title: Prediction of drugs target groups based on ChEBI ontology.
abstract: Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset-the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.
authors.affiliation: Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China.
authors.forename: Yu-Fei
authors.initials: YF
authors.lastname: Gao
authors.affiliation: College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai 200444, China.
authors.forename: Guo-Hua
authors.initials: GH
authors.lastname: Huang
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai 200444, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Beijing Genomics Institute, Shenzhen Beishan Industrial Zone, Shenzhen 518083, China.
authors.forename: Kai-Yan
authors.initials: KY
authors.lastname: Feng
authors.affiliation: CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Hai-Peng
authors.initials: HP
authors.lastname: Li
authors.affiliation: Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Jiang
mesh_terms: D064229:Biological Ontologies; D016208:Databases, Factual; D016503:Drug Delivery Systems / Q000379:methods*; D011506:Proteins / Q000378:metabolism
chemical_list: D011506:Proteins