ABSTRACT
Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (rext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC50 values in the range of 0.0508–0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.
Acknowledgements
The project was supported by the Director of Global Institute of Pharmaceutical Education and Research, Kashipur and Teerthanker Mahaveer College of Pharmacy, Moradabad. The authors thank Dr. Shome S. Bhunia for helpful discussions.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplementary material
Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2023.2225872