Abstract
Tuberculosis (TB) is a global threat with a large burden across the continents in terms of mortality, morbidity, and financial losses. The disease has evolved into multi-drug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) tuberculosis owing to numerous factors ranging from patients' non-compliance to demographical implications. There have been very few new drugs for resistant TB. Resistance has already been reported even for the newly introduced drug bedaquiline. An attempt has been made to integrate both structure-based and QSAR drug design techniques (QSAR-SBDD) for the identification of novel leads. The docking scores normally do not correlate with the activity. Hence, the docking results have been analysed in terms of the number of interactions rather than docking scores. The parameters derived from interactions have been used in developing the QSAR models. The best model shows a good correlation (r = 0.908) between the activity and interaction parameter 'C' describing the sum of all the interactions with each amino acid residue. This model also predicts external dataset with a good correlation (r (ext) = 0.851) and can be used for the identification of novel chemical entities (NCEs) and repurposed drugs for TB therapeutics.