QSAR study of benzimidazole derivatives inhibition on escherichia coli methionine Aminopeptidase

  • Zahra Garkani-Nejad Department of Chemistry, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran
  • Fereshteh Saneie Department of Chemistry, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran
Keywords: QSAR, Artificial neural network, Multiple linear regression, Molecular descriptors, Escherichia coli methionine aminopeptidase

Abstract

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.

KEY WORDS: QSAR, Artificial neural network, Multiple linear regression, Molecular descriptors, Escherichia coli methionine aminopeptidase

 

 

 

 

Bull. Chem. Soc. Ethiop. 2010, 24(3), 317-325.

 

 

 

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eISSN: 1726-801X
print ISSN: 1011-3924