Drug repositioning via matrix completion with multi‐view side information

Abstract

In the process of drug discovery and disease treatment, drug repositioning is broadly studied to identify biological targets for existing drugs. Many methods have been proposed for drug–target interaction prediction by taking into account different kinds of data sources. However, most of the existing methods only use one side information for drugs or targets to predict new targets for drugs. Some recent works have improved the prediction accuracy by jointly considering multiple representations of drugs and targets. In this work, the authors propose a drug–target prediction approach by matrix completion with multi‐view side information (MCM) of drugs and proteins from both structural view and chemical view. Different from existing studies for drug–target prediction, they predict drug–target interaction by directly completing the interaction matrix between them. The experimental results show that the MCM method could obtain significantly higher accuracies than the comparison methods. They finally report new drug–target interactions for 26 FDA‐approved drugs, and biologically discuss these targets using existing references.

Efficient prediction of drug–drug interaction using deep learning models

“…Generally, cancer patients need an efficient combination of various drugs so called drug–drug interaction (DDI) [2]. A DDI means a variation in the effect of given drug on the human body when a drug is taken at the same time with another drug [3]. It may increase, delay, decrease or cause adverse effects of either drug.…”