Activity Modeling of Some Potent Inhibitors Against Mycobacterium tuberculosis Using Genetic Function Approximation Approach

Objectives: The research aimed to develop a theoretical (QSAR) model for predicting the activity of 1,2,4-Triazole derivatives as anti-tubercular antagonist. Methods: Genetic function approximation (GFA) was employed on a dataset of 1,2,4-Triazole derivatives to investigate their activities behavior on mycobacterium tuberculosis. This approach led to selection of the optimum descriptors and to generate the correlation QSAR model that relate their activities values against mycobacterium tuberculosis with the molecular structures of the inhibitors. Results: The built model was validated and was found to have squared correlation coefficient (R2) of 0.9134, adjusted squared correlation coefficient (Radj) of 0.8753 and Leave one out (LOO) cross validation coefficient () value of 0.8231. The external validation set used for confirming the predictive power of the model has R2pred of 0.7482. Conclusion: Reliability, stability and robustness of the model obtained by the validation test indicate that the model can be used to design and synthesis other 1,2,4-Triazole derivatives with improved anti-tubercular activities