A Review of Computational Methods for Predicting Drug Targets

Abstract

Drug discovery and development is not only a time-consuming and labor-intensive process but also full of risk. Identifying targets of small molecules helps evaluate safety of drugs and find new therapeutic applications. The biotechnology measures a wide variety of properties related to drug and targets from different perspectives, thus generating a large body of data. This undoubtedly provides a solid foundation to explore relationships between drugs and targets. A large number of computational techniques have recently been developed for drug target prediction. In this paper, we summarize these computational methods and classify them into structure-based, molecular activity-based, side-effectbased and multi-omics-based predictions according to the used data for inference. The multi-omicsbased methods are further grouped into two types: classifier-based and network-based predictions. Furthermore， the advantages and limitations of each type of methods are discussed. Finally, we point out the future directions of computational predictions for drug targets.

Keywords: Drug targets; gene expression profile; heterogeneous network; machine learning; off-target; side-effect..