科研之友
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摘要
The activation of pregnane X receptor (PXR), a member of the nuclear receptor (NR) superfamily, can mediate potential drugdrug interactions, and therefore, prediction of PXR activation is of great importance for evaluating drug metabolism and toxicity. In this study, based on 532 structurally diverse compounds, we present a comprehensive analysis with the aim to build accurate classification models for distinguishing PXR activators from nonactivators by using a naive Bayesian classification technique. First, the distributions of eight important molecular physicochemical properties of PXR activators versus nonactivators were compared, illustrating that the hydrophobicity-related molecular descriptors (AlogP and log D) show slightly better capability to discriminate PXR activators from nonactivators than the others. Then, based on molecular physicochemical properties, VolSurf descriptors, and molecular fingerprints, naive Bayesian classifiers were developed to separate PXR activators from nonactivators. The results demonstrate that the introduction of molecular fingerprints is quite essential to enhance the prediction accuracy of the classifiers. The best Bayesian classifier based on the 21 physicochemical properties, VolSurf descriptors, and LCFC_10 fingerprints descriptors yields a prediction accuracy of 92.7% for the training set based on leave-one-out (LOO) cross-validation and of 85.2% for the test set. Moreover, by exploring the important structural fragments derived from the best Bayesian classifier, we observed that flexibility is an important structural pattern for PXR activation. In addition, chemical compounds containing more halogen atoms, unsaturated alkanes chains relevant to pi-pi stacking, and fewer nitrogen atoms tend to be PXR activators. We believe that the naive Bayesian classifier can be used as a reliable virtual screening tool to predict PXR activation in the drug design and discovery pipeline.
