Torchdrug

If you're doing ML on molecules or proteins, this is a solid PyTorch toolkit that handles the graph structure natively. It includes 40+ datasets (BBBP, Tox21, QM9 for molecules; EnzymeCommission, PDBBind for proteins), 20+ GNN architectures (GIN, GAT, SchNet), and pre-built tasks for property prediction, retrosynthesis, and molecular generation. The knowledge graph reasoning over biomedical data like Hetionet is surprisingly useful for drug repurposing. It integrates well with RDKit and plays nice with pre-trained protein models like ESM. The scaffold splitting and property prediction workflows are production-ready, though you'll want the full installation for all features.

skills.sh installs: 237
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill torchdrug --agent claude-code

Installs into .claude/skills of the current project.

Files

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

SKILL.md on GitHub

Details

Repository: github.com/davila7/claude-code-templates
Categories: Data Science & ML, Sales & Marketing, Go
First seen: Jun 3, 2026

citation-validatorliangdabiao/claude-code-stock-deep-research-agent验证研究报告中所有声明的引用准确性、来源质量和格式规范性。确保每个事实性声明都有可验证的来源，并提供来源质量评级。当最终确定研究报告、审查他人研究、发布或分享研究之前使用此技能。217skills.sh installs

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