  In silico prediction of drug-target interactions (DTI) is significant for
drug discovery because it can largely reduce timelines and costs in the drug
development process. Specifically, deep learning-based DTI approaches have been
shown promising results in terms of accuracy and low cost for the prediction.
However, they pay little attention to the interpretability of their prediction
results and feature-level interactions between a drug and a target. In this
study, we propose a novel interpretable framework that can provide reasonable
cues for the interaction sites. To this end, we elaborately design a gated
cross-attention mechanism that crossly attends drug and target features by
constructing explicit interactions between these features. The gating function
in the method enables neural models to focus on salient regions over entire
sequences of drugs and proteins, and the byproduct from the function, which is
the attention map, could serve as interpretable factors. The experimental
results show the efficacy of the proposed method in two DTI datasets.
Additionally, we show that gated cross-attention can sensitively react to the
mutation, and this result could provide insights into the identification of
novel drugs targeting mutant proteins.
