An In Silico Method for Predicting Drug Synergy Based on Multitask Learning

AbstractTo make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein –protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug–target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validat ion experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug–target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demon strate its ability to predict drug synergy.Graphic Abstract
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research