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Computational Biology

Awesome Computational Biology Awesome

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

Interface

Browse and search the resources via the GitHub Pages UI: https://inoue0426.github.io/awesome-computational-biology/

Databases

scRNA

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

Genome

Disease

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

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) — 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.

Clinical Trial

Benchmarks & Datasets

API

Preprocessing Tools

  • 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.

Machine Learning Tasks and Models

Drug Discovery

Drug Response Prediction

  • 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.

Drug Repurposing

  • DeepPurpose — Deep learning library for drug repurposing.

Drug Target Interaction

  • 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.

Compound-Protein Interaction

  • MCPINN — Drug discovery via compound-protein interaction and machine learning.
  • TransformerCPI — CPI prediction using Transformer.

Molecular Generation

  • 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.

LLM for Biology

  • 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.

Foundation Models

Single-cell Foundation Models

Transcriptomics Foundation Models
  • 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.
Spatial Foundation Models
  • 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.
Multi-Omics Foundation Models
  • 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.
Domain Alignment
  • 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.

Protein Foundation Models

Pre-trained Embedding
Protein Structure Prediction and Design
  • 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.

Multi-Modal Foundation Models

  • 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.

Genomics Foundation Models

  • 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.