Deep learning in target prediction and drug repositioning: Recent advances and challenges

Abstract

Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.