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Tuberculosis is one of the leading cause for deaths due to infectious disease worldwide. There is an urgent need for developing new drugs due to the rising incidents of drug resistance. Triazoles have previously been reported to show antitubercular activity. Various computational tools pave the way for a rational approach in understanding the structural importance of these compounds in inhibiting Mycobacterium tuberculosis growth. The aim of this study is to develop and compare two different QSAR models based on a set of previously reported molecules and use the best one for gaining structural insights in to the Triazole molecules. In the current study, two separate models were generated with CoMFA and CoMSIA descriptors respectively based on a dataset of triazole molecules showing antitubercular activity. Several one dimensional (1D) descriptors were added to each of the models and the validation results and the contour data generated from them were compared. The best model was studied to give a detailed understanding of the triazole molecules and their role in the antitubercular activity.The r2, q2, predicted r2 and SEP (Standard error of prediction) for the CoMFA model were 0.866, 0.573, 0.119 and 0.736 respectively and for the CoMSIA model the r2, q2, predicted r2 and SEP were calculated to be 0.998, 0.634, 0.013 and 0.869 respectively. Although both the QSAR models produced acceptable internal and external validation scores but the CoMSIA results were significantly better. The CoMSIA contours also provided a better match than CoMFA with most of the features of the active compound 30b. Hence, the CoMSIA model was chosen and its contours were explored for gaining structural insights on the triazole molecules. The CoMSIA contours helped us to understand the role of several atoms and groups of the triazole molecules in their biological activity. The possibilities for substitution in the triazole compounds that would enhance the activity were also analysed. Thus, this study paves the way for designing new antitubercular drugs in future.
Structural insights into the mechanism defining substrate affinity in Arabidopsis thaliana dUTPase: the role of tryptophan 93 in ligand orientation