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TDC Datasets Comprehensive Catalog

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TDC Datasets Comprehensive Catalog

This document provides a comprehensive catalog of all available datasets in the Therapeutics Data Commons, organized by task category.

Single-Instance Prediction Datasets

ADME (Absorption, Distribution, Metabolism, Excretion)

Absorption:

  • Caco2_Wang - Caco-2 cell permeability (906 compounds)
  • Caco2_AstraZeneca - Caco-2 permeability from AstraZeneca (700 compounds)
  • HIA_Hou - Human intestinal absorption (578 compounds)
  • Pgp_Broccatelli - P-glycoprotein inhibition (1,212 compounds)
  • Bioavailability_Ma - Oral bioavailability (640 compounds)
  • F20_edrug3d - Oral bioavailability F>=20% (1,017 compounds)
  • F30_edrug3d - Oral bioavailability F>=30% (1,017 compounds)

Distribution:

  • BBB_Martins - Blood-brain barrier penetration (1,975 compounds)
  • PPBR_AZ - Plasma protein binding rate (1,797 compounds)
  • VDss_Lombardo - Volume of distribution at steady state (1,130 compounds)

Metabolism:

  • CYP2C19_Veith - CYP2C19 inhibition (12,665 compounds)
  • CYP2D6_Veith - CYP2D6 inhibition (13,130 compounds)
  • CYP3A4_Veith - CYP3A4 inhibition (12,328 compounds)
  • CYP1A2_Veith - CYP1A2 inhibition (12,579 compounds)
  • CYP2C9_Veith - CYP2C9 inhibition (12,092 compounds)
  • CYP2C9_Substrate_CarbonMangels - CYP2C9 substrate (666 compounds)
  • CYP2D6_Substrate_CarbonMangels - CYP2D6 substrate (664 compounds)
  • CYP3A4_Substrate_CarbonMangels - CYP3A4 substrate (667 compounds)

Excretion:

  • Half_Life_Obach - Half-life (667 compounds)
  • Clearance_Hepatocyte_AZ - Hepatocyte clearance (1,020 compounds)
  • Clearance_Microsome_AZ - Microsome clearance (1,102 compounds)

Solubility & Lipophilicity:

  • Solubility_AqSolDB - Aqueous solubility (9,982 compounds)
  • Lipophilicity_AstraZeneca - Lipophilicity (logD) (4,200 compounds)
  • HydrationFreeEnergy_FreeSolv - Hydration free energy (642 compounds)

Toxicity

Organ Toxicity:

  • hERG - hERG channel inhibition/cardiotoxicity (648 compounds)
  • hERG_Karim - hERG blockers extended dataset (13,445 compounds)
  • DILI - Drug-induced liver injury (475 compounds)
  • Skin_Reaction - Skin reaction (404 compounds)
  • Carcinogens_Lagunin - Carcinogenicity (278 compounds)
  • Respiratory_Toxicity - Respiratory toxicity (278 compounds)

General Toxicity:

  • AMES - Ames mutagenicity (7,255 compounds)
  • LD50_Zhu - Acute toxicity LD50 (7,385 compounds)
  • ClinTox - Clinical trial toxicity (1,478 compounds)
  • SkinSensitization - Skin sensitization (278 compounds)
  • EyeCorrosion - Eye corrosion (278 compounds)
  • EyeIrritation - Eye irritation (278 compounds)

Environmental Toxicity:

  • Tox21-AhR - Nuclear receptor signaling (8,169 compounds)
  • Tox21-AR - Androgen receptor (9,362 compounds)
  • Tox21-AR-LBD - Androgen receptor ligand binding (8,343 compounds)
  • Tox21-ARE - Antioxidant response element (6,475 compounds)
  • Tox21-aromatase - Aromatase inhibition (6,733 compounds)
  • Tox21-ATAD5 - DNA damage (8,163 compounds)
  • Tox21-ER - Estrogen receptor (7,257 compounds)
  • Tox21-ER-LBD - Estrogen receptor ligand binding (8,163 compounds)
  • Tox21-HSE - Heat shock response (8,162 compounds)
  • Tox21-MMP - Mitochondrial membrane potential (7,394 compounds)
  • Tox21-p53 - p53 pathway (8,163 compounds)
  • Tox21-PPAR-gamma - PPAR gamma activation (7,396 compounds)

HTS (High-Throughput Screening)

SARS-CoV-2:

  • SARSCoV2_Vitro_Touret - In vitro antiviral activity (1,484 compounds)
  • SARSCoV2_3CLPro_Diamond - 3CL protease inhibition (879 compounds)
  • SARSCoV2_Vitro_AlabdulKareem - In vitro screening (5,953 compounds)

Other Targets:

  • Orexin1_Receptor_Butkiewicz - Orexin receptor screening (4,675 compounds)
  • M1_Receptor_Agonist_Butkiewicz - M1 receptor agonist (1,700 compounds)
  • M1_Receptor_Antagonist_Butkiewicz - M1 receptor antagonist (1,700 compounds)
  • HIV_Butkiewicz - HIV inhibition (40,000+ compounds)
  • ToxCast - Environmental chemical screening (8,597 compounds)

QM (Quantum Mechanics)

  • QM7 - Quantum mechanics properties (7,160 molecules)
  • QM8 - Electronic spectra and excited states (21,786 molecules)
  • QM9 - Geometric, energetic, electronic, thermodynamic properties (133,885 molecules)

Yields

  • Buchwald-Hartwig - Reaction yield prediction (3,955 reactions)
  • USPTO_Yields - Yield prediction from USPTO (853,879 reactions)

Epitope

  • IEDBpep-DiseaseBinder - Disease-associated epitope binding (6,080 peptides)
  • IEDBpep-NonBinder - Non-binding peptides (24,320 peptides)

Develop (Development)

  • Manufacturing - Manufacturing success prediction
  • Formulation - Formulation stability

CRISPROutcome

  • CRISPROutcome_Doench - Gene editing efficiency prediction (5,310 guide RNAs)

Multi-Instance Prediction Datasets

DTI (Drug-Target Interaction)

Binding Affinity:

  • BindingDB_Kd - Dissociation constant (52,284 pairs, 10,665 drugs, 1,413 proteins)
  • BindingDB_IC50 - Half-maximal inhibitory concentration (991,486 pairs, 549,205 drugs, 5,078 proteins)
  • BindingDB_Ki - Inhibition constant (375,032 pairs, 174,662 drugs, 3,070 proteins)

Kinase Binding:

  • DAVIS - Davis kinase binding dataset (30,056 pairs, 68 drugs, 442 proteins)
  • KIBA - KIBA kinase binding dataset (118,254 pairs, 2,111 drugs, 229 proteins)

Binary Interaction:

  • BindingDB_Patent - Patent-derived DTI (8,503 pairs)
  • BindingDB_Approval - FDA-approved drug DTI (1,649 pairs)

DDI (Drug-Drug Interaction)

  • DrugBank - Drug-drug interactions (191,808 pairs, 1,706 drugs)
  • TWOSIDES - Side effect-based DDI (4,649,441 pairs, 645 drugs)

PPI (Protein-Protein Interaction)

  • HuRI - Human reference protein interactome (52,569 interactions)
  • STRING - Protein functional associations (19,247 interactions)

GDA (Gene-Disease Association)

  • DisGeNET - Gene-disease associations (81,746 pairs)
  • PrimeKG_GDA - Gene-disease from PrimeKG knowledge graph

DrugRes (Drug Response/Resistance)

  • GDSC1 - Genomics of Drug Sensitivity in Cancer v1 (178,000 pairs)
  • GDSC2 - Genomics of Drug Sensitivity in Cancer v2 (125,000 pairs)

DrugSyn (Drug Synergy)

  • DrugComb - Drug combination synergy (345,502 combinations)
  • DrugCombDB - Drug combination database (448,555 combinations)
  • OncoPolyPharmacology - Oncology drug combinations (22,737 combinations)

PeptideMHC

  • MHC1_NetMHCpan - MHC class I binding (184,983 pairs)
  • MHC2_NetMHCIIpan - MHC class II binding (134,281 pairs)

AntibodyAff (Antibody Affinity)

  • Protein_SAbDab - Antibody-antigen affinity (1,500+ pairs)

MTI (miRNA-Target Interaction)

  • miRTarBase - Experimentally validated miRNA-target interactions (380,639 pairs)

Catalyst

  • USPTO_Catalyst - Catalyst prediction for reactions (11,000+ reactions)

TrialOutcome

  • TrialOutcome_WuXi - Clinical trial outcome prediction (3,769 trials)

Generation Datasets

MolGen (Molecular Generation)

  • ChEMBL_V29 - Drug-like molecules from ChEMBL (1,941,410 molecules)
  • ZINC - ZINC database subset (100,000+ molecules)
  • GuacaMol - Goal-directed benchmark molecules
  • Moses - Molecular sets benchmark (1,936,962 molecules)

RetroSyn (Retrosynthesis)

  • USPTO - Retrosynthesis from USPTO patents (1,939,253 reactions)
  • USPTO-50K - Curated USPTO subset (50,000 reactions)

PairMolGen (Paired Molecule Generation)

  • Prodrug - Prodrug to drug transformations (1,000+ pairs)
  • Metabolite - Drug to metabolite transformations

Using retrieve_dataset_names

To programmatically access all available datasets for a specific task:

python
from tdc.utils import retrieve_dataset_names

# Get all datasets for a specific task
adme_datasets = retrieve_dataset_names('ADME')
tox_datasets = retrieve_dataset_names('Tox')
dti_datasets = retrieve_dataset_names('DTI')
hts_datasets = retrieve_dataset_names('HTS')

Dataset Statistics

Access dataset statistics directly:

python
from tdc.single_pred import ADME
data = ADME(name='Caco2_Wang')

# Print basic statistics
data.print_stats()

# Get label distribution
data.label_distribution()

Loading Datasets

All datasets follow the same loading pattern:

python
from tdc.<problem_type> import <TaskType>
data = <TaskType>(name='<DatasetName>')

# Get full dataset
df = data.get_data(format='df')  # or 'dict', 'DeepPurpose', etc.

# Get train/valid/test split
split = data.get_split(method='scaffold', seed=1, frac=[0.7, 0.1, 0.2])

Notes

  • Dataset sizes and statistics are approximate and may be updated
  • New datasets are regularly added to TDC
  • Some datasets may require additional dependencies
  • Check the official TDC website for the most up-to-date dataset list: https://tdcommons.ai/overview/