torchdrug

Molecular property prediction pipelines are built by training graph neural networks like GIN on molecular datasets. This framework provides modular components for models, tasks, and datasets to enable reproducible end-to-end PyTorch experiments.

Yes, graph neural networks can be applied to retrosynthesis planning. The toolkit supports molecular generation and retrosynthesis tasks, allowing researchers to model chemical reactions and plan synthesis routes using PyTorch integration.

Yes, PyTorch and RDKit are required supporting libraries for chemistry tasks. Optional dependencies like AlphaFold and ESM can also be integrated for advanced protein modeling and biomedical knowledge graph reasoning workflows.

A unified graph-based framework enables joint modeling of molecules, proteins, and knowledge graphs. It covers diverse drug discovery tasks like property prediction and knowledge graph reasoning with 40+ datasets and pre-trained models.

Yes, the framework provides 40+ rich datasets and pre-trained models for drug discovery. These modular components integrate directly with PyTorch to facilitate reproducible molecular, protein, and knowledge graph experiments.

Graph-based approaches capture the relational structure of molecules and biomedical knowledge graphs better than traditional ML. This enables more accurate molecular property prediction, protein modeling, and retrosynthesis planning within a reproducible PyTorch pipeline.