Monn: a multi-objective neural network for predicting pairwise non-covalent interactions and binding affinities between compounds and proteins

Abstract

Background. Computational approaches for inferring the mechanisms of compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, although a number of deep learning based methods have been proposed to predict binding affinities of CPIs and attempt to capture local interaction sites in compounds and proteins through neural attentions, they still lack a systematic evaluation on the interpretability of the identified local features [1–3]. In this work, we constructed the first benchmark dataset containing the pairwise inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs. Our comprehensive evaluation suggested that current neural attention based approaches have difficulty in automatically capturing the accurate local non-covalent interactions between compounds and proteins.