Machine learning methods and docking for predicting human pregnane X receptor activation.

ABSTRACT: The pregnane X receptor (PXR) regulates the expression of genes involved in xenobiotic metabolism and transport. In vitro methods to screen for PXR agonists are used widely. In the current study, computational models for human PXR activators and PXR nonactivators were developed using recursive partitioning (RP), random forest (RF), and support vector machine (SVM) algorithms with VolSurf descriptors. Following 10-fold randomization, the models correctly predicted 82.6-98.9% of activators and 62.0-88.6% of nonactivators. The models were validated using separate test sets. The overall ( n = 15) test set prediction accuracy for PXR activators with RP, RF, and SVM PXR models is 80-93.3%, representing an improvement over models previously reported. All models were tested with a second test set

SUBMITTER: Khandelwal A 

PROVIDER: S-EPMC2574557 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature