It is of great use to find out and clear up the interactions between enzymes and small molecules, for understanding the molecular and cellular functions of organisms. In this study, we developed a novel method for the prediction of enzyme-small molecules interactions based on machine learning approach. The biochemical and physicochemical description of proteins and the functional group composition of small molecules are used for representing enzyme-small molecules pairs. Tested by jackknife cross-validation, our predictor achieved an overall accuracy of 87.47%, showing an acceptable efficiency. The 39 features selected by feature selection were analyzed for further understanding of enzyme-small molecule interactions.
Keywords: Biochemical and physicochemical description of proteins, Chemical functional group, Feature selection, Forward Feature Selection, Incremental Feature Selection, Minimum Redundancy Maximum Relevance, jackknife cross-validation, NNA, KEGG, mRMR, FFS, hydrophobicity, FRET, RTK signaling, NMR
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