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In Silico SAR Studies of HIV-1 Inhibitors

Quantitative Structure Activity Relationships (QSAR or SAR) have helped scientists to establish mathematical relationships between molecular structures and their biological activities. In the present article, SAR studies have been carried out on 89 tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIB...

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Autores principales: Hdoufane, Ismail, Bjij, Imane, Soliman, Mahmoud, Tadjer, Alia, Villemin, Didier, Bogdanov, Jane, Cherqaoui, Driss
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6160994/
https://www.ncbi.nlm.nih.gov/pubmed/30011783
http://dx.doi.org/10.3390/ph11030069
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author Hdoufane, Ismail
Bjij, Imane
Soliman, Mahmoud
Tadjer, Alia
Villemin, Didier
Bogdanov, Jane
Cherqaoui, Driss
author_facet Hdoufane, Ismail
Bjij, Imane
Soliman, Mahmoud
Tadjer, Alia
Villemin, Didier
Bogdanov, Jane
Cherqaoui, Driss
author_sort Hdoufane, Ismail
collection PubMed
description Quantitative Structure Activity Relationships (QSAR or SAR) have helped scientists to establish mathematical relationships between molecular structures and their biological activities. In the present article, SAR studies have been carried out on 89 tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives using different classifiers, such as support vector machines, artificial neural networks, random forests, and decision trees. The goal is to propose classification models that will be able to classify TIBO compounds into two groups: high and low inhibitors of HIV-1 reverse transcriptase. Each molecular structure was encoded by 10 descriptors. To check the validity of the established models, all of them were subjected to various validation tests: internal validation, Y-randomization, and external validation. The established classification models have been successful. The correct classification rates reached 100% and 90% in the learning and test sets, respectively. Finally, molecular docking analysis was carried out to understand the interactions between reverse transcriptase enzyme and the TIBO compounds studied. Hydrophobic and hydrogen bond interactions led to the identification of active binding sites. The established models could help scientists to predict the inhibition activity of untested compounds or of novel molecules prior to their synthesis. Therefore, they could reduce the trial and error process in the design of human immunodeficiency virus (HIV) inhibitors.
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spelling pubmed-61609942018-10-01 In Silico SAR Studies of HIV-1 Inhibitors Hdoufane, Ismail Bjij, Imane Soliman, Mahmoud Tadjer, Alia Villemin, Didier Bogdanov, Jane Cherqaoui, Driss Pharmaceuticals (Basel) Article Quantitative Structure Activity Relationships (QSAR or SAR) have helped scientists to establish mathematical relationships between molecular structures and their biological activities. In the present article, SAR studies have been carried out on 89 tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives using different classifiers, such as support vector machines, artificial neural networks, random forests, and decision trees. The goal is to propose classification models that will be able to classify TIBO compounds into two groups: high and low inhibitors of HIV-1 reverse transcriptase. Each molecular structure was encoded by 10 descriptors. To check the validity of the established models, all of them were subjected to various validation tests: internal validation, Y-randomization, and external validation. The established classification models have been successful. The correct classification rates reached 100% and 90% in the learning and test sets, respectively. Finally, molecular docking analysis was carried out to understand the interactions between reverse transcriptase enzyme and the TIBO compounds studied. Hydrophobic and hydrogen bond interactions led to the identification of active binding sites. The established models could help scientists to predict the inhibition activity of untested compounds or of novel molecules prior to their synthesis. Therefore, they could reduce the trial and error process in the design of human immunodeficiency virus (HIV) inhibitors. MDPI 2018-07-13 /pmc/articles/PMC6160994/ /pubmed/30011783 http://dx.doi.org/10.3390/ph11030069 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hdoufane, Ismail
Bjij, Imane
Soliman, Mahmoud
Tadjer, Alia
Villemin, Didier
Bogdanov, Jane
Cherqaoui, Driss
In Silico SAR Studies of HIV-1 Inhibitors
title In Silico SAR Studies of HIV-1 Inhibitors
title_full In Silico SAR Studies of HIV-1 Inhibitors
title_fullStr In Silico SAR Studies of HIV-1 Inhibitors
title_full_unstemmed In Silico SAR Studies of HIV-1 Inhibitors
title_short In Silico SAR Studies of HIV-1 Inhibitors
title_sort in silico sar studies of hiv-1 inhibitors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6160994/
https://www.ncbi.nlm.nih.gov/pubmed/30011783
http://dx.doi.org/10.3390/ph11030069
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