Please use this identifier to cite or link to this item: https://doi.org/10.1124/mol.106.027623
DC FieldValue
dc.titleIn silico prediction of pregnane X receptor activators by machine learning approaches
dc.contributor.authorUng, C.Y.
dc.contributor.authorLi, H.
dc.contributor.authorYap, C.W.
dc.contributor.authorChen, Y.Z.
dc.date.accessioned2014-10-29T01:54:03Z
dc.date.available2014-10-29T01:54:03Z
dc.date.issued2007
dc.identifier.citationUng, 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
dc.identifier.issn0026895X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/106034
dc.description.abstractPregnane 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1124/mol.106.027623
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentPHARMACY
dc.description.doi10.1124/mol.106.027623
dc.description.sourcetitleMolecular Pharmacology
dc.description.volume71
dc.description.issue1
dc.description.page158-168
dc.description.codenMOPMA
dc.identifier.isiut000243010200017
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

70
checked on Aug 11, 2020

WEB OF SCIENCETM
Citations

70
checked on Aug 3, 2020

Page view(s)

37
checked on Aug 1, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.