Prediction of Drug-Target Protein Interaction Based on the Minimization of Weighted Nuclear Norm and Similarity Graph between Drugs and Target Proteins

Abstract

Identification of drug-target protein interaction plays an important role in the drug discovery process. Given the fact that prediction experiments are time-consuming, tedious, and very costly, the computational prediction could be a proper solution for decreasing search space for evaluation of the interaction between drug and target. In this paper, a novel approach based on the known drug-target interactions based on similarity graphs is proposed. It was shown that use of this method was a low-ranking issue and WNNM (weighted nuclear norm minimization) method was applied to detect the drug-target interactions. In the proposed method, the interaction between the drug and the target is encoded by graphs. Also known drug-target interaction, drug-drug similarity, target-target and combination of similarities were used as input. The proposed method was performed on four benchmark datasets, including enzymes (Es), ion channels (IC), G protein-coupled receptors (GPCRs), and nuclear receptors (NRs) based on the AUC and AUPR criteria. Finally, the results showed the improved performance of the proposed method.