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

Abstract A dataset of (E)-N’-benzylideneisonicotinohydrazide derivatives as a potent anti-mycobacterium tuberculosis 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 relate 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 selected has squared correlation coefficient (R2) of 0.9202, adjusted squared correlation coefficient (Radj) of 0.91012, Leave one out (LOO) cross validation coefficient (Q_cv^2) value of 0.8954. The external validation set used for confirming the predictive power of the model has its R2pred of 0.8842. Stability and robustness of the model obtained by the validation test indicate that the model can be used to design and synthesis other (E)-N’-benzylideneisonicotinohydrazide derivatives with improved anti-mycobacterium tuberculosis activity.