Individual skill
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.
skills-imported/torchdrug/SKILL.md
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- Oct 10, 2026
- License
- MIT
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SKILL.mdcore · 13,992 bytesSKILL.mdcore · 13,992 bytesSKILL.mdcore · 13,992 bytesSKILL.mdcore · 13,992 bytesSKILL.mdcore · 13,992 bytesSKILL.mdcore · 13,992 bytesreferences/core_concepts.mddoc · 14,394 bytesreferences/datasets.mddoc · 10,951 bytesreferences/knowledge_graphs.mddoc · 8,480 bytesreferences/models_architectures.mddoc · 13,694 bytesreferences/molecular_generation.mddoc · 10,074 bytesreferences/molecular_property_prediction.mddoc · 5,667 bytesreferences/protein_modeling.mddoc · 7,737 bytesreferences/retrosynthesis.mddoc · 11,537 bytes
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# 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