Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction

Abstract

The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called drug repositioning with the use of computational methods. Similarity-based methods are common in predicting drug-target association or the interaction between drugs and targets based on various features the drugs and targets have. Heterogeneous network topology involving many biomedical entities interactions has yet to be used in drug-target association. Deep learning can disclose features of vertices in a large network, which can be incorporated with heterogeneous network topology in order to assist similarity-based solutions to provide more flexibility for drug-target prediction. Here we describe a similarity-based drug-target prediction method that utilizes a topology-based similarity measure and two inference methods based on the similarities. We used DeepWalk, a deep learning method, to calculate the vertex similarities based on Linked Tripartite Network (LTN), which is a heterogeneous network created from different biomedical-linked datasets. The similarities are further used to feed to the inference methods, drug-based similarity inference (DBSI) and target-based similarity inference (TBSI), to obtain the predicted drug-target associations. Our previous experiments have shown that by utilizing deep learning and heterogeneous network topology, the proposed method can provide more promising results than current topology-based similarity computation methods.