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2000
Volume 31, Issue 26
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

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.

Results

The best QSAR model displays the r2 value of 0.715, Q2 value of 0.797 and Q2 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.

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QSAR and Molecular Docking Studies on Uracil-based Benzoic Acid and Ester Derivatives to Explore Novel Dipeptidyl Peptidase-4 Inhibitors
Curr. Pharm. Des. 31, 2129 (2025); https://doi.org/10.2174/0113816128331664250206113701
/content/journals/cpd/10.2174/0113816128331664250206113701
/content/journals/cpd/10.2174/0113816128331664250206113701
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  • Article Type:     Research Article
Keyword(s): Applicability Domain (AD); Combinatorial Protocol in Multiple Linear Regression (CP-MLR); diabetes mellitus type-2; dragon descriptors; Partial Least Squares (PLS); Quantitative Structure-activity Relationship (QSAR)

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