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B. Abbasi, “A Neural Network Applied to Estimate Process Capability of Non-Normal Processes,” Expert Systems with Applications, Vol. 36, No. 2, 2009, pp. 3093-3100. doi:10.1016/j.ejmech.2008.02.041

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

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

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

  JOURNAL NAME: Open Journal of Medicinal Chemistry, Vol.3 No.1, March 29, 2013

  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.