A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against <i>Mycobacterium tuberculosis</i>: A Theoretical Approach

Development of more potent antituberculosis agents is as a result of emergence of multidrug resistant strains of M. tuberculosis . Novel compounds are usually synthesized by trial approach with a lot of errors, which is time consuming and expensive. QSAR is a theoretical approach, which has the potential to reduce the aforementioned problem in discovering new potent drugs against M. tuberculosis . This approach was employed to develop multivariate QSAR model to correlate the chemical structures of the 2,4-disubstituted quinoline analogues with their observed activities using a theoretical approach. In order to build the robust QSAR model, Genetic Function Approximation (GFA) was employed as a tool for selecting the best descriptors that could efficiently predict the activities of the inhibitory agents. The developed model was influenced by molecular descriptors: AATS5e, VR1_Dzs, SpMin7_Bhe, TDB9e, and RDF110s. The internal validation test for the derived model was found to have correlation coefficient (R 2 ) of 0.9265, adjusted correlation coefficient (R 2 adj) value of 0.9045, and leave-one-out cross-validation coefficient (Q_cv ∧ 2) value of 0.8512, while the external validation test was found to have (R 2 test) of 0.8034 and Y-randomization coefficient (cR_p ∧ 2) of 0.6633. The proposed QSAR model provides a valuable approach for modification of the lead compound and design and synthesis of more potent antitubercular agents.