pytdc

Topics in the source

• PyTDC (Therapeutics Data Commons)
• Overview
• When to Use This Skill
• Installation & Setup
• Quick Start
• Single-Instance Prediction Tasks
• Available Task Categories
• 1. ADME (Absorption, Distribution, Metabolism, Excretion)

PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery and development. Access curated datasets spanning the entire therapeutics pipeline with standardized evaluation metrics and meaningful data splits, organized into three categories: single-instance prediction (molecular/protein properties), multi-instance prediction (drug-target interactions, DDI), and generation (molecule generation, retrosynthesis). This skill should be used when: Working with drug discovery or therapeutic ML datasets Benchmarking machine learning models on standardized pharmaceutical tasks Predicting molecular properties (ADME, toxicity, bioactivity) Predicting drug-target or drug-drug interactions Generating novel molecules with desired properties Accessing curated datasets with proper train/test splits (scaffold, cold-split) Using molecular oracles for property optimization Install PyTDC using pip: To upgrade to the latest version: Core dependencies (automatically installed): numpy, pandas, tqdm, seaborn, scikitlearn, fuzzywuzzy Additional packages are installed automatically as needed for specific features. The basic pattern for accessing any TDC dataset follows thi

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