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torchdrug-english

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PyTorch-native Graph Neural Network framework for molecules and proteins. Suitable for building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, and retrosynthesis. If you need pretrained models and diverse feature extractors, use deepchem; if you need benchmark datasets, use pytdc.

scientific-skills/Data Analysis/TorchDrug-English/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

# TorchDrug

## When to Use

- Use this skill when you need pytorch-native graph neural network framework for molecules and proteins. suitable for building custom gnn architectures for drug discovery, protein modeling, or knowledge graph reasoning. best for custom model development, protein property prediction, and retrosynthesis. if you need pretrained models and diverse feature extractors, use deepchem; if you need benchmark datasets, use pytdc in a reproducible workflow.
- Use this skill when a data analytics task needs a packaged method instead of ad-hoc freeform output.
- Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
- Use this skill when `the documented workflow in this package` is the most direct path to complete the request.
- Use this skill when you need the `TorchDrug (English)` package behavior rather than a generic answer.

## Key Features

- Scope-focused workflow aligned to: PyTorch-native Graph Neural Network framework for molecules and proteins. Suitable for building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, and retrosynthesis. If you need pretrained models and diverse feature extractors, use deepchem; if you need benchmark datasets, use pytdc.
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