ArticleChemical research in toxicology2008
Machine learning methods and docking for predicting human pregnane X receptor activation.
Article in Chemical research in toxicology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Exploring Adverse Event Associations of Predicted PXR Agonists Using the FAERS Database.International journal of molecular sciences · 2025Article
- Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction.Journal of cheminformatics · 2024Article
- Article
- Population pharmacokinetic model selection assisted by machine learning.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Identification of novel pregnane X receptor (PXR) agonists by In silico and biological activity analyses and reversal of cigarette smoke-induced PXR downregulation.Biochemical and biophysical research communications · 2021Article
- Back to the future: Advances in development of broad-spectrum capsid-binding inhibitors of enteroviruses.European journal of medicinal chemistry · 2019Review
- Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor.Environmental science and pollution research international · 2017Article
- Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.Journal of chemical information and modeling · 2015Article
- Toward predicting drug-induced liver injury: parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors.Drug metabolism and disposition: the biological fate of chemicals · 2015Article
- Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation.BioMed research international · 2015Article
- Thiazide-like diuretic drug metolazone activates human pregnane X receptor to induce cytochrome 3A4 and multidrug-resistance protein 1.Biochemical pharmacology · 2014Article
- Binary and ternary combinations of anti-HIV protease inhibitors: effect on gene expression and functional activity of CYP3A4 and efflux transporters.Drug metabolism and drug interactions · 2014Article
- An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters.Journal of pharmaceutical sciences · 2013Review
- Induction of P-glycoprotein by antiretroviral drugs in human brain microvessel endothelial cells.Antimicrobial agents and chemotherapy · 2013Article
- Identification and validation of novel human pregnane X receptor activators among prescribed drugs via ligand-based virtual screening.Drug metabolism and disposition: the biological fate of chemicals · 2011Article
- Identification of clinically used drugs that activate pregnane X receptors.Drug metabolism and disposition: the biological fate of chemicals · 2011Article
- Evaluation of computational docking to identify pregnane X receptor agonists in the ToxCast database.Environmental health perspectives · 2010Article
- The structural basis of pregnane X receptor binding promiscuity.Biochemistry · 2009Article
- Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR.PLoS computational biology · 2009Article
- Elucidating the 'Jekyll and Hyde' nature of PXR: the case for discovering antagonists or allosteric antagonists.Pharmaceutical research · 2009Review
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Authors and funding
6 authors.
Funding
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 ( n = 145), and the prediction accuracy ranged from 63 to 67% overall. These test set molecules were found to cover the same area in a principal component analysis plot as the training set, suggesting that the predictions were within the applicability domain. The FlexX docking method combined with logistic regression performed poorly in classifying this PXR test set as compared with RP, RF, and SVM but may be useful for qualitative interpretion of interactions within the LBD. From this analysis, VolSurf descriptors and machine learning methods had good classification accuracy and made reliable predictions within the model applicability domain. These methods could be used for high throughput virtual screening to assess for PXR activation, prior to in vitro testing to predict potential drug-drug interactions.
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