A Novel QSAR Model for the Evaluation and Prediction of (E)-N’- Benzylideneisonicotinohydrazide Derivatives as the Potent Anti-mycobacterium Tuberculosis Antibodies Using Genetic Function Approach

A dataset of (E)-N’-benzylideneisonicotinohydrazide derivatives as the potent anti-mycobacterium tuberculosis antibodies has been investigated utilizing quantitative structure-activity relationship (QSAR) techniques. Genetic function algorithm (GFA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation QSAR models that correlate the minimum inhibitory concentration (MIC) values against mycobacterium tuberculosis with the molecular structures of the active molecules. The models were validated, and the best model with squared correlation coefficient (R2) of 0.9202, adjusted squared correlation coefficient (Radj) of 0.91012, and leave one out (LOO) cross validation coefficient ( 2 CV Q ) value of 0.8954 was selected. R2 pred of 0.8842 was achieved for the external validation set used for confirming the predictive power of the model. Stability and robustness of the model obtained by the validation test indicate that the model can be used to design and synthesize the other (E)-N’- benzylideneisonicotinohydrazide derivatives with improved anti-mycobacterium tuberculosis activity.