Multi-linear regression model, molecular binding interactions and ligand-based design of some prominent compounds against Mycobacterium tuberculosis

Multidrug-resistant strains of Mycobacterium tuberculosis still remain a major challenge in creating first-hand drugs for treating tuberculosis. Development and syntheses of novel compounds with more potent anti-tubercular agents are usually by the trial and error approach 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 the multivariate QSAR model to correlate the chemical structures of the 1,2,4-triazole analogues with their observed activities using a theoretical approach. To build the robust QSAR model, genetic function approximation 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 with molecular descriptors, AATS7s, nHBint3, minHCsatu, TDB9e, RDF90i and RDF110s which have been validated through internal and external validation tests. Molecular docking studies were also carried for all the studied compounds to show the interactions and binding modes between the ligand and the receptor (DNA gyrase). The lead compound (compound 34) with higher anti-tubercular activity was observed with prominent binding affinity of 21.9 kcal/mol compared to the recommended drugs: isoniazid (− 14.6 kcal/mol) and ethambutol (− 5.8 kcal/mol). Therefore, compound 34 served as a template structure to designed compounds with more efficient activities. Among the compounds designed, compounds 34p, 34q and 34r were observed with better anti-tubercular activities with more prominent binding affinities which ranges from − 24.3 to − 28.6 kcal/mol. The outcome of this research is recommended for pharmaceutical and medicinal chemists to synthesize and carry out an in vivo and in vitro screening for the proposed designed compounds to substantiate the computational findings.