Title: QSAR study of 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using genetic algorithms and artificial neural networks

Authors: Houda Labjar; Mohamed Kissi; Rokaya Mouhibi; Omar Khadir; Hassan Chaair; Mohamed Zahouily

Addresses: Laboratoire de génie des procédés et environnement, Faculté des Sciences et Techniques, Université Hassan II-Casablanca, BP. 146, 20650 Mohammedia, Morocco ' Laboratoire d'informatique et de mathématiques et leurs applications, Faculté des Sciences, Université Chouaib Doukkali, BP. 20, Route Ben Maachou, 24000 El Jadida, Morocco ' Laboratoire de Catalyse, Chimiométrie et Environnement (URAC24), Faculté des Sciences et Techniques, Université Hassan II-Casablanca, BP. 146, 20650 Mohammedia, Morocco ' Laboratoire de Mathématiques, Cryptographie et Mécanique, Faculté des Sciences et Techniques, Université Hassan II-Casablanca, BP. 146, 20650 Mohammedia, Morocco ' Laboratoire de génie des procédés et environnement, Faculté des Sciences et Techniques, Université Hassan II-Casablanca, BP. 146, 20650 Mohammedia, Morocco ' Laboratoire de Catalyse, Chimiométrie et Environnement (URAC24), Faculté des Sciences et Techniques, Université Hassan II-Casablanca, BP. 146, 20650 Mohammedia, Morocco

Abstract: Quantitative Structure Activity Relationships (QSAR) were studied for a series of 54 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas derivatives by means of Multiple Linear Regression (MLR), Genetic Algorithm (GA) and Artificial Neural Network (ANN) techniques. The values of pIC₅₀ (dose of compound required to reduce the proliferation of normal uninfected cells by 50%) of the studied compounds were correlated with the descriptors or variables encoding the chemical structures. An approach that combines GA and MLR (GA-MLR) was used to select the pertinent descriptors to explain the activity pIC₅₀. The descriptors revealed by GA-MLR were used to characterise the non-linear aspect in the activity parameter. The results obtained from this study indicate that the activity pIC₅₀ is strongly dependent on the highest occupied molecular orbital, molecular weight, molecular volume, molar refractivity and LogP parameters.

Keywords: QSAR; quantitative structure activity relationships; genetic algorithms; artificial neural networks; ANNs; CCR5; multiple linear regression; 1-(3, 3-diphenylpropyl)-piperidinyl amides; ureas; molecular orbital; molecular weight; molecular volume; molar refractivity; LogP parameters; human immunodeficiency virus; HIV; acquired immune-deficiency syndrome; AIDS; bioinformatics.

DOI: 10.1504/IJBRA.2016.077123

International Journal of Bioinformatics Research and Applications, 2016 Vol.12 No.2, pp.116 - 128

Accepted: 07 Dec 2015
Published online: 21 Jun 2016 *

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