torchdruglisted

# 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 Lightning - Integrates with AlphaFold and ESM for proteins ## Getting Started ### Installation ```bash uv pip install torchdrug # Or with optional dependencies uv pip install torchdrug[full] ``` ### Quick Example ```python from torchdrug import datasets, models, tasks from torch.utils.dat