Agent skill · research science · aiskillstore
pytdc
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
Why this skill is useful
Provides access to specialized drug discovery datasets and benchmarks that enhance the AI's ability to perform therapeutic ML tasks.
What it needs
Requires pip installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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: 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. Quick Start The basic pattern for accessing any TDC dataset follows this structure: Where: <problem>: One of singlepred, multipred, or generation <Task>: Specific task category (e.g., ADME, DTI, MolGen) <Dataset>: Dataset name within that task Example - Loading ADME data: Single-Instance Prediction Tasks Single-instance prediction involves forecasting properties of individual biomedical entities (molecules, proteins, etc.). Available Task Categories 1. ADME (Absorption, Distribution, Metabolism, Excretion) Predict pharmacokinetic properties of drug molecules. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pytdc