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

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.

Automated summary

Identification of drug-target protein interactions is crucial in drug discovery, and a novel method based on known interactions and similarity graphs was proposed in this paper. The method utilized WNNM to detect interactions and showed improved performance on benchmark datasets across various criteria like AUC and AUPR.