Find open-source science resources

Decentralized self-organizing teams of AI agents for long-running computational scientific experimentation; agents critique each other's proposals before spending compute and share successes/failures to avoid redundant exploration, achieving +8.33% on BioML-Bench, 1.9× faster nanoGPT optimization, and +12.5% on ProteinGym ACE2-Spike (425+ stars, 2026)

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)

End-to-end autonomous AI research engine that turns an idea into a complete LaTeX paper by dispatching real computational experiments to local GPUs or SLURM clusters, collecting actual results, generating figures/tables, and writing a data-grounded manuscript rather than LLM hallucinations (OpenRaiser, 1.5K+ stars, MIT License, 2026)

Generalized biological foundation model with unified nucleic acid and protein language, integrating DNA/RNA/protein sequences (Nature Machine Intelligence 2025)

Automates and standardizes ligand preparation for AutoDock Vina.

Simulation of large-scale brain models

3D vision-language model for computed tomography that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining, enabling multimodal medical understanding and radiology report generation (447+ stars, MIT License, 2026)

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)

Automate downloading, opening, and parsing DrugBank.

LLM agents for working with the SRA (Sequence Read Archive) and associated bioinformatics databases, enabling natural language querying of high-throughput sequencing data and metadata across genomic repositories (Arc Institute, 169+ stars, 2024-2026)

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)

Biological vision foundation model trained on TreeOfLife-200M, yielding extraordinary accuracy on diverse biological visual tasks including habitat classification and trait prediction despite a narrow training objective (Ohio State University Imageomics Institute)

Open-ended AI scientist for computational fluid dynamics that spans literature-grounded ideation, OpenFOAM execution via Foam-Agent, vision-language physics verification of rendered flow fields, source-code modification for new physical models, and figure-grounded LaTeX manuscript writing within a single inspectable workflow (43+ stars, Python)

A package for accessing data from the NIST webbook...

- Molecular Manipulation Made Easy. A light wrapper build on top of RDKit.

Convert AMBER forcefields from ANTECHAMBER to GROMACS format.

Unified pre-trained model for general physics simulation via lifted geometric pre-training, augmenting static geometry with synthetic dynamics to enable dynamics-aware self-supervision without physics labels; improves industrial-fidelity benchmarks spanning fluid mechanics and solid mechanics while reducing labeled data requirements by 20–60% (Physics-Scaling, 224+ stars)

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

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)

Parallel symbolic regression network evaluating millions of expressions on GPU with automated subtree reuse, Nature Computational Science cover article (MIT, 2026)

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)

First fully customizable open-source multiagent framework automating complete research lifecycle from idea conception to LaTeX papers with dynamic workflows

Behavioral and neuroimaging experiments.

General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)

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)

Generates pre-miRNA and mature miRNA count tables from read alignments to pre-miRNA sequences and a gff file, both downloaded from mirBase. Produces also read coverage plots of pre-miRNAs.

The "FRamewOrk for Molecular AGgregate Excitations" enables localised QM/QM' excited state calculations in a solid state environment.

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

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)

Fast, all-atom SE(3)-equivariant diffusion model for protein design achieving state-of-the-art performance on unconditional generation, motif scaffolding, and binder design while retaining the computational efficiency of equivariant architectures (bioRxiv 2026)

Medical large vision-language model unifying comprehension and generation via heterogeneous knowledge adaptation, enabling holistic medical image understanding, visual question answering, and clinical report generation across diverse modalities (ZJU4HealthCare, 1.6K+ stars)

Trainable PyTorch reproduction of AlphaFold 3

Benchmark quantifying end-to-end autonomous AI research abilities of LLM agents across 20 tasks from SOTA machine learning papers spanning NLP, code, math, biochemical modelling, and time series forecasting, with normalized score metrics against human SOTA and HuggingFace dataset

General-purpose RNA language model with 650M parameters pretrained on 36M non-coding RNA sequences, achieving strong generalization on structure prediction tasks including secondary structure prediction, splice-site prediction, mean ribosome loading, and ncRNA classification (lbcb-sci, 165+ stars, Apache-2.0)

First physics-aligned interactive benchmark for LLM agents in engineering construction, designing rockets/cars/bridges in physics simulator with 3D spatial geometry library

Simple RDKit molecule editor GUI using PySide.

Semi-flexible molecular diffusion model for structure-based drug design with reinforcement learning, achieving 20× faster sampling and providing a no-code web platform for molecular design (ISPC Lab, Tongji University, 2026)

Incremental knowledge graph construction using LLMs with entity extraction and Neo4j visualization

Fast spike sorting with drift correction for extracellular electrophysiology, enabling universal neural spike sorting via deep learning on high-density neural probe recordings (MouseLand, 609+ stars)

The application of 2nd and 3rd generation High Throughput Sequencing (HTS) technologies has deeply reshaped experimental method to investigate microbial communities and obtain a taxonomic and functional profile of the invetigated community. Shotgun Metagenomics allow to quickly obtain a representation of microorganisms genomes characterizing a particular environment. In order to obtain a fast e reliable taxonomic classification of microorganisms genomes we present kMetaShot, an alignment-free taxonomic classifier based on k-mer/minimizer counting.

Trackastra is a transformer-based cell-tracking tool for live-cell microscopy. It links already segmented cell instances across time by predicting associations between detections. It supports greedy tracking with or without cell divisions, optional ILP-based linking, pretrained tracking models, and export of tracked masks and lineage information in Cell Tracking Challenge format.

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%)

Protein structure prediction

Distributional flow matching model for robust single-cell perturbation prediction, modeling the full distribution of perturbed cellular expression profiles conditioned on control states via PAD-Transformer and multi-kernel MMD regularization; reduces MSE by 19.6% over the strongest baseline in combinatorial settings (Westlake University, 41+ stars, MIT License)

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)

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

End-to-end molecular dynamics engine built on PyTorch, enabling differentiable simulations with neural network potentials and GPU acceleration for machine learning-accelerated molecular dynamics (MIT License, 707+ stars)

Scaling efficient, expressive, and general SE(3)-equivariant graph attention transformers for atomic systems and machine-learned interatomic potentials (MIT License, 2026)

Pretrained machine-learned force field for (bio)molecular simulations combining the fast SO3krates neural network for semi-local interactions with universal pairwise force fields for short-range repulsion, long-range electrostatics, and dispersion interactions; supports geometry optimization, NVT/NPT/NVE MD, fine-tuning, ASE calculator, and JAX-MD integration (JACS 2025, 218+ stars, MIT License)

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)