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Literature DB >> 18704735

Prediction of compounds' biological function (metabolic pathways) based on functional group composition.

Yu-Dong Cai¹, Ziliang Qian, Lin Lu, Kai-Yan Feng, Xin Meng, Bing Niu, Guo-Dong Zhao, Wen-Cong Lu.

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.

Mesh：

Substances：
Small Molecule Libraries

Year: 2008 PMID： 18704735 DOI： 10.1007/s11030-008-9085-9

Source DB: PubMed Journal: Mol Divers ISSN： 1381-1991 Impact factor: 2.943

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