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

Table of Contents

• Awesome Computational Biology
  • Table of Contents
  • Auto-Generated Resources
  • Databases
    • scRNA
    • Compound
    • Pathway
    • Mass Spectra
    • Protein
    • Genome
    • Disease
    • Interaction
      • Drug-Gene Interaction
      • Drug (Cell Line) Response
      • Chemical-Protein Interaction
      • Protein-Protein Interaction
      • Knowledge Graph
    • Clinical Trial
  • Benchmarks & Datasets
  • API
  • Preprocessing Tools
  • Machine Learning Tasks and Models
    • Drug Discovery
      • Drug Response Prediction
      • Drug Repurposing
      • Drug Target Interaction
      • Compound-Protein Interaction
      • Molecular Generation
    • LLM for Biology
    • Foundation Models
      • Single-cell Foundation Models
        • Transcriptomics Foundation Models
        • Spatial Foundation Models
        • Multi-Omics Foundation Models
        • Domain Alignment
      • Protein Foundation Models
        • Pre-trained Embedding
        • Protein Structure Prediction and Design
      • Multi-Modal Foundation Models
      • Genomics Foundation Models

Auto-Generated Resources

Generated from data/resources.yml.

API

• ChEMBL Web Services — REST API providing programmatic access to the ChEMBL database for bioactive molecules, targets, assays, and compound ADMET properties.

Benchmark

• MoleculeNet — Curated collection of benchmark datasets for molecular machine learning, spanning quantum mechanics, physical chemistry, biophysics, and physiology.
• Therapeutics Data Commons (TDC) — Unified resource of AI-ready datasets and learning tasks for therapeutic science, covering drug-target interaction, ADMET, clinical trials, and more.

Database

• AlphaFold Protein Structure Database — 3D protein structure predictions powered by AlphaFold.
• ChEMBL — Manually curated database of bioactive molecules with drug-like properties, containing binding, functional, and ADMET data for millions of compounds.
• CZ CELLxGENE — Single-cell dataset repository and interactive explorer from the Chan Zuckerberg Initiative.
• Dependency Map (DepMap) — CRISPR-Cas9 screens in cancer cell lines.
• ENCODE — Encyclopedia of DNA Elements; regulatory and functional genomic elements across the genome.
• Gene Expression Omnibus — Public functional genomics database.
• Genomics of Drug Sensitivity in Cancer (GDSC) — Drug sensitivity measurements for ~1 000 human cancer cell lines tested against hundreds of compounds, paired with rich genomic characterization.
• MassBank — Open source databases and tools for mass spectrometry reference spectra.
• PrimeKG (⭐706) — Precision medicine knowledge graph integrating 20 high-quality biological databases across diseases, drugs, genes, and biological processes.
• PubChem — One of the largest chemical databases (compounds, genes, and proteins).
• Reactome — Expert-curated, peer-reviewed pathway database with detailed reaction mechanisms.
• UniProt — Functional information on proteins.

Model

• AlphaFold 3 (⭐7.7k) — Deep learning model from Google DeepMind that predicts the joint 3-D structure of proteins, nucleic acids, small molecules, and their complexes.
• Enformer (⭐15k) — Transformer model that predicts gene expression and epigenetic tracks directly from long DNA sequences (200 kb context window).
• scGPT (⭐1.5k) — Transformer-based foundation model pretrained on millions of single-cell transcriptomic profiles; supports cell-type annotation, perturbation response prediction, and multi-batch integration.

Toolkit

• Scanpy — Scalable Python toolkit for analyzing single-cell gene expression data, covering preprocessing, visualization, clustering, and trajectory inference.
• Squidpy — Python library for the analysis and visualization of spatial molecular data, built on top of AnnData and Scanpy.

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.
• 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.
• SIGNOR — Database of causal signaling interactions and pathways.
• 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.
• 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.

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.
• 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.

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.

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.
• 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) (⭐69) — Mechanisms of action from drug to disease.
• DRKG (⭐671) — Large-scale biological knowledge graph for drug discovery.
• Hetionet (⭐343) — Heterogeneous network integrating genes, diseases, drugs, pathways, and more.
• PrimeKG (⭐706) — Multi-modal precision medicine knowledge graph integrating clinical, genetic, and drug data.

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

• 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.
• CrossDocked2020 — Large-scale dataset for structure-based virtual screening.
• Genomics of Drug Sensitivity in Cancer (GDSC) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
• GuacaMol (⭐500) — Benchmark suite for generative molecular design models.
• MoleculeNet — Benchmark datasets for molecular machine learning.
• MOSES (⭐957) — Benchmarking platform for molecular generation models.
• NCI60 — Drug sensitivity benchmark across 60 diverse human cancer cell lines.
• OpenBioLink (⭐158) — Benchmark datasets for biological knowledge graph completion.
• Therapeutics Data Commons (TDC) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.

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 (⭐571) — Cheminformatics software & machine learning tools.
• Biopython — Collection of Python tools for biological computation including sequence analysis, structure parsing, and database access.
• FlashDeconv (⭐13) — High-performance spatial transcriptomics deconvolution (~1M spots in ~3 min).
• RDKit (⭐3.3k) — Cheminformatics software & machine learning toolkit.
• DeepChem (⭐6.6k) — Deep learning library for drug discovery, quantum chemistry, and materials science.
• ChatSpatial (⭐18) — 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 (⭐457) — 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.

Machine Learning Tasks and Models

Drug Discovery

Drug Response Prediction

• drGAT (⭐1) — Attention-based model for drug response prediction with gene explainability.
• MOFGCN (⭐6) — GCN + heterogeneous network.
• DeepDSC — Autoencoder + fully connected NN.
• DGDRP (⭐0) — Multi-view embedding neural network.
• DeepAEG (⭐3) — GNN embedding + attention mechanism.

Drug Repurposing

• DeepPurpose (⭐1.1k) — Deep learning library for drug repurposing.

Drug Target Interaction

• NeoDTI (⭐77) — Library for drug-target interaction prediction.
• DTINet (⭐185) — Network-based framework integrating heterogeneous biological data for DTI prediction.
• DeepDTA (⭐293) — Deep learning model using CNNs on protein sequences and drug SMILES.
• GraphDTA (⭐293) — Graph neural network–based DTI prediction using molecular graphs.
• MolTrans (⭐225) — Transformer-based DTI model leveraging molecular substructures.
• DrugBAN (⭐138) — Bilinear attention network for interpretable DTI prediction.

Compound-Protein Interaction

• MCPINN (⭐3) — Drug discovery via compound-protein interaction and machine learning.
• TransformerCPI (⭐153) — CPI prediction using Transformer.

Molecular Generation

• REINVENT (⭐370) — Reinforcement learning for de novo drug design.
• MolGPT (⭐169) — Transformer-based model for molecular generation.
• Molecular Transformer (⭐413) — Sequence-to-sequence model for retrosynthesis prediction.
• TargetDiff (⭐323) — 3D equivariant diffusion model for structure-based drug design.

LLM for Biology

• AI4Chem/ChemLLM-7B-Chat — LLM for chemical & molecular science.
• BioGPT (⭐4.5k) — LLM for biomedical text generation.
• GeneGPT (⭐423) — LLM for biomedical information, integrated with various APIs.
• GenePT (⭐310) — Foundation LLM for single-cell data.
• scPRINT (⭐142) — Pretrained on 50M cells for scRNA-seq denoising & zero imputation.
• ClawBio (⭐101) — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.

Foundation Models

Single-cell Foundation Models

Transcriptomics Foundation Models

• scFoundation (⭐392) — Large-scale foundation model for single-cell gene expression, enabling multiple downstream tasks.
• scGPT (⭐1.5k) — 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 (⭐42) — Foundation model for bulk RNA-seq data; learns general transcriptomic representations.
• scBERT (⭐347) — BERT-based foundation model pretrained on large-scale scRNA-seq data for cell type annotation.
• CellPLM (⭐101) — Cell pre-trained language model with inter-cell transformer architecture for diverse single-cell analysis tasks.

Spatial Foundation Models

• GigaPath (⭐578) — Slide-level digital pathology foundation model pretrained on 1.3 billion pathology image tokens from whole-slide images.
• UNI (⭐681) — General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse computational pathology tasks.
• CONCH (⭐472) — 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.

Multi-Omics Foundation Models

• scMulan (⭐62) — Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics for cross-omics transfer tasks.
• totalVI (⭐1.6k) — 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 (⭐1.6k) — Multi-modal variational autoencoder for integrating paired and unpaired single-cell RNA-seq and ATAC-seq measurements into a unified latent space.
• MIRA (⭐67) — Probabilistic multimodal topic model jointly modeling single-cell transcriptomics and chromatin accessibility for regulatory network inference.
• GLUE (⭐455) — Graph-Linked Unified Embedding framework for unpaired single-cell multi-omics data integration across RNA, ATAC, methylation, and protein modalities.
• BABEL (⭐47) — Cross-modality translation model enabling prediction between scRNA-seq and scATAC-seq profiles without requiring paired single-cell measurements.
• Multigrate (⭐31) — Asymmetric multi-omics variational autoencoder for integrating single-cell data across RNA, ATAC, and protein modalities with missing-modality support.
• MOFA+ (⭐384) — 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 (⭐111) — Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.
• UnitedNet (⭐52) — 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 (⭐62) — Mosaic integration and differential accessibility model for single-cell multi-omics data that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.

Domain Alignment

• scArches (⭐399) — 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.

Protein Foundation Models

Pre-trained Embedding

• Evolutionary Scale Modeling (ESM) (⭐4k) — Protein embeddings.
• ChemBERTa-2 (⭐487) — Chemical embeddings & prediction.

Protein Structure Prediction and Design

• AlphaFold3 (⭐7.7k) — Predicts structures of proteins, nucleic acids, small molecules, and their complexes.
• Boltz-1 (⭐3.8k) — Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their complexes achieving AlphaFold3-level accuracy.
• Chai-1 (⭐1.9k) — Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes.
• ESM3 (⭐2.3k) — Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering.
• ESMFold (⭐4k) — Fast protein structure prediction using language model embeddings.
• RFdiffusion (⭐2.8k) — Generative model for protein backbone design using diffusion.
• ProteinMPNN (⭐1.6k) — Deep learning model for protein sequence design given backbone structure.
• OmegaFold (⭐612) — High-resolution de novo protein structure prediction from sequence.
• RoseTTAFold (⭐2.2k) — Three-track neural network for protein structure prediction.

Multi-Modal Foundation Models

• CHIEF (⭐688) — 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.

Genomics Foundation Models

• Nucleotide Transformer (⭐830) — Foundation model for genomic sequences across multiple species.
• DNABERT (⭐743) — Pre-trained bidirectional encoder for DNA sequence analysis.
• DNABERT-2 (⭐459) — Improved genome foundation model with efficient tokenization.
• Enformer (⭐15k) — Transformer model predicting gene expression from DNA sequence.
• Basenji (⭐466) — Sequential regulatory activity prediction from DNA sequences.
• Caduceus (⭐226) — Bidirectional equivariant long-range DNA sequence model based on Mamba.
• Evo (⭐1.5k) — Long-context genomic foundation model (up to 1M tokens).
• HyenaDNA (⭐764) — Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention.