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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)

A Simulation Tool for Fractured and Deformable Porous Media.

MITE (Minimum Information about a Tailoring Enzyme) is a data repository and associated data standard designed to capture the reaction- and substrate-specificities of tailoring enzymes. Community-driven and fully expert-reviewed, it represents enzymatic reactions using reaction SMARTS and links to established resources such as UniProt, NCBI GenPept, Rhea, and MIBiG. MITE serves as a knowledgebase for enzyme and pathway annotation, in silico biosynthesis, and machine learning applications.

The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset).

Healthcare Brain Procedure Surgery NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of surgical procedures, diagnostic tests, interventions, and procedural details from unstructured clinical text.

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)

Healthcare Brain Vitals NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of vital signs, body measurements, and physiological parameters from clinical text.

Healthcare Brain Laboratory NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of laboratory test results, values, units, reference ranges, and abnormality flags from unstructured clinical text.

Healthcare Brain Diagnosis ICD NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of diagnoses, conditions, and support for ICD-10/SNOMED code mapping from unstructured clinical text.

Healthcare Brain Medication NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of medication names, dosages, routes, frequencies, and administration details from unstructured clinical text.

Healthcare Brain Clinical Findings NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of clinical findings, diseases, conditions, anatomical locations, and clinical modifiers from unstructured clinical text.

Healthcare Brain NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated detection and de-identification of Protected Health Information (PHI) and Personally Identifiable Information (PII) in unstructured clinical text.

JCVI is a versatile toolkit for comparative genomics analysis. It is a collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.

High-performance ML research

Molecular dynamics in JAX

CliniGuard Laboratory NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of laboratory test results, values, units, reference ranges, and abnormality flags from unstructured clinical text.

CliniGuard Diagnosis ICD NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of diagnoses, conditions, and support for ICD-10/SNOMED code mapping from unstructured clinical text.

CliniGuard Medication NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of medication names, dosages, routes, frequencies, and administration details from unstructured clinical text.

CliniGuard Clinical Findings NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of clinical findings, diseases, conditions, anatomical locations, and clinical modifiers from unstructured clinical text.

Comprehensive Claude Code skill suite covering the full academic pipeline from deep research and paper writing to multi-perspective peer review, revision, and finalization; features multi-agent teams, PRISMA systematic review, style calibration, claim-level citation audits, integrity gates, and human-in-the-loop safeguards (38K+ stars, CC BY-NC 4.0, 2026)

Official Jupyter extension with `%%ai` magic commands and sidebar chat assistant, connecting multiple model providers and local inference

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)

# 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.

The gEAR portal is a website for visualization and analysis of multi-omic data both in public and private domains.

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

METPO (Microbial Ecophysiological Trait and Phenotype Ontology) provides standardized terms for describing microbial phenotypes, growth characteristics, and culture conditions. It includes classes for growth media, temperature tolerances, pH tolerances, and relationships like "grows in" and "does not grow in".

Curated list of atomistic ML projects for materials science

Biological simulation tools

Inflexa is an open-source, agentic orchestration platform for computational biology and translational medicine. It is designed to assist researchers in analyzing multi-omics, cheminformatics, and imaging data by reading published literature, designing multi-step analysis plans, and executing experiments with full reproducibility.

A molecule manipulation library.

This is an ONNX version of OpenMed/OpenMed-NER-PharmaDetect-SuperClinical-434M. It was automatically converted and uploaded using this Hugging Face Space.

AI co-author covering the entire research lifecycle — from an under-specified research direction to a published paper; includes ResearchStudio-Idea for evidence-grounded research ideation and ResearchStudio-Reel for turning finished papers into posters, narrated videos, blogs, and interactive reels; runs as skills on Claude Code and Codex (1.2K+ stars, MIT License, 2026)

Galaxy workflow for BlockClust pipeline.

RetroAgent is a 4B-parameter LLM agent for multi-step retrosynthesis planning. It decomposes a target molecule into commercially available building blocks by searching over an AND-OR graph of molecules and reactions, driven entirely by tool calls.

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)

Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.

Open-source deep learning toolbox for bioimage analysis providing a unified, configuration-driven framework for 2D/3D semantic segmentation, instance segmentation, classification, denoising, super-resolution, and self-supervised learning; integrates state-of-the-art architectures including U-Net, Vision Transformers, and ConvNeXt, designed for microscopy and biomedical imaging researchers without extensive coding expertise (MIT License, actively maintained)

Structural variant discovery by integrated paired-end and split-read analysis.

Advanced OCR with PP-StructureV3 document parsing, 13% accuracy improvement, supports 80+ languages

Autonomous ML experimentation for biomedical data.

Segment Anything Model for microscopy: interactive and automatic segmentation of light, electron, and fluorescence microscopy images in 2D and 3D, with domain-specific fine-tuning workflows for scientific imaging (1.5K+ stars)

A Workflow Management System geared towards scientific workflows.

The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages.

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)

GAIn is a platform for annotating genetic variants, genomic positions, and regions with reproducible, declarative pipelines using curated Genomic Resource Repositories.

atomate2 is a library of computational materials science workflows.