Quantitative structure–activity relationship on a series of imidazole [1, 2-a] pyridinecarboxamide derivatives as anti-tuberculosis agents
Subject Areas : Others
Mohsen
Nekoeinia
1
(Department of chemistry, Payame Noor Unvierstiy, Tehran, Iran)
Saeed
Yousefinejad
2
(Research Center for Health Sciences, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.)
Keywords: Anti-tuberculosis, QSAR, 2-a] pyridinecarboxamide derivatives, Imidazole [1, Artificial Neural Networks,
Abstract :
Tuberculosis drug resistance is still one of the most important challenges in the treatment of this infectious disease, and therefore the discovery and development of new effective anti-tuberculosis drugs are always of interest to researchers. In this study, Quantitative structure – activity relationship (QSAR) analysis was applied on a series of imidazole[1,2-a] pyridinecarboxamide derivatives as anti-tuberculosis agents. The biological activity of the 18 derivatives were estimated by multiple linear regression and artificial neural network approaches. The four molecular descriptors (nCl, MATS8m, BELe4 and GATS8e) were selected by using stepwise multiple linear regression. The best results of artificial neural network were obtained with a 5-5-1 architecture trained with the feed forward backpropagation algorithm. An external test set containing 5 compounds for evaluating the model's predictive ability was used. The results showed that the artificial neural network approach provides better predictive power compared with multiple linear regression. According to the results of this study, electronegativity, atomic masses and molecular geometry have been found to be important factors controlling the anti-tuberculosis activity.
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