Find open-source science resources

# DrugGen 2: A disease-aware language model for enhancing drug discovery DrugGen-2 is a disease‑aware language model specialized for generating drug-like SMILES structures based on both disease pathways and protein sequence.

Plain-text, git-tracked electronic lab notebook (ELN) for reproducible bioinformatics — threads your R & Python figures into living lab notes with full provenance. Built for single-cell / CyTOF / flow cytometry; works with Obsidian, Quarto & Jupyter.

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

Fast, interactive, multi-dimensional image viewer for Python, foundational platform for scientific imaging AI with a rich plugin ecosystem integrating deep learning segmentation, object tracking, and microscopy analysis workflows (2.6K+ stars)

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)

💻 Github | 📄 Report (Coming soon...) | 🚀 Demo

!image

Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science with 140+ ready-to-use skills and 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Antigravity, and the open Agent Skills standard (K-Dense-AI, 26K+ stars, 2025)

A 1.7B-parameter causal language model distilled from Qwen3-30B-A3B on 6,122 STEM chain-of-thought samples using discrepancy-informed knowledge distillation. The training objective emphasizes proof structure, detects reasoning pivot tokens through token-level divergence dynamics, smooths…

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

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)

> NEXUS domain specialist for medical Q&A and clinical reasoning — lightweight & uncensored.

Machine learning interatomic potentials

A library and command-line tool for building and analyzing complex homogeneous microkinetic models from quantum chemistry calculations, with support for quasi-harmonic thermochemistry, quantum tunnelling corrections, molecular symmetries and more.

MEG and EEG.

A quantum chemistry package written in Python.

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)

Probabilistic framework for inferring cell fate decisions and trajectory dynamics from multi-view single-cell data using Markov chains and machine learning, integrating RNA velocity, pseudotime, and metabolic labeling to predict differentiation paths and terminal states (scverse/Theis Lab, 449+ stars, BSD 3-Clause)

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)

Modular Python suite for Neuro-AI research across all modalities, providing efficient data loaders (NeuralSet), curated datasets (NeuralFetch), scalable training (NeuralTrain), and unified benchmarking (NeuralBench) for building and evaluating neuroscience foundation models (Meta FAIR, 270+ stars, MIT License, 2026)

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)

This repository contains LoRA finetunes of DiffusionGemma (image-conditioned discrete-diffusion LLM) for radiology visual question answering, each paired with an autoregressive Gemma-4 finetune as a controlled baseline. It corresponds to the paper Discrete Diffusion Language Models for Interactive…

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)

Composite-objective protein design framework integrating Boltz, AlphaFold2, OpenFold3, ProteinMPNN, and ESM via JAX-based gradient optimization over continuous relaxed sequence space for multi-property binder design (319+ stars, MIT License, 2025)

Toolkit for large-scale whole-slide image processing supporting 22+ patch encoders (UNI, CONCH, Virchow, H-Optimus-0, etc.), slide encoders (TITAN, GigaPath, PRISM, CHIEF, Madeleine, Feather), tissue segmentation, and multi-GPU inference with end-to-end pipeline and smart resume for standardized deployment of computational pathology foundation models (Mahmood Lab, Harvard Medical School, 553+ stars)

Agent skills (SKILL.md + deterministic tools) for the AI4S workflow — topic exploration, literature survey, runnable experiments, publication-grade papers, and integrity audit, with every citation and number traceable to its source (by ai4s-research, maintainers of this list; MIT, 2026)

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/.

Collection of SKILLS.md guiding AI coding agents (Claude Code, OpenAI Codex, Google Gemini, OpenCode, OpenClaw) through common bioinformatics workflows from basic sequence manipulation to advanced analyses such as single-cell RNA-seq and population genetics; evaluated on the Bio-Task Bench dataset (GPTomics, 969+ stars, MIT License, 2026)

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)

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)

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)

🤗 Blog | 📄 Paper | 💻 Code | 🌐 FineMed | 🩺 DoctoBERT

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)

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)

Parameter/topology editor and molecular simulator with visualization capability.

Directed message passing neural networks for property prediction of molecules and reactions with uncertainty and interpretation.

Heretic-abliterated version of Qwen/Qwen2.5-0.5B-Instruct for the Evolva drug discovery pipeline.

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)

First agentic LLM for autonomous data science with end-to-end pipeline from data to analyst-grade reports

A data model for managing information about chemical entities, ranging from atoms through molecules to complex mixtures.

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)