Drug Target Interaction Prediction using Multi-task Learning and Co-attention

Various machine learning models have been proposed as cost-effective means to predict Drug-Target Interactions (DTI). Most existing researches treat DTI prediction either as a classification task (i.e. output negative or positive labels to indicate existence of interaction) or as a regression task (i.e. output numerical values as the strength of interaction). However, classifiers are more prone to higher bias and regression models tend to overfit the training data to generate large variance. In this paper, we explore to balance the bias and variance by a multi-task learning framework. We propose an architecture to both predict accurate values of strength of interaction and decide correct boundary between positive and negative interactions. Furthermore, the two tasks are performed on a shared feature representation, which is learnt using a co-attention mechanism. Comprehensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art methods.