torchdrug

Topics in the source

• TorchDrug
• Overview
• When to Use This Skill
• Getting Started
• Installation
• Quick Example
• Core Capabilities
• 1. Molecular Property Prediction

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. 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 Lightning Integrates with AlphaFold and ESM for proteins Predict chemical, physical, and biol

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