QSAR study of 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using genetic algorithms and artificial neural networks Online publication date: Tue, 21-Jun-2016
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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