Predicting Drug-Target Interactions Using Weisfeiler-Lehman Neural Network

Abstract

Predicting missing drug-target relationships can help to speed up the process of identifying unknown interactions between chemical drugs and target proteins in pharmaceutical research. In this paper we employ Weisfeiler-Lehman Neural Network method to capture features, purely based on topological network and learn the pattern of drug-target interactions. We show our approach is able to learn sophisticated drug-target topological features and outperform other similarity based methods in terms of AUROC.