (2025)
 - [LeanDojo](https://github.com/lean-dojo/LeanDojo) - Open-source toolkit and benchmark for learning-based theorem proving in Lean, providing programmatic Lean interaction, a 98K+ theorem dataset extracted from 217 Lean projects, and ReProver—the first retrieval-augmented LLM-based theorem prover for Lean—with reproducible training pipelines underpinning much subsequent Lean prover research (Caltech & NVIDIA, NeurIPS 2023 Outstanding Paper, Datasets & Benchmarks)
 - [Lean Copilot](https://github.com/lean-dojo/LeanCopilot) - LLMs as copilots for theorem proving in Lean 4, exposing native tactics (`suggest_tactics`, `search_proof`, `select_premises`) that embed language model inference and premise retrieval directly inside the Lean proof environment, supporting local CTranslate2/CUDA inference as well as remote model APIs for interactive and automated proof search (Caltech & NVIDIA, NeurIPS 2024, 1.2K+ stars)
- [MathCode](https://github.com/math-ai-org/mathcode) - Terminal AI coding assistant with a built-in math formalization engine that converts plain-language math problems into Lean 4 theorems and attempts formal proofs; bundles a local Lean toolchain and WebUI for interactive mathematical reasoning (math-ai-org, 582+ stars, 2026)
 - [Get Physics Done (PSI)](https://github.com/psi-oss/get-physics-done) - First open-source agentic AI physicist turning research questions into structured workflows with rigorous verification and multi-step analytical work for long-horizon physics projects; integrates with Claude Code, Codex, Gemini CLI, and OpenCode (804+ stars, Apache 2.0, 2026)
 - [Foam-Agent (NeurIPS 2025)](https://github.com/csml-rpi/Foam-Agent) - End-to-end composable multi-agent framework for automating OpenFOAM-based CFD simulations from natural language prompts, managing meshing, case setup, execution, error correction, and post-processing; achieves 100% success rate on 110 FoamBench tasks with Claude Opus 4.6 through Architect-Input Writer-Runner-Reviewer agent collaboration with RAG-enhanced generation and MCP tool integration (RPI CSML, 242+ stars, MIT License)
 - [Zephyrus (ICLR 2026)](https://github.com/Rose-STL-Lab/Zephyrus) - First agentic framework for weather science, pairing an LLM with ZephyrusWorld (a code-execution environment exposing WeatherBench 2 data, geolocation, forecasting, simulation, and climatology tools) and ZephyrusBench (2,230 Q&A pairs across 49 weather-science tasks); outperforms text-only baselines by up to 44.2 percentage points (UC San Diego Rose-STL-Lab, 99+ stars, MIT License, 2026)
@@ -638,7 +625,6 @@ validated: false
 - [SpikeInterface](https://github.com/SpikeInterface/spikeinterface) - Unified Python framework for extracellular electrophysiology, standardizing interfaces to 10+ ML-based spike sorting algorithms including Kilosort for reproducible neural spike sorting workflows (792+ stars, actively maintained)
 - [CaImAn (Flatiron Institute)](https://github.com/flatironinstitute/CaImAn) - Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization, using machine learning for automated neuron detection and activity inference in two-photon and one-photon calcium imaging data (723+ stars, actively maintained)
 - [TRIBE v2](https://github.com/facebookresearch/tribev2) - Meta FAIR's foundation model of vision, audition, and language for in-silico neuroscience, predicting fMRI brain responses to naturalistic multimodal stimuli (video, audio, text) through unified Transformer architecture mapped to the cortical surface (2026)
- [Brain2Qwerty (Meta FAIR, Nature Neuroscience 2026)](https://github.com/facebookresearch/brain2qwerty) - Non-invasive decoding of typed sentences from MEG and EEG brain recordings using a convolutional encoder, transformer, and character-level language model; official code for the Nature Neuroscience paper and Meta blog post on brain-AI communication (Meta FAIR, 894+ stars, CC BY-NC 4.0, 2026)
 - [braindecode](https://github.com/braindecode/braindecode) - Deep learning software to decode EEG, ECG or MEG signals, providing standardized neural network models, preprocessing pipelines, and evaluation workflows for brain-computer interfaces and cognitive neuroscience research (1.2K+ stars, BSD 3-Clause, actively maintained)
 - [snntorch](https://github.com/jeshraghian/snntorch) - Deep learning with spiking neural networks in Python, providing gradient-based training of SNNs via PyTorch autodifferentiation for brain-inspired computing and neuromorphic research, with online learning capabilities and extensive tutorials (1.9K+ stars, actively maintained)
 - [nilearn](https://github.com/nilearn/nilearn) - Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)
@@ -658,7 +644,6 @@ validated: false
 - [PathChat (Nature Medicine 2024)](https://github.com/MahmoodLab/PathChat) - Multimodal generative AI assistant for computational pathology enabling interactive visual-language conversations over histopathology images for diagnostic reasoning, case discussion, and education, built on a Mistral-7B backbone with domain-specific fine-tuning (Mahmood Lab, Harvard Medical School, 1.2K+ stars)
 - [SlideChat (CVPR 2025)](https://github.com/uni-medical/SlideChat) - First large vision-language assistant for gigapixel whole-slide pathology image understanding, released with the SlideInstruction dataset and SlideBench benchmark (uni-medical, Apache 2.0, 2025)
 - [HEST (NeurIPS 2024)](https://github.com/mahmoodlab/HEST) - Dataset and benchmarking framework integrating histology and spatial transcriptomics, enabling multimodal analysis of whole-slide images with matched spatial gene expression for advancing computational pathology and tissue microenvironment research (Mahmood Lab, Harvard Medical School, 411+ stars)
- [spmind (ICML 2026)](https://github.com/tomtommyyuan/spmind) - Autonomous AI agent for end-to-end spatial proteomics analysis, featuring SP-Bench for agentic multiplexed-imaging workflows (tomtommyyuan, 140+ stars, 2026)

 #### Medical AI & Clinical Applications
 - [Cellpose](https://github.com/MouseLand/cellpose) - Generalist deep learning algorithm for cell and nucleus segmentation across diverse image types, with human-in-the-loop training (2.0) and one-click image restoration (3.0), 70K+ training objects (Nature Methods 2021/2022/2025)
@@ -786,7 +771,6 @@ validated: false
 - [SkySensePlusPlus](https://github.com/kang-wu/SkySensePlusPlus) - Semantic-enhanced multi-modal remote sensing foundation model for Earth observation (Nature Machine Intelligence 2025), enabling universal interpretation across diverse satellite imagery modalities with open-source weights and benchmarks
 - [TESSERA (CVPR 2026)](https://github.com/ucam-eo/tessera) - University of Cambridge's foundation model for time-series satellite imagery, enabling efficient extraction of temporal patterns from Earth observation for land classification, canopy height prediction, and other remote sensing tasks
 - [TerraMind (IBM & ESA, 2025)](https://github.com/IBM/terramind) - First any-to-any generative foundation model for Earth Observation, enabling unified multimodal understanding and generation across diverse satellite sensors and geospatial tasks through a single architecture (258+ stars)
- [GeoAgent (opengeos, 2026)](https://github.com/opengeos/GeoAgent) - Shared multimodal AI agent layer for geospatial Python packages (leafmap, geoai, geemap, STAC, NASA Earthdata) and QGIS, exposing geospatial tools to LLMs with structured metadata, confirmation hooks, and support for OpenAI, Anthropic, Google Gemini, Ollama, and more; includes the OpenGeoAgent QGIS plugin (456+ stars, MIT License)
 - [Awesome Remote Sensing Foundation Models](https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models) - Curated collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs), tracking the rapidly evolving landscape of vision, vision-language, generative, and agent-based geospatial AI (1.9K+ stars, 2024-2026)

 ### 🌾 Agriculture & Ecology
@@ -811,7 +795,6 @@ validated: false
 - [AgentSociety](https://github.com/tsinghua-fib-lab/AgentSociety) - Modern LLM-native agent simulation platform for social science research and experimental design, providing a flexible framework for creating and managing intelligent agents in simulated environments (Tsinghua FIB Lab, 984+ stars, 2025)
 - [Auto-Empirical-Research-Skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills) - Curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines, enabling reproducible social science research with AI agents (Stanford REAP & CoPaper.AI, 3K+ stars, 2026)
 - [EDSL](https://github.com/expectedparrot/edsl) - Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs (460+ stars, 2024)
- [GABRIEL (OpenAI, 2026)](https://github.com/openai/GABRIEL) - Generalized Attribute Based Ratings Information Extraction Library; official OpenAI toolkit that turns messy qualitative corpora into analysis-ready datasets for social scientists and data scientists, measuring quantitative attributes in text, images, or audio using the GPT API. See the [official blog post](https://openai.com/index/scaling-social-science-research/) and [NBER working paper](http://www.nber.org/papers/w34834) (413+ stars, Apache 2.0)

 ---

@@ -898,7 +881,6 @@ validated: false

 ### Specialized Frameworks
 - [MDAnalysis](https://github.com/MDAnalysis/mdanalysis) - Molecular dynamics analysis
- [Nano World Model](https://github.com/simchowitzlabpublic/nano-world-model) - Minimalist, batteries-included repository for training video world models with diffusion-forcing, supporting long-horizon rollouts, 3D point-cloud generation, and model-predictive control with pretrained checkpoints (Simchowitz Lab, 700+ stars, MIT License, 2026)
 - [e3nn](https://github.com/e3nn/e3nn) - Euclidean neural networks for arbitrary point transformations enabling E(3)-equivariant deep learning, foundational library for building geometry-aware neural networks in molecular dynamics, materials science, and physics
 - [MDtrajNet](https://arxiv.org/abs/2505.16301) - Neural network foundation model that directly generates MD trajectories bypassing force calculations, accelerating simulations by up to 100× with equivariant Transformer architecture (2025)
 - [ASE](https://wiki.fysik.dtu.dk/ase/) - Atomic Simulation Environment for materials modeling
@@ -978,7 +960,6 @@ This project builds upon and complements several excellent resources:
 - [awesome-ai4s](https://github.com/hyperai/awesome-ai4s) - 200+ AI for Science papers with Chinese interpretations
 - [Awesome AI Scientist Papers](https://github.com/openags/Awesome-AI-Scientist-Papers) - Autonomous AI scientist research
 - [Awesome Scientific Machine Learning](https://github.com/MartinuzziFrancesco/awesome-scientific-machine-learning) - Physics-informed ML and SciML
- [Awesome Scientific Skills](https://github.com/InternScience/Awesome-Scientific-Skills) - Curated collection of agent skills for scientific research (InternScience, 493+ stars, 2026)
 - [Awesome Agents for Science](https://github.com/OSU-NLP-Group/awesome-agents4science) - LLM agents across scientific domains
 - [Awesome LLM Agents Scientific Discovery](https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery) - Biomedical AI agents
 - [Awesome Foundation Models for Weather and Climate](https://github.com/shengchaochen82/Awesome-Foundation-Models-for-Weather-and-Climate) - Comprehensive survey of foundation models for weather and climate data understanding
@@ -1,61 +0,0 @@
---
-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
-prompt_class: unknown
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-name: AI4Bio Schema Check
-
-on:
-  pull_request:
-    paths:
-      - data/resources.yml
-      - data/enrichment.yml
-      - 'data/enrichment.*.yml'
-      - data/vocabulary.yml
-      - docs/data/resource.schema.json
-      - scripts/enrichment_fragments.py
-      - scripts/validate_resources.py
-      - scripts/build_resources_v2.py
-  push:
-    branches: [main]
-    paths:
-      - data/resources.yml
-      - data/enrichment.yml
-      - 'data/enrichment.*.yml'
-      - data/vocabulary.yml
-      - docs/data/resource.schema.json
-      - scripts/enrichment_fragments.py
-      - scripts/validate_resources.py
-      - scripts/build_resources_v2.py
-
-permissions:
-  contents: read
-
-jobs:
-  validate:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v3
-      - name: Validate schema and enrichment
-        run: uv run --with pyyaml python scripts/validate_resources.py
-      - name: Build enriched artifacts
-        run: uv run --with pyyaml python scripts/build_resources_v2.py
-      - name: Verify enriched artifacts are committed
-        run: |
-          if git diff --quiet; then
-            echo "AI4Bio artifacts are in sync."
-            exit 0
-          fi
-          echo "Generated AI4Bio artifacts are out of date. Run:"
-          echo "  uv run --with pyyaml python scripts/build_resources_v2.py"
-          git status --short
-          exit 1
@@ -1,43 +0,0 @@
---
-title: "Landscape Ui Check"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/landscape-ui-check.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: unknown
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-name: Landscape UI Check
-
-on:
-  pull_request:
-    paths:
-      - docs/landscape.html
-      - docs/landscape.css
-      - docs/landscape.js
-      - .github/workflows/landscape-ui-check.yml
-  push:
-    branches: [main]
-    paths:
-      - docs/landscape.html
-      - docs/landscape.css
-      - docs/landscape.js
-      - .github/workflows/landscape-ui-check.yml
-
-permissions:
-  contents: read
-
-jobs:
-  ui-check:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-node@v4
-        with:
-          node-version: 'lts/*'
-      - name: Check landscape JavaScript syntax
-        run: node --check docs/landscape.js
@@ -2,9 +2,9 @@
 title: "Sync Resources"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/sync_resources.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/sync_resources.yml
+upstream_sha: 12d87583
+imported_at: 2026-06-26
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -20,8 +20,6 @@ on:
    paths:
      - README.md
      - data/resources.yml
-      - data/enrichment.yml
-      - 'data/enrichment.*.yml'
      - scripts/*.py
      - scripts/**/*.py

@@ -57,10 +55,7 @@ jobs:

      - name: Sync From README
        if: contains(steps.changes.outputs.changed, 'README.md')
-        run: uv run --with pyyaml python scripts/sync_resources_from_readme.py
-
-      - name: Validate Resource Schema
-        run: uv run --with pyyaml python scripts/validate_resources.py
+        run: uv run python scripts/sync_resources_from_readme.py

      - name: Build Artifacts
        run: uv run --with pyyaml python scripts/build_resources.py
@@ -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/7a064bf0/README.md
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/README.md
+upstream_sha: 12d87583
+imported_at: 2026-06-26
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -70,7 +70,6 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
  - [Machine Learning Tasks and Models](#machine-learning-tasks-and-models)
    - [Drug Discovery](#drug-discovery)
      - [Drug Response Prediction](#drug-response-prediction)
-      - [Drug Perturbation](#drug-perturbation)
      - [Drug Repurposing](#drug-repurposing)
      - [Drug Target Interaction](#drug-target-interaction)
      - [Compound-Protein Interaction](#compound-protein-interaction)
@@ -266,8 +265,6 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [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.
 - [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.
@@ -323,7 +320,6 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [sciPENN](https://github.com/jlakkis/sciPENN) — RNN-based method for simultaneous protein expression prediction, uncertainty estimation, and cell-type label transfer from CITE-seq and scRNA-seq data.
 - [MOGONET](https://github.com/txWang/MOGONET) — Multi-omics graph convolutional network framework for patient classification and biomarker identification.
 - [AutoZyme](https://github.com/ElliotXie/autozyme) — Autonomous agentic framework that speeds up bioinformatics software (e.g. Scanpy, Seurat) on CPUs while preserving the original results.
- [SeqBench](https://seqbench.com/) — Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server.

 ---

@@ -341,15 +337,10 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [RECOVER](https://github.com/RECOVERcoalition/Recover) — Machine learning framework for predicting synergistic drug combination responses across cell lines.
 - [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.
-
-#### Drug Perturbation
-
- [CellOT](https://github.com/bunnech/cellot) — Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.
- [CMonge](https://github.com/AI4SCR/conditional-monge-gap) — Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses.
+- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
 - [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.
+- [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.

 #### Drug Repurposing

@@ -421,10 +412,6 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [Phikon](https://huggingface.co/owkin/phikon) — ViT-based pathology foundation model pretrained with iBOT self-supervision on TCGA whole-slide images.
 - [Nicheformer](https://github.com/theislab/nicheformer) — Foundation model for single-cell and spatial omics using a transformer architecture with positional embeddings to encode spatial cell information.
 - [scGPT-spatial](https://github.com/bowang-lab/scGPT-spatial) — Extension of scGPT for spatial transcriptomics with continual pretraining and a mixture-of-experts decoder for spatial gene expression analysis.
- [DeepSpot](https://github.com/ratschlab/DeepSpot) — Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.
- [DeepSpot2Cell](https://github.com/ratschlab/DeepSpot2Cell) — Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).
- [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.

 ##### Multi-Omics Foundation Models

@@ -2,9 +2,9 @@
 title: "Cspell"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/cspell.json
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/cspell.json
+upstream_sha: 12d87583
+imported_at: 2026-06-26
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -157,21 +157,7 @@ validated: false
    "Pacc",
    "multiomics",
    "Pathomic",
-    "PLIP",
-    "Omni",
-    "Bento",
-    "FFPE",
-    "Xenium",
-    "Zyme",
-    "Neur",
-    "Imageomics",
-    "AESTETIK",
-    "CellOT",
-    "CMonge",
-    "bowang",
-    "ctheodoris",
-    "OpenAI",
-    "GPT"
+    "PLIP"
  ],
  "ignorePaths": [
    "node_modules/**"
@@ -1,56 +0,0 @@
---
-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
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed single-cell and biomedical benchmark enrichment batch.
-resources:
-  scmulan:
-    entities: [cell, gene]
-    methods: [language-model, transformer]
-    modalities: [epigenomics, multi-omics, proteomics, single-cell-rna-seq, transcriptomics]
-    tasks: [foundation-model-pretraining, representation-learning]
-    github: https://github.com/SuperBianC/scMulan
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/SuperBianC/scMulan
-
-  proteingym:
-    entities: [protein]
-    modalities: [protein-sequence]
-    tasks: [regression]
-    github: https://github.com/OATML-Markslab/ProteinGym
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/OATML-Markslab/ProteinGym
-
-  lincs_l1000:
-    entities: [cell, compound, gene]
-    modalities: [transcriptomics]
-    tasks: [perturbation-prediction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data
-
-  prism:
-    entities: [cell, drug]
-    tasks: [drug-response-prediction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://depmap.org/portal/prism/
-
-  pharmgkb:
-    entities: [drug, gene, phenotype, variant]
-    modalities: [clinical, genomics]
-    tasks: [drug-response-prediction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.pharmgkb.org/
@@ -1,91 +0,0 @@
---
-title: "Provenance-backed database and API enrichment batch."
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.database-api-v1.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed database and API enrichment batch.
-resources:
-  chembl_web_services:
-    entities: [molecule, protein]
-    modalities: [chemical-structure]
-    documentation: https://www.ebi.ac.uk/chembl/api/data/docs
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.ebi.ac.uk/chembl/api/data/docs
-
-  clinicaltrials_gov_api:
-    entities: [disease, drug]
-    modalities: [clinical]
-    documentation: https://clinicaltrials.gov/data-api/api
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://clinicaltrials.gov/data-api/api
-
-  ensembl_rest_api:
-    entities: [gene, genome, transcript, variant]
-    modalities: [genomics]
-    documentation: https://rest.ensembl.org/
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://rest.ensembl.org/
-
-  kegg_rest_api:
-    entities: [compound, gene, pathway]
-    documentation: https://www.kegg.jp/kegg/rest/keggapi.html
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.kegg.jp/kegg/rest/keggapi.html
-
-  ncbi_e_utilities:
-    entities: [gene, genome, protein, transcript, variant]
-    modalities: [genomics, transcriptomics]
-    documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.ncbi.nlm.nih.gov/books/NBK25501/
-
-  open_targets_platform_api:
-    entities: [disease, drug, gene, variant]
-    modalities: [genomics, knowledge-graph]
-    documentation: https://platform.opentargets.org/api
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://platform.opentargets.org/api
-
-  pubmed_e_utilities_esearch_efetch:
-    documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.ncbi.nlm.nih.gov/books/NBK25501/
-
-  uniprot_rest_api:
-    entities: [protein]
-    modalities: [protein-sequence, proteomics]
-    documentation: https://www.uniprot.org/help/api
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.uniprot.org/help/api
-
-  drugbank:
-    entities: [disease, drug, protein]
-    modalities: [chemical-structure]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://go.drugbank.com/
-
-  string:
-    entities: [protein]
-    modalities: [knowledge-graph, proteomics]
-    documentation: https://string-db.org/help/api/
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://string-db.org/
-      - https://string-db.org/help/api/
@@ -1,73 +0,0 @@
---
-title: "Provenance-backed foundation model enrichment batch 2."
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.foundation-models-v2.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed foundation model enrichment batch 2.
-resources:
-  nicheformer:
-    entities: [cell, gene, tissue]
-    methods: [self-supervised-learning, transformer]
-    modalities: [single-cell-rna-seq, spatial-transcriptomics, transcriptomics]
-    tasks: [foundation-model-pretraining, representation-learning]
-    year: 2024
-    github: https://github.com/theislab/nicheformer
-    paper: https://doi.org/10.1101/2024.04.15.589472
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/theislab/nicheformer
-      - https://doi.org/10.1101/2024.04.15.589472
-
-  genept:
-    entities: [cell, gene]
-    methods: [language-model]
-    modalities: [single-cell-rna-seq, transcriptomics]
-    tasks: [batch-correction, classification, representation-learning]
-    year: 2023
-    github: https://github.com/yiqunchen/GenePT
-    paper: https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/yiqunchen/GenePT
-      - https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
-
-  scgpt_spatial:
-    entities: [cell, gene, tissue]
-    methods: [generative-model, self-supervised-learning, transformer]
-    modalities: [multi-omics, single-cell-rna-seq, spatial-transcriptomics]
-    tasks: [foundation-model-pretraining, imputation, representation-learning]
-    year: 2025
-    github: https://github.com/bowang-lab/scGPT-spatial
-    paper: https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/bowang-lab/scGPT-spatial
-      - https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
-
-  scprint:
-    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: 2025
-    github: https://github.com/cantinilab/scPRINT
-    documentation: https://www.jkobject.com/scPRINT/
-    paper: https://www.nature.com/articles/s41467-025-58699-1
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/cantinilab/scPRINT
-      - https://www.nature.com/articles/s41467-025-58699-1
@@ -1,62 +0,0 @@
---
-title: "Provenance-backed molecular model and benchmark enrichment batch."
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.molecular-v2.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed molecular model and benchmark enrichment batch.
-resources:
-  chemberta_2:
-    entities: [molecule]
-    methods: [language-model, self-supervised-learning, transformer]
-    modalities: [chemical-structure]
-    tasks: [representation-learning]
-    github: https://github.com/seyonechithrananda/bert-loves-chemistry
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/seyonechithrananda/bert-loves-chemistry
-
-  molformer:
-    entities: [molecule]
-    methods: [language-model, self-supervised-learning, transformer]
-    modalities: [chemical-structure]
-    tasks: [representation-learning]
-    github: https://github.com/IBM/molformer
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/IBM/molformer
-
-  grover:
-    entities: [molecule]
-    methods: [graph-neural-network, self-supervised-learning, transformer]
-    modalities: [chemical-structure]
-    tasks: [representation-learning]
-    github: https://github.com/tencent-ailab/grover
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/tencent-ailab/grover
-
-  moleculenet:
-    entities: [molecule]
-    modalities: [chemical-structure]
-    tasks: [classification, regression]
-    github: https://github.com/deepchem/moleculenet
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/deepchem/moleculenet
-
-  guacamol:
-    entities: [molecule]
-    modalities: [chemical-structure]
-    tasks: [molecular-generation]
-    github: https://github.com/BenevolentAI/guacamol
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/BenevolentAI/guacamol
@@ -1,92 +0,0 @@
---
-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
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed drug-response and pharmacogenomics enrichment batch.
-resources:
-  beat_aml:
-    entities: [cell, disease, drug, gene]
-    modalities: [genomics]
-    tasks: [drug-response-prediction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://biodev.github.io/BeatAML2/
-
-  cancer_therapeutics_response_portal_ctrp:
-    entities: [cell, drug]
-    tasks: [drug-response-prediction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://portals.broadinstitute.org/ctrp/
-
-  bindingdb_curated_sets:
-    entities: [molecule, protein]
-    modalities: [chemical-structure]
-    tasks: [drug-target-interaction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.bindingdb.org/
-
-  bace:
-    entities: [molecule, protein]
-    modalities: [chemical-structure]
-    tasks: [classification, regression]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.kaggle.com/datasets/gokturkkoch/bace
-
-  clintox:
-    entities: [drug]
-    modalities: [clinical]
-    tasks: [classification]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://tdcommons.ai/single_pred_tasks/tox/#clintox
-
-  sider_side_effect_resource:
-    entities: [drug, phenotype]
-    modalities: [clinical]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - http://sideeffects.embl.de/
-
-  pk_db:
-    entities: [drug]
-    modalities: [clinical]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://pk-db.com/
-
-  scperturb:
-    entities: [cell, drug, gene]
-    modalities: [single-cell-rna-seq]
-    tasks: [perturbation-prediction]
-    github: https://github.com/sanderlab/scPerturb
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/sanderlab/scPerturb
-
-  genomics_of_drug_sensitivity_in_cancer_gdsc:
-    entities: [cell, drug, gene]
-    modalities: [genomics]
-    tasks: [drug-response-prediction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://www.cancerrxgene.org/
-
-  cellminer_cross_database_cellminercdb:
-    entities: [cell, drug, gene]
-    modalities: [genomics]
-    tasks: [drug-response-prediction]
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://discover.nci.nih.gov/cellminercdb/
@@ -1,62 +0,0 @@
---
-title: "Provenance-backed protein and drug-discovery enrichment batch."
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-drug-v1.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed protein and drug-discovery enrichment batch.
-resources:
-  esmfold:
-    entities: [protein]
-    methods: [language-model, transformer]
-    modalities: [molecular-structure, protein-sequence]
-    tasks: [representation-learning, structure-prediction]
-    year: 2023
-    github: https://github.com/facebookresearch/esm
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/facebookresearch/esm
-
-  proteinmpnn:
-    entities: [protein]
-    methods: [graph-neural-network, message-passing-neural-network]
-    modalities: [molecular-structure, protein-sequence]
-    tasks: [protein-sequence-design]
-    year: 2022
-    github: https://github.com/dauparas/ProteinMPNN
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/dauparas/ProteinMPNN
-
-  diffdock:
-    entities: [molecule, protein]
-    methods: [diffusion, geometric-deep-learning]
-    modalities: [molecular-structure]
-    tasks: [docking]
-    year: 2023
-    github: https://github.com/gcorso/DiffDock
-    paper: https://openreview.net/forum?id=kKF8_K-mBbS
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/gcorso/DiffDock
-      - https://openreview.net/forum?id=kKF8_K-mBbS
-
-  uni_mol:
-    entities: [molecule, protein]
-    methods: [self-supervised-learning, transformer]
-    modalities: [chemical-structure, molecular-structure]
-    tasks: [docking, representation-learning]
-    year: 2023
-    github: https://github.com/deepmodeling/Uni-Mol
-    paper: https://openreview.net/forum?id=6K2RM6wVqKu
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/deepmodeling/Uni-Mol
-      - https://openreview.net/forum?id=6K2RM6wVqKu
@@ -1,64 +0,0 @@
---
-title: "Provenance-backed protein model enrichment batch."
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-v2.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed protein model enrichment batch.
-resources:
-  esm3:
-    entities: [protein]
-    methods: [generative-model, language-model, transformer]
-    modalities: [molecular-structure, protein-sequence]
-    tasks: [protein-sequence-design, representation-learning]
-    github: https://github.com/evolutionaryscale/esm
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/evolutionaryscale/esm
-
-  evolutionary_scale_modeling_esm:
-    entities: [protein]
-    methods: [language-model, self-supervised-learning, transformer]
-    modalities: [protein-sequence]
-    tasks: [representation-learning]
-    github: https://github.com/facebookresearch/esm
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/facebookresearch/esm
-
-  prottrans:
-    entities: [protein]
-    methods: [language-model, self-supervised-learning, transformer]
-    modalities: [protein-sequence]
-    tasks: [representation-learning]
-    github: https://github.com/agemagician/ProtTrans
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/agemagician/ProtTrans
-
-  progen2:
-    entities: [protein]
-    methods: [generative-model, language-model, transformer]
-    modalities: [protein-sequence]
-    tasks: [protein-sequence-design, representation-learning]
-    github: https://github.com/salesforce/progen
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/salesforce/progen
-
-  alphafold3:
-    entities: [molecule, protein, protein-complex]
-    methods: [diffusion]
-    modalities: [molecular-structure, protein-sequence]
-    tasks: [structure-prediction]
-    github: https://github.com/google-deepmind/alphafold3
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/google-deepmind/alphafold3
@@ -1,110 +0,0 @@
---
-title: "Provenance-backed spatial transcriptomics and imaging enrichment batch."
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.spatial-imaging-v1.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Provenance-backed spatial transcriptomics and imaging enrichment batch.
-resources:
-  aestetik:
-    entities: [cell, gene, tissue]
-    methods: [autoencoder]
-    modalities: [histopathology, spatial-transcriptomics]
-    tasks: [representation-learning]
-    github: https://github.com/ratschlab/aestetik
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/ratschlab/aestetik
-
-  conch:
-    entities: [tissue]
-    methods: [contrastive-learning, transformer]
-    modalities: [histopathology, imaging]
-    tasks: [foundation-model-pretraining, representation-learning]
-    github: https://github.com/mahmoodlab/CONCH
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/mahmoodlab/CONCH
-
-  deepspot:
-    entities: [gene, tissue]
-    modalities: [histopathology, spatial-transcriptomics]
-    tasks: [regression]
-    github: https://github.com/ratschlab/DeepSpot
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/ratschlab/DeepSpot
-
-  deepspot_m:
-    entities: [gene, tissue]
-    modalities: [histopathology, spatial-transcriptomics, transcriptomics]
-    tasks: [foundation-model-pretraining, regression]
-    github: https://github.com/ratschlab/DeepSpotM
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/ratschlab/DeepSpotM
-
-  deepspot2cell:
-    entities: [cell, gene, tissue]
-    modalities: [histopathology, spatial-transcriptomics]
-    tasks: [regression]
-    github: https://github.com/ratschlab/DeepSpot2Cell
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/ratschlab/DeepSpot2Cell
-
-  gigapath:
-    entities: [tissue]
-    methods: [self-supervised-learning, transformer]
-    modalities: [histopathology, imaging]
-    tasks: [foundation-model-pretraining, representation-learning]
-    github: https://github.com/prov-gigapath/prov-gigapath
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/prov-gigapath/prov-gigapath
-
-  phikon:
-    entities: [tissue]
-    methods: [self-supervised-learning, transformer]
-    modalities: [histopathology, imaging]
-    tasks: [foundation-model-pretraining, representation-learning]
-    documentation: https://huggingface.co/owkin/phikon
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://huggingface.co/owkin/phikon
-
-  plip:
-    entities: [tissue]
-    methods: [contrastive-learning]
-    modalities: [histopathology, imaging]
-    tasks: [classification, representation-learning]
-    github: https://github.com/PathologyFoundation/plip
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/PathologyFoundation/plip
-
-  uni:
-    entities: [tissue]
-    methods: [self-supervised-learning, transformer]
-    modalities: [histopathology, imaging]
-    tasks: [foundation-model-pretraining, representation-learning]
-    github: https://github.com/mahmoodlab/UNI
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/mahmoodlab/UNI
-
-  hest_xenium_virtual_spatial_transcriptomics:
-    entities: [cell, gene, tissue]
-    modalities: [histopathology, spatial-transcriptomics, transcriptomics]
-    tasks: [regression]
-    documentation: https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
@@ -1,128 +0,0 @@
---
-title: "AI4Bio landscape enrichment overlay"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# AI4Bio landscape enrichment overlay
-#
-# README.md remains the canonical source for resource membership and basic fields.
-# Add richer, independently curated metadata here, keyed by the stable resource id.
-# scripts/build_resources.py merges these fields into generated JSON/CSV artifacts.
-#
-# Enrichment values should be source-verifiable. Controlled vocabulary fields are
-# validated against data/vocabulary.yml.
-
-resources:
-  scgpt:
-    entities: [cell, gene]
-    methods: [generative-model, self-supervised-learning, transformer]
-    modalities: [multi-omics, single-cell-rna-seq, transcriptomics]
-    tasks:
-      - cell-type-annotation
-      - foundation-model-pretraining
-      - gene-regulatory-network-inference
-      - perturbation-prediction
-      - representation-learning
-    year: 2024
-    github: https://github.com/bowang-lab/scGPT
-    documentation: https://scgpt.readthedocs.io/en/latest/
-    paper: https://www.nature.com/articles/s41592-024-02201-0
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/bowang-lab/scGPT
-      - https://www.nature.com/articles/s41592-024-02201-0
-
-  geneformer:
-    entities: [cell, gene]
-    methods: [self-supervised-learning, transformer]
-    modalities: [single-cell-rna-seq, transcriptomics]
-    tasks:
-      - classification
-      - foundation-model-pretraining
-      - perturbation-prediction
-      - representation-learning
-    year: 2023
-    documentation: https://geneformer.readthedocs.io/
-    paper: https://www.nature.com/articles/s41586-023-06139-9
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://huggingface.co/ctheodoris/Geneformer
-      - https://www.nature.com/articles/s41586-023-06139-9
-
-  scfoundation:
-    entities: [cell, gene]
-    methods: [self-supervised-learning, transformer]
-    modalities: [single-cell-rna-seq, transcriptomics]
-    tasks:
-      - cell-type-annotation
-      - drug-response-prediction
-      - foundation-model-pretraining
-      - perturbation-prediction
-      - representation-learning
-    year: 2024
-    github: https://github.com/biomap-research/scFoundation
-    paper: https://www.nature.com/articles/s41592-024-02305-7
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/biomap-research/scFoundation
-      - https://www.nature.com/articles/s41592-024-02305-7
-
-  genecompass:
-    entities: [cell, gene]
-    methods: [self-supervised-learning, transformer]
-    modalities: [single-cell-rna-seq, transcriptomics]
-    tasks: [foundation-model-pretraining, representation-learning]
-    year: 2024
-    github: https://github.com/xCompass-AI/GeneCompass
-    paper: https://www.nature.com/articles/s41422-024-01034-y
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/xCompass-AI/GeneCompass
-      - https://www.nature.com/articles/s41422-024-01034-y
-
-  uce:
-    entities: [cell]
-    methods: [self-supervised-learning]
-    modalities: [single-cell-rna-seq, transcriptomics]
-    tasks: [foundation-model-pretraining, representation-learning]
-    year: 2026
-    github: https://github.com/snap-stanford/UCE
-    paper: https://www.nature.com/articles/s41586-026-10689-z
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/snap-stanford/UCE
-      - https://www.nature.com/articles/s41586-026-10689-z
-
-  cellplm:
-    entities: [cell, gene]
-    methods: [self-supervised-learning, transformer]
-    modalities: [single-cell-rna-seq, transcriptomics]
-    tasks: [foundation-model-pretraining, representation-learning]
-    year: 2023
-    github: https://github.com/OmicsML/CellPLM
-    paper: https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/OmicsML/CellPLM
-      - https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
-
-  scbert:
-    entities: [cell, gene]
-    methods: [language-model, self-supervised-learning, transformer]
-    modalities: [single-cell-rna-seq, transcriptomics]
-    tasks: [cell-type-annotation, classification, foundation-model-pretraining]
-    year: 2022
-    github: https://github.com/TencentAILabHealthcare/scBERT
-    paper: https://www.nature.com/articles/s42256-022-00534-z
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://github.com/TencentAILabHealthcare/scBERT
-      - https://www.nature.com/articles/s42256-022-00534-z
@@ -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/7a064bf0/data/resources.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/data/resources.yml
+upstream_sha: 12d87583
+imported_at: 2026-06-26
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -248,17 +248,6 @@ resources:
    organism: []
    api: false

-  - id: hest_xenium_virtual_spatial_transcriptomics
-    name: "HEST Xenium virtual spatial transcriptomics"
-    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]
-    tasks: []
-    modalities: []
-    organism: []
-    api: false
-
  - id: jump_cell_painting_datasets
    name: "JUMP Cell Painting Datasets"
    type: benchmark
@@ -446,17 +435,6 @@ resources:
    organism: []
    api: false

-  - id: tcga_virtual_spatial_transcriptomics_atlas
-    name: "TCGA virtual spatial transcriptomics atlas"
-    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]
-    tasks: []
-    modalities: []
-    organism: []
-    api: false
-
  - id: the_cancer_genome_atlas_tcga
    name: "The Cancer Genome Atlas (TCGA)"
    type: benchmark
@@ -1513,17 +1491,6 @@ resources:
    organism: []
    api: false

-  - id: aestetik
-    name: "AESTETIK"
-    type: model
-    url: https://github.com/ratschlab/aestetik
-    description: "Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge."
-    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
-    tasks: [Foundation Model]
-    modalities: [Single Cell, Spatial Transcriptomics]
-    organism: []
-    api: false
-
  - id: ai4chem_chemllm_7b_chat
    name: "AI4Chem/ChemLLM-7B-Chat"
    type: model
@@ -1678,17 +1645,6 @@ resources:
    organism: []
    api: false

-  - id: cellot
-    name: "CellOT"
-    type: model
-    url: https://github.com/bunnech/cellot
-    description: "Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations."
-    tags: [drug-discovery, drug-perturbation]
-    tasks: [Drug Discovery, Drug Perturbation]
-    modalities: [Small Molecule]
-    organism: []
-    api: false
-
  - id: cellplm
    name: "CellPLM"
    type: model
@@ -1738,8 +1694,8 @@ resources:
    type: model
    url: https://github.com/theislab/chemCPA
    description: "Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations."
-    tags: [drug-discovery, drug-perturbation]
-    tasks: [Drug Discovery, Drug Perturbation]
+    tags: [drug-discovery, drug-response-prediction]
+    tasks: [Drug Discovery, Drug Response Prediction]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -1766,17 +1722,6 @@ resources:
    organism: []
    api: false

-  - id: cmonge
-    name: "CMonge"
-    type: model
-    url: https://github.com/AI4SCR/conditional-monge-gap
-    description: "Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses."
-    tags: [drug-discovery, drug-perturbation]
-    tasks: [Drug Discovery, Drug Perturbation]
-    modalities: [Small Molecule]
-    organism: []
-    api: false
-
  - id: concerto
    name: "Concerto"
    type: model
@@ -1804,8 +1749,8 @@ resources:
    type: model
    url: https://github.com/hliulab/cycleCDR
    description: "Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations."
-    tags: [drug-discovery, drug-perturbation]
-    tasks: [Drug Discovery, Drug Perturbation]
+    tags: [drug-discovery, drug-response-prediction]
+    tasks: [Drug Discovery, Drug Response Prediction]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -1865,39 +1810,6 @@ resources:
    organism: []
    api: false

-  - id: deepspot
-    name: "DeepSpot"
-    type: model
-    url: https://github.com/ratschlab/DeepSpot
-    description: "Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution."
-    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
-    tasks: [Foundation Model]
-    modalities: [Single Cell, Spatial Transcriptomics]
-    organism: []
-    api: false
-
-  - id: deepspot_m
-    name: "DeepSpot-M"
-    type: model
-    url: https://github.com/ratschlab/DeepSpotM
-    description: "Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology."
-    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
-    tasks: [Foundation Model]
-    modalities: [Single Cell, Spatial Transcriptomics]
-    organism: []
-    api: false
-
-  - id: deepspot2cell
-    name: "DeepSpot2Cell"
-    type: model
-    url: https://github.com/ratschlab/DeepSpot2Cell
-    description: "Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics)."
-    tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
-    tasks: [Foundation Model]
-    modalities: [Single Cell, Spatial Transcriptomics]
-    organism: []
-    api: false
-
  - id: dgdrp
    name: "DGDRP"
    type: model
@@ -2497,8 +2409,8 @@ resources:
    type: model
    url: https://github.com/Perturbation-Response-Prediction/PRnet
    description: "Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery."
-    tags: [drug-discovery, drug-perturbation]
-    tasks: [Drug Discovery, Drug Perturbation]
+    tags: [drug-discovery, drug-response-prediction]
+    tasks: [Drug Discovery, Drug Response Prediction]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -3185,17 +3097,6 @@ resources:
    organism: []
    api: false

-  - id: seqbench
-    name: "SeqBench"
-    type: toolkit
-    url: https://seqbench.com/
-    description: "Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server."
-    tags: [preprocessing-tools]
-    tasks: [Preprocessing]
-    modalities: []
-    organism: []
-    api: false
-
  - id: seurat
    name: "Seurat"
    type: toolkit
@@ -1,105 +0,0 @@
---
-title: "Canonical vocabulary for new AI4Bio enrichment metadata."
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/vocabulary.yml
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: unknown
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Canonical vocabulary for new AI4Bio enrichment metadata.
-#
-# These values are enforced only for fields explicitly added through
-# data/enrichment.yml. README-derived legacy values remain backward compatible.
-# Canonical terms use lowercase kebab-case.
-
-version: 1
-
-controlled_fields:
-  entities:
-    - cell
-    - compound
-    - disease
-    - drug
-    - gene
-    - genome
-    - molecule
-    - organism
-    - pathway
-    - phenotype
-    - protein
-    - protein-complex
-    - regulatory-element
-    - tissue
-    - transcript
-    - variant
-
-  methods:
-    - autoencoder
-    - contrastive-learning
-    - convolutional-neural-network
-    - diffusion
-    - generative-model
-    - geometric-deep-learning
-    - graph-neural-network
-    - knowledge-graph
-    - language-model
-    - message-passing-neural-network
-    - multi-agent-system
-    - optimal-transport
-    - recurrent-neural-network
-    - reinforcement-learning
-    - retrieval-augmented-generation
-    - self-supervised-learning
-    - state-space-model
-    - supervised-learning
-    - transformer
-    - unsupervised-learning
-    - variational-autoencoder
-
-  modalities:
-    - cell-painting
-    - chemical-structure
-    - clinical
-    - dna-sequence
-    - electronic-health-record
-    - epigenomics
-    - genomics
-    - histopathology
-    - imaging
-    - knowledge-graph
-    - metabolomics
-    - molecular-structure
-    - multi-omics
-    - protein-sequence
-    - proteomics
-    - rna-sequence
-    - single-cell-rna-seq
-    - spatial-transcriptomics
-    - transcriptomics
-
-  tasks:
-    - batch-correction
-    - cell-type-annotation
-    - classification
-    - dimensionality-reduction
-    - docking
-    - drug-response-prediction
-    - drug-target-interaction
-    - foundation-model-pretraining
-    - gene-regulatory-network-inference
-    - imputation
-    - link-prediction
-    - molecular-generation
-    - perturbation-prediction
-    - protein-function-prediction
-    - protein-sequence-design
-    - regression
-    - representation-learning
-    - structure-prediction
-    - trajectory-inference
-    - virtual-screening
@@ -1,73 +0,0 @@
---
-title: "AI4Bio Landscape Database"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/AI4BIO_LANDSCAPE.md
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# AI4Bio Landscape Database
-
-The landscape view treats the existing computational biology registry as a multidimensional database rather than a single hierarchical list.
-
-## Design goals
-
- Keep the current curated resource records and generation pipeline intact.
- Expose orthogonal facets so one resource can be explored by resource type, biological/ML task, data modality, organism, and domain tag.
- Make the landscape useful without introducing a server or build-time dependency.
- Keep the data model extensible for richer AI4Bio metadata over time.
-
-## Current facet model
-
-The landscape UI derives the following dimensions from `docs/data/resources.json`:
-
-| Dimension | Source field | Example values |
-|---|---|---|
-| Resource type | `type` | `database`, `benchmark`, `model`, `toolkit`, `api` |
-| Task | `tasks` | `drug-response-prediction`, `cell-type-annotation`, `molecular-generation` |
-| Modality | `modalities` | `transcriptomics`, `spatial-transcriptomics`, `protein-sequence` |
-| Organism | `organism` | `human`, `mouse`, `multi-species` |
-| Domain/tag | `tags` | `drug-discovery`, `single-cell`, `foundation-model` |
-
-These are deliberately treated as separate axes. A model can therefore be, for example, a `model` that performs `perturbation-prediction` on `single-cell-rna-seq` data for `human` and carry tags such as `drug-discovery` and `foundation-model`.
-
-## Recommended schema evolution
-
-The current schema is compatible with a richer landscape database. New fields should be added incrementally and only when they can be curated consistently.
-
-Suggested fields:
-
-| Field | Type | Purpose |
-|---|---|---|
-| `entities` | array of strings | Biological entities such as `gene`, `protein`, `compound`, `cell`, `disease` |
-| `methods` | array of strings | Method families such as `transformer`, `gnn`, `diffusion`, `optimal-transport` |
-| `organizations` | array of strings | Primary organizations responsible for the resource |
-| `year` | integer | Initial public release/publication year |
-| `github` | string | Source repository when distinct from the canonical landing page |
-| `documentation` | string | Documentation URL |
-| `maintenance_status` | string | Curated status such as `active`, `maintenance`, `archived`, `unknown` |
-| `last_checked` | string | Date the metadata/link was last manually or automatically checked |
-
-Avoid adding dynamic popularity metrics such as GitHub stars directly to canonical records unless a reproducible refresh pipeline is introduced. Such values become stale quickly and should be stored as generated metadata rather than curated facts.
-
-## Canonical-source policy
-
-At present, `README.md` is the canonical curated list, with generated YAML/JSON/CSV artifacts. The landscape page intentionally consumes `docs/data/resources.json` without changing that policy.
-
-A future migration may make `data/resources.yml` the canonical source once all README-only categorization semantics can be represented explicitly in structured fields. That migration should be a separate change because it changes contribution workflow and source-of-truth semantics.
-
-## Landscape page
-
-Open `docs/landscape.html` through GitHub Pages. It provides:
-
- full-text search across names, descriptions, tasks, modalities, organisms, and tags;
- filters for type, task, modality, organism, and tag;
- summary counts for resources and major dimensions;
- frequency bars recalculated for the current filtered result set;
- direct resource and paper links;
- client-side rendering with no additional dependencies.
@@ -1,72 +0,0 @@
---
-title: "Foundation Model Enrichment"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/FOUNDATION_MODEL_ENRICHMENT.md
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Foundation Model Enrichment
-
-This document tracks the first curated metadata-enrichment pass for AI4Bio foundation models.
-
-## Scope
-
-The initial pass focuses on representative single-cell and transcriptomics foundation models already present in the resource registry, beginning with:
-
- scGPT
- Geneformer
-
-The scope may be expanded incrementally once the curation rules below are validated in practice.
-
-## Curation rules
-
-Metadata must be supported by at least one primary or official source:
-
- official project repository or model card;
- official documentation;
- primary peer-reviewed publication or preprint.
-
-Unknown or ambiguous metadata is omitted rather than inferred.
-
-For each resource, curate fields where evidence is available:
-
- `entities`
- `methods`
- `organizations`
- `year`
- `github`
- `documentation`
- `maintenance_status`
- `last_checked`
- `metadata_sources`
-
-`maintenance_status` should only be marked `active` when there is direct evidence of ongoing maintenance, such as a recent official release or repository activity. Otherwise use `unknown` or omit the field.
-
-## Initial evidence targets
-
-### scGPT
-
-Primary evidence should include the official `bowang-lab/scGPT` repository and the Nature Methods publication.
-
-### Geneformer
-
-Primary evidence should include the official `ctheodoris/Geneformer` model repository/model card and the primary Nature publication.
-
-## Completion criteria
-
-A resource is considered enriched when:
-
-1. all added metadata is supported by `metadata_sources`;
-2. no unsupported organization, method, year, or maintenance claim is introduced;
-3. generated JSON/CSV artifacts are regenerated and committed;
-4. schema validation and resource-consistency CI checks pass.
-
-## Provenance
-
-This enrichment pass is being prepared with assistance from OpenAI GPT-5.6 Sol. Final metadata is intended to remain source-verifiable and reviewable through the recorded provenance URLs.
@@ -1,21 +0,0 @@
---
-title: "AI4Bio data files"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/README.md
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# AI4Bio data files
-
- `resources.json`: generated merged resource registry consumed by GitHub Pages.
- `resource.schema.json`: JSON Schema 2020-12 contract for one resource object.
- `SCHEMA.md`: original schema notes.
- `SCHEMA_V2.md`: richer AI4Bio landscape schema and enrichment workflow.
-
-The enriched build path is `scripts/build_resources_v2.py`, which combines `data/resources.yml` with `data/enrichment.yml` and runs `scripts/validate_resources.py` before writing artifacts.
@@ -1,132 +0,0 @@
---
-title: "AI4Bio Resource Schema v2"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/SCHEMA_V2.md
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# AI4Bio Resource Schema v2
-
-This document defines the richer landscape metadata layered on top of the curated Awesome Computational Biology list.
-
-## Source model
-
-The repository intentionally separates **membership/basic metadata** from **landscape enrichment**:
-
-1. `README.md` is the canonical curated resource list.
-2. `scripts/sync_resources_from_readme.py` derives `data/resources.yml` from README headings and bullets.
-3. `data/enrichment.yml` stores richer metadata keyed by stable resource `id`.
-4. `data/vocabulary.yml` defines canonical terms for controlled enrichment dimensions.
-5. `scripts/build_resources.py` merges base records and enrichment, validates them, and writes `data/resources.json`, `data/resources.csv`, and `docs/data/resources.json`.
-
-This separation prevents hand-curated AI4Bio metadata from being erased by README synchronization.
-
-## Core identity fields
-
-These fields are required and may not be overridden by `data/enrichment.yml`:
-
-| Field | Type | Meaning |
-|---|---|---|
-| `id` | string | Stable lowercase `snake_case` identifier |
-| `name` | string | Official display name |
-| `type` | enum | `api`, `benchmark`, `database`, `model`, `resource`, or `toolkit` |
-| `url` | URL | Canonical landing page |
-| `description` | string | Short factual description |
-
-## Landscape dimensions
-
-| Field | Type | Meaning |
-|---|---|---|
-| `tasks` | string[] | Biological or ML tasks performed |
-| `modalities` | string[] | Input/output data modalities |
-| `organism` | string[] | Covered organisms or species groups |
-| `entities` | string[] | Biological entities: gene, protein, compound, cell, disease, etc. |
-| `methods` | string[] | Method families: transformer, GNN, diffusion, optimal transport, etc. |
-| `tags` | string[] | Broad domain and curation labels |
-| `organizations` | string[] | Organizations maintaining or primarily responsible for the resource |
-
-These dimensions are deliberately orthogonal. Do not encode a task as a modality or a biological entity as a resource type.
-
-## Controlled vocabulary
-
-New values added through `data/enrichment.yml` for `entities`, `methods`, `modalities`, and `tasks` must use canonical terms from `data/vocabulary.yml`.
-
-Canonical terms use lowercase kebab-case, for example:
-
-```yaml
-entities: [cell, gene]
-methods: [transformer, self-supervised-learning]
-modalities: [single-cell-rna-seq, transcriptomics]
-tasks: [foundation-model-pretraining, cell-type-annotation]
-```
-
-This rule is intentionally applied only to enrichment metadata. Existing README-derived values remain valid for backward compatibility and can be migrated separately without blocking routine resource updates.
-
-When a required concept is missing, add a reusable canonical term to `data/vocabulary.yml` instead of inventing a one-off spelling in an enrichment record. `tags`, `organism`, and `organizations` remain free-form because their vocabularies are broader or context dependent.
-
-## Provenance and lifecycle fields
-
-| Field | Type | Meaning |
-|---|---|---|
-| `year` | integer | Initial public release or primary publication year |
-| `github` | URL | Source repository when available |
-| `documentation` | URL | Documentation landing page |
-| `paper` | URL | Primary publication or preprint |
-| `license` | string | SPDX identifier preferred |
-| `api` | boolean | Programmatic API availability |
-| `access` | enum | `open`, `registration`, `restricted`, `commercial`, `unknown` |
-| `maintenance_status` | enum | `active`, `maintenance`, `archived`, `unknown` |
-| `updated` | date | Last-known upstream update date |
-| `last_checked` | date | Date this repository verified the metadata |
-| `metadata_sources` | URL[] | Sources supporting enriched metadata |
-
-`last_checked` is a curation timestamp, not an upstream release date. `updated` should only be populated when an upstream update date is known.
-
-## Enrichment rules
-
-`data/enrichment.yml` is a mapping keyed by resource id:
-
-```yaml
-resources:
-  example_resource:
-    entities: [gene, disease]
-    methods: [transformer]
-    organizations: [Example Lab]
-    year: 2025
-    github: https://github.com/example/project
-    documentation: https://example.org/docs
-    maintenance_status: active
-    access: open
-    last_checked: 2026-08-08
-    metadata_sources:
-      - https://example.org/about
-```
-
-Enrichment cannot override `id`, `name`, `type`, `url`, or `description`. A referenced id must already exist in `data/resources.yml`.
-
-## Validation contract
-
-`python scripts/validate_resources.py` checks:
-
- required fields and field types;
- stable id format and id uniqueness;
- allowed enum values;
- HTTP(S) URL shape;
- ISO `YYYY-MM-DD` dates;
- list uniqueness and non-empty values;
- enrichment references and forbidden identity overrides;
- controlled enrichment terms against `data/vocabulary.yml`;
- vocabulary uniqueness and lowercase kebab-case normalization;
- unknown field names.
-
-The machine-readable resource counterpart is `docs/data/resource.schema.json` (JSON Schema 2020-12). Controlled vocabulary enforcement is performed at the enrichment layer because legacy README-derived values intentionally remain backward compatible.
-
-## Curation guidance
-
-Prefer verified metadata over exhaustive metadata. Unknown fields should be omitted rather than guessed. For facts likely to change, include `last_checked` and at least one `metadata_sources` URL. Dynamic popularity metrics such as GitHub stars should remain generated telemetry rather than canonical curated fields.
@@ -1,53 +0,0 @@
---
-title: "Resource.Schema"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/resource.schema.json
-upstream_sha: 7a064bf0
-imported_at: 2026-08-08
-prompt_class: catalogue
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-{
-  "$schema": "https://json-schema.org/draft/2020-12/schema",
-  "$id": "https://inoue0426.github.io/awesome-computational-biology/data/resource.schema.json",
-  "title": "AI4Bio Resource",
-  "type": "object",
-  "required": ["id", "name", "type", "url", "description"],
-  "additionalProperties": false,
-  "properties": {
-    "id": {"type": "string", "pattern": "^[a-z0-9]+(?:_[a-z0-9]+)*$"},
-    "name": {"type": "string", "minLength": 1},
-    "type": {"enum": ["api", "benchmark", "database", "model", "resource", "toolkit"]},
-    "url": {"type": "string", "format": "uri", "pattern": "^https?://"},
-    "description": {"type": "string", "minLength": 1},
-    "tags": {"$ref": "#/$defs/stringArray"},
-    "tasks": {"$ref": "#/$defs/stringArray"},
-    "modalities": {"$ref": "#/$defs/stringArray"},
-    "organism": {"$ref": "#/$defs/stringArray"},
-    "entities": {"$ref": "#/$defs/stringArray"},
-    "methods": {"$ref": "#/$defs/stringArray"},
-    "organizations": {"$ref": "#/$defs/stringArray"},
-    "metadata_sources": {"type": "array", "items": {"type": "string", "format": "uri", "pattern": "^https?://"}, "uniqueItems": true},
-    "license": {"type": "string"},
-    "api": {"type": "boolean"},
-    "paper": {"type": "string", "format": "uri", "pattern": "^https?://"},
-    "github": {"type": "string", "format": "uri", "pattern": "^https://github\\.com/"},
-    "documentation": {"type": "string", "format": "uri", "pattern": "^https?://"},
-    "year": {"type": "integer", "minimum": 1900, "maximum": 2100},
-    "maintenance_status": {"enum": ["active", "maintenance", "archived", "unknown"]},
-    "access": {"enum": ["open", "registration", "restricted", "commercial", "unknown"]},
-    "updated": {"type": "string", "format": "date"},
-    "last_checked": {"type": "string", "format": "date"}
-  },
-  "$defs": {
-    "stringArray": {
-      "type": "array",
-      "items": {"type": "string", "minLength": 1},
-      "uniqueItems": true
-    }
-  }
-}
@@ -1,352 +0,0 @@
---
-title: "Awesome AI Agents for Scientific Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
-task: ""
-lineage_type: import
-upstream_source: https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery/blob/bb3be5bc/README.md
-upstream_sha: bb3be5bc
-imported_at: 2026-08-08
-prompt_class: prompt
-upstream_changes: accepted
-author: upstream
-validated: false
---
-
-# Awesome AI Agents for Scientific Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
-<div align="center">
-  <img src="agents4science.webp" alt="AI Agents for Scientific Discovery" width="600px">
-</div>
-
-A curated list of papers about AI agents for scientific discovery and research automation.
-
-
-Maintained by [Jieli Zhou](mailto:[email protected])
-
-If you use this paper list for your research, please cite it using:
-```bibtex
-@misc{zhou2024awesome,
-  title={Awesome AI Agents for Scientific Discovery},
-  author={Zhou, Jieli},
-  year={2024},
-  publisher={GitHub},
-  journal={GitHub repository},
-  howpublished={\url{https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery}}
-}
-```
-
-
-## Introduction
-
-The convergence of large language models (LLMs) and autonomous agents has ushered in a new era in scientific discovery, fundamentally transforming how research is conducted across disciplines. This emerging paradigm, articulated in Kitano's seminal "Nobel Turing Challenge" (2021), envisions AI systems capable of making scientific discoveries worthy of Nobel Prize recognition. Recent advances in LLM-based agents have brought us closer to this vision, enabling increasingly sophisticated automation of scientific workflows and decision-making processes.
-
-### Evolution and Current Landscape
-
-The field has evolved rapidly since early visions of AI-driven scientific discovery. While traditional AI systems focused on narrow tasks, modern LLM-based agents demonstrate remarkable capabilities in complex scientific reasoning, experimental design, and hypothesis generation. The breakthrough capabilities of models like GPT-4 have catalyzed this transition, enabling agents to engage in sophisticated scientific discourse, interpret complex data, and even design novel experiments.
-
-### Key Research Directions
-
-Several major research themes have emerged in this space:
-
-1. **Multi-Agent Architectures**: Research has increasingly focused on collaborative multi-agent systems, where specialized agents work together to tackle complex scientific problems.
-
-2. **Domain-Specific Applications**: The healthcare sector has seen particularly rapid adoption, with agents being developed for clinical decision support, medical diagnosis, and healthcare administration.
-
-3. **Scientific Process Automation**: Agents are being developed to automate various aspects of the research pipeline, from literature review and hypothesis generation to experimental design and data analysis.
-
-### Impact and Future Directions
-
-The emergence of AI agents in scientific discovery represents more than just technological advancement; it signals a fundamental shift in how science is conducted. These systems promise to:
- Accelerate the pace of scientific discovery
- Enable exploration of previously intractable research questions
- Democratize access to scientific expertise
- Foster more efficient use of research resources
-
-# Awesome LLM Agents for Scientific Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
-<div align="center">
-  <img src="agents4science.webp" alt="AI Agents for Scientific Discovery" width="600px">
-</div>
-
-A curated list of papers about AI agents for scientific discovery and research automation.
-
-
-Maintained by [Jieli Zhou](mailto:[email protected])
-
-If you use this paper list for your research, please cite it using:
-```bibtex
-@misc{zhou2024awesome,
-  title={Awesome AI Agents for Scientific Discovery},
-  author={Zhou, Jieli},
-  year={2024},
-  publisher={GitHub},
-  journal={GitHub repository},
-  howpublished={\url{https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery}}
-}
-```
-
-
-## Introduction
-
-The convergence of large language models (LLMs) and autonomous agents has ushered in a new era in scientific discovery, fundamentally transforming how research is conducted across disciplines. This emerging paradigm, articulated in Kitano's seminal "Nobel Turing Challenge" (2021), envisions AI systems capable of making scientific discoveries worthy of Nobel Prize recognition. Recent advances in LLM-based agents have brought us closer to this vision, enabling increasingly sophisticated automation of scientific workflows and decision-making processes.
-
-### Evolution and Current Landscape
-
-The field has evolved rapidly since early visions of AI-driven scientific discovery. While traditional AI systems focused on narrow tasks, modern LLM-based agents demonstrate remarkable capabilities in complex scientific reasoning, experimental design, and hypothesis generation. The breakthrough capabilities of models like GPT-4 have catalyzed this transition, enabling agents to engage in sophisticated scientific discourse, interpret complex data, and even design novel experiments.
-
-### Key Research Directions
-
-Several major research themes have emerged in this space:
-
-1. **Multi-Agent Architectures**: Research has increasingly focused on collaborative multi-agent systems, where specialized agents work together to tackle complex scientific problems.
-
-2. **Domain-Specific Applications**: The healthcare sector has seen particularly rapid adoption, with agents being developed for clinical decision support, medical diagnosis, and healthcare administration.
-
-3. **Scientific Process Automation**: Agents are being developed to automate various aspects of the research pipeline, from literature review and hypothesis generation to experimental design and data analysis.
-
-### Impact and Future Directions
-
-The emergence of AI agents in scientific discovery represents more than just technological advancement; it signals a fundamental shift in how science is conducted. These systems promise to:
- Accelerate the pace of scientific discovery
- Enable exploration of previously intractable research questions
- Democratize access to scientific expertise
- Foster more efficient use of research resources
-
-## Table of Contents
-
-1. [Foundations & Vision](#foundations--vision)
-2. [Core Technologies](#core-technologies)
-3. [Scientific Process Automation](#scientific-process-automation)
-4. [Domain Applications](#domain-applications)
-5. [Infrastructure & Tools](#infrastructure--tools)
-8. [AI Agent Frameworks & Tools](#ai-agent-frameworks--tools)
-6. [Evaluation & Benchmarking](#evaluation--benchmarking)
-7. [Surveys & Reviews](#surveys--reviews)
-
-## Foundations & Vision
-
-### Vision Papers
- **[Nobel Turing Challenge: Creating the Engine for Scientific Discovery](https://www.nature.com/articles/s41592-021-01091-w)**  
-  *Hiroaki Kitano.* NPJ Systems Biology and Applications 2021
-
- **[Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery](https://www.aaai.org/ojs/index.php/aimagazine/article/view/2624)**  
-  *Hiroaki Kitano.* AI Magazine 2016
-
- **[The AI Scientist: Towards Fully Automated Open-ended Scientific Discovery](https://arxiv.org/abs/2408.06292)**  
-  *Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha.* arXiv 2024
-
- **[Emergent autonomous scientific research capabilities of large language models](https://arxiv.org/abs/2304.05332)**  
-  *Daniil A Boiko, Robert MacKnight, Gabe Gomes.* arXiv 2023
-
- **[What is missing in autonomous discovery: open challenges for the community](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00089c)**  
-  *Phillip M Maffettone, Pascal Friederich, Sterling G Baird, et al.* Digital Discovery 2023
-
- **[The future of fundamental science led by generative closed-loop artificial intelligence](https://arxiv.org/abs/2307.07522)**  
-  *Hector Zenil, Jesper Tegnér, Felipe S Abrahão, Alexander Lavin, et al.* arXiv 2023
-
-## Core Technologies
-
-### Multi-Agent Systems & Architectures
- **[CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://proceedings.neurips.cc/paper_files/paper/2023/hash/9a86e0c5-e09e-4ad7-96d6-b2ed61855e37-Abstract-Conference.html)**  
-  *Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem.* NeurIPS 2023
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- **[Dynamic LLM-Agent Network: An LLM-Agent Collaboration Framework with Agent Team Optimization](https://arxiv.org/abs/2310.02170)**  
-  *Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, Diyi Yang.* arXiv 2023
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- **[AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155)**  
-  *Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, et al.* arXiv 2023
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-### Reasoning & Knowledge Systems
- **[Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://ojs.aaai.org/index.php/AAAI/article/view/28877)**  
-  *Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, et al.* AAAI 2024
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- **[KnowAgent: Knowledge-augmented Planning for LLM-based Agents](https://arxiv.org/abs/2403.03101)**  
-  *Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, et al.* arXiv 2024
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- **[Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://arxiv.org/abs/2305.14325)**  
-  *Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch.* arXiv 2023
-
-## Scientific Process Automation
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-### Research Planning & Literature Review
- **[ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models](https://arxiv.org/abs/2404.07738)**  
-  *Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, Sung Ju Hwang.* arXiv 2024
-
- **[SciMon: Scientific Inspiration Machines Optimized for Novelty](https://arxiv.org/abs/2305.14259)**  
-  *Qingyun Wang, Doug Downey, Heng Ji, Tom Hope.* arXiv 2023
-
- **[AutoSurvey: Large Language Models Can Automatically Write Surveys](https://arxiv.org/abs/2406.10252)**  
-  *Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, et al.* arXiv 2024
-
-### Experimental Design & Workflow
- **[DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents](https://arxiv.org/abs/2406.06769)**  
-  *Peter Jansen, Marc-Alexandre Côté, Tushar Khot, Erin Bransom, et al.* arXiv 2024
-
- **[Genesis: Towards the Automation of Systems Biology Research](https://arxiv.org/abs/2408.10689)**
-  *Ievgeniia A Tiukova, Daniel Brunnsåker, Erik Y Bjurström, Alexander H Gower, et al.* arXiv 2024
-
- **[AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing](https://arxiv.org/abs/2602.17607)** ([code](https://github.com/Daviddjddu/Autonumerics))
-  *Jianda Du, Youran Sun, Haizhao Yang.* arXiv 2026
-
-## Domain Applications
-
-### Healthcare & Medicine
-
-#### Clinical Decision Support & Diagnosis
- **[MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://arxiv.org/abs/2411.00248)**  
-  *Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, et al.* NeurIPS 2024
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- **[Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis](https://arxiv.org/abs/2401.16107)**  
-  *Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, et al.* arXiv 2024
-
- **[MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration](https://arxiv.org/abs/2410.12532)**  
-  *Jinjie Wei, Dingkang Yang, Yanshu Li, Qingyao Xu, et al.* arXiv 2024
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- **[Large Language Models as Agents in the Clinic](https://arxiv.org/abs/2309.10895)**  
-  *Nikita Mehandru, Brenda Y. Miao, Eduardo Rodriguez Almaraz, et al.* NPJ Digital Medicine 2024
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- **[MAGDA: Multi-Agent Guideline-Driven Diagnostic Assistance](https://link.springer.com/chapter/10.1007/978-3-031-49673-3_15)**  
-  *David Bani-Harouni, Nassir Navab, Matthias Keicher.* FMGMAI 2024
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-#### Healthcare Systems & Management
- **[ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration](https://arxiv.org/abs/2410.02551)**  
-  *Zixiang Wang, Yinghao Zhu, Huiya Zhao, Xiaochen Zheng, et al.* arXiv 2024
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- **[Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://arxiv.org/abs/2405.02957)**  
-  *Junkai Li, Siyu Wang, Meng Zhang, Weitao Li, et al.* arXiv 2024
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- **[ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World](https://arxiv.org/abs/2406.13890)**  
-  *Weixiang Yan, Haitian Liu, Tengxiao Wu, Qian Chen, et al.* arXiv 2024
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- **[AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow](https://arxiv.org/abs/2409.18924)**  
-  *Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, et al.* arXiv 2024
-
-#### Medical Education & Training
- **[Medco: Medical education copilots based on a multi-agent framework](https://arxiv.org/abs/2408.12496)**  
-  *Hao Wei, Jianing Qiu, Haibao Yu, Wu Yuan.* arXiv 2024
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- **[AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://arxiv.org/abs/2405.07960)**  
-  *Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, et al.* arXiv 2024
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-#### Medical Imaging & Pathology
- **[CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting](https://arxiv.org/abs/2407.08811)**  
-  *Naman Sharma.* arXiv 2024
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- **[PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration](https://arxiv.org/abs/2407.00203)**  
-  *Yuxuan Sun, Yunlong Zhang, Yixuan Si, Chenglu Zhu, et al.* arXiv 2024
-
-#### Medical Research
- **[OpenLens AI: Fully Autonomous Research Agent for Health Infomatics](https://arxiv.org/abs/2509.14778)**  
-  *Yuxiao Cheng, Jinli Suo* arXiv 2025, [GitHub Repo](https://github.com/jarrycyx/openlens-ai)
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-### Biology & Life Sciences
-
-#### Genomics & Molecular Biology
- **[BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments](https://arxiv.org/abs/2405.17631)**  
-  *Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora, et al.* arXiv 2024
-
- **[GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases](https://arxiv.org/abs/2405.16205)**  
-  *Zhizheng Wang, Qiao Jin, Chih-Hsuan Wei, Shubo Tian, et al.* arXiv 2024
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- **[Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation](https://arxiv.org/abs/2407.08940)**  
-  *Biqing Qi, Kaiyan Zhang, Kai Tian, Haoxiang Li, et al.* arXiv 2024
-
-#### Bioinformatics Tools & Platforms
- **[BIA: BioInformatics Agent - Unleashing the Power of Large Language Models to Reshape Bioinformatics Workflow](https://www.biorxiv.org/content/10.1101/2024.05.22.595240v1)**  
-  *Qi Xin, Quyu Kong, Hongyi Ji, Yue Shen, et al.* bioRxiv 2024
-
- **[CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis](https://www.biorxiv.org/content/10.1101/2024.05.13.593861v1)**  
-  *Yihang Xiao, Jinyi Liu, Yan Zheng, Xiaohan Xie, et al.* bioRxiv 2024
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- **[SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing](https://arxiv.org/abs/2407.03381)**
-  *Devam Mondal, Atharva Inamdar.* arXiv 2024
-
- **[ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics](https://doi.org/10.64898/2026.02.26.708361)**
-  *Chen Yang, Xianyang Zhang, Jun Chen.* bioRxiv 2026
-  An MCP server that enables spatial transcriptomics analysis via natural language, integrating 60+ methods across Python and R into a single conversational workflow. [Code](https://github.com/cafferychen777/ChatSpatial)
-
-### Chemistry & Materials Science
-
-#### Drug Discovery & Development
- **[DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning](https://arxiv.org/abs/2408.13378)**  
-  *Yoshitaka Inoue, Tianci Song, Tianfan Fu.* arXiv 2024
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- **[Malade: Orchestration of LLM-powered agents with retrieval augmented generation for pharmacovigilance](https://arxiv.org/abs/2408.01869)**  
-  *Jihye Choi, Nils Palumbo, Prasad Chalasani, Matthew M Engelhard, et al.* arXiv 2024
-
-#### Molecular Modeling & Computation
- **[ChatMol Copilot: An Agent for Molecular Modeling and Computation Powered by LLMs](https://aclanthology.org/2024.lm-1.6/)**  
-  *Jinyuan Sun, Auston Li, Yifan Deng, Jiabo Li.* L+M Workshop 2024
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- **[A review of large language models and autonomous agents in chemistry](https://arxiv.org/abs/2407.01603)**  
-  *Mayk Caldas Ramos, Christopher J Collison, Andrew D White.* arXiv 2024
-
-### Earth & Environmental Sciences
- **[An LLM Agent for Automatic Geospatial Data Analysis](https://arxiv.org/abs/2410.18792)**  
-  *Yuxing Chen, Weijie Wang, Sylvain Lobry, Camille Kurtz.* arXiv 2024
-
-## Evaluation & Benchmarking
-
-### General Benchmarks
- **[ClawBench: A Comprehensive Benchmark for Evaluating AI Web Agents](https://arxiv.org/abs/2604.08523)**  
-  *Reacher et al.* arXiv 2026. An open benchmark for browser agents on everyday tasks across live websites, with 153 V1 and 130 V2 tasks and reproducible execution traces ([code](https://github.com/reacher-z/ClawBench), [project](https://claw-bench.com/)).
-
- **[AgentBench: Evaluating LLMs as Agents](https://arxiv.org/abs/2308.03688)**  
-  *Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, et al.* arXiv 2023
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- **[ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://arxiv.org/abs/2308.07201)**  
-  *Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, et al.* arXiv 2023
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- **[Benchmarking large language models as ai research agents](https://arxiv.org/abs/2311.12741)**  
-  *Qian Huang, Jian Vora, Percy Liang, Jure Leskovec.* NeurIPS 2023 Workshop
-
-### Domain-Specific Benchmarks
- **[BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science](https://arxiv.org/abs/2407.00466)**  
-  *Xinna Lin, Siqi Ma, Junjie Shan, Xiaojing Zhang, et al.* arXiv 2024
-
- **[GenoTEX: A Benchmark for Evaluating LLM-Based Exploration of Gene Expression Data](https://arxiv.org/abs/2406.15341)**  
-  *Haoyang Liu, Haohan Wang.* arXiv 2024
-
- **[IdeaBench: Benchmarking Large Language Models for Research Idea Generation](https://arxiv.org/abs/2411.02429)**  
-  *Sikun Guo, Amir Hassan Shariatmadari, Guangzhi Xiong, Albert Huang, et al.* arXiv 2024
-
-## Surveys & Reviews
-
-### Comprehensive Surveys
- **[Scientific discovery in the age of artificial intelligence](https://www.nature.com/articles/s41586-023-06221-2)**  
-  *Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, et al.* Nature 2023
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- **[The rise and potential of large language model based agents: A survey](https://arxiv.org/abs/2309.07864)**  
-  *Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, et al.* arXiv 2023
-
- **[Large language model based multi-agents: A survey of progress and challenges](https://arxiv.org/abs/2402.01680)**  
-  *Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, et al.* arXiv 2024
-
- **[A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges](https://link.springer.com/article/10.1007/s44223-024-00009-0)**  
-  *Xinyi Li, Sai Wang, Siqi Zeng, Yu Wu, Yi Yang.* Vicinagearth 2024
-
-### Domain-Specific Reviews
- **[AI for Biomedicine in the Era of Large Language Models](https://arxiv.org/abs/2403.15673)**  
-  *Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol, Sindhura Kommu, et al.* arXiv 2024
-
- **[A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://arxiv.org/abs/2406.03712)**  
-  *Lei Liu, Xiaoyan Yang, Junchi Lei, Xiaoyang Liu, et al.* arXiv 2024
-
- **[From LLMs to LLM-based Agents for Software Engineering: A Survey](https://arxiv.org/abs/2408.02479)**  
-  *Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, et al.* arXiv 2024
-
-## AI Agent Frameworks & Tools
-
- **[Bride](https://tools.gracestack.se/bride-live.html)** — Cognitive AI agent with Active Inference, HDC, and anomaly detection for hypothesis generation. [Rust, MIT] `2026`
-
-## Contributing
-
-Please feel free to send a pull request if you want to:
- Add new papers
- Fix errors
- Update paper information
-
-## License
-
-[![CC0](https://licensebuttons.net/p/zero/1.0/88x31.png)](https://creativecommons.org/publicdomain/zero/1.0/)
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