A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models

We performed studies on extended series of 79 HEPT ligands (1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine), inhibitors of HIV reverse-transcriptase with anti-HIV biological activity, using quantitative structure–activity relationship (QSAR) methods that imply analysis of correlations and represe...

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Detalles Bibliográficos
Autores principales: Noorizadeh, Hadi, Sajjadifar, Sami, Farmany, Abbas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2013
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3785711/
https://www.ncbi.nlm.nih.gov/pubmed/24098069
http://dx.doi.org/10.1007/s00044-013-0525-4
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author Noorizadeh, Hadi
Sajjadifar, Sami
Farmany, Abbas
author_facet Noorizadeh, Hadi
Sajjadifar, Sami
Farmany, Abbas
author_sort Noorizadeh, Hadi
collection PubMed
description We performed studies on extended series of 79 HEPT ligands (1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine), inhibitors of HIV reverse-transcriptase with anti-HIV biological activity, using quantitative structure–activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated, and the genetic algorithm was employed to select those descriptors which resulted in the best-fit models. The kernel partial least square and Levenberg–Marquardt artificial neural network were utilized to construct the nonlinear QSAR models. The proposed methods will be of great significance in this research, and would be expected to apply to other similar research fields.
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spelling pubmed-37857112013-10-04 A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models Noorizadeh, Hadi Sajjadifar, Sami Farmany, Abbas Med Chem Res Original Research We performed studies on extended series of 79 HEPT ligands (1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine), inhibitors of HIV reverse-transcriptase with anti-HIV biological activity, using quantitative structure–activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated, and the genetic algorithm was employed to select those descriptors which resulted in the best-fit models. The kernel partial least square and Levenberg–Marquardt artificial neural network were utilized to construct the nonlinear QSAR models. The proposed methods will be of great significance in this research, and would be expected to apply to other similar research fields. Springer US 2013-02-27 2013 /pmc/articles/PMC3785711/ /pubmed/24098069 http://dx.doi.org/10.1007/s00044-013-0525-4 Text en © The Author(s) 2013 https://creativecommons.org/licenses/by/2.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Original Research
Noorizadeh, Hadi
Sajjadifar, Sami
Farmany, Abbas
A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models
title A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models
title_full A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models
title_fullStr A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models
title_full_unstemmed A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models
title_short A quantitative structure–activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models
title_sort quantitative structure–activity relationship study of anti-hiv activity of substituted hept using nonlinear models
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3785711/
https://www.ncbi.nlm.nih.gov/pubmed/24098069
http://dx.doi.org/10.1007/s00044-013-0525-4
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