[upstream-sync] README.md from ai-boost/awesome-ai-for-science@03ff6200 [catalogue]

Reviewed-on: #37

upstream/ai-boost-awesome-ai-for-science/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/e0df96d7/README.md
-upstream_sha: e0df96d7
-imported_at: 2026-07-06
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/03ff6200/README.md
+upstream_sha: 03ff6200
+imported_at: 2026-07-10
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
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 - [ScholarAIO](https://github.com/ZimoLiao/scholaraio) - Agent-agnostic research infrastructure providing AI agents with a structured scientific workspace for deep PDF parsing, hybrid semantic/keyword literature search, citation-graph analysis, topic discovery, and academic writing workflows; natively integrates with Claude Code, Codex, Cursor, Cline, and AgentSkills.io (530+ stars, MIT License, 2026)
 - [BioMCP](https://github.com/genomoncology/biomcp) - Biomedical Model Context Protocol (MCP) server unifying literature search across PubMed/Europe PMC, entity pivoting across genes/variants/drugs/diseases/pathways/proteins, local study analytics, and Claude Code/Codex integration for agentic biomedical research (531+ stars, MIT License, 2025-2026)
 - [MATLAB Agentic Toolkit](https://github.com/matlab/matlab-agentic-toolkit) - Official MathWorks toolkit connecting AI agents to MATLAB via the MATLAB MCP Server and curated skills, enabling trusted engineering and scientific computing workflows with idiomatic code generation, testing, and error diagnosis in Claude Code, GitHub Copilot, OpenAI Codex, and Gemini CLI (686+ stars, BSD-3-Clause, 2026)
+- [BioNeMo Agent Toolkit (NVIDIA)](https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit) - Turn any AI agent into a life science expert with NVIDIA BioNeMo skills, enabling agentic workflows for drug discovery, protein engineering, and biomolecular design (329+ stars, Apache 2.0 / CC-BY-4.0, 2026)
 - [open-science](https://github.com/ai4s-research/open-science) - Local-first, open-source AI workbench for scientists — an open alternative to Claude Science (by ai4s-research, maintainers of this list; TypeScript, MIT, 2026)
+- [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)
 
 ### 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)
@@ -490,6 +493,7 @@ validated: false
 - [Proteina-Complexa](https://github.com/NVIDIA-Digital-Bio/Proteina-Complexa) - Flow-based generative model for atomistic protein binder design with test-time optimization, SOTA on binder benchmarks (ICLR 2026 Oral, NVIDIA)
 - [PXDesign (ByteDance, 2025)](https://github.com/bytedance/PXDesign) - Fast, modular, and accurate de novo design of protein binders based on the Protenix foundation model, achieving 17-82% nanomolar hit rates across diverse targets with 2-6× improvement over prior methods like AlphaProteo and RFdiffusion (229+ stars, Apache 2.0)
 - [ODesign (OTeam-AI4S, 2025)](https://github.com/OTeam-AI4S/ODesign) - All-atom generative world model for all-to-all biomolecular interaction design, enabling cross-modality generation of proteins, nucleic acids, small molecules, and cyclic peptides with fine-grained epitope-level control and 2-4 orders of magnitude faster design throughput than modality-specific baselines (316+ stars, Apache 2.0)
+- [OpenDDE (Aureka Research, 2026)](https://github.com/aurekaresearch/OpenDDE) - Open-source, all-atom biomolecular foundation model that turns co-folding into a scalable engine for structure prediction, design, and optimization across proteins, nucleic acids, and small molecules in drug discovery; ranked first on PXMeter-AB, FoldBench-AB, and 2026ARK-AB antibody-antigen benchmarks (263+ stars, Apache 2.0)
 - [La-Proteina (NVIDIA)](https://github.com/NVIDIA-Digital-Bio/la-proteina) - Partially latent flow matching model for the joint generation of a protein's amino acid sequence and full atomistic structure, including both backbone and side chains (2025)
 - [Proteina (NVIDIA, ICLR 2025 Oral)](https://github.com/NVIDIA-Digital-Bio/proteina) - Large-scale flow-based protein backbone generator utilizing hierarchical fold class labels for conditioning with a tailored scalable transformer architecture, enabling controllable de novo protein design (264+ stars)
 - [xfold](https://github.com/Shenggan/xfold) - Democratizing AlphaFold3: PyTorch reimplementation to accelerate protein structure prediction research
@@ -530,6 +534,7 @@ validated: false
 - [DeepMol](https://github.com/BioSystemsUM/DeepMol) - Unified ML/DL framework for drug discovery workflows, integrating RDKit, DeepChem, and scikit-learn with SHAP explainability
 - [Chemprop](https://github.com/chemprop/chemprop) - Message passing neural networks for molecule property prediction, ADMET modeling, and reaction prediction, achieving SOTA on MoleculeNet and widely used in pharmaceutical drug discovery (MIT, 2.3K+ stars)
 - [RDKit](https://github.com/rdkit/rdkit) - Cheminformatics toolkit
+- [nvMolKit (NVIDIA BioNeMo, 2025)](https://github.com/NVIDIA-BioNeMo/nvMolKit) - High-performance, GPU-accelerated library for key computational chemistry tasks including molecular similarity, conformer generation, and geometry relaxation, designed to accelerate drug-discovery and molecular-modeling workflows (264+ stars, Apache 2.0)
 - [Open Targets](https://www.opentargets.org/) - Open-source data integration platform for systematic drug target identification and prioritization, combining genetics, genomics, chemistry, and pharmacology data from EMBL-EBI, Wellcome Sanger Institute, and pharmaceutical partners to accelerate therapeutic discovery
 - [ESM3](https://github.com/evolutionaryscale/esm) - 98B-parameter frontier generative model jointly reasoning over protein sequence, structure, and function, trained on 2.78 billion proteins; generated a novel fluorescent protein (esmGFP) with only 58% sequence identity to known GFPs (EvolutionaryScale, 2024)
 - [ProtTrans](https://github.com/agemagician/ProtTrans) - State-of-the-art pretrained language models for proteins trained on thousands of GPUs and Google TPUs using Transformer architectures, enabling protein property prediction, feature extraction, and transfer learning across diverse downstream tasks (1.3K+ stars, MIT, 2020-2026)
@@ -592,6 +597,7 @@ validated: false
 - [AlphaMissense](https://github.com/google-deepmind/alphamissense) - Google DeepMind's AlphaFold-derived classifier for proteome-wide missense variant effect prediction, providing pathogenicity scores for all ~71M possible human missense variants and classifying 89% with 90% precision; pre-computed predictions are integrated into Ensembl VEP and UCSC Genome Browser to support clinical variant interpretation (Science 2023)
 - [AlphaGenome](https://github.com/google-deepmind/alphagenome) - Google DeepMind's unified DNA sequence foundation model predicting molecular consequences of genetic variants from single-base resolution up to 1 megabase context, jointly outputting thousands of regulatory tracks (RNA expression, splicing, chromatin accessibility, TF binding, contact maps) for human and mouse genomes via a Python client and non-commercial API (2025)
 - [GPN-Star (Song Lab, UC Berkeley, bioRxiv 2025)](https://github.com/songlab-cal/gpn) - Phylogeny-aware genomic language model trained on whole-genome alignments across multiple evolutionary timescales, predicting functional constraints and variant effects for human, mouse, chicken, fly, worm, and Arabidopsis genomes (344+ stars, MIT License)
+- [GENERanno (bioRxiv 2025)](https://github.com/GenerTeam/GENERanno) - Genomic foundation model for metagenomic and genome annotation, featuring an 8k base-pair context and 500M parameters trained on 386B base pairs of eukaryotic DNA; provides expert models and a unified CLI for prokaryotic/eukaryotic coding-sequence annotation with strong performance on Genomic Benchmarks, Nucleotide Transformer tasks, and custom Gener tasks (GenerTeam, 314+ stars, MIT License)
 - [DeepVariant](https://github.com/google/deepvariant) - Google DeepMind's deep learning analysis pipeline for calling genetic variants (SNPs and indels) from next-generation DNA sequencing data, achieving human expert-level accuracy and widely adopted in clinical genomics, population genetics, and precision medicine; pre-trained models available for multiple sequencing platforms and organismal genomes (Nature Biotechnology 2018, 3.7K+ stars)
 - [Casanovo](https://github.com/Noble-Lab/casanovo) - Transformer encoder-decoder for de novo peptide sequencing from tandem mass spectrometry, translating MS/MS spectra directly to peptide sequences without reference databases, enabling identification of novel peptides for immunopeptidomics, antibody repertoires, and metaproteomes (Noble Lab UW, Nature Communications 2024)
 
@@ -617,6 +623,7 @@ validated: false
 - [PLIP (Nature Medicine 2023)](https://github.com/PathologyFoundation/plip) - First vision-and-language foundation model for pathology AI, fine-tuned from CLIP on 249K image-caption pairs, enabling open-ended visual-semantic search and zero-shot diagnosis across histopathology (Pathology Foundation, 376+ stars)
 - [TITAN (Nature Medicine 2024)](https://github.com/mahmoodlab/TITAN) - Multimodal whole-slide pathology foundation model jointly pretrained on H&E histology and diagnostic text reports, enabling zero-shot cancer subtyping, biomarker prediction, and multimodal reasoning across diverse cancer types (Mahmood Lab, 341+ stars)
 - [Virchow (Nature Medicine 2024)](https://huggingface.co/paige-ai/Virchow) - Self-supervised pathology foundation model (ViT-Huge, 632M parameters) pretrained via DINOv2 on 1.5M whole-slide images from Memorial Sloan Kettering across 17 cancer types, with Virchow2 follow-up scaling to 3.1M slides and mixed magnifications, achieving SOTA on biomarker prediction, mutation classification, and rare cancer detection (Paige AI & MSK)
+- [H-Optimus (Bioptimus, Nature Medicine 2025)](https://huggingface.co/bioptimus/H-optimus-0) - Open-weights pathology foundation model family (H-Optimus-0: 1.1B-parameter ViT pretrained via DINOv2 on 500M+ diagnostic image tiles; H-Optimus-1 follow-up) for whole-slide image analysis, achieving strong zero-shot and fine-tuned transfer across biomarker prediction, cancer subtyping, and mutation classification (Bioptimus, Apache 2.0)
 - [TRIDENT (2025)](https://github.com/mahmoodlab/TRIDENT) - Toolkit for large-scale whole-slide image processing supporting 22+ patch encoders (UNI, CONCH, Virchow, H-Optimus-0, etc.), slide encoders (TITAN, GigaPath, PRISM, CHIEF, Madeleine, Feather), tissue segmentation, and multi-GPU inference with end-to-end pipeline and smart resume for standardized deployment of computational pathology foundation models (Mahmood Lab, Harvard Medical School, 553+ stars)
 - [Feather (Mahmood Lab, ICML 2025 Spotlight)](https://github.com/mahmoodlab/MIL-Lab) - Lightweight supervised slide foundation model with 0.9M parameters pretrained on 24K whole-slide images for pan-cancer morphological classification, achieving competitive performance with much larger self-supervised models (TITAN, GigaPath) while enabling finetuning on consumer-grade GPUs; includes standardized MIL implementations and benchmarking across 15+ classification tasks (Mahmood Lab, Harvard Medical School, 153+ stars)
 - [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)
@@ -847,6 +854,7 @@ validated: false
 - [DiffEqFlux.jl](https://github.com/SciML/DiffEqFlux.jl) - Neural ordinary differential equations with O(1) backprop and GPU support (900+ stars)
 - [Optimization.jl](https://github.com/SciML/Optimization.jl) - Unified interface for local, global, gradient-based and derivative-free optimization (800+ stars)
 - [PaddleScience](https://github.com/PaddlePaddle/PaddleScience) - SDK & library for AI-driven scientific computing applications
+- [Tesseract Core (Pasteur Labs, SciPy 2025 / JOSS)](https://github.com/pasteurlabs/tesseract-core) - Universal components for differentiable scientific computing, packaging heterogeneous scientific tools into self-contained, portable, gradient-propagating components with auto-generated schemas, CLI/REST API/Python SDK interfaces, and reproducible deployment across local, cloud, and HPC environments (105+ stars, Apache 2.0)
 - [Flux.jl](https://github.com/FluxML/Flux.jl) - Machine learning in Julia
 
 ### Specialized Frameworks