Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas

Rokaya Mouhibi, Mohamed Zahouily, Khalid El Akri, Naîma Hanafi

Laboratoire de Catalyse, Chimiométrie et Environnement, Faculté des Sciences et Techniques Mohammedia (URAC24), Université Hassan II Mohammedia-Casablanca, Casablanca, Maroc.
Laboratoire de Chimie Organique, Catalyse et Environnement, Faculté des Sciences Ben M’sik, Université Hassan II Mohammedia-Casablanca, Casablanca, Maroc.

DOI: 10.4236/ojmc.2013.31002                

Abstract

Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R₇), the partition coefficient between n-octanol and water logP and logP(R₁) and Molecular weight MW(R₇), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

Keywords

Artificial Neural Network, Descriptors; CCR5; Multiple Linear Regression; Structure-Activity Relationship

Conflicts of Interest

The authors declare no conflicts of interest.

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