QSAR tool for optimization of nitrobenzamide pharmacophore for antitubercular activity

Tuberculosis (TB) is a leading cause of death worldwide from a single infectious agent, Mycobacterium tuberculosis (MTB), especially due to the development of resistant strains and its co-infections in HIV. Quantitative-structure activity relationship (QSAR) studies aid rapid drug discovery. In this work, 2D and 3D QSAR studies were carried out on a series of nitrobenzamide derivatives to design newer analogues for antitubercular activity. 2D QSAR was performed using MLR on a data set showing antitubercular activity. The 3D-QSAR studies were performed by kNN-MFA using simulated annealing variable selection method. Alignment of given set of molecules was carried out by the template-based alignment method and then was used to build the 3D-QSAR model. Robustness and predictive ability of the models were evaluated by using various traditional validating parameters. Different physiochemical, alignment-based, topological, electrostatic, and steric descriptors were generated, which indicated the key structural requirements for optimizing the pharmacophore for better antitubercular activity. For 2D QSAR, the best statistical model was generated using SA-MLR method (r(2) = 0.892, q(2) = 0.819) while 3D QSAR model was derived using the SA KNN method (q(2) = 0.722). The positively contributing descriptors can be incorporated to design new chemical entities for future study.

Automated summary

Tuberculosis is a major global health concern, with drug resistance and HIV co-infections posing significant challenges. This study utilized 2D and 3D QSAR to design new antitubercular drug analogues. The models generated provided insights into key structural requirements for optimizing pharmacophore and guiding future drug discovery efforts.