<b>QSAR study of benzimidazole derivatives inhibition on escherichia coli methionine Aminopeptidase</b>

• Main Authors: Zahra Garkani-Nejad, Fereshteh Saneie
• Format: Article
• Language: English
• Published: Chemical Society of Ethiopia 2010-06-01
• Series: Bulletin of the Chemical Society of Ethiopia
• Subjects: QSAR; Artificial neural network; Multiple linear regression; Molecular descriptors; Escherichia coli methionine aminopeptidase
• Online Access: http://ajol.info/index.php/bcse/article/view/60661
• ISSN: 1011-3924; 1726-801X

The paper describes a quantitative structure-activity relationship (QSAR) study of IC₅₀ values of benzimidazole derivatives on escherichia coli methionine aminopeptidase. The activity of the 32 inhibitors has been estimated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques. The results obtained using the MLR method indicate that the activity of derivatives of benzimidazoles on CoII-loaded escherichia coli methionine aminopeptidase depend on different parameters containing topological descriptors, Burden eigen values, 3D MoRSE descriptors and 2D autocorrelation descriptors. The best artificial neural network model is a fully-connected, feed forward back propagation network with a 5-4-1 architecture. Standard error for the training set using this network was 0.193 with correlation coefficient 0.996 and for the prediction set standard error was 1.41 with correlation coefficient 0.802. Comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive power.