Ligand-based design of potent Dipeptidyl Peptidase-IV (DPP-IV) inhibitors in heterocyclic compounds using QSAR, molecular docking, dynamic simulations, ADMET, and pharmacological profiling for anti-diabetic potential
Abstract
Type 2 Diabetes Mellitus (T2DM) is characterized by high glucose levels in the blood and impaired insulin function, often leading to complications like visual impairment, amputation, and nephropathy. It is a global health challenge and is projected to become the seventh leading cause of mortality. This research aimed to design effective and safer Dipeptidyl Peptidase-IV (DPP-IV) inhibitors as potential T2DM treatments. Five QSAR models were developed; Model 3 demonstrated high performance, with R² = 0.9904, Q²cv = 0.9836, and R²pred = 0.8989. Based on this model, the newly designed compounds, yielding pIC50 values ranging from 8.10 to 8.27, are more effective than the template (pIC50 = 8.0) and the reference drug Sitagliptin (pIC50 = 7.88). Docking studies revealed better binding affinities for the newly designed compounds. The ADMET predictions suggest that the compounds exhibit favourable pharmacokinetics and low risk of toxicity profiles. Using Material Studio v8.0, a Quantitative structure-activity relationship (QSAR) model was constructed and validated through both internal and external assessment procedures. Virtual screening identified a template compound that underwent structural modifications to enhance efficacy. Molecular docking studies using Protein Data Bank data (PDB ID: 3c59) pinpointed active site residues, and a molecular dynamics simulation study using the Software Amber24 was performed at 500 ns. The data showed that the compound DPI7 was thermodynamically stable and effectively bound to the target protein in its active binding pockets without much movement. The pharmacological characteristics of the compounds were evaluated using ADMETlab, SwissADME, and pKCSM. The findings suggest the designed DPP-IV inhibitors are potential candidates for T2DM treatment, giving better results and safety compared to the template and reference drug (Sitagliptin) used in this study.
Fields of Study & Subjects
Computational Drug Discovery Methods
Natural Antidiabetic Agents Studies
Diabetes Treatment and Management
Publication details
- Journal / source
- In Silico Research in Biomedicine
- Volume
- 2
- Pages
- 100184–100184
- Publication date
- 1 January 2026
- Language
- EN