Find open-source science resources

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)

A structural pharmacology workbench for cognition-related CNS targets, built so that a displayed number must trace to a computation. Runs Boltz-2 locally for structure prediction, validates chemistry with RDKit, enforces a provenance record on every value, and reports eight studies pre-registered under content hashes before any data was seen. Its headline result is negative: designed peptides did not separate from composition-matched shuffles of their own amino acids.

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)

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

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)

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 PDF parser for AI-ready data, converting PDFs into Markdown/JSON/HTML/Tagged PDF with layout analysis and reading-order detection; ranks #1 overall on extraction benchmarks with deterministic bounding boxes and hybrid AI mode (26K+ stars, Apache 2.0)

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)

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)

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)

Multi-type data labeling and annotation tool

Python toolkit for fine-tuning geospatial foundation models

A client to simplify fetching predictions from the Koina web service. Koina is a model repository enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic.

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.

Universal molecular toolkit that can be used for molecular fingerprinting, substructure search, and molecular visualization written in C++ package, with Java, C#, and Python wrappers.

Machine learning software for extracting structured metadata from scholarly documents

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)

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)

Multimodal AI system generating virtual populations for tumor microenvironment modeling from H&E and multiplex immunofluorescence pathology images, enabling large-scale spatial analysis of cancer biology and therapeutic response prediction (Microsoft Research & Providence, 370+ stars)

High-accuracy PDF→Markdown/JSON/HTML conversion, specialized for tables/formulas/code blocks with benchmark scripts

ECMWF's unified framework and command-line tool to run AI-based weather forecasting models (GraphCast, Aurora, Pangu, NeuralGCM, FourCastNet) with operational ECMWF data infrastructure, enabling standardized inference and benchmarking across state-of-the-art meteorological AI systems (ECMWF, 576+ stars)

linkset-automation is a set of tools to automatically generates CyTargetLinker linksets from different resources, starting with WikiPathways.

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)

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)

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)

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)

Production-grade ETL for transforming complex documents into structured formats, with open-source API

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)

ReviewAid is an open-source AI-assisted tool for full-text screening and data extraction in systematic reviews. It supports evidence synthesis workflows by using large language models to classify articles according to user-defined PICO criteria and extract structured information from full-text publications. ReviewAid is designed as a supplementary reviewer rather than a replacement for human judgement. It aims to reduce manual workload, improve consistency, and assist researchers during screening and data extraction while maintaining human oversight throughout the evidence synthesis process.

NOS-TLPlot is an open-source tool for visualizing Newcastle–Ottawa Scale (NOS) risk-of-bias assessments in systematic reviews. It converts NOS star ratings into publication-ready traffic-light plots and 12 specialized visualizations, enabling reviewers and readers to interpret study-level risk-of-bias results clearly and reproducibly.

RejuvenationKit is an open-source Python toolkit for reproducible auditing and analysis of longitudinal preclinical rejuvenation studies. It provides protocol-aware missingness checks, experimental-confounding diagnostics, attrition and analysis-readiness profiling, covariance-aware multichannel change detection, sequential response monitoring, randomized longitudinal inference, visualization, and integrity-tracked report bundles.

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

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/.

PyTorch-based embedding instance segmentation algorithm optimized for accurate, efficient, and portable cell and nucleus segmentation across fluorescence and brightfield microscopy images, achieving state-of-the-art speed and accuracy with lightweight model sizes suitable for edge deployment (224+ stars, Apache 2.0)

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)

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)

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

nnU-Net is a self-configuring method for deep learning-based biomedical image segmentation, developed by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ). It is designed to automatically adapt to a given dataset, analyzing the provided training cases to configure a matching U-Net-based segmentation pipeline without requiring expertise from the user. The tool provides pretrained models for Pancreas and Pancreas tumor segmentation, Colon cancer primaries segmentation, Abdominal organ segmentation, Liver and liver tumor segmentation, Kidney and kidney tumor segmentation, Brain Tumor segmentation and Hippocampus (MR data) segmentation

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

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)

SDK & library for AI-driven scientific computing applications

200+ AI for Science papers with Chinese interpretations

Auto-generates clean, customizable academic CVs from open research data (OpenAlex, ORCID, Crossref, DataCite, Open Editors Plus). A single canonical CV object drives every output format (HTML, PDF, DOCX, LaTeX, Markdown); citations render through CSL; and the account holder is matched by persistent identifier (ORCID / OpenAlex ID) rather than name string. Free for individuals, open-source, and FAIR by design.

Robust, lightweight infrastructure for multi-agent autonomous self-evolution, built for autoresearch; agents run in isolated git worktrees, share knowledge through a common state directory, and are scored by a grader daemon; natively integrated with Claude Code, Codex, Cursor Agent, OpenCode, and Kiro (672+ stars, Apache 2.0)

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)

SMBGC Annotation using Neural Networks Trained on Interpro Signatures

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)

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)