Drug target interaction prediction via multi-task co-attention

Abstract

Drug-Target Interaction (DTI) prediction is a key step in drug discovery and drug repurposing. A variety of machine learning models are considered to be effective means of predicting DTI. Most current studies regard DTI prediction as a classification task (that is, negative or positive labels are applied to indicate the intensity of interaction) or regression tasks (numerical value is used to measure detailed DTI). In this article, we explore how to balance bias and variance through a multi-task learning framework. Because the classifier is more likely to produce higher bias, and the regression models are more prone to create a significant variance and overfit the training data. We propose a novel model, named Multi-DTI, that can predict the precise value and determine the correct labels of positive or negative interactions. Besides, these two tasks are performed with similar feature representations of CNN, which is adopted with a co-attention mechanism. Detailed experiments show that Multi-DTI is superior to state-of-the-art methods.