QSAR study of 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using genetic algorithms and artificial neural networks
by Houda Labjar; Mohamed Kissi; Rokaya Mouhibi; Omar Khadir; Hassan Chaair; Mohamed Zahouily
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 12, No. 2, 2016

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.

Online publication date: Tue, 21-Jun-2016

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