Hybrid scoring and classification approaches to predict human pregnane X receptor activators

Sandhya Kortagere, Dmitriy Chekmarev, William J. Welsh, Sean Ekins

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Purpose: The human pregnane X receptor (PXR) is a transcriptional regulator of many genes involved in xenobiotic metabolism and excretion. Reliable prediction of high affinity binders with this receptor would be valuable for pharmaceutical drug discovery to predict potential toxicological responses Materials and Methods: Computational models were developed and validated for a dataset consisting of human PXR (PXR) activators and non-activators. We used support vector machine (SVM) algorithms with molecular descriptors derived from two sources, Shape Signatures and the Molecular Operating Environment (MOE) application software. We also employed the molecular docking program GOLD in which the GoldScore method was supplemented with other scoring functions to improve docking results. Results: The overall test set prediction accuracy for PXR activators with SVM was 72% to 81%. This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor. The best docking prediction accuracy (61%) was obtained using 1D Shape Signature descriptors as a weighting factor to the GoldScore. By pooling the available human PXR data sets we revealed those molecular features that are associated with human PXR activators. Conclusions: These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators.

Original languageEnglish (US)
Pages (from-to)1001-1011
Number of pages11
JournalPharmaceutical research
Volume26
Issue number4
DOIs
StatePublished - Apr 2009

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Molecular Medicine
  • Pharmacology
  • Pharmaceutical Science
  • Organic Chemistry
  • Pharmacology (medical)

Keywords

  • Docking
  • Hybrid methods
  • Machine learning
  • Pregnane X receptor
  • Shape signatures descriptors
  • Support vector machine

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