Please use this identifier to cite or link to this item: https://doi.org/10.1124/mol.106.027623
Title: In silico prediction of pregnane X receptor activators by machine learning approaches
Authors: Ung, C.Y. 
Li, H. 
Yap, C.W. 
Chen, Y.Z. 
Issue Date: 2007
Citation: Ung, C.Y., Li, H., Yap, C.W., Chen, Y.Z. (2007). In silico prediction of pregnane X receptor activators by machine learning approaches. Molecular Pharmacology 71 (1) : 158-168. ScholarBank@NUS Repository. https://doi.org/10.1124/mol.106.027623
Abstract: Pregnane X receptor (PXR) regulates drug metabolism and is involved in drug-drug interactions. Prediction of PXR activators is important for evaluating drug metabolism and toxicity. Computational pharmacophore and quantitative structure-activity relationship models have been developed for predicting PXR activators. Because of the structural diversity of PXR activators, more efforts are needed for exploring methods applicable to a broader spectrum of compounds. We explored three machine learning methods (MLMs) for predicting PXR activators, which were trained and tested by using significantly higher number of compounds, 128 PXR activators (98 human) and 77 PXR non-activators, than those of previous studies. The recursive feature-selection method was used to select molecular descriptors relevant to PXR activator prediction, which are consistent with conclusions from other computational and structural studies. In a 10-fold cross-validation test, our MLM systems correctly predicted 81.2 to 84.0% of PXR activators, 80.8 to 85.0% of hPXR activators, 61.2 to 70.3% of PXR nonactivators, and 67.7 to 73.6% of hPXR nonactivators. Our systems also correctly predicted 73.3 to 86.7% of 15 newly published hPXR activators. MLMs seem to be useful for predicting PXR activators and for providing clues to physicochemical features of PXR activation. Copyright © 2007 The American Society for Pharmacology and Experimental Therapeutics.
Source Title: Molecular Pharmacology
URI: http://scholarbank.nus.edu.sg/handle/10635/106034
ISSN: 0026895X
DOI: 10.1124/mol.106.027623
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