In silico molecular docking approach to investigate the anti-diabetic potential of natural, semi-synthetic, and synthetic compounds for diabetes management

*Corresponding author: Harshitha V and Dhanush GS Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. In silico molecular docking approach to investigate the anti-diabetic potential of natural, semi-synthetic, and synthetic compounds for diabetes management Harshitha V *, Smitha Jose, Dhanush GS * and Digvijaymandal Visveswarapura Institute of Pharmaceutical Sciences, Bengaluru 560070, Karnataka. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 Publication history: Received on 07 July 2025; revised on 16 August; accepted on 19 August 2025 Article DOI: https://doi.org/10.30574/wjbphs.2025.23.2.0755 Abstract Oxidative stress plays a critical role in the pathogenesis of diabetes mellitus, and the nuclear factor erythroid 2 related factor 2 (Nrf2) pathway has emerged as a promising target for its regulation. Activation of Nrf2 enhances the transcription of antioxidant and cytoprotective genes, offering a novel therapeutic approach for managing diabetes. In silico molecular docking study, we employed molecular docking software to evaluate and compare the binding affinities of selected natural, semi-synthetic, and synthetic compounds against key Nrf2 pathway proteins, particularly the Keap1Nrf2 interaction site. The compounds were docked to assess their potential to disrupt the Keap1-Nrf2 complex and thereby promote Nrf2 activation. The evaluated compounds possessed good docking scores and followed Lipinski Rule of 5, with additional pharmacokinetic predictions using SwissADME to evaluate binding energies, hydrogen bond formation, and the specific amino acid residues involved in the interactions. These findings support the role of both natural and synthetic molecules in modulating oxidative stress through the Nrf2 pathway. Further in vitro and in vivo studies are warranted to validate these results and explore their therapeutic applications in diabetes management. Keywords: Nrf2 pathway; Diabetes mellitus; Oxidative stress; Molecular docking; Keap1; Natural compounds; Synthetic drugs; Drug repurposing; Antioxidant response 1. Introduction Diabetes mellitus is a rapidly escalating global health burden, characterized by chronic hyperglycemia resulting from defects in insulin secretion, insulin action, or both. This heterogeneous metabolic disorder affects over 537 million adults globally and continues to be a leading cause of morbidity and mortality. In 2024, diabetes contributed to nearly 7 million deaths worldwide, equivalent to one death every 4.5 seconds. Despite medical advances, the incidence and associated complications of diabetes, such as cardiovascular disease, nephropathy, neuropathy, and retinopathy remain prevalent, particularly in lowand middle-income countries, including India. Diabetes mellitus is broadly classified into Type 1 and Type 2. Type 1 diabetes, often diagnosed in children and young adults, arises from autoimmune destruction of pancreatic β-cells, leading to absolute insulin deficiency. In contrast, Type 2 diabetes is primarily associated with insulin resistance, obesity, aging, and genetic predisposition. It is the most common form, accounting for approximately 90% of diabetes cases globally. Over time, patients with Type 2 diabetes may also exhibit a progressive decline in β-cell function, worsening glycemic control. [1] A major contributing factor to the pathogenesis and progression of diabetes is oxidative stress. Persistent hyperglycemia increases the generation of reactive oxygen species (ROS), which impair insulin signalling pathways, damage β-cells, and exacerbate complications. This has led to considerable interest in the therapeutic potential of antioxidants and related molecular pathways that regulate oxidative homeostasis in cells. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 302 Among these, the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway has emerged as a central regulator of cellular defence mechanisms. Under oxidative or electrophilic stress, Nrf2 dissociates from its repressor Keap1 and translocates into the nucleus, where it activates antioxidant response element (ARE)-driven genes. These include HO1, NQO1, SOD, CAT, and GPx, which collectively enhance antioxidant capacity, reduce inflammation, and protect cellular integrity. Activation of this pathway by natural compounds such as curcumin, resveratrol, and quercetin has shown promising effects in reducing oxidative stress and improving insulin sensitivity. [2-4] In addition to Nrf2, the NF-κB pathway is also intricately involved in the pathophysiology of diabetes, particularly through its role in inflammation. High glucose levels, free fatty acids, and oxidative stress activate NF-κB, leading to the upregulation of pro-inflammatory cytokines such as TNF-α and IL-6. These inflammatory mediators impair insulin action, accelerate β-cell dysfunction, and contribute to chronic complications. India bears a disproportionate burden of diabetes-related complications due to delayed diagnosis, poor glycemic control, and limited access to healthcare in rural settings. National surveys, including the ICMR-INDIAB study, report over 101 million diagnosed diabetic individuals, with another 136 million classified as prediabetic. States such as Tamil Nadu, Kerala, and Karnataka report some of the highest prevalence rates, with cardiovascular disease being the leading cause of death among diabetics. [5] In light of the growing health crisis, there is increasing emphasis on identifying and validating novel anti-diabetic agents. Natural, semi-synthetic, and synthetic compounds are being investigated for their ability to target oxidative stress, enhance insulin sensitivity, and modulate key metabolic pathways. Natural products like berberine, sulforaphane, gymnemic acid, and quercetin have shown to regulate blood glucose levels via mechanisms including inhibition of αglucosidase, activation of AMPK, and stimulation of insulin secretion. Semi-synthetic agents such as metformin and acarbose have long been used as first-line treatments, acting through inhibition of hepatic gluconeogenesis and delayed carbohydrate digestion, respectively. Novel synthetic drugs including SGLT2 inhibitors, DPP-4 inhibitors, and GLP-1 receptor agonists continue to expand therapeutic options, especially for patients with advanced disease or comorbidities. Given the complexity of diabetes pathogenesis, computational approaches such as molecular docking using software like PyRx have become valuable tools in drug discovery. These techniques allow in italics screening of compounds against molecular targets such as Nrf2 and NF-κB, facilitating the identification of promising therapeutic candidates with enhanced efficacy and safety profiles. [6-7] This study aims to investigate the anti-diabetic potential of selected natural, semi-synthetic, and synthetic compounds targeting the Nrf2 pathway through molecular docking analysis. By identifying effective Nrf2 activators, this research may contribute to the development of novel therapeutic strategies to combat oxidative stress and insulin resistance in diabetes mellitus.[8] 2. Materials and methods 2.1. Selection of Ligands A wide range of natural, semi-synthetic, and synthetic compounds known for their antidiabetic potential were selected for the study. The compounds were chosen based on literature support and reported interaction with the Nrf2 pathway. The selected ligands were retrieved from public databases in SDF format, including PubChem and ChEMBL or the compounds can be drawn from chemdraw. 2.2. Protein Preparation Preparation of protein was done using protein preparation BIOVIA -Discovery studio. The typical structure PDB may not be suitable for immediate use in molecular modelling. It may contain co-crystallized ligand, water molecules, metal ions and also co-factors. Some structure may be multimeric, hence need to be reduced to single unit. Protein prepared in 2 steps first involved, Removal of crystallographic water molecules, ions, and co-crystallized ligands to avoid interference during docking. The polar hydrogen was added in second step. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 303 2.3. Preparation of Ligands To prepare the ligand, first visit the PubChem website (www.pubchem.ncbi.nlm.nih.gov) and search for the required compound. Once located, download the 3D conformer of the compound in CDF format. Alternatively, the structure can be drawn using ChemSketch and saved in MDL molfile format for further use. 2.4. Molecular Docking Procedure To load the protein and ligand into PyRx for molecular docking, begin by navigating to the ‘File’ menu and selecting the ‘Load Molecule’ option. Open the preferred folder and choose the downloaded or prepared protein file. Right-click on the selected protein and choose the ‘Autodock’ option, followed by selecting ‘Make Macromolecule’. Next, open Open Babel and click on ‘Insert New Item’ icon. From there, navigate to the folder containing the prepared ligand, and select the appropriate ligand file. Right-click on the molecular formula and select ‘Minimize All’ to optimize the structure. Note the energy value (E) after minimization for reference. Once minimized, right-click again on the molecular formula and convert it to the Autodock ligand format. Proceed to the ‘Vina Wizard’ and click on ‘Start’. Select both the ligand and the macromolecule, then click on ‘Forward’. Finally, click on ‘Maximize’ to visualize the grid box on the display screen, indicating the docking area. A grid area was generated around the binding site of the receptor. 2.5. Visualization and Interaction Analysis The compounds with highest binding affinity docked complexes were visualized using BIOVIA Discovery Studio Visualizer. Detailed binding interactions were analyzed based on: • Hydrogen bonds: Identification of donor-acceptor hydrogen bonds within the active site. • Van der Waals interactions: Evaluation of contact residues stabilizing the ligand. • π–π and cation–π interactions: Detection of aromatic stacking interactions. 2D interaction diagrams were generated to visually represent the interactions for inclusion in results and discussion. [9] 2.6. ADME and Drug-Likeness Prediction The SwissADME online server (http://www.swissadme.ch) was utilized to predict the pharmacokinetic profiles and drug-likeness of the selected steroidal drugs. The canonical SMILES of each ligand were input to calculate: • Physicochemical properties: Molecular weight, topological polar surface area (TPSA), number of hydrogen bond donors and acceptors, and rotatable bonds. • Lipophilicity: LogP values estimated using various predictive models (iLOGP, XLOGP3, WLOGP, MLOGP, SILICOS IT). • Water solubility: Qualitative and quantitative predictions of solubility. • Pharmacokinetics: Gastrointestinal (GI) absorption, blood-brain barrier (BBB) permeability, P-glycoprotein substrate likelihood, and cytochrome P450 enzyme inhibition profile. • Drug-likeness rules: Evaluation using Lipinski’s Rule of Five, Ghose, Veber, Egan, and Muegge filters. • Medicinal chemistry friendliness: Synthetic accessibility score and PAINS filter alerts. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 304 2.7. Structure, binding affinity and log p value of natural, synthetic and semi synthetic compounds Table 1 Natural compounds Sl no: Structure of compound Binding affinity Log P 2FLU 2V92 1. Diosgenin -11.8 -8.6 5.02 2. Gamma Oryzanol -10.4 -10.2 9.01 3. Mangiferin -10.9 -9.0 -0.77 4. Mahanimbine -10.3 -9.6 5.62 5. Kaemferol -10.1 -8.0 1.58 World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 305 6. Quercetin -9.9 -8.1 1.23 7. Ellagic acid -9.9 -9.5 1.00 8. Cyanidin 3-O-Glucoside -9.6 -8.0 -1.13 9. Allicin -4.1 -4.0 1.61 10. Sulforaphane -4.4 -3.2 1.93 World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 306 Tabel 2 Semi synthetic compounds Sl no: Structure of compound Binding affinity Log P 2FLU 2V92 1. Kotalanol -8.3 -6.4 -3.77 2. Salacinol -7.8 -6.0 -2.52 3. Deoxynojirimycin -6.2 -5.5 -1.77 4. Cinnamic Acid -5.8 -6.5 1.79 Tabel 3 Synthetic compounds Sl no: Structure of compound Binding affinity Log p 2FLU 2V92 1. Saxagliptin -11.9 -11.8 1.24 World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 307 2. Sitagliptin -10.5 -8.7 2.51 3. Empagliflozin -9.7 -8.1 1.97 4. Glibenclamide -9.5 -9.0 3.58 5. Giliclazide -9.3 -7.8 1.60 6. Dapagliflozin -9.2 -7.6 2.17 7. Sotagliflozin -9.1 -7.2 2.86 World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 308 8. Miglitol -9.0 -7.3 -1.94 9. Metformin -5.9 -5.0 -0.89 Among the selected 23 compounds, Mangiferin (−10.9 kcal/mol), Mahanimbine (−10.3 kcal/mol), Kaempferol (−10.1 kcal/mol), Gamma Oryzanol (−10.4 kcal/mol) exhibited significant receptor interactions binding properties compared to the standard drug Metformin (-5.9 kcal/mol). These compounds not only showed better docking scores but were also associated with lesser toxicity when compared to other selected ligands. Their predicted interactions suggest a higher potential for therapeutic efficacy, making them promising candidates for further investigation. The toxicity study of the above compounds was studied using the software Protox 3.0. [10-12] 2.7.1. Binding Interactions Table 4 Predicted binding interaction of the compounds with targeted protein Mangiferin World Journal of Biology Pharmacy and Health Sciences, 2025, 23(02), 301-314 309 Gamma Oryzanol Mahanimbine Kaempferol