BRNS + SSFSM-DTI: A hybrid method for drug-target interaction prediction based on balanced reliable negative samples and semi-supervised feature selection

Abstract

De novo drug discovery is a time-consuming and costly process. Drug repositioning, which means finding new applications for existing drugs, is one of the most effective approaches to reduce time and cost of detecting a new drug. Predicting drug-target interactions (DTIs) can facilitate the drug repositioning and consequently accelerate the process of drug discovery. The diversity of drug descriptors and protein features as well as the lack of access to experimentally-confirmed non-interacting drug-target pairs as negative samples are the major challenges in predicting new DTIs. In this study, we present a modified algorithm for extracting balanced reliable negative samples named BRNS. Moreover, we propose a semi-supervised feature selection method called SSFSM-DTI and compare the performance of our hybrid predictive model with the models constructed by random selection of negative samples and the other well-known feature selection methods on benchmark DTI datasets. The results show that the combination of BRNS and SSFSM-DTI is superior to the random selection of negative samples and the state-of-the-art feature selection methods in most cases. Hence, it can be used as a guideline in the drug repositioning process. The source codes and all datasets used in this study are available at https://github.com/LBDSoft/BRNS.