In Silico Screening of Natural Compounds for Identification of GLP-1 Receptor Agonists for Type 2 Diabetes Mellitus

Aniket Aayush, Karthik Prakash, Shivam Verma, Ashish Kumar, Ashutosh Mani*

Department of Biotechnology, Motilal Nehru National Institute of Technology Allahabad, India
*Corresponding author: amani@mnnit.ac.in

Received: 11 Nov 2025 | Accepted: 23 Dec 2025 | Published: 26 Dec 2025

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with insulin resistance and impaired insulin secretion. GLP-1 receptor agonists are effective therapeutic agents but are largely peptide-based and require parenteral administration. This study employed a computer-aided drug design (CADD) approach to identify potential small-molecule GLP-1 receptor agonists from natural compounds. Virtual screening of 4000 compounds from the AnalytiCon Discovery library was performed using AutoDock Vina against the GLP-1 receptor (PDB ID: 5TTG). Compounds with binding energy ≤ −11.0 kcal/mol were shortlisted and further evaluated using SwissADME. Several compounds demonstrated strong binding affinity, favorable pharmacokinetics, and stable receptor interactions, suggesting their potential as orally active GLP-1 receptor modulators.

Keywords

Type 2 diabetes mellitus, GLP-1 receptor, molecular docking, virtual screening, SwissADME

Introduction

Type 2 diabetes mellitus is a widespread metabolic disorder characterized by chronic hyperglycemia due to insulin resistance and pancreatic dysfunction. GLP-1 receptor activation plays a critical role in glucose homeostasis, but existing peptide-based therapies have limitations. Natural compounds combined with computational screening provide a promising approach for discovering novel therapeutic agents.

Materials and Methods

The crystal structure of the GLP-1 receptor (PDB ID: 5TTG) was retrieved and prepared for docking. A dataset of 4000 natural compounds was obtained and processed using OpenBabel. Molecular docking was performed using AutoDock Vina, and compounds were ranked based on binding affinity. Pharmacokinetic properties were evaluated using SwissADME, and interaction analysis was performed using PyMOL and Discovery Studio.

Results and Discussion

Virtual screening identified several compounds with strong binding affinity (≤ −11.0 kcal/mol). Structural validation confirmed the suitability of the receptor model. SwissADME analysis indicated favorable pharmacokinetic properties for shortlisted compounds. Interaction analysis revealed stable binding involving hydrogen bonds and hydrophobic interactions, supporting their potential as GLP-1 receptor agonists.

Conclusion

This study demonstrates the effectiveness of a CADD-based approach for identifying natural compounds as potential GLP-1 receptor agonists. Acetoxy guaianolide emerged as a promising lead compound, warranting further experimental validation.

Acknowledgements

The authors acknowledge the Department of Biotechnology, Motilal Nehru National Institute of Technology Allahabad for providing computational facilities.

References

(Include full reference list from PDF)

Download Full PDF

← Back to Issue