Find open-source science resources

Machine learning interatomic potentials

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

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)

Neural network-based exchange-correlation functional for density functional theory (DFT) that surpasses state-of-the-art hybrid functionals in accuracy for main-group thermochemistry, kinetics, and non-covalent interactions at semi-local DFT cost; includes PySCF/GPU4PySCF/ASE bindings and C++/Fortran integrations (248+ stars, MIT License)

RAiSD-AI is a tool for training, testing, and deploying Convolutional Neural Networks to detect selective sweeps in genomic data, extending the functionality of the original RAiSD software with machine learning capabilities. It supports SNP data processing, CNN model training with TensorFlow or PyTorch, and genome-wide selective sweep detection.

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)

Universal graph neural network framework for global-to-regional Earth system forecasting, combining multi-grid theory with a dynamic-system perspective to build multi-scale graphs that densify target regions for local high-frequency features; adaptive message passing with dynamic gating units is theoretically proven to act as high-pass filtering against over-smoothing, and a neural nested-grid method mitigates boundary information loss in high-resolution regional forecasts; extended to causally-coupled ocean-atmosphere cross-sphere modeling with strong extreme-event prediction, releasing inference/training code, pretrained weights, and preprocessed data (Renmin University & PolyU, 213+ 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)

Deep learning library for Chemistry based on Tensorflow

Deep learning atomistic model across elements, temperatures, and pressures

Python package for simulation-based inference enabling likelihood-free Bayesian parameter estimation from scientific simulators, with flexible interfaces for neural posterior estimation, sequential methods, and MCMC/variational backends (Mackelab, 825+ stars)

Manipulation and analysis of geometric objects.

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)

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

Fully autonomous research from idea to paper with multi-agent debate, citation verification, and OpenClaw integration (11K+ stars, 2026)

Open-source PyMOL plugin for membrane-aware review of predicted, designed and experimental protein structures. Membrane Visual QC provides planar membrane-relative geometry, residue core/interface classification, hydropathy and ligand-context review, solvent-accessibility context, PDBTM/OPM orientation-source checks, and reproducible batch reporting. It is designed as a review assistant rather than a biological structure validator.

Scikit-learn compatible tabular foundation model for zero-shot classification and regression on mixed-type tabular datasets via in-context learning; applicable to diverse scientific datasets (1.8K+ stars, Apache 2.0)

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)

A Python package useful for chemistry (mainly physical/inorganic/analytical chemistry)

Multi-LLM consensus framework for automated cell type annotation in single-cell transcriptomics, integrating predictions from 10+ large language models with iterative discussion and uncertainty quantification to reduce single-model biases, achieving up to 95% accuracy without reference datasets; available as CRAN R package and PyPI Python package with Scanpy/Seurat integration (2025)

Deep learning library for solving PDEs

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)

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.

LLM-driven machine learning engineering agent using agentic tree search to autonomously draft, debug and benchmark ML code; wins 4× more medals than the best linear agent on OpenAI's MLE-Bench (75 Kaggle competitions) (1.3K+ stars, MIT License)

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.

Knowledge graph-guided synthetic data generation for LLM fine-tuning, achieving strong performance on scientific QA (GPQA-Diamond) and math reasoning (AIME)

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

A package for working with nuclear magnetic resonance (NMR) data including functions for reading common binary file formats and processing NMR data.

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)

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)

A quantum chemistry package written in Python.

Calculate mass, elemental composition, and mass distribution spectrum of a molecule given by its chemical formula, relative element weights, or sequence.

MCP server enabling spatial transcriptomics analysis via natural language, integrating 60+ methods including SpaGCN, Cell2location, LIANA+, CellRank for Visium, Xenium, MERFISH platforms

Exact, validated excision of coordinate-defined genomic regions from transposed NEXUS matrices.

Semi-autonomous AI scientist for scientific theory discovery and verifiable goal solving, using adversarial review-refinement loops and evolution-inspired candidate populations; integrates with Claude Code, Gemini CLI, Antigravity, and Codex harnesses (Imbue, 31+ stars, AGPL-3.0, 2026)

Pretrained time series foundation model for zero-shot forecasting across diverse scientific and real-world domains; tokenizes continuous time series into discrete bins to train transformer language models on large-scale corpora, achieving strong zero-shot generalization and competitive performance with task-specific supervised models on climate, energy, and health benchmarks (5.3K+ stars, Apache 2.0, 2024-2026)

End-to-end composable multi-agent framework for automating OpenFOAM-based CFD simulations from natural language prompts, managing meshing, case setup, execution, error correction, and post-processing; achieves 100% success rate on 110 FoamBench tasks with Claude Opus 4.6 through Architect-Input Writer-Runner-Reviewer agent collaboration with RAG-enhanced generation and MCP tool integration (RPI CSML, 242+ stars, MIT License)

Modular toolchain for an extensible and customizable ETL pipeline that extracts, transforms, and loads clinical data and medical imaging metadata, applying dataset-specific mappings to generate outputs compatible with the EUCAIM Common Data Model (CDM). Its design aims to minimize manual data preparation efforts and facilitate customization and integration with other components, such as data quality assurance tools. Containerized, currently supports input datasets in CSV, JSON, XLSX.

AI-assisted structural engineering workspace for AEC workflows: natural language to structural model, analysis, code-check, and report (171+ stars, MIT License, 2026)

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)

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

Open-source JAX-based software suite for variational optimization of deep-learning molecular wave functions, solving electronic ground and excited states via neural-network trial wave functions with configurable FermiNet, PauliNet, Psiformer, LapNet, and DeepErwin ansätze, geometric transferability across molecular configurations, and effective core potential support (FU Berlin / Noé group, J. Chem. Phys. 2023, 420+ stars, MIT License)

U-Net-style deep neural network for P/S seismic arrival-time picking trained on millions of waveforms from the Northern California Earthquake Data Center, achieving near-analyst picking precision at orders-of-magnitude higher speed and robustness to low signal-to-noise traces where STA/LTA fails; a foundational reference for deep-learning phase picking, integrated into SeisBench model collections and national seismic networks, with PhaseNet-DAS extending it to distributed acoustic sensing (Stanford AI4EPS, 386+ stars, MIT License, actively maintained)

Human-centered research OS with terminal-first harness and local browser Studio, turning research work into reproducible artifact-backed runs through a 9-stage workflow with human approval gates, resume/rollback controls, and venue-aware manuscript packaging (1K+ stars, 2026)

EcoliTyper is a revolutionary bioinformatics pipeline that eliminates workflow fragmentation in E. coli genomic surveillance. By integrating nine core analyses into a single automated workflow, EcoliTyper transforms disconnected genomic data into coherent biological narratives with actionable public health intelligence. It is a species-optimized computational pipeline for comprehensive genotyping and surveillance of Escherichia coli, perfect for clinical microbiology, outbreak investigations, and genomic research.

ProSeqGO predicts Gene Ontology (GO) terms for protein sequences using ESM2 embeddings and a trained 1-Dimensional Convolutional Neural Network multi-label classifier. By integrating recent advances in protein language models, ProSeqGO facilitates large-scale, automated functional annotation directly from sequence input, empowering researchers to infer protein function, explore biological mechanisms, and accelerate discovery in genomics and proteomics.

RiSPICE (Rice SNP Prioritization Integrating Chromatin Effects) is a computational framework for prioritizing non-coding rice variants by integrating predicted chromatin effects from a fine-tuned DNA language model.

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)

Vision foundation model for the tree of life, pretrained on diverse biological imagery across taxa for zero-shot species identification, trait extraction, and biodiversity research (Ohio State University Imageomics Institute)