QSAR and Molecular Docking Studies on Uracil-based Benzoic Acid and Ester Derivatives to Explore Novel Dipeptidyl Peptidase-4 Inhibitors.

BACKGROUND

Today, diabetes mellitus (DM) is considered a major global health problem and, especially diabetes mellitus type-2 (T2DM), which accounts for 90-95% of all diabetes cases. Among the novel glucose-lowering agents, dipeptidyl peptidase-4 (DPP-4) inhibitors have been extensively studied in recent years.

OBJECTIVES

This paper integrates a QSAR study and docking analysis of a series of uracil-based benzoic acid and ester derivatives as novel DPP-4 inhibitors.

METHODS

The correlation of chemical structure with the biological activity in CP-MLR led to the detection of eleven descriptors from various classes of Dragon descriptors for modeling the activity. The resulting QSAR model has been validated internally and externally using CP-MLR and PLS. Further, the applicability domain analysis revealed the acceptable predictivity of the highest significant model.

RESULTS

The best QSAR model displays the r value of 0.715, Q value of 0.797 and Q value of 0.809 and this model is used to predict novel compounds with high potency. Further docking study was executed using Autodock 4.2 against DPP-4 protein (PDB ID: 2RGU) that reflects the significant binding potential in newly proposed compounds.

CONCLUSION

From the results, four new congeners have been predicted and validated with good inhibitory activity against DPP-4. Present work reflects that with further optimization of these scaffolds, more selective, potent, and bioavailable DPP-4 inhibitors can be developed for the treatment of T2DM.