In-silico studies of anti-tuberculosis drugs using advanced computational techniques

As per the 2022 global tuberculosis (TB) report, TB ranked among the top ten causes of mortality worldwide, surpassing both HIV and malaria in fatalities. It is a communicable disease induced by the Mycobacterium tuberculosis (MTB). Herein the role of molecular modeling tools in the search for new TB therapeutics involves quantitative structure-activity relationship (QSAR), pharmacophore modeling, and molecular docking. In the present work, we compiled approximately 1000 anti-TB candidates from the literature, along with their experimentally determined activities, and classified into three classes based on their biological activities, viz.,pIC, pIC_50, and pMIC_50. All selected compounds were optimized followed by docking them to anti-TB receptors retrieved from the Protein Data Bank. The docking results delineate the nature of interactions between the ligands and their respective receptors, which are prevalently noncovalent, dominated by hydrogen bonds and van der Waals’ interactions. Additionally, DFT-based descriptors were calculated such as constitutional, geometrical, topological, quantum chemical and docking based descriptors basis on their biological activities. The best models were selected on the basis of statistical parameters and were validated by training and test set division.