A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

摘要

Drug targets are special molecules that can bind to drugs and produce effects in cells, the main molecular targets for drugs are proteins [1]. Drug-target interactions (DTIs) prediction is of great significance for drug repositioning [2], drug discovery [3], side-effect prediction [4] and drug resistance [5]. However, identifying the drug-target interactions via biochemical and chemical biological experiments is costly and time-consuming [6]. Recently, as genomic, chemical, and pharmacological data become more and more complete, new opportunities for identifying drug target interactions have been emerged [2]. Therefore, many researchers have attempted to predict DTIs by using silico or computational approaches to guide in vivo validation in recent years, and thus significantly reduce the cost and time for identifying the drug-target interactions [2].