title: 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.
authors.affiliation: Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, Hunan 422000, China.|College of Information Engineering, Shaoyang University, Shaoyang, Hunan 422000, China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Huang
authors.affiliation: College of Science, National University Defense Technology, Changsha, Hunan 410073, China.
authors.forename: Fengxia
authors.initials: F
authors.lastname: Yan
authors.affiliation: School of Finance and Statistics, Hunan University, Changsha, Hunan 410081, China.
authors.forename: Duoduo
authors.initials: D
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D015870:Gene Expression; D006801:Humans; D000069550:Machine Learning; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins
keywords: Drug targets; gene expression profile; heterogeneous network; machine learning; off-target; side-effect.