pytdclisted

# PyTDC (Therapeutics Data Commons) ## Overview 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). ## When to Use This Skill 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 ## Installation & Setup Install PyTDC using pip: ```bash uv pip install PyTDC ``` To upgrade to the latest version: ```bash uv pip install PyTDC --upgrade ``` Core dependencies (automatically installed): - numpy, pandas, tqdm, seaborn, scikit_learn, fuzzywuzzy Additional packages are installed automatically as needed for specific features. ## Quick Start The basic pattern for accessing any TDC dataset follows this structure: ```python from tdc.<problem> import <Task> data = <Task>(name='<Dat