Acta Phys. -Chim. Sin. ›› 2010, Vol. 26 ›› Issue (02): 471-477.doi: 10.3866/PKU.WHXB20100125

• BIOPHYSICAL CHEMISTRY • Previous Articles     Next Articles

Activity Prediction of Hormone-Sensitive Lipase Inhibitors Based on Machine Learning Methods

LV Wei, XUE Ying   

  1. Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, P. R. China; State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P. R. China
  • Received:2009-09-17 Revised:2009-10-25 Published:2010-01-26
  • Contact: XUE Ying


Hormone-sensitive lipase (HSL) is known as the key rate-limiting enzyme responsible for regulating free fatty acids (FFAs) metabolismin adipose tissue. Recently, HSLhas been found to be useful in the treatment of diabetes so the discovery of new HSL inhibitors (HSLIs) is of interest. Methods for the prediction of HSLIs are highly desired to facilitate the design of novel diabetes therapeutic agents because limited knowledge exists concerning the mechanism and three dimensional (3D) structure of hormone-sensitive lipase. We have explored several machine learning methods (support vectormachines (SVM), k-nearest neighbor (k-NN), and C4.5 decision tree (C4.5 DT)) to predict desirable HSLIs from a comprehensive set of known HSLIs and non-HSLIs. Our prediction system was tested using 252 compounds (123 HSLIs and 129 non-HSLIs) and these are significantly more diverse in chemical structure than those in other studies. The recursive feature elimination selection method was used to improve the prediction accuracy and to select the molecular descriptors responsible for distinguishing HSLIs and non-HSLIs. Prediction accuracies were 85.7%-90.5% for HSLIs, 63.2%-68.4% for non-HSLIs, and 75.0%-80.0% for all structures based on three kinds of machine learning methods using an independent validation set. SVMgave the best total accuracy of 80.0% for all the structures. This work suggests that machine learning methods such as SVM are useful to predict the potential HSLIs among unknown sets of compounds and to characterize the molecular descriptors associated with HSLIs.

Key words: Support vector machine, Hormone-sensitive lipase, Machine learning method, Molecular descriptor, Recursive feature elimination


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