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Pharmaceuticals 2018, 11(3), 69; https://doi.org/10.3390/ph11030069

In Silico SAR Studies of HIV-1 Inhibitors

1
Department of Chemistry, Faculty of Science Semlalia, BP 2390 Marrakech, Morocco
2
School of Health Sciences, University of KwaZulu-Natal, Westville, Durban 4000, South Africa
3
Faculty of Chemistry and Pharmacy, 1 James Bourchier Avenue 1164, Sofia University “St. Kliment Ohridski”, Sofia 1164, Bulgaria
4
Ecole Nationale Supérieure d'Ingénieurs (ENSICAEN) LCMT, UMR CNRS n° 6507, 6 Boulevard Maréchal Juin, 14050 Caen, France
5
Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, 1000 Skopje, Macedonia
*
Author to whom correspondence should be addressed.
Received: 21 June 2018 / Revised: 1 July 2018 / Accepted: 2 July 2018 / Published: 13 July 2018
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Abstract

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. View Full-Text
Keywords: structure activity relationship; TIBO; HIV inhibitors; support vector machines; decision trees; random forests and artificial neural networks structure activity relationship; TIBO; HIV inhibitors; support vector machines; decision trees; random forests and artificial neural networks
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Hdoufane, I.; Bjij, I.; Soliman, M.; Tadjer, A.; Villemin, D.; Bogdanov, J.; Cherqaoui, D. In Silico SAR Studies of HIV-1 Inhibitors. Pharmaceuticals 2018, 11, 69.

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