Computational Prediction of Drug-Target Interactions Using Chemical, Biological, and Network Features

Drug-target interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drug-target interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68% and 84.72% for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.