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In silico investigation of agonist activity of a structurally diverse set of drugs to hPXR using HM-BSM and HM-PNN / 华中科技大学学报(医学)(英德文版)
Journal of Huazhong University of Science and Technology (Medical Sciences) ; (6): 463-468, 2016.
Article in English | WPRIM | ID: wpr-285245
ABSTRACT
The human pregnane X receptor (hPXR) plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards hPXR. Heuristic method (HM)-Best Subset Modeling (BSM) and HM-Polynomial Neural Networks (PNN) were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain (AD) of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved (for HM-BSM, r (2)=0.881, q LOO (2) =0.797, q EXT (2) =0.674; for HM-PNN, r (2)=0.882, q LOO (2) =0.856, q EXT (2) =0.655). The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to hPXR.
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Full text: Available Index: WPRIM (Western Pacific) Main subject: Computer Simulation / Receptors, Steroid / Chemistry / Models, Statistical / Neural Networks, Computer / Quantitative Structure-Activity Relationship / Small Molecule Libraries / Static Electricity / Molecular Weight Type of study: Prognostic study / Risk factors Limits: Humans Language: English Journal: Journal of Huazhong University of Science and Technology (Medical Sciences) Year: 2016 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Computer Simulation / Receptors, Steroid / Chemistry / Models, Statistical / Neural Networks, Computer / Quantitative Structure-Activity Relationship / Small Molecule Libraries / Static Electricity / Molecular Weight Type of study: Prognostic study / Risk factors Limits: Humans Language: English Journal: Journal of Huazhong University of Science and Technology (Medical Sciences) Year: 2016 Type: Article