torchdrug

Molecular property prediction is enabled by loading large chemical datasets into graph neural network models and wrapping them in a task to run a training loop. This workflow supports multi-task training directly within the PyTorch ecosystem.

Yes, PyTorch ecosystem compatibility allows for protein modeling, structure-aware reasoning, and biomedical knowledge graph completion. It enables end-to-end experimentation across biology and biomedical data integration workflows.

Retrosynthesis planning for drug discovery is handled through graph-based models that support de novo molecule generation. This approach applies modular graph neural network architectures to evaluate chemical synthesis pathways.

Yes, multi-task training is supported alongside flexible graph neural network architectures. It allows researchers to handle extensive dataset collections and perform end-to-end experimentation across molecular property prediction and protein modeling.

To generate molecules, you need to install the toolkit, load a dataset, choose a graph neural model, and wrap it in a property-prediction task. This enables de novo molecule generation and retrosynthesis planning.