Single-cell dataset repository and interactive explorer from the Chan Zuckerberg Initiative.

Public functional genomics database.

Open global atlas of all cells in the human body.

Public database for single-cell RNA.

Public database for single-cell RNA.

One of the largest chemical databases (compounds, genes, and proteins).

Database focused on small chemical compounds.

Bioactive molecules with drug-like properties.

Chemical structure database.

Community platform for curating and integrating experimental bioactivity data across drugs and targets.

Comprehensive database of small molecule metabolites found in the human body.

Collection of small molecules and biopolymers.

Database of lipids.

Database of chemical reactions.

Online drug compendium with drug mode of action and indication information.

Collections of drug repurposing data (drug, MoA, target, etc).

Drug-target, target-disease, and drug-disease datasets.

Free database of commercially-available compounds for virtual screening.

Database of pathways and interactions.

Collection of pathway maps.

Database of biological pathways.

Expert-curated, peer-reviewed pathway database with detailed reaction mechanisms.

Collection of pathway/genome databases across thousands of organisms.

Comprehensive resource integrating protein interactions, signaling pathways, gene regulatory networks, and miRNA…

Database of causal signaling interactions and pathways, with signed and directed relationships between proteins.

Curated gene sets derived from pathways and biological processes.

Open source databases and tools for mass spectrometry reference spectra.

Meta-database of metabolite mass spectra, metadata, and associated compounds.

Comprehensive human protein database (cells, tissues, organs).

3D structures of proteins, nucleic acids, complexes.

Functional information on proteins.

3D protein structure predictions.

Assessing methods for protein structure prediction.

Clustered protein sequence databases.

Non-redundant sequence database clustering UniProtKB entries at multiple sequence identity thresholds.

Hierarchical classification of protein domain structures.

Structural Antibody Database containing all antibody structures in the PDB.

Database of antibody sequences from immune repertoire sequencing.

Protein families, domains, and functional sites database integrating 14 member databases including Pfam and PROSITE.

Database of protein families described by multiple sequence alignments and hidden Markov models.

Expert knowledge base on human proteins with deep functional annotation, complementary to UniProt.

Encyclopedia of DNA Elements; regulatory and functional genomic elements across the genome.

Genome browser and annotation database for vertebrate and other eukaryotic genomes.

Database for genomics, proteomics, transcriptomics, and systems biology.

NCBI's database of genetic sequences.

UCSC's genome browser.

Cancer genomics database; aggregating many patient datasets.

Precision oncology knowledge base of cancer genes, variants, and therapeutic implications.

Collection of single-cell datasets.

Human gene expression and regulation resource.

CRISPR-Cas9 screens in cancer cell lines.

Resource on somatic mutations in cancers.

Resource for metagenomic and metatranscriptomic data.

Database of transcription factor binding profiles.

Genome Aggregation Database; genetic variation from large-scale sequencing projects.

Database of RNA families with sequence alignments and consensus structures.

Reference epigenome maps for 111 primary human cell types and tissues, including histone modifications, chromatin…

Functional annotation of mammalian genome; comprehensive atlas of active enhancers, promoters, and transcription start…

Comprehensive, approved drug information.

Database of drugs and targets (University of Alberta).

Database of gene-disease associations integrating expert-curated and GWAS data.

Comprehensive database of human genes and genetic disorders.

Systematic target identification and prioritization platform integrating genetics, genomics, and drug data for drug…

Standardized vocabulary of phenotypic abnormalities in human disease, linking genes, variants, and clinical features.

Gene–disease association database integrating evidence from text mining, curated databases, and experimental data.

Drug-gene interactions and the druggable genome.

Chemical-gene interactions, chemical-disease and gene-disease associations, chemical-phenotype associations.

Dataset of drug-gene interactions.

Focuses on 60 cancer cell lines and many drugs.

Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.

Database of ~1000 cancer cell lines.

Integrates multiple cancer cell line databases.

Chemical-protein interactions.

Compounds and target database.

Experimental kinase inhibitor binding affinity dataset for protein–ligand interaction research.

Integrated bioactivity scores for kinase inhibitors combining Ki, Kd, and IC50 measurements.

Binding affinity data for biomolecular complexes.

PPI networks for multiple organisms.

Protein, genetic, and chemical interactions.

Human protein-protein interaction database.

Open-source molecular interaction database and analysis system from EMBL-EBI.

Mechanisms of action from drug to disease.

Large-scale biological knowledge graph for drug discovery.

Heterogeneous network integrating genes, diseases, drugs, pathways, and more.

Multi-modal precision medicine knowledge graph integrating clinical, genetic, and drug data.

Manually curated database of human and mouse transcriptional regulatory interactions between transcription factors and…

Database of gene regulatory networks covering transcription factor–target gene and miRNA–gene interaction data across…

Reference repository for microRNA gene annotations, sequences, and experimentally validated targets.

Privately and publicly funded clinical studies.

International Classification of Diseases, 10th revision.

European clinical trial database.

Freely accessible critical care database.

Reference panel of human genetic variation from 2,504 individuals across 26 populations.

Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.

Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.

Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks.

Curated binding affinity datasets for protein–ligand interaction benchmarking.

Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.

Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.

Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.

Large-scale dataset for structure-based virtual screening.

Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein…

Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.

Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.

Benchmark suite for generative molecular design models.

Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery…

Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.

Benchmark datasets for molecular machine learning.

Benchmarking platform for molecular generation models.

Focuses on 60 cancer cell lines and many drugs.

Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and…

Benchmark datasets for biological knowledge graph completion.

Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.

Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies.

Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.

Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models.

Quantum chemistry properties for 134K stable small organic molecules computed at DFT level.

Comprehensive benchmarking framework for single-cell data integration methods.

Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.

Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.

Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.

Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.

Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.

Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across…

DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper:…

DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: DeepSpot-M.

Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.

12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.

Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics…

APIs for searching and retrieving biomedical literature from PubMed.

Unified APIs for accessing NCBI databases (Gene, GEO, SRA, PubChem, etc).

Programmatic access to protein sequence and functional annotation data.

API for genomic annotations, variants, genes, and comparative genomics.

API for accessing KEGG pathways, compounds, genes, and reactions.

REST API for bioactive molecules, targets, and bioassays.

API for target–disease associations integrating genetics, genomics, and drug data.

API for querying clinical trial metadata and results.

Cheminformatics software & machine learning tools.

Collection of Python tools for biological computation including sequence analysis, structure parsing, and database…

High-performance spatial transcriptomics deconvolution (~1M spots in ~3 min).

Cheminformatics software & machine learning toolkit.

Deep learning library for drug discovery, quantum chemistry, and materials science.

MCP server for spatial transcriptomics analysis via natural language.

Python library for scRNA-seq analysis.

R library for scRNA-seq analysis.

Probabilistic models for single-cell omics data analysis.

Automated cell type annotation for scRNA-seq.

Python library for spatial single-cell analysis.

Molecular dynamics simulation package for biochemical molecules.

Python library for analyzing and altering molecular dynamics simulation trajectories.

High-performance toolkit for molecular simulation and GPU-accelerated MD.

RNA velocity estimation for single-cell transcriptomics, inferring the direction and speed of cell differentiation.

Ultrafast universal RNA-seq aligner with support for spliced alignment and single-cell quantification via STARsolo.

Near-optimal RNA-seq quantification using pseudoalignment for fast transcript abundance estimation.

Fast and scalable integration of single-cell data across datasets, conditions, technologies, and species.

Single-cell trajectory analysis tool for learning developmental trajectories and ordering cells in pseudotime.

Inference and analysis of cell-cell communication ligand-receptor networks from single-cell transcriptomics data.

Single-cell regulatory network inference and clustering linking transcription factors to co-expressed gene modules.

Machine learning approach for detecting multiplet (doublet) artifacts in single-cell RNA-seq data.

Haplotype-aware copy number variation inference from single-cell RNA-seq using hidden Markov models.

CNV identification and visualization by integrative analysis of single-cell or bulk RNA-seq data.

Identification and characterization of spatial cell niches from spatial transcriptomics using VAEs and Gaussian…

Adaptive graph attention auto-encoder for spatial domain identification in spatial transcriptomics.

GNN-based model for learning intercellular communication from spatial graphs of cells.

Graph attention network for deciphering cell-cell communication from spatial transcriptomics.

Optimal transport-based framework for screening cell-cell communication in spatial transcriptomics.

Neural optimal transport method for reconstructing growth and dynamic trajectories from single-cell transcriptomics.

Neural network for gene regulatory network inference from single-cell multiome (RNA+ATAC-seq) data with bulk data…

RNN-based method for simultaneous protein expression prediction, uncertainty estimation, and cell-type label transfer…

Multi-omics graph convolutional network framework for patient classification and biomarker identification.

Autonomous agentic framework that speeds up bioinformatics software (e.g. Scanpy, Seurat) on CPUs while preserving the…

Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction…

Attention-based model for drug response prediction with gene explainability.

GCN + heterogeneous network.

Autoencoder + fully connected NN.

Multi-view embedding neural network.

GNN embedding + attention mechanism.

Machine learning framework for predicting synergistic drug combination responses across cell lines.

Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.

Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction.

Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs…

Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.

Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and…

Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug…

Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations.

Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.

Deep learning library for drug repurposing.

Dual-VAE architecture for ligand-based virtual screening, drug response prediction, and drug repurposing using…

Library for drug-target interaction prediction.

Network-based framework integrating heterogeneous biological data for DTI prediction.

Deep learning model using CNNs on protein sequences and drug SMILES.

Graph neural network–based DTI prediction using molecular graphs.

Transformer-based DTI model leveraging molecular substructures.

Bilinear attention network for interpretable DTI prediction.

Drug discovery via compound-protein interaction and machine learning.

CPI prediction using Transformer.

Reinforcement learning for de novo drug design.

Transformer-based model for molecular generation.

Sequence-to-sequence model for retrosynthesis prediction.

Multi-stage Riemannian flow matching model for physically valid molecular docking with scoring, pose filtering, and…

3D equivariant diffusion model for structure-based drug design.

Diffusion generative model for molecular docking, predicting the binding pose of small molecules to protein targets.

Junction tree variational autoencoder for molecular graph generation that guarantees chemical validity via a…

Equivariant diffusion model for structure-based drug design that generates molecules and binding conformations for…

Deep reinforcement learning framework for de novo drug design combining a generative and predictive model.

Reinforcement learning-based generative model for de novo hit-like anticancer molecule design from transcriptomic data.

Bayesian neural network integrating protein language model embeddings and physicochemical features to predict nanobody…

LLM for chemical & molecular science.

LLM for biomedical text generation.

LLM for biomedical information, integrated with various APIs.

Foundation LLM for single-cell data.

Pretrained on 50M cells for scRNA-seq denoising & zero imputation.

Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity,…

2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical…

Language model for molecular tasks bridging text and SMILES, enabling molecule captioning and text-driven molecule…

LLM-based conversational pipeline for drug discovery, using natural language prompts for iterative drug editing and…

Multi-agent LLM for reference-free, interpretable cell-type annotation of single-cell RNA-seq data, with dedicated…

Large-scale foundation model for single-cell gene expression, enabling multiple downstream tasks.

Transformer-based foundation model pretrained on millions of single-cell profiles.

Context-aware, attention-based deep learning model pretrained on a large corpus of single-cell transcriptomes.

Foundation model for bulk RNA-seq data; learns general transcriptomic representations.

BERT-based foundation model pretrained on large-scale scRNA-seq data for cell type annotation.

Cell pre-trained language model with inter-cell transformer architecture for diverse single-cell analysis tasks.

Universal Cell Embeddings: zero-shot single-cell embedding model trained on 36M cells across species, tissues, and…

Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using…

Transformer-based model integrating gene expression and protein sequences via a protein language model to learn…

Single-cell RNA-seq foundation model trained exclusively on a curated dataset of malignant cells to learn…

Slide-level digital pathology foundation model pretrained on 1.3 billion pathology image tokens from whole-slide images.

General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse…

Vision-language foundation model for computational pathology trained with contrastive captioning on pathology…

ViT-based pathology foundation model pretrained with iBOT self-supervision on TCGA whole-slide images.

Foundation model for single-cell and spatial omics using a transformer architecture with positional embeddings to…

Extension of scGPT for spatial transcriptomics with continual pretraining and a mixture-of-experts decoder for spatial…

Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.

Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).

Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.

Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.

Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics…

Probabilistic framework for joint analysis of paired scRNA-seq and protein (CITE-seq) data enabling multi-modal cell…

Probabilistic multimodal topic model jointly modeling single-cell transcriptomics and chromatin accessibility for…

Graph-Linked Unified Embedding framework for unpaired single-cell multi-omics data integration across RNA, ATAC,…

Cross-modality translation model enabling prediction between scRNA-seq and scATAC-seq profiles without requiring…

Asymmetric multi-omics variational autoencoder for integrating single-cell data across RNA, ATAC, and protein…

Multi-Omics Factor Analysis framework identifying shared axes of variation across bulk and single-cell datasets…

Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells…

Interpretable multi-task deep neural network for single-cell multi-omics integration spanning transcriptomics,…

Graph attention network for spatial multi-omics integration jointly embedding spatial transcriptomics with chromatin…

Mosaic integration and differential accessibility model for single-cell multi-omics that handles arbitrary…

Contrastive self-supervised learning framework for single-cell multimodal data integration, batch correction, and…

Dual-aligned variational autoencoder for single-cell cross-modality translation between paired and unpaired multiomics…

Joint variational autoencoder for multimodal single-cell data imputation and embedding.

Bidirectional feedforward network for single-cell multimodal analysis with cross-modality prediction leveraging…

Transfer learning framework for mapping new single-cell datasets onto pre-trained reference atlases across batches,…

Transformer-based framework for one-stop interpretable cell-type annotation supporting cross-dataset and cross-species…

RoBERTa-based molecular language model pretrained on SMILES for small-molecule representation learning.

Self-supervised graph transformer for large-scale molecular representation learning from unlabeled compounds.

Unsupervised molecular embedding method inspired by Word2Vec for learning vector representations of chemical…

Linear attention transformer pretrained on millions of SMILES strings for efficient molecular embeddings.

3D molecular pretraining framework for universal representation learning on molecules and protein pockets.

Protein embeddings.

Suite of protein language models (ProtBERT, ProtT5, ProtXLNet) trained on billions of protein sequences from UniRef…

Protein language model trained on diverse protein families for sequence generation and fitness prediction.

Efficient protein language model optimized for downstream prediction tasks including secondary structure,…

Predicts structures of proteins, nucleic acids, small molecules, and their complexes.

Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their…

Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes.

Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein…

Generative model for protein backbone design using diffusion.

Deep learning model for protein sequence design given backbone structure.

High-resolution de novo protein structure prediction from sequence.

Three-track neural network for protein structure prediction.

Trainable, memory-efficient open-source reproduction of AlphaFold2 enabling custom protein structure prediction…

Structure-aware protein language model using structure-aware tokens that encode both sequence and backbone geometry…

Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting…

Clinical Histopathology Imaging Evaluation Foundation model integrating histology images and clinical context for…

CLIP-based vision-language foundation model for biomedical images and text trained on PubMed figure–caption pairs.

Pan-cancer integrative histology-genomic analysis framework using multimodal deep learning for patient stratification.

Integrated framework fusing histopathology and genomic features via CNN, GNN, and attention gating for cancer…

Million-slide digital pathology foundation model using a vision transformer and self-supervised distillation for…

Tumor Origin Assessment via Deep-learning; weakly-supervised multi-task model predicting cancer primary origin from…

Vision-language foundation model for pathology trained with contrastive learning on pathology image–text pairs for…

Vision-language foundation model for precision oncology analyzing multimodal paired text and pathology image data for…

Foundation model for genomic sequences across multiple species.

Pre-trained bidirectional encoder for DNA sequence analysis.

Improved genome foundation model with efficient tokenization.

Transformer model predicting gene expression from DNA sequence.

Sequential regulatory activity prediction from DNA sequences.

Bidirectional equivariant long-range DNA sequence model based on Mamba.

Long-context genomic foundation model (up to 1M tokens).

Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention.

Extended successor to Enformer for predicting RNA-seq coverage from long genomic sequence windows (524 kb) with…

Deep learning framework for predicting chromatin effects of sequence alterations with single-nucleotide sensitivity…

Sequence-to-function framework learning a genome-wide regulatory activity code from DNA sequences for variant effect…

Masked language model for DNA sequences enabling zero-shot variant effect prediction without requiring functional…