Machine learning methods and docking for predicting human pregnane X receptor activation.
about
Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXROpen Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery DatasetsHybrid scoring and classification approaches to predict human pregnane X receptor activatorsIdentification of clinically used drugs that activate pregnane X receptorsUnderstanding nuclear receptors using computational methods.Elucidating the 'Jekyll and Hyde' nature of PXR: the case for discovering antagonists or allosteric antagonists.Evaluation of computational docking to identify pregnane X receptor agonists in the ToxCast database.Thiazide-like diuretic drug metolazone activates human pregnane X receptor to induce cytochrome 3A4 and multidrug-resistance protein 1.An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters.Binary and ternary combinations of anti-HIV protease inhibitors: effect on gene expression and functional activity of CYP3A4 and efflux transporters.Toward predicting drug-induced liver injury: parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors.Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation.Identification and validation of novel human pregnane X receptor activators among prescribed drugs via ligand-based virtual screening.Induction of P-glycoprotein by antiretroviral drugs in human brain microvessel endothelial cells.Polymer-drug interactions in tyrosine-derived triblock copolymer nanospheres: a computational modeling approach.The structural basis of pregnane X receptor binding promiscuity.Computational modeling of P450s for toxicity prediction.Evaluation of pregnane X receptor (PXR)-mediated CYP3A4 drug-drug interactions in drug development.Regulation of human pregnane X receptor and its target gene cytochrome P450 3A4 by Chinese herbal compounds and a molecular docking study.Establishment of In Silico Prediction Models for CYP3A4 and CYP2B6 Induction in Human Hepatocytes by Multiple Regression Analysis Using Azole Compounds.PXR ligand classification model with SFED-weighted WHIM and CoMMA descriptors.Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor.From Molecular Docking to 3D-Quantitative Structure-Activity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors.LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening
P2860
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P2860
Machine learning methods and docking for predicting human pregnane X receptor activation.
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
2008年论文
@zh
2008年论文
@zh-cn
name
Machine learning methods and docking for predicting human pregnane X receptor activation.
@en
type
label
Machine learning methods and docking for predicting human pregnane X receptor activation.
@en
prefLabel
Machine learning methods and docking for predicting human pregnane X receptor activation.
@en
P2093
P2860
P50
P356
P1476
Machine learning methods and docking for predicting human pregnane X receptor activation.
@en
P2093
Akash Khandelwal
Erica J Reschly
Michael W Sinz
P2860
P304
P356
10.1021/TX800102E
P577
2008-06-12T00:00:00Z