The development of new therapies to treat tuberculosis effectively is currently an intensive area of research, as there is development of total resistance to present marketed drugs available for tuberculosis. To achieve this objective, quantitative structure activity relationship (QSAR) study was carried out on a series of 1,4- dihydropyridine derivatives reported as inhA inhibitors as it provides the rationale for the changes in the pharmacophore to have more potent and less toxic analogues. In this article, we report 2D and 3D QSAR studies for the set of 20 molecules. Statistically significant models were generated, and the most robust model for 2D quantitative structure activity relationship was obtained using Multiple linear regression (MLR), Principle component regression (PCR) and Partial least square regression (PLSR) technique. The 3D QSAR model was developed by Simulated Annealing kohonen Nearest Neighbor Molecular Field Analysis (SA kNN MFA). By performing 2D QSAR, we found that multiple linear regression method showed best statistical result when compared with other methods. The model has shown correlation coefficient (r2), cross validation (q2) and external validation (pred_r2) values of 0.8052, 0.7096 and 0.7567 respectively. The 3D QSAR models were generated to study the effect of steric, electrostatic and hydrophobic descriptors on antitubercular activity. The generated model with good external and internal predectivity for the training and test set that shown cross validation (q2) and external validation (pred_r2) values of 0.7353 and 0.8043, respectively. The electronic, steric and hydrophobic descriptors generated at the points E_796, S_755 and H_944 play an important role in the design of new molecule. Thus 2D and 3D QSAR studies were found to reliable clues for further optimization of 1,4-dihydropyridine pharmacophore as effective antitubercular agents.
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