PyTDC (Therapeutics Data Commons)

PyTDC (Therapeutics Data Commons)

Overview

PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery. It organizes therapeutics data into three categories: single-instance prediction (molecular/protein properties), multi-instance prediction (drug-target interactions), and generation (molecule design, retrosynthesis). All datasets come with standardized splits, evaluation metrics, and molecular oracles.

When to Use

• Loading curated ADME, toxicity, or bioactivity datasets for ML model training
• Benchmarking drug discovery models with standardized 5-seed evaluation protocols
• Predicting drug-target or drug-drug interactions with proper cold-split evaluation
• Generating novel molecules and scoring them with molecular oracles (QED, SA, DRD2, GSK3B)
• Accessing scaffold-based or temporal train/test splits for pharmaceutical ML
• Converting molecular representations (SMILES to PyG graphs, ECFP fingerprints, SELFIES)
• For chemical database queries (compound search, bioactivity), use chembl-database-bioactivity instead
• For molecular featurization beyond format conversion, use molfeat instead

Prerequisites

uv pip install PyTDC
# Core deps: numpy, pandas, scikit-learn, tqdm, fuzzywuzzy
# Optional: rdkit (scaffold splits), torch-geometric (PyG conversion)

API Note: TDC downloads datasets on first access (~10-500 MB per dataset). Specify path='data/' to control download location. No API key required.

Quick Start

from tdc.single_pred import ADME
from tdc import Evaluator

# Load dataset with scaffold split
data = ADME(name='Caco2_Wang')
split = data.get_split(method='scaffold', seed=42, frac=[0.7, 0.1, 0.2])
train, valid, test = split['train'], split['valid'], split['test']
print(f"Train: {len(train)}, Valid: {len(valid)}, Test: {len(test)}")
# Train: ~640, Valid: ~91, Test: ~182