  Predicting compound-protein affinity is critical for accelerating drug
discovery. Recent progress made by machine learning focuses on accuracy but
leaves much to be desired for interpretability. Through molecular contacts
underlying affinities, our large-scale interpretability assessment finds
commonly-used attention mechanisms inadequate. We thus formulate a hierarchical
multi-objective learning problem whose predicted contacts form the basis for
predicted affinities. We further design a physics-inspired deep relational
network, DeepRelations, with intrinsically explainable architecture.
Specifically, various atomic-level contacts or "relations" lead to
molecular-level affinity prediction. And the embedded attentions are
regularized with predicted structural contexts and supervised with partially
available training contacts. DeepRelations shows superior interpretability to
the state-of-the-art: without compromising affinity prediction, it boosts the
AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test,
compound-unique, protein-unique, and both-unique sets, respectively. Our study
represents the first dedicated model development and systematic model
assessment for interpretable machine learning of compound-protein affinity.
