title: Prediction of compounds' biological function (metabolic pathways) based on functional group composition.
abstract: Efficient in silico screening approaches may provide valuable hints on biological functions of the compound-candidates, which could help to screen functional compounds either in basic researches on metabolic pathways or drug discovery. Here, we introduce a machine learning method (Nearest Neighbor Algorithm) based on functional group composition of compounds to the analysis of metabolic pathways. This method can quickly map small chemical molecules to the metabolic pathway that they likely belong to. A set of 2,764 compounds from 11 major classes of metabolic pathways were selected for study. The overall prediction rate reached 73.3%, indicating that functional group composition of compounds was really related to their biological metabolic functions.
authors.affiliation: CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China. cyd@picb.ac.cn
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Ziliang
authors.initials: Z
authors.lastname: Qian
authors.forename: Lin
authors.initials: L
authors.lastname: Lu
authors.forename: Kai-Yan
authors.initials: KY
authors.lastname: Feng
authors.forename: Xin
authors.initials: X
authors.lastname: Meng
authors.forename: Bing
authors.initials: B
authors.lastname: Niu
authors.forename: Guo-Dong
authors.initials: GD
authors.lastname: Zhao
authors.forename: Wen-Cong
authors.initials: WC
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D006019:Glycolysis; D053858:Metabolic Networks and Pathways; D015203:Reproducibility of Results; D054852:Small Molecule Libraries / Q000378:metabolism*
chemical_list: D054852:Small Molecule Libraries