Prediction of compounds' biological function (metabolic pathways) based on functional group composition.
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.
Cai
Yu-Dong
YD
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
Qian
Ziliang
Z
Lu
Lin
L
Feng
Kai-Yan
KY
Meng
Xin
X
Niu
Bing
B
Zhao
Guo-Dong
GD
Lu
Wen-Cong
WC
D000465:Algorithms; D006019:Glycolysis; D053858:Metabolic Networks and Pathways; D015203:Reproducibility of Results; D054852:Small Molecule Libraries / Q000378:metabolism*
D054852:Small Molecule Libraries