pytdc-therapeutics-data-commons
Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.
npx skills add BioTender-max/awesome-bio-agent-skills --skill pytdc-therapeutics-data-commons --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyTDC (Therapeutics Data Commons) ## Overview PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery. It organizes therapeutics data into three categories: single-instance prediction (molecular/protein properties), multi-instance prediction (drug-target interactions), and generation (molecule design, retrosynthesis). All datasets come with standardized splits, evaluation metrics, and molecular oracles. ## When to Use - Loading curated ADME, toxicity, or bioactivity datasets for ML model training - Benchmarking drug discovery models with standardized 5-seed evaluation protocols - Predicting drug-target or drug-drug interactions with proper cold-split evaluation - Generating novel molecules and scoring them with molecular oracles (QED, SA, DRD2, GSK3B) - Accessing scaffold-based or temporal train/test splits for pharmaceutical ML - Converting molecular representations (SMILES to PyG graphs, ECFP fingerprints, SELFIES) - For chemical database queries (compound search, bioactivity), use `chembl-database-bioactivity` instead - For molecular featurization beyond format conversion, use `molfeat` instead ## Prerequisites ```bash uv pip install PyT
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Single-Instance Prediction — Dataset Access
- Module 2: Multi-Instance Prediction — Interaction Datasets
- Module 3: Generation Tasks — Molecular Design
- Module 4: Data Splits and Evaluation
- Module 5: Benchmark Groups
- Key Concepts
- Dataset Organization
- Oracle Categories
- Data Processing Utilities
uv pip install PyTDC Core deps: numpy, pandas, scikit-learn, tqdm, fuzzywuzzy
What does the pytdc-therapeutics-data-commons skill do?
Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill pytdc-therapeutics-data-commons --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.