Provides AI-ready datasets and benchmarks for therapeutic machine learning and drug discovery tasks.
The PyTDC skill equips Claude with specialized capabilities for pharmacological research and drug development. It provides access to the Therapeutics Data Commons, an open-science platform containing curated datasets spanning the entire therapeutics pipeline, including ADME, toxicity, and drug-target interactions. This skill is essential for researchers building machine learning models for molecule generation, property prediction, and benchmarking. It includes support for standardized evaluation metrics, advanced data splitting strategies like scaffold and cold-start, and molecular oracles for property optimization, enabling autonomous discovery workflows within Claude Code.
Key Features
01Curated datasets for ADME, toxicity, and bioactivity prediction
02Comprehensive utilities for molecular format conversion and data processing
03Standardized benchmark groups for systematic model evaluation
04Advanced data splitting strategies including scaffold and cold-splits
05324 GitHub stars
06Molecular oracles for property optimization and molecule generation
Use Cases
01Generating novel lead molecules with optimized chemical properties
02Predicting pharmacokinetic and toxicity properties of drug candidates
03Benchmarking machine learning models on standardized pharmaceutical tasks
