Find open-source science resources

World's first fully open, accelerated weather AI software stack with Medium Range forecasting and Nowcasting models using generative AI (January 2026)

Open-source, all-atom biomolecular foundation model that turns co-folding into a scalable engine for structure prediction, design, and optimization across proteins, nucleic acids, and small molecules in drug discovery; ranked first on PXMeter-AB, FoldBench-AB, and 2026ARK-AB antibody-antigen benchmarks (263+ stars, Apache 2.0)

Next-generation benchmark for data-driven global weather models with standardized evaluation framework and curated datasets for ML forecasting (Google Research, 2024)

A quantum chemistry package written in Python.

Universal components for differentiable scientific computing, packaging heterogeneous scientific tools into self-contained, portable, gradient-propagating components with auto-generated schemas, CLI/REST API/Python SDK interfaces, and reproducible deployment across local, cloud, and HPC environments (105+ stars, Apache 2.0)

OpenProteo is the open-source Rust stack for proteomics raw-file access. It reads Thermo, Bruker, and Waters acquisitions through a single API (via the sibling OpenTFRaw, OpenTimsTDF, and OpenWRaw readers), converts them to PSI-MS mzML 1.1.0 with a canonical writer, and provides a zero-copy read_arrow() API (enabled by default) that loads directly into Polars or Pandas via PyArrow. No vendor SDKs, no Windows-only DLLs, no binary blobs in the release pipeline. Includes a one-shot vendor2mzml CLI.

OpenWRaw is a standalone, cross-platform reader for Waters MassLynx .raw acquisition directories, implemented in pure Rust with no dependency on vendor DLLs. Python bindings built on PyO3 expose functions, scans, and ion-mobility data as native Python objects from Waters QTof and SYNAPT instrument families, ready to be assembled into a Pandas or Polars DataFrame.

OpenTimsTDF is a standalone, cross-platform reader for Bruker timsTOF .tdf and .tdf_bin acquisition files, implemented in pure Rust with no dependency on vendor SDKs. Python bindings built on PyO3 expose frame, scan, and peak data as native Python objects, providing ion-mobility-aware access that can be assembled into a Pandas or Polars DataFrame.

OpenTFRaw is a standalone, cross-platform reader for Thermo Fisher Scientific .raw mass-spectrometry files, implemented in pure Rust with no dependency on vendor DLLs or .NET. Python bindings built on PyO3 return NumPy arrays for spectral data, straightforward to load into Pandas or Polars. Covers format versions 8 through 66 (LCQ Classic through Orbitrap Astral and modern TSQ instruments), supporting both centroid and profile spectra.

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)

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)

Open-source framework for building physics-ML models at scale (renamed from Modulus, 2025)

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)

SQUARNA is a tool for RNA secondary structure prediction. It can take a single RNA sequence or an alignment of sequences as input. SQUARNA handles pseudoknots and can predict alternative structures. SQUARNA allows structural restraints and chemical probing data as additional input and is available at https://github.com/febos/SQUARNA and https://larnal.imol.institute/.

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)

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)

AlphaFold 3 inference pipeline for unified biomolecular structure prediction of proteins, nucleic acids, small molecules, ions, and post-translational modifications (Google DeepMind, Nature 2024)

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)

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)

Generalist autonomous research agent that grows a hypothesis tree to optimize any measurable task, beating Claude Code and Codex by 2.5× on the same compute budget across BrowseComp, Terminal-Bench 2.0, math reasoning, and MLE-Bench Lite; supports native CLI, keyless Claude Code/Codex integration, and an MCP tool server (RUC-NLPIR, 866+ stars, Apache 2.0, 2026)

Local-first, open-source healthcare AI toolkit for clinical NLP and PHI/PII de-identification across 12 languages, running entirely on-device with 1,000+ specialized medical models; provides Python SDK, REST API, Docker deployment, and native Swift apps via OpenMedKit with Apple MLX/CoreML acceleration, supporting HIPAA-aware de-identification with 247 PII checkpoints (3K+ stars, Apache 2.0, arXiv 2508.01630)

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)

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)

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)

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)

Python toolkit for fine-tuning geospatial foundation models

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)

Py-HLA-Match is a Python library for standardised, rule-based HLA (Human Leukocyte Antigen) matching in retrospective analyses, method development, benchmarking, and in-silico studies in immunogenetics and related fields.

The information resource registry is a listing of data sources present in the NCATS Data Translator system. Each information resource has an identifier, a short description, and a URL to more information about that resource.

A Molecular Interaction-Guided Graph Learning Framework for Multi-Omics Cancer Classification

Python library for blazing-fast genomic interval operations and genomic file formats I/O on Polars DataFrames

Machine learning toolkit for many-body quantum systems, implementing neural quantum states, variational Monte Carlo, and tensor network algorithms to solve ground-state and dynamical problems in condensed matter physics and quantum chemistry (EPFL & collaborators, Nature Physics 2019/2022+, 670+ stars)

Ontology representation of the [International Committee on Taxonomy of Viruses (ICTV)](https://ictv.global/) for the [EVORA project](https://evora-project.eu/)

Controllable foundation model for general and specialized biomolecular structure prediction across proteins, nucleic acids, and complexes, featuring a public web server for interactive prediction workflows (IntelliGen AI, 223+ stars, Apache 2.0, 2025)

Open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives, enabling quantum algorithm development for quantum chemistry, materials science, and optimization research (IBM, 7.4K+ stars, Apache 2.0)

Agricultural machine learning platform

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)

Fully open-source (Apache 2.0) biomolecular structure prediction reproducing AlphaFold3, free for academic and commercial use (Columbia AlQuraishi Lab & OpenFold Consortium, 2025)

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)

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)

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)

Democratizing AI scientists by transforming any LLM into research systems with 600+ scientific tools (Harvard MIMS)

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)

Google DeepMind's official collection of agentic science skills accelerating scientific workflows with better grounding and higher token efficiency, integrating insights from AlphaGenome, AFDB, UniProt and 30+ other databases and tools (2026)

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)

High-performance ML research

Family of causal genomic foundation models trained on 1T tokens (~6T DNA base pairs) from the Carbon Pretraining Corpus, combining eukaryote genes, mRNA transcripts, and prokaryote genomes with a hybrid text/6-mer tokenizer; Carbon-3B matches or beats Evo2-7B on zero-shot DNA evaluations including sequence recovery, variant effect prediction, and perturbations (Apache 2.0, 201+ stars)

High-accuracy RAG for scientific PDFs with citation support, agentic RAG, and contradiction detection

Language agent gymnasium for challenging scientific tasks including DNA manipulation, literature search, and protein engineering