In-silico Estimation of Flavonoids through Molecular Docking and Assessment of ADMET properties as DPP-IV inhibitor in the treatment of Diabetes Mellitus

Abstract

Flavonoids are polyphenolic compounds identified in greater quantities in plants, fruits, and vegetables with varying proportions. Their subgroups comprise six main classes, including flavanols, flavones, flavanones, and isoflavones. The focus of the work was to identify their receptor-drug interactions against dipeptidyl peptidase IV (DPP-IV) enzymes through in-silico drug design. Their chemical structure was drawn using ACD labs Chemsketch and energy minimized using Avogadro energy minimization software. To test the binding interaction and mode of flavonoids with the DPP-IV receptor, molecular docking simulations were executed using AutoDock Tools 4.2v, and ADMET predictions were performed. The overall quality of the protein chosen for performing docking analysis was assessed using ERRAT and the score of the model was around 98.386%. Among the 19 flavonoids used in this experiment for in-silico studies, their binding energy was calculated using Autodock. Molecular docking analysis revealed quercetin and kaempferol as the most promising candidates based on their low binding energy of -9.3 and -9.2 kcal/mol, respectively. These compounds demonstrated strong molecular interactions through H-bonding and other hydrophobic interactions with key amino acid residues (Pro475, Gly476, Met509, Pro510, Ser511, Lys512, Gln527, Ile529, Asp545, Asp556, Val558, Phe559, Arg560, Asn562, Ala564 and Thr565). Lipinski’s rule of five identified the drug-like behavior of the molecules and all the compounds were screened using the SWISS ADME software tool. Both quercetin and kaempferol exhibited optimal drug-like characteristics, and showed complete compliance with Lipinski’s rule, supporting their potential for therapeutic development.