Predicting drug-target interaction based on sequence and structure information

Abstract

It is well known that discovering a new drug is a cumbersome, time-consuming and expensive process. Computational approaches for identifying interactions between drug compounds and target proteins have become important in drug discovery which is helpful to reduce these obstacles. The difficulties of drug-target interaction identification include the lack of known drug-target associations and no experimentally verified negative examples. In this study, we present a method, called PUDT, to predict drug-target interactions. Instead of treating unknown interactions as negative examples, we consider unknown interactions as unlabeled examples. The unlabeled examples are divided into two parts: reliable negative examples and likely negative examples based on protein structure similarity. Then, a weighted support vector machine is used to build a classifier to predict drug-target interactions based on protein sequence and drug structure information. Four data sets (enzymes, ion channels, GPCRs and nuclear receptors) are used to evaluate the performance of the proposed method PUDT. The experimental results demonstrate that our method PUDT outperforms recent state-of-the-art approaches.