  Expanding the scope of graph-based, deep-learning models to noncovalent
protein-ligand interactions has earned increasing attention in structure-based
drug design. Modeling the protein-ligand interactions with graph neural
networks (GNNs) has experienced difficulties in the conversion of
protein-ligand complex structures into the graph representation and left
questions regarding whether the trained models properly learn the appropriate
noncovalent interactions. Here, we proposed a GNN architecture, denoted as
InteractionNet, which learns two separated molecular graphs, being covalent and
noncovalent, through distinct convolution layers. We also analyzed the
InteractionNet model with an explainability technique, i.e., layer-wise
relevance propagation, for examination of the chemical relevance of the model's
predictions. Separation of the covalent and noncovalent convolutional steps
made it possible to evaluate the contribution of each step independently and
analyze the graph-building strategy for noncovalent interactions. We applied
InteractionNet to the prediction of protein-ligand binding affinity and showed
that our model successfully predicted the noncovalent interactions in both
performance and relevance in chemical interpretation.
