Drug–target interaction prediction by random walk on the heterogeneous network

TLDR

Predicting potential drug‑target interactions from heterogeneous biological data is critical for understanding interactions, biological processes, and developing novel drugs. The authors develop NRWRH, a network‑based random walk with restart on a heterogeneous network, to predict large‑scale drug‑target interactions. NRWRH integrates protein‑protein, drug‑drug, and known drug‑target similarity networks into a heterogeneous graph and applies random walk with restart to fuse information and predict associations. NRWRH outperforms prior methods on enzymes, ion channels, GPCRs, and nuclear receptors, and identifies numerous promising new drug‑target pairs.

Abstract

Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.