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Expanding the scope of graph-based, deep-learning models to noncovalentprotein-ligand interactions has earned increasing attention in structure-baseddrug design. Modeling the protein-ligand interactions with graph neuralnetworks (GNNs) has experienced difficulties in the conversion ofprotein-ligand complex structures into the graph representation and leftquestions regarding whether the trained models properly learn the appropriatenoncovalent interactions. Here, we proposed a GNN architecture, denoted asInteractionNet, which learns two separated molecular graphs, being covalent andnoncovalent, through distinct convolution layers. We also analyzed theInteractionNet model with an explainability technique, i.e., layer-wiserelevance propagation, for examination of the chemical relevance of the model'spredictions. Separation of the covalent and noncovalent convolutional stepsmade it possible to evaluate the contribution of each step independently andanalyze the graph-building strategy for noncovalent interactions. We appliedInteractionNet to the prediction of protein-ligand binding affinity and showedthat our model successfully predicted the noncovalent interactions in bothperformance and relevance in chemical interpretation.

InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand  Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance  Propagation