title: Hybrid scoring and classification approaches to predict human pregnane X receptor activators.
abstract: OBJECTIVE
  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
  
  
  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.
authors.affiliation: Department of Pharmacology and Environmental Bioinformatics and Computational Toxicology Center (ebCTC), University of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA.
authors.forename: Sandhya
authors.initials: S
authors.lastname: Kortagere
authors.forename: Dmitriy
authors.initials: D
authors.lastname: Chekmarev
authors.forename: William J
authors.initials: WJ
authors.lastname: Welsh
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D017076:Computer-Aided Design; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D015195:Drug Design; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D015394:Molecular Structure; D000077297:Pregnane X Receptor; D011487:Protein Conformation; D011987:Receptors, Steroid / Q000819:agonists / Q000737:chemistry*; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship
chemical_list: D008024:Ligands; D000077297:Pregnane X Receptor; D011987:Receptors, Steroid