Awesome Computational Biology Overview

Awesome list of computational biology.

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Awesome Computational Biology

A curated collection of databases, software, and papers related to computational biology.

Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of biological, ecological, behavioural, and social systems. — Wikipedia

Overview

Interactive version: Resource Overview page
Regenerate the figure: python scripts/generate_overview.py

GitHub Pages UI

Browse and search the resources via the GitHub Pages UI.

For treatment-response work, the Dataset Explorer compares curated cell-line, patient, and PDX datasets by year, sample type, perturbation metadata, matched pre/post availability, SMILES coverage, and clinical outcomes.

The Method Explorer compares drug-response and perturbation methods by task, molecular/context representation, unseen-drug support, dose/time conditioning, and patient transfer.

The Foundation Model Explorer compares foundation models by fine-grained modality, parameter count, pretraining scale, species, zero-shot support, weights/code availability, perturbation support, and spatial support.

The Agent Explorer compares agentic AI systems by scientific domain, single- vs multi-agent architecture, tool/code execution, literature and web retrieval, omics and wet-lab support, autonomy, and human-in-the-loop design. The Agent Explorer table is sortable and supports domain, architecture, omics, code-execution, and year filters.

All explorer tables support sortable columns and focused filters for quick comparison, including a Recent (≥2025) toggle.

The Pages home screen also summarizes total resources, profiled datasets, methods, foundation models, agents, and the most common foundation-model modalities.

Chemical and genetic perturbation datasets can be filtered separately in the Dataset Explorer. Genetic screens can also be filtered by CRISPRi, CRISPRa, knockout, enhancer-targeting, combinatorial, or mixed perturbation modes, including mixed-resource collections.

The explorer also includes the eight perturbation datasets used in the Bison unseen-compound benchmark.

Search matches name, description, tasks, modalities, and tags.
The Task, Modality, and Type filters map directly to tasks, modalities, and type in docs/data/resources.json.
Clicking badges on cards applies the corresponding filter.

Awesome Computational Biology

Databases

scRNA

CZ CELLxGENE — Single-cell dataset repository and interactive explorer from the Chan Zuckerberg Initiative.
Gene Expression Omnibus — Public functional genomics database.
Human Cell Atlas — Open global atlas of all cells in the human body.
Single Cell PORTAL — Public database for single-cell RNA.
Single Cell Expression Atlas — Public database for single-cell RNA.

Compound

PubChem — One of the largest chemical databases (compounds, genes, and proteins).
ChEBI — Database focused on small chemical compounds.
ChEMBL — Bioactive molecules with drug-like properties.
ChemSpider — Chemical structure database.
DrugTargetCommons — Community platform for curating and integrating experimental bioactivity data across drugs and targets.
HMDB (Human Metabolome Database) — Comprehensive database of small molecule metabolites found in the human body.
KEGG COMPOUND — Collection of small molecules and biopolymers.
LIPID MAPS — Database of lipids.
Rhea — Database of chemical reactions.
DrugCentral — Online drug compendium with drug mode of action and indication information.
Drug Repurposing Hub — Collections of drug repurposing data (drug, MoA, target, etc).
Therapeutic Target Database — Drug-target, target-disease, and drug-disease datasets.
ZINC ligand discovery database — Free database of commercially-available compounds for virtual screening.

Pathway

PathwayCommons — Database of pathways and interactions.
KEGG PATHWAY — Collection of pathway maps.
WikiPathways — Database of biological pathways.
Reactome — Expert-curated, peer-reviewed pathway database with detailed reaction mechanisms.
BioCyc — Collection of pathway/genome databases across thousands of organisms.
OmniPath — Comprehensive resource integrating protein interactions, signaling pathways, gene regulatory networks, and miRNA targets from over 100 databases.
SIGNOR 2.0 — Database of causal signaling interactions and pathways, with signed and directed relationships between proteins.
MSigDB (Molecular Signatures Database) — Curated gene sets derived from pathways and biological processes.

Mass Spectra

MassBank — Open source databases and tools for mass spectrometry reference spectra.
MoNA MassBank of North America — Meta-database of metabolite mass spectra, metadata, and associated compounds.

Protein

THE HUMAN PROTEIN ATLAS — Comprehensive human protein database (cells, tissues, organs).
PROTEIN DATA BANK (PDB) — 3D structures of proteins, nucleic acids, complexes.
UniProt — Functional information on proteins.
AlphaFold Protein Structure Database — 3D protein structure predictions.
RCSB Protein Data Bank — Repository for structural data of biological molecules.
Critical Assessment of Structure Prediction (CASP) — Assessing methods for protein structure prediction.
Uniclust — Clustered protein sequence databases.
UniRef — Non-redundant sequence database clustering UniProtKB entries at multiple sequence identity thresholds.
CATH database — Hierarchical classification of protein domain structures.
SAbDab — Structural Antibody Database containing all antibody structures in the PDB.
OADB (Observed Antibody Space Database) — Database of antibody sequences from immune repertoire sequencing.
InterPro — Protein families, domains, and functional sites database integrating 14 member databases including Pfam and PROSITE.
Pfam — Database of protein families described by multiple sequence alignments and hidden Markov models.
NeXtProt — Expert knowledge base on human proteins with deep functional annotation, complementary to UniProt.

Genome

ENCODE — Encyclopedia of DNA Elements; regulatory and functional genomic elements across the genome.
Ensembl — Genome browser and annotation database for vertebrate and other eukaryotic genomes.
Human Genome Resources at NCBI — Database for genomics, proteomics, transcriptomics, and systems biology.
GenBank — NCBI's database of genetic sequences.
UCSC Genome Browser — UCSC's genome browser.
cBioPortal — Cancer genomics database; aggregating many patient datasets.
OncoKB — Precision oncology knowledge base of cancer genes, variants, and therapeutic implications.
10x Genomics Dataset — Collection of single-cell datasets.
The Genotype-Tissue Expression (GTEx) — Human gene expression and regulation resource.
Dependency Map (DepMap) — CRISPR-Cas9 screens in cancer cell lines.
Catalogue Of Somatic Mutations In Cancer (COSMIC) — Resource on somatic mutations in cancers.
MGnify — Resource for metagenomic and metatranscriptomic data.
JASPAR — Database of transcription factor binding profiles.
gnomAD — Genome Aggregation Database; genetic variation from large-scale sequencing projects.
Rfam — Database of RNA families with sequence alignments and consensus structures.
ROADMAP Epigenomics — Reference epigenome maps for 111 primary human cell types and tissues, including histone modifications, chromatin accessibility, and DNA methylation.
FANTOM5 — Functional annotation of mammalian genome; comprehensive atlas of active enhancers, promoters, and transcription start sites across human and mouse cell types.

Disease

KEGG DRUG — Comprehensive, approved drug information.
DrugBank — Database of drugs and targets (University of Alberta).
DisGeNET — Database of gene-disease associations integrating expert-curated and GWAS data.
OMIM (Online Mendelian Inheritance in Man) — Comprehensive database of human genes and genetic disorders.
Open Targets Platform — Systematic target identification and prioritization platform integrating genetics, genomics, and drug data for drug discovery.
Human Phenotype Ontology (HPO) — Standardized vocabulary of phenotypic abnormalities in human disease, linking genes, variants, and clinical features.
DISEASES — Gene–disease association database integrating evidence from text mining, curated databases, and experimental data.

Interaction

Drug-Gene Interaction

DGIdb — Drug-gene interactions and the druggable genome.
Comparative Toxicogenomics Database — Chemical-gene interactions, chemical-disease and gene-disease associations, chemical-phenotype associations.
SNAP — Dataset of drug-gene interactions.

Drug (Cell Line) Response

NCI60 — Focuses on 60 cancer cell lines and many drugs.
Genomics of Drug Sensitivity in Cancer (GDSC) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
Cancer Cell Line Encyclopedia — Database of ~1000 cancer cell lines.
CellMiner Cross Database (CellMinerCDB) — Integrates multiple cancer cell line databases.

Chemical-Protein Interaction

STITCH — Chemical-protein interactions.
BindingDB — Compounds and target database.
Davis kinase inhibitors DB — Experimental kinase inhibitor binding affinity dataset for protein–ligand interaction research.
Kinase Inhibitor Bioactivity Data (KIBA) — Integrated bioactivity scores for kinase inhibitors combining Ki, Kd, and IC50 measurements.
PDBBind — Binding affinity data for biomolecular complexes.

Protein-Protein Interaction

STRING — PPI networks for multiple organisms.
BioGRID — Protein, genetic, and chemical interactions.
HIPPIE — Human protein-protein interaction database.
IntAct — Open-source molecular interaction database and analysis system from EMBL-EBI.

Knowledge Graph

Drug Mechanism Database (DrugMechDB) (⭐79) — Mechanisms of action from drug to disease.
DRKG (⭐704) — Large-scale biological knowledge graph for drug discovery.
Hetionet (⭐363) — Heterogeneous network integrating genes, diseases, drugs, pathways, and more.
PrimeKG (⭐831) — Multi-modal precision medicine knowledge graph integrating clinical, genetic, and drug data.

Gene Regulatory Network

TRRUST v2 — Manually curated database of human and mouse transcriptional regulatory interactions between transcription factors and their target genes, expanded with literature-derived evidence.
RegNetwork — Database of gene regulatory networks covering transcription factor–target gene and miRNA–gene interaction data across multiple species.
miRBase — Reference repository for microRNA gene annotations, sequences, and experimentally validated targets.

Clinical Trial

ClinicalTrials.gov — Privately and publicly funded clinical studies.
ICD10 — International Classification of Diseases, 10th revision.
EU Drug Regulating Authorities Clinical Trials DB (EudraCT) — European clinical trial database.
MIMIC-IV — Freely accessible critical care database.

Benchmarks & Datasets

Drug Response & Perturbation

BEAT AML — Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.
Cancer Therapeutics Response Portal (CTRP) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.
Chem-PerturBridge (⭐2) — Harmonized compendium and processing pipelines for small-molecule perturbation transcriptomics across heterogeneous assays and gene panels.
GSE191127 Breast Cancer Pre/Post Chemotherapy — Bulk RNA-seq and genomics from matched pre- and post-neoadjuvant chemotherapy breast tumors with treatment outcomes.
Genomics of Drug Sensitivity in Cancer (GDSC) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
JUMP Cell Painting Datasets (⭐190) — Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research.
LINCS L1000 — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.
LINCS L1000 Phase 1 (⭐2) — Phase-1 L1000 benchmark view used by Bison with 692,787 profiles, 9,233 molecules, 70 contexts, and 978 landmark genes.
LINCS L1000 Phase 2 (⭐2) — Phase-2 L1000 benchmark view used by Bison with 333,263 profiles, 1,760 molecules, 30 contexts, and 978 landmark genes.
MIX-Seq — Multiplexed single-cell transcriptional profiling of chemical and genetic perturbation responses across pools of cancer cell lines.
NCI60 — Drug sensitivity benchmark across 60 diverse human cancer cell lines.
Novartis Perturbation Dataset (⭐2) — Large chemical perturbation transcriptomic screen used in Chem-PerturBridge; the Bison benchmark view contains 46,748 profiles and 3,770 molecules.
OP3 (⭐2) — Chemical perturbation dataset used by Chem-PerturBridge and the Bison unseen-compound benchmark; the Bison benchmark view contains 1,813 profiles, 138 molecules, and 4 cellular contexts.
PDX-Atlas — Portal integrating clinical, genomic, transcriptomic, and drug-response data from patient-derived xenograft models.
PharmGKB — Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.
PRISM — Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.
sci-Plex — Single-cell chemical perturbation screen of ~650,000 transcriptomes across three cancer cell lines, 188 compounds, and four doses.
scPerturb (⭐189) — Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.
Tahoe-100M — Giga-scale single-cell perturbation atlas with >100 million profiles from 50 cancer cell lines exposed to ~1,100 small molecules.
VCPI-0001 (⭐2) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 27,517 profiles and 2,272 molecules in one cellular context.
VCPI-0002 (⭐2) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 18,139 profiles and 1,488 molecules in one cellular context.

Genetic Perturbation & Perturb-seq

Dixit et al. 2016 Perturb-seq — Pooled CRISPR knockout with single-cell RNA-seq across K562 and dendritic-cell screens, including stimulated conditions and combinatorial perturbations.
Adamson et al. 2016 Perturb-seq — CRISPRi Perturb-seq in K562 cells for systematic dissection of the unfolded protein response.
Datlinger et al. 2017 CROP-seq — Pooled CRISPR knockout screen with single-cell transcriptomic readout in Jurkat cells under T-cell receptor stimulation.
Norman et al. 2019 CRISPRa Perturb-seq — Large-scale CRISPR activation Perturb-seq in K562 cells spanning single-gene and combinatorial perturbations.
Gasperini et al. 2019 CRISPRi enhancer screen — High-MOI single-cell CRISPRi screen in K562 cells targeting thousands of candidate enhancers to map enhancer-gene regulation.
Replogle et al. 2022 K562 Genome-wide Perturb-seq — Genome-scale CRISPRi Perturb-seq targeting nearly all expressed genes in K562 cells.
Replogle et al. 2022 K562 Essential Perturb-seq — CRISPRi Perturb-seq focused on essential genes in K562 cells.
Replogle et al. 2022 RPE1 Essential Perturb-seq — CRISPRi Perturb-seq focused on essential genes in RPE1 cells.
Frangieh et al. 2021 Perturb-CITE-seq — Multimodal CRISPR knockout screen with RNA and surface-protein readouts in patient-derived melanoma models under immune-related conditions.

Single-Cell & Spatial

HEST Xenium virtual spatial transcriptomics — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: DeepSpot-M.
scIB (Single-cell Integration Benchmarks) (⭐432) — Comprehensive benchmarking framework for single-cell data integration methods.
Tabula Muris — Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.
Tabula Sapiens — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.
TCGA virtual spatial transcriptomics atlas — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: DeepSpot-M.

Molecular, Protein & Drug Discovery

BACE — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.
Bento (⭐13) — Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks.
BindingDB Curated Sets — Curated binding affinity datasets for protein–ligand interaction benchmarking.
ClinTox — Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.
CrossDocked2020 — Large-scale dataset for structure-based virtual screening.
DUD-E (Directory of Useful Decoys, Enhanced) — Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets.
FLIP (Fitness Landscape Inference for Proteins) (⭐141) — Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.
GuacaMol (⭐534) — Benchmark suite for generative molecular design models.
MoleculeNet — Benchmark datasets for molecular machine learning.
MOSES (⭐991) — Benchmarking platform for molecular generation models.
PK-DB — Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies.
ProteinGym (⭐474) — Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models.
QM9 — Quantum chemistry properties for 134K stable small organic molecules computed at DFT level.
SIDER (Side Effect Resource) — Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.
TAPE (Tasks Assessing Protein Embeddings) (⭐745) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.
Therapeutics Data Commons (TDC) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.
Tox21 — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.

Genomics, Cancer & Biomedical Cohorts

1000 Genomes Project — Reference panel of human genetic variation from 2,504 individuals across 26 populations.
CPTAC (Clinical Proteomic Tumor Analysis Consortium) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.
The Cancer Genome Atlas (TCGA) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.
UK Biobank — Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.

General Biological ML & Knowledge Graph Benchmarks

OGB (Open Graph Benchmark) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.
OpenBioLink (⭐164) — Benchmark datasets for biological knowledge graph completion.

API

PubMed E-utilities (esearch/efetch) — APIs for searching and retrieving biomedical literature from PubMed.
NCBI E-utilities — Unified APIs for accessing NCBI databases (Gene, GEO, SRA, PubChem, etc).
UniProt REST API — Programmatic access to protein sequence and functional annotation data.
Ensembl REST API — API for genomic annotations, variants, genes, and comparative genomics.
KEGG REST API — API for accessing KEGG pathways, compounds, genes, and reactions.
ChEMBL Web Services — REST API for bioactive molecules, targets, and bioassays.
Open Targets Platform API — API for target–disease associations integrating genetics, genomics, and drug data.
ClinicalTrials.gov API — API for querying clinical trial metadata and results.

Preprocessing Tools

Chemistry Development Kit (⭐606) — Cheminformatics software & machine learning tools.
Biopython — Collection of Python tools for biological computation including sequence analysis, structure parsing, and database access.
FlashDeconv (⭐26) — High-performance spatial transcriptomics deconvolution (~1M spots in ~3 min).
RDKit (⭐3.6k) — Cheminformatics software & machine learning toolkit.
DeepChem (⭐7k) — Deep learning library for drug discovery, quantum chemistry, and materials science.
ChatSpatial (⭐45) — MCP server for spatial transcriptomics analysis via natural language.
Scanpy — Python library for scRNA-seq analysis.
Seurat — R library for scRNA-seq analysis.
scvi-tools — Probabilistic models for single-cell omics data analysis.
CellTypist (⭐506) — Automated cell type annotation for scRNA-seq.
Squidpy — Python library for spatial single-cell analysis.
GROMACS — Molecular dynamics simulation package for biochemical molecules.
MDAnalysis — Python library for analyzing and altering molecular dynamics simulation trajectories.
OpenMM — High-performance toolkit for molecular simulation and GPU-accelerated MD.
scVelo (⭐512) — RNA velocity estimation for single-cell transcriptomics, inferring the direction and speed of cell differentiation.
STAR (⭐2.3k) — Ultrafast universal RNA-seq aligner with support for spliced alignment and single-cell quantification via STARsolo.
kallisto — Near-optimal RNA-seq quantification using pseudoalignment for fast transcript abundance estimation.
Harmony (⭐674) — Fast and scalable integration of single-cell data across datasets, conditions, technologies, and species.
Monocle3 — Single-cell trajectory analysis tool for learning developmental trajectories and ordering cells in pseudotime.
CellChat (⭐791) — Inference and analysis of cell-cell communication ligand-receptor networks from single-cell transcriptomics data.
SCENIC (⭐498) — Single-cell regulatory network inference and clustering linking transcription factors to co-expressed gene modules.
DoubletFinder (⭐569) — Machine learning approach for detecting multiplet (doublet) artifacts in single-cell RNA-seq data.
Numbat (⭐230) — Haplotype-aware copy number variation inference from single-cell RNA-seq using hidden Markov models.
CaSpER (⭐92) — CNV identification and visualization by integrative analysis of single-cell or bulk RNA-seq data.
CellCharter (⭐197) — Identification and characterization of spatial cell niches from spatial transcriptomics using VAEs and Gaussian mixture models.
STAGATE (⭐54) — Adaptive graph attention auto-encoder for spatial domain identification in spatial transcriptomics.
NCEM (⭐121) — GNN-based model for learning intercellular communication from spatial graphs of cells.
DeepTalk (⭐30) — Graph attention network for deciphering cell-cell communication from spatial transcriptomics.
COMMOT (⭐146) — Optimal transport-based framework for screening cell-cell communication in spatial transcriptomics.
TIGON (⭐60) — Neural optimal transport method for reconstructing growth and dynamic trajectories from single-cell transcriptomics.
LINGER (⭐140) — Neural network for gene regulatory network inference from single-cell multiome (RNA+ATAC-seq) data with bulk data pretraining.
sciPENN (⭐19) — RNN-based method for simultaneous protein expression prediction, uncertainty estimation, and cell-type label transfer from CITE-seq and scRNA-seq data.
MOGONET (⭐188) — Multi-omics graph convolutional network framework for patient classification and biomarker identification.
AutoZyme (⭐51) — Autonomous agentic framework that speeds up bioinformatics software (e.g. Scanpy, Seurat) on CPUs while preserving the original results.
SeqBench — Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server.

Machine Learning Tasks and Models

Drug Discovery

Drug Response Prediction

drGAT (⭐2) — Attention-based model for drug response prediction with gene explainability.
MOFGCN (⭐8) — GCN + heterogeneous network.
DeepDSC — Autoencoder + fully connected NN.
DGDRP (⭐1) — Multi-view embedding neural network.
DeepAEG (⭐4) — GNN embedding + attention mechanism.
RECOVER (⭐26) — Machine learning framework for predicting synergistic drug combination responses across cell lines.
TGSA (⭐24) — Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.
HiDRA — Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction.
DRUML (⭐12) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.
PASO (⭐8) — Pathway-aware multi-omics drug response model combining pathway-difference features, multi-scale convolutions, Transformer encoding, and drug SMILES.
DTLCDR — Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization.
THERAPI (⭐1) — Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction.
EXPRESSO — Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies.
PerturbRx — Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction.

Drug Perturbation

CellOT (⭐182) — Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.
CMonge (⭐25) — Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses.
chemCPA (⭐160) — Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations.
cycleCDR (⭐5) — Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations.
PRNet (⭐91) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
Bison — Cross-dataset model for globally unseen-compound response prediction using a shared gene representation, discrete diffusion models, and matched drug-contrast supervision.
biolord (⭐102) — Deep generative model that disentangles known and unknown attributes for conditional generation of single-cell states.
PerturbNet (⭐69) — Conditional generative model for predicting distributions of single-cell states under unseen chemical and genetic perturbations.
CellFlow (⭐160) — Conditional flow-matching framework for modeling and predicting cellular phenotypes under chemical, genetic, and other perturbations.
Prophet (⭐41) — Transformer model for predicting cellular phenotypes under unseen chemical or genetic perturbations across heterogeneous assays and contexts.
PrePR-CT (⭐8) — Graph-based model using cell-type-specific co-expression networks as inductive priors for small-data chemical perturbation response prediction.
XPert (⭐27) — Knowledge-informed dual-branch Transformer for drug-induced transcriptional perturbation prediction across dose, time, and cellular context.
State (⭐707) — Transition model for predicting cellular perturbation responses across diverse contexts and sets of cells.
TxPert (⭐52) — Knowledge-graph-informed latent-transfer model for transcriptomic perturbation prediction across unseen single perturbations, combinations, and cross-context settings.
LPM (⭐54) — Large perturbation model that jointly learns heterogeneous perturbation experiments by disentangling perturbation, readout, and context representations.

Drug Repurposing

DeepPurpose (⭐1.2k) — Deep learning library for drug repurposing.
TranSiGen (⭐39) — Dual-VAE architecture for ligand-based virtual screening, drug response prediction, and drug repurposing using chemical-induced transcriptional profiles.

Drug Target Interaction

NeoDTI (⭐78) — Library for drug-target interaction prediction.
DTINet (⭐191) — Network-based framework integrating heterogeneous biological data for DTI prediction.
DeepDTA (⭐306) — Deep learning model using CNNs on protein sequences and drug SMILES.
GraphDTA (⭐308) — Graph neural network–based DTI prediction using molecular graphs.
MolTrans (⭐248) — Transformer-based DTI model leveraging molecular substructures.
DrugBAN (⭐154) — Bilinear attention network for interpretable DTI prediction.

Compound-Protein Interaction

MCPINN (⭐4) — Drug discovery via compound-protein interaction and machine learning.
TransformerCPI (⭐159) — CPI prediction using Transformer.

Molecular Generation

REINVENT (⭐377) — Reinforcement learning for de novo drug design.
MolGPT (⭐178) — Transformer-based model for molecular generation.
Molecular Transformer (⭐429) — Sequence-to-sequence model for retrosynthesis prediction.
Matcha (⭐35) — Multi-stage Riemannian flow matching model for physically valid molecular docking with scoring, pose filtering, and benchmarks.
TargetDiff (⭐347) — 3D equivariant diffusion model for structure-based drug design.
DiffDock (⭐1.6k) — Diffusion generative model for molecular docking, predicting the binding pose of small molecules to protein targets.
JTVAE (⭐566) — Junction tree variational autoencoder for molecular graph generation that guarantees chemical validity via a hierarchical tree decomposition.
DiffSBDD (⭐531) — Equivariant diffusion model for structure-based drug design that generates molecules and binding conformations for protein targets.
ReLeaSE (⭐373) — Deep reinforcement learning framework for de novo drug design combining a generative and predictive model.
PaccMannRL (⭐11) — Reinforcement learning-based generative model for de novo hit-like anticancer molecule design from transcriptomic data.

Protein Property Prediction

NbBayesLM (⭐4) — Bayesian neural network integrating protein language model embeddings and physicochemical features to predict nanobody thermostability with uncertainty estimates. Paper

LLM for Biology

AI4Chem/ChemLLM-7B-Chat — LLM for chemical & molecular science.
BioGPT (⭐4.5k) — LLM for biomedical text generation.
GeneGPT (⭐431) — LLM for biomedical information, integrated with various APIs.
GenePT (⭐324) — Foundation LLM for single-cell data.
scPRINT (⭐162) — Pretrained on 50M cells for scRNA-seq denoising & zero imputation.
BioMedLM — 2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical question answering and text generation.
MolT5 (⭐195) — Language model for molecular tasks bridging text and SMILES, enabling molecule captioning and text-driven molecule generation.
ChatDrug (⭐163) — LLM-based conversational pipeline for drug discovery, using natural language prompts for iterative drug editing and optimization.

Agentic AI for Biology

General Biomedical Agents

Biomni (⭐3.9k) — General-purpose biomedical AI agent integrating planning, code execution, specialized tools, databases, and software across diverse biomedical research tasks.
ToolUniverse (⭐1.7k) — Unified scientific tool ecosystem for building AI scientists that can discover, select, and execute biomedical tools and databases.
ClawBio (⭐1.1k) — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.
BioMedAgent (⭐143) — Self-evolving multi-agent framework for autonomous biomedical data analysis with tool discovery, workflow planning, code generation, execution, correction, and cross-omics analysis.
BioMaster (⭐114) — Multi-agent system for automated and auditable bioinformatics workflows spanning RNA-seq, ChIP-seq, single-cell, spatial omics, Hi-C, long reads, metagenomics, and proteomics.
BRAD (⭐64) — Retrieval-augmented bioinformatics assistant integrating scientific literature, databases, external tools, and executable workflows.

Therapeutics & Drug Discovery Agents

TxAgent (⭐655) — Therapeutic reasoning agent using multi-step reasoning and a large scientific tool universe for drug interactions, contraindications, and personalized treatment analysis.
Medea (⭐131) — Multi-agent therapeutic discovery system combining research planning, biological data analysis, literature reasoning, and multi-LLM deliberation across single-cell, cell-line, and patient contexts.
DrugAgent (⭐1) — Multi-agent biomedical evidence synthesis framework for computational drug discovery with reliability-aware aggregation.

Bioinformatics & Omics Agents

CASSIA (⭐107) — Multi-agent LLM framework for reference-free and interpretable single-cell cell-type annotation with dedicated annotation, validation, scoring, and reporting agents.
STELLA (⭐156) — Self-evolving biomedical research agent that expands its tool repertoire and supports literature reasoning, computational analysis, and laboratory-oriented scientific workflows.
AutoBA (⭐239) — Automated multi-omics analysis agent that plans, executes, and repairs bioinformatics workflows from natural-language objectives.
GenoMAS (⭐135) — Multi-agent framework for code-driven gene-expression analysis with planning, execution, debugging, backtracking, and GEO/TCGA-based scientific discovery.
BIA (⭐45) — Bioinformatics agent for GEO search, sample metadata extraction, count-matrix processing, and pipeline extraction from papers.
BioAgents (⭐47) — Multi-agent bioinformatics assistant using specialized language models and retrieval for genomics workflow development and troubleshooting.
Genomi (⭐484) — Local-first genomics agent runtime that indexes personal variants, queries evidence, and generates evidence-grounded reports while keeping raw genome data on-device.

Multi-Agent Scientific Labs

Virtual Lab (⭐738) — Human–AI collaborative research environment in which an LLM principal investigator coordinates specialized scientist agents for scientific discovery.
Agent Laboratory (⭐5.9k) — End-to-end multi-agent research workflow for literature review, experimentation, implementation, analysis, and report generation.

Paper & Workflow Agents

Paper2Agent (⭐3.6k) — Multi-agent system that transforms research papers and associated code into interactive, testable scientific agents and MCP tools.

Foundation Models

Single-cell Foundation Models

Transcriptomics Foundation Models

scFoundation (⭐431) — Large-scale foundation model for single-cell gene expression, enabling multiple downstream tasks.
scGPT (⭐1.6k) — Transformer-based foundation model pretrained on millions of single-cell profiles.
Geneformer — Context-aware, attention-based deep learning model pretrained on a large corpus of single-cell transcriptomes.
BulkFormer (⭐85) — Foundation model for bulk RNA-seq data; learns general transcriptomic representations.
scBERT (⭐364) — BERT-based foundation model pretrained on large-scale scRNA-seq data for cell type annotation.
CellPLM (⭐107) — Cell pre-trained language model with inter-cell transformer architecture for diverse single-cell analysis tasks.
UCE (⭐345) — Universal Cell Embeddings: zero-shot single-cell embedding model trained on 36M cells across species, tissues, and assays without fine-tuning.
GEARS (⭐414) — Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using biological priors.
SATURN (⭐174) — Transformer-based model integrating gene expression and protein sequences via a protein language model to learn unified multi-species cell embeddings.
CancerFoundation (⭐32) — Single-cell RNA-seq foundation model trained exclusively on a curated dataset of malignant cells to learn cancer-specific embeddings.
CellFM (⭐115) — 800M-parameter single-cell foundation model pretrained on transcriptomics from 100 million human cells for annotation, integration, gene-function, and perturbation tasks.
scPRINT-2 (⭐39) — Next-generation single-cell foundation model pretrained on 350M+ cells across 22K+ datasets and 16 species for embeddings, denoising, annotation, gene-network inference, and cross-species transfer.

Spatial Foundation Models

GigaPath (⭐638) — Slide-level digital pathology foundation model pretrained on 1.3 billion pathology image tokens from whole-slide images.
UNI (⭐784) — General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse computational pathology tasks.
CONCH (⭐534) — Vision-language foundation model for computational pathology trained with contrastive captioning on pathology image–text pairs.
Phikon — ViT-based pathology foundation model pretrained with iBOT self-supervision on TCGA whole-slide images.
Nicheformer (⭐175) — Foundation model for single-cell and spatial omics using a transformer architecture with positional embeddings to encode spatial cell information.
scGPT-spatial (⭐142) — Extension of scGPT for spatial transcriptomics with continual pretraining and a mixture-of-experts decoder for spatial gene expression analysis.
DeepSpot (⭐101) — Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.
DeepSpot2Cell (⭐21) — Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).
DeepSpot-M (⭐58) — Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.
AESTETIK (⭐28) — Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.

Pathology Foundation Models

TITAN (⭐371) — Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis.
Virchow2 — 632M-parameter pathology vision transformer pretrained on 3.1M whole-slide images with mixed-magnification self-supervision.
H-Optimus-0 — 1.1B-parameter histopathology foundation model trained with self-supervised learning on a large multi-center slide corpus.
H-Optimus-1 — 1.1B-parameter pathology foundation model trained on billions of histology images from more than one million slides and 800K+ patients.
UNI2-h — Billion-parameter histopathology vision foundation model for tile-level feature extraction and downstream computational pathology tasks.
Phikon-v2 — Updated pathology foundation model for general-purpose histology feature extraction and transfer learning.
GenBio-PathFM (⭐40) — 1.1B-parameter histopathology foundation model trained on public data using morphology-aware curation and dual-stage JEPA+DINO learning.

Multi-Omics Foundation Models

scMulan (⭐63) — Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics for cross-omics transfer tasks.
totalVI (⭐1.7k) — Probabilistic framework for joint analysis of paired scRNA-seq and protein (CITE-seq) data enabling multi-modal cell state representation across single-cell datasets.
MultiVI (⭐1.7k) — Multi-modal variational autoencoder for integrating paired and unpaired single-cell RNA-seq and ATAC-seq measurements into a unified latent space.
MIRA (⭐70) — Probabilistic multimodal topic model jointly modeling single-cell transcriptomics and chromatin accessibility for regulatory network inference.
GLUE (⭐480) — Graph-Linked Unified Embedding framework for unpaired single-cell multi-omics data integration across RNA, ATAC, methylation, and protein modalities.
BABEL (⭐53) — Cross-modality translation model enabling prediction between scRNA-seq and scATAC-seq profiles without requiring paired single-cell measurements.
Multigrate (⭐35) — Asymmetric multi-omics variational autoencoder for integrating single-cell data across RNA, ATAC, and protein modalities with missing-modality support.
MOFA+ (⭐426) — Multi-Omics Factor Analysis framework identifying shared axes of variation across bulk and single-cell datasets including RNA, ATAC, proteomics, methylation, and copy number.
GeneCompass (⭐124) — Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.
UnitedNet (⭐53) — Interpretable multi-task deep neural network for single-cell multi-omics integration spanning transcriptomics, chromatin accessibility, and proteomics.
SpatialGlue — Graph attention network for spatial multi-omics integration jointly embedding spatial transcriptomics with chromatin accessibility or proteomics.
MIDAS (⭐72) — Mosaic integration and differential accessibility model for single-cell multi-omics that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.
Concerto (⭐41) — Contrastive self-supervised learning framework for single-cell multimodal data integration, batch correction, and reference-query mapping.
scButterfly (⭐30) — Dual-aligned variational autoencoder for single-cell cross-modality translation between paired and unpaired multiomics data.
JAMIE (⭐17) — Joint variational autoencoder for multimodal single-cell data imputation and embedding.
scPair (⭐11) — Bidirectional feedforward network for single-cell multimodal analysis with cross-modality prediction leveraging single-cell atlases.

Domain Alignment

scArches (⭐411) — Transfer learning framework for mapping new single-cell datasets onto pre-trained reference atlases across batches, conditions, and modalities.
TOSICA — Transformer-based framework for one-stop interpretable cell-type annotation supporting cross-dataset and cross-species transfer.

Compound Foundation Models

Compound Embedding

ChemBERTa-2 (⭐501) — RoBERTa-based molecular language model pretrained on SMILES for small-molecule representation learning.
GROVER (⭐395) — Self-supervised graph transformer for large-scale molecular representation learning from unlabeled compounds.
Mol2Vec (⭐293) — Unsupervised molecular embedding method inspired by Word2Vec for learning vector representations of chemical substructures.
MolFormer (⭐415) — Linear attention transformer pretrained on millions of SMILES strings for efficient molecular embeddings.
Uni-Mol (⭐1.2k) — 3D molecular pretraining framework for universal representation learning on molecules and protein pockets.
Uni-Mol2 (⭐1.2k) — Scaled molecular pretraining model using atomic, graph, and 3D geometry features, with models up to 1.1B parameters pretrained on 800M conformations.
ChemFM (⭐87) — 1B/3B-parameter chemical language model pretrained on 178M molecules for molecular representation, property prediction, generation, and synthesis tasks.

Protein Foundation Models

Pre-trained Embedding

Evolutionary Scale Modeling (ESM) (⭐4.2k) — Protein embeddings.
ProtTrans (⭐1.3k) — Suite of protein language models (ProtBERT, ProtT5, ProtXLNet) trained on billions of protein sequences from UniRef and BFD.
ProGen2 (⭐705) — Protein language model trained on diverse protein families for sequence generation and fitness prediction.
Ankh (⭐250) — Efficient protein language model optimized for downstream prediction tasks including secondary structure, localization, and function annotation.
ESM Cambrian (ESM C) (⭐3k) — Protein representation foundation-model family designed as an efficient next-generation successor to ESM2, spanning 300M to multi-billion-parameter models.

Protein Structure Prediction and Design

AlphaFold3 (⭐8.6k) — Predicts structures of proteins, nucleic acids, small molecules, and their complexes.
Boltz-1 (⭐4.2k) — Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their complexes achieving AlphaFold3-level accuracy.
Boltz-2 (⭐4.2k) — Biomolecular foundation model jointly predicting complex structures and binding affinities for protein–ligand interaction modeling and virtual screening.
Protenix (⭐2.1k) — Trainable biomolecular structure-prediction framework for proteins, nucleic acids, ligands, and complexes with open training and inference pipelines.
Chai-1 (⭐2k) — Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes.
ESM3 (⭐3k) — Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering.
ESMFold (⭐4.2k) — Fast protein structure prediction using language model embeddings.
RFdiffusion (⭐3.1k) — Generative model for protein backbone design using diffusion.
ProteinMPNN (⭐1.9k) — Deep learning model for protein sequence design given backbone structure.
OmegaFold (⭐627) — High-resolution de novo protein structure prediction from sequence.
RoseTTAFold (⭐2.3k) — Three-track neural network for protein structure prediction.
OpenFold (⭐3.4k) — Trainable, memory-efficient open-source reproduction of AlphaFold2 enabling custom protein structure prediction workflows.
SaProt — Structure-aware protein language model using structure-aware tokens that encode both sequence and backbone geometry for improved function prediction.
EvoDiff (⭐686) — Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ paper-2023 ]

CHIEF (⭐719) — Clinical Histopathology Imaging Evaluation Foundation model integrating histology images and clinical context for pan-cancer analysis.
BiomedCLIP — CLIP-based vision-language foundation model for biomedical images and text trained on PubMed figure–caption pairs.
PORPOISE (⭐252) — Pan-cancer integrative histology-genomic analysis framework using multimodal deep learning for patient stratification.
PathomicFusion (⭐327) — Integrated framework fusing histopathology and genomic features via CNN, GNN, and attention gating for cancer diagnosis and prognosis.
Virchow — Million-slide digital pathology foundation model using a vision transformer and self-supervised distillation for tile-level pathology image representation.
TOAD (⭐186) — Tumor Origin Assessment via Deep-learning; weakly-supervised multi-task model predicting cancer primary origin from H&E whole-slide images.
PLIP (⭐380) — Vision-language foundation model for pathology trained with contrastive learning on pathology image–text pairs for image classification and text-to-image retrieval.
MUSK (⭐247) — Vision-language foundation model for precision oncology analyzing multimodal paired text and pathology image data for biomarker prediction and retrieval.

Cancer Genome Foundation Models

TESSERA (⭐6) — Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning.
MutationProjector — Pan-cancer genotype foundation model trained on mutations and copy-number alterations from >30K tumors for clinical representation learning.

Single-Cell Epigenomics Foundation Models

EpiAgent (⭐72) — scATAC-seq foundation model pretrained on ~5M cells and >35B tokens for representation learning, annotation, imputation, perturbation prediction, and in-silico cCRE knockout.
SCARF — Single-cell RNA+ATAC foundation model pretrained on >2.7M cells for multimodal representation, matching, cross-omics translation, and few-shot annotation.
scDNAm-GPT (⭐20) — Foundation model for single-cell whole-genome bisulfite sequencing with whole-genome context modeling at single-CpG resolution.
EpiFoundation — Foundation model for scATAC-seq using peak-to-gene aligned pretraining for cell representation, annotation, batch correction, and gene-expression prediction.
ChromFound (⭐6) — Genome-aware scATAC-seq foundation model pretrained on 1.97M cells across tissues and disease contexts for zero-shot cell representations, annotation, and cross-omics prediction.
Atacformer — Transformer foundation model for scATAC-seq that learns embeddings of cis-regulatory elements for clustering, annotation, and reference mapping.
CLM-X — Multi-way Transformer foundation model jointly handling RNA-only, ATAC-only, and paired RNA–ATAC single-cell inputs for integration, translation, annotation, and perturbation prediction.

Other Omics Foundation Models

CAPTAIN — Multimodal foundation model pretrained on co-assayed single-cell RNA and protein for joint representation learning and cross-modal downstream tasks.
HiCFoundation — Hi-C foundation model pretrained on large-scale chromatin-contact maps for 3D-genome analysis, epigenomic prediction, and single-cell Hi-C adaptation.
OmicsFM (⭐6) — Modality-agnostic molecular-expression foundation model with matched proteomics, bulk-transcriptomics, and single-cell-transcriptomics checkpoints.
VirTues (⭐156) — Spatial-proteomics foundation model learning marker-aware representations across proteins, cells, niches, and tissues from multiplexed imaging.
MethylGPT (⭐67) — Transformer foundation model for DNA methylation pretrained on >150K human methylomes across thousands of datasets, with 3M/7M/15M parameter variants.
CpGPT (⭐82) — DNA methylation foundation model pretrained on >150K samples for zero-shot imputation, array conversion, reference mapping, and downstream phenotype prediction.
Casanovo Foundation (⭐1) — Tandem mass-spectrometry proteomics foundation model that reuses a pretrained Casanovo spectrum encoder for spectrum quality, chimericity, and post-translational-modification prediction.

RNA Foundation Models

RNA-FM (⭐392) — General-purpose RNA foundation model pretrained on large-scale RNA sequences for structural and functional representation learning.
RiNALMo (⭐177) — RNA language-model family pretrained on tens of millions of RNA sequences for secondary-structure and functional prediction tasks.

Genomics Foundation Models

Nucleotide Transformer (⭐923) — Foundation model for genomic sequences across multiple species.
DNABERT (⭐780) — Pre-trained bidirectional encoder for DNA sequence analysis.
DNABERT-2 (⭐516) — Improved genome foundation model with efficient tokenization.
Enformer (⭐15k) — Transformer model predicting gene expression from DNA sequence.
Basenji (⭐475) — Sequential regulatory activity prediction from DNA sequences.
Caduceus (⭐252) — Bidirectional equivariant long-range DNA sequence model based on Mamba.
Evo (⭐1.6k) — Long-context genomic foundation model (up to 1M tokens).
Evo 2 (⭐4.2k) — Genome foundation model trained on 9 trillion DNA base pairs across all domains of life with a 1M-token context window and single-nucleotide resolution.
AlphaGenome (⭐2.2k) — Long-context DNA model predicting multimodal regulatory outputs including expression, splicing, chromatin features, and contact maps at near base-pair resolution.
modernGENA (⭐232) — ModernBERT-style DNA foundation-model family pretrained on hundreds of vertebrate genome assemblies for efficient long-sequence regulatory modeling.
HyenaDNA (⭐809) — Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention.
Borzoi (⭐265) — Extended successor to Enformer for predicting RNA-seq coverage from long genomic sequence windows (524 kb) with improved resolution.
DeepSEA — Deep learning framework for predicting chromatin effects of sequence alterations with single-nucleotide sensitivity across thousands of chromatin features.
Sei (⭐118) — Sequence-to-function framework learning a genome-wide regulatory activity code from DNA sequences for variant effect prediction.
GPN (Genomic Pre-trained Network) (⭐382) — Masked language model for DNA sequences enabling zero-shot variant effect prediction without requiring functional annotations.

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