A Review of Computational Methods for Predicting Drug Targets

作者：Huang, Guohua^*; Yan, Fengxia; Tan, Duoduo

来源：Current Protein & Peptide Science, 2018, 19(6): 562-572.

DOI：10.2174/1389203718666161114113212

• Drug targets
• off-target
• side-effect
• machine learning
• heterogeneous network
• gene expression profile

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-effect-based and multi-omics-based predictions according to the used data for inference. The multi-omics-based 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.

• 出版日期2018
• 单位邵阳学院; 中国人民解放军国防科学技术大学; 湖南大学