title: Identification of drug-target interaction from interactome network with 'guilt-by-association' principle and topology features.
abstract: Identifying drug-target protein interaction is a crucial step in the process of drug research and development. Wet-lab experiment are laborious, time-consuming and expensive. Hence, there is a strong demand for the development of a novel theoretical method to identify potential interaction between drug and target protein.
  
  
  
  We use all known proteins and drugs to construct a nodes- and edges-weighted biological relevant interactome network. On the basis of the 'guilt-by-association' principle, novel network topology features are proposed to characterize interaction pairs and random forest algorithm is employed to identify potential drug-protein interaction. Accuracy of 92.53% derived from the 10-fold cross-validation is about 10% higher than that of the existing method. We identify 2272 potential drug-target interactions, some of which are associated with diseases, such as Torg-Winchester syndrome and rhabdomyosarcoma. The proposed method can not only accurately predict the interaction between drug molecule and target protein, but also help disease treatment and drug discovery.
  
  
  
  zhanchao8052@gmail.com or ceszxy@mail.sysu.edu.cn
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China.
authors.forename: Zhan-Chao
authors.initials: ZC
authors.lastname: Li
authors.affiliation: School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China.
authors.forename: Meng-Hua
authors.initials: MH
authors.lastname: Huang
authors.affiliation: School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China.
authors.forename: Wen-Qian
authors.initials: WQ
authors.lastname: Zhong
authors.affiliation: School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China.
authors.forename: Zhi-Qing
authors.initials: ZQ
authors.lastname: Liu
authors.affiliation: School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Xie
authors.affiliation: School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Zong
authors.initials: Z
authors.lastname: Dai
authors.affiliation: SYSU-CMU Shunde International Joint Research Institute, Shunde 528300, People's Republic of China and School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Xiao-Yong
authors.initials: XY
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D016503:Drug Delivery Systems; D055808:Drug Discovery; D006801:Humans; D011487:Protein Conformation; D060066:Protein Interaction Maps; D011506:Proteins
chemical_list: D011506:Proteins