Skip to content

ai-boost/awesome-ai-for-science

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

179 Commits
assets
assets
 
 
.gitignore
.gitignore
 
 
.gitmessage
.gitmessage
 
 
CONTRIBUTING.md
CONTRIBUTING.md
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 

Repository files navigation

✨ Awesome AI for Science (AI4Science) ✨

Awesome AI for Science Banner

A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery across all disciplines.

Deutsch | English | Español | français | 日本語 | 한국어 | Português | Русский | 中文

Awesome License: MIT GitHub stars GitHub forks

AI is revolutionizing scientific research - from drug discovery and materials design to climate modeling and astrophysics. This repository collects the best resources to help researchers leverage AI in their work.

📚 Contents

🧪 AI Tools for Research

Literature & Knowledge Management

  • Semantic Scholar - AI-powered academic search (Allen AI)
  • arXiv - Open-access repository of electronic preprints and postprints
  • OpenAlex - Open catalog of scholarly papers and authors
  • CORE - Aggregator of open access research papers

Data Analysis & Visualization

  • PandasAI - Conversational data analysis using natural language
  • DeepAnalyze - First agentic LLM for autonomous data science with end-to-end pipeline from data to analyst-grade reports
  • AutoViz - Automated data visualization with minimal code
  • Chat2Plot - Secure text-to-visualization through standardized chart specifications

Data Labeling & Annotation

  • Label Studio - Multi-type data labeling and annotation tool
  • Snorkel - Programmatic data labeling and weak supervision

Research Workbench & Plugins

  • Claude Scientific Skills - Comprehensive collection of 125+ ready-to-use scientific skill modules for Claude AI across bioinformatics, cheminformatics, clinical research, ML, and materials science

📄 Paper→Poster / Slides / Graphical Abstract

Poster Generation

  • Paper2Poster - Multi-agent system with Parser-Planner-Painter architecture converting paper.pdf to editable poster.pptx, outperforms GPT-4o with 87% fewer tokens
  • mPLUG-PaperOwl - Multimodal LLM for scientific charts and diagrams understanding/generation

Slides & Presentation Generation

  • Auto-Slides - Multi-agent academic paper to high-quality presentation slides with interactive refinement
  • PPTAgent - Beyond text-to-slides generation with PPTEval multi-dimensional evaluation (EMNLP 2025)
  • paper2slides - Transform arXiv papers into Beamer slides using LLMs
  • PaperToSlides - AI-powered tool that automatically converts academic papers (PDF) into presentation slides
  • pdf2slides - Convert PDF files into editable slides with three lines of code
  • SlideDeck AI - Co-create PowerPoint presentations with Generative AI from documents or topics
  • AI Multi-Agent Presentation Builder - Azure Semantic Kernel multi-agent PPT generation reference

Video & Media Generation

  • Paper2Video - First benchmark for automatic video generation from scientific papers (NeurIPS 2025)
  • paper2video - Transform arXiv research papers into engaging presentations and YouTube-ready videos

Website & Interactive Content Generation

  • Paper2All - AI-powered pipeline converting papers into interactive websites, posters, and multimedia presentations with "Let's Make Your Paper Alive!" philosophy

Figure & Illustration Generation

  • PaperBanana - Automated academic illustration generation for AI scientists, converting research papers into publication-ready figures using VLMs and diffusion models with iterative refinement (PKU & Google Research, 6.2K+ stars, 2026)

Chart & Visualization Generation

Note: For comprehensive chart understanding and code generation tools, see 📊 Chart Understanding & Generation section

📊 Chart Understanding & Generation

Chart-to-Code & Reproducibility

Scientific Visualization Tools

  • Chat2Plot - Secure text-to-visualization through standardized chart specifications
  • AutoViz - Automated data visualization with minimal code
  • PlotlyAI - AI-powered data visualization and dashboard creation

🔄 Paper-to-Code & Reproducibility

Automated Code Generation

  • Paper2Code - Automated code generation from machine learning research papers into runnable implementations (4.5K+ stars, 2025)
  • AutoP2C - LLM agent framework generating runnable repositories from academic papers
  • ResearchCodeAgent - Multi-agent system for automated codification of research methodologies
  • ToolMaker - Convert papers with code into callable agent tools

Experiment Automation

  • BioProBench - Comprehensive benchmark for automatic evaluation of LLMs on biological protocols and procedural understanding
  • Alhazen - Extract experimental metadata and protocol information from scientific documents

📋 Scientific Documentation & Parsing

High-Performance Document Processing

  • MinerU (2024/2025) - SOTA multimodal document parsing with 1.2B parameters outperforming GPT-4o, converts PDFs to LLM-ready Markdown/JSON
  • PDF-Extract-Kit (2024) - Comprehensive toolkit for high-quality PDF content extraction with layout detection, formula recognition, and OCR
  • Docling (IBM, AAAI 2025) - Multi-format (PDF/DOCX/PPTX/HTML/Images) → structured data (Markdown/JSON) with layout reconstruction, table/formula recovery
  • Nougat (Meta AI) - Neural optical understanding for academic documents, transforms scientific PDFs to Markdown with mathematical formula support
  • PaddleOCR 3.0 (2024/2025) - Advanced OCR with PP-StructureV3 document parsing, 13% accuracy improvement, supports 80+ languages
  • Unstructured - Production-grade ETL for transforming complex documents into structured formats, with open-source API
  • Marker - High-accuracy PDF→Markdown/JSON/HTML conversion, specialized for tables/formulas/code blocks with benchmark scripts
  • S2ORC doc2json (AllenAI) - Large-scale PDF/LaTeX/JATS parsing to standardized JSON for millions of papers
  • GROBID - Machine learning software for extracting structured metadata from scholarly documents
  • Science-Parse / SPv2 (AllenAI) - Parse scientific papers to structured fields (title/author/sections/references)

Production Pipelines & Data Preparation

Figure & Table Extraction

  • PDFFigures2 - Extract figures, tables, captions, and section titles from scholarly PDFs
  • TableBank - Large-scale table detection and recognition dataset with pre-trained models

Scientific Literature RAG & Analysis

  • PaperQA2 - High-accuracy RAG for scientific PDFs with citation support, agentic RAG, and contradiction detection
  • OpenScholar - Retrieval-augmented LM synthesizing scientific literature from 45M papers with human-expert-level citation accuracy, outperforming GPT-4o by 5% on ScholarQABench (Nature 2026, UW & Ai2)
  • Valsci - Self-hostable scientific claim-verification and literature-review tool combining Semantic Scholar retrieval, bibliometric scoring, and LLM-based evidence synthesis for large-batch validation workflows
  • paper-reviewer - Generate comprehensive reviews from arXiv papers and convert to blog posts

🧰 Research Workbench & Plugins

Interactive Research Environments

Literature Management Plugins

Scientific Writing & Collaboration

  • Notion AI - AI-powered research note-taking and knowledge management
  • Obsidian Smart Connections - AI-powered note linking and research graph navigation
  • Research Rabbit - AI-powered literature discovery and research network mapping
  • 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)

🕸 Knowledge Extraction & Scholarly KGs

Knowledge Graph Construction

  • iText2KG - Incremental knowledge graph construction using LLMs with entity extraction and Neo4j visualization
  • GraphGen - Knowledge graph-guided synthetic data generation for LLM fine-tuning, achieving strong performance on scientific QA (GPQA-Diamond) and math reasoning (AIME)
  • KoPA - Structure-aware prefix adaptation for integrating LLMs with knowledge graphs (ACM MM 2024)
  • Scholarly KGQA - LLM-powered question answering over scholarly knowledge graphs (ArXiv paper)

Knowledge Graph Resources

  • Awesome-LLM-KG - Comprehensive collection of papers on unifying LLMs and knowledge graphs

🤖 Research Agents & Autonomous Workflows

Autonomous Research Systems (2023-2025 Breakthroughs)

  • FunSearch (DeepMind, Nature 2023) - 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 - 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)
  • The AI Scientist v1 (2024) - First fully autonomous research system: hypothesis→experiment→writing→review simulation
  • The AI Scientist v2 (2025) - Enhanced with Agentic Tree Search, reduced template dependency, first workshop-level accepted paper
  • 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
  • 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)
  • AlphaResearch - Autonomous algorithm discovery combining evolutionary search with peer-review reward models, achieving best-known performance on circle packing problems
  • AutoResearchClaw - Fully autonomous research from idea to paper with multi-agent debate, citation verification, and OpenClaw integration (11K+ stars, 2026)
  • AI-Researcher - Autonomous pipeline from literature review→hypothesis→algorithm implementation→publication-level writing with Scientist-Bench evaluation
  • Agent Laboratory - Multi-agent workflows for complete research cycles with AgentRxiv for cumulative discovery
  • InternAgent - Closed-loop multi-agent system from hypothesis to verification across 12 scientific tasks, #1 on MLE-Bench (36.44%)
  • freephdlabor - First fully customizable open-source multiagent framework automating complete research lifecycle from idea conception to LaTeX papers with dynamic workflows
  • ToolUniverse - Democratizing AI scientists by transforming any LLM into research systems with 600+ scientific tools (Harvard MIMS)
  • LabClaw - Skill operating layer for biomedical AI agents with 211 production-ready SKILL.md files across 7 domains (biology, pharmacology, medicine, data science, literature search), enabling modular dry-lab reasoning and protocol composition for Stanford LabOS-compatible agents
  • Robin - FutureHouse's end-to-end scientific discovery multi-agent system orchestrating literature search (Crow/Falcon) and data analysis (Finch) agents, first AI-generated drug discovery identifying ripasudil as novel dry AMD therapeutic (2025)
  • Aviary - Language agent gymnasium for challenging scientific tasks including DNA manipulation, literature search, and protein engineering
  • Curie - Automated and rigorous experiments using AI agents for scientific discovery
  • POPPER - Automated hypothesis testing with agentic sequential falsifications
  • autoresearch - Andrej Karpathy's autonomous LLM research framework: AI agent runs overnight experiments on a real training setup, auto-editing code→5min training→evaluation in a loop, ~100 experiments per night on a single GPU
  • UniScientist - Universal scientific research intelligence covering 50+ disciplines, repositioning LLMs as cross-disciplinary generators with human experts as verifiers; 30B model outperforms Claude Opus and GPT on 5 research benchmarks
  • EvoScientist - Self-evolving AI scientist with 6 specialized sub-agents (plan/research/code/debug/analyze/write) and persistent memory, #1 on DeepResearch Bench II and AstaBench, supporting multi-provider LLMs and multi-channel deployment (Apache 2.0, 2026)

Evaluation & Benchmarking

  • ScienceAgentBench (ICLR 2025) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
  • PaperBench (OpenAI, 2025) - 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
  • MLE-Bench (OpenAI, 2024) - 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) - 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 - First physics-aligned interactive benchmark for LLM agents in engineering construction, designing rockets/cars/bridges in physics simulator with 3D spatial geometry library
  • SciTrust (2024) - Trustworthiness evaluation framework for scientific LLMs (truthfulness, hallucination, sycophancy)
  • SciCode - Research coding benchmark curated by scientists with 338 subproblems across 16 subdomains (physics, math, materials, biology, chemistry), evaluating LLMs on realistic scientific programming tasks with gold-standard solutions (NeurIPS 2024)
  • SciBench - College-level scientific problem-solving evaluation across multiple domains
  • NewtonBench (ICLR 2026) - 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)

Academic Review & Evaluation

  • AgentReview - LLM agents simulating academic peer review ecosystems
  • LLM-Peer-Review - Web application for LLM-assisted manuscript review and annotation

Domain-Specific Research Agents

  • Aletheia - 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)
  • 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 - 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 - 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 - 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 - 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)
  • BioDiscoveryAgent - AI agent for biological discovery and research automation
  • 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)
  • STAgent - Multimodal LLM-based AI agent enabling deep research in spatial transcriptomics, automating analysis and interpretation of spatial gene expression data (Harvard LiuLab, bioRxiv 2025)
  • Camyla - Fully autonomous medical image segmentation research system that generates complete manuscripts end-to-end from datasets with zero human intervention, beating strongest baselines on 24 of 31 datasets and achieving T1-T2 tier manuscript quality in double-blind evaluations (USTC & Shanghai AI Lab, 2026)
  • MOOSE - Large Language Models for automated open-domain scientific hypotheses discovery (ACL 2024, ICML Best Poster)
  • ChemCrow - LLM agents for chemistry research with tool integration
  • Coscientist - Autonomous chemical experiment planning and execution
  • 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)

🏷 Data Labeling & Curation

Weak Supervision & Auto-Labeling

  • Snorkel - Programmatic data labeling and weak supervision for scientific datasets
  • PandasAI - Conversational data analysis and visualization using natural language

⚗ Scientific Machine Learning

Neural Differential Equations

Physics-Informed Neural Networks

  • DeepXDE - Deep learning library for solving PDEs
  • Lang-PINN - LLM-driven multi-agent system that builds trainable PINNs from natural language task descriptions, achieving 3-5 orders of magnitude MSE reduction and 50%+ execution success improvement (ICLR 2026)
  • PINNs - Physics-informed neural networks
  • NVIDIA PhysicsNeMo - Open-source framework for building physics-ML models at scale (renamed from Modulus, 2025)
  • PINA - Physics-Informed Neural networks for Advanced modeling in PyTorch
  • SciANN - Keras-based scientific neural networks
  • NeuralPDE.jl - Physics-informed neural networks in Julia

Neural Operators & Model Discovery

  • DeepONet - Learning nonlinear operators
  • PySINDy - Sparse identification of nonlinear dynamics
  • PySR - High-performance symbolic regression for discovering interpretable scientific equations from data, multi-population evolutionary search with Python/Julia backend, widely used in physics and astronomy (Cambridge, NeurIPS 2023)
  • LLM-SR - Scientific equation discovery and symbolic regression using LLMs, combining code generation with evolutionary search (ICLR 2025 Oral)
  • PSRN - Parallel symbolic regression network evaluating millions of expressions on GPU with automated subtree reuse, Nature Computational Science cover article (MIT, 2026)
  • Fourier Neural Operator - Learning operators in Fourier space
  • 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)
  • 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)

📖 Papers & Reviews

Foundational Papers

📊 Comprehensive Surveys & Reviews (2024-2025)

AI for Scientific Research

Scientific Large Language Models

Scientific Machine Learning

Uncertainty Quantification

Automation & Self-Driving Laboratories

Policy & Strategic Perspectives

  • Artificial Intelligence for Science (CSIRO 2022) - Landmark report analyzing AI adoption across 98% of scientific fields over 60 years
  • AI for Science 2025 (Fudan University & Nature 2025) - Comprehensive report on AI's transformative impact across 7 scientific fields, 28 research directions, and 90+ challenges
  • AI in science evidence review (European Scientific Advice 2024) - Policy-focused evidence review on AI's impact in research

🚀 AI Scientist & Autonomous Research (2024-2025 Breakthroughs)

Recent Advances & Domain Applications

📈 Evaluation & Benchmarking

🔬 Domain-Specific Applications

🧬 Biology & Medicine

Protein & Drug Discovery

  • CryoDRGN - Neural network-based cryo-EM heterogeneous reconstruction, modeling continuous 3D structure distributions from single-particle images, with CryoDRGN-ET extending to in-cell cryo-electron tomography (MIT CSAIL, Nature Methods 2021/2024)
  • AlphaFold - Protein structure prediction
  • 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)
  • ColabFold (2025 Updates) - AlphaFold/ESMFold accessible implementation with AF3 JSON export, database updates
  • OpenFold3 - Fully open-source (Apache 2.0) biomolecular structure prediction reproducing AlphaFold3, free for academic and commercial use (Columbia AlQuraishi Lab & OpenFold Consortium, 2025)
  • Protenix - Trainable PyTorch reproduction of AlphaFold 3
  • HelixFold3 - Baidu's open-source reproduction of AlphaFold3 in PaddlePaddle, providing pretrained weights and inference pipelines for unified biomolecular structure prediction across proteins, nucleic acids, ligands, ions, and post-translational modifications within the PaddleHelix biocomputing platform (Baidu, bioRxiv 2024)
  • RoseTTAFold-All-Atom - All-atom biomolecular structure prediction for protein-nucleic acid-small molecule-metal ion complexes, enabling accurate modeling of covalent modifications and assemblies beyond proteins (Baker Lab, Science 2024)
  • Chai-1 - Multi-modal foundation model for biomolecular structure prediction (proteins, small molecules, DNA, RNA, glycans) achieving SOTA across benchmarks, with optional MSA/template support (Chai Discovery, 2024)
  • NeuralPLexer - State-specific protein-ligand complex structure prediction with a multi-scale deep generative model, enabling conformational state-aware modeling of molecular interactions (329+ stars, 2024)
  • Boltz - First fully open-source model achieving AlphaFold3-level accuracy with 1000x faster binding affinity prediction (MIT)
  • BoltzGen - De novo protein binder design via generative model, achieving nanomolar binding for 66% of novel targets tested (MIT, 2025)
  • Proteina-Complexa - Flow-based generative model for atomistic protein binder design with test-time optimization, SOTA on binder benchmarks (ICLR 2026 Oral, NVIDIA)
  • xfold - Democratizing AlphaFold3: PyTorch reimplementation to accelerate protein structure prediction research
  • MegaFold - Cross-platform system optimizations for accelerating AlphaFold3 training with 1.73x speedup and 1.23x memory reduction
  • Graphormer - General-purpose deep learning backbone for molecular modeling
  • DiffDock - Diffusion-based molecular docking achieving SOTA blind docking performance, treating ligand pose prediction as generative diffusion over SE(3), with DiffDock-L update for improved generalization (MIT CSAIL, ICLR 2023)
  • PLACER - Graph neural network operating entirely at the atomic level for protein-ligand conformational ensemble prediction and docking, generating diverse solutions through rapid stochastic denoising to model conformational heterogeneity (Baker Lab, bioRxiv 2025)
  • targetdiff - 3D Equivariant Diffusion for Target-Aware Molecule Generation (ICLR2023)
  • ReQFlow - Rectified Quaternion Flow for efficient protein backbone generation, 37× faster than RFDiffusion with 0.972 designability (ICML 2025)
  • BioEmu - Microsoft's generative model for sampling protein equilibrium conformations 100,000× faster than MD simulations, predicting domain motions, local unfolding and cryptic binding pockets on a single GPU (Science 2025)
  • ProteinMPNN - Deep learning-based protein sequence design (inverse folding) from backbone structures, achieving 52.4% sequence recovery vs 32.9% for Rosetta, core tool in modern protein design pipelines (Baker Lab, Science 2022)
  • LigandMPNN - Extension of ProteinMPNN for protein sequence design in the context of small-molecule ligands, metal ions, and nucleic acids, enabling binding site engineering and co-factor redesign (Baker Lab)
  • ColabDesign - Accessible protein design platform via Google Colab integrating AlphaFold2, RoseTTAFold, and ProteinMPNN for de novo hallucination, fixed backbone design, and binder design (Sergey Ovchinnikov, 2022+)
  • BindCraft - Simple and accurate de novo protein binder design pipeline using AlphaFold2 backpropagation, MPNN, and PyRosetta for automated binder discovery (bioRxiv 2024)
  • Genie 2 - Diffusion model for scalable protein structure design with multi-motif scaffolding capabilities, achieving state-of-the-art designability, diversity, and novelty through SE(3)-equivariant attention and massive data augmentation (AlQuraishi Lab, 2024)
  • Chroma - Generative model for programmable protein design using diffusion modeling, equivariant graph neural networks, and conditional random fields to efficiently sample diverse all-atom structures; supports conditional generation via composable conditioners for substructure, symmetry, shape, and neural-network predictions; validated crystallographically (Generate Biomedicines, Nature 2023)
  • RFdiffusion3 - Latest RFdiffusion for protein structure design with 10× speedup and atom-level precision (December 2025)
  • RFantibody - Structure-based de novo antibody design pipeline built on RFdiffusion for computational generation of target-specific antibodies (RosettaCommons, 2025)
  • IgGM - Generative foundation model for functional antibody and nanobody design, supporting de novo generation, affinity maturation, inverse design, structure prediction, and humanization (Tencent AI4S, ICLR 2025)
  • DrugAssist - LLM-based molecular optimization tool
  • GenMol - ICML 2025 drug discovery generalist using masked discrete diffusion and fragment-based generation with molecular context guidance (NVIDIA)
  • REINVENT - Industrial-grade reinforcement-learning-based generative platform for de novo molecular design with transformer architectures, supporting multi-objective optimization, scaffold decoration, and curriculum learning (AstraZeneca MolecularAI, REINVENT 4, 2024)
  • mint - Learning the language of protein-protein interactions
  • Mol-Instructions - Large-scale biomolecular instruction dataset for chemistry/biology LLMs (ICLR2024)
  • Uni-Mol - Universal 3D molecular pretraining framework with 209M conformations, scaling to 1.1B parameters (Uni-Mol2) on 800M conformations for molecular property prediction, docking, and quantum chemistry (ICLR 2023, NeurIPS 2024)
  • ChemBERTa - Chemical language model
  • DeepChem - Machine learning for chemistry
  • DeepMol - Unified ML/DL framework for drug discovery workflows, integrating RDKit, DeepChem, and scikit-learn with SHAP explainability
  • Chemprop - Message passing neural networks for molecule property prediction, ADMET modeling, and reaction prediction, achieving SOTA on MoleculeNet and widely used in pharmaceutical drug discovery (MIT, 2.3K+ stars)
  • RDKit - Cheminformatics toolkit
  • ESM3 - 98B-parameter frontier generative model jointly reasoning over protein sequence, structure, and function, trained on 2.78 billion proteins; generated a novel fluorescent protein (esmGFP) with only 58% sequence identity to known GFPs (EvolutionaryScale, 2024)
  • ESMFold - Protein structure prediction from ESM models
  • SaProt - Structure-aware protein language model using 3D structural vocabulary (Foldseek) for joint sequence-structure pretraining, achieving SOTA on protein engineering and fitness prediction benchmarks (ICML 2024, Westlake University & Repl)
  • Foldseek - Fast and accurate protein structure search using a learned 3Di structural alphabet (VQ-VAE) that discretizes tertiary interactions into structural tokens, enabling protein-universe-scale structural alignment at sequence-search speeds (4-5 orders of magnitude faster than DALI/TM-align) and underpinning many AI4S tools such as SaProt, ESMAtlas search, and AFDB clustering pipelines (Steinegger Lab, Nature Biotechnology 2023)

Genomics & Bioinformatics

  • RhoFold+ - End-to-end RNA 3D structure prediction using RNA language model pretrained on 23.7M sequences, outperforming existing methods and human expert groups on RNA-Puzzles and CASP15 (Nature Methods 2024)
  • AIDO.ModelGenerator - GenBio AI's software stack for the AI-Driven Digital Organism, supporting adaptation and finetuning of multiscale biological foundation models across DNA, RNA, protein, structure, and single-cell tasks with reproducible CLIs and pretrained model zoo (2025)
  • Evo 2 - Arc Institute's 40B-parameter genome foundation model trained on 9 trillion nucleotides from all domains of life, supporting 1M base pair context for generalist DNA/RNA/protein prediction and design (Nature 2026)
  • Nucleotide Transformer - Foundation models for genomics and transcriptomics pretrained on 3,000+ human genomes and 850+ diverse species, enabling chromatin accessibility prediction, splice site detection, and promoter classification across multiple model scales (InstaDeep, NVIDIA & TUM, Nature Methods 2023)
  • LucaOne - Generalized biological foundation model with unified nucleic acid and protein language, integrating DNA/RNA/protein sequences (Nature Machine Intelligence 2025)
  • Geneformer - Single-cell transformer foundation model pretrained on 104M human transcriptomes via masked gene prediction, enabling transfer learning for cell type classification, gene network analysis, and in silico perturbation with limited labeled data (Nature 2023, V2 2024)
  • scFoundation - 100M-parameter foundation model pretrained on 50M+ human single-cell transcriptomes covering ~20,000 genes, achieving SOTA on gene expression enhancement, drug response and perturbation prediction (Nature Methods 2024)
  • Tahoe-x1 - Apache 2.0 single-cell foundation model family scaling to 3B parameters, pretrained on 266M cell profiles including perturbation data and released with training, embedding, and downstream benchmarking workflows for disease-relevant single-cell tasks (2025)
  • Stack - Arc Institute's single-cell foundation model enabling in-context learning at inference time via a novel tabular attention architecture, trained on 150M uniformly-preprocessed cells for generalizing biological effects and generating unseen cell profiles in novel contexts (2025)
  • scvi-tools - Deep probabilistic framework for single-cell and spatial omics analysis, integrating scVI, scANVI, totalVI and other VAE-based models for batch correction, cell annotation, multi-omics integration, and RNA velocity (scverse/NumFOCUS, Nature Methods 2018/2024)
  • GEARS - Geometric deep learning model predicting transcriptional outcomes of novel single- and multi-gene perturbations using gene–gene knowledge graphs, 40% higher precision than prior methods on combinatorial perturbation prediction (Stanford, Nature Biotechnology 2024)
  • scGPT - Single-cell analysis with transformers
  • CellTypist - Automated cell type annotation tool for single-cell transcriptomics using gradient boosting and logistic regression with reference atlases, enabling standardized classification across datasets (Wellcome Sanger Institute, Nature Biotechnology 2022)
  • Cell2Sentence - Teaching Large Language Models the Language of Biology through single-cell transcriptomics (ICML 2024)
  • OmicVerse - Unified Python framework for bulk, single-cell, and spatial RNA-seq multi-omics analysis with deep learning deconvolution (VAE) and graph neural networks, bridging Bindea, Bindea, scanpy and squidpy ecosystems (Nature Communications 2024)
  • ChatSpatial - MCP server enabling spatial transcriptomics analysis via natural language, integrating 60+ methods including SpaGCN, Cell2location, LIANA+, CellRank for Visium, Xenium, MERFISH platforms
  • Enformer - Gene expression prediction
  • DNABERT - DNA sequence analysis
  • scBERT - Single-cell BERT for gene expression
  • GenePT - Generative pre-training for genomics
  • DNA Claude Analysis - Interactive personal genome analysis toolkit using Claude Code and Python. Parses raw genotyping data from consumer DNA services and analyzes SNPs across 17 categories including health risks, pharmacogenomics, ancestry, and nutrition, with a terminal-style HTML dashboard.
  • OpenCRISPR - First open-source AI-generated gene editing systems developed with protein language models, enabling programmable CRISPR-Cas nucleases for synthetic biology and therapeutic genome editing (Profluent, 2024)
  • AlphaMissense - Google DeepMind's AlphaFold-derived classifier for proteome-wide missense variant effect prediction, providing pathogenicity scores for all ~71M possible human missense variants and classifying 89% with 90% precision; pre-computed predictions are integrated into Ensembl VEP and UCSC Genome Browser to support clinical variant interpretation (Science 2023)
  • AlphaGenome - 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)

Neuroscience & Behavioral Analysis

  • 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)
  • SLEAP - Deep learning-based multi-animal pose tracking and behavior classification, enabling automated quantification of social interactions and collective behavior across species (Nature Methods 2022, 2.2K+ stars)
  • CEBRA (Nature 2023) - Learnable latent embeddings for joint behavioral and neural analysis, enabling consistent and interpretable mapping of neural activity to behavior across modalities, species, and experiments (EPFL & Harvard, 1K+ stars)
  • TRIBE v2 - 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)

Computational Pathology & Digital Pathology

  • UNI (Nature Medicine 2024) - 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)
  • Prov-GigaPath (Nature 2024) - Whole-slide pathology foundation model trained on 1.3 billion image tiles from 171K slides using a LongNet-based architecture to encode gigapixel-scale WSIs for cancer subtyping and biomarker prediction (Microsoft Research & Providence, 601+ stars)
  • CONCH (Nature Medicine 2024) - Vision-language pathology foundation model using contrastive learning on histopathology image-text pairs, enabling zero-shot classification, slide-level retrieval, and multimodal reasoning across diverse cancer types (Mahmood Lab, 494+ stars)
  • PLIP (Nature Medicine 2023) - First vision-and-language foundation model for pathology AI, fine-tuned from CLIP on 249K image-caption pairs, enabling open-ended visual-semantic search and zero-shot diagnosis across histopathology (Pathology Foundation, 376+ stars)
  • TITAN (Nature Medicine 2024) - Multimodal whole-slide pathology foundation model jointly pretrained on H&E histology and diagnostic text reports, enabling zero-shot cancer subtyping, biomarker prediction, and multimodal reasoning across diverse cancer types (Mahmood Lab, 341+ stars)
  • Virchow (Nature Medicine 2024) - Self-supervised pathology foundation model (ViT-Huge, 632M parameters) pretrained via DINOv2 on 1.5M whole-slide images from Memorial Sloan Kettering across 17 cancer types, with Virchow2 follow-up scaling to 3.1M slides and mixed magnifications, achieving SOTA on biomarker prediction, mutation classification, and rare cancer detection (Paige AI & MSK)

Medical AI & Clinical Applications

  • 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)
  • 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 - 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 - 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)
  • MedSegX - 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)
  • 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)
  • MedAgents - Multi-disciplinary collaboration framework for zero-shot medical reasoning using role-playing LLM agents (ACL 2024)
  • MedAgentGym - Scalable agentic training environment for code-centric reasoning in biomedical data science
  • MedRAG - Systematic medical RAG toolkit for question answering over PubMed, StatPearls, textbooks, and Wikipedia, supporting multiple retrievers, domain LLMs, and follow-up-query workflows for benchmarked clinical/biomedical QA (ACL Findings 2024)
  • NVIDIA Biomedical AI-Q Research Agent - Deployable biomedical deep-research agent blueprint combining on-prem multimodal RAG, report generation, human-in-the-loop editing, and virtual screening with MolMIM and DiffDock for drug discovery workflows (2025)
  • nnU-Net - Self-configuring deep learning framework for semantic segmentation of biomedical images requiring no manual hyperparameter tuning; automatically adapts preprocessing, network topology, and training parameters to achieve state-of-the-art results across 120+ international competitions and benchmarks out-of-the-box (DKFZ, Nature Methods 2021, 8.3k+ stars)
  • TotalSegmentator - Robust deep learning-based segmentation of >100 anatomical structures in CT and MR images, built on nnU-Net and widely adopted in clinical radiology and surgical planning workflows (2.6K+ stars)
  • MONAI - NVIDIA and King's College London's open-source AI toolkit for healthcare imaging, providing foundational frameworks for medical image annotation (MONAI Label), training (MONAI Core), and deployment (MONAI Deploy) across radiology, pathology, and endoscopy (8K+ stars, Apache 2.0)

⚛ Chemistry & Materials

LLM for Chemistry

  • LLM4Chemistry - Curated paper list about LLMs for chemistry covering fine-tuning, reasoning, multi-modal models, agents, and benchmarks (COLING 2025)
  • ChemMCP - Extensible chemistry toolkit for MCP-enabled AI assistants, exposing molecule analysis, property prediction, and reaction synthesis tools through unified Python/MCP interfaces for chemistry agents and research workflows (Apache 2.0, 2025)

Materials Discovery

  • GNoME - 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) - Meta's comprehensive ML ecosystem for materials/chemistry with 118M+ DFT calculations, EquiformerV2 models achieving top Matbench Discovery performance
  • 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)
  • SchNetPack - PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)
  • pymatgen - Python Materials Genomics: robust materials analysis library defining classes for structures and molecules with support for many electronic structure codes; foundational toolkit powering the Materials Project (Berkeley Lab, 1.8K+ stars)
  • MACE - Machine learning interatomic potentials
  • CHGNet - Universal pretrained neural network potential with charge and magnetic moment awareness, trained on 1.5M+ Materials Project inorganic structures for charge-informed molecular dynamics and phase diagram prediction (Berkeley, Nature Machine Intelligence 2023 Cover)
  • MatterGen - Diffusion-based generative model for inorganic materials design, steering generation by chemistry, symmetry, bulk modulus, band gap, or magnetic properties, 2× more likely to produce stable novel structures than prior methods, experimentally validated with synthesized TaCr₂O₆ (Microsoft, Nature 2025)
  • MatterSim - Deep learning atomistic model across elements, temperatures, and pressures
  • ORB - Universal machine learning interatomic potential for atomistic simulation of materials, molecules, and biomolecules across the periodic table, with open-source pretrained models and inference tools (Orbital Materials, 2024-2025)
  • Crystal Graph CNNs - Crystal property prediction
  • MatBench - Materials informatics benchmark
  • Best of Atomistic Machine Learning - Curated list of atomistic ML projects for materials science

Chemical Synthesis

  • AiZynthFinder - AstraZeneca's industrial-grade retrosynthetic planning tool using MCTS to recursively decompose molecules into purchasable precursors, with multi-step route scoring and support for custom one-step models (v4.0, 2024)
  • Molecular Transformers - AI for chemical reaction prediction and synthesis planning

🌌 Physics & Astronomy

Machine Learning for Physics

  • FermiNet - DeepMind's neural network for ab-initio quantum chemistry, directly solving the many-electron Schrödinger equation via variational Monte Carlo with antisymmetric wavefunctions, extended to excited states (Phys. Rev. Research 2020, Science 2024)
  • JAX-MD - Molecular dynamics in JAX
  • Neural ODEs - Differential equations with neural networks
  • Physics-Informed Neural Networks - Physics-constrained ML
  • EquiformerV2 - Improved equivariant Transformer for 3D atomic graphs (ICLR2024)
  • Equiformer - Equivariant graph attention Transformer (ICLR2023)
  • 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)

Astronomy & Astrophysics

  • 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)
  • AION (arXiv 2025) - Polymathic AI's large omnimodal foundation model for astronomical surveys, seamlessly integrating 39 distinct data modalities including imaging, spectra, photometry, and catalog entries for similarity search, property prediction, and generative modeling across legacy surveys (MIT)
  • AstroPy - Python astronomy tools
  • Gaia Archive - Stellar data for ML
  • DeepSphere - Spherical CNNs for astronomy

🌍 Earth & Climate Science

Climate Modeling

  • GenCast - Google DeepMind's diffusion-based ensemble weather forecasting model at 0.25° resolution, outperforming ECMWF ENS on 97.2% of targets up to 15 days ahead, with open-source code and weights (Nature 2024)
  • Aurora - Microsoft's foundation model for the Earth system supporting weather, air pollution, and ocean wave forecasting at multiple resolutions, trained on 1M+ hours of diverse atmospheric data (Nature 2025)
  • Earth-Copilot - Microsoft's AI-powered geospatial Earth science application for natural-language exploration, visualization, and analysis of 130+ satellite collections, with STAC integration, multi-agent backend, MCP server, and deployable React/FastAPI stack (MIT, 2025)
  • ClimaX - First foundation model for weather and climate by Microsoft, Vision Transformer-based architecture trained on heterogeneous datasets (ICML 2023)
  • NeuralGCM - Google Research's hybrid ML/physics atmospheric model combining learned dynamics with physical constraints, outperforming traditional models on 2-15 day forecasts and 40-year climate simulation, developed with ECMWF (Nature 2024)
  • NVIDIA Earth-2 - World's first fully open, accelerated weather AI software stack with Medium Range forecasting and Nowcasting models using generative AI (January 2026)
  • Pangu-Weather - Huawei's 3D high-resolution global weather forecast model at 0.25° resolution, first AI method to comprehensively outperform traditional NWP across all variables and lead times, integrated into ECMWF operational forecasts (Nature 2023)
  • Prithvi WxC - IBM-NASA open-source 2.3B parameter weather and climate foundation model trained on 160 MERRA-2 variables, runs on desktop with fine-tuned variants for climate downscaling and gravity wave parameterization
  • ClimateBench - Climate data benchmark for ML models
  • WeatherBench - Weather prediction benchmark
  • WeatherBench2 - Next-generation benchmark for data-driven global weather models with standardized evaluation framework and curated datasets for ML forecasting (Google Research, 2024)
  • WeatherGFT - Physics-AI hybrid modeling for fine-grained weather forecasting (NeurIPS'24)
  • GeoAI - High-level open-source geospatial AI package for satellite/aerial imagery analysis, model training, inference, interactive visualization, and QGIS integration, bridging PyTorch/Transformers with remote sensing workflows (MIT, 2026)
  • segment-geospatial - Python package for segmenting geospatial data with the Segment Anything Model (SAM), enabling zero-shot object segmentation in satellite and aerial imagery for remote sensing and Earth observation (MIT, 4k+ stars)
  • Awesome Large Weather Models - Curated list of large weather models for AI Earth science
  • TerraTorch - Python toolkit for fine-tuning geospatial foundation models
  • Earth-Agent - LLM agent framework for Earth Observation with 104 specialized tools across 5 functional kits
  • AI for Earth - Microsoft's environmental AI

Remote Sensing & Geospatial AI

  • TorchGeo - PyTorch domain library for geospatial deep learning providing standardized datasets, samplers, transforms, and pre-trained models for remote sensing, land cover mapping, and environmental monitoring (Microsoft, 4K+ stars)
  • Clay Foundation Model - Open-source self-supervised vision foundation model for Earth observation by Clay Foundation (non-profit), a Masked Autoencoder ViT pretrained on multimodal satellite imagery (Sentinel-1/2, Landsat 8-9, NAIP, MODIS, LINZ DEM) with location/time embeddings, supporting classification, segmentation, change detection, similarity search, and few-shot downstream geospatial tasks (Apache 2.0, v1.5 2024-2025)
  • Satlas - Allen Institute for AI's global geospatial foundation model for satellite imagery analysis, enabling large-scale mapping of buildings, wind turbines, trees, and land cover from Sentinel-2 data with open-source weights and inference tools (2024)
  • 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) - 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

🌾 Agriculture & Ecology

Agricultural AI

  • PlantNet - Plant identification using AI and citizen science
  • AgML - Agricultural machine learning platform
  • 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)

Ecological Modeling

🤖 Foundation Models for Science

General Science Models

  • Galactica - Large language model for science
  • Intern-S1 - Open-source scientific multimodal foundation model built on a 235B MoE LLM and 6B vision encoder, continually pretrained on 5T tokens including 2.5T scientific-domain tokens, with strong results across chemistry, materials, life science, and earth science benchmarks (2025)
  • Llemma - Open language model for mathematics (7B/34B) trained on Proof-Pile-2, outperforming Minerva at equal scale on MATH benchmark, with tool use and formal theorem proving in Lean without finetuning (EleutherAI, ICLR 2024)
  • MinervaAI - Mathematical reasoning
  • PaLM-2 - Scientific reasoning capabilities

Domain-Specific Models

  • ESM - Protein language models
  • 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)
  • IBM FM4M - 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 - Chemistry-focused language model
  • BioGPT - Biomedical text generation

📈 Datasets & Benchmarks

Multidisciplinary

Biology & Medicine

  • TDC - Therapeutics Data Commons: 66 AI-ready datasets across 22 drug discovery tasks with 29 leaderboards, covering target identification, molecular generation, ADMET prediction, and clinical trial outcomes (Harvard MIMS, NeurIPS 2021/2024)
  • Protein Data Bank - Protein structures
  • ChEMBL - Chemical bioactivity data
  • Human Protein Atlas - Protein expression data
  • Chinese Medical Dataset - Comprehensive collection of Chinese medical datasets for AI research

Chemistry & Materials

Physics

  • The Well - 15TB collection of 16 large-scale numerical simulation datasets spanning fluid dynamics, MHD, astrophysics, biological systems, and acoustic scattering, with unified PyTorch dataloaders and benchmarks for training foundation models on physical sciences (Polymathic AI, NeurIPS 2024)
  • LIGO Open Science Center - Gravitational wave data
  • Particle Data Group - Particle physics data
  • OpenQuantumMaterials - Quantum materials data

💻 Computing Frameworks

Machine Learning

  • PyTorch - Deep learning framework
  • JAX - High-performance ML research
  • TensorFlow - End-to-end ML platform

Scientific Computing

Scientific Machine Learning Frameworks

  • SciML - Scientific machine learning ecosystem
  • DifferentialEquations.jl - Multi-language suite for high-performance differential equation solving and scientific machine learning (3.0k+ stars)
  • ModelingToolkit.jl - Acausal modeling framework for automatically parallelized scientific machine learning (1.5k+ stars)
  • SciMLBenchmarks.jl - Scientific machine learning benchmarks & differential equation solvers
  • NeuralPDE.jl - Physics-informed neural networks (PINNs) for solving partial differential equations (1.1k+ stars)
  • DiffEqFlux.jl - Neural ordinary differential equations with O(1) backprop and GPU support (900+ stars)
  • Optimization.jl - Unified interface for local, global, gradient-based and derivative-free optimization (800+ stars)
  • PaddleScience - SDK & library for AI-driven scientific computing applications
  • Flux.jl - Machine learning in Julia

Specialized Frameworks

  • MDAnalysis - Molecular dynamics analysis
  • 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 - Neural network foundation model that directly generates MD trajectories bypassing force calculations, accelerating simulations by up to 100× with equivariant Transformer architecture (2025)
  • ASE - Atomic Simulation Environment for materials modeling
  • PyMC - Probabilistic programming
  • AI2BMD - Microsoft's AI-powered ab initio biomolecular dynamics simulation achieving quantum-mechanical accuracy for proteins with 10,000+ atoms, orders of magnitude faster than DFT using protein fragmentation and ML force fields (Nature 2024)
  • OpenMM - High-performance molecular simulation toolkit
  • DeePMD-kit - Deep learning package for many-body potential energy representation and molecular dynamics, achieving quantum-mechanical accuracy with classical MD efficiency (DeepModeling, Gordon Bell Prize 2020, 1.9k+ stars)
  • Newton - GPU-accelerated differentiable physics simulation engine built on NVIDIA Warp, supporting rigid/soft body, cloth, and gradient-based optimization for scientific ML, initiated by Disney Research, DeepMind, and NVIDIA (Linux Foundation, Apache 2.0, 2025)
  • PennyLane - Cross-platform library for differentiable programming of quantum computers with automatic differentiation, enabling hybrid quantum-classical machine learning for quantum chemistry, quantum physics, and NISQ algorithm research (Xanadu, 3k+ stars)

🎓 Educational Resources

Courses & Tutorials

Open Access Educational Materials

📋 Paper Collections & Repositories

YouTube Channels

🏛 Research Communities

Conferences

Organizations

Online Communities

📚 Related Awesome Lists

This project builds upon and complements several excellent resources:

🎯 Specialized Collections

📊 Paper & Research Collections

🌟 Key Insights from These Collections

  • Current Focus: Shift from tool-level assistance to autonomous scientific agents
  • Emerging Trends: Multi-modal scientific models, self-improving research systems
  • Research Gaps: Evaluation frameworks, ethical governance, human-AI collaboration
  • Future Directions: Fully autonomous discovery cycles, robotic lab integration

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

How to Contribute

  1. Fork this repository
  2. Add your resource in the appropriate section
  3. Ensure the format matches existing entries
  4. Submit a pull request with a clear description

Contribution Guidelines

  • Ensure the resource is actively maintained
  • Include a brief, clear description
  • Check for duplicates before adding
  • Use proper markdown formatting

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Special thanks to all researchers and developers pushing the boundaries of AI for Science. This list is inspired by the awesome community and the transformative potential of AI in scientific discovery.

Star ⭐ this repository if you find it helpful!

Last updated: May 2026 - Enhanced with 2025-2026 breakthroughs in autonomous research, equation discovery, scientific foundation models, spatial transcriptomics agents, biomedical image analysis foundation models, and deep potential molecular dynamics; added FunSearch to Research Agents & Autonomous Workflows; added pymatgen to Chemistry & Materials; added TESSERA to Earth & Climate Science; added nnU-Net to Biology & Medicine; added OpenCRISPR to Biology & Medicine; added Camyla to Research Agents; added TRIBE v2 to Biology & Medicine; added SciWrite to Research Workbench & Plugins; added Chroma to Biology & Medicine; added AlphaMissense to Genomics & Bioinformatics; added Clay Foundation Model to Remote Sensing & Geospatial AI; added MONAI to Medical AI & Clinical Applications; added TotalSegmentator to Medical AI & Clinical Applications; added LeanDojo to Domain-Specific Research Agents; added Biomni to Domain-Specific Research Agents; added TxAgent to Domain-Specific Research Agents; added ScienceBoard to Research Agents Evaluation & Benchmarking; added RFantibody to Biology & Medicine; added NewtonBench to Research Agents Evaluation & Benchmarking; added HelixFold3 to Protein & Drug Discovery; added Genie 2 to Protein & Drug Discovery; added MLE-Bench to Research Agents Evaluation & Benchmarking; added Stack to Genomics & Bioinformatics; added UNI, Prov-GigaPath, CONCH, and PLIP to Computational Pathology & Digital Pathology; added Virchow to Computational Pathology & Digital Pathology; added MedSAM2 to Medical AI & Clinical Applications; added PLACER to Protein & Drug Discovery; added CEBRA to Neuroscience & Behavioral Analysis; added TorchGeo to Remote Sensing & Geospatial AI; added AION to Astronomy & Astrophysics; added SchNetPack to Chemistry & Materials; added micro-sam to Medical AI & Clinical Applications; added LigandMPNN to Protein & Drug Discovery; added AlphaGenome to Genomics & Bioinformatics; added REINVENT to Protein & Drug Discovery; added Poseidon to Scientific Machine Learning; added TITAN to Computational Pathology & Digital Pathology; added PaperBanana to Paper→Poster / Slides / Graphical Abstract; added NeuralPLexer to Protein & Drug Discovery; added Lean Copilot to Domain-Specific Research Agents; added Foldseek to Protein & Drug Discovery; added OpenEvolve to Research Agents & Autonomous Workflows

About

A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery — from physics and chemistry to biology, materials, and beyond.

github.com/ai-boost/awesome-ai-for-science

Topics

awesome bioinformatics awesome-list scientific-ai ai4science ai-for-science ai4s

Resources

Readme

License

MIT license

Contributing

Contributing
Activity

Stars

1.6k stars

Watchers

14 watching

Forks

163 forks
Report repository

Releases

No releases published

Packages

 
 
 

Contributors