Graph Neural Networks

TorchDrug is a documentation skill that provides comprehensive guidance for using the TorchDrug PyTorch library in drug discovery and molecular science. It covers graph neural networks for molecules and proteins, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis planning with 40+ curated datasets and 20+ model architectures.

This skill provides comprehensive guidance for PyTorch Geometric (PyG), a library for developing Graph Neural Networks. It covers graph creation, GNN architectures (GCN, GAT, GraphSAGE, GIN), node/graph classification, molecular property prediction, and large-scale graph learning with extensive reference documentation and utility scripts.