torchdrug
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Profile →npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-torchdrug --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.
# TorchDrug ## Overview TorchDrug is a comprehensive PyTorch-based machine learning toolbox for drug discovery and molecular science. Apply graph neural networks, pre-trained models, and task definitions to molecules, proteins, and biological knowledge graphs, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis planning, with 40+ curated datasets and 20+ model architectures. ## When to Use This Skill This skill should be used when working with: **Data Types:** - SMILES strings or molecular structures - Protein sequences or 3D structures (PDB files) - Chemical reactions and retrosynthesis - Biomedical knowledge graphs - Drug discovery datasets **Tasks:** - Predicting molecular properties (solubility, toxicity, activity) - Protein function or structure prediction - Drug-target binding prediction - Generating new molecular structures - Planning chemical synthesis routes - Link prediction in biomedical knowledge bases - Training graph neural networks on scientific data **Libraries and Integration:** - TorchDrug is the primary library - Often used with RDKit for cheminformatics - Compatible with PyTorch and PyTorch L
- Overview
- When to Use This Skill
- Getting Started
- Installation
- Quick Example
- Core Capabilities
- 1. Molecular Property Prediction
- 2. Protein Modeling
- 3. Knowledge Graph Reasoning
- 4. Molecular Generation
- 5. Retrosynthesis
- 6. Graph Neural Network Models
- 7. Datasets
- Common Workflows
pip install torchdrug Or with optional dependencies pip install torchdrug[full]
What does the torchdrug skill do?
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-torchdrug --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 majiayu000/claude-skill-registry, a repository with 534 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.