An integrative computational strategy for antidiabetic drug discovery: From QSAR modeling to retrosynthesis

Abstract

Introduction: The α-amylase enzyme plays a critical role in the digestion of complex carbohydrates. Inhibiting this enzyme offers a promising strategy for improving glucose regulation in diabetic patients.
Methods: In this study, a comprehensive computational approach, combining 3D-QSAR modeling, ADMET profiling, molecular docking, molecular dynamics, ligand transport analysis, and retrosynthesis, was used to identify novel ligands with potent inhibitory activity against various indenoquinoxaline-phenylacrylohydrazide hybrids.
Results: The optimal 3D-QSAR model, developed using partial least squares (PLS) and Comparative Molecular Similarity Indices Analysis (CoMSIA), demonstrated strong correlation and predictive power (Q² = 0.541, R² = 0.973, SEE = 0.076). ADMET analysis showed that the designed ligands possess acceptable pharmacokinetic and toxicological profiles, supporting their potential for further drug development. Molecular docking revealed that the designed ligands effectively interacted with the active site of α-amylase (PDB ID: 7TAA). Furthermore, molecular dynamics simulations (100 ns) and MM-PBSA free energy calculations confirmed the stability of ligand-enzyme complexes. Ligand transport was further examined using the CaverDock program, tracking the movement of molecules from the enzyme’s active site to its surface. Finally, retrosynthetic analysis was performed to propose feasible synthesis routes for the most active compound.
Conclusion: Overall, the findings highlight a promising lead compound for further in vitro and in vivo investigations targeting α-amylase inhibition.

Keywords: 3D-QSAR, Docking, Molecular dynamics, α-Amylase, Biological efficacy, Retrosynthesis