Computer Regression Models for P-Glycoprotein Transport of Drugs

Autor:	John C. Dearden, D.E. Polianczyk, Oleg A. Raevsky, V. Yu. Grigor’ev, S. L. Solodova
Rok vydání:	2019
Předmět:	Pharmacology Quantitative structure–activity relationship biology 010405 organic chemistry Chemistry Regression analysis Statistical model Computational biology 01 natural sciences 0104 chemical sciences Random forest Support vector machine 010404 medicinal & biomolecular chemistry Molecular descriptor Drug Discovery Linear regression biology.protein P-glycoprotein
Zdroj:	Pharmaceutical Chemistry Journal. 52:975-979
ISSN:	1573-9031 0091-150X
Popis:	Regression models of the cellular substrate specificity of 177 drugs for P-glycoprotein were built using linear regression, random forest, and support vector methods. QSAR modeling used a full-trial search of all possible combinations of the seven most significant molecular descriptors with clear physicochemical interpretations. The statistics of the obtained models were satisfactory according to an internal cross-validation and external validation tests using 44 new compounds. H-bond descriptors were components of almost all most significant QSAR models. This confirmed that H-bonds played an important role in penetration of the compounds through the blood–brain barrier. The developed statistical models could be used to assess P-glycoprotein transport of investigational new drugs.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::aefc71ab328f202e120b5dc206db0bfc https://doi.org/10.1007/s11094-019-01936-x Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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