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.
npx skills add LeonChaoX/qinyan-academic-skills --skill 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
uv pip install torchdrug Or with optional dependencies uv pip install torchdrug[full]
What does the torchdrug skill do?
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.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill 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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.