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

Abstract

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.

Result: The best QSAR model displays the r2 Test value of 0.715, Q2 LOO value of 0.797 and Q2 L5O 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.