In silico prediction of pregnane X receptor activators by machine learning approaches

C. Y. Ung, H. Li, C. W. Yap, Y. Z. Chen

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)158-168
Number of pages11
JournalMolecular pharmacology
Volume71
Issue number1
DOIs
StatePublished - Jan 4 2007

ASJC Scopus subject areas

  • Molecular Medicine
  • Pharmacology

Fingerprint Dive into the research topics of 'In silico prediction of pregnane X receptor activators by machine learning approaches'. Together they form a unique fingerprint.

Cite this