Influence of Molecular Descriptors on Antimycobacterial Activity: A Chemometric Approach

Tuberculosis is a leading cause of death from infectious disease. Because of this an urgent needs to develop potent and effective agents against tuberculosis. Computational chemistry prediction of the biological activity based on Quantitative Structure–Activity Relationship (QSAR) studies substantially increases the potentialities of this kind of networks avoiding time and resource consuming experiments. Hence, the set of furanylamide analogs was considered for QSAR study and divided into two subsets. The first subset contained the 54 compounds that were used as the training compounds. The remaining 39 compounds were used as the test set. The consensus model showed a good correlative and predictive ability with correlation coefficient of determination (0.853), cross validated correlation co-efficient (0.827) and predictive correlation coefficient (0.474) values. A detailed investigation was made on the structural basis for the inhibitory activity and the study revealed that the inhibitory activity largely explained by the topological (molecular connectivity indices), constitutional (presence of aromatic nitro group) and geometric descriptors (moment of inertia, molecular volume, MoRSE code). Insights gleaned from the study could be usefully employed to design inhibitors with a much more enhanced potency.