DeepREAL: A Deep Learning Powered Multi-scale Modeling Framework Towards Predicting Out-of-distribution Receptor Activity of Ligand Binding

Abstract

Drug discovery has witnessed intensive exploration of the problem of drug-target physical interactions over two decades, however, a strong drug binding affinity to a single target often fails to translate into desired clinical outcomes. A critical knowledge gap needs to be filled for correlating drug-target interactions with phenotypic responses: predicting the receptor activities or function selectivity upon the ligand binding (i.e., agonist vs. antagonist) on a genome-scale and for novel chemicals. Two major obstacles compound the difficulty on this direction: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions. To address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide receptor activities of ligand binding. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies that simulate real-world scenarios demonstrate that DeepREAL achieves state-of-the-art performance in out-of-distribution settings.