Organic Molecular Strategy to Prevent Diabetes: An In Silico Screening of Berberine and Biguanides as Visfatin Antagonists

Organic Molecular Strategy to Prevent Diabetes: An In Silico Screening of Berberine and Biguanides as Visfatin Antagonists | 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 Organic Molecular Strategy to Prevent Diabetes: An In Silico Screening of Berberine and Biguanides as Visfatin Antagonists Hersh Ibrahim Rashid, Rebin Omer Ahmed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8809541/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diabetes mellitus remains a critical health problem for the world population. Visfatin is a potential target for the effective treatment of type 2 diabetes mellitus (T2DM). Several biguanides, such as Berberine (a natural isoquinoline alkaloid) and Metformin, Phenformin, and Buformin (biguanide-derived antidiabetic drugs), present well-recognized antidiabetic effects. Thus, in this study, using molecular docking, density functional theory (DFT), ADMET, and drug-likeness approaches, the visfatin-inhibiting activity of Berberine and Biguanides for T2DM treatment was investigated. Berberine and Biguanide compounds have favorable pharmacokinetic and physicochemical properties, suggesting that both can be considered safe, orally bioavailable candidates. As lipophilicity, solubility, and Csp3 refinement improve, drugs exhibit better drug-like properties and are more advanced in the drug discovery process. Berberine had a binding free energy of -8.5 kcal/mol. The complex formed 2 hydrogen bonds, including a classical H-bond to Gln92 and Lys189 at an unusually short distance of 2.0 Å, as well as eleven hydrophobic interactions, rendering the compound, compared to Biguanides, the most efficient of all solids bearing potential visfatin. Visfatin T2DM computational chemistry Molecular docking Density functional theory (DFT) ADMET Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Diabetes mellitus is a chronic and systemic metabolic disorder characterized by hyperglycemia, which leads to glycosuria as one of many symptoms, in which cells are unable to take up glucose for energy generation[ 1 ]. It is divided into insulin-dependent diabetes mellitus and non-insulin-dependent diabetes mellitus[ 1 , 2 ]. It has been established that the disease emerges as a consequence of a glucose metabolic pathway disorder, caused by inactivity or excessive activity of diabetic enzymes[ 3 ]. Visfatin (also named nicotinamide phosphoribosyl transferase) is an adipokine associated with T2DM and is primarily secreted by white adipose tissue[ 4 ]. Increased circulating visfatin is found in obese and insulin-resistant individuals. In many studies, elevated plasma levels of visfatin were observed in different groups of patients with obesity and/or other features of metabolic syndrome[ 5 ]. The results of the meta-analysis revealed that circulating visfatin levels were increased in obesity, metabolic syndrome, and type 2 diabetes mellitus (T2DM) and were positively correlated with insulin resistance [ 6 ]. This relationship indicates that visfatin secretion is stimulated by diabetes mellitus as part of a counterregulatory response, as visfatin enhances insulin release and insulin receptor phosphorylation in mouse pancreatic beta-cells[ 7 , 8 ]. Therefore, suppression of the visfatin enzyme is suggested as one of the plausible strategies for type 2 diabetes control [ 8 , 9 ]. Natural products, particularly Berberine, are a well-known reservoir for new medicines to treat various types of diseases[ 10 ], The in vivo study revealed that BBR treatment (5 mg/kg/day) markedly decreased the serum level of visfatin[ 11 ]. Biguanides, a class of antidiabetic agents primarily used to treat Type-2 diabetes mellitus, are gaining attention as promising drugs for clinical or medicinal therapy due to their numerous biological activities [ 12 ]. The use of computational methods is safe, faster, more efficient, and less expensive in the drug discovery and development process [ 13 ]. Molecular docking and Density functional theory can assist in screening, identification of phytochemicals as potential therapeutic agents against enzymes and prediction of their binding affinity[ 14 ], investigation of pharmacological properties and electric properties (pharmacologically active compounds), however, being putative drugs [ 15 ]. Computational methods can also help identify new target binding sites of a protein for inhibition. The areas of bioinformatics and cheminformatics include database generation and manipulation, which use statistical algorithms to handle and analyze biological or chemical data. Their instruments are used to discover and analyze drug targets, macromolecular biological structures, and active sites. They can also predict a drug lead molecule for a drug target and estimate its drug-likeness to docking and ADMET studies [ 16 ]. The binding affinities of drug candidates are ordered, and in appeal, their binding properties can be fine-tuned. Molecular drug discovery using computational approaches is called structure-based drug design (SBDD), and in silico techniques have broad potential for discovering new target proteins and predicting their biological activity. Due to the rapid growth in computational power, molecular dynamics is a well-established computational approach that has been used effectively to simulate protein-ligand complexes and monitor the action of drugs on biological macromolecules at the atomic level over time[ 17 ]. Computational methods applied to identify natural organic compounds, such as Berberine, vs synthetic drugs like Biguanides, and those designed to predict small inhibitors for 2G95. Molecular docking was performed between the identified novel drugs and the 2G95 target protein to understand their interactions with 2G95. This work is crucial for investigating potential drugs against 2G95, which can be used as a therapeutic strategy to treat T2DM and reduce the rising issues associated with high blood sugar, thereby improving long-term control. 2. Materials and Methods (Computational Portion) 2.1. Protein recovery and preparation (protein model preparation) The protein target (AChE; PDB ID: 2G95) was obtained from the Protein Data Bank (PDB) of the Research Collaboratory for Structural Bioinformatics (RCSB; https://www.rcsb.org/ ). PDB IDs were selected based on corresponding X-ray crystallographic structures, lower resolution (< 2.5 Å), and percentile scores in global validation metrics, which indicated better structural quality. The protein structures were processed using PyMOL version 2.5[ 18 ]. This step prepared the protein models by removing heteroatoms, such as water and other molecules. Hydrogen atoms were added to improve protein docking performance. The processed macromolecules were refined and validated, then energy-minimized in the Swiss-PDB Viewer. 2.2 Ligand Database Searching and Preparation (library preparation) Four bioactive ligands from plants and their synthesis, as reported (Metformin, Phenformin, Buformin, Berberine), were chosen. Structures of the molecules in 3D were retrieved from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) as.sdf files. Open Babel prepared ligand structures as a default built-in program of PyRx 2.3 Molecular Docking. Ligand-containing compounds were constructed and saved in mol file format via ChemDraw Ultra 12.0 [ 19 ].7 We downloaded the enzymes from the RCSB protein data bank, and the Visfatin enzyme was chosen for the docking study. We queried RCSB for "Visfatin" and selected PDB entry 2G95. The PDB alarm change file 2G95 was then downloaded, and the .pdb file was imported into Biovia Discovery Studio Visualizer for cleaning. The active site pocket was identified from the crystal structure, and water molecules were stripped for cleaning. The assembly consisted of two chains, A and B, which were selected for further investigation. Details of the active cavity are recorded and presented in Fig. 2 . The cleaned 2G95 chains were saved in PDB format, along with the ligand and enzyme. Docking studies were performed using the PyRx Virtual Screening tool with AutoDock Vina[ 20 , 21 ]. After importing the ligand files with Open Babel, they were prepared for energy minimization using the Universal Force Field (UFF).7 Ligands were minimized and converted into pdbqt format. The enzyme file was opened and loaded as a molecule before being converted to a macromolecule. And then we performed docking experiments on all the obtained files using the Vina Wizard program to identify the active cavity. Potential binding patterns of the enzyme for ligands were then explored. The designed ligands and the 2G95 enzyme were subjected to molecular docking. The PyRx Virtual Screening tool was opened in AutoDock Vina to perform the docking study. (Autodock 4) We imported the ligand les with the Open Babel option and used the Universal Force Field (UFF) for energy optimization[ 22 ]. Ligands were then converted to PDBQT format after minimization. The purified enzyme file was loaded and converted to macromolecular format using "Load Molecule" on the Menu bar. Once we acquired all the information, we performed the docking experiments with Vina Wizard, targeting the active binding site. Then we analyzed how those ligands might bind and what else they are compatible with. 2.4 Molecular Contact Map STRUCTURAL Interaction Fingerprinting (SIFt) analysis SIFt is a novel method for a complicated problem: the modeling and prediction of protein-ligand interactions in three dimensions. The binary digit interaction fingerprint (BDIF) model is constructed with the SIFt method, which reflects the three-dimensional binding pattern of a ligand-protein complex. Each molecular fingerprint encodes high-level information for the organization, analysis, and presentation of ligand-receptor complex data sets mined from the database.[ 23 ]. The interaction fingerprints of the selected phytochemicals were generated for four docking complexes using the SIFt panel in Schrödinger Suite 2020-3. The input files were the receptor grid and the ligands. After the fingerprint is formed, one can visualize in an Excel sheet which residues and interaction types (hydrophobic, H-bond donor, or acceptor) contribute most to the binding. Similar colors were assigned to the interaction types between residues, with 1 and 0 representing the presence and absence of an interaction [ 24 ]. For building the SIFt model, additional information was available from previously published work [ 25 , 26 ]. 2.5 Pharmacokinetic parameters The ADMET properties of the newly designed compounds were also calculated using SwissADME ( http://www.swissadme.ch ), which was used to assess their synthetic feasibility[ 27 ] —a computer-aided ADME study. The pkCSM ( http://biosig.unimelb.edu.au/pkcsm/ ) webserver was used to predict the pharmacokinetic properties of our in-house compounds [ 28 ]. These studies encompass analyses of features such as oral absorption, volume of distribution (VDss), CYP-mediated metabolism, excretion (total clearance), AMES toxicity prediction, and synthetic accessibility using the free online tool SwissADME ( http://www.swissadme.ch/index.php )[ 29 ]. Two servers, such as ProToxII [ 30 ] and StopTox [ 31 ], were used for toxicity assessment. ProTox-II server estimates different toxicity endpoints, such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, and adverse outcomes (Tox21) pathways, along with specific/unknown target toxicity using models based on fragment propensity scores derived from molecular similarity scores to the most frequent features and further refined by machine learning. The StopTox server utilizes a set of Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models to estimate the toxicity of compounds across various endpoints, including acute inhalation toxicity, oral toxicity, and eye irritation/corrosion/skin sensitization, following the curation and integration of publicly available datasets. Predicted macromolecular targets for the top-selected candidates were generated using the Swiss Target Prediction server [ 32 ]. The library of many known bioactive molecules available on the server is mapped to ∼ 3068 proteins using two-dimensional (2D) and three-dimensional (3D) similarity. The server Swiss Similarity was used to identify possible structural analogs for repurposing against the selected receptor [ 33 ]. 2.6 DFT studies (MESP/HOMO/LUMO analysis) The DFT calculations were performed under the same conditions as in a previous report [ 34 ], with slight modifications. The Gaussian 06 package (Rev. E.01) [ 35 ] with the default settings, and all structure SVP-basis set calculations were performed using the B3LYP functional. This theory allows for the efficient determination of the electronic structure of atoms and molecules. In this work, we shall compute the most relevant geometric parameters, the molecular electrostatic potential (MEP), the frontier molecular orbital (FMO), and global and local reactivity descriptors. The optimized geometries were also examined using GaussView 6. 2.7 Molecular Dynamics (MD) simulations We performed molecular dynamics simulations of the lowest-energy, best-posed docking complex derived from the docking data. To understand, predict, and estimate the parameters of a model research system, molecular dynamics (MD) simulations numerically probe the dense phases of the model system [ 36 ]. MD simulations identified the stability of the best-selected natural product molecule with the target. MD simulations (iMODS) were performed using the iMod server ( https://imods.iqfr.csic.es ). The iMod server provides easy access to improved standard-mode analysis (NMA) in internal coordinates. The online interface works across all browsers and most modern mobile devices. Flux simulations of transitioning between two conformations; users might then interactively explore the resulting structures, trajectories, and animations, and, for large macromolecules, 3D [ 37 ]. 3. Result and Discussion 3.1 Molecular Docking Analysis Molecular docking has become increasingly relevant for accelerating and reducing the cost of drug discovery. In our study, molecular docking was employed to identify the top-ranked visfatin inhibitors, with cut-off values set for different binding energies (Table 1 ). Metformin demonstrated potent inhibition of visfatin, with a binding energy of − 4.35 kcal/mol. The molecule's atom formed hydrogen-bond interactions with six amino acid residues at distances of 2.54–3.97 Å. Also, hydrophobic interactions between the nitrogen of the ligand and His191 of the visfatin enzyme. (Fig. 1a) Phenformin elicited considerable binding energy of − 6.23 kcal/mol at the binding pocket of the visfatin enzyme—this promising visfatin inhibitor formed hydrophobic interactions with TYR240 and VAL242. Similarly, (Fig. 1b). Furthermore, five hydrogen bonding interactions were observed between the Phenformin moiety and the visfatin enzyme at 1.97 to 3.48 Å. Buformin had a nearly identical binding energy to metformin, − 4.68 kcal/mol, and elicited interesting binding interactions with the visfatin enzyme. Only the nitrogen atom of the Buformin moiety participated in two hydrogen bonding interactions with PHE91 and SER241 at bond distances of 2.66Å and 2.61 Å. The ligand was not further stabilized in the visfatin enzyme's binding pocket by hydrophobic interactions (Fig. 1c). Berberine had a binding energy of − 7.85 kcal/mol after interacting with the amino acid residues at the binding pocket of the visfatin enzyme. The two-oxygen atom of the Berberine ligand formed two strong hydrogen bonding interactions with Lys189 at 2.04 Å and GLN92 at 1.97Å. Also, the Berberine moiety formed hydrophobic interactions with TYR188, TYR240, PRO273, PRO307, and ARG349 (Fig. 1d). The hydrophobic, hydrogen bonding, and pi-interactions observed between the ligands and the amino acid residues at the binding pocket of the visfatin enzyme contributed favorably to the high affinities and compression stability elicited by Metformin, Phenformin, Buformin, and Berberine. But, the visfatin-inhibitor of the Berberine is more responsible than biguanides, due to making a strong hydrogen interaction between the berberine ligand and the target protein. Figure 1 shows 2-D and 3-D visualization of the interaction of visfatin's amino acid residues with (a) Metformin, (b)Phenformin, (c) Buformin, and (d) Berberine 3.2 Molecular dynamics simulations Conformational dynamics of the protein−ligand complex were examined using molecular dynamics simulations and iMODS normal-mode analysis (NMA). The values of the deformability indicate that the well-deformed residues, polym, and pmbn are the eigenvalues of this complex: 1.31427e-04. Figure 2 shows the MD simulation results. 3.3 PK and oral bioavailability of the best drug candidates Computed ADME properties and evaluation of Bioactivity/toxicity profiles were calculated using pKCSM, SWISSADME Swiss Target Prediction Tool, StopTox, and ProtoxII tools. Four compounds were back-searched against visfatin as a target. PubChem SMILES, and all were identified as previously known compounds6. ADMET Profiles The ADMET properties of the potential visfatin inhibitors were evaluated based on their drug ability, including solubility, human intestinal absorption, brain–blood barrier permeability, and maximum. The maximum dose (100 mg/kg/day in humans) [ 38 ] and hepatotoxicity were considered in our study (Fig. 3 A, Table 2 ). Solubility profiling of the ligands revealed their compatibility was between − 6.0 and 0.5 at compromiseable levels for all ligands screened throughout the study period. Using Swiss Target Prediction analysis, the chemical–biological interaction profiles of (−)-epicatechin, (−)-catechin, and (+)-taxifolin were markedly distinct from those of caffeic acid. Selectivity assays: Phenformin displayed strong selectivity for Family-A G-protein–coupled receptors (73.3%). Table 2 pkCSM pharmacokinetic parameters of the selected ligand Pharmacokinetic properties Metformin Phenformin Buformin Berberine Water solubility -2.707 -2.671 -2.755 -2.872 Intestinal absorption (human) 59.401 68.08 57.741 99.024 Fraction unbound (human) 0.811 0.53 0.615 0.388 BBB permeability -0.946 -0.74 -1.446 0.419 CNS permeability -4.238 -2.969 -4.634 -1.647 Max. tolerated dose (human) 0.902 0.005 0.869 -0.012 Hepatotoxicity No No No No Metformin, Buformin, and Berberine were more generalist, with a more evenly distributed affinity profile across enzymes, membrane receptors, and transport proteins (Fig. 3 B). Introduction: StopTox and ProTox-II are in silico toxicology systems for the prediction of primary toxicological endpoints of small molecules based purely on their chemical structures and molecular properties. Through the employment of QSAR models and machine learning approaches, these platforms make it possible to predict hepatotoxicity (liver toxicity), cytotoxicity, as well as acute toxicity in drug discovery. ProTox-II analysis also supported the predicted non-inhibitory potential for hepatotoxicity or carcinogenicity for all four organic ligands (Metformin, Phenformin, Buformin, and Berberine), implying an organosafety profile (Table 3 ). The Tox21 nuclear receptor panel revealed that there is no apparent major endocrine receptor-ligand crosstalk between the androgen, estrogen, and PPARγ receptors for any of the compounds studied; thus, there is a low likelihood of endocrine-disruptive toxicity. Herein, all the compounds exhibit very low or no activity toward p53, ATAD5, long interspersed nuclear elements 2/antioxidant response element (Nrf2/ARE), and heat shock element (HSE), suggesting their major genomic and cellular predicted harmlessness (Table 3 ) (Fig. C). All the compounds had potential acute inhalation toxicity based on our StopTox predictions, and lower oral and dermal toxicity for Berberine was obtained compared to Metformin, Phenformin, and Buformin. In summary, berberine possessed the most suitable acute exposure profile among all the compounds tested herein, while the other three may need additional optimizations to improve their safety (Table 4 ). Table 4 stopTox toxicity parameters of the selected phytochemicals. Ligands Acute inhalation toxicity Acute oral toxicity Acute dermal toxicity Eye irritation and corrosion Skin sensitization Skin irritation and corrosion Metformin T (+) T (+) Toxic (+) NT (-) NS (-) Positive (+) Phenformin T (+) T (+) Toxic (+) NT (-) S (+) Negative (-) Buformin T (+) T (+) Toxic (+) T (+) NS (-) Positive (+) Berberine T (+) NT (-) NT (-) T (+) NS (-) Negative (-) T: toxic; NT: non-toxic; S: sensitizer; NS: non-sensitizer Table 3 ProtoxII toxicological parameters of the identified phytoconstituents of ligands Classification Target Metformin Phenformin Buformin Berberine Pre Pro Pre Pro Pre Pro Pre Pro Organ toxicity Hepatotoxicity I 0.74 I 0.87 I 0.85 I 0.82 Toxicity endpoints Carcinogenicity I 0.56 I 0.62 I 0.63 I 0.72 Tox21-Nuclear receptor signaling pathways AR I 1 I 0.99 I 1 I 0.98 AR-LBD I 0.99 I 0.99 I 0.99 I 0.99 Aromatase I 0.99 I 0.96 I 0.99 I 0.91 ER I 0.98 I 0.83 I 0.97 I 0.95 ER-LBD I 0.99 I 0.99 I 0.99 I 0.89 PPAR-Gamma I 0.99 I 0.99 I 1 I 0.98 nrf2/ARE I 0.99 I 0.96 I 0.94 I 0.89 Tox21 -Stress Response Pathways HSE I 0.99 I 0.96 I 0.94 I 0.89 p53 I 0.98 I 0.94 I 0.92 I 0.94 ATAD5 I 0.97 I 0.98 I 0.99 I 0.96 3.4 Structural interaction fingerprint (SIFt) In this work, the SIFt technique proves helpful in comparing docking poses of new ligands and for constructing target-dependent scoring functions. This procedure is a way to develop ligands with different structures based on interaction patterns rather than solely on their molecular structure. SIFt maps facilitated the interpretation of docking results for four compounds on 2G95. This approach effectively matched inhibitors to key hotspot active-site residues that are essential for the formation of ligand-protein complexes. This requires the development of a robust structure interaction fingerprint, such as SIFt, which allows us to understand how large ligand databases interact at the active site of a protein. To identify the most critical hot-spot residues contributing to ligand-protein complex formation, we generated an SIFt (supplemental Information 4SIFT) analysis for all virtual screening hits that bind in 2G95. The docked poses overlay and show that all four ligands bind to the common, active cavity of the 2G95 (visfatin/NAMPT) protein. Residues including His90, Gln92, Ser241, and Tyr246, positioned surrounding the binding pocket in proximity to Val242.DoesNotExist Yet these residues participate in ligand recognition: His90, Arg349, Thr304, Phe305, Gln92, Glu376, & Ser379. Several hydrogen bonds, especially with Ser241, Thr304, Glu376, and Ser379, indicate strong polar stabilization in the ligand–protein complexes. These residues reside in the catalytic pocket of NAMPT; ligand binding may likely affect the enzyme's activity. Hydrophobic contacts with Val242, Tyr246, Phe305, Pro273, and Val272 also anchor the ligands in the binding pocket. The aromatic rings of the ligands are also held close to Tyr246 and Phe305, involving π–π interactions as well as π–alkyl interactions. While the four ligands bind in the same manner, distinctive orientations result in variations in interaction strength. Among them, the ligand that forms the most H-bonds and hydrophobic interactions is expected to have the most potent inhibitory activity. The partially overlapping conformations demonstrated that all of the compounds occupy the same active site region of visfatin. Altogether, the interaction pattern is suggestive of the potential of these ligands as potent visfatin inhibitors and confirms the applied docking strategy presented in Fig. 4 a. Figure 4 b: Heatmap between ligands based on Schrödinger fingerprint. The heatmap constructed from Schrödinger fingerprints reveals clustering of interactions between the ligands Metformin, Phenformin, Buformin, and Berberine within the active site of protein 2G95 (visfatin/NAMPT). Each colored block corresponds to one pair of ligand-residue contacts, with darker colors indicating stronger or more frequent interactions. Eight positions show high contact frequencies, suggesting that these six amino acid positions are iteratively reused for ligand binding. The bar plots above and to the right show the total interaction count per residue or per ligand, respectively, allowing easy comparison of binding strength across all four compounds. These ligands all interact with the active site of 2G95, but with slightly different numbers of residues. The extensive interactions among Ser241, Val242, Thr304, Glu376, and Ser379 indicated that these residues are crucial for ligand recognition and inhibition. In conclusion, this docking fingerprint analysis provides further validation of the docking results for the interaction of these two compounds with visfatin. It already suggests which amino acids are essential for stabilizing the ligand–visfatin complex. Berberine has more hydrogen bonds and hydrophobic contacts among ligands, which will ensure stable binding. 3.5 DFT and MESP studies The Molecular Electrostatic Potential (MESP) mapping has compared the electronic features of the 2G95 inhibitors Biguanides with those of Berberine, establishing them above those considered moderately active, as in the case of the 2G95 inhibitor Berberine. These are presented in Figs. 5 and 6 . They illustrate typical electronic properties responsible for the particular biomolecular interactions with 2G95. The electronic structures and reactivity profiles of Berberine, buformin, Phenformin, and metformin were explored for their putative roles in gas-phase visfatin inhibition using Density Functional Theory (DFT) calculations. The spatial arrangement of the HOMO and LUMO orbitals shown in Fig. 5 gives a pictorial representation of exchangeable electron-donating/accepting sites for each ligand. These two experimental observations are supported by quantitative interpretations from the present theoretical findings based on global reactivity descriptors listed in Table 5 . Phenformin and Berberine have higher HOMO energies, thus better electron-donating abilities than others, which is in favor of interactions with electron-deficient residues forming the visfatin binding site. The HOMO–LUMO energy gap (ΔE) provides essential information about chemical stability and reactivity. As shown in Table 5 buformin exhibits the lowest ΔE of the set of buformins under each phase (≈ 0.0966–0.0970 a.u.), which implies that they are softer on the electron cloud(frontier), and therefore see between berberines, where we observed larger ΔE(0.24086–0.26660 a.u.) with high molecular stability but low flexibility of electrons(close). The phenformin shows medium ΔE (0.16134–0.19178 a.u.), thereby stabilizing the drug, making it reactive enough in specific cases like a ligand-protein interaction. Metformin has moderate ΔE values (~ 0.139 a.u.), consistent with its less adaptive binding. The ionization potential (IP) and the electron affinity (EA) are also shown to describe the charge transfer properties of these ligands. Both Phenformin and buformin have high IP values, suggesting electron-loss insensitivity; however, buformin has a much larger EA than Phenformin (i.e., the strongest electron-accepting power). These findings are consistent with the LUMO localization shown in Fig. 5 and indicate that buformin, as well as Phenformin, can be efficiently engaged in visfatin charge-transfer interactions. All of the compounds have negative electrochemical potential (µ) values, indicating their thermodynamic stability under biological conditions. Further information on molecular flexibility is obtained from the hardness (η) and softness (S) descriptors. Buformin has the lowest hardness and highest softness, indicating its greatest electronic adaptability in the visfatin active site. Table 5 DFT calculation (Quantum chemical descriptors) of the selected ligands. phase Dipole Moment HOMO (a.u) LUMO (a.u) ΔE gap (a.u) Ionization Potential IP (eV) Electron Affinity EA (eV) Electrochemical Potential µ (eV) Hardness η (eV) Softness S (eV⁻¹) Electrophilicity ω (eV) Berberine Gas phase 1.8682 -0.15220 -0.01339 0.13881 4.1419 0.3647 -2.253 1.8885 0.529 1.34 Berberine Solvent phase 2.4602 -0.16253 -0.02315 0.13938 4.4239 0.6299 -2.5269 1.8970 0.527 1.68 Buformin Gas phase 1.2725 −0.14185 −0.04523 0.09662 3.861 1.231 −2.546 1.315 0.380 2.466 Buformin solvent phase 1.5817 −0.15333 −0.05634 0.09699 4.169 1.533 −2.851 1.318 0.380 3.089 Metformin Gas phase 5.3315 −0.16121 + 0.07965 0.24086 4.386 −2.167 −1.110 3.277 0.305 0.188 Metformin solvent phase 6.9244 −0.17490 + 0.09170 0.26660 4.760 −2.495 −1.133 3.628 0.276 0.177 Phenformin Gas phase 5.6049 −0.17679 −0.01545 0.16134 4.811 0.420 −2.616 2.195 0.456 1.558 Phenformin solvent phase 6.8077 −0.18678 + 0.00500 0.19178 5.083 −0.136 −2.474 2.610 0.383 1.17 On the other hand, Berberine has the highest hardness, suggesting a stiffer electronic structure. Phenformin crumb shows IR values intermediate in hardness, consistent with electronic redistribution on binding. The electrophilicity index (ω) shows that both buformin and Phenformin exhibit a higher electrophilic nature and are predominantly large in the solvent phase, indicating a preference for nucleophilic residues present in the visfatin binding pocket. These results are strongly supported by the molecular electrostatic potential (MEP) surfaces in Fig. 6 , which show significant charge separation in these ligands. Conclusion An integrated computational approach that relies on molecular docking, molecular dynamics (MD) simulation, Structure interaction fingerprinting (SIFt), ADMET profile and density functional theory (DFT) calculations has been applied in the present work to predict inhibitory efficacy of Berberine and three biguanides (metformin Phenformin & buformin) as modulator against visfatin (NAMPT) enzyme for type 2 diabetes mellitus. Docking results indicated that all four compounds could be docked into an identical catalytic cleft of visfatin, and Berberine exhibited the lowest binding energy (− 7.85 kcal/mol). It forms strong H-bonds with Lys189 and Gln92, as well as extensive hydrophobic contacts with Tyr188, Tyr240, Pro273, Pro307, and Arg349. These interactions suggest a highly stable ligand–protein complex and hence appear to have a better inhibitory nature than biguanides. The stability of the complex was also confirmed by the presence of a low eigenvalue and reduced residue deformability, indicating that berberine appropriately bound in the active site throughout the simulation. SIFT all ligands share a single binding mode. But Berberine formed more hydrogen bonds and hydrophobic contacts with crucial hotspot residues, which may further strengthen its binding profile and functional significance within the visfatin active domain. Pharmacokinetic and toxicological profiles of Berberine predict that it offers high intestinal absorption, moderate permeation across the blood-brain barrier (BBB), acceptable solubility compared to biguanides, and a suitable safety profile, with low acute oral and dermal toxicities. ProTox-II and StopTox predictions also indicated that all the compounds are non-hepatotoxic, non-carcinogenic, and inactive against several primary endocrine and stress-response system targets, with respect to their drug-likeness. Quantum chemical calculations also supported the electronic nature of the ligands. Berberine possessed a relatively large HOMO–LUMO energy gap, indicating good molecular stability. It transferred with Phenformin + buformin (greater softness and electrophilicity) to achieve stronger charge-transfer abilities. However, the greater stability and bonding of Berberine with the visfatin active site indicate that not only structural complementarity but also intermolecular organization are more responsible than electronic softness for controlling inhibitory efficacy. In summary, the combined molecular docking, MD simulations, SIFT, ADMET studies, and DFT calculations unequivocally establish Berberine as the most effective visfatin inhibitor among the studied compounds. The high binding capacity, stable binding pattern, and favourable pharmacokinetic properties of Berberine described above, along with its good toxicity profile, suggest that it may be a promising lead compound for further preclinical and clinical evaluation as an effective new anti-T2DM drug candidate. These results may be followed by further in vitro and in vivo investigations of Berberine in modulating visfatin-regulated metabolic control. Declarations Funding: The authors declare that no financial support was received from any public, commercial, or not-for-profit funding agencies. Author Contribution The study design was conceived and designed by both authors—material preparation, data collection, and analysis: Hersh Ibrahim Rashid; Rebin Omer Ahmed. The first draft of the manuscript was prepared by Hersh Ibrahim Rashid, and both authors commented on previous versions. The author has read and approved the final manuscript Acknowledgements: The authors are very grateful to Sulaimani Technical Institute, Sulaimani Polytechnic University, Sulaimaniyah, Iraq, for providing institutional assistance as well as research facilities and an inspiring academic atmosphere that fully supported the successful accomplishment of this research. The author is also thankful for the technical and administrative support provided during this study, which helped in computational processing, data analysis, and manuscript writing. Data Availability All datasets generated during the current study are included in this article and its supplementary files. Such data comprise docked conformations, binding energies, interaction analyses, ADMET predictions, molecular dynamics (MD) simulation results and density functional theory (DFT) estimated descriptors. More input and output files from the calculations, if needed, can be provided by the corresponding author upon reasonable request for the sake of transparency and reproducibility of the results. References Soumya, D. and B. Srilatha, Late stage complications of diabetes and insulin resistance . J Diabetes Metab, 2011. 2(9): p. 1000167. http://dx.doi.org/10.4172/2155-6156.1000167 Thomas, M.C., M.E. Cooper, and P. Zimmet, Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease . Nature Reviews Nephrology, 2016. 12(2): p. 73–81. https://doi.org/10.1038/nrneph.2015.173 Galicia-Garcia, U., et al., Pathophysiology of type 2 diabetes mellitus . International journal of molecular sciences, 2020. 21(17): p. 6275 .https://doi.org/10.3390/ijms21176275 Di Raimo, T., et al., Adipokines and their involvement as a target of new drugs . Journal of Pharmacovigilance, 2015. 3(3): p. 1–10. https://doi.org/10.4172/2329-6887.1000166 Kyrou, I., et al., Fat hormones, adipokines , in Endocrinology of the Heart in health and disease . 2017, Elsevier. p. 167–205. Ratajczak-Pawłowska, A.E., et al., Relationship of visfatin with obesity and osteoporosis in patients with inflammatory bowel disease: a narrative review . Frontiers in Immunology, 2025. 16: p. 1533955 .https://doi.org/10.3389/fimmu.2025.1533955 Chang, Y.H., et al., Visfatin in overweight/obesity, type 2 diabetes mellitus, insulin resistance, metabolic syndrome and cardiovascular diseases: a meta-analysis and systemic review . Diabetes/metabolism research and reviews, 2011. 27(6): p. 515–527. https://doi.org/10.1002/dmrr.1115 Famuyiwa, S.O., et al., Comprehensive computational studies of naturally occurring kuguacins as antidiabetic agents by targeting visfatin . Chemistry Africa, 2023. 6(3): p. 1415–1427. https://doi.org/10.1007/s42250-023-00643-5 Esteghamati, A., et al., Serum visfatin is associated with type 2 diabetes mellitus independent of insulin resistance and obesity . Diabetes research and clinical practice, 2011. 91(2): p. 154–158. https://doi.org/10.1016/j.diabres.2010.11.014 Elekofehinti, O.O., et al., Discovery of potential visfatin activators using in silico docking and ADME predictions as therapy for type 2 diabetes . Beni-Suef University Journal of Basic and Applied Sciences, 2018. 7(2): p. 241–249. https://doi.org/10.1016/j.bjbas.2018.03.005 Wan, Q., et al., Suppressive effects of berberine on atherosclerosis via downregulating visfatin expression and attenuating visfatin-induced endothelial dysfunction . International journal of molecular medicine, 2018. 41(4): p. 1939–1948. https://doi.org/10.3892/ijmm.2018.3455 Rena, G., D.G. Hardie, and E.R. Pearson, The mechanisms of action of metformin . Diabetologia, 2017. 60(9): p. 1577–1585. https://doi.org/10.1007/s00125-017-4342-z Pitchakarn, P., et al., Kuguacin J isolated from bitter melon leaves modulates paclitaxel sensitivity in drug-resistant human ovarian cancer cells . Journal of natural medicines, 2017. 71(4): p. 693–702. https://doi.org/10.1007/s11418-017-1116-8 Anwar, S., et al., Deciphering quinazoline derivatives’ interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations . Frontiers in Pharmacology, 2024. 15: p. 1399372 .https://doi.org/10.3389/fphar.2024.1399372 Marinho, M.M., et al., Quantum computational investigations and molecular docking studies on amentoflavone . Heliyon, 2021. 7(1). https://doi.org/10.1016/j.heliyon.2021.e05958 Zainab, R., et al., Finding inhibitors for PCSK9 using computational methods . PLoS One, 2021. 16(8): p. e 0255523.https://doi.org/10.1371/journal.pone.0255523 Ramharack, P. and M.E. Soliman, Bioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks . Drug discovery today, 2018. 23(9): p. 1658–1665. https://doi.org/10.1016/j.drudis.2018.06.005 Schrodinger, L., The PyMOL molecular graphics system . Version, 2015. 1: p. 8 .http://doi.org/10.5281/zenodo.592552 Cousins, K.R., Computer review of ChemDraw ultra 12.0 . 2011, ACS Publications. Pawar, R.P. and S.H. Rohane, Role of autodock vina in PyRx molecular docking . Asian Journal of Research in Chemistry, 2021. 14(2): p. 132–134. https://doi.org/10.5958/0974-4150.2021.00022.4 Morris, G.M., et al., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility . Journal of computational chemistry, 2009. 30(16): p. 2785–2791. https://doi.org/10.1002/jcc.21256 Kresse, G. and J. Furthmüller, Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set . Computational materials science, 1996. 6(1): p. 15–50. https://doi.org/10.1016/0927-0256(96)00008-0 Fajardo-Díaz, E., et al., InterMap: Accelerated Detection of Interaction Fingerprints on Large-Scale Molecular Ensembles. bioRxiv, 2025: p. 2025.12. 15. 694195.https://doi.org/10.1101/2025.12.15.694195 Manzoor, H., et al., Identification and evaluation of pyrimidine based CDK6 inhibitors against glioblastoma using integrated computational approaches . Scientific Reports, 2025. 15(1): p. 25387. https://doi.org/10.1038/s41598-025-25387-x Munir, M., et al., Evaluating Allium lycaonicum Phytochemicals as CCR5 Inhibitors for HIV Therapy: A Computational Study . Chemistry & Biodiversity, 2025: p. e00980 .https://doi.org/10.1002/cbdv.202400980 Manzoor, H., et al., Computational identification and evaluation of novel PD-L1 inhibitors for cancer immunotherapy . Scientific Reports, 2025. 15(1): p. 33787. https://doi.org/10.1038/s41598-025-33787-1 Daina, A., O. Michielin, and V. Zoete, SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules . Scientific reports, 2017. 7(1): p. 42717. https://doi.org/10.1038/srep42717 Juliaty, A., et al., Interaction between Cytochrome P450 Isozymes and Antiseizure Medications: A Literature Review . Research Journal of Pharmacy and Technology, 2025. 18(3): p. 1089–1095 .https://doi.org/10.52711/0974-360X.2025.00156 Pires, D.E., T.L. Blundell, and D.B. Ascher, pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures . Journal of medicinal chemistry, 2015. 58(9): p. 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104 Banerjee, P., et al., ProTox-II: a webserver for the prediction of toxicity of chemicals . Nucleic acids research, 2018. 46(W1): p. W257-W263. https://doi.org/10.1093/nar/gky318 Borba, J.V., et al., STopTox: An in silico alternative to animal testing for acute systemic and topical toxicity . Environmental Health Perspectives, 2022. 130(2): p. 027012 .https://doi.org/10.1289/EHP8498 Daina, A., O. Michielin, and V. Zoete, SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules . Nucleic acids research, 2019. 47(W1): p. W357-W 364.https://doi.org/10.1093/nar/gkz382 Zoete, V., et al., SwissSimilarity: a web tool for low to ultra high throughput ligand-based virtual screening . 2016, ACS Publications. Ejaz, S.A., et al., In-silico Investigations of quinine and quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and structural characterization . Plos one, 2022. 17(10): p. e 0271602.https://doi.org/10.1371/journal.pone.0271602 Geerlings, P., F. De Proft, and W. Langenaeker, Conceptual density functional theory . Chemical reviews, 2003. 103(5): p. 1793–1874. https://doi.org/10.1021/cr990029p Shafiq, N., et al., A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential . Journal of Biomolecular Structure and Dynamics, 2025. 43(9): p. 4512–4533. https://doi.org/10.1080/07391102.2025.2321457 Yun, Y., et al., Molecular dynamics simulations in semiconductor material processing: A comprehensive review . Measurement, 2025. 241: p. 115708. https://doi.org/10.1016/j.measurement.2024.115708 Yilmaz, D.T., et al., Investigation of Serum Visfatin and Chemerin Levels in Type 2 Diabetes and Obesity Patients: Their Potential Role as Clinical and Biomarkers . Biomedicines, 2025. 13(11): p. 2619. https://doi.org/10.3390/biomedicines13112619 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8809541","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589812766,"identity":"58f47123-fb8c-403a-bee7-99d2e9b27f64","order_by":0,"name":"Hersh Ibrahim Rashid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAyBmZmCQSGBg4GE4wFABEZUAI+K0nCFeCwNYCwNjG1wLbmDO3vv4cwGDRZ55+9mDh27Oq8szOMB88DYPg4VsAw4tlj3HzaRnMEgUy5zJSzicu+1wscEBtmRrHgYJY1xaDG6ksTEDFSTOYMgxAGo5kLjhAI+ZNEgEp5b7z5g/g7XwvwFqmVMH1ML/Db+WG2wMYAUzJEC2NDCDbGHDr+VMGlCBgUSxhATQlpxjh4slD7MZW84xwOOX48eADquoy5PgzzH+nFNTl8d3vPnhjTcVdThDDKoRwUwARxNQhBG/FiSQAGMQr2UUjIJRMAqGOwAA8UtR3ViupkEAAAAASUVORK5CYII=","orcid":"","institution":"Sulaimani Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Hersh","middleName":"Ibrahim","lastName":"Rashid","suffix":""},{"id":589812767,"identity":"3ce8dbd5-1e28-4b4a-a0ec-422f3e88c227","order_by":1,"name":"Rebin Omer Ahmed","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rebin","middleName":"Omer","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2026-02-06 17:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8809541/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8809541/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102532946,"identity":"77df16be-99bc-4935-a9ae-311e673f3893","added_by":"auto","created_at":"2026-02-12 16:47:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":555864,"visible":true,"origin":"","legend":"\u003cp\u003eshows 2-D and 3-D visualization of the interaction of visfatin's amino acid residues with (a) Metformin, (b)Phenformin, (c) Buformin, and (d) Berberine\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8809541/v1/23c1f7122e6481c0b416a0c8.png"},{"id":102532951,"identity":"4bb303f6-52d0-49c5-aa8e-9b906492453c","added_by":"auto","created_at":"2026-02-12 16:47:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1150300,"visible":true,"origin":"","legend":"\u003cp\u003eThe molecular dynamics simulation study of docked berberine with visfatin. Deformability(a),β-factor (b), eigenvalues (c), variance (d), covariance map (e), and elastic network (f). In (d), green indicates cumulative variances and red indicates individual variances, while in (f), darker grey indicates the highest-stiffness regions. In(e), the covariance map represents correlated, anticorrelated, and uncorrelated motions shown in red, blue, or white color, respectively.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8809541/v1/2cf1defeac4292659adf54f6.jpeg"},{"id":102532949,"identity":"0c0f3ae9-48ea-4bcc-a3e7-51cbe877b569","added_by":"auto","created_at":"2026-02-12 16:47:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":411459,"visible":true,"origin":"","legend":"\u003cp\u003e(a)Bioavailability radar, (b)Swiss target prediction, and(c) toxicity radar of selected phytochemicals of Metformin, Phenformin, Buformin, Berberine\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8809541/v1/00d37dee00ac1a5e8454e82a.png"},{"id":102532948,"identity":"96e150cf-9b9c-476f-ba48-28dcadef3086","added_by":"auto","created_at":"2026-02-12 16:47:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126692,"visible":true,"origin":"","legend":"\u003cp\u003eSIFt Analysis:(a) All ligands docked into the active site of 2G95, (b) Protein-ligand interaction\u003cstrong\u003e \u003c/strong\u003efingerprints for modeling compounds.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8809541/v1/6d44a6fca12112b28f0c1dad.jpeg"},{"id":102532947,"identity":"3cc02349-bac8-4912-8714-00061a232b5e","added_by":"auto","created_at":"2026-02-12 16:47:13","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":438936,"visible":true,"origin":"","legend":"\u003cp\u003eESP structures (in both gas and solvent phases) formed by mapping of total density over electrostatic potential, and optimized structures of Metformin, Phenformin, Buformin, and Berberine. Calculated HOMO and LUMO orbitals of potent derivatives at the B3LYP/SVP level of DFT calculations for all selected ligands.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8809541/v1/e2d6e9b1a1c8aa47e0e631aa.jpeg"},{"id":102962341,"identity":"94575c4b-95f8-447b-8ef6-4a4d55428014","added_by":"auto","created_at":"2026-02-19 04:07:13","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":278189,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Representation of the Visfatin–Berberine Complex with Active-Site Interaction Map. (A)Docked conformation of visfatin protein inhibited by berberine ligand. (B)Selected ligands occupy the same binding site as the visfatin, where they interact with nearly all residues in a similar fashion\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8809541/v1/eed2fe94ed311a18b960e26a.jpeg"},{"id":106401414,"identity":"405ef302-0fa4-4fd4-a0fb-2efcb50e659e","added_by":"auto","created_at":"2026-04-08 08:49:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3909227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8809541/v1/771c623e-889b-4451-afbb-e4dc8ba22e21.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Organic Molecular Strategy to Prevent Diabetes: An In Silico Screening of Berberine and Biguanides as Visfatin Antagonists","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetes mellitus is a chronic and systemic metabolic disorder characterized by hyperglycemia, which leads to glycosuria as one of many symptoms, in which cells are unable to take up glucose for energy generation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is divided into insulin-dependent diabetes mellitus and non-insulin-dependent diabetes mellitus[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It has been established that the disease emerges as a consequence of a glucose metabolic pathway disorder, caused by inactivity or excessive activity of diabetic enzymes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Visfatin (also named nicotinamide phosphoribosyl transferase) is an adipokine associated with T2DM and is primarily secreted by white adipose tissue[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increased circulating visfatin is found in obese and insulin-resistant individuals. In many studies, elevated plasma levels of visfatin were observed in different groups of patients with obesity and/or other features of metabolic syndrome[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The results of the meta-analysis revealed that circulating visfatin levels were increased in obesity, metabolic syndrome, and type 2 diabetes mellitus (T2DM) and were positively correlated with insulin resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This relationship indicates that visfatin secretion is stimulated by diabetes mellitus as part of a counterregulatory response, as visfatin enhances insulin release and insulin receptor phosphorylation in mouse pancreatic beta-cells[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, suppression of the visfatin enzyme is suggested as one of the plausible strategies for type 2 diabetes control [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Natural products, particularly Berberine, are a\u0026ensp;well-known reservoir for new medicines to treat various types of diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], The in vivo study\u0026ensp;revealed that BBR treatment (5 mg/kg/day) markedly decreased the serum level of visfatin[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Biguanides, a class of antidiabetic agents primarily used to treat Type-2 diabetes mellitus, are gaining attention as promising drugs for clinical or medicinal therapy due to their numerous biological activities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The use of computational methods is safe, faster, more efficient, and less expensive in the drug discovery and development process [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Molecular docking and Density functional theory can assist in screening, identification of phytochemicals as potential therapeutic agents against enzymes and\u0026ensp;prediction of their binding affinity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], investigation of pharmacological properties and electric properties (pharmacologically active compounds), however, being putative drugs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Computational methods can also help identify new target binding sites of a protein for inhibition. The areas of bioinformatics and cheminformatics include database generation and manipulation, which use statistical algorithms to handle and analyze biological or chemical data. Their instruments are used to discover and analyze drug targets, macromolecular biological\u0026ensp;structures, and active sites. They can also predict a drug lead molecule for a drug target and estimate its drug-likeness\u0026ensp;to docking and ADMET studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The binding affinities of drug candidates are ordered, and in appeal, their\u0026ensp;binding properties can be fine-tuned. Molecular drug discovery using computational approaches is called structure-based drug design (SBDD), and in silico techniques have broad potential for discovering new target proteins and predicting their biological activity. Due to the rapid growth in computational power, molecular dynamics is a well-established computational approach that has been used effectively to simulate protein-ligand complexes and monitor the action of drugs on biological macromolecules at the atomic level over time[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Computational methods applied to identify natural organic compounds, such as Berberine, vs synthetic\u0026ensp;drugs like Biguanides, and those designed to predict small inhibitors for 2G95. Molecular docking was performed between the identified novel drugs and the 2G95 target protein to understand their interactions with 2G95. This work is crucial for investigating potential drugs against 2G95, which can be used as a therapeutic strategy to treat T2DM and reduce the rising issues associated with high blood sugar,\u0026ensp;thereby improving long-term control.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e(Computational Portion)\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Protein recovery and preparation (protein model preparation)\u003c/h2\u003e \u003cp\u003eThe protein target (AChE;\u0026ensp;PDB ID: 2G95) was obtained from the Protein Data Bank (PDB) of the Research Collaboratory for Structural Bioinformatics (RCSB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PDB IDs were selected based on corresponding X-ray crystallographic structures, lower resolution (\u0026lt;\u0026thinsp;2.5 \u0026Aring;), and percentile scores in global validation metrics, which indicated better structural quality. The protein structures were processed using PyMOL version 2.5[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This step prepared the protein models by removing heteroatoms, such as water and other molecules. Hydrogen atoms were added to improve protein docking performance. The processed macromolecules were refined and validated, then energy-minimized in the Swiss-PDB Viewer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ligand Database Searching and Preparation (library\u0026ensp;preparation)\u003c/h2\u003e \u003cp\u003eFour bioactive ligands\u0026ensp;from plants and their synthesis, as reported (Metformin, Phenformin, Buformin, Berberine), were chosen. Structures of the molecules in\u0026ensp;3D were retrieved from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as.sdf files. Open Babel prepared ligand structures as a default built-in program\u0026ensp;of PyRx\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Molecular Docking.\u003c/h2\u003e \u003cp\u003eLigand-containing compounds were constructed and saved in mol file format via\u0026ensp;ChemDraw Ultra 12.0 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].7 We downloaded the enzymes from the RCSB protein data bank, and\u0026ensp;the Visfatin enzyme was chosen for the docking study. We queried RCSB for \"Visfatin\" and selected PDB entry 2G95. The PDB alarm change file 2G95 was then downloaded, and the .pdb file was imported into Biovia Discovery Studio Visualizer for cleaning. The active\u0026ensp;site pocket was identified from the crystal structure, and water molecules were stripped for cleaning. The assembly consisted of two chains, A and B, which were selected for further investigation. Details of\u0026ensp;the active cavity are recorded and presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The cleaned 2G95 chains were saved in PDB format, along with the ligand and enzyme. Docking studies were performed using the PyRx Virtual Screening tool with AutoDock Vina[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. After importing the ligand files with Open Babel, they were prepared for energy minimization using the Universal Force Field (UFF).7 Ligands were\u0026ensp;minimized and converted into pdbqt format. The enzyme file was opened and loaded as a molecule before being converted to a macromolecule. And then we performed docking experiments on all the obtained files using the Vina Wizard program to identify the active cavity. Potential\u0026ensp;binding patterns of the enzyme for ligands were then explored. The designed ligands and the 2G95 enzyme were subjected to molecular docking. The PyRx Virtual Screening tool was opened in AutoDock Vina to perform the docking study. (Autodock 4)\u0026ensp;We imported the ligand les with the Open Babel option and used the Universal Force Field (UFF) for energy optimization[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Ligands were then converted to PDBQT format after minimization. The purified enzyme file was loaded and converted to macromolecular format using \"Load Molecule\" on the Menu bar. Once we acquired all the information, we performed the docking experiments with Vina Wizard, targeting the active binding site. Then we analyzed how those ligands might bind and what else they are compatible with.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular Contact Map STRUCTURAL Interaction Fingerprinting (SIFt)\u0026ensp;analysis\u003c/h2\u003e \u003cp\u003eSIFt is a novel method for a complicated problem: the modeling and prediction of protein-ligand interactions in three dimensions. The binary digit interaction fingerprint\u0026ensp;(BDIF) model is constructed with the SIFt method, which reflects the three-dimensional binding pattern of a ligand-protein complex. Each molecular fingerprint encodes high-level information for the organization, analysis, and presentation of ligand-receptor complex data sets mined from the database.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The interaction fingerprints of the selected phytochemicals were generated for four docking complexes using the SIFt panel in Schr\u0026ouml;dinger Suite 2020-3. The input files were the receptor grid and the ligands. After the fingerprint is formed, one can visualize in an Excel sheet which residues and interaction types (hydrophobic, H-bond donor, or acceptor) contribute most to the binding. Similar colors were assigned to the interaction types between residues, with 1 and 0 representing the presence and absence of an interaction [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For building the SIFt model, additional information was available from previously published work [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Pharmacokinetic parameters\u003c/h2\u003e \u003cp\u003eThe ADMET properties of the newly designed compounds were also calculated using SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which was used to assess their synthetic feasibility[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] \u0026mdash;a computer-aided ADME study. The pkCSM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biosig.unimelb.edu.au/pkcsm/\u003c/span\u003e\u003cspan address=\"http://biosig.unimelb.edu.au/pkcsm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) webserver was used to predict the pharmacokinetic properties of our in-house compounds [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These studies encompass analyses of features such as oral absorption, volume of distribution (VDss), CYP-mediated metabolism, excretion (total clearance), AMES toxicity prediction, and synthetic accessibility using the free online tool SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/index.php\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Two servers, such as ProToxII [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and StopTox [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], were used for toxicity assessment. ProTox-II server estimates different toxicity endpoints, such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, and adverse outcomes (Tox21) pathways, along with specific/unknown target toxicity using models based on fragment propensity scores derived from molecular similarity scores to the most frequent features and further refined by machine learning. The StopTox server utilizes a set of Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models to estimate the toxicity of compounds across various endpoints, including acute inhalation toxicity, oral toxicity, and eye irritation/corrosion/skin sensitization, following the curation and integration of publicly available datasets. Predicted macromolecular targets for the top-selected candidates were generated using the Swiss Target Prediction server [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The library of many known bioactive molecules available on the server is mapped to \u0026sim; 3068 proteins using two-dimensional (2D) and three-dimensional (3D) similarity. The server Swiss Similarity was used to identify possible structural analogs for repurposing against the selected receptor [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 DFT studies (MESP/HOMO/LUMO analysis)\u003c/h2\u003e \u003cp\u003eThe DFT calculations were performed under the same conditions as in a previous report [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with slight modifications. The Gaussian 06 package (Rev. E.01) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] with the default settings, and all structure SVP-basis set calculations were performed using the B3LYP functional. This theory allows for the efficient determination\u0026ensp;of the electronic structure of atoms and molecules. In this work, we shall compute the most relevant geometric parameters, the molecular electrostatic potential (MEP), the frontier molecular orbital (FMO), and global and local reactivity descriptors. The optimized geometries were also examined\u0026ensp;using GaussView 6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular Dynamics (MD) simulations\u003c/h2\u003e \u003cp\u003eWe performed molecular dynamics simulations of the lowest-energy, best-posed docking complex derived from the docking data. To understand, predict, and estimate the parameters of a model research system, molecular dynamics (MD) simulations numerically\u0026ensp;probe the dense phases of the model system [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. MD simulations identified the stability of the best-selected natural product molecule with the target. MD simulations (iMODS) were performed using the iMod server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imods.iqfr.csic.es\u003c/span\u003e\u003cspan address=\"https://imods.iqfr.csic.es\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The iMod server provides easy access to improved standard-mode analysis (NMA) in internal coordinates. The online interface works across all browsers and most modern mobile devices. Flux simulations of transitioning between two conformations; users might then interactively explore the resulting structures, trajectories, and animations, and, for large macromolecules, 3D [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Molecular Docking Analysis\u003c/h2\u003e\n \u003cp\u003eMolecular docking has become increasingly relevant for accelerating and reducing the cost of drug discovery. In our study, molecular docking was employed to identify the top-ranked visfatin inhibitors, with cut-off values set for different binding energies (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Metformin demonstrated potent inhibition of visfatin, with a binding energy of \u0026minus;\u0026thinsp;4.35 kcal/mol. The molecule\u0026apos;s atom formed hydrogen-bond interactions with six amino acid residues at distances of 2.54\u0026ndash;3.97 \u0026Aring;. Also, hydrophobic interactions between the nitrogen of the ligand and His191 of the visfatin enzyme. (Fig. 1a) Phenformin elicited considerable binding energy of \u0026minus;\u0026thinsp;6.23 kcal/mol at the binding pocket of the visfatin enzyme\u0026mdash;this promising visfatin inhibitor formed hydrophobic interactions with TYR240 and VAL242. Similarly, (Fig. 1b). Furthermore, five hydrogen bonding interactions were observed between the Phenformin moiety and the visfatin enzyme at 1.97 to 3.48 \u0026Aring;. Buformin had a nearly identical binding energy to metformin, \u0026minus;\u0026thinsp;4.68 kcal/mol, and elicited interesting binding interactions with the visfatin enzyme. Only the nitrogen atom of the Buformin moiety participated in two hydrogen bonding interactions with PHE91 and SER241 at bond distances of 2.66\u0026Aring; and 2.61 \u0026Aring;. The ligand was not further stabilized in the visfatin enzyme\u0026apos;s binding pocket by hydrophobic interactions (Fig. 1c).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1770914405.png\"\u003e\u003c/p\u003e\n \u003cp\u003eBerberine had a binding energy of \u0026minus;\u0026thinsp;7.85 kcal/mol after interacting with the amino acid residues at the binding pocket of the visfatin enzyme. The two-oxygen atom of the Berberine ligand formed two strong hydrogen bonding interactions with Lys189 at 2.04 \u0026Aring; and GLN92 at 1.97\u0026Aring;. Also, the Berberine moiety formed hydrophobic interactions with TYR188, TYR240, PRO273, PRO307, and ARG349 (Fig.\u0026nbsp;1d). The hydrophobic, hydrogen bonding, and pi-interactions observed between the ligands and the amino acid residues at the binding pocket of the visfatin enzyme contributed favorably to the high affinities and compression stability elicited by Metformin, Phenformin, Buformin, and Berberine. But, the visfatin-inhibitor of the Berberine is more responsible than biguanides, due to making a strong hydrogen interaction between the berberine ligand and the target protein.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eshows 2-D and 3-D visualization of the interaction of visfatin\u0026apos;s amino acid residues with (a) Metformin, (b)Phenformin, (c) Buformin, and (d) Berberine\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Molecular dynamics simulations\u003c/h2\u003e\n \u003cp\u003eConformational dynamics of the protein\u0026minus;ligand complex were examined using molecular dynamics simulations and iMODS normal-mode analysis (NMA). The values of the deformability indicate that the well-deformed\u0026ensp;residues, polym, and pmbn are the eigenvalues of this complex: 1.31427e-04. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the MD simulation results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 PK and oral\u0026ensp;bioavailability of the best drug candidates\u003c/h2\u003e\n \u003cp\u003eComputed ADME properties and evaluation of Bioactivity/toxicity profiles were calculated using pKCSM, SWISSADME Swiss Target Prediction Tool, StopTox, and ProtoxII tools. Four compounds were back-searched against visfatin as a target. PubChem SMILES, and all were identified as previously known compounds6. ADMET Profiles The ADMET properties of the potential visfatin inhibitors were evaluated based on their drug ability, including solubility, human intestinal absorption, brain\u0026ndash;blood barrier permeability, and maximum. The maximum dose (100 mg/kg/day in humans) [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] and hepatotoxicity were considered in our study (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Solubility profiling of the ligands revealed their compatibility was between \u0026minus;\u0026thinsp;6.0\u0026ensp;and 0.5 at compromiseable levels for all ligands screened throughout the study period. Using Swiss Target Prediction analysis, the chemical\u0026ndash;biological interaction profiles of (\u0026minus;)-epicatechin, (\u0026minus;)-catechin, and (+)-taxifolin were markedly distinct from those of caffeic acid. Selectivity assays: Phenformin displayed\u0026ensp;strong selectivity for Family-A G-protein\u0026ndash;coupled receptors (73.3%).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003epkCSM pharmacokinetic parameters of the selected ligand\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePharmacokinetic properties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetformin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhenformin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBuformin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBerberine\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater solubility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntestinal absorption (human)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFraction unbound (human)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNS permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax. tolerated dose (human)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMetformin, Buformin, and Berberine were more generalist, with a more evenly distributed affinity profile across enzymes, membrane receptors, and transport proteins (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Introduction: StopTox and ProTox-II are in silico toxicology systems for the prediction of primary toxicological endpoints\u0026ensp;of small molecules based purely on their chemical structures and molecular properties. Through the employment of QSAR models and machine learning approaches, these platforms make it possible to predict hepatotoxicity (liver toxicity),\u0026ensp;cytotoxicity, as well as acute toxicity in drug discovery. ProTox-II analysis also supported the predicted non-inhibitory potential for hepatotoxicity or carcinogenicity for all four organic ligands (Metformin, Phenformin, Buformin, and Berberine), implying an organosafety profile (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The Tox21 nuclear receptor panel revealed that there is no apparent major endocrine receptor-ligand crosstalk between the androgen, estrogen, and PPAR\u0026gamma; receptors for any of the compounds studied; thus, there is a low likelihood of endocrine-disruptive toxicity. Herein, all the compounds exhibit very low or no activity toward p53, ATAD5, long interspersed nuclear elements 2/antioxidant response element (Nrf2/ARE), and heat shock element (HSE), suggesting their major genomic and cellular predicted harmlessness (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) (Fig. C). All the compounds had potential acute inhalation toxicity based on our\u0026ensp;StopTox predictions, and lower oral and dermal toxicity for Berberine was obtained compared to Metformin, Phenformin, and Buformin. In summary, berberine possessed the most suitable acute exposure profile among all the compounds tested\u0026ensp;herein, while the other three may need additional optimizations to improve their safety (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003estopTox toxicity parameters of the selected phytochemicals.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLigands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcute inhalation toxicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcute oral toxicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcute dermal toxicity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEye irritation and corrosion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkin sensitization\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSkin irritation and corrosion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetformin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eToxic (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNS (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenformin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eToxic (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuformin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eToxic (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNS (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBerberine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNS (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eT: toxic; NT: non-toxic; S: sensitizer; NS: non-sensitizer\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProtoxII toxicological parameters of the identified phytoconstituents of ligands\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMetformin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePhenformin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBuformin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBerberine\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePro\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePro\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePro\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePro\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrgan toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eToxicity endpoints\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarcinogenicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eTox21-Nuclear receptor signaling pathways\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR-LBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER-LBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePPAR-Gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enrf2/ARE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTox21 -Stress Response Pathways\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATAD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Structural interaction fingerprint (SIFt)\u003c/h2\u003e\n \u003cp\u003eIn this work, the SIFt technique proves helpful in comparing docking poses of new ligands and for constructing target-dependent scoring functions. This procedure is a way to develop ligands with different structures based on interaction patterns rather than solely on their molecular structure. SIFt maps facilitated the interpretation of\u0026ensp;docking results for four compounds on 2G95. This approach effectively matched inhibitors to key hotspot active-site residues that are essential for the formation of ligand-protein complexes. This requires the development of a robust structure interaction fingerprint, such as SIFt, which allows us to\u0026ensp;understand how large ligand databases interact at the active site of a protein. To identify the most critical hot-spot residues contributing to ligand-protein complex formation, we generated an SIFt\u0026ensp;(supplemental Information 4SIFT) analysis for all virtual screening hits that bind in 2G95. The docked poses overlay and show that all four ligands bind to the common, active cavity of the 2G95 (visfatin/NAMPT) protein. Residues including His90, Gln92, Ser241, and Tyr246, positioned surrounding\u0026ensp;the binding pocket in proximity to Val242.DoesNotExist Yet these residues participate in ligand recognition: His90, Arg349, Thr304, Phe305, Gln92, Glu376, \u0026amp; Ser379. Several hydrogen bonds,\u0026ensp;especially with Ser241, Thr304, Glu376, and Ser379, indicate strong polar stabilization in the ligand\u0026ndash;protein complexes. These residues reside in\u0026ensp;the catalytic pocket of NAMPT; ligand binding may likely affect the enzyme\u0026apos;s activity. Hydrophobic contacts with Val242, Tyr246, Phe305, Pro273, and Val272 also anchor the ligands in the binding pocket. The aromatic rings of\u0026ensp;the ligands are also held close to Tyr246 and Phe305, involving \u0026pi;\u0026ndash;\u0026pi; interactions as well as \u0026pi;\u0026ndash;alkyl interactions. While the four ligands bind in the same manner, distinctive orientations result in variations in interaction strength. Among them, the ligand that forms the most H-bonds and hydrophobic interactions is expected to have the most potent inhibitory activity. The partially overlapping conformations demonstrated that all of the compounds occupy the same active site region\u0026ensp;of visfatin. Altogether, the interaction pattern is suggestive of the potential of these ligands as potent visfatin inhibitors and confirms the applied docking strategy presented in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb: Heatmap\u0026ensp;between ligands based on Schr\u0026ouml;dinger fingerprint. The heatmap constructed from Schr\u0026ouml;dinger fingerprints reveals clustering of interactions between the ligands Metformin, Phenformin, Buformin, and Berberine within the active site of protein 2G95 (visfatin/NAMPT). Each colored block corresponds to one pair of ligand-residue contacts, with darker colors indicating stronger or\u0026ensp;more frequent interactions. Eight positions show high contact frequencies, suggesting that these six amino acid positions are iteratively reused for ligand binding. The bar plots above and to the right show the total interaction count per residue or per ligand, respectively, allowing easy comparison of binding strength across all four compounds. These ligands all interact with the active site of 2G95, but with slightly different numbers of residues. The extensive interactions among Ser241, Val242, Thr304, Glu376, and Ser379 indicated that these residues are crucial for ligand recognition and inhibition. In conclusion, this docking fingerprint analysis provides further validation of the docking results for the interaction of these two compounds with visfatin. It already suggests which amino acids are essential for stabilizing the ligand\u0026ndash;visfatin complex. Berberine has more hydrogen bonds and hydrophobic contacts among ligands, which\u0026ensp;will ensure stable binding.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 DFT and MESP studies\u003c/h2\u003e\n \u003cp\u003eThe Molecular Electrostatic Potential (MESP) mapping has compared the electronic features of the 2G95 inhibitors Biguanides with those of Berberine, establishing them above those considered moderately active, as in the case of the 2G95 inhibitor Berberine. These are presented in Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. They illustrate typical\u0026ensp;electronic properties responsible for the particular biomolecular interactions with 2G95. The electronic structures and reactivity profiles of Berberine, buformin, Phenformin, and metformin were explored for their putative roles in gas-phase visfatin inhibition using Density Functional Theory (DFT) calculations. The spatial arrangement of the HOMO and\u0026ensp;LUMO orbitals shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e gives a pictorial representation of exchangeable electron-donating/accepting sites for each ligand. These two experimental observations are supported by quantitative interpretations from the present theoretical findings based on global reactivity descriptors listed in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Phenformin and Berberine have higher HOMO energies, thus better electron-donating abilities\u0026ensp;than others, which is in favor of interactions with electron-deficient residues forming the visfatin binding site. The HOMO\u0026ndash;LUMO energy gap (\u0026Delta;E) provides essential information\u0026ensp;about chemical stability and reactivity.\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e buformin exhibits the lowest \u0026Delta;E of the set of buformins under each phase (\u0026asymp;\u0026thinsp;0.0966\u0026ndash;0.0970 a.u.), which implies that they\u0026ensp;are softer on the electron cloud(frontier), and therefore see between berberines, where we observed larger \u0026Delta;E(0.24086\u0026ndash;0.26660 a.u.) with high molecular stability but low flexibility of electrons(close). The phenformin shows medium \u0026Delta;E\u0026ensp;(0.16134\u0026ndash;0.19178 a.u.), thereby stabilizing the drug, making it reactive enough in specific cases like a ligand-protein interaction. Metformin has moderate \u0026Delta;E values (~\u0026thinsp;0.139 a.u.), consistent with its less adaptive binding. The ionization potential (IP) and\u0026ensp;the electron affinity (EA) are also shown to describe the charge transfer properties of these ligands. Both Phenformin and buformin have high IP values, suggesting electron-loss insensitivity; however, buformin has a much larger EA\u0026ensp;than Phenformin (i.e., the strongest electron-accepting power). These findings are consistent with the LUMO localization shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and indicate that buformin, as well as Phenformin, can be efficiently engaged in visfatin charge-transfer interactions. All of\u0026ensp;the compounds have negative electrochemical potential (\u0026micro;) values, indicating their thermodynamic stability under biological conditions. Further information on molecular flexibility is obtained from\u0026ensp;the hardness (\u0026eta;) and softness (S) descriptors. Buformin has the lowest hardness and highest softness, indicating its greatest electronic adaptability in the visfatin active site.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDFT calculation (Quantum chemical descriptors) of the selected ligands.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ephase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDipole Moment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHOMO (a.u)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLUMO (a.u)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;E gap (a.u)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIonization Potential IP (eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElectron Affinity EA (eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElectrochemical Potential \u0026micro; (eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHardness \u0026eta; (eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoftness S (eV⁻\u0026sup1;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElectrophilicity \u0026omega; (eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBerberine Gas phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBerberine Solvent phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuformin\u003c/p\u003e\n \u003cp\u003eGas phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.14185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.04523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuformin\u003c/p\u003e\n \u003cp\u003esolvent phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.15333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.05634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetformin Gas phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.16121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.07965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetformin solvent phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.17490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.09170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenformin Gas phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.17679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.01545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenformin solvent phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.18678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;0.00500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOn the other hand, Berberine has the highest hardness, suggesting a stiffer electronic structure. Phenformin crumb shows IR values intermediate in hardness, consistent with electronic redistribution on binding. The electrophilicity index (\u0026omega;) shows that both buformin and Phenformin exhibit a higher electrophilic nature and are predominantly large in the solvent phase, indicating a preference for nucleophilic residues present in the visfatin binding pocket. These results are strongly supported by the molecular electrostatic potential (MEP) surfaces in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, which show significant charge separation in these ligands.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAn integrated computational approach that relies on molecular docking, molecular dynamics (MD) simulation, Structure interaction fingerprinting\u0026ensp;(SIFt), ADMET profile and density functional theory (DFT) calculations has been applied in the present work to predict inhibitory efficacy of Berberine and three biguanides (metformin Phenformin \u0026amp; buformin) as modulator against visfatin (NAMPT) enzyme for type 2 diabetes mellitus. Docking results indicated that all four compounds could be docked into an identical catalytic cleft of visfatin, and Berberine exhibited the lowest binding energy (\u0026minus;\u0026thinsp;7.85 kcal/mol). It forms strong H-bonds with Lys189 and Gln92, as well as extensive hydrophobic contacts with Tyr188, Tyr240, Pro273, Pro307, and Arg349. These interactions suggest\u0026ensp;a highly stable ligand\u0026ndash;protein complex and hence appear to have a better inhibitory nature than biguanides. The stability of the complex was also confirmed by the presence of a low eigenvalue and reduced residue deformability, indicating that berberine appropriately bound in the active site throughout the simulation. SIFT\u0026ensp;all ligands share a single binding mode. But Berberine formed more hydrogen bonds and hydrophobic contacts with crucial hotspot residues, which may further strengthen its binding profile and functional significance within the visfatin active domain. Pharmacokinetic and toxicological profiles of Berberine predict that it offers high intestinal absorption, moderate permeation across the blood-brain barrier (BBB), acceptable solubility compared to biguanides, and a suitable safety profile, with low acute oral and dermal toxicities. ProTox-II and StopTox predictions also indicated that all the compounds are non-hepatotoxic, non-carcinogenic, and inactive against several primary endocrine and stress-response system targets, with respect to their drug-likeness. Quantum chemical calculations also supported the electronic\u0026ensp;nature of the ligands. Berberine possessed a relatively large HOMO\u0026ndash;LUMO energy gap, indicating good molecular stability. It transferred with Phenformin\u0026thinsp;+\u0026thinsp;buformin (greater softness and electrophilicity) to achieve stronger charge-transfer abilities. However, the greater stability and bonding of Berberine with the visfatin active site indicate that not only structural complementarity but also intermolecular organization are more responsible than electronic softness for controlling inhibitory efficacy. In summary, the combined molecular docking, MD simulations, SIFT, ADMET studies, and DFT calculations unequivocally establish Berberine as the most effective visfatin inhibitor among the studied compounds. The high binding capacity, stable binding pattern, and favourable pharmacokinetic properties of Berberine described above, along with its good toxicity profile, suggest that it may be a promising lead compound for further preclinical and clinical evaluation as an effective new anti-T2DM drug candidate. These results may be followed by further in vitro and in\u0026ensp;vivo investigations of Berberine in modulating visfatin-regulated metabolic control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors declare that no financial support was received from any public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe study design\u0026ensp;was conceived and designed by both authors\u0026mdash;material preparation, data collection, and analysis: Hersh Ibrahim Rashid; Rebin Omer Ahmed. The first draft of the manuscript was\u0026ensp;prepared by Hersh Ibrahim Rashid, and both authors commented on previous versions. The author has read and approved the final manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors are very grateful to\u0026ensp;Sulaimani Technical Institute, Sulaimani Polytechnic University, Sulaimaniyah, Iraq, for providing institutional assistance as well as research facilities and an inspiring academic atmosphere that fully supported the successful accomplishment of this research. The\u0026ensp;author is also thankful for the technical and administrative support provided during this study, which helped in computational processing, data analysis, and manuscript writing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll datasets generated during the current study are included in this article and its supplementary files. Such data comprise docked conformations, binding energies, interaction analyses, ADMET predictions, molecular dynamics (MD) simulation results and density functional theory\u0026ensp;(DFT) estimated descriptors. More input and output files from the calculations, if needed, can be provided by the corresponding author upon reasonable request for the sake of transparency and reproducibility of the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSoumya, D. and B. Srilatha, \u003cem\u003eLate stage complications of diabetes and insulin resistance\u003c/em\u003e. J Diabetes Metab, 2011. 2(9): p. 1000167.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.4172/2155-6156.1000167\u003c/span\u003e\u003cspan address=\"10.4172/2155-6156.1000167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas, M.C., M.E. Cooper, and P. Zimmet, \u003cem\u003eChanging epidemiology of type 2 diabetes mellitus and associated chronic kidney disease\u003c/em\u003e. Nature Reviews Nephrology, 2016. 12(2): p. 73\u0026ndash;81.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrneph.2015.173\u003c/span\u003e\u003cspan address=\"10.1038/nrneph.2015.173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalicia-Garcia, U., et al., \u003cem\u003ePathophysiology of type 2 diabetes mellitus\u003c/em\u003e. International journal of molecular sciences, 2020. 21(17): p. 6275\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.3390/ijms21176275\u003c/span\u003e\u003cspan address=\".10.3390/ijms21176275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Raimo, T., et al., \u003cem\u003eAdipokines and their involvement as a target of new drugs\u003c/em\u003e. Journal of Pharmacovigilance, 2015. 3(3): p. 1\u0026ndash;10.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4172/2329-6887.1000166\u003c/span\u003e\u003cspan address=\"10.4172/2329-6887.1000166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyrou, I., et al., \u003cem\u003eFat hormones, adipokines\u003c/em\u003e, in \u003cem\u003eEndocrinology of the Heart in health and disease\u003c/em\u003e. 2017, Elsevier. p. 167\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRatajczak-Pawłowska, A.E., et al., \u003cem\u003eRelationship of visfatin with obesity and osteoporosis in patients with inflammatory bowel disease: a narrative review\u003c/em\u003e. Frontiers in Immunology, 2025. 16: p. 1533955\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.3389/fimmu.2025.1533955\u003c/span\u003e\u003cspan address=\".10.3389/fimmu.2025.1533955\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, Y.H., et al., \u003cem\u003eVisfatin in overweight/obesity, type 2 diabetes mellitus, insulin resistance, metabolic syndrome and cardiovascular diseases: a meta-analysis and systemic review\u003c/em\u003e. Diabetes/metabolism research and reviews, 2011. 27(6): p. 515\u0026ndash;527.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/dmrr.1115\u003c/span\u003e\u003cspan address=\"10.1002/dmrr.1115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFamuyiwa, S.O., et al., \u003cem\u003eComprehensive computational studies of naturally occurring kuguacins as antidiabetic agents by targeting visfatin\u003c/em\u003e. Chemistry Africa, 2023. 6(3): p. 1415\u0026ndash;1427.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42250-023-00643-5\u003c/span\u003e\u003cspan address=\"10.1007/s42250-023-00643-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsteghamati, A., et al., \u003cem\u003eSerum visfatin is associated with type 2 diabetes mellitus independent of insulin resistance and obesity\u003c/em\u003e. Diabetes research and clinical practice, 2011. 91(2): p. 154\u0026ndash;158.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.diabres.2010.11.014\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2010.11.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElekofehinti, O.O., et al., \u003cem\u003eDiscovery of potential visfatin activators using in silico docking and ADME predictions as therapy for type 2 diabetes\u003c/em\u003e. Beni-Suef University Journal of Basic and Applied Sciences, 2018. 7(2): p. 241\u0026ndash;249.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bjbas.2018.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.bjbas.2018.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan, Q., et al., \u003cem\u003eSuppressive effects of berberine on atherosclerosis via downregulating visfatin expression and attenuating visfatin-induced endothelial dysfunction\u003c/em\u003e. International journal of molecular medicine, 2018. 41(4): p. 1939\u0026ndash;1948.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/ijmm.2018.3455\u003c/span\u003e\u003cspan address=\"10.3892/ijmm.2018.3455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRena, G., D.G. Hardie, and E.R. Pearson, \u003cem\u003eThe mechanisms of action of metformin\u003c/em\u003e. Diabetologia, 2017. 60(9): p. 1577\u0026ndash;1585.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-017-4342-z\u003c/span\u003e\u003cspan address=\"10.1007/s00125-017-4342-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePitchakarn, P., et al., \u003cem\u003eKuguacin J isolated from bitter melon leaves modulates paclitaxel sensitivity in drug-resistant human ovarian cancer cells\u003c/em\u003e. Journal of natural medicines, 2017. 71(4): p. 693\u0026ndash;702.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11418-017-1116-8\u003c/span\u003e\u003cspan address=\"10.1007/s11418-017-1116-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnwar, S., et al., \u003cem\u003eDeciphering quinazoline derivatives\u0026rsquo; interactions with EGFR: a computational quest for advanced cancer therapy through 3D-QSAR, virtual screening, and MD simulations\u003c/em\u003e. Frontiers in Pharmacology, 2024. 15: p. 1399372\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.3389/fphar.2024.1399372\u003c/span\u003e\u003cspan address=\".10.3389/fphar.2024.1399372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarinho, M.M., et al., \u003cem\u003eQuantum computational investigations and molecular docking studies on amentoflavone\u003c/em\u003e. Heliyon, 2021. 7(1).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2021.e05958\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2021.e05958\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZainab, R., et al., \u003cem\u003eFinding inhibitors for PCSK9 using computational methods\u003c/em\u003e. PLoS One, 2021. 16(8): p. e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e0255523.https://doi.org/10.1371/journal.pone.0255523\u003c/span\u003e\u003cspan address=\"0255523.10.1371/journal.pone.0255523\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamharack, P. and M.E. Soliman, \u003cem\u003eBioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks\u003c/em\u003e. Drug discovery today, 2018. 23(9): p. 1658\u0026ndash;1665.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drudis.2018.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.drudis.2018.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrodinger, L., \u003cem\u003eThe PyMOL molecular graphics system\u003c/em\u003e. Version, 2015. 1: p. 8\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.5281/zenodo.592552\u003c/span\u003e\u003cspan address=\".10.5281/zenodo.592552\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCousins, K.R., \u003cem\u003eComputer review of ChemDraw ultra 12.0\u003c/em\u003e. 2011, ACS Publications.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePawar, R.P. and S.H. Rohane, \u003cem\u003eRole of autodock vina in PyRx molecular docking\u003c/em\u003e. Asian Journal of Research in Chemistry, 2021. 14(2): p. 132\u0026ndash;134.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5958/0974-4150.2021.00022.4\u003c/span\u003e\u003cspan address=\"10.5958/0974-4150.2021.00022.4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris, G.M., et al., \u003cem\u003eAutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility\u003c/em\u003e. Journal of computational chemistry, 2009. 30(16): p. 2785\u0026ndash;2791.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcc.21256\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKresse, G. and J. Furthm\u0026uuml;ller, \u003cem\u003eEfficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set\u003c/em\u003e. Computational materials science, 1996. 6(1): p. 15\u0026ndash;50.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0927-0256(96)00008-0\u003c/span\u003e\u003cspan address=\"10.1016/0927-0256(96)00008-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFajardo-D\u0026iacute;az, E., et al., \u003cem\u003eInterMap: Accelerated Detection of Interaction Fingerprints on Large-Scale Molecular Ensembles.\u003c/em\u003e bioRxiv, 2025: p. 2025.12. 15.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e694195.https://doi.org/10.1101/2025.12.15.694195\u003c/span\u003e\u003cspan address=\"694195.10.1101/2025.12.15.694195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManzoor, H., et al., \u003cem\u003eIdentification and evaluation of pyrimidine based CDK6 inhibitors against glioblastoma using integrated computational approaches\u003c/em\u003e. Scientific Reports, 2025. 15(1): p. 25387.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-25387-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-25387-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunir, M., et al., \u003cem\u003eEvaluating Allium lycaonicum Phytochemicals as CCR5 Inhibitors for HIV Therapy: A Computational Study\u003c/em\u003e. Chemistry \u0026amp; Biodiversity, 2025: p. e00980\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.1002/cbdv.202400980\u003c/span\u003e\u003cspan address=\".10.1002/cbdv.202400980\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManzoor, H., et al., \u003cem\u003eComputational identification and evaluation of novel PD-L1 inhibitors for cancer immunotherapy\u003c/em\u003e. Scientific Reports, 2025. 15(1): p. 33787.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-33787-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-33787-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina, A., O. Michielin, and V. Zoete, \u003cem\u003eSwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules\u003c/em\u003e. Scientific reports, 2017. 7(1): p. 42717.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep42717\u003c/span\u003e\u003cspan address=\"10.1038/srep42717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuliaty, A., et al., \u003cem\u003eInteraction between Cytochrome P450 Isozymes and Antiseizure Medications: A Literature Review\u003c/em\u003e. Research Journal of Pharmacy and Technology, 2025. 18(3): p. 1089\u0026ndash;1095\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.52711/0974-360X.2025.00156\u003c/span\u003e\u003cspan address=\".10.52711/0974-360X.2025.00156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePires, D.E., T.L. Blundell, and D.B. Ascher, \u003cem\u003epkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures\u003c/em\u003e. Journal of medicinal chemistry, 2015. 58(9): p. 4066\u0026ndash;4072.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jmedchem.5b00104\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.5b00104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee, P., et al., \u003cem\u003eProTox-II: a webserver for the prediction of toxicity of chemicals\u003c/em\u003e. Nucleic acids research, 2018. 46(W1): p. W257-W263.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gky318\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky318\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorba, J.V., et al., \u003cem\u003eSTopTox: An in silico alternative to animal testing for acute systemic and topical toxicity\u003c/em\u003e. Environmental Health Perspectives, 2022. 130(2): p. 027012\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.1289/EHP8498\u003c/span\u003e\u003cspan address=\".10.1289/EHP8498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina, A., O. Michielin, and V. Zoete, \u003cem\u003eSwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules\u003c/em\u003e. Nucleic acids research, 2019. 47(W1): p. W357-W\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e364.https://doi.org/10.1093/nar/gkz382\u003c/span\u003e\u003cspan address=\"364.10.1093/nar/gkz382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoete, V., et al., \u003cem\u003eSwissSimilarity: a web tool for low to ultra high throughput ligand-based virtual screening\u003c/em\u003e. 2016, ACS Publications.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEjaz, S.A., et al., \u003cem\u003eIn-silico Investigations of quinine and quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and structural characterization\u003c/em\u003e. Plos one, 2022. 17(10): p. e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e0271602.https://doi.org/10.1371/journal.pone.0271602\u003c/span\u003e\u003cspan address=\"0271602.10.1371/journal.pone.0271602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeerlings, P., F. De Proft, and W. Langenaeker, \u003cem\u003eConceptual density functional theory\u003c/em\u003e. Chemical reviews, 2003. 103(5): p. 1793\u0026ndash;1874.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/cr990029p\u003c/span\u003e\u003cspan address=\"10.1021/cr990029p\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShafiq, N., et al., \u003cem\u003eA virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential\u003c/em\u003e. Journal of Biomolecular Structure and Dynamics, 2025. 43(9): p. 4512\u0026ndash;4533.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07391102.2025.2321457\u003c/span\u003e\u003cspan address=\"10.1080/07391102.2025.2321457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun, Y., et al., \u003cem\u003eMolecular dynamics simulations in semiconductor material processing: A comprehensive review\u003c/em\u003e. Measurement, 2025. 241: p. 115708.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.measurement.2024.115708\u003c/span\u003e\u003cspan address=\"10.1016/j.measurement.2024.115708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz, D.T., et al., \u003cem\u003eInvestigation of Serum Visfatin and Chemerin Levels in Type 2 Diabetes and Obesity Patients: Their Potential Role as Clinical and Biomarkers\u003c/em\u003e. Biomedicines, 2025. 13(11): p. 2619.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biomedicines13112619\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines13112619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Visfatin, T2DM, computational chemistry, Molecular docking, Density functional theory (DFT), ADMET","lastPublishedDoi":"10.21203/rs.3.rs-8809541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8809541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetes mellitus remains a critical health problem for the world population. Visfatin is a potential target for the effective treatment of type 2 diabetes mellitus (T2DM). Several biguanides, such as Berberine (a natural isoquinoline alkaloid) and Metformin, Phenformin, and Buformin (biguanide-derived antidiabetic drugs), present well-recognized antidiabetic effects. Thus, in this study, using molecular docking, density functional theory (DFT), ADMET, and drug-likeness approaches, the visfatin-inhibiting activity of Berberine and Biguanides for T2DM treatment was investigated. Berberine and Biguanide compounds have favorable pharmacokinetic and physicochemical properties, suggesting that both can be considered safe, orally bioavailable candidates. As lipophilicity, solubility, and Csp3 refinement improve, drugs exhibit better drug-like properties and are more advanced in the drug discovery process. Berberine had a binding free energy of -8.5 kcal/mol. The complex formed 2 hydrogen bonds, including a classical H-bond to Gln92 and Lys189 at an unusually short distance of 2.0 \u0026Aring;, as well as eleven hydrophobic interactions, rendering the compound, compared to Biguanides, the most efficient of all solids bearing potential visfatin.\u003c/p\u003e","manuscriptTitle":"Organic Molecular Strategy to Prevent Diabetes: An In Silico Screening of Berberine and Biguanides as Visfatin Antagonists","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 16:47:08","doi":"10.21203/rs.3.rs-8809541/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fee6a57b-5cce-4d4f-a6b3-d1f60148301b","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-13T08:57:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 16:47:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8809541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8809541","identity":"rs-8809541","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}