Computational Assessment of Substituted 2-Mercaptobenzimidazole Schiff Bases Derivatives Targeting α-Amylase, α-Glucosidase, and PPAR-γ Receptor in Type 2 Diabetes Mellitus

Computational Assessment of Substituted 2-Mercaptobenzimidazole Schiff Bases Derivatives Targeting α-Amylase, α-Glucosidase, and PPAR-γ Receptor in Type 2 Diabetes Mellitus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Computational Assessment of Substituted 2-Mercaptobenzimidazole Schiff Bases Derivatives Targeting α-Amylase, α-Glucosidase, and PPAR-γ Receptor in Type 2 Diabetes Mellitus Vaishnavi Sanjay Patil, Sushant Prakash Kokane, Gajanan Ganesh Deshmane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6892367/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Type 2 diabetes mellitus (T2DM) is a progressive metabolic disorder marked by elevated blood glucose levels due to insulin resistance and impaired insulin secretion. Current therapeutic strategies often focus on inhibiting carbohydrate-hydrolyzing enzymes such as α-amylase and α-glucosidase to reduce postprandial hyperglycemia. Additionally, increasing insulin sensitivity is mostly dependent on PPAR-γ receptor activity. In this study, a computational docking approach was employed to assess the antidiabetic potential of fifteen substituted 2-mercaptobenzimidazole Schiff base derivatives targeting α-amylase, α-glucosidase, and PPAR-γ receptor. Molecular docking was conducted using AutoDock Tools version 1.5.7 to evaluate binding affinity and interaction profiles. As the standard reference medication, acarbose was employed. Among the designed compounds, five derivatives showing the highest binding affinity were selected for detailed comparative analysis. The docking results revealed that several compounds exhibited stronger binding energies and stable interactions within the active sites of the target proteins compared to Acarbose. Key interactions included hydrogen bonds and hydrophobic contacts with catalytically important amino acids. These findings suggest that substituted 2-mercaptobenzimidazole Schiff bases hold promise as multi-target antidiabetic agents. The study provides valuable insight for further in vitro validation and potential lead optimization in the development of novel antidiabetic therapies. 2-Mercaptobenzimidazole Schiff Bases Molecular Docking α-Amylase Inhibition α-Glucosidase Inhibition PPAR-γ Inhibition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Insulin resistance and inadequate insulin production cause chronic hyperglycemia, which is a hallmark of type 2 diabetes mellitus (T2DM), a common metabolic disease. Effective multi-target therapy approaches are necessary because it plays a major role in cardiovascular diseases, neuropathy, nephropathy, and other problems. Complex carbohydrates are hydrolyzed into glucose by two essential enzymes, α-amylase and α-glucosidase. Since their inhibition lessens postprandial blood glucose surges, they are important targets for the treatment of type 2 diabetes [ 1 ]. Although acarbose, a common α-glucosidase inhibitor, successfully postpones the breakdown of carbohydrates, it has gastrointestinal adverse effects [ 2 ]. Insulin sensitivity and lipid metabolism depend on the nuclear receptor known as peroxisome proliferator-activated receptor gamma (PPAR-γ), which is another target of interest. Although PPAR-γ agonists, like thiazolidinediones, enhance the action of insulin, they can also have negative side effects, such as weight gain and fluid retention. The creation of multi-target-directed ligands (MTDLs), which can alter several diabetes pathways, is a focus of current drug research initiatives [ 3 , 4 ]. Schiff bases of 2-mercaptobenzimidazole in particular have demonstrated encouraging biological actions, including the potential to treat diabetes. Their structural adaptability enables a variety of replacements that could improve binding particular to a target. Using molecular docking, fifteen substituted 2-mercaptobenzimidazole Schiff base derivatives were created and tested for their ability to inhibit α-amylase, α-glucosidase, and their interaction with PPAR-γ. Using AutoDock Tools version 1.5.7, docking simulations were run. Acarbose served as the standard reference material. Five of the most promising adaptations were chosen for comparison based on their docking scores. The objective is to find drugs with multi-target efficacy that may be used as leads for additional in vitro and in vivo testing in the treatment of type 2 diabetes [ 5 – 7 ]. 2. MATERIALS AND METHODS 2.1 Design strategy The main scaffold, N'-(benzylidene)-2-[[(1H-benzimidazol-2-yl)thio]acetyl]hydrazinecarboxamide, was altered to produce a special set of fifteen 2-mercaptobenzimidazole-based Schiff base derivatives in order to increase potential antidiabetic efficacy (Fig. 1 ).Structure–activity connections and pharmacodynamic considerations led to the introduction of structural variants. For comparison, Acarbose, the typical medication, was utilized (Fig. 2 ). ChemDraw Professional 8.0 was used to create the SMILES notations for each chemical. For later computer studies, the structures were stored in MDL SDF format [ 8 ]. 2.2 Computational Docking: Ligand Preparation : ChemDraw and ChemSketch were used to design fifteen substituted 2-mercaptobenzimidazole Schiff base derivatives, and PyMOL was used to create their three-dimensional structures. Geometry was optimized using energy minimization. The optimized structures were converted from MOL to PDB format using Open Babel. In order to create PDBQT files, these PDB files were further processed in AutoDock Tools (version 1.5.7) by combining non-polar hydrogens, allocating rotatable bonds, and adding Gasteiger charges. The reference standard, acarbose, was prepared in a similar manner [ 9 ]. Receptor Preparation : The SuperPred web server, which uses molecular target prediction, was used to identify three protein targets related with type 2 diabetes. PDB IDs for α-amylase, α-glucosidase, and PPAR-γ are 4GQR, 3TOP, and 1QSE respectively. The RCSB Protein Data Bank provided the protein structures. Kollman charges were applied, polar hydrogens were added, and all water molecules and heteroatoms were eliminated using AutoDock Tools. The PDBQT format was used to store the cleaned structures [ 10 , 11 ]. Selection of Top Derivatives : We compared the docking scores of 15 Schiff base derivatives across three targets. The five compounds with the best binding energies and consistent interactions were chosen for comparative docking study. Interaction Analysis : The top five compounds' protein-ligand complexes were visualized with BIOVIA Discovery Studio Visualizer to assess hydrogen bonding, hydrophobic contacts, and interactions with critical amino acid residues in the active site [ 12 ]. 2.3 Drug likeness studies and ADME prediction Calculations of molecular properties and drug-likeness parameters were performed for each of the design compounds (VSP-1 to VSP-5). The drug-likeness study is important because it identifies compounds that fit the criteria as being similar to drugs. It is crucial to investigate ADME qualities because of the pharmacokinetic properties of pharmaceutical compounds, including their oral bioavailability, cell pen etration, metabolism, and elimination. SWISS ADME techniques were used to calculate Lipinski's rule of five, and other physicochemical parameters such molecular refractivity, GI absorption, water solubility, and the number of rotatable bonds were predicted. Several software tools were used in this study to ensure accurate projections and in-depth analysis [ 13 ]. Using SWISS ADME, which provided crucial details on the pharmacokinetic properties of the medications, predictions for Absorption, Distribution, Metabolism, and Excretion (ADME) were established. ProTox 3.0 was used to assess the toxicity profiles of the compounds and ensure their safety for further studies [ 14 ]. 2.4 BoiledEGG analysis The selected drugs' gastrointestinal absorption and blood–brain barrier penetration were evaluated using the BOILED-Egg model [ 15 ]. The study discovered that chemicals in the white region were linked to greater intestine absorption potential, whereas compounds in the yellow zone were expected to have greater permeability across the blood–brain barrier. The SwissADME digital platform was used to conduct the study [ 16 ]. 2.5 Toxicity studies To evaluate the safety profile of the synthesized chemicals, a toxicology investigation was conducted. Acute toxicity (LD₅₀), toxicity class, and organ-specific toxicities like hepatotoxicity, mutagenicity, and carcinogenicity were estimated using the in silico prediction platform ProTox-II (version 3.0) [ 17 ]. Using machine learning and molecular similarity techniques, it facilitates effective early-stage screening of possible hazardous effects based on SMILES input [ 18 ]. 2.6 Selection of the target protein α-Amylase Inhibitory Activity : One important target for controlling postprandial hyperglycemia in type 2 diabetes is human pancreatic α-amylase. The chosen structure (PDB ID: 4GQR) was resolved by X-ray crystallography at a resolution of 1.20 Å, as seen in Fig. 3 a. It provides comprehensive information on the active site and consists of a single chain (Chain A) with 496 amino acids. The logical design of new 2-mercaptobenzimidazole Schiff base derivatives is supported by this structural information. Developing strong α-amylase inhibitors requires precise prediction of binding modes and interactions, which is made possible by docking experiments employing 4GQR [ 19 , 20 ]. α-Glucosidase Inhibition Assay : α The α-Glucosidase Inhibition Assay is a proven target for regulating postprandial blood glucose levels in type 2 diabetes and is an essential enzyme in carbohydrate metabolism. X-ray crystallography was used to resolve the chosen protein structure (PDB ID: 3TOP), which is displayed in Fig. 3 b, at a resolution of 2.10 Å. Its active site is fully visible because to its single polypeptide chain (Chain A), which has 952 amino acids. The evaluation of binding interactions for newly discovered 2-mercaptobenzimidazole Schiff base derivatives as possible α-glucosidase inhibitors is made easier by using this structure in docking investigations [ 21 , 22 ]. PPAR-γ inhibitory activity : PPAR-γ inhibitory activity: A key player in lipid metabolism and glucose homeostasis, peroxisome proliferator-activated receptor gamma (PPAR-γ) is a viable target for type 2 diabetes treatment. The single polypeptide chain (Chain A) with 275 amino acids makes up the crystal structure of PPAR-γ (PDB ID: 1QSE), which is displayed in Fig. 3 c and was determined at a 2.30 Å resolution. Important information about the ligand-binding domain is provided by this high-resolution structure. Docking investigations with 1QSE enable precise evaluation of binding affinities and interactions of 2-mercaptobenzimidazole Schiff base derivatives as suspected PPAR-γ antagonists [ 23 , 24 ]. 2.7 In Silico Bioactivity Prediction The probable biological activities of the developed Schiff base derivatives were assessed utilizing their SMILES notations and PASS (Prediction of Activity Spectra for Substances). The expected antidiabetic effects were thought to be likely to be exhibited by compounds with a probability of activity (Pa) > 0.5. Promising candidates for additional pharmacological research could be identified early thanks to this technique [ 25 – 27 ]. Table 1 2D Chemical Structures of the Top Five Derivatives with Their Compound Codes 2.8 Target Prediction Using SuperPred Predicting the likely protein targets of the synthesized Schiff base derivatives was done using the SuperPred web server ( https://prediction.charite.de ). SuperPred uses known ligand-target interactions to connect chemicals to possible biological targets based on structural similarity and machine learning methods. To help identify pertinent pathways and receptor families, the SMILES of each molecule were uploaded, and the results were assessed using probability scores and target classes. This prediction method improves knowledge of potential modes of action and aids in the rationalization of docking targets [ 28 – 30 ]. 2.9 Interactions between analogs and protein The obtained protein structures were loaded into BIOVIA Discovery Studio Visualizer (v21.1.0.20298) to conduct interaction analysis. The produced 2-mercaptobenzimidazole Schiff base compounds were designated as ligands, and the target proteins served as receptors. 2D interaction diagrams were used to show hydrogen bonds, hydrophobic contacts, and other important molecular interactions. Furthermore, 3D interaction patterns of the ligands were compared to native and standard equivalents across all three selected protein targets. The purpose of this research was to elucidate the binding patterns and anticipate the derivatives' potential efficacy by identifying important ligand-protein interactions that are relevant to biological activity [ 31 – 33 ]. 3. RESULTS 3.1 Designed 2-Mercaptobenzimidazole Schiff base derivatives ChemDraw Professional 8.0 was used to design fifteen 2-mercaptobenzimidazole Schiff base derivatives and produce SMILES notations for them. All compounds were tested using molecular docking against three protein targets. Based on binding affinity scores and interaction consistency, five derivatives with the best docking results were chosen for a thorough comparison investigation. Table 1 shows the 2D chemical structures of the top five derivatives, as well as their compound codes. 3.2 ADME prediction The SwissADME online tool was used to assess the synthetic 2-mercaptobenzimidazole Schiff base derivatives' ADME characteristics. In order to predict a compound's pharmacokinetic profile prior to in vivo research, this in silico method is essential in the early stages of drug development. Many drugs fail in clinical phases because of inadequate absorption, metabolism, or bioavailability, even while they show promising in vitro activity. Important factors like gastrointestinal (GI) absorption, water solubility, Lipinski's Rule of Five, and other physicochemical characteristics were examined in order to evaluate drug-likeness. By looking at molecular weight (MW < 500 g/mol), lipophilicity (Log P ≤ 5), hydrogen bond donors (HBD ≤ 5), and hydrogen bond acceptors (HBA ≤ 10), Lipinski's Rule provides a standard for assessing oral bioavailability. The majority of substances demonstrated good compliance with these requirements when all derivatives were evaluated against them, suggesting a high potential for oral delivery. Additionally, favorable membrane permeability and bioavailability were corroborated by the computed topological polar surface area (TPSA), HBA, and HBD values. These findings imply that the chosen Schiff base derivatives have appropriate pharmacokinetic characteristics for additional drug candidate optimization. Table 2 Drug-likeliness properties of the designed compounds Ligand Molecular Formula MW (g/mol) Log P HBA HBD Lipinski Rule Met TPSA (Ų) MR (cm³/mol) nRotB <500 <5 <10 <5 VSP1 C₁₇H₁₅N₅OS 337.10 1.80 5 3 Yes 119.29 97.00 7 VSP2 C₁₉H₁₇N₃O₂S 351.10 2.46 5 3 Yes 110.84 102.99 7 VSP3 C₂₀H₁₉N₃O₂S 365.12 2.79 5 2 Yes 99.84 107.46 8 VSP4 C₁₇H₁₄ClN₅OS 371.06 2.44 5 3 Yes 119.29 102.01 7 VSP5 C₁₇H₁₅N₆O₃S 383.09 0.26 7 4 Yes 159.60 102.07 8 MW molecular weight, nHBA no. of hydrogen bond acceptor, nHBD no. of hydrogen bond donor, TPSA topological polar surface area, nViola tions No. of the rule of five violations Table 3 ADMET features of selected ligand Ligand In-silico ADMET Absorption Distribution Metabolism Toxicity Water Solubility BBB Permeability 1A2 2C19 2C9 2D6 3A4 Predicted LD₅₀ VSP1 −2.51 (Moderately soluble) 2.77 No Yes Yes Yes Yes 500 mg/kg (Class 4) VSP2 −2.14 (Moderately soluble) 3.03 No No No No Yes 1000 mg/kg (Class 4) VSP3 −3.02 (Low soluble) 3.84 Yes Yes Yes Yes Yes 258 mg/kg (Class 3) VSP4 −3.40 (Low soluble) 2.71 No No No No No 910 mg/kg (Class 4) VSP5 −4.20 (Very low soluble) 2.12 No No No Yes No 500 mg/kg (Class 4) 3.3 BoiledEGG analysis The pharmacokinetic behavior of the five best-performing 2-mercaptobenzimidazole Schiff base derivatives was assessed using the BOILED-Egg (Brain Or Intestinal Estimated Permeation technique) model. In order to forecast passive gastrointestinal absorption and blood-brain barrier penetration, this graphical method displays WLOGP (lipophilicity) on the Y-axis versus TPSA (topological polar surface area) on the X-axis. The white ellipse zone contains all five molecules, suggesting a high likelihood of efficient gastrointestinal absorption. Nevertheless, none of the compounds fall into the yellow zone, indicating that these derivatives do not have a high probability of crossing the blood-brain barrier, which is a desired characteristic for preventing adverse effects on the central nervous system in peripheral drug targets. Additionally, the absence of P-glycoprotein substrate affinity (PGP−), seen by the red circular boundaries surrounding all molecules, suggests improved cellular absorption and decreased efflux. The systemic availability and oral acceptability of the chosen analogs are supported by these ADME-related characteristics. 3.4 Toxicity studies The web-based program ProTox-II (version 3.0) was used to evaluate the toxicity profiles of the developed 2-mercaptobenzimidazole Schiff base derivatives. Table No. 3 provides a summary of the expected LD₅₀ values (in mg/kg) and associated toxicity classes. Under Class 4, compounds D1, D2, D4, and D5 showed comparatively reduced toxicity; their LD₅₀ values ranged from 500 to 1000 mg/kg, suggesting that they are only dangerous at greater dosages. D3 on the other hand, which was categorized under Class 3, showed greater toxicity. A greater risk of acute toxicity was suggested by its lower LD₅₀ value of 258 mg/kg. In order to choose drug-like candidates for preclinical assessments, these projections offer early safety insights. 3.5 Docking study: Molecular docking was used to analyze the interaction patterns of fifteen 2-mercaptobenzimidazole Schiff base derivatives against three diabetes-related targets: α-glucosidase (3TOP), α-amylase (4GQR), and PPAR-γ (1QSE). AutoDock Vina was used to dock refined protein structures and energy-minimized ligands, and PyMOL and Discovery Studio were used to display interactions. To find the most promising antidiabetic possibilities, the top five derivatives were selected for in-depth comparative study based on binding affinities and recurring interaction patterns. Table 4 Docking Score of Various Designed 2-Mercaptobenzimidazole Schiff base Compounds Concerning the Receptors Sr. No Compound Code R DOCKING SCORES Antidiabetic α-Amylase α-Glucosidase PPAR-γ 3. VSP1 -H -8.1 -9.1 -9.7 4. VSP2 -3OH -7.9 -10.0 -8.9 5. VSP3 -3OCH 3 -8.1 -9.9 -8.7 6. VSP4 -2Cl -7.9 -9.6 -8.7 7. VSP5 -3OH -7.9 -10.0 -8.9 8. VSP6 -2Br -7.8 -9.1 -8.6 9. VSP7 -3Br -7.6 -9.0 -8.6 10. VSP8 -4Br -7.6 -9.0 -8.6 11. VSP9 -2NO -7.5 -8.9 -8.4 12. VSP10 -4NO -7.3 -8.9 -8.4 13. VSP11 -2OH -7.2 -8.8 -8.3 14. VSP12 -4OH -7.2 -8.7 -8.2 15. VSP13 -2OCH 3 -7.1 -8.7 -8.2 16. VSP14 -4OCH 3 -7.1 -8.5 -8,1 17. VSP15 -2Cl -7.0 -8.4 -7.9 18. Standard Acarbose -10.8 -10.0 -11.0 I] ANTIDIABETIC (α-AMYLASE) DOCKING INTERACTION II] ANTIDIABETIC (α-GLUCOSIDASE) DOCKING INTERACTION III] ANTIDIABETIC ( PPAR-γ ) DOCKING INTERACTION 3.6 In Silico Bioactivity Prediction Table 5 Predicted Biological Activities of Top Five 2-Mercaptobenzimidazole Schiff Base Derivatives Using PASS Analysis Compound Pa Pi Biological Activity VSP1 0.775 0.002 Antidiabetic VSP2 0.755 0.002 Antidiabetic VSP3 0.745 0.002 Antidiabetic VSP4 0.735 0.008 Antidiabetic VSP5 0.752 0.010 Antidiabetic Utilizing the Prediction of Activity Spectra for Substances (PASS) online application, the anticipated bioactivity profiles of the five optimized 2-mercaptobenzimidazole Schiff base derivatives (VSP1–VSP5) were evaluated. For a particular biological function, this in silico method calculates the likelihood that a molecule is pharmacologically active (Pa) or inert (Pi). Standard criteria state that compounds are expected to exhibit the relevant activity under biological conditions if their Pa value is greater than 0.7. According to Table 4 , all five analogs had low Pi values and Pa values more than 0.73, indicating a strong probability of antidiabetic potential. Interestingly, VSP1 had the best prediction with a Pa of 0.775, suggesting that it could be a top contender for additional research. The logical priority of these derivatives for experimental validation and structure–activity relationship (SAR) studies to verify their therapeutic significance is supported by these computational results. 3.7 Target Prediction Using SuperPred For the top five 2-mercaptobenzimidazole Schiff base derivatives, the chemical targets were predicted using the SuperPred web server. This tool correlates compound structures with known biological targets by combining cheminformatics techniques with machine learning methods. The anticipated targets for the compounds' antidiabetic activity are listed in Table 5 and include α-amylase (PDB ID: 4GQR), α-glucosidase (PDB ID: 3TOP), and peroxisome proliferator-activated receptor gamma (PPAR-γ, PDB ID: 1QSE). These predictions, which are quite congruent with the therapeutic goals of type 2 diabetes care, encourage the selection of these proteins for molecular docking and interaction studies. Table 6 Target and PDB ID of the compounds were predicted through the SuperPred web server. Sr. No. Biological Activity Target PBD Id 1. Antidiabetic α-Amylase 4GQR 2. Antidiabetic α-Glucosidase 3TOP 3. Antidiabetic PPAR-γ 1QSE 3.8 Visualization of the selected designed compounds The top five 2-mercaptobenzimidazole Schiff base derivatives were examined using BIOVIA Discovery Studio Visualizer to examine their 2D and 3D interactions with α-amylase, α-glucosidase, and PPAR-γ. Strong binding affinities were supported by the identification of important interactions, including hydrophobic contacts and hydrogen bonding. The complexes can be seen in Figs. 6 , 7 , and 8 . 4. DISCUSSION The current computational assessment of 2-mercaptobenzimidazole Schiff base derivatives targeting α-amylase, α-glucosidase, and PPAR-γ provides promising insight into their potential as antidiabetic agents for managing type 2 diabetes mellitus (T2DM). These multifunctional targets were rationally selected due to their significant roles in modulating postprandial glucose levels and improving insulin sensitivity. The in silico strategies employed here including ADME prediction, molecular docking, toxicity analysis, and bioactivity profiling collectively supported the efficacy, safety, and drug-likeness of the designed ligands. Among the fifteen designed compounds, VSP1 to VSP5 were shortlisted based on superior docking scores across all three diabetic targets, coupled with favorable physicochemical and pharmacokinetic parameters. Docking interactions revealed that VSP1 exhibited the most potent binding affinities toward α-amylase (− 8.1 kcal/mol), α-glucosidase (− 9.1 kcal/mol), and PPAR-γ (− 9.7 kcal/mol), in comparison to other Schiff bases, however it was marginally less than that of the common medication acarbose. Notably, VSP2 and VSP5 also demonstrated strong interactions, particularly with α-glucosidase (− 10.0 kcal/mol), suggesting effective inhibition of carbohydrate metabolism. The ADME predictions using SwissADME indicated all top five compounds comply with Lipinski’s Rule of Five, demonstrating acceptable oral bioavailability. The TPSA values (ranging from ~ 99 to 160 Ų) were within optimal ranges, indicating potential for good membrane permeability. VSP5, though having the highest TPSA and lowest LogP, still maintained drug-likeness with good solubility, showing promise for systemic circulation without central nervous system (CNS) penetration, as further supported by the BOILED-EGG analysis. Additionally, four of the top five compounds were classified as belonging to toxicity class 4, which denotes modest acute toxicity (LD₅₀ range from 500–1000 mg/kg), according to toxicity evaluation using ProTox-II. However, VSP3 was identified as relatively more toxic (Class 3, LD₅₀ = 258 mg/kg), which warrants caution in its further development. Despite this, the predicted toxicity values were generally acceptable for early drug candidates. Interaction profiling using BIOVIA Discovery Studio unveiled that the selected ligands established stable hydrogen bonds and diverse hydrophobic contacts with critical active site residues. For α-amylase, GLN63 and TYR62 were recurrent interaction points contributing to stable complex formation. In the case of α-glucosidase, π–π stacking and electrostatic interactions with key residues such as TRP1369, PHE1559, and ASP1526 played a dominant role in ligand stabilization. Within the PPAR-γ binding site, hydrogen bonding with residues including TYR7, ARG97, and TRP147, accompanied by π–alkyl and π–π interactions, confirmed the ligands' strong binding affinity and structural compatibility with the receptor pocket. As shown in Table No. 6, 7, and 8, these interaction patterns suggest that the designed 2-mercaptobenzimidazole Schiff base derivatives exhibit favorable multi-target binding characteristics, indicating their potential as antidiabetic agents. Table 7 Overview of Ligand–α-Amylase (PDB ID: 4GQR) Interactions at the Active Site Compound code Conventional hydrogen bond Hydrophobic bond VSP1 GLN63 (×2) TYR62, ILE51 (×2) VSP2 THR163 (×3), GLN63 TYR62 (×2) VSP3 — TRP58, TYR62, ILE235, LEU165 VSP4 THR163 TRP59, LEU165 VSP5 ASN105 (×2), ALA106, VAL107 TRP58 (×2), TYR62, LEU165 Acarbose TRP59, TYR151, ILE235, GLN63, ASP197, GLU233 (×2), GLU233:O TRP59 (π-Sigma) Table 8 Overview of Ligand– α-Glucosidase (PDB ID: 5NN5) Interactions at the Active Site Compound code Conventional hydrogen bond Hydrophobic bond VSP1 ASP1526, LYS1460 MET1421 (π-Sigma), TRP1369 (π–π Stacked), PHE1559 (π–π T-shaped ×2), LYS1460 (π-Alkyl) VSP2 — TRP1369 (π–π Stacked ×4), TYR1251 (π–π T-shaped) VSP3 LYS1460 TRP1369 (π–π Stacked ×2, π-Alkyl), TYR1251 (π–π T-shaped ×2), PHE1559 (π–π T-shaped ×2), LYS1460 (Alkyl) VSP4 ASP1157, ASP1526 ARG1510 (π–Sigma), ASP1420, MET1421 (π–π Stacked), TRP1355, TRP1369 (π–π T-shaped, π-Alkyl), PHE1559 VSP5 ASP1526 (also Electrostatic), TRP1369 LYS1460 (π–Cation), MET1421 (π–Sigma), TRP1355, PHE1560 (π–Sulfur), TRP1369 (π–π Stacked ×2), PHE1559 Acarbose SER1292, GLN1372, ARG1377 (multiple H-bonds), LEU1367, ASP1357, TRP1369 (carbon H-bond), ASP1281 (carbon H-bond) — Table 9 Overview of Ligand– PPAR-γ (PDB ID: 1QSE) Interactions at the Active Site Compound code Conventional hydrogen bond Hydrophobic bond VSP1 TRP147, LEU98 THR143, VAL152 (Pi-Sigma); LEU81, LEU156 (Pi-Alkyl) VSP2 TYR7, HIS70, TYR99 TYR7 (Pi-Pi T-shaped); VAL152, PRO103, LYS66 (Pi-Alkyl) VSP3 ARG97 (4 interactions) TYR99 (Pi-Pi Stacked); TYR159 (Pi-Pi T-shaped); TRP147 (Pi-Alkyl) VSP4 SER31, GLN30 TYR159 (Pi-Pi T-shaped); PRO103, GLU105 (Amide-Pi Stacked); PRO103, LEU156 (Pi-Alkyl) VSP5 TYR7, LYS66, TYR159 TYR7 (Pi-Pi T-shaped); LYS66 (Alkyl); HIS70, TYR159, VAL152, PRO103, LYS66 (Pi-Alkyl) Acarbose HIS70, ARG97 (2 H-bonds), HIS114, GLN30, THR98, SER100, ASP77 (2 H-bonds), THR73 (carbon H-bond), LYS146 (carbon H-bond), ASP99 (carbon H-bond) — In addition, PASS bioactivity predictions yielded Pa values greater than 0.73 for all selected derivatives, with VSP1 scoring 0.775, suggesting a high likelihood of antidiabetic activity under physiological conditions. This outcome is consistent with molecular docking and target prediction results from SuperPred, which identified all three biological targets (α-amylase, α-glucosidase, and PPAR-γ) as relevant interaction partners for these ligands. Overall, the study confirms that 2-mercaptobenzimidazole Schiff bases, particularly VSP1, VSP2, and VSP5, hold significant promise as multifunctional antidiabetic agents with desirable pharmacokinetic profiles, acceptable safety margins, and potent inhibitory interactions across critical enzymatic and nuclear receptor targets. These in silico findings justify further in vitro, in vivo, and SAR (structure–activity relationship) studies to validate their therapeutic potential and optimize structural frameworks for enhanced efficacy. 5. CONCLUSION This study presents the rational design and comprehensive in silico evaluation of fifteen novel 2-mercaptobenzimidazole Schiff base derivatives targeting three pivotal proteins implicated in type 2 diabetes mellitus: α-amylase, α-glucosidase, and the PPAR-γ receptor. Molecular docking analyses revealed that five compounds—VSP1, VSP2, VSP3, VSP4, and VSP5—exhibited superior binding affinities, characterized by stable conventional hydrogen bonds and diverse hydrophobic interactions with critical active site residues, including GLN63 and TYR62 in α-amylase, and TRP1369 and PHE1559 in PPAR-γ. Among these, VSP1 emerged as the lead candidate, demonstrating the highest docking scores coupled with exemplary drug-likeness parameters, including optimal molecular weight, favorable lipophilicity (Log P), and adherence to Lipinski’s Rule of Five, indicative of excellent oral bioavailability. Complementary in silico ADMET profiling corroborated VSP1’s superior pharmacokinetic properties and minimal toxicity risks, underscoring its potential as a safe and efficacious therapeutic agent. VSP2 and VSP3 also exhibited robust binding and promising ADMET profiles, while VSP4 and VSP5 showed moderate yet significant interactions and acceptable safety parameters. Collectively, these findings establish 2-mercaptobenzimidazole Schiff bases, particularly VSP1, as potent multifunctional inhibitors capable of modulating key enzymatic and nuclear receptor targets involved in glucose metabolism. By providing a solid basis for further experimental confirmations, this work advances the search for innovative, effective antidiabetic medications with better clinical prospects. Declarations Funding For this docking article, no specific grant from any public, private, or nonprofit financing entity was obtained. Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics Approval Not applicable. The study did not involve human participants or animal experimentation requiring ethical approval. Consent to Participate Not applicable. Consent for Publication Not applicable. Availability of Data and Material All data generated or analyzed during this study are included in this published article. Code Availability Not applicable. Clinical trial number Not applicable. Authors’ Contributions V. S. Patil conceptualized the study, designed the research protocol, performed molecular docking, ADME analysis, and bioactivity prediction. She was responsible for data collection, compound selection, visualization and result interpretation. All authors contributed to data analysis, manuscript drafting, and approved the final version, taking full responsibility for its content and integrity. S. P. Kokane supervised the research, provided continuous guidance throughout the study, and critically reviewed the manuscript for intellectual and scientific accuracy. Acknowledgments The author gratefully acknowledges the Department of Pharmaceutical Chemistry, Appasaheb Birnale College of Pharmacy, Sangli, for providing necessary laboratory facilities and technical support throughout the research work. Also, special thanks to Sushant P. Kokane for their valuable guidance and encouragement. References Thabet HK, Al‑Sanea MM, Ahmed MM, Ali NF, Shoman ME. Discovery of novel 6-(piperidin-1-ylsulfonyl)-2H-chromenes targeting α-glucosidase, α-amylase, and PPAR-γ: Design, synthesis, virtual screening, and antidiabetic activity. J Mol Struct. 2024; 130154. https://doi.org/10.1016/j.molstruc.2024.130154 Domínguez-Mendoza Y, Salgado-Zamora H, Mendieta-Wejebe JE, Trujillo-Ferrara JG, Correa-Basurto J, Rosales-Hernández MC. Design of a multi-target focused library for antidiabetic targets using a comprehensive set of chemical transformation rules. J Comput Aided Mol Des. 2021; 35(1):47–68. https://doi.org/10.1007/s10822-020-00344-3 Sanna M, Gadau SD, Boi C, Pinna GA, Dallocchio R, et al. A new PPARα/γ dual agonist with mitochondrial pyruvate carrier inhibition. Bioorg Chem. 2023; 139:106657. https://doi.org/10.1016/j.bioorg.2023.106657 Francque SM, Ip E, Jin Y, Yoshida EM, Bedossa P. Emerging role of PPAR agonists in the treatment of metabolic dysfunction and NAFLD. Nat Rev Endocrinol. 2024; 20:112–127. https://doi.org/10.1038/s41574-024-00912-7 Hussain S, Taha M, Rahim F, et al. Synthesis of benzimidazole derivatives: in vitro α‑amylase activity and molecular docking study. Russ J Org Chem. 2021;57(7):968–975. doi:10.1134/S1070428021060130 Khan SA, Ali M, Latif A, Ahmad M, Khan A, Al‑Harrasi A. Mercaptobenzimidazole‑based 1,3‑thiazolidin‑4‑ones as antidiabetic agents: synthesis, in vitro α‑glucosidase inhibition activity, and molecular docking studies. ACS Omega. 2022;7(32):28041–28051. doi:10.1021/acsomega.2c01969 Sattar S, Ullah H, Taha M, Khan F, Rahim F, Uddin I, et al. Synthesis, in vitro α‑amylase activity, and molecular docking study of new benzimidazole derivatives. Russ J Org Chem. 2021;57:968–975. Ubeid MT, Thabet HK, Abu Shuheil MY. Synthesis of 4-[(1H-benzimidazol-2-yl)sulfanyl]benzaldehyde and 2-({4-[(1H-benzimidazol-2-yl)sulfanyl]phenyl}methylidene)hydrazine-1-carbothioamide. Molbank. 2021; 2021(3):M1273. doi:10.3390/M1273. Alam MM, Naeem A, Siddiqui MH, Alreshidi MM, Alghanem SB. Design, synthesis, molecular docking, and in silico ADMET analysis of novel benzimidazole derivatives as dual inhibitors of α-amylase and α-glucosidase. J Mol Struct. 2023;128679. doi:10.1016/j.molstruc.2023.128679 Thafeni MA, Mosa RA, Musyoka TM, Tastan Bishop Ö. Identification of potential novel inhibitors for alpha-glucosidase using computational approaches. J Biomol Struct Dyn. 2022;40(1):292–304. doi:10.1080/07391102.2020.1805462 Qamar MT, Mumtaz A, Ashfaq UA, Fatima I, Shahid F, Bilal M, et al. In silico screening of potential compounds from natural sources targeting PPAR-γ for the management of diabetes mellitus. Comput Biol Med. 2021; 133:104362. doi:10.1016/j.compbiomed.2021.104362 Khan T, Abbasi MA, Shah SA, Khan A, Ali S, Park TJ, et al. Benzimidazole derivatives as potential antidiabetic agents: synthesis, biological evaluation, and molecular docking studies. Eur J Med Chem. 2021; 211:113111. doi:10.1016/j.ejmech.2020.113111 Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug‑likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017; 7:42717. doi:10.1038/srep42717 Banerjee P, Kemmler E, Dunkel M, Preissner R. ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024; 52(W1):W513–W520. doi:10.1093/nar/gkae303 Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017; 7:42717. doi:10.1038/srep42717 Daina A, Zoete V. A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem. 2016; 11(11):1117-21. doi:10.1002/cmdc.201600182 Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257–W263. doi:10.1093/nar/gky318 Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, et al. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics. 2019; 35(6):1067–1069. doi:10.1093/bioinformatics/bty707 Williams LK, Li C, Withers SG, Brayer GD. Order and disorder: differential structural impacts of myricetin and ethyl caffeate on human amylase, an antidiabetic target. J Med Chem. 2012; 55(20):10177–86. Brayer GD, Luo Y, Withers SG. High-resolution crystal structures of human pancreatic α‑amylase: implications for enzyme mechanism and drug design. Structure. 2012;20(3):454–462 Shen Y, Qin XH, Ren LM, Wang LL, Bai F, Bai G, et al. Structural insight into substrate specificity of human intestinal maltase-glucoamylase. Protein Cell. 2011;2(8):645–654 Sidra Rafi Q‑A, Khairullah, Saeedullah, et al. Benzophenone semicarbazones as potential α‑glucosidase inhibitors: in vitro and in silico study. bioRxiv. 2023. Shi GQ, Dropinski JF, McKeever BM, Adams AD, MacNaul KL, Elbrecht A, et al. Design and synthesis of alpha‑aryloxyphenylacetic acid derivatives: a novel class of PPAR α/γ dual agonists. J Med Chem. 2005; 48(13):4457–4468. Nemetchek MD, Chrisman IM, Rayl ML, Voss AH, Hughes TS. A structural mechanism of nuclear receptor biased agonism. Proc Natl Acad Sci USA. 2022; 119(20):e2215333119. Parasuraman S. Prediction of activity spectra for substances. J Pharmacol Pharmacother. 2011; 2(1):52–53. Rafi Q‑A S, Khairullah, Saeedullah, et al. Computer-aided prediction of biological activity spectra: study of correlation between predicted and observed activities for coumarin-4-acetic acids. Indian J Pharm Sci. 2012; 74(4):340–346. Panda SP, Khuntia BK, Dash AK, Choudhury C. Synthesis, characterization, thermal, antimicrobial, and in silico studies of a Schiff base ligand: Potential bioactivity prediction using PASS. ACS Omega. 2023;8(5):4521–4532. doi:10.1021/acsomega.2c07625 Nickel J, Gohlke BO, Erehman J, Banerjee P, Rong WW, Goede A, et al. SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 2014;42(W1):W26–31. doi:10.1093/nar/gku477 Erehman J, Ahmed J, Baumbach J, Rücker C, Dunkel M, Preissner R. SuperPred 3.0: drug classification and target prediction—a machine learning approach. Nucleic Acids Res. 2022; 50(W1):W74–W81. doi:10.1093/nar/gkac402 Voigt JH, Bienfait B, Wang S, Nicklaus MC. Comparison of structural databases for target prediction: implications for SuperPred performance. J Chem Inf Comput Sci. 2001; 41(4):702–12. doi:10.1021/ci000392m Baroroh US, Muscifa ZS, Destiarani W, Rohmatullah FG, Yusuf M. Molecular interaction analysis and visualization of protein–ligand docking using BIOVIA Discovery Studio Visualizer. Indones J Comput Biol. 2022; 2(1):1–13. doi:10.24198/ijcb.v2i1.46322 Dassault Systèmes. BIOVIA Discovery Studio Modeling Environment. Release 21.1. San Diego: Dassault Systèmes; 2023. Baskaran SG, Sharp TP, Sharp KA. Computational graphics software for interactive docking and visualization of ligand–protein complementarity. J Chem Inf Model. 2021; 61(3):1427–35. doi:10.1021/acs.jcim.0c01367 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":12444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eN'-(benzylidene)-2-[[(1H-benzimidazol-2-yl)thio] acetyl] hydrazinecarboxamide\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/9aa733da7a3eef82b457c46f.png"},{"id":86640773,"identity":"18b11a9a-841b-4ec3-be9d-b9adf5723d8e","added_by":"auto","created_at":"2025-07-14 08:09:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21358,"visible":true,"origin":"","legend":"\u003cp\u003eAcarbose\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/796ec54a642cf2a2b5930b05.png"},{"id":86642425,"identity":"8cf2dade-f5ff-4085-ab69-b86b32833f59","added_by":"auto","created_at":"2025-07-14 08:25:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":355128,"visible":true,"origin":"","legend":"\u003cp\u003e3D \u003cem\u003ecrystal structures of selected target proteins used in the molecular docking study: a) α-Amylase (PDB ID: 4GQR), b) α-Glucosidase (PDB ID: 3TOP), and c) PPAR-γ receptor (PDB ID: 1QSE).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/3248a66f4d7e57b105c155cb.png"},{"id":86640771,"identity":"0fde2f93-71b4-4e1f-81ab-5ae2d2542a28","added_by":"auto","created_at":"2025-07-14 08:09:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107492,"visible":true,"origin":"","legend":"\u003cp\u003eIn the BOILED-EGG Model, the yellow zone represents the biophysical space of chemicals that are more likely to enter the brain, while the white zone represents the physicochemical region of compounds most likely to be absorbed within the digestive tract.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/13181f8c68db976a0d0f4b36.png"},{"id":86640752,"identity":"61e02f77-4e02-49cf-8249-63346812f629","added_by":"auto","created_at":"2025-07-14 08:09:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":196686,"visible":true,"origin":"","legend":"\u003cp\u003eRadar plots showing ADME properties (LIPO, SIZE, POLAR, INSOLU, INSATU, FLEX) of top five 2-mercaptobenzimidazole Schiff base derivatives (VSP1–VSP5).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/d36a871754d90700c88780fd.png"},{"id":86640758,"identity":"554f58b6-2dd2-4194-875d-0c11730dd4db","added_by":"auto","created_at":"2025-07-14 08:09:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":381962,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D Docking Interactions of VSP1–VSP5 and Standard with α-Amylase (PDB ID: 4GQR)\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/9bdb4e9d11235d32d8b23a91.jpg"},{"id":86641241,"identity":"c3cb791e-5e5b-4858-81ec-2ba52a400625","added_by":"auto","created_at":"2025-07-14 08:17:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":451647,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D Docking Interactions of VSP1–VSP5 and Standard with α-Glucosidase (PDB ID: 3TOP)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/579dee08b445a5bb2118b182.jpg"},{"id":86640751,"identity":"8f6d31d2-c625-480c-8fe0-ced225345240","added_by":"auto","created_at":"2025-07-14 08:09:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":481700,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D Docking Interactions of VSP1–VSP5 and Standard with PPAR-γ (PDB ID: 1QSE)\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/6b1a59d282b00e8097db9e6b.jpg"},{"id":86642955,"identity":"ae84bb63-2943-4cad-ba80-8567406f51e9","added_by":"auto","created_at":"2025-07-14 08:33:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3351255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/ef000e53-5d2d-4bae-bc98-3e47580359cd.pdf"},{"id":86642420,"identity":"aeb1f1be-2c52-4f48-ae0f-1fba64e86c55","added_by":"auto","created_at":"2025-07-14 08:25:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1744868,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6892367/v1/e85513ac8930fe7d17f334be.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational Assessment of Substituted 2-Mercaptobenzimidazole Schiff Bases Derivatives Targeting α-Amylase, α-Glucosidase, and PPAR-γ Receptor in Type 2 Diabetes Mellitus","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eInsulin resistance and inadequate insulin production cause chronic hyperglycemia, which is a hallmark of type 2 diabetes mellitus (T2DM), a common metabolic disease. Effective multi-target therapy approaches are necessary because it plays a major role in cardiovascular diseases, neuropathy, nephropathy, and other problems. Complex carbohydrates are hydrolyzed into glucose by two essential enzymes, α-amylase and α-glucosidase. Since their inhibition lessens postprandial blood glucose surges, they are important targets for the treatment of type 2 diabetes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although acarbose, a common α-glucosidase inhibitor, successfully postpones the breakdown of carbohydrates, it has gastrointestinal adverse effects [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Insulin sensitivity and lipid metabolism depend on the nuclear receptor known as peroxisome proliferator-activated receptor gamma (PPAR-γ), which is another target of interest. Although PPAR-γ agonists, like thiazolidinediones, enhance the action of insulin, they can also have negative side effects, such as weight gain and fluid retention. The creation of multi-target-directed ligands (MTDLs), which can alter several diabetes pathways, is a focus of current drug research initiatives [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSchiff bases of 2-mercaptobenzimidazole in particular have demonstrated encouraging biological actions, including the potential to treat diabetes. Their structural adaptability enables a variety of replacements that could improve binding particular to a target. Using molecular docking, fifteen substituted 2-mercaptobenzimidazole Schiff base derivatives were created and tested for their ability to inhibit α-amylase, α-glucosidase, and their interaction with PPAR-γ. Using AutoDock Tools version 1.5.7, docking simulations were run. Acarbose served as the standard reference material. Five of the most promising adaptations were chosen for comparison based on their docking scores. The objective is to find drugs with multi-target efficacy that may be used as leads for additional in vitro and in vivo testing in the treatment of type 2 diabetes [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Design strategy\u003c/h2\u003e\u003cp\u003eThe main scaffold, N'-(benzylidene)-2-[[(1H-benzimidazol-2-yl)thio]acetyl]hydrazinecarboxamide, was altered to produce a special set of fifteen 2-mercaptobenzimidazole-based Schiff base derivatives in order to increase potential antidiabetic efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).Structure\u0026ndash;activity connections and pharmacodynamic considerations led to the introduction of structural variants. For comparison, Acarbose, the typical medication, was utilized (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ChemDraw Professional 8.0 was used to create the SMILES notations for each chemical. For later computer studies, the structures were stored in MDL SDF format [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Computational Docking:\u003c/h2\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-alpha;\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLigand Preparation\u003c/b\u003e: ChemDraw and ChemSketch were used to design fifteen substituted 2-mercaptobenzimidazole Schiff base derivatives, and PyMOL was used to create their three-dimensional structures. Geometry was optimized using energy minimization. The optimized structures were converted from MOL to PDB format using Open Babel. In order to create PDBQT files, these PDB files were further processed in AutoDock Tools (version 1.5.7) by combining non-polar hydrogens, allocating rotatable bonds, and adding Gasteiger charges. The reference standard, acarbose, was prepared in a similar manner [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eReceptor Preparation\u003c/b\u003e: The SuperPred web server, which uses molecular target prediction, was used to identify three protein targets related with type 2 diabetes. PDB IDs for α-amylase, α-glucosidase, and PPAR-γ are 4GQR, 3TOP, and 1QSE respectively. The RCSB Protein Data Bank provided the protein structures. Kollman charges were applied, polar hydrogens were added, and all water molecules and heteroatoms were eliminated using AutoDock Tools. The PDBQT format was used to store the cleaned structures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSelection of Top Derivatives\u003c/b\u003e: We compared the docking scores of 15 Schiff base derivatives across three targets. The five compounds with the best binding energies and consistent interactions were chosen for comparative docking study.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInteraction Analysis\u003c/b\u003e: The top five compounds' protein-ligand complexes were visualized with BIOVIA Discovery Studio Visualizer to assess hydrogen bonding, hydrophobic contacts, and interactions with critical amino acid residues in the active site [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Drug likeness studies and ADME prediction\u003c/h2\u003e\u003cp\u003eCalculations of molecular properties and drug-likeness parameters were performed for each of the design compounds (VSP-1 to VSP-5). The drug-likeness study is important because it identifies compounds that fit the criteria as being similar to drugs. It is crucial to investigate ADME qualities because of the pharmacokinetic properties of pharmaceutical compounds, including their oral bioavailability, cell pen etration, metabolism, and elimination. SWISS ADME techniques were used to calculate Lipinski's rule of five, and other physicochemical parameters such molecular refractivity, GI absorption, water solubility, and the number of rotatable bonds were predicted. Several software tools were used in this study to ensure accurate projections and in-depth analysis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUsing SWISS ADME, which provided crucial details on the pharmacokinetic properties of the medications, predictions for Absorption, Distribution, Metabolism, and Excretion (ADME) were established. ProTox 3.0 was used to assess the toxicity profiles of the compounds and ensure their safety for further studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 BoiledEGG analysis\u003c/h2\u003e\u003cp\u003eThe selected drugs' gastrointestinal absorption and blood\u0026ndash;brain barrier penetration were evaluated using the BOILED-Egg model [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The study discovered that chemicals in the white region were linked to greater intestine absorption potential, whereas compounds in the yellow zone were expected to have greater permeability across the blood\u0026ndash;brain barrier. The SwissADME digital platform was used to conduct the study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Toxicity studies\u003c/h2\u003e\u003cp\u003eTo evaluate the safety profile of the synthesized chemicals, a toxicology investigation was conducted. Acute toxicity (LD₅₀), toxicity class, and organ-specific toxicities like hepatotoxicity, mutagenicity, and carcinogenicity were estimated using the in silico prediction platform ProTox-II (version 3.0) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Using machine learning and molecular similarity techniques, it facilitates effective early-stage screening of possible hazardous effects based on SMILES input [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Selection of the target protein\u003c/h2\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-alpha;\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eα-Amylase Inhibitory Activity\u003c/b\u003e: One important target for controlling postprandial hyperglycemia in type 2 diabetes is human pancreatic α-amylase. The chosen structure (PDB ID: 4GQR) was resolved by X-ray crystallography at a resolution of 1.20 \u0026Aring;, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. It provides comprehensive information on the active site and consists of a single chain (Chain A) with 496 amino acids. The logical design of new 2-mercaptobenzimidazole Schiff base derivatives is supported by this structural information. Developing strong α-amylase inhibitors requires precise prediction of binding modes and interactions, which is made possible by docking experiments employing 4GQR [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eα-Glucosidase Inhibition Assay\u003c/b\u003e: α The α-Glucosidase Inhibition Assay is a proven target for regulating postprandial blood glucose levels in type 2 diabetes and is an essential enzyme in carbohydrate metabolism. X-ray crystallography was used to resolve the chosen protein structure (PDB ID: 3TOP), which is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, at a resolution of 2.10 \u0026Aring;. Its active site is fully visible because to its single polypeptide chain (Chain A), which has 952 amino acids. The evaluation of binding interactions for newly discovered 2-mercaptobenzimidazole Schiff base derivatives as possible α-glucosidase inhibitors is made easier by using this structure in docking investigations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePPAR-γ inhibitory activity\u003c/b\u003e: PPAR-γ inhibitory activity: A key player in lipid metabolism and glucose homeostasis, peroxisome proliferator-activated receptor gamma (PPAR-γ) is a viable target for type 2 diabetes treatment. The single polypeptide chain (Chain A) with 275 amino acids makes up the crystal structure of PPAR-γ (PDB ID: 1QSE), which is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and was determined at a 2.30 \u0026Aring; resolution. Important information about the ligand-binding domain is provided by this high-resolution structure. Docking investigations with 1QSE enable precise evaluation of binding affinities and interactions of 2-mercaptobenzimidazole Schiff base derivatives as suspected PPAR-γ antagonists [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 In Silico Bioactivity Prediction\u003c/h2\u003e\u003cp\u003eThe probable biological activities of the developed Schiff base derivatives were assessed utilizing their SMILES notations and PASS (Prediction of Activity Spectra for Substances). The expected antidiabetic effects were thought to be likely to be exhibited by compounds with a probability of activity (Pa)\u0026thinsp;\u0026gt;\u0026thinsp;0.5. Promising candidates for additional pharmacological research could be identified early thanks to this technique [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e2D Chemical Structures of the Top Five Derivatives with Their Compound Codes\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"680\" height=\"573\"\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Target Prediction Using SuperPred\u003c/h2\u003e\u003cp\u003ePredicting the likely protein targets of the synthesized Schiff base derivatives was done using the SuperPred web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prediction.charite.de\u003c/span\u003e\u003cspan address=\"https://prediction.charite.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SuperPred uses known ligand-target interactions to connect chemicals to possible biological targets based on structural similarity and machine learning methods. To help identify pertinent pathways and receptor families, the SMILES of each molecule were uploaded, and the results were assessed using probability scores and target classes. This prediction method improves knowledge of potential modes of action and aids in the rationalization of docking targets [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Interactions between analogs and protein\u003c/h2\u003e\u003cp\u003eThe obtained protein structures were loaded into BIOVIA Discovery Studio Visualizer (v21.1.0.20298) to conduct interaction analysis. The produced 2-mercaptobenzimidazole Schiff base compounds were designated as ligands, and the target proteins served as receptors. 2D interaction diagrams were used to show hydrogen bonds, hydrophobic contacts, and other important molecular interactions. Furthermore, 3D interaction patterns of the ligands were compared to native and standard equivalents across all three selected protein targets. The purpose of this research was to elucidate the binding patterns and anticipate the derivatives' potential efficacy by identifying important ligand-protein interactions that are relevant to biological activity [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.1 Designed 2-Mercaptobenzimidazole Schiff base derivatives\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eChemDraw Professional 8.0 was used to design fifteen 2-mercaptobenzimidazole Schiff base derivatives and produce SMILES notations for them. All compounds were tested using molecular docking against three protein targets. Based on binding affinity scores and interaction consistency, five derivatives with the best docking results were chosen for a thorough comparison investigation. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the 2D chemical structures of the top five derivatives, as well as their compound codes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 ADME prediction\u003c/h2\u003e\u003cp\u003eThe SwissADME online tool was used to assess the synthetic 2-mercaptobenzimidazole Schiff base derivatives' ADME characteristics. In order to predict a compound's pharmacokinetic profile prior to in vivo research, this in silico method is essential in the early stages of drug development. Many drugs fail in clinical phases because of inadequate absorption, metabolism, or bioavailability, even while they show promising in vitro activity. Important factors like gastrointestinal (GI) absorption, water solubility, Lipinski's Rule of Five, and other physicochemical characteristics were examined in order to evaluate drug-likeness.\u003c/p\u003e\u003cp\u003eBy looking at molecular weight (MW\u0026thinsp;\u0026lt;\u0026thinsp;500 g/mol), lipophilicity (Log P\u0026thinsp;\u0026le;\u0026thinsp;5), hydrogen bond donors (HBD\u0026thinsp;\u0026le;\u0026thinsp;5), and hydrogen bond acceptors (HBA\u0026thinsp;\u0026le;\u0026thinsp;10), Lipinski's Rule provides a standard for assessing oral bioavailability. The majority of substances demonstrated good compliance with these requirements when all derivatives were evaluated against them, suggesting a high potential for oral delivery. Additionally, favorable membrane permeability and bioavailability were corroborated by the computed topological polar surface area (TPSA), HBA, and HBD values. These findings imply that the chosen Schiff base derivatives have appropriate pharmacokinetic characteristics for additional drug candidate optimization.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDrug-likeliness properties of the designed compounds\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLigand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMolecular Formula\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMW (g/mol)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLog P\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHBA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHBD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLipinski Rule Met\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTPSA (\u0026Aring;\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMR (cm\u0026sup3;/mol)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003enRotB\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;500\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC₁₇H₁₅N₅OS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e337.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e119.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e97.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC₁₉H₁₇N₃O₂S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e351.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e110.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e102.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC₂₀H₁₉N₃O₂S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e365.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e99.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e107.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC₁₇H₁₄ClN₅OS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e371.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e119.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e102.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC₁₇H₁₅N₆O₃S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e383.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e159.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e102.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMW molecular weight, nHBA no. of hydrogen bond acceptor, nHBD no. of hydrogen bond donor, TPSA topological polar surface area, nViola tions No. of the rule of five violations\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eADMET features of selected ligand\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLigand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eIn-silico ADMET\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbsorption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDistribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u003cp\u003eMetabolism\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eToxicity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater Solubility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBBB Permeability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1A2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2C19\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2C9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2D6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3A4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePredicted LD₅₀\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;2.51 (Moderately soluble)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e500 mg/kg (Class 4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;2.14 (Moderately soluble)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1000 mg/kg (Class 4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;3.02 (Low soluble)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e258 mg/kg (Class 3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;3.40 (Low soluble)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e910 mg/kg (Class 4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;4.20 (Very low soluble)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e500 mg/kg (Class 4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 BoiledEGG analysis\u003c/h2\u003e\u003cp\u003eThe pharmacokinetic behavior of the five best-performing 2-mercaptobenzimidazole Schiff base derivatives was assessed using the BOILED-Egg (Brain Or Intestinal Estimated Permeation technique) model. In order to forecast passive gastrointestinal absorption and blood-brain barrier penetration, this graphical method displays WLOGP (lipophilicity) on the Y-axis versus TPSA (topological polar surface area) on the X-axis.\u003c/p\u003e\u003cp\u003eThe white ellipse zone contains all five molecules, suggesting a high likelihood of efficient gastrointestinal absorption. Nevertheless, none of the compounds fall into the yellow zone, indicating that these derivatives do not have a high probability of crossing the blood-brain barrier, which is a desired characteristic for preventing adverse effects on the central nervous system in peripheral drug targets. Additionally, the absence of P-glycoprotein substrate affinity (PGP\u0026minus;), seen by the red circular boundaries surrounding all molecules, suggests improved cellular absorption and decreased efflux. The systemic availability and oral acceptability of the chosen analogs are supported by these ADME-related characteristics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Toxicity studies\u003c/h2\u003e\u003cp\u003eThe web-based program ProTox-II (version 3.0) was used to evaluate the toxicity profiles of the developed 2-mercaptobenzimidazole Schiff base derivatives. Table No. 3 provides a summary of the expected LD₅₀ values (in mg/kg) and associated toxicity classes. Under Class 4, compounds D1, D2, D4, and D5 showed comparatively reduced toxicity; their LD₅₀ values ranged from 500 to 1000 mg/kg, suggesting that they are only dangerous at greater dosages. D3 on the other hand, which was categorized under Class 3, showed greater toxicity. A greater risk of acute toxicity was suggested by its lower LD₅₀ value of 258 mg/kg. In order to choose drug-like candidates for preclinical assessments, these projections offer early safety insights.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Docking study:\u003c/h2\u003e\u003cp\u003eMolecular docking was used to analyze the interaction patterns of fifteen 2-mercaptobenzimidazole Schiff base derivatives against three diabetes-related targets: α-glucosidase (3TOP), α-amylase (4GQR), and PPAR-γ (1QSE). AutoDock Vina was used to dock refined protein structures and energy-minimized ligands, and PyMOL and Discovery Studio were used to display interactions. To find the most promising antidiabetic possibilities, the top five derivatives were selected for in-depth comparative study based on binding affinities and recurring interaction patterns.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDocking Score of Various Designed 2-Mercaptobenzimidazole Schiff base Compounds Concerning the Receptors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSr. No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCompound Code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eDOCKING SCORES\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eα-Amylase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eα-Glucosidase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePPAR-γ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-9.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3OH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3OCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2Cl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3OH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2Br\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3Br\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4Br\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2NO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4NO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2OH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4OH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2OCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4OCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8,1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVSP15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2Cl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcarbose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003e\u003cu\u003eI] ANTIDIABETIC (\u0026alpha;-AMYLASE) DOCKING INTERACTION\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u003cstrong\u003e\u003cu\u003eII] ANTIDIABETIC (\u0026alpha;-GLUCOSIDASE) DOCKING INTERACTION\u003c/u\u003e\u003c/strong\u003e\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u003cstrong\u003e\u003cu\u003e\u003cstrong\u003e\u003cu\u003eIII]\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eANTIDIABETIC (\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003ePPAR-\u0026gamma;\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003e) DOCKING INTERACTION\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/u\u003e\u003c/strong\u003e\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6 In Silico Bioactivity Prediction\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredicted Biological Activities of Top Five 2-Mercaptobenzimidazole Schiff Base Derivatives Using PASS Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiological Activity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUtilizing the Prediction of Activity Spectra for Substances (PASS) online application, the anticipated bioactivity profiles of the five optimized 2-mercaptobenzimidazole Schiff base derivatives (VSP1\u0026ndash;VSP5) were evaluated. For a particular biological function, this in silico method calculates the likelihood that a molecule is pharmacologically active (Pa) or inert (Pi). Standard criteria state that compounds are expected to exhibit the relevant activity under biological conditions if their Pa value is greater than 0.7.\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, all five analogs had low Pi values and Pa values more than 0.73, indicating a strong probability of antidiabetic potential. Interestingly, VSP1 had the best prediction with a Pa of 0.775, suggesting that it could be a top contender for additional research. The logical priority of these derivatives for experimental validation and structure\u0026ndash;activity relationship (SAR) studies to verify their therapeutic significance is supported by these computational results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Target Prediction Using SuperPred\u003c/h2\u003e\u003cp\u003eFor the top five 2-mercaptobenzimidazole Schiff base derivatives, the chemical targets were predicted using the SuperPred web server. This tool correlates compound structures with known biological targets by combining cheminformatics techniques with machine learning methods. The anticipated targets for the compounds' antidiabetic activity are listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e and include α-amylase (PDB ID: 4GQR), α-glucosidase (PDB ID: 3TOP), and peroxisome proliferator-activated receptor gamma (PPAR-γ, PDB ID: 1QSE). These predictions, which are quite congruent with the therapeutic goals of type 2 diabetes care, encourage the selection of these proteins for molecular docking and interaction studies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTarget and PDB ID of the compounds were predicted through the SuperPred web server.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSr. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBiological Activity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTarget\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePBD Id\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Amylase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4GQR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Glucosidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3TOP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntidiabetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePPAR-γ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1QSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Visualization of the selected designed compounds\u003c/h2\u003e\u003cp\u003eThe top five 2-mercaptobenzimidazole Schiff base derivatives were examined using BIOVIA Discovery Studio Visualizer to examine their 2D and 3D interactions with α-amylase, α-glucosidase, and PPAR-γ. Strong binding affinities were supported by the identification of important interactions, including hydrophobic contacts and hydrogen bonding. The complexes can be seen in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe current computational assessment of 2-mercaptobenzimidazole Schiff base derivatives targeting α-amylase, α-glucosidase, and PPAR-γ provides promising insight into their potential as antidiabetic agents for managing type 2 diabetes mellitus (T2DM). These multifunctional targets were rationally selected due to their significant roles in modulating postprandial glucose levels and improving insulin sensitivity. The in silico strategies employed here including ADME prediction, molecular docking, toxicity analysis, and bioactivity profiling collectively supported the efficacy, safety, and drug-likeness of the designed ligands.\u003c/p\u003e\u003cp\u003eAmong the fifteen designed compounds, VSP1 to VSP5 were shortlisted based on superior docking scores across all three diabetic targets, coupled with favorable physicochemical and pharmacokinetic parameters. Docking interactions revealed that VSP1 exhibited the most potent binding affinities toward α-amylase (\u0026minus;\u0026thinsp;8.1 kcal/mol), α-glucosidase (\u0026minus;\u0026thinsp;9.1 kcal/mol), and PPAR-γ (\u0026minus;\u0026thinsp;9.7 kcal/mol), in comparison to other Schiff bases, however it was marginally less than that of the common medication acarbose. Notably, VSP2 and VSP5 also demonstrated strong interactions, particularly with α-glucosidase (\u0026minus;\u0026thinsp;10.0 kcal/mol), suggesting effective inhibition of carbohydrate metabolism.\u003c/p\u003e\u003cp\u003eThe ADME predictions using SwissADME indicated all top five compounds comply with Lipinski\u0026rsquo;s Rule of Five, demonstrating acceptable oral bioavailability. The TPSA values (ranging from ~\u0026thinsp;99 to 160 \u0026Aring;\u0026sup2;) were within optimal ranges, indicating potential for good membrane permeability. VSP5, though having the highest TPSA and lowest LogP, still maintained drug-likeness with good solubility, showing promise for systemic circulation without central nervous system (CNS) penetration, as further supported by the BOILED-EGG analysis.\u003c/p\u003e\u003cp\u003eAdditionally, four of the top five compounds were classified as belonging to toxicity class 4, which denotes modest acute toxicity (LD₅₀ range from 500\u0026ndash;1000 mg/kg), according to toxicity evaluation using ProTox-II. However, VSP3 was identified as relatively more toxic (Class 3, LD₅₀ = 258 mg/kg), which warrants caution in its further development. Despite this, the predicted toxicity values were generally acceptable for early drug candidates.\u003c/p\u003e\u003cp\u003eInteraction profiling using BIOVIA Discovery Studio unveiled that the selected ligands established stable hydrogen bonds and diverse hydrophobic contacts with critical active site residues. For α-amylase, GLN63 and TYR62 were recurrent interaction points contributing to stable complex formation. In the case of α-glucosidase, π\u0026ndash;π stacking and electrostatic interactions with key residues such as TRP1369, PHE1559, and ASP1526 played a dominant role in ligand stabilization. Within the PPAR-γ binding site, hydrogen bonding with residues including TYR7, ARG97, and TRP147, accompanied by π\u0026ndash;alkyl and π\u0026ndash;π interactions, confirmed the ligands' strong binding affinity and structural compatibility with the receptor pocket. As shown in Table No. 6, 7, and 8, these interaction patterns suggest that the designed 2-mercaptobenzimidazole Schiff base derivatives exhibit favorable multi-target binding characteristics, indicating their potential as antidiabetic agents.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of Ligand\u0026ndash;α-Amylase (PDB ID: 4GQR) Interactions at the Active Site\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompound code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConventional hydrogen bond\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHydrophobic bond\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGLN63 (\u0026times;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTYR62, ILE51 (\u0026times;2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTHR163 (\u0026times;3), GLN63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTYR62 (\u0026times;2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRP58, TYR62, ILE235, LEU165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTHR163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRP59, LEU165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASN105 (\u0026times;2), ALA106, VAL107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRP58 (\u0026times;2), TYR62, LEU165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcarbose\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRP59, TYR151, ILE235, GLN63, ASP197, GLU233 (\u0026times;2), GLU233:O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRP59 (π-Sigma)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of Ligand\u0026ndash; α-Glucosidase (PDB ID: 5NN5) Interactions at the Active Site\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompound code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConventional hydrogen bond\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHydrophobic bond\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASP1526, LYS1460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMET1421 (π-Sigma), TRP1369 (π\u0026ndash;π Stacked), PHE1559 (π\u0026ndash;π T-shaped \u0026times;2), LYS1460 (π-Alkyl)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRP1369 (π\u0026ndash;π Stacked \u0026times;4), TYR1251 (π\u0026ndash;π T-shaped)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLYS1460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTRP1369 (π\u0026ndash;π Stacked \u0026times;2, π-Alkyl), TYR1251 (π\u0026ndash;π T-shaped \u0026times;2), PHE1559 (π\u0026ndash;π T-shaped \u0026times;2), LYS1460 (Alkyl)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASP1157, ASP1526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARG1510 (π\u0026ndash;Sigma), ASP1420, MET1421 (π\u0026ndash;π Stacked), TRP1355, TRP1369 (π\u0026ndash;π T-shaped, π-Alkyl), PHE1559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASP1526 (also Electrostatic), TRP1369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLYS1460 (π\u0026ndash;Cation), MET1421 (π\u0026ndash;Sigma), TRP1355, PHE1560 (π\u0026ndash;Sulfur), TRP1369 (π\u0026ndash;π Stacked \u0026times;2), PHE1559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcarbose\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSER1292, GLN1372, ARG1377 (multiple H-bonds), LEU1367, ASP1357, TRP1369 (carbon H-bond), ASP1281 (carbon H-bond)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of Ligand\u0026ndash; PPAR-γ (PDB ID: 1QSE) Interactions at the Active Site\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompound code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConventional hydrogen bond\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHydrophobic bond\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTRP147, LEU98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTHR143, VAL152 (Pi-Sigma); LEU81, LEU156 (Pi-Alkyl)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTYR7, HIS70, TYR99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTYR7 (Pi-Pi T-shaped); VAL152, PRO103, LYS66 (Pi-Alkyl)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eARG97 (4 interactions)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTYR99 (Pi-Pi Stacked); TYR159 (Pi-Pi T-shaped); TRP147 (Pi-Alkyl)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSER31, GLN30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTYR159 (Pi-Pi T-shaped); PRO103, GLU105 (Amide-Pi Stacked); PRO103, LEU156 (Pi-Alkyl)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVSP5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTYR7, LYS66, TYR159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTYR7 (Pi-Pi T-shaped); LYS66 (Alkyl); HIS70, TYR159, VAL152, PRO103, LYS66 (Pi-Alkyl)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcarbose\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHIS70, ARG97 (2 H-bonds), HIS114, GLN30, THR98, SER100, ASP77 (2 H-bonds), THR73 (carbon H-bond), LYS146 (carbon H-bond), ASP99 (carbon H-bond)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition, PASS bioactivity predictions yielded Pa values greater than 0.73 for all selected derivatives, with VSP1 scoring 0.775, suggesting a high likelihood of antidiabetic activity under physiological conditions. This outcome is consistent with molecular docking and target prediction results from SuperPred, which identified all three biological targets (α-amylase, α-glucosidase, and PPAR-γ) as relevant interaction partners for these ligands.\u003c/p\u003e\u003cp\u003eOverall, the study confirms that 2-mercaptobenzimidazole Schiff bases, particularly VSP1, VSP2, and VSP5, hold significant promise as multifunctional antidiabetic agents with desirable pharmacokinetic profiles, acceptable safety margins, and potent inhibitory interactions across critical enzymatic and nuclear receptor targets. These in silico findings justify further in vitro, in vivo, and SAR (structure\u0026ndash;activity relationship) studies to validate their therapeutic potential and optimize structural frameworks for enhanced efficacy.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study presents the rational design and comprehensive in silico evaluation of fifteen novel 2-mercaptobenzimidazole Schiff base derivatives targeting three pivotal proteins implicated in type 2 diabetes mellitus: α-amylase, α-glucosidase, and the PPAR-γ receptor. Molecular docking analyses revealed that five compounds\u0026mdash;VSP1, VSP2, VSP3, VSP4, and VSP5\u0026mdash;exhibited superior binding affinities, characterized by stable conventional hydrogen bonds and diverse hydrophobic interactions with critical active site residues, including GLN63 and TYR62 in α-amylase, and TRP1369 and PHE1559 in PPAR-γ. Among these, VSP1 emerged as the lead candidate, demonstrating the highest docking scores coupled with exemplary drug-likeness parameters, including optimal molecular weight, favorable lipophilicity (Log P), and adherence to Lipinski\u0026rsquo;s Rule of Five, indicative of excellent oral bioavailability. Complementary in silico ADMET profiling corroborated VSP1\u0026rsquo;s superior pharmacokinetic properties and minimal toxicity risks, underscoring its potential as a safe and efficacious therapeutic agent. VSP2 and VSP3 also exhibited robust binding and promising ADMET profiles, while VSP4 and VSP5 showed moderate yet significant interactions and acceptable safety parameters. Collectively, these findings establish 2-mercaptobenzimidazole Schiff bases, particularly VSP1, as potent multifunctional inhibitors capable of modulating key enzymatic and nuclear receptor targets involved in glucose metabolism. By providing a solid basis for further experimental confirmations, this work advances the search for innovative, effective antidiabetic medications with better clinical prospects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;For this docking article, no specific grant from any public, private, or nonprofit financing entity was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable. The study did not involve human participants or animal experimentation requiring ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eV. S. Patil conceptualized the study, designed the research protocol, performed molecular docking, ADME analysis, and bioactivity prediction. She was responsible for data collection, compound selection, visualization and result interpretation. All authors contributed to data analysis, manuscript drafting, and approved the final version, taking full responsibility for its content and integrity. S. P. Kokane supervised the research, provided continuous guidance throughout the study, and critically reviewed the manuscript for intellectual and scientific accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author gratefully acknowledges the Department of Pharmaceutical Chemistry, Appasaheb Birnale College of Pharmacy, Sangli, for providing necessary laboratory facilities and technical support throughout the research work. Also, special thanks to Sushant P. Kokane for their valuable guidance and encouragement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThabet HK, Al‑Sanea MM, Ahmed MM, Ali NF, Shoman ME. Discovery of novel 6-(piperidin-1-ylsulfonyl)-2H-chromenes targeting \u0026alpha;-glucosidase, \u0026alpha;-amylase, and PPAR-\u0026gamma;: Design, synthesis, virtual screening, and antidiabetic activity. J Mol Struct. 2024; 130154. https://doi.org/10.1016/j.molstruc.2024.130154\u003c/li\u003e\n\u003cli\u003eDom\u0026iacute;nguez-Mendoza Y, Salgado-Zamora H, Mendieta-Wejebe JE, Trujillo-Ferrara JG, Correa-Basurto J, Rosales-Hern\u0026aacute;ndez MC. Design of a multi-target focused library for antidiabetic targets using a comprehensive set of chemical transformation rules. J Comput Aided Mol Des. 2021; 35(1):47\u0026ndash;68. https://doi.org/10.1007/s10822-020-00344-3\u003c/li\u003e\n\u003cli\u003eSanna M, Gadau SD, Boi C, Pinna GA, Dallocchio R, et al. 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ACS Omega. 2022;7(32):28041\u0026ndash;28051. doi:10.1021/acsomega.2c01969 \u003c/li\u003e\n\u003cli\u003eSattar S, Ullah H, Taha M, Khan F, Rahim F, Uddin I, et al. Synthesis, in vitro \u0026alpha;‑amylase activity, and molecular docking study of new benzimidazole derivatives. Russ J Org Chem. 2021;57:968\u0026ndash;975.\u003c/li\u003e\n\u003cli\u003eUbeid MT, Thabet HK, Abu Shuheil MY. Synthesis of 4-[(1H-benzimidazol-2-yl)sulfanyl]benzaldehyde and 2-({4-[(1H-benzimidazol-2-yl)sulfanyl]phenyl}methylidene)hydrazine-1-carbothioamide. Molbank. 2021; 2021(3):M1273. doi:10.3390/M1273.\u003c/li\u003e\n\u003cli\u003eAlam MM, Naeem A, Siddiqui MH, Alreshidi MM, Alghanem SB. Design, synthesis, molecular docking, and in silico ADMET analysis of novel benzimidazole derivatives as dual inhibitors of \u0026alpha;-amylase and \u0026alpha;-glucosidase. J Mol Struct. 2023;128679. doi:10.1016/j.molstruc.2023.128679\u003c/li\u003e\n\u003cli\u003eThafeni MA, Mosa RA, Musyoka TM, Tastan Bishop \u0026Ouml;. Identification of potential novel inhibitors for alpha-glucosidase using computational approaches. J Biomol Struct Dyn. 2022;40(1):292\u0026ndash;304. doi:10.1080/07391102.2020.1805462\u003c/li\u003e\n\u003cli\u003eQamar MT, Mumtaz A, Ashfaq UA, Fatima I, Shahid F, Bilal M, et al. In silico screening of potential compounds from natural sources targeting PPAR-\u0026gamma; for the management of diabetes mellitus. Comput Biol Med. 2021; 133:104362. doi:10.1016/j.compbiomed.2021.104362\u003c/li\u003e\n\u003cli\u003eKhan T, Abbasi MA, Shah SA, Khan A, Ali S, Park TJ, et al. Benzimidazole derivatives as potential antidiabetic agents: synthesis, biological evaluation, and molecular docking studies. Eur J Med Chem. 2021; 211:113111. doi:10.1016/j.ejmech.2020.113111\u003c/li\u003e\n\u003cli\u003eDaina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug‑likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017; 7:42717. doi:10.1038/srep42717 \u003c/li\u003e\n\u003cli\u003eBanerjee P, Kemmler E, Dunkel M, Preissner R. ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024; 52(W1):W513\u0026ndash;W520. doi:10.1093/nar/gkae303\u003c/li\u003e\n\u003cli\u003eDaina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017; 7:42717. doi:10.1038/srep42717\u003c/li\u003e\n\u003cli\u003eDaina A, Zoete V. A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem. 2016; 11(11):1117-21. doi:10.1002/cmdc.201600182\u003c/li\u003e\n\u003cli\u003eBanerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257\u0026ndash;W263. doi:10.1093/nar/gky318\u003c/li\u003e\n\u003cli\u003eYang H, Lou C, Sun L, Li J, Cai Y, Wang Z, et al. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics. 2019; 35(6):1067\u0026ndash;1069. doi:10.1093/bioinformatics/bty707\u003c/li\u003e\n\u003cli\u003eWilliams LK, Li C, Withers SG, Brayer GD. Order and disorder: differential structural impacts of myricetin and ethyl caffeate on human amylase, an antidiabetic target. J Med Chem. 2012; 55(20):10177\u0026ndash;86. \u003c/li\u003e\n\u003cli\u003eBrayer GD, Luo Y, Withers SG. High-resolution crystal structures of human pancreatic \u0026alpha;‑amylase: implications for enzyme mechanism and drug design. Structure. 2012;20(3):454\u0026ndash;462\u003c/li\u003e\n\u003cli\u003eShen Y, Qin XH, Ren LM, Wang LL, Bai F, Bai G, et al. Structural insight into substrate specificity of human intestinal maltase-glucoamylase. Protein Cell. 2011;2(8):645\u0026ndash;654\u003c/li\u003e\n\u003cli\u003eSidra Rafi Q‑A, Khairullah, Saeedullah, et al. Benzophenone semicarbazones as potential \u0026alpha;‑glucosidase inhibitors: in vitro and in silico study. bioRxiv. 2023. \u003c/li\u003e\n\u003cli\u003eShi GQ, Dropinski JF, McKeever BM, Adams AD, MacNaul KL, Elbrecht A, et al. Design and synthesis of alpha‑aryloxyphenylacetic acid derivatives: a novel class of PPAR \u0026alpha;/\u0026gamma; dual agonists. J Med Chem. 2005; 48(13):4457\u0026ndash;4468. \u003c/li\u003e\n\u003cli\u003eNemetchek MD, Chrisman IM, Rayl ML, Voss AH, Hughes TS. A structural mechanism of nuclear receptor biased agonism. Proc Natl Acad Sci USA. 2022; 119(20):e2215333119. \u003c/li\u003e\n\u003cli\u003eParasuraman S. Prediction of activity spectra for substances. J Pharmacol Pharmacother. 2011; 2(1):52\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eRafi Q‑A S, Khairullah, Saeedullah, et al. 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Nucleic Acids Res. 2022; 50(W1):W74\u0026ndash;W81. doi:10.1093/nar/gkac402\u003c/li\u003e\n\u003cli\u003eVoigt JH, Bienfait B, Wang S, Nicklaus MC. Comparison of structural databases for target prediction: implications for SuperPred performance. J Chem Inf Comput Sci. 2001; 41(4):702\u0026ndash;12. doi:10.1021/ci000392m\u003c/li\u003e\n\u003cli\u003eBaroroh US, Muscifa ZS, Destiarani W, Rohmatullah FG, Yusuf M. Molecular interaction analysis and visualization of protein\u0026ndash;ligand docking using BIOVIA Discovery Studio Visualizer. Indones J Comput Biol. 2022; 2(1):1\u0026ndash;13. doi:10.24198/ijcb.v2i1.46322\u003c/li\u003e\n\u003cli\u003eDassault Syst\u0026egrave;mes. BIOVIA Discovery Studio Modeling Environment. Release 21.1. San Diego: Dassault Syst\u0026egrave;mes; 2023.\u003c/li\u003e\n\u003cli\u003eBaskaran SG, Sharp TP, Sharp KA. Computational graphics software for interactive docking and visualization of ligand\u0026ndash;protein complementarity. J Chem Inf Model. 2021; 61(3):1427\u0026ndash;35. doi:10.1021/acs.jcim.0c01367\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"2-Mercaptobenzimidazole, Schiff Bases, Molecular Docking, α-Amylase Inhibition, α-Glucosidase Inhibition, PPAR-γ Inhibition","lastPublishedDoi":"10.21203/rs.3.rs-6892367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6892367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a progressive metabolic disorder marked by elevated blood glucose levels due to insulin resistance and impaired insulin secretion. Current therapeutic strategies often focus on inhibiting carbohydrate-hydrolyzing enzymes such as α-amylase and α-glucosidase to reduce postprandial hyperglycemia. Additionally, increasing insulin sensitivity is mostly dependent on PPAR-γ receptor activity. In this study, a computational docking approach was employed to assess the antidiabetic potential of fifteen substituted 2-mercaptobenzimidazole Schiff base derivatives targeting α-amylase, α-glucosidase, and PPAR-γ receptor. Molecular docking was conducted using AutoDock Tools version 1.5.7 to evaluate binding affinity and interaction profiles. As the standard reference medication, acarbose was employed. Among the designed compounds, five derivatives showing the highest binding affinity were selected for detailed comparative analysis. The docking results revealed that several compounds exhibited stronger binding energies and stable interactions within the active sites of the target proteins compared to Acarbose. Key interactions included hydrogen bonds and hydrophobic contacts with catalytically important amino acids. These findings suggest that substituted 2-mercaptobenzimidazole Schiff bases hold promise as multi-target antidiabetic agents. The study provides valuable insight for further in vitro validation and potential lead optimization in the development of novel antidiabetic therapies.\u003c/p\u003e","manuscriptTitle":"Computational Assessment of Substituted 2-Mercaptobenzimidazole Schiff Bases Derivatives Targeting α-Amylase, α-Glucosidase, and PPAR-γ Receptor in Type 2 Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 08:09:33","doi":"10.21203/rs.3.rs-6892367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-01T17:28:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-26T04:45:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T16:43:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-19T04:52:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196780924123935088876147444003205689772","date":"2025-07-14T18:50:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104349924824887694140099855843628055418","date":"2025-07-14T14:01:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162194515088719228180274702945463475833","date":"2025-07-14T01:55:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3873780614573169071442268312245805912","date":"2025-07-13T08:52:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-12T16:15:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4301211551959424438449250047828899954","date":"2025-07-09T21:35:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271415219925889988987446496991521169673","date":"2025-07-09T21:33:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T21:31:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-04T08:44:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-19T11:49:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T11:49:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Chemistry","date":"2025-06-14T06:54:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b1d37f4-3580-4233-96e2-7135848b6ed6","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T08:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 08:09:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6892367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6892367","identity":"rs-6892367","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}