Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#94 ) from upstream-sync/awesome-ai-for-science-20260904-e18934-poor into main

Reviewed-on: #94
[upstream-sync] README.md from ai-boost/awesome-ai-for-science@e1893484 [catalogue]
2026-09-04 15:26:37 +00:00 · 2026-09-04 11:32:47 +00:00 · 2026-08-21 14:37:05 +00:00 · 2026-08-21 10:38:52 +00:00 · 2026-08-09 18:32:11 +00:00 · 2026-08-09 17:02:16 +00:00
26 changed files with 4215 additions and 388 deletions
@@ -2,9 +2,9 @@
 title: "Awesome Genomic Skills [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/GoekeLab/awesome-genomic-skills/blob/f88d9494/README.md
-upstream_sha: f88d9494
-imported_at: 2026-06-26
+upstream_source: https://github.com/GoekeLab/awesome-genomic-skills/blob/8cf42e1d/README.md
+upstream_sha: 8cf42e1d
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -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:** 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.
+  - **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).
  - **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.
@@ -83,6 +83,9 @@ 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/230e8724/README.md
-upstream_sha: 230e8724
-imported_at: 2026-07-10
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/e1893484/README.md
+upstream_sha: e1893484
+imported_at: 2026-09-04
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -63,6 +63,7 @@ validated: false
  - [🌍 Earth & Climate Science](#-earth--climate-science)
  - [🌾 Agriculture & Ecology](#-agriculture--ecology)
  - [🧠 Social Sciences](#-social-sciences)
+  - [🏗 Engineering & Built Environment](#-engineering--built-environment)
 - [🤖 Foundation Models for Science](#-foundation-models-for-science)
 - [📈 Datasets & Benchmarks](#-datasets--benchmarks)
 - [💻 Computing Frameworks](#-computing-frameworks)
@@ -144,6 +145,7 @@ validated: false
 - [Chat2Plot](https://github.com/nyanp/chat2plot) - Secure text-to-visualization through standardized chart specifications
 - [AutoViz](https://github.com/AutoViML/AutoViz) - Automated data visualization with minimal code
 - [PlotlyAI](https://plotly.com/ai/) - AI-powered data visualization and dashboard creation
+- [Flint (Microsoft)](https://github.com/microsoft/flint-chart) - Visualization intermediate language that lets AI agents create expressive, polished charts from simple, human-editable specs, compiling the same input to 30+ chart types across Vega-Lite, ECharts, and Chart.js with an MCP server for agent integration (1.9K+ stars, MIT License, 2026)

 ---

@@ -205,6 +207,7 @@ validated: false
 - [Jupyter AI (JupyterLab Extension)](https://github.com/jupyterlab/jupyter-ai) - Official Jupyter extension with `%%ai` magic commands and sidebar chat assistant, connecting multiple model providers and local inference
 - [Notebook Intelligence (NBI)](https://github.com/notebook-intelligence/notebook-intelligence) - AI coding assistant for JupyterLab with agent mode, supporting arbitrary LLM providers (2025+)
 - [Google Colab AI Features](https://colab.research.google.com/) - Integrated AI assistance for data science and research notebooks
+- [OpenAI4S](https://github.com/PKU-YuanGroup/OpenAI4S) - Open-source hybrid scientific research agent and workbench replicating Claude Science, combining JSON tool orchestration with persistent Python/R Code-as-Action kernels, 604 bundled science skills, MCP connectors, sandboxed local execution, and multi-provider LLM support for end-to-end scientific workflows (PKU–YuanKong Intelligence, 377+ stars, MIT License, 2026)
 - [OpenBioMed](https://github.com/PharMolix/OpenBioMed) - Open-source biomedical AI platform integrating multimodal foundation models (BioMedGPT, PharmolixFM, LangCell) with agentic workflows and 45+ Claude Code skills for drug discovery, protein engineering, and single-cell omics analysis (PharMolix & Tsinghua AIR, 1K+ stars, 2023-2026)
 - [AutoR](https://github.com/AutoX-AI-Labs/AutoR) - Human-centered research OS with terminal-first harness and local browser Studio, turning research work into reproducible artifact-backed runs through a 9-stage workflow with human approval gates, resume/rollback controls, and venue-aware manuscript packaging (1K+ stars, 2026)
 - [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)
@@ -214,6 +217,15 @@ validated: false
 - [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)
+- [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)
@@ -226,6 +238,7 @@ validated: false
 - [Obsidian Smart Connections](https://github.com/brianpetro/obsidian-smart-connections) - AI-powered note linking and research graph navigation
 - [Research Rabbit](https://www.researchrabbit.ai/) - AI-powered literature discovery and research network mapping
 - [SciWrite](https://github.com/labarba/sciwrite) - Agent skill for AI-assisted scientific manuscript writing review distilled from Stanford's *Writing in the Sciences* course, performing five sequential editorial audit passes on clarity, voice, structure, consistency, and integrity (2026)
+- [PaperSpine](https://github.com/WUBING2023/PaperSpine) - Motivation-driven academic writing system for Claude Code, Codex, OpenClaw, and Hermes CLI that learns from strong papers, builds evidence-aware central-argument blueprints, and rewrites manuscripts with revision matrices and LaTeX-safe audits (4.9K+ stars, MIT License, 2026)
 - [Claude Prism](https://github.com/delibae/claude-prism) - Offline-first scientific writing workspace powered by Claude, integrating LaTeX, Python, and 100+ scientific skills with local execution, Zotero integration, and privacy-focused design (2026)

 ---
@@ -237,6 +250,7 @@ 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
@@ -249,11 +263,15 @@ validated: false
 - [FunSearch (DeepMind, Nature 2023)](https://github.com/google-deepmind/funsearch) - First system to make novel, verifiable scientific discoveries by pairing LLMs with evolutionary search, solving open problems in combinatorics (cap set problem) and discovering faster matrix multiplication algorithms
 - [OpenEvolve](https://github.com/algorithmicsuperintelligence/openevolve) - Open-source implementation of AlphaEvolve's evolutionary coding agent paradigm, enabling LLMs to autonomously discover and optimize algorithms through iterative evolution, matching the approach behind DeepMind's breakthrough matrix multiplication discovery (6.2K+ stars, 2025)
 - [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
 - [The AI Scientist v2 (2025)](https://arxiv.org/abs/2504.08066) - Enhanced with Agentic Tree Search, reduced template dependency, first workshop-level accepted paper
+- [FAROS (OpenNSWM-Lab)](https://github.com/OpenNSWM-Lab/FAROS) - Foundation AutoResearch Operating System: blueprint-driven runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review (OpenNSWM-Lab, 2.4K+ stars, 2026)
 - [DeepScientist](https://github.com/ResearAI/DeepScientist) - First system progressively surpassing human SOTA on frontier AI tasks (183.7%, 1.9%, 7.9% improvements), month-long autonomous discovery with 20,000+ GPU hours
 - [ASI-Arch (GAIR-NLP, arXiv 2025)](https://github.com/GAIR-NLP/ASI-Arch) - Autonomous multi-agent research loop for model architecture discovery that ran 1,773 experiments over 20,000 GPU hours and produced 106 state-of-the-art linear-attention architectures, surpassing human-designed baselines including Mamba2 and DeltaNet (1.1K+ stars, Apache 2.0)
 - [Kosmos](https://github.com/jimmc414/Kosmos) - Extended autonomy AI scientist with 200 parallel agent rollouts, 42K lines of code execution, 1.5K papers analyzed per run, achieving 79.4% accuracy and 7 scientific discoveries (Edison Scientific)
@@ -287,11 +305,17 @@ validated: false
 - [Science-Star (USTC AI4Science, 2025)](https://github.com/ustc-ai4science/Science-Star) - Open-source platform for building, extending, and experimenting with scientific agents, providing modular agent construction tools and standardized evaluation pipelines for accelerating autonomous scientific discovery research (748+ stars, MIT License)
 - [SR-Scientist (ICLR 2026)](https://github.com/GAIR-NLP/SR-Scientist) - Scientific equation discovery with agentic AI, elevating LLMs from equation proposers to autonomous scientists that write code, analyze data, implement equations, and optimize based on experimental feedback; outperforms baselines by 6-35% across four science disciplines with robustness to noise and out-of-domain generalization (GAIR-NLP / SJTU, 49+ stars, Apache 2.0)
 - [ARA (Agent-Native Research Artifact)](https://github.com/ARA-Labs/Agent-Native-Research-Artifact) - Research ecosystem for rigorous and trustworthy AI scientists — a protocol and skill bundle that makes autonomous research verifiable, crystallized, and observable through structured, machine-executable research artifacts and five agent skills for research management, compilation, verification, visualization, and publication (ARA-Labs, 447+ stars, MIT License, 2026)
+- [XScientist](https://github.com/smileformylove/XScientist) - Local-first autonomous research system implementing a Git-like research protocol for long-running scientific discovery; explores competing explanations, executes experiments inside an isolation boundary, self-criticizes results, and exports the entire path as typed Agent-Native Research Artifacts (ARA) with exploration DAGs, claim-to-evidence anchors, content hashes, and re-execution hooks (126+ stars, Apache 2.0, arXiv 2026)
+- [Scholar Loop](https://github.com/renee-jia/scholar-loop) - Autonomous multi-agent AI scientist that mirrors a PhD workflow: literature review → grounded hypothesis → real ML experiments → self-critique → write-up; features a deterministic harness with frozen-metric scoring, edit allowlists, and a verified registry to make reward-hacking and hallucination impossible, plus 108 unit tests runnable without API keys or GPUs (461+ stars, MIT License, 2026)
+- [ResearchStudio (Microsoft)](https://github.com/microsoft/ResearchStudio) - AI co-author covering the entire research lifecycle — from an under-specified research direction to a published paper; includes ResearchStudio-Idea for evidence-grounded research ideation and ResearchStudio-Reel for turning finished papers into posters, narrated videos, blogs, and interactive reels; runs as skills on Claude Code and Codex (1.2K+ stars, MIT License, 2026)
+- [Principia](https://github.com/pzqpzq/Principia) - Principle-first scientific idea discovery framework that extracts reusable principles from public literature and private research materials, composes them into traceable Idea Cards with prior-art comparisons, and exports validation-ready research packs; emphasizes inspectable scientific objects, risk disclosure, and falsification paths (ICML 2026, 411+ stars, MIT License)
+- [Imbue Catalyst](https://github.com/imbue-ai/catalyst) - Semi-autonomous AI scientist for scientific theory discovery and verifiable goal solving, using adversarial review-refinement loops and evolution-inspired candidate populations; integrates with Claude Code, Gemini CLI, Antigravity, and Codex harnesses (Imbue, 31+ stars, AGPL-3.0, 2026)

 ### Evaluation & Benchmarking
 - [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
@@ -300,6 +324,7 @@ validated: false
 - [SciBench](https://arxiv.org/abs/2307.10635) - College-level scientific problem-solving evaluation across multiple domains
 - [NewtonBench (ICLR 2026)](https://github.com/HKUST-KnowComp/NewtonBench) - First benchmark evaluating LLMs' ability to rediscover scientific laws through interactive experimentation across 324 tasks in 12 physics domains, featuring memorization-resistant metaphysical shifts of canonical laws (HKUST)
 - [ResearchClawBench (InternScience, arXiv 2026)](https://github.com/InternScience/ResearchClawBench) - Benchmark evaluating AI agents for end-to-end automated research from re-discovery to new-discovery, with 40 real-science tasks across 10 disciplines, curated datasets from published papers, and expert-curated multimodal rubrics (170+ stars, MIT License)
+- [Terminal-Bench Science (Harbor Framework, 2026)](https://github.com/harbor-framework/terminal-bench-science) - Benchmark evaluating AI agents on complex real-world scientific workflows in terminal environments across life, physical, earth, and mathematical sciences; featured on model cards for Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro (200+ stars, Apache 2.0)

 ### Academic Review & Evaluation
 - [AgentReview](https://agentreview.github.io/) - LLM agents simulating academic peer review ecosystems
@@ -307,13 +332,18 @@ 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)
+- [Ten Proofs (OpenAI, 2026)](https://github.com/openai/ten-proofs) - Lean 4 formalizations of ten major advances in mathematics and theoretical computer science, including improved sphere-packing bounds, non-sofic groups, a counterexample to Connes's rigidity conjecture, and quantum parallel repetition; released with the OpenAI paper and reasoning walkthroughs (57+ 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)
+- [TorchLean (lean-dojo, 2026)](https://github.com/lean-dojo/TorchLean) - First unified Lean 4 framework for neural-network specification, execution, and verification; tensor shapes are part of the types, models are executable Lean programs, and the same definitions can be used by training code, graph transformations, certificate checkers, and proofs with CPU/CUDA backends (123+ stars, MIT License)
 - [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)
+- [AI CFD Scientist (RPI CSML, arXiv 2026)](https://github.com/csml-rpi/AI-CFD-Scientist) - Open-ended AI scientist for computational fluid dynamics that spans literature-grounded ideation, OpenFOAM execution via Foam-Agent, vision-language physics verification of rendered flow fields, source-code modification for new physical models, and figure-grounded LaTeX manuscript writing within a single inspectable workflow (43+ stars, Python)
 - [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)
 - [BioDiscoveryAgent](https://github.com/snap-stanford/BioDiscoveryAgent) - AI agent for biological discovery and research automation
 - [Biomni](https://github.com/snap-stanford/Biomni) - General-purpose biomedical AI agent integrating LLM reasoning with retrieval-augmented planning and code-based execution to autonomously execute diverse biomedical research tasks and generate testable hypotheses (Stanford SNAP, bioRxiv 2025)
@@ -326,6 +356,7 @@ validated: false
 - [Coscientist](https://www.nature.com/articles/s41586-023-06792-1) - Autonomous chemical experiment planning and execution
 - [SciAgents](https://github.com/lamm-mit/SciAgentsDiscovery) - Bioinspired multi-agent intelligent graph reasoning system that autonomously traverses ontological knowledge graphs to generate, critique, and refine novel research hypotheses, demonstrated on bio-inspired materials discovery with cross-disciplinary connection mining (MIT Lamm Group, 2024)
 - [TxAgent](https://github.com/mims-harvard/TxAgent) - AI agent for therapeutic reasoning across a universe of tools, achieving 92.1% accuracy in drug reasoning and outperforming GPT-4o by 25.8% (Harvard MIMS, 2025)
+- [ATHENA-R1 (Harvard MIMS)](https://github.com/mims-harvard/ATHENA) - Reinforcement-learning-trained AI agent for treatment reasoning over a universe of 212 biomedical tools, performing multi-step evidence gathering and spawning parallel reasoning branches to reach evidence-grounded clinical decisions (55+ stars, MIT License, 2026)
 - [ClawBio](https://github.com/ClawBio/ClawBio) - First bioinformatics-native AI agent skill library enabling local-first, reproducible genomic and population-genetics research workflows built on OpenClaw (871+ stars, MIT License, 2026)

 ---
@@ -371,6 +402,7 @@ validated: false
 - [Fourier Neural Operator](https://github.com/neuraloperator/neuraloperator) - Learning operators in Fourier space
 - [Poseidon](https://github.com/camlab-ethz/poseidon) - Efficient foundation models for PDEs with pretrained transformer-based neural operators and downstream task fine-tuning pipelines, HuggingFace integration for models and datasets (ETH Zurich CAMLab, arXiv 2024)
 - [GAOT (NeurIPS 2025)](https://github.com/camlab-ethz/GAOT) - Geometry Aware Operator Transformer serving as an efficient and accurate neural surrogate for PDEs on arbitrary domains, combining geometric priors with transformer architectures for scientific computing (ETH Zurich CAMLab, 92+ stars)
+- [TensorMesh (ETH Zurich CAMLab, arXiv 2026)](https://github.com/camlab-ethz/TensorMesh) - Fast, differentiable, JIT-free finite element library for PyTorch enabling GPU-native PDE solving with native autograd, tensorized assembly, and sparse linear algebra; part of the TensorGalerkin framework (218+ stars, Apache 2.0)
 - [PhiFlow](https://github.com/tum-pbs/PhiFlow) - Differentiable PDE solving framework for machine learning with built-in fluid simulation, supporting PyTorch/JAX/TensorFlow backends and enabling neural network training within physical simulations (TUM, MIT License)
 - [exponax](https://github.com/Ceyron/exponax) - Efficient differentiable n-dimensional PDE solvers built on JAX and Equinox, shipping 46+ built-in equations with Fourier spectral methods, exponential time differencing, and full auto-differentiation for physics-based deep learning workflows (MIT, 200+ stars, 2024)

@@ -437,6 +469,7 @@ validated: false
 - [ChemCrow: Augmenting large-language models with chemistry tools](https://arxiv.org/abs/2304.05376) (2023.04) - LLM agents for chemistry research
 - [Autonomous chemical research with large language models](https://www.nature.com/articles/s41586-023-06792-0) - Automated chemical experimentation
 - [Coscientist: Autonomously planning and executing scientific experiments](https://www.nature.com/articles/s41586-023-06792-1) - Robotic lab automation
+- [The AutoResearch Moment: From Experimenter to Research Director](https://www.preprints.org/manuscript/202603.1329) (2026.03) - Position paper on claim governance for autonomous research: proposes a research-director bundle (objective sheet, discovery trace, verification ledger, provenance bundle) for evaluating agent-driven science

 ### Recent Advances & Domain Applications
 - [AlphaFold: Protein Structure Prediction](https://www.nature.com/articles/s41586-021-03819-2)
@@ -474,6 +507,7 @@ validated: false
 - [ModelAngelo](https://github.com/3dem/model-angelo) - Automatic atomic model building program for cryo-EM maps using deep learning, enabling rapid de novo protein structure determination from electron density with high accuracy (3DEM/EMBL, 169+ stars)
 - [AlphaFold](https://github.com/google-deepmind/alphafold) - Protein structure prediction
 - [AlphaFold3](https://github.com/google-deepmind/alphafold3) - AlphaFold 3 inference pipeline for unified biomolecular structure prediction of proteins, nucleic acids, small molecules, ions, and post-translational modifications (Google DeepMind, Nature 2024)
+- [AlphaFold Server](https://alphafoldserver.com/) - Free, easy-to-use web platform by Google DeepMind and Isomorphic Labs for running AlphaFold 3 predictions of biomolecular structures and interactions, enabling researchers without local infrastructure to model proteins, nucleic acids, small molecules, ions, and post-translational modifications through a searchable proteome interface (2024)
 - [AlphaProteo](https://github.com/google-deepmind/alphaproteo) - Deep learning system for de novo design of high-affinity protein binders, achieving strong binding across diverse target classes including challenging intracellular proteins with significantly higher success rates than traditional wet-lab screening methods (Google DeepMind, Nature 2024)
 - [AlphaPulldown](https://github.com/KosinskiLab/AlphaPulldown) - Automated pipeline for proteome-scale protein-protein interaction screening with AlphaFold-Multimer and AlphaFold 3, supporting flexible inputs (UniProt IDs, FASTA, residue regions, multimers, AF3 JSON features) and integrated downstream analysis for hit prioritization (Kosinski Lab, EMBL, Nature Protocols 2024, 317+ stars, GPL-3.0)
 - [RareFold](https://github.com/PatrickBryant1/RareFold) - Structure prediction and design of proteins with noncanonical amino acids, enabling AI-powered modeling of synthetic biology constructs and expanded genetic code systems (133+ stars, 2025)
@@ -599,8 +633,10 @@ validated: false
 - [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)
+- [GENERator (bioRxiv 2026)](https://github.com/GenerTeam/GENERator) - Long-context generative genomic foundation model using 6-mer tokenization for DNA sequence modeling and generation, with v2 model families for prokaryote and eukaryote genomes and pretrained weights available on HuggingFace (GenerTeam, 460+ stars, MIT License, 2025-2026)
 - [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)
+- [Dorado](https://github.com/nanoporetech/dorado) - Oxford Nanopore's official deep-learning basecaller for nanopore sequencing, converting raw electrical signals into DNA/RNA sequences with integrated modified-base (methylation) detection and efficient CPU/GPU inference; foundational tool for long-read genomics, epigenetics, and real-time sequencing analysis (nanoporetech, 846+ stars, actively maintained)

 #### Neuroscience & Behavioral Analysis
 - [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) - Markerless pose estimation of user-defined features with deep learning for all animals including humans, enabling quantitative behavioral analysis in neuroscience and ethology (Nature Neuroscience 2018, 5.6K+ stars)
@@ -611,10 +647,12 @@ 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)
 - [BrainIAC (Nature Neuroscience 2026)](https://github.com/AIM-KannLab/BrainIAC) - Self-supervised vision foundation model for generalized structural brain MRI analysis, pretrained on ~49,000 scans from diverse datasets and generalizing across brain age prediction, dementia/MCI classification, IDH mutation detection, glioma survival prediction, time-to-stroke estimation, MR sequence classification, and brain tumor segmentation; outperforms task-specific models especially with limited training data (Mass General Brigham & Harvard Medical School, 129+ stars)
+- [Zapbench (Google Research, 2025)](https://github.com/google-research/zapbench) - The Zebrafish Activity Prediction Benchmark for forecasting cellular-resolution neural activity throughout an entire vertebrate brain, combining light-sheet microscopy calcium-imaging data, forecasting tasks, and evaluation tools to advance whole-brain neural dynamics modeling (77+ stars, Apache 2.0)

 #### Computational Pathology & Digital Pathology
 - [UNI (Nature Medicine 2024)](https://github.com/mahmoodlab/UNI) - General-purpose pathology foundation model pretrained on 100K+ diagnostic whole-slide images across 20 major tissue types, achieving state-of-the-art transfer learning across 30+ clinical tasks and serving as a universal feature extractor for digital pathology (Mahmood Lab, 722+ stars)
@@ -630,6 +668,7 @@ 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)
@@ -640,12 +679,14 @@ validated: false
 - [micro-sam](https://github.com/computational-cell-analytics/micro-sam) - Segment Anything Model for microscopy: interactive and automatic segmentation of light, electron, and fluorescence microscopy images in 2D and 3D, with domain-specific fine-tuning workflows for scientific imaging (1.5K+ stars)
 - [MedSAM](https://github.com/bowang-lab/MedSAM) - Universal medical image segmentation foundation model trained on 1.57M image-mask pairs across 10 imaging modalities and 30+ cancer types (Nature Communications 2024)
 - [MedSAM2](https://github.com/bowang-lab/MedSAM2) - Segment Anything in 3D medical images and videos, extending SAM2 to volumetric and temporal medical imaging with state-of-the-art zero-shot segmentation performance across CT, MRI, and surgical video (arXiv 2025)
+- [Medical SAM3 (AIM Research Lab, arXiv 2026)](https://github.com/AIM-Research-Lab/Medical-SAM3) - Foundation model for universal prompt-driven medical image segmentation extending SAM3 to clinical imaging, supporting 2D public benchmarks and 3D training/evaluation with text and box prompts; pretrained weights available on HuggingFace (189+ stars)
 - [MedSegX](https://github.com/MedSegX/MedSegX-code) - Generalist foundation model and database for open-world medical image segmentation, enabling universal segmentation of diverse anatomical structures and pathologies with zero-shot generalization to unseen tasks and modalities (Nature Biomedical Engineering 2025)
 - [VoxTell (MIC-DKFZ, 2025)](https://github.com/MIC-DKFZ/VoxTell) - Free-text promptable universal 3D medical image segmentation foundation model enabling zero-shot segmentation of diverse anatomical structures and pathologies via natural language prompts across CT, MRI, and other volumetric imaging modalities (DKFZ, 195+ stars, Apache 2.0)
 - [BiomedParse](https://github.com/microsoft/BiomedParse) - Foundation model for joint segmentation, detection, and recognition of biomedical objects across nine imaging modalities, with v2 introducing BoltzFormer architecture for end-to-end 3D inference (Microsoft, Nature Methods 2025)
 - [UniBiomed (Nature Communications 2026)](https://github.com/Luffy03/UniBiomed) - Universal foundation model for grounded biomedical image interpretation, enabling comprehensive visual understanding, reasoning, and grounding across diverse biomedical imaging modalities with strong zero-shot generalization (55+ stars, Apache 2.0, 2025-2026)
 - [MIRA (NeurIPS 2025)](https://github.com/microsoft/MIRA) - Medical time series foundation model pretrained on 454B time points from heterogeneous clinical corpora spanning ICU physiological signals and hospital EHR, with continuous-time rotary positional encoding, frequency-specialized Mixture-of-Experts, and neural ODE extrapolation for zero-shot forecasting across irregular and multimodal temporal health data (Microsoft, 399+ stars, MIT License)
 - [HealthGPT (ICML 2025 Spotlight)](https://github.com/ZJU4HealthCare/HealthGPT) - Medical large vision-language model unifying comprehension and generation via heterogeneous knowledge adaptation, enabling holistic medical image understanding, visual question answering, and clinical report generation across diverse modalities (ZJU4HealthCare, 1.6K+ stars)
+- [Merlin (Stanford MIMI, Nature 2026)](https://github.com/StanfordMIMI/Merlin) - 3D vision-language model for computed tomography that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining, enabling multimodal medical understanding and radiology report generation (447+ stars, MIT License, 2026)
 - [MedAgents](https://github.com/gersteinlab/MedAgents) - Multi-disciplinary collaboration framework for zero-shot medical reasoning using role-playing LLM agents (ACL 2024)
 - [MedAgentGym](https://github.com/wshi83/MedAgentGym) - Scalable agentic training environment for code-centric reasoning in biomedical data science
 - [MedRAX (ICML 2025)](https://github.com/bowang-lab/MedRAX) - First versatile medical reasoning agent for chest X-ray interpretation, dynamically integrating state-of-the-art CXR analysis tools and multimodal LLMs into a unified framework; introduces ChestAgentBench with 2,500 complex medical queries across 7 categories (bowang-lab, 1.1K+ stars)
@@ -670,9 +711,11 @@ validated: false
 #### Materials Discovery
 - [GNoME](https://github.com/google-deepmind/materials_discovery) - DeepMind's graph neural network for materials exploration, discovering 2.2M new crystal structures (380K most stable) equivalent to 800 years of traditional research, with 520K+ materials dataset open-sourced (Nature 2023)
 - [FAIRChem (OMat24)](https://github.com/FAIR-Chem/fairchem) - Meta's comprehensive ML ecosystem for materials/chemistry with 118M+ DFT calculations, EquiformerV2 models achieving top Matbench Discovery performance
+- [Skala 1.1 (Microsoft Research, 2026)](https://github.com/microsoft/skala) - Neural network-based exchange-correlation functional for density functional theory (DFT) that surpasses state-of-the-art hybrid functionals in accuracy for main-group thermochemistry, kinetics, and non-covalent interactions at semi-local DFT cost; includes PySCF/GPU4PySCF/ASE bindings and C++/Fortran integrations (248+ stars, MIT License)
 - [All-atom Diffusion Transformers (ADiT)](https://github.com/facebookresearch/all-atom-diffusion-transformer) - Unified latent diffusion transformer that jointly generates periodic crystals and non-periodic molecules, scaling to 500M parameters with SOTA results on QM9, MP20, and GEOM-DRUGS (Meta FAIR, ICML 2025, 310+ stars)
 - [JARVIS](https://github.com/usnistgov/jarvis) - NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)
 - [NVIDIA ALCHEMI Toolkit](https://github.com/NVIDIA/nvalchemi-toolkit) - Developer toolkit for accelerating training and inference for AI in chemistry and material science, providing optimized GPU-accelerated workflows for molecular and materials machine learning (NVIDIA, 2026)
+- [CatGo (UCSD)](https://github.com/Hello-QM/catgo-LRG) - AI-driven desktop workbench for computational materials science with an interactive 3D structure editor, natural-language CatBot assistant, visual DAG workflow engine, remote-cluster access, and HPC job submission for VASP, ORCA, CP2K, Quantum ESPRESSO, GPAW, DFTB+, SIESTA, and LAMMPS (172+ stars, AGPL-3.0, 2026)
 - [NequIP](https://github.com/mir-group/nequip) - E(3)-equivariant neural network interatomic potentials achieving DFT accuracy with up to 1000× less training data than invariant models, foundational architecture behind MACE and Allegro (Harvard, MIT, Nature Communications 2022)
 - [Allegro](https://github.com/mir-group/allegro) - Highly scalable equivariant deep learning interatomic potentials enabling million-atom molecular dynamics simulations with ab initio accuracy, building on E(3)-equivariant architectures for large-scale atomistic modeling (mir-group, MIT License, 480+ stars)
 - [SchNetPack](https://github.com/atomistic-machine-learning/schnetpack) - PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)
@@ -695,6 +738,7 @@ validated: false

 #### Lab Automation & Robotics
 - [PyLabRobot](https://github.com/PyLabRobot/pylabrobot) - Interactive and hardware-agnostic SDK for laboratory automation, enabling programmatic control of liquid handlers, plate readers, and other lab instruments across multiple vendors; foundational infrastructure for self-driving laboratories and AI-driven experimental execution (447+ stars)
+- [RoboChem-Flex](https://github.com/Noel-Research-Group/Robochem_Flex) - Low-cost, modular self-driving laboratory platform democratizing autonomous chemical experimentation with open control software, device CAD/PCB files, and example optimization campaigns (Noël Research Group, University of Amsterdam, Apache 2.0, 2026)

 ### 🌌 Physics & Astronomy

@@ -706,10 +750,12 @@ validated: false
 - [Neural ODEs](https://github.com/rtqichen/torchdiffeq) - Differential equations with neural networks
 - [Physics-Informed Neural Networks](https://github.com/maziarraissi/PINNs) - Physics-constrained ML
 - [EquiformerV2](https://github.com/atomicarchitects/equiformer_v2) - Improved equivariant Transformer for 3D atomic graphs (ICLR2024)
+- [EquiformerV3](https://github.com/atomicarchitects/equiformer_v3) - Scaling efficient, expressive, and general SE(3)-equivariant graph attention transformers for atomic systems and machine-learned interatomic potentials (MIT License, 2026)
 - [Equiformer](https://github.com/atomicarchitects/equiformer) - Equivariant graph attention Transformer (ICLR2023)
 - [TORAX](https://github.com/google-deepmind/torax) - Differentiable tokamak core transport simulator for fusion energy research, coupling PDE solvers with JAX auto-differentiation and neural-network surrogates for fast forward modelling, pulse-design, and trajectory optimization (Google DeepMind, Apache 2.0)
 - [DiffPhysDrone (Nature Machine Intelligence 2025)](https://github.com/HenryHuYu/DiffPhysDrone) - First real quadrotor robot trained end-to-end with differentiable physics for vision-based agile flight, bridging simulation-based learning and real-world deployment with physics-informed neural network controllers (558+ stars)
 - [Walrus (arXiv 2025)](https://github.com/PolymathicAI/walrus) - Cross-domain foundation model for continuum dynamics trained on 19 physical scenarios spanning 63 variables, featuring adaptive compute via stride modulation and patch jittering for long-run stability (Polymathic AI, 293+ stars, MIT License)
+- [GeoPT (ICML 2026)](https://github.com/Physics-Scaling/GeoPT) - Unified pre-trained model for general physics simulation via lifted geometric pre-training, augmenting static geometry with synthetic dynamics to enable dynamics-aware self-supervision without physics labels; improves industrial-fidelity benchmarks spanning fluid mechanics and solid mechanics while reducing labeled data requirements by 20–60% (Physics-Scaling, 224+ stars)

 #### Astronomy & Astrophysics
 - [AstroCLIP](https://github.com/PolymathicAI/AstroCLIP) - Cross-modal self-supervised foundation model for galaxies by Polymathic AI, jointly embedding multi-band galaxy imaging and optical spectra into a shared latent space to enable zero/few-shot redshift estimation, galaxy property prediction, morphology classification, and cross-modal similarity search (MNRAS Letters 2024)
@@ -754,6 +800,7 @@ 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
@@ -763,6 +810,8 @@ validated: false
 - [AgML](https://github.com/Project-AgML/AgML) - Agricultural machine learning platform
 - [FarmVibes.AI](https://github.com/microsoft/farmvibes-ai) - Multi-modal geospatial ML platform for agriculture and sustainability, fusing satellite imagery (RGB, SAR, multispectral), drone imagery, weather data, and sensor data for crop identification, carbon footprint estimation, and microclimate prediction (Microsoft Research, MIT License)
 - [PlantCV](https://github.com/danforthcenter/plantcv) - Open-source image analysis toolkit for high-throughput plant phenotyping, extracting morphological, color, and texture traits from RGB, hyperspectral, and thermal imagery with modular Python workflows for crop improvement, stress detection, and plant biology research (Donald Danforth Plant Science Center, 795+ stars, MPL-2.0)
+- [Virdis](https://github.com/Thanas-R/Virdis) - Satellite-powered agricultural and land analytics platform combining Sentinel-2 imagery, Google Earth Engine processing, real-time weather data, soil science databases, and AI-driven crop planning into a unified web dashboard (145+ stars, AGPL-3.0, 2026)
+- [Agribound](https://github.com/montimaj/agribound) - AI-powered field boundary delineation toolkit combining satellite foundation models, embeddings, and global training data for accurate agricultural parcel/field boundary mapping, with Google Earth Engine integration and PyPI distribution (84+ stars, Apache 2.0, 2026)

 #### Ecological Modeling
 - [BioSimulators](https://github.com/biosimulators/Biosimulators) - Biological simulation tools
@@ -776,8 +825,28 @@ validated: false

 #### Social Science Research & Simulation
 - [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)
- [Awesome Agent Skills for Empirical Research](https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research) - 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, 1.1K+ stars, 2026)
+- [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)
+
+---
+
+## 🏗 Engineering & Built Environment
+
+### Mechanical, Aerospace & Industrial Engineering
+- [Noether (Emmi AI)](https://github.com/Emmi-AI/noether) - Open software framework for Engineering AI built on transformer building blocks, enabling teams to build, train, and operate industrial simulation models across engineering verticals; includes ready-to-use recipes for CFD (AB-UPT on DrivAerML), external aerodynamics, and heat transfer (234+ stars, ENPL non-commercial license, 2026)
+
+### Structural & Civil Engineering
+- [StructureClaw](https://github.com/structureclaw/structureclaw) - AI-assisted structural engineering workspace for AEC workflows: natural language to structural model, analysis, code-check, and report (171+ stars, MIT License, 2026)
+
+### Construction & Built Environment Management
+- [OpenConstructionERP](https://github.com/datadrivenconstruction/OpenConstructionERP) - Open-source construction ERP with AI-powered cost matching, BOQ generation, and PDF/CAD/BIM takeoff; 42 regional catalogues, 21 languages, 71 modules (DataDrivenConstruction, 717+ stars, AGPL-3.0, 2026)
+
+### Architectural Design & BIM
+- [Aedifex](https://github.com/TangSY/aedifex) - Open-source 3D architectural editor with an AI design assistant; build floor plans with walls, doors, windows, and furniture using natural language, with real-time WebGPU-powered previews (TangSY, 59+ stars, MIT License, 2026)
+
+### Electrical & Electronics Engineering
+- [kicad-happy](https://github.com/aklofas/kicad-happy) - AI coding agent skills for KiCad electronics design that turn Claude Code, Codex, Gemini CLI, and other coding agents into full electronics design assistants; parses schematics and PCB layouts, builds power trees, audits connectors/ESD protection, validates passive networks, runs SPICE simulation, sources components from major distributors, and prepares boards for fabrication (aklofas, 974+ stars, MIT License, 2026)

 ---

@@ -790,12 +859,17 @@ validated: false
 - [TimesFM (Google Research)](https://github.com/google-research/timesfm) - Pretrained time series foundation model for long-horizon forecasting across diverse scientific domains including climate variables, biomedical signals, and physical observations; decoder-only Transformer architecture with strong zero-shot generalization (19.8K+ stars, Apache 2.0, 2024-2025)
 - [Chronos (Amazon Science, NeurIPS 2024)](https://github.com/amazon-science/chronos-forecasting) - Pretrained time series foundation model for zero-shot forecasting across diverse scientific and real-world domains; tokenizes continuous time series into discrete bins to train transformer language models on large-scale corpora, achieving strong zero-shot generalization and competitive performance with task-specific supervised models on climate, energy, and health benchmarks (5.3K+ stars, Apache 2.0, 2024-2026)
 - [TabPFN (Prior Labs, Nature 2025)](https://github.com/PriorLabs/tabpfn) - Foundation model for tabular data that predicts on unseen real-world tables in a single forward pass, achieving accurate small-data classification and regression without task-specific training; widely applicable to scientific datasets with limited samples (7.4K+ stars, 2022-2026)
+- [TabFM (Google Research, 2026)](https://github.com/google-research/tabfm) - Scikit-learn compatible tabular foundation model for zero-shot classification and regression on mixed-type tabular datasets via in-context learning; applicable to diverse scientific datasets (1.8K+ stars, Apache 2.0)
+- [DeepInnovator (HKUDS, arXiv 2026)](https://github.com/HKUDS/DeepInnovator) - Scientific foundation model and AI research copilot for idea generation, cross-disciplinary connection discovery, and hypothesis formation; trained with a decoupled reward-comment RL architecture and achieves GPT-4o-competitive novelty/rationale on STEM and social-science idea-generation benchmarks (270+ stars, MIT License, 2026)
+- [AI Can Learn Scientific Taste (OpenMOSS, arXiv 2026)](https://github.com/tongjingqi/AI-Can-Learn-Scientific-Taste) - Scientific taste learning framework showing AI can judge and propose research ideas with long-term impact; trains Scientific Judge as a generative reward model and Scientific Thinker as an ideation policy using Reinforcement Learning from Community Feedback (RLCF) on large-scale citation signals, with SciJudgeBench and released HuggingFace model weights (425+ stars, Apache 2.0, 2026)
+- [LOGOS (arXiv 2026)](https://github.com/LOGOS-Hub/LOGOS) - First multi-domain generative foundation model for the natural sciences built on a unified scientific grammar, encoding proteins, antibodies, small molecules, chemical reactions, materials, and their spatial interactions into a shared token vocabulary; enables unified generation, prediction, and design across domains under a purely autoregressive paradigm (134+ stars, Apache 2.0, 2026)
 - [MinervaAI](https://github.com/google-research/minerva) - Mathematical reasoning
 - [PaLM-2](https://ai.google/discover/palm2) - Scientific reasoning capabilities

 ### Domain-Specific Models
 - [ESM](https://github.com/facebookresearch/esm) - Protein language models
 - [BioNeMo Framework](https://github.com/NVIDIA/bionemo-framework) - NVIDIA's open-source platform for building and adapting biological AI models at scale, bundling ESM-2, Geneformer, MolMIM and DNA embedding models with recipes for single-GPU to multi-node training (2025)
+- [BioNeMo Recipes (NVIDIA)](https://github.com/NVIDIA-BioNeMo/bionemo-recipes) - TransformerEngine-accelerated checkpoints and training recipes for scaling biological foundation models (ESM-2, AMPLIFY, Geneformer, CodonFM) from single-GPU prototyping to multi-node FSDP training with FP8/MXFP8/NVFP4 precision, compatible with PyTorch, HF Accelerate, and PyTorch Lightning, plus sparse-autoencoder interpretability tools for biological foundation models (851+ stars, 2026)
 - [IBM FM4M](https://github.com/IBM/materials) - IBM's open foundation model family for materials and chemistry, covering SMILES, SELFIES, molecular graphs, 3D atom positions, and electron density grids, with a unified toolkit for representation learning and downstream prediction/generation (Apache 2.0, 2024-2025)
 - [ChemGPT](https://huggingface.co/ncfrey/ChemGPT-1.2B) - Chemistry-focused language model
 - [BioGPT](https://github.com/microsoft/BioGPT) - Biomedical text generation
@@ -860,6 +934,7 @@ 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
@@ -939,12 +1014,14 @@ 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

 ### 📊 Paper & Research Collections
 - [Scientific LLM Papers](https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models) - 260+ scientific language models
+- [Awesome Scientific LLM Benchmarks](https://github.com/subinium/Awesome-Scientific-LLM-Benchmarks) - Curated, accuracy-first collection of benchmarks for evaluating LLMs on scientific reasoning and discovery across mathematics, physics, chemistry, materials science, biology, and agentic science (subinium, 29+ stars, MIT License, 2026)
 - [LLM4SR Repository](https://github.com/du-nlp-lab/LLM4SR) - LLM for scientific research survey materials
 - [PINNs Paper Collection](https://github.com/idrl-lab/PINNpapers) - Physics-informed neural networks research
 - [SciML Papers](https://sciml.ai/papers/) - Scientific computing and machine learning papers
@@ -0,0 +1,61 @@
+---
+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
@@ -0,0 +1,43 @@
+---
+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/12d87583/.github/workflows/sync_resources.yml
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/sync_resources.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -20,6 +20,8 @@ on:
    paths:
      - README.md
      - data/resources.yml
+      - data/enrichment.yml
+      - 'data/enrichment.*.yml'
      - scripts/*.py
      - scripts/**/*.py

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

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

      - 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/12d87583/README.md
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/README.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -70,6 +70,7 @@ 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)
@@ -265,6 +266,8 @@ 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.
@@ -320,6 +323,7 @@ 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.

 ---

@@ -337,10 +341,15 @@ 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.
- [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 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.
 - [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.
- [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.
+- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.

 #### Drug Repurposing

@@ -412,6 +421,10 @@ 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/12d87583/cspell.json
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/cspell.json
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -157,7 +157,21 @@ validated: false
    "Pacc",
    "multiomics",
    "Pathomic",
-    "PLIP"
+    "PLIP",
+    "Omni",
+    "Bento",
+    "FFPE",
+    "Xenium",
+    "Zyme",
+    "Neur",
+    "Imageomics",
+    "AESTETIK",
+    "CellOT",
+    "CMonge",
+    "bowang",
+    "ctheodoris",
+    "OpenAI",
+    "GPT"
  ],
  "ignorePaths": [
    "node_modules/**"
@@ -0,0 +1,56 @@
+---
+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/
@@ -0,0 +1,91 @@
+---
+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/
@@ -0,0 +1,73 @@
+---
+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
@@ -0,0 +1,62 @@
+---
+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
@@ -0,0 +1,92 @@
+---
+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/
@@ -0,0 +1,62 @@
+---
+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
@@ -0,0 +1,64 @@
+---
+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
@@ -0,0 +1,110 @@
+---
+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
@@ -0,0 +1,128 @@
+---
+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/12d87583/data/resources.yml
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/resources.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -248,6 +248,17 @@ 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
@@ -435,6 +446,17 @@ 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
@@ -1491,6 +1513,17 @@ 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
@@ -1645,6 +1678,17 @@ 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
@@ -1694,8 +1738,8 @@ 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-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -1722,6 +1766,17 @@ 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
@@ -1749,8 +1804,8 @@ 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-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -1810,6 +1865,39 @@ 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
@@ -2409,8 +2497,8 @@ 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-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
    modalities: [Small Molecule]
    organism: []
    api: false
@@ -3097,6 +3185,17 @@ 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
@@ -0,0 +1,105 @@
+---
+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
@@ -0,0 +1,73 @@
+---
+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.
@@ -0,0 +1,72 @@
+---
+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.
@@ -0,0 +1,21 @@
+---
+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.
@@ -0,0 +1,132 @@
+---
+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.
@@ -0,0 +1,53 @@
+---
+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
+    }
+  }
+}
@@ -0,0 +1,352 @@
+---
+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
+
+- **[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
+
+- **[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
+
+### 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
+
+- **[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
+
+- **[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
+
+### 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
+
+- **[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
+
+- **[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
+
+- **[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
+
+#### 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
+
+- **[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
+
+- **[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
+
+- **[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
+
+- **[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
+
+#### 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
+
+- **[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)
+
+### 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
+
+- **[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
+
+- **[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
+
+- **[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
+
+- **[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
+
+- **[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
+
+- **[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
+
+- **[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/)
+
+
+
+
Author	SHA1	Message	Date
promptadmin	b8639aa3d1	Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#94 ) from upstream-sync/awesome-ai-for-science-20260904-e18934-poor into main Reviewed-on: #94	2026-09-04 15:26:37 +00:00
promptadmin	31d939ab41	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@e1893484 [catalogue]	2026-09-04 11:32:47 +00:00
promptadmin	0a25b5b792	Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#83 ) from upstream-sync/awesome-ai-for-science-20260821-ec6f36-ihmj into main Reviewed-on: #83	2026-08-21 14:37:05 +00:00
promptadmin	27303f36c1	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@ec6f3650 [catalogue]	2026-08-21 10:38:52 +00:00
promptadmin	ea7701920e	Merge pull request '[Upstream sync] inoue0426/awesome-computational-biology (github) — 17 added, 6 modified' (#67 ) from upstream-sync/awesome-computational-biology-20260808-7a064b-pniq into main Reviewed-on: #67	2026-08-09 18:32:11 +00:00
promptadmin	fcd5fbc473	Merge pull request '[Upstream sync] ai-boost/awesome-ai-for-science (github) — 0 added, 1 modified' (#68 ) from upstream-sync/awesome-ai-for-science-20260809-024f42-tzfc into main Reviewed-on: #68	2026-08-09 17:02:16 +00:00
promptadmin	1fdd6c59ed	[upstream-sync] README.md from ai-boost/awesome-ai-for-science@024f42bf [catalogue]	2026-08-09 15:54:26 +00:00
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promptadmin	4478db9429	Merge pull request '[Upstream sync] inoue0426/awesome-computational-biology (github) — 0 added, 4 modified' (#53 ) from upstream-sync/awesome-computational-biology-20260717-478be8-wkjf into main Reviewed-on: #53	2026-08-08 15:42:14 +00:00
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