Abstract
BackgroundWet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates.ResultsIn this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug–target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input.ConclusionsThe proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.
Highlights
Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive
drug–target interactions (DTIs) means binding a drug to a target location, that leads to a change in its behavior or function
The results show that the use of Convolution Neural Networks (CNNs) to obtain data display, as an alternative to traditional descriptors, improves performance in DTI
Summary
Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The study of DTIs has been attracted many researchers’ attention in the field of pharmaceutical science in recent years [1,2,3,4] In this regard, many efforts have been made to investigate drug repositioning as well as the discovery of the interaction between new targets and existing drugs. Performing wet-lab experiments is a significant challenge in terms of cost, time and effort [5] In this regard, Computational Prediction (CP) methods have been used in recent years [6]. Using traditional approaches of experiments to confirm these connections often requires a great deal of materials and time which are expected computational methods to be used to predict these associations Many of these algorithms use profile-based methods (for example, NCPLP [7] in ADM and BLM-NPAI [8] in LDA) to predict these associations
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