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
Browse and search the resources via the GitHub Pages UI.
- Awesome Computational Biology
- 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.
- 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.
- 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.
- 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.
- SIGNOR — Database of causal signaling interactions and pathways.
- MSigDB (Molecular Signatures Database) — Curated gene sets derived from pathways and biological processes.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- STITCH — Chemical-protein interactions.
- BindingDB — Compounds and target database.
- PDBBind — Binding affinity data for biomolecular complexes.
- 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.
- Drug Mechanism Database (DrugMechDB) — Mechanisms of action from drug to disease.
- DRKG — Large-scale biological knowledge graph for drug discovery.
- Hetionet — Heterogeneous network integrating genes, diseases, drugs, pathways, and more.
- PrimeKG — Multi-modal precision medicine knowledge graph integrating clinical, genetic, and drug data.
- 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.
- 1000 Genomes Project — Reference panel of human genetic variation from 2,504 individuals across 26 populations.
- BACE — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.
- BEAT AML — Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.
- BindingDB Curated Sets — Curated binding affinity datasets for protein–ligand interaction benchmarking.
- Cancer Therapeutics Response Portal (CTRP) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.
- ClinTox — Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.
- CPTAC (Clinical Proteomic Tumor Analysis Consortium) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.
- CrossDocked2020 — Large-scale dataset for structure-based virtual screening.
- FLIP (Fitness Landscape Inference for Proteins) — Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.
- Genomics of Drug Sensitivity in Cancer (GDSC) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
- GuacaMol — Benchmark suite for generative molecular design models.
- LINCS L1000 — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.
- MoleculeNet — Benchmark datasets for molecular machine learning.
- MOSES — Benchmarking platform for molecular generation models.
- NCI60 — Drug sensitivity benchmark across 60 diverse human cancer cell lines.
- OGB (Open Graph Benchmark) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.
- OpenBioLink — Benchmark datasets for biological knowledge graph completion.
- 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.
- ProteinGym — 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.
- scIB (Single-cell Integration Benchmarks) — Comprehensive benchmarking framework for single-cell data integration methods.
- SIDER (Side Effect Resource) — Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.
- 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.
- TAPE (Tasks Assessing Protein Embeddings) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.
- The Cancer Genome Atlas (TCGA) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.
- 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.
- UK Biobank — Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.
- 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.
- Chemistry Development Kit — Cheminformatics software & machine learning tools.
- Biopython — Collection of Python tools for biological computation including sequence analysis, structure parsing, and database access.
- FlashDeconv — High-performance spatial transcriptomics deconvolution (~1M spots in ~3 min).
- RDKit — Cheminformatics software & machine learning toolkit.
- DeepChem — Deep learning library for drug discovery, quantum chemistry, and materials science.
- ChatSpatial — 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 — 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 — RNA velocity estimation for single-cell transcriptomics, inferring the direction and speed of cell differentiation.
- drGAT — Attention-based model for drug response prediction with gene explainability.
- MOFGCN — GCN + heterogeneous network.
- DeepDSC — Autoencoder + fully connected NN.
- DGDRP — Multi-view embedding neural network.
- DeepAEG — GNN embedding + attention mechanism.
- RECOVER — Machine learning framework for predicting synergistic drug combination responses across cell lines.
- DeepPurpose — Deep learning library for drug repurposing.
- NeoDTI — Library for drug-target interaction prediction.
- DTINet — Network-based framework integrating heterogeneous biological data for DTI prediction.
- DeepDTA — Deep learning model using CNNs on protein sequences and drug SMILES.
- GraphDTA — Graph neural network–based DTI prediction using molecular graphs.
- MolTrans — Transformer-based DTI model leveraging molecular substructures.
- DrugBAN — Bilinear attention network for interpretable DTI prediction.
- MCPINN — Drug discovery via compound-protein interaction and machine learning.
- TransformerCPI — CPI prediction using Transformer.
- REINVENT — Reinforcement learning for de novo drug design.
- MolGPT — Transformer-based model for molecular generation.
- Molecular Transformer — Sequence-to-sequence model for retrosynthesis prediction.
- TargetDiff — 3D equivariant diffusion model for structure-based drug design.
- DiffDock — Diffusion generative model for molecular docking, predicting the binding pose of small molecules to protein targets.
- AI4Chem/ChemLLM-7B-Chat — LLM for chemical & molecular science.
- BioGPT — LLM for biomedical text generation.
- GeneGPT — LLM for biomedical information, integrated with various APIs.
- GenePT — Foundation LLM for single-cell data.
- scPRINT — Pretrained on 50M cells for scRNA-seq denoising & zero imputation.
- ClawBio — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.
- BioMedLM — 2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical question answering and text generation.
- scFoundation — Large-scale foundation model for single-cell gene expression, enabling multiple downstream tasks.
- scGPT — 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 — Foundation model for bulk RNA-seq data; learns general transcriptomic representations.
- scBERT — BERT-based foundation model pretrained on large-scale scRNA-seq data for cell type annotation.
- CellPLM — Cell pre-trained language model with inter-cell transformer architecture for diverse single-cell analysis tasks.
- UCE — Universal Cell Embeddings: zero-shot single-cell embedding model trained on 36M cells across species, tissues, and assays without fine-tuning.
- GEARS — Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using biological priors.
- GigaPath — Slide-level digital pathology foundation model pretrained on 1.3 billion pathology image tokens from whole-slide images.
- UNI — General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse computational pathology tasks.
- CONCH — 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.
- scMulan — Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics for cross-omics transfer tasks.
- totalVI — 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 — Multi-modal variational autoencoder for integrating paired and unpaired single-cell RNA-seq and ATAC-seq measurements into a unified latent space.
- MIRA — Probabilistic multimodal topic model jointly modeling single-cell transcriptomics and chromatin accessibility for regulatory network inference.
- GLUE — Graph-Linked Unified Embedding framework for unpaired single-cell multi-omics data integration across RNA, ATAC, methylation, and protein modalities.
- BABEL — Cross-modality translation model enabling prediction between scRNA-seq and scATAC-seq profiles without requiring paired single-cell measurements.
- Multigrate — Asymmetric multi-omics variational autoencoder for integrating single-cell data across RNA, ATAC, and protein modalities with missing-modality support.
- MOFA+ — 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 — Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.
- UnitedNet — 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 — Mosaic integration and differential accessibility model for single-cell multi-omics data that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.
- scArches — 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.
- Evolutionary Scale Modeling (ESM) — Protein embeddings.
- ChemBERTa-2 — Chemical embeddings & prediction.
- AlphaFold3 — Predicts structures of proteins, nucleic acids, small molecules, and their complexes.
- Boltz-1 — Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their complexes achieving AlphaFold3-level accuracy.
- Chai-1 — Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes.
- ESM3 — Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering.
- ESMFold — Fast protein structure prediction using language model embeddings.
- RFdiffusion — Generative model for protein backbone design using diffusion.
- ProteinMPNN — Deep learning model for protein sequence design given backbone structure.
- OmegaFold — High-resolution de novo protein structure prediction from sequence.
- RoseTTAFold — Three-track neural network for protein structure prediction.
- OpenFold — 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.
- CHIEF — 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.
- Nucleotide Transformer — Foundation model for genomic sequences across multiple species.
- DNABERT — Pre-trained bidirectional encoder for DNA sequence analysis.
- DNABERT-2 — Improved genome foundation model with efficient tokenization.
- Enformer — Transformer model predicting gene expression from DNA sequence.
- Basenji — Sequential regulatory activity prediction from DNA sequences.
- Caduceus — Bidirectional equivariant long-range DNA sequence model based on Mamba.
- Evo — Long-context genomic foundation model (up to 1M tokens).
- HyenaDNA — Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention.
- Borzoi — Extended successor to Enformer for predicting RNA-seq coverage from long genomic sequence windows (524 kb) with improved resolution.