pytdc
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
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---
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:
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 piHow to Use
Recommended: Install to project (local)
mkdir -p .claude/skills
curl -o .claude/skills/pytdc.md \
https://raw.githubusercontent.com/K-Dense-AI/claude-scientific-skills/main/scientific-skills/pytdc/SKILL.mdSkill is scoped to this project only. Add .claude/skills/ to your .gitignoreif you don't want to commit it.
Alternative: Clone full repo
git clone https://github.com/K-Dense-AI/claude-scientific-skillsThen reference at scientific-skills/pytdc/SKILL.md
Related Skills
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
deepchemdeepchemml
by K-Dense-AI · claude-scientific-skills
torchdrug
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
torchdrugtorchdrug
by K-Dense-AI · claude-scientific-skills