scientific-graph-neural-networks

To predict molecular properties using graph neural networks, you need to construct molecular graphs from your data and train a GNN model. This Skill provides end-to-end workflows covering data construction, model selection, training, and evaluation using PyTorch Geometric and RDKit.

Yes, graph neural networks can be applied to protein structures and knowledge graphs to predict protein interactions and infer relations. This Skill unifies molecular graphs, protein structures, and knowledge graphs under a single GNN-centric approach for classification and link prediction tasks.

Yes, PyTorch Geometric and TorchDrug are required dependencies to construct graphs, train models, and evaluate results. You also need auxiliary tools like RDKit for processing molecular structures and preparing scientific data for the GNN workflows.

The best way to prepare molecular graph data for deep learning is using auxiliary tools like RDKit to construct the graphs. This Skill guides you through data preparation, including converting molecules into graph structures suitable for training GNN models.

Graph-based deep learning handles knowledge-graph reasoning by applying GNN models to infer missing relations between entities. This approach allows you to perform link prediction across biological knowledge graphs, extracting insights from complex scientific data.

Applying GNNs to both molecular and protein data requires careful model selection and data construction for each specific domain. The Skill addresses these variations by providing tailored guidance on training and evaluating models for property prediction, protein function, and knowledge-graph relations.