Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
---
name: pytdc
description: Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
license: MIT license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---
# 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… load the full skill through Skill Me