Computational investigation and molecular docking simulation of some bioactive compounds as potent inhibitors against tuberculosis receptor

Abstract

Abstract

Multi-drug resistant strains of Mycobacterium Tuberculosis still remain a major challenge toward the first hand drugs for treating tuberculosis.   Development and syntheses of novel compounds with more potent anti-tubercular agents are usually by trial approach with lots 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 1, 2, 4-Triazole 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 validated through internal and external validation test. Molecular docking studies was as well carried for all the studied compounds in order to show the interactions and binding modes between the ligand and the receptor (DNA gyrase). The lead compound (compound 3) with higher anti-tubercular activity was observed with prominent binding affinity of -11.8 kcal/mol. Therefore, compound 3 could serve as a template structure to designed compounds with more efficient activities. The outcome of this research is recommended for pharmaceutical and medicinal chemists to design and synthesis more potent compounds with prominent anti-tubercular activities using the model designed in this study.