{"paper_id":"080fe8bf-e7ba-4510-a1ba-ff59faee20f5","body_text":"In-Silico Screening and Molecular Dynamics Evaluation of Spirulina Platensis-Derived Compounds as Potential Antiviral Agents Against Dengue Virus NS2b/NS3 Protease | 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 In-Silico Screening and Molecular Dynamics Evaluation of Spirulina Platensis-Derived Compounds as Potential Antiviral Agents Against Dengue Virus NS2b/NS3 Protease Joyanti Biswas, Md. Raisul Islam, Sumaiya Nousheen, Md. Abdul Jalil, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9667555/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 Dengue virus (DENV) remains a global health concern, with no clinically approved antiviral treatment available to date. The NS2b/NS3 serine protease is an essential viral enzyme responsible for polyprotein processing and represents a promising therapeutic target. This computational study employed molecular docking and molecular dynamics (MD) simulations to identify potential antiviral compounds from Spirulina platensis. A total of 19 bioactive compounds isolated from Spirulina platensis were screened against the dengue virus NS2b/NS3 protease (PDB ID: 2FOM) using PyRx virtual screening with AutoDock Vina. The seven compounds with the highest binding affinities were subjected to 100 nanosecond molecular dynamics simulations using the AMBER14 force field at physiological conditions (pH 7.4, 298 K). ADMET analysis and toxicity profiling were performed to evaluate drug-likeness and safety parameters. Tannin (− 8.9 kcal/mol) and Rutin (− 7.8 kcal/mol) exhibited the strongest binding affinities and remained stable throughout the simulation. RMSD analysis confirmed complex stability (< 3.3 Å for six of seven compounds), while hydrogen bonding patterns revealed sustained interactions between ligands and protein residues. ADMET screening identified gallic acid, oleic acid, alpha-terpineol, and beta-sitosterol as possessing favorable oral bioavailability characteristics. Notably, tannin demonstrated minimal toxicity across major organ systems. Our findings suggest that tannin and rutin from Spirulina platensis are promising lead compounds warranting further experimental validation for dengue antiviral drug development. This study demonstrates the potential of marine-derived natural products in accelerating the discovery of new therapeutic agents against viral infections. Dengue virus molecular docking molecular dynamics simulation Spirulina platensis NS2b/NS3 protease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Dengue represents one of the most significant vector-borne viral diseases globally, with transmission mediated by Aedes aegypti and Aedes albopictus mosquitoes. The causative agent, dengue virus (DENV), encompasses four serotypes (DENV-1 to DENV-4) and a recently identified fifth serotype (DENV-5) discovered in Sarawak, Malaysia in 2013 [ 1 – 3 ]. The epidemiological burden has reached unprecedented levels, with the World Health Organization (WHO) reporting more than 14.2 million dengue cases in 2024, including 7.5 million confirmed cases, over 52,000 severe manifestations, and approximately 10,000 deaths[ 4 ]. In Bangladesh, dengue fever was first documented in 1964, with subsequent major outbreaks in 2000, 2013, 2019, and 2022. Between January and September 2023, the country recorded 203,406 infections with 989 deaths, representing a case fatality rate of 0.49%, with 96.1% of infections occurring between July and September[ 5 ].Despite its clinical significance and increasing prevalence, dengue lacks specific antiviral therapy, with current management limited to supportive care measures. Several drug candidates, including chloroquine, prednisolone, lovastatin, and celgosivir, have entered clinical trials but failed to demonstrate efficacy in reducing viremia or providing significant therapeutic benefit[ 6 – 7 ]. This therapeutic gap, combined with the emergence of drug-resistant viral strains and adverse effects associated with synthetic antivirals, has catalyzed investigations into natural product-based drug discovery approaches. The dengue virus NS2b/NS3 serine protease is a bifunctional enzyme complex consisting of the NS3 catalytic domain and the NS2B cofactor. This complex is essential for viral replication as it catalyzes the post-translational proteolytic processing of the viral polyprotein into functional domains required for viral assembly and replication. The three-dimensional crystal structure of this protease (PDB ID: 2FOM) has been elucidated, enabling structure-based rational drug design approaches[ 8 – 10 ]. The NS2b/NS3 protease represents a superior therapeutic target compared to other viral enzymes because of its essential role in the viral lifecycle and its evolutionary conservation across DENV serotypes, suggesting potential cross-serotype activity of inhibitors. Spirulina platensis (Arthrospira platensis) is a nutrient-dense cyanobacterium classified as a superfood due to its exceptional concentrations of essential nutrients and bioactive compounds. Beyond its well-established nutritional profile, clinical and laboratory research has documented significant immune-stimulating, anti-inflammatory, and antiviral properties. Calcium spirulan (Ca-SP), an acidic polysaccharide derived from Spirulina platensis, has demonstrated potent inhibitory activity against multiple enveloped viruses[ 11 – 13 ]. Previous studies have confirmed antiviral efficacy of polysaccharides and methanol extracts of Spirulina against rotavirus, adenovirus, and coxsackievirus. Recent computational screening identified eight Spirulina-derived molecules with promising activity against SARS-CoV-2 using consensus docking methodologies. However, systematic evaluation of Spirulina compounds against dengue virus using integrated molecular docking and molecular dynamics simulation remains unexplored[ 14 – 16 ]. Traditional drug discovery encompasses a protracted timeline of 10–15 years from initial research to commercial availability, with substantial financial investment. Computer-aided drug design has emerged as a transformative approach, significantly accelerating the drug discovery pipeline and reducing development costs. CADD methodologies, particularly molecular docking and molecular dynamics simulations, enable virtual screening of large compound libraries and prediction of binding modes and stability of drug-target complexes under physiological conditions[ 17 – 21 ]. The integration of structure-based docking with ligand-based design, complemented by molecular dynamics simulation and ADMET profiling, provides a comprehensive framework for rational identification of lead compounds with optimal pharmacokinetic properties[ 22 – 23 ]. The absence of an approved dengue antiviral, the failure of multiple clinical-stage synthetic candidates, and the continued epidemiological expansion of all four DENV serotypes collectively constitute an urgent unmet medical need. Natural products have historically furnished a disproportionate fraction of antiviral and antibacterial drugs, owing to their structural complexity and inherent biocompatibility. Spirulina platensis, a globally cultivated and clinically safe organism, has demonstrated broad-spectrum antiviral activity across multiple viral families, yet its constituent compounds have not been systematically evaluated against the dengue NS2b/NS3 protease[ 24 – 26 ]. Computational screening of this well-characterized phytochemical library against a crystallographically resolved viral target therefore represents a scientifically justified and resource-efficient first step toward dengue antiviral discovery. Furthermore, the integration of MD simulation—a methodology rarely applied to spirulina-derived compounds—enables validation of docking predictions under physiologically realistic conditions, increasing the translational relevance of computational hits[ 27 – 29 ]. Despite growing interest in natural product-based antiviral development and the established therapeutic relevance of the dengue NS2b/NS3 protease, several critical gaps remain in the existing literature: No prior study has combined molecular docking, 100-ns MD simulation, ADMET profiling, and multi-organ toxicity assessment for Spirulina platensis compounds against DENV NS2b/NS3 protease. Most published natural product docking studies against dengue targets report binding affinities without MD validation, failing to account for time-dependent conformational changes that determine real-world binding feasibility. Incomplete compound space coverage: Previous spirulina-SARS-CoV-2 computational studies screened only a subset of compounds [ 28 ]; a full library screen against dengue-specific targets has not been performed. Existing studies rarely combine ADMET with multi-organ toxicity profiling using machine-learning models, leaving safety-related attrition risks unaddressed at the computational stage [ 29 – 33 ]. Limited structure-activity relationship data: The physicochemical and structural determinants of spirulina compound binding to the dengue protease active site remain unexplored, impeding rational analog design. Our study carries significance at multiple levels: It establishes the first integrated computational framework for evaluating spirulina-derived compounds against DENV NS2b/NS3 protease, generating novel structure-activity insights and validating binding stability through extended MD simulation. Identification of safe, bioavailable lead compounds from a widely consumed, sustainably cultivated organism directly addresses the therapeutic gap in dengue management, which affects hundreds of millions annually [ 34 – 35 ]. Noteworthy, The study demonstrates that MD instability can overturn docking-based rankings (sulfoquinovosyldiglyceride), validating the importance of dynamic sampling in computational antiviral campaigns and providing a methodological template for related flaviviral targets[ 36 ]. By generating ADMET- and toxicity-stratified lead compound tiers, this work directly informs the design of focused experimental follow-up studies, reducing resource expenditure in early-stage antiviral discovery. This work contributes to the growing evidence base supporting marine- and cyanobacterium-derived natural products as underexplored reservoirs of antiviral scaffolds, with potential applications beyond dengue to related flaviviral pathogens including Zika and West Nile virus [ 37 – 38 ]. This study was designed to: (1) screen 19 bioactive compounds from Spirulina platensis against dengue NS2b/NS3 protease by molecular docking; (2) evaluate binding stability of the seven highest-affinity compounds through 100-ns molecular dynamics simulation; (3) characterize ADMET pharmacokinetic profiles and multi-organ toxicity; and (4) generate a rational, tiered lead compound shortlist for experimental validation[ 39 – 40 ]. 2. Methods The three-dimensional crystal structure of dengue virus NS2b/NS3 protease (PDB ID: 2FOM) was downloaded from the RCSB Protein Data Bank ( https://www.rcsb.org ). The protein structure contained 697 amino acid residues distributed across two chains with missing residues in specific regions. Protein preparation was performed using BIOVIA Discovery Studio (DassaultSystèmes, 2016), employing the following workflow: (1) visualization of the protein structure in three dimensions; (2) deletion of heteroatoms, water molecules, and non-essential ligands; (3) identification and computational addition of missing amino acid residues based on the FASTA sequence; (4) addition of hydrogen atoms using the CHARMM force field; and (5) energy minimization using Swiss PDB Viewer ( https://spdbv.unil.ch/ ). The prepared protein was saved in PDB format for subsequent docking studies[ 41 – 43 ]. Nineteen bioactive compounds previously identified from Spirulina platensis in peer-reviewed literature were selected for this study (Table 1 ). Compound structures were retrieved from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov ) in Structure Data File (SDF) format. The selected compounds represented diverse chemical classes including fatty acids, polysaccharides, polyphenols, and phytosterols. Ligand preparation involved: (1) conversion of SDF files to three-dimensional PDB format using Online SMILES Translator; (2) visualization in BIOVIA Discovery Studio; (3) addition of polar hydrogen atoms; (4) energy minimization using Swiss PDB Viewer; and (5) conversion to PDBQT format using PyRx. All ligand structures were subject to energy minimization to remove steric clashes and generate conformations suitable for docking[ 44 – 46 ]. Table 1 Bioactive compounds from Spirulina platensis selected for computational screening. Compound Chemical Class Reference Ca-SP (Calcium spirulan) Polysaccharide Hayashi et al., 2008 Sulfoquinovosyldiglyceride Glycolipid Chirasuwan et al., 2009 Palmitic acid Fatty acid Hetta et al., 2014 Palmitoleic acid Unsaturated fatty acid Hetta et al., 2014 Myristic acid Fatty acid Hetta et al., 2014 Margaric acid Fatty acid Hetta et al., 2014 Oleic acid Unsaturated fatty acid Hetta et al., 2014 Linoleic acid Polyunsaturated fatty acid Hetta et al., 2014 Capric acid Fatty acid Hetta et al., 2014 Lauric acid Fatty acid Hetta et al., 2014 Stearic acid Fatty acid Hetta et al., 2014 β-sitosterol Phytosterol Hetta et al., 2014 n-heptadecane Alkane Hetta et al., 2014 α-pinene Monoterpene Akram et al., 2010 α-terpineol Monoterpenol Akram et al., 2010 Gallic acid Polyphenol Hetta et al., 2014 Rutin Flavonoid glycoside Nuhu, 2013 Tannin Polyphenolic compound Nuhu, 2013 Na-SP (Sodium spirulan) Polysaccharide Lee et al., 2007 Molecular docking was performed using PyRx (version 0.8), a virtual screening tool integrating AutoDock Vina. The prepared protein (2FOM) was designated as the macromolecule, while all 19 compounds served as ligands. The docking grid box was centered at coordinates: X = 0.097 Å, Y = − 17.0563 Å, Z = 13.8011 Å, with dimensions encompassing the entire protein surface to enable blind docking. Blind docking was selected to improve hit enrichment and comprehensively explore the ligand pose space without predetermined assumptions regarding the binding site. AutoDock Vina conducted exhaustive conformational sampling, generating multiple ligand poses for each compound and calculating binding free energies for each pose[ 47 – 49 ]. The workflow encompassed: (1) loading of protein and ligand structures; (2) conversion to PDBQT format; (3) specification of docking parameters; (4) execution of docking simulations; and (5) ranking of results by binding affinity (ΔG in kcal/mol). The seven compounds exhibiting the lowest binding affinities (highest binding strengths) were selected for subsequent analysis. Visualization of protein-ligand interactions was performed using PyMOL (version 3.1.4) and BIOVIA Discovery Studio 2021. The PyMOL visualization pipeline included: (1) importation of docked complexes[ 50 – 52 ]; (2) color coding of protein chains; (3) representation of protein structures as cartoon diagrams or molecular surfaces; (4) identification and visualization of hydrogen bonds; (5) highlighting of hydrophobic interactions; and (6) labeling of key interacting residues with three-letter codes and position identifiers. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties were assessed for the seven top-ranked compounds using Swiss ADME ( http://www.swissadme.ch/ ) and ADMETlab 3.0 ( https://admetlab3.scbdd.com/ ). ADMET analysis evaluated: (1) molecular weight; (2) water solubility predictions; (3) gastrointestinal absorption (GI absorption classification: high/low); (4) blood-brain barrier (BBB) permeability; (5) Lipinski's Rule of Five compliance (molecular weight < 500 Da, LogP < 5, hydrogen bond donors < 5, hydrogen bond acceptors < 10); and (6) oral bioavailability potential. Toxicity profiling assessed organ-specific toxicity including hepatotoxicity, nephrotoxicity, neurotoxicity, respiratory toxicity, and cardiotoxicity using machine learning models trained on experimental toxicity datasets[ 53 – 56 ] The molecular dynamics (MD) simulations were performed using the AMBER14 force field for a total duration of 100 nanoseconds, with an integration time step of 2.50 femtoseconds. The system temperature was maintained at 298 K using the Berendsen thermostat, while the pressure was controlled at 1 atm to mimic standard conditions. Physiological pH (7.4) was assumed throughout the simulation. The system was solvated using the TIP3P water model with a density of 0.997 g/cm³, and a salt concentration of 0.9% NaCl was applied to replicate biological ionic strength. Trajectory data were recorded at intervals of every 250 picoseconds for subsequent analysis.MD simulations were initiated from the docked poses of the seven lead compounds[ 57 – 59 ]. The trajectory data generated over the 100 ns simulation were analyzed to compute the following structural and dynamical parameters: Root Mean Square Deviation (RMSD): Calculated for Cα atoms to assess structural stability. RMSD measures the deviation of the complex structure from its initial configuration over simulation time, with values < 3.0 Å generally indicating stable complexes. Root Mean Square Fluctuation (RMSF): Evaluated for individual amino acid residues to quantify local flexibility and residual vibration. RMSF values provide insights into regions of protein flexibility and identify critical residues involved in ligand binding. Radius of Gyration (Rg): Computed to assess the compactness and rigidity of the protein backbone. Stable Rg values indicate maintained structural integrity, while increasing Rg suggests protein expansion or unfolding. Solvent-Accessible Surface Area (SASA): Calculated to evaluate changes in surface exposure during the simulation. Increased SASA values indicate protein expansion or conformational changes affecting complex stability. Hydrogen Bond Analysis: Total hydrogen bonds formed between the ligand-protein complex and solvent were monitored throughout the simulation. Persistent hydrogen bonding indicates favorable binding interactions and complex stability. Software tools employed included PyRx, BIOVIA Discovery Studio, PyMOL, YASARA, Swiss PDB Viewer, and online analytical platforms (Swiss ADME, ADMETlab 3.0, PubChem, RCSB PDB). 3. Results and Discussion Among the 19 Spirulina-derived compounds screened, seven exhibited favorable binding interactions with the dengue NS2b/NS3 protease, ranked by binding affinity (Table 2 ). Tannin demonstrated the highest binding affinity (− 8.9 kcal/mol), followed by Rutin (− 7.8 kcal/mol), β-sitosterol (− 7.1 kcal/mol), Sulfoquinovosyldiglyceride(− 5.9 kcal/mol),Gallic acid(− 5.7 kcal/mol), α-terpineol (− 5.3 kcal/mol), and Oleic acid (− 5.1 kcal/mol). All seven compounds exhibited RMSD values of 0 for both upper and lower bounds, indicating geometrically optimal docking poses with minimal conformational adjustment[ 60 – 61 ]. Table 2 Molecular docking results for the seven lead compounds against dengue NS2b/NS3 protease. Rank Compound Binding Affinity (kcal/mol) RMSD/UB RMSD/LB 1 Tannin −8.9 0 0 2 Rutin −7.8 0 0 3 β-sitosterol −7.1 0 0 4 Sulfoquinovosyldiglyceride −5.9 0 0 5 Gallic acid −5.7 0 0 6 α-terpineol −5.3 0 0 7 Oleic acid −5.1 0 0 ADMET analysis of the seven lead compounds revealed critical information regarding their pharmacokinetic profiles and drug-likeness (Table 3 ). Gallic acid, oleic acid, α-terpineol, and β-sitosterol demonstrated high GI absorption potential, suggesting favorable intestinal uptake. Molecular weight analysis indicated that all compounds except tannin and rutin complied with Lipinski's Rule of Five, predicting enhanced oral bioavailability. Water solubility profiles ranged from poor (tannin, β-sitosterol, sulfoquinovosyldiglyceride) to soluble (gallic acid, α-terpineol), with intermediate solubility observed for rutin and oleic acid. Blood-brain barrier permeability analysis indicated that only α-terpineol demonstrated potential BBB penetration, while all other compounds showed restricted CNS entry, reducing neurotoxicity risk[ 62 – 64 ]. Table 3 ADMET properties of the seven lead compounds. Compound MW (g/mol) Solubility GI Absorption BBB Penetration Lipinski Violation Tannin 1700.17 Poor Low No 3 Rutin 610.52 Moderate Low No 3 β-sitosterol 414.71 Poor Low No 1 Sulfoquinovosyldiglyceride 841.14 Poor Low No 2 Gallic acid 170.12 Soluble High No 0 α-terpineol 154.25 Soluble High Yes 0 Oleic acid 282.46 Moderate High No 1 Toxicity profiling (Table 4 ) demonstrated that tannin exhibited the most favorable toxicity profile with \"No\" classification across hepatotoxicity, nephrotoxicity, and neurotoxicity, and \"Medium\" risk for respiratory and cardiotoxicity. Rutin showed similar favorable characteristics with \"No\" hepatotoxicity and neurotoxicity, though medium respiratory and cardiotoxicity risk.β-sitosterol, sulfoquinovosyldiglyceride, and oleic acid exhibited elevated toxicity concerns in specific organ systems, particularly hepatic and renal toxicity. Gallic acid demonstrated medium-risk toxicity across multiple systems, while α-terpineol showed medium neurotoxicity risk. Table 4 Toxicity profiling of the seven lead compounds across organ systems. Compound Hepatotoxicity Nephrotoxicity Neurotoxicity Respiratory Toxicity Cardiotoxicity Tannin No No No Medium Medium Rutin Medium No No Medium Medium β-sitosterol Medium Medium No High Medium Sulfoquinovosyldiglyceride High High No Medium Medium Gallic acid Medium No No Medium Medium α-terpineol Medium No Medium No Medium Oleic acid No Medium No High Medium Molecular dynamics simulations of the seven ligand-protein complexes over 100 ns provided comprehensive insights into binding stability and complex dynamics under physiological conditions.RMSD analysis of Cα atoms revealed critical differences in complex stability patterns (Fig. 1 ). Six of the seven compounds exhibited overall RMSD trends below 3.3 Å, indicating stable protein conformations throughout the 100 ns simulation. Oleic acid maintained the most consistent RMSD profile with a horizontal trend, despite minor deviations at specific timepoints (23.5 ns: 3.4 Å; 27.5 ns: 3.9 Å; 42.5 ns: 3.5 Å)[ 65 ]. Notably, sulfoquinovosyldiglyceride (SQD) exhibited an abnormal increasing trend after 80 ns, with RMSD values progressively rising above 3.3 Å, indicating loss of structural stability during the final 20 ns of the simulation. This destabilization suggests that SQD, despite moderate initial binding affinity, does not maintain favorable binding interactions under extended physiological conditions[ 66 ]. RMSF analysis of individual amino acid residues across the 100 ns simulation revealed residue-specific flexibility patterns (Fig. 2 ). For tannin, rutin, β-sitosterol, gallic acid, α-terpineol, and oleic acid, RMSF values demonstrated remarkable consistency, indicating uniform residual flexibility and stable ligand-induced stabilization of protein regions. Initial residues (positions 1–54) showed maximum variation across all complexes, with RMSF values ranging from 3.4 to 10.6 Å, reflecting the inherent flexibility of terminal protein regions[ 67 ]. In contrast, SQD exhibited anomalous fluctuation patterns in the region spanning residues 513–698, with RMSF values ranging abnormally from 7.89 to 12.91 Å. This pronounced regional fluctuation, substantially exceeding values observed for other complexes, confirms the instability of this compound and suggests unsuccessful stabilization of critical protein regions. Radius of gyration analysis assessed structural compactness and rigidity maintenance throughout the simulation (Fig. 3 ). Most complexes exhibited stable Rg values ranging from 16.8 to 17.1 Å, demonstrating maintained structural compactness[ 68 – 69 ]. Gallic acid displayed minor Rg elevation between 57.75 and 67.5 ns (17.514 to 17.743 Å) before stabilizing, suggesting transient conformational adjustment followed by recovery. Importantly, SQD demonstrated continuous and pronounced Rg increase beginning at 81 ns and continuing through the end of the simulation, indicating progressive protein expansion and loss of structural organization, consistent with RMSD and RMSF findings. SASA analysis monitored surface exposure changes during the simulation (Fig. 4 ). The average SASA values for six of the seven complexes ranged consistently from 10,002 to 10,741 Ų, indicating stable surface characteristics. SQD displayed a marked increase in SASA at approximately 60 ns, with values diverging from the stable range and remaining elevated through the end of the simulation[ 70 – 71 ]. This elevation in surface area corresponds with observations from RMSD, RMSF, and Rg analyses, further supporting the conclusion that SQD undergoes deleterious conformational changes affecting complex stability. Hydrogen Bond Formation Hydrogen bonding between the ligand-protein complex and the solvent environment was comprehensively monitored (Fig. 5 ). All seven compounds established hydrogen bonds throughout the simulation, reflecting sustained interactions within the binding pocket and with solvent molecules. Tannin and Rutin exhibited the most extensive hydrogen bonding patterns, with tannin establishing 11–23 hydrogen bonds and rutin maintaining 16–34 hydrogen bonds throughout the simulation. These elevated hydrogen bond counts correlate with their superior binding affinities and suggest multiple favorable intermolecular interactions. In contrast, β-sitosterol and α-terpineol formed fewer hydrogen bonds (1–3 bonds), consistent with their lower binding affinities and reduced polar surface areas. The sustained hydrogen bonding observed for tannin and rutin throughout the 100 ns simulation indicates persistent favorable electrostatic interactions that stabilize the protein-ligand complex[ 72 ]. 4. Discussion Dengue remains among the most significant vector-borne viral diseases globally, with alarming epidemiological trends. The WHO's documentation of > 14.2 million dengue cases in 2024, including > 7.5 million confirmed cases and > 10,000 deaths, underscores the urgent need for effective therapeutic interventions. Bangladesh has experienced recurring outbreaks since 2000, with increasing severity and mortality rates. The absence of clinically approved antiviral therapy, combined with limited vaccine coverage and suboptimal vector control measures, perpetuates disease transmission and morbidity. These epidemiological realities justify the investment in computational drug discovery approaches targeting dengue-specific vulnerabilities[ 73 ]. The dengue virus NS2b/NS3 serine protease was selected as the pharmacological target based on its essential role in viral replication and proteolytic processing of the viral polyprotein. This protease complex catalyzes the sequential cleavage of the dengue-encoded polyprotein into mature structural and non-structural proteins required for viral genome replication and virion assembly. The evolutionary conservation of this enzyme across all four major dengue serotypes and the fifth serotype suggests that inhibitors developed against this target possess potential for broad-spectrum activity. Previous literature has validated the NS2b/NS3 protease as a productive drug discovery target, with numerous structure-based drug design studies and clinical development efforts aimed at identifying selective protease inhibitors. Spirulina platensis represents a compelling natural product source for antiviral drug discovery. Extensive clinical and laboratory evidence has documented the immunostimulatory, anti-inflammatory, and antiviral properties of spirulina extracts and their constituent bioactive compounds. The documented efficacy against multiple viral pathogens (rotavirus, adenovirus, coxsackievirus, SARS-CoV-2, influenza) provides precedent for evaluating spirulina components against dengue virus. The safety profile established through decades of human consumption and clinical applications, combined with the accessibility and sustainability of spirulina cultivation, positions this organism as an excellent source for lead compound identification. The 19 compounds selected for this study represent the chemical diversity present within spirulina, including polysaccharides, fatty acids, polyphenols, and phytosterols[ 74 ]. The ranking of compounds by binding affinity identified tannin and rutin as superior lead candidates. The binding affinities of − 8.9 and − 7.8 kcal/mol, respectively, indicate favorable thermodynamic binding, with tannin's affinity among the strongest reported for natural products against dengue proteases. These binding energies suggest favorable enthalpic interactions, likely mediated through hydrogen bonding and hydrophobic interactions within the substrate-binding pocket of the NS2b/NS3 protease. The structural features of tannin and rutin supporting favorable binding reflect their polyphenolic nature, with multiple hydroxyl groups capable of hydrogen bond formation with backbone amide and polar side chains of catalytic residues. Rutin's superior performance relative to structurally simpler polyphenols and oleic acid likely derives from its larger size and complex ring structure enabling multiple binding interactions. The modest binding affinities of fatty acids (oleic acid: −5.1 kcal/mol) and simple aromatics (α-terpineol: −5.3 kcal/mol) compared to polyphenols reflect their reduced capacity for specific polar interactions. ADMET analysis revealed important distinctions between compounds regarding their suitability for pharmaceutical development. Gallic acid and α-terpineol exhibited superior characteristics for drug candidates based on zero Lipinski violations and high GI absorption, coupled with favorable water solubility[ 75 ]. These properties predict efficient intestinal absorption and systemic bioavailability essential for oral formulations. The fact that these compounds maintain favorable ADMET profiles while still exhibiting binding affinities ≥ − 5.3 kcal/mol suggests their viability for further development. Tannin and rutin, despite exhibiting the strongest binding affinities, demonstrate elevated molecular weights and Lipinski violations, predicting suboptimal oral bioavailability[ 76 ]. This characteristic does not preclude their development as therapeutic agents but suggests alternative formulation strategies (parenteral administration, topical delivery, sustained-release formulations) or chemical modification to enhance bioavailability. The three Lipinski violations observed for both compounds warrant synthetic modification to reduce molecular weight and hydrogen bonding capacity while maintaining antiviral potency. The toxicity profiles demonstrated that tannin poses minimal risks across most organ systems, with only medium-level respiratory and cardiac toxicity concerns. This favorable safety profile, combined with superior binding affinity, positions tannin as the highest-priority candidate for lead compound optimization and experimental validation[ 77 ]. The 100 ns molecular dynamics simulations provided critical validation of binding stability predictions derived from static docking poses. This extended sampling of conformational space under physiological conditions revealed that six of seven compounds maintained stable protein-ligand complexes throughout the simulation period[ 78 ]. Tannin and rutin, the two compounds with the strongest docking affinities, both demonstrated excellent stability profiles across all assessed parameters (RMSD, RMSF, Rg, SASA). The sustained hydrogen bonding observed for these compounds (23 bonds for tannin; 34 bonds for rutin) indicates persistent favorable interactions that stabilize the complex[ 79 ]. These findings suggest that the docking predictions accurately reflected the favorable binding thermodynamics and binding modes of these compounds. Oleic acid maintained the most consistent RMSD trajectory despite its modest binding affinity, suggesting that even less energetically favorable compounds can maintain stable complexes under physiological conditions. This finding underscores the value of MD simulations in identifying compounds that, while exhibiting lower docking scores, may maintain functional binding stability. The marked instability of sulfoquinovosyldiglyceride (SQD) beginning at 80 ns, evidenced by increasing RMSD, RMSF, Rg, and SASA values, indicates that docking affinity alone provides insufficient information regarding practical binding stability. Despite its − 5.9 kcal/mol binding affinity, SQD's inability to maintain stable interactions under physiological conditions argues against its selection as a lead candidate. This finding illustrates the critical importance of incorporating MD simulations into computational drug discovery workflows, as docking-only approaches would have incorrectly ranked SQD above oleic acid and α-terpineol. Integration of docking affinity, ADMET properties, toxicity profiles, and MD simulation stability enables comprehensive lead compound prioritization: Tier 1 (Highest Priority): Tannin: Superior binding affinity (− 8.9 kcal/mol), excellent MD stability, minimal toxicity profile, but requires bioavailability optimization through chemical modification or alternative formulation strategies. Tier 2 (High Priority): Rutin: Strong binding affinity (− 7.8 kcal/mol), excellent MD stability, favorable toxicity profile, but similar bioavailability limitations requiring optimization. Gallic acid: Moderate binding affinity (− 5.7 kcal/mol), favorable ADMET properties with high GI absorption, excellent water solubility, and minimal Lipinski violations, making it suitable for direct advancement to experimental validation. Tier 3 (Moderate Priority): Oleic acid: Modest binding affinity but excellent MD stability and favorable ADMET properties. α-terpineol: Moderate binding affinity with favorable ADMET characteristics, though medium neurotoxicity risk warrants careful evaluation[ 80 ]. The superior performance of tannin and rutin compared to simpler polyphenols and non-polar compounds reflects the importance of structural complexity and polar interactions in dengue NS2b/NS3 protease inhibition[ 81 ]. Polyphenolic compounds, with their multiple aromatic rings and hydroxyl groups, provide a structural framework accommodating multiple hydrogen bonds and π-π interactions with protein residues. The correlation between binding affinity and hydrogen bonding capacity (tannin: 23 H-bonds; rutin: 34 H-bonds) suggests that electrostatic interactions dominate the binding thermodynamics of these compounds. In contrast, fatty acids and monoterpenes, while capable of establishing hydrophobic interactions within the binding pocket, lack the polar groups necessary for extensive hydrogen bonding, resulting in weaker overall binding. This structure-activity relationship provides a rational basis for designing second-generation analogs with enhanced binding properties through incorporation of additional polar moieties or optimization of molecular geometry. While computational studies provide critical insights into molecular interactions and stability predictions, these approaches cannot definitively establish antiviral efficacy without experimental validation. The current findings identify lead compounds warranting progression to: In vitro cell-based assays assessing dengue virus replication inhibition using authenticated viral strains and standardized plaque reduction or quantitative RT-PCR methodologies. Enzymatic assays directly measuring NS2b/NS3 protease inhibition using recombinant protein and synthesized substrate peptides. Structural biology studies including X-ray crystallography or cryo-EM to visualize ligand-protein interactions and confirm predicted binding modes. Pharmacokinetic studies in appropriate animal models to establish absorption, distribution, metabolism, elimination, and oral bioavailability. 5. Toxicology studies assessing in vivo safety in animal models and potential for off-target effects. 5.Limitations and Contextual Considerations This computational study operates within several important limitations. First, molecular docking and MD simulations represent static and semi-dynamic representations of a highly dynamic biological system. The protein and ligands are treated as relatively rigid structures, whereas in vivo binding likely involves conformational adaptation and induced-fit mechanisms not fully captured by these methodologies. Second, blind docking without prior knowledge of the substrate-binding pocket may identify artifactual binding modes at non-functional sites. Although the present study was constrained to the protein surface through grid box configuration, more targeted approaches incorporating known binding site information might refine predictions. Third, the ADMET and toxicity predictions rely on machine learning models trained on limited experimental datasets, introducing inherent uncertainty in quantitative predictions. Empirical laboratory testing remains essential to validate these computational estimates. Fourth, only 19 of the 48 known compounds in spirulina could be evaluated due to time and computational constraints, potentially missing additional promising candidates. Finally, the dengue NS2b/NS3 protease structure represents a single serotype (likely derived from serotype 2 based on sequence identity), and binding characteristics may vary across serotypes due to natural genetic variation. As stated earlier that our computational investigation identified tannin and rutin from Spirulina platensis as promising lead compounds against dengue virus NS2B/NS3 protease based on molecular docking, ADMET profiling, toxicity assessment, and molecular dynamics simulation. Tannin demonstrated the strongest binding affinity (− 8.9 kcal/mol) with minimal organ-system toxicity while maintaining excellent stability throughout 100 ns MD simulation. Previous experimental studies have reported that flavonoids and polyphenolic compounds can effectively inhibit dengue NS2B/NS3 protease, supporting the therapeutic relevance of the present findings [ 82 – 88 ] 6. Conclusion Computational investigation identified tannin and rutin from Spirulina platensis as promising lead compounds against dengue virus NS2b/NS3 protease based on molecular docking, ADMET profiling, toxicity assessment, and molecular dynamics simulation. Tannin demonstrated the strongest binding affinity (− 8.9 kcal/mol) with minimal organ-system toxicity, while maintaining excellent stability throughout 100 ns MD simulation. Rutin exhibited comparable stability and favorable toxicity profiles despite slightly lower binding affinity. ADMET analysis identified gallic acid and oleic acid as compounds with superior bioavailability characteristics suitable for direct experimental advancement, while tannin and rutin require bioavailability optimization through chemical modification or alternative formulation strategies. The marked instability of sulfoquinovosyldiglyceride during MD simulations, despite its reasonable docking affinity, underscores the critical importance of dynamical sampling in computational drug discovery. The integration of binding affinity predictions, ADMET properties, toxicity profiles, and MD stability assessment provides a comprehensive framework for rational lead compound identification. These findings establish a strong computational foundation for experimental validation through in vitro cell-based dengue replication assays, enzymatic protease inhibition studies, and in vivo pharmacokinetic and toxicology evaluations. Success of these subsequent experimental studies as stated earlier in the discussion section could generate novel antiviral therapeutic options for dengue treatment and contribute to addressing a significant global health challenge affecting hundreds of millions of individuals annually. Declarations Competing Interests The authors declare no competing financial interests. Funding This research was supported by computational resources provided by the Bangladesh Medical University Department of Pharmacology. Author Contribution Author contributions are summarized below:• Dr. Joyanti Biswas (ORCID: 0009-0008-9629-3894), Department of Pharmacology, Bangladesh Medical University — Conceptualization, pharmacological interpretation, manuscript review.• Dr. Md. Raisul Islam (ORCID: 0009-0001-4252-5863), Department of Pharmacology, Sher-E-Bangla Medical College — Data interpretation, literature review, manuscript editing.• Dr. Sumaiya Nousheen (ORCID: 0009-0007-7374-0105), Department of Pharmacology and Therapeutics, Holy Family Red Crescent Medical College — ADMET analysis support, manuscript preparation.• Dr. Md. Abdul Jalil (ORCID: 0009-0001-9237-3140), Department of Pharmacology, Dinajpur Medical College — Toxicity analysis, validation, manuscript revision.• Dr. Milan Kumar Saha, Consultant (Surgery), DGHS, Mohakhali, Dhaka, Bangladesh — Clinical interpretation and biomedical significance assessment.• Dr. Kalyan Dhar, Department of Chemical Engineering, Politecnico di Milano, Italy; Shyamoli Engineering College, University of Dhaka, Bangladesh — Corresponding author; molecular docking, molecular dynamics simulations, computational analysis, visualization, and primary manuscript drafting.We appreciate your time and consideration of our manuscript. We believe the interdisciplinary nature and translational relevance of this study will be of interest to the readership of the Journal of Molecular Modeling, and we look forward to your response.Sincerely,Dr. Kalyan Dhar, PhDCorresponding AuthorDepartment of Chemical EngineeringPolitecnico di Milano20133 Milan, ItalyShyamoli Engineering CollegeUniversity of DhakaDhaka-1000, BangladeshEmail: [ [email protected] ](mailto: [email protected] ) Data Availability All datas will be available after acceptance of the MS with the SA GitHub repo: www.github.com/onepartho References Schaefer, T. J.; Wolford, R. W. Dengue Fever. Nih.gov. https://www.ncbi.nlm.nih.gov/books/NBK430732/ . World Health Organization. Dengue and severe dengue. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue . Yang, Z.-S.; Baua, A. D.; Hemdan, M. S.; Assavalapsakul, W.; Wang, W.-H.; Lin, C.-Y.; Chao, D.-Y.; Chen, Y.-H.; Wang, S.-F. Dengue Virus Infection: A Systematic Review of Pathogenesis, Diagnosis and Management. Journal of Infection and Public Health 2025, 18 (12), 102982. https://doi.org/10.1016/j.jiph.2025.102982 . Disease Outbreak News: Dengue - Global Situation (30 May 2024) - World | ReliefWeb. reliefweb.int. https://reliefweb.int/report/world/disease-outbreak-news-dengue-global-situation-30-may-2024 . Ikponmwosa Jude Ogieuhi; Ahmed, M. M.; Jamil, S.; Olalekan John Okesanya; Bonaventure Michael Ukoaka; Eshun, G.; Jerico Bautista Ogaya; Eliseo, D. Dengue Fever in Bangladesh: Rising Trends, Contributing Factors, and Public Health Implications. Tropical Diseases Travel Medicine and Vaccines 2025, 11 (1). https://doi.org/10.1186/s40794-025-00251-6 . Obi, J. O.; Gutiérrez-Barbosa, H.; Chua, J. V.; Deredge, D. J. Current Trends and Limitations in Dengue Antiviral Research. Tropical Medicine and Infectious Disease 2021, 6 (4), 180. https://doi.org/10.3390/tropicalmed6040180 . Gan, V. C. Dengue: Moving from Current Standard of Care to State-of-The-Art Treatment. Current Treatment Options in Infectious Diseases 2014, 6 (3), 208–226. https://doi.org/10.1007/s40506-014-0025-1 . Frimayanti, N.; Chee, C. F.; Zain, S.; Rahman, N. Abd. Design of New Competitive Dengue Ns2b/Ns3 Protease Inhibitors—a Computational Approach. International Journal of Molecular Sciences 2011, 12 (2), 1089–1100. https://doi.org/10.3390/ijms12021089 . Lin, Y.-F.; Lai, H.-C.; Lin, C.-S.; Hung, P.-Y.; Kan, J.-Y.; Chiu, S.-W.; Lu, C.-H.; Petrova, S. F.; Baltina, L.; Lin, C.-W. Discovery of Potent Dengue Virus NS2B-NS3 Protease Inhibitors among Glycyrrhizic Acid Conjugates with Amino Acids and Dipeptides Esters. Viruses 2024, 16 (12), 1926. https://doi.org/10.3390/v16121926 . Lang, J.; Dutta, S. K.; Leuthold, M. M.; Reichert, L.; Kühl, N.; Martina, B.; Klein, C. D. Antiviral Drug Discovery with an Optimized Biochemical Dengue Protease Assay: Improved Predictive Power for Antiviral Efficacy. Antiviral Research 2024, 234, 106053. https://doi.org/10.1016/j.antiviral.2024.106053 . Harun Norshidah; Chiuan Herng Leow; Kamarulzaman Ezatul Ezleen; Wahab, H. A.; Ramachandran Vignesh; Rasul, A.; Ngit Shin Lai. Assessing the Potential of NS2B/NS3 Protease Inhibitors Biomarker in Curbing Dengue Virus Infections: In Silico vs. in Vitro Approach. Assessing the potential of NS2B/NS3 protease inhibitors biomarker in curbing dengue virus infections: In silico vs. In vitro approach 2023, 13. https://doi.org/10.3389/fcimb.2023.1061937 . Mader, J.; Gallo, A.; Schommartz, T.; Handke, W.; Nagel, C.-H.; Günther, P.; Brune, W.; Reich, K. Calcium Spirulan Derived from Spirulina Platensis Inhibits Herpes Simplex Virus 1 Attachment to Human Keratinocytes and Protects against Herpes Labialis. Journal of Allergy and Clinical Immunology 2016, 137 (1), 197–203.e3. https://doi.org/10.1016/j.jaci.2015.07.027 . Hayashi, T.; Hayashi, K.; Maeda, M.; Kojima, I. Calcium Spirulan, an Inhibitor of Enveloped Virus Replication, from a Blue-Green AlgaSpirulina Platensis. Journal of Natural Products 1996, 59 (1), 83–87. https://doi.org/10.1021/np960017o . Verani, M.; Manera, C.; Pagani, A.; Banti, M.; Carducci, A.; Gasperin, F.; Cannaos, A.; Di Giuseppe, G.; Palego, L.; Nieri, P.; Federigi, I. In Vitro Evaluation of Virucidal Effect of Polysaccharides Extracted and Purified from Arthrospira Platensis and Dunaliella Salina on Human Adenovirus Type 5 in A549 Cells. Molecules 2026, 31 (6), 1023. https://doi.org/10.3390/molecules31061023 . Rabaan, A. A.; Kaabi, A.; None Muzaheed; Mubarak Alfaresi; Garout, M.; Alotaibi, N.; Ameen; Alsayyah, A.; Alali, N. A.; Sulaiman, T.; Alotaibi, J.; Alissa, M. Antiviral Actions of Natural Compounds against Dengue Virus RNA Dependent RNA Polymerase: Insights from Molecular Dynamics and Gibbs Free Energy Landscape. Journal of Biomolecular Structure and Dynamics 2024, 1–18. https://doi.org/10.1080/07391102.2024.2325120 . Cordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592–7618. https://doi.org/10.3390/cimb46070451 . Al Khzem, A. H.; Wali, S. M. Drug Repurposing as an Effective Drug Discovery Strategy: A Critical Review. Drug design, development and therapy 2025, 19, 12019–12034. https://doi.org/10.2147/DDDT.S576701 . Gourab Ray. The Technological Re-Engineering of Pharmaceutical Research and Development: A Quantitative Analysis of Innovation’s Impact on the Drug Discovery Value Chain. World Journal of Advanced Research and Reviews 2025, 27 (2), 533–550. https://doi.org/10.30574/wjarr.2025.27.2.2857 . Nobuaki Yasuo; Ishida, T.; Masakazu Sekijima. Computer Aided Drug Discovery Review for Infectious Diseases with Case Study of Anti-Chagas Project. Parasitology international 2021, 83, 102366–102366. https://doi.org/10.1016/j.parint.2021.102366 . Al Khzem, A. H.; Wali, S. M. Drug Repurposing as an Effective Drug Discovery Strategy: A Critical Review. Drug design, development and therapy 2025, 19, 12019–12034. https://doi.org/10.2147/DDDT.S576701 . Fahim, A. M. Advances in Computer-Aided Drug Design: From Molecular Docking to AI-Driven Therapeutic Discovery. ASPET Discovery 2026, 100024. https://doi.org/10.1016/j.aspetd.2026.100024 . Fahim, A. M. Advances in Computer-Aided Drug Design: From Molecular Docking to AI-Driven Therapeutic Discovery. ASPET Discovery 2026, 100024. https://doi.org/10.1016/j.aspetd.2026.100024 . Challapa-Mamani, M. R.; Tomás-Alvarado, E.; Espinoza-Baigorria, A.; León-Figueroa, D. A.; Sah, R.; Rodríguez-Morales, A. J.; Barboza, J. J. Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania Donovani: An Update and Literature Review. Tropical Medicine and Infectious Disease 2023, 8 (10), 457–457. https://doi.org/10.3390/tropicalmed8100457 . Lim, L.; Dang, M.; Roy, A.; Kang, J.; Song, J. Curcumin Allosterically Inhibits the Dengue NS2B-NS3 Protease by Disrupting Its Active Conformation. ACS Omega 2020, 5 (40), 25677–25686. https://doi.org/10.1021/acsomega.0c00039 . Saqallah, F. G.; Abbas, M. A.; Wahab, H. A. Recent Advances in Natural Products as Potential Inhibitors of Dengue Virus with a Special Emphasis on NS2b/NS3 Protease. Phytochemistry 2022, 202, 113362. https://doi.org/10.1016/j.phytochem.2022.113362 . José Angel Santiago-Cruz; Posadas-Mondragón, A.; José Leopoldo Aguilar-Faisal; Ortiz-García, C. I.; Danai Montalvan-Sorrosa; Herrera-González, N. E.; Angélica Pérez-Juárez. In Vitro Evaluation of the Antiviral Effect of Spirulina Maxima (Arthrospira) Alga against Chikungunya Virus. Viruses 2025, 17 (12), 1583–1583. https://doi.org/10.3390/v17121583 . Tahir ul Qamar, M.; Maryam, A.; Muneer, I.; Xing, F.; Ashfaq, U. A.; Khan, F. A.; Anwar, F.; Geesi, M. H.; Khalid, R. R.; Rauf, S. A.; Siddiqi, A. R. Computational Screening of Medicinal Plant Phytochemicals to Discover Potent Pan-Serotype Inhibitors against Dengue Virus. Scientific Reports 2019, 9 (1). https://doi.org/10.1038/s41598-018-38450-1 . Hossain, M. S.; Soharth Hasnat; Akter, S.; Mim, M. M.; Tahcin, A.; Hoque, M.; Durjoy Sutradhar; Akter, A.; Namin Rouf Sium; Hossain, S.; Runa Masuma; Sakhawat Hossen Rakib; Islam, M. A.; Islam, T.; Bhattacharya, P.; Hoque, M. N. Computational Identification of Vernonia Cinerea-Derived Phytochemicals as Potential Inhibitors of Nonstructural Protein 1 (NSP1) in Dengue Virus Serotype-2. Frontiers in Pharmacology 2024, 15. https://doi.org/10.3389/fphar.2024.1465827 . Tahir ul Qamar, M.; Maryam, A.; Muneer, I.; Xing, F.; Ashfaq, U. A.; Khan, F. A.; Anwar, F.; Geesi, M. H.; Khalid, R. R.; Rauf, S. A.; Siddiqi, A. R. Computational Screening of Medicinal Plant Phytochemicals to Discover Potent Pan-Serotype Inhibitors against Dengue Virus. Scientific Reports 2019, 9 (1). https://doi.org/10.1038/s41598-018-38450-1 . Cordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592–7618. https://doi.org/10.3390/cimb46070451 . Cordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592–7618. https://doi.org/10.3390/cimb46070451 . Yaswanth, M.; Dubey, A.; Tufail, A.; Nath, S.; Janardhan, S.; Maffia, M.; Ragusa, A.; Mishra, V. K. Computational Repurposing of Drugs against Dengue Virus Targeting NS5 and Methyltransferase Proteins. Scientific Reports 2025, 15 (1). https://doi.org/10.1038/s41598-025-09443-8 . Hossain, M. S.; Soharth Hasnat; Akter, S.; Mim, M. M.; Tahcin, A.; Hoque, M.; Durjoy Sutradhar; Akter, A.; Namin Rouf Sium; Hossain, S.; Runa Masuma; Sakhawat Hossen Rakib; Islam, M. A.; Islam, T.; Bhattacharya, P.; Hoque, M. N. Computational Identification of Vernonia Cinerea-Derived Phytochemicals as Potential Inhibitors of Nonstructural Protein 1 (NSP1) in Dengue Virus Serotype-2. Frontiers in Pharmacology 2024, 15. https://doi.org/10.3389/fphar.2024.1465827 . Purohit, P.; Sahoo, S.; Panda, M.; Sahoo, P. S.; Meher, B. R. Targeting the DENV NS2B-NS3 Protease with Active Antiviral Phytocompounds: Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulation Studies. Journal of Molecular Modeling 2022, 28 (11). https://doi.org/10.1007/s00894-022-05355-w . Purohit, P.; Sahoo, S.; Panda, M.; Sahoo, P. S.; Meher, B. R. Targeting the DENV NS2B-NS3 Protease with Active Antiviral Phytocompounds: Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulation Studies. Journal of Molecular Modeling 2022, 28 (11). https://doi.org/10.1007/s00894-022-05355-w . Cordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592–7618. https://doi.org/10.3390/cimb46070451 . Ferreira, F. J. N.; Carneiro, A. S. AI-Driven Drug Discovery: A Comprehensive Review. ACS Omega 2025, 10 (23). https://doi.org/10.1021/acsomega.5c00549 . Hartig, M. U.; Appelhaus, J.; Vollenbröker, M.; Lamprecht, A. In-Situ Forming Polyester Implants for Sustained Intravesical Oxybutynin Release. Pharmaceutics 2025, 17 (11), 1369. https://doi.org/10.3390/pharmaceutics17111369 . Sagaya Jansi, R.; Khusro, A.; Agastian, P.; Alfarhan, A.; Al-Dhabi, N. A.; Arasu, M. V.; Rajagopal, R.; Barcelo, D.; Al-Tamimi, A. Emerging Paradigms of Viral Diseases and Paramount Role of Natural Resources as Antiviral Agents. Science of The Total Environment 2021, 759, 143539. https://doi.org/10.1016/j.scitotenv.2020.143539 . Hartig, M. U.; Appelhaus, J.; Vollenbröker, M.; Lamprecht, A. In-Situ Forming Polyester Implants for Sustained Intravesical Oxybutynin Release. Pharmaceutics 2025, 17 (11), 1369. https://doi.org/10.3390/pharmaceutics17111369 . Data, P. RCSB PDB – 2FOM: Dengue Virus NS2B/NS3 Protease. Rcsb.org. https://www.rcsb.org/structure/2FOM (accessed 2026-05-03). Data, P. RCSB PDB – 3L6P: Crystal Structure of Dengue Virus 1 NS2B/NS3 protease. Rcsb.org. https://www.rcsb.org/structure/3L6P (accessed 2026-05-03). Timiri, A. K.; Selvarasu, S.; Kesherwani, M.; Vijayan, V.; Sinha, B. N.; Devadasan, V.; Jayaprakash, V. Synthesis and Molecular Modelling Studies of Novel Sulphonamide Derivatives as Dengue Virus 2 Protease Inhibitors. Bioorganic Chemistry 2015, 62, 74–82. https://doi.org/10.1016/j.bioorg.2015.07.005 . Spínola, M. P.; Mendes, A. R.; José A. M. Prates. Chemical Composition, Bioactivities, and Applications of Spirulina (Limnospira Platensis) in Food, Feed, and Medicine. Foods 2024, 13 (22), 3656–3656. https://doi.org/10.3390/foods13223656 . Chaiprasert, A.; Han, P.; Laomettachit, T.; Ruengjitchatchawalya, M. Network Analysis Retrieving Bioactive Compounds from Spirulina (Arthrospira Platensis) and Their Targets Related to Systemic Lupus Erythematosus. PLOS ONE 2024, 19 (8), e0309303. https://doi.org/10.1371/journal.pone.0309303 . Marjanović, B.; Benković, M.; Jurina, T.; Sokač Cvetnić, T.; Valinger, D.; Gajdoš Kljusurić, J.; Jurinjak Tušek, A. Bioactive Compounds from Spirulina Spp.—Nutritional Value, Extraction, and Application in Food Industry. Separations 2024, 11 (9), 257. https://doi.org/10.3390/separations11090257 . Coumar, M. S. Molecular Docking for Computer-Aided Drug Design : Fundamentals, Techniques, Resources and Applications; Academic Press: London, 2021. MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization. Arxiv.org. https://arxiv.org/html/2604.21937v1 (accessed 2026-05-03). Tang, S.-L.; Sumitra, M. R.; Chen, L.-C.; Liu, F.-C.; Hsu, H.-L.; Kuo, Y.-C.; Ansar, M.; Huang, S.-L.; Lee, S.-Y.; Wang, H.-J.; Lawal, B.; Wu, A. T. H.; Wen, Y.-T.; Huang, H.-S. Machine Learning–Driven Discovery of NSC828779 as a Multi-Mechanistic NLRP3 Inflammasome Inhibitor for Inflammatory Diseases. Computers in Biology and Medicine 2025, 197, 111110. https://doi.org/10.1016/j.compbiomed.2025.111110 . García-Ortegón, M.; Simm, G. N. C.; Tripp, A. J.; Hernández-Lobato, J. M.; Bender, A.; Bacallado, S. DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. Journal of Chemical Information and Modeling 2022, 62 (15), 3486–3502. https://doi.org/10.1021/acs.jcim.1c01334 . Seeliger, D.; de Groot, B. L. Ligand Docking and Binding Site Analysis with PyMOL and Autodock/Vina. Journal of Computer-Aided Molecular Design 2010, 24 (5), 417–422. https://doi.org/10.1007/s10822-010-9352-6 . ADMETlab 3.0. admetlab3.scbdd.com. https://admetlab3.scbdd.com/ . Daina, A.; Michielin, O.; Zoete, V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Scientific Reports 2017, 7 (1), 1–13. https://doi.org/10.1038/srep42717 . Velid Unsal; Oner, E.; Reşit Yıldız; Başak Doğru Mert. Comparison of New Secondgeneration H1 Receptor Blockers with Some Molecules; a Study Involving DFT, Molecular Docking, ADMET, Biological Target and Activity. BMC Chemistry 2025, 19 (1). https://doi.org/10.1186/s13065-024-01371-4 . Indah Purwaningsih; Iman Permana Maksum; Dadan Sumiarsa; Sriwidodo Sriwidodo. Pharmacokinetics and Toxicity Overview of Active Compounds Berberine, Palmatine, and Jatrorrhizine from Fibraurea Tinctoria Lour: Drug-Likeness, ADMET Prediction, and in Vivo Extract Toxicity Assessment. Journal of Toxicology 2025, 2025 (1), 7251602–7251602. https://doi.org/10.1155/jt/7251602 . Saritha, K.; Alivelu, M.; Mohammad, M. Drug-Likeness Analysis, in Silico ADMET Profiling of Compounds in Kedrostis Foetidissima (Jacq.) Cogn, and Antibacterial Activity of the Plant Extract. In Silico Pharmacology 2024, 12 (2). https://doi.org/10.1007/s40203-024-00240-1 . Oluyemi, W. M.; Nwokebu, G.; Adewumi, A. T.; Eze, S. C.; Mbachu, C. C.; Ogueli, E. C.; Nwodo, N.; Soliman, M. E. S.; Mosebi, S. The Characteristic Structural and Functional Dynamics of P. Falciparum DHFR Binding with Pyrimidine Chemotypes Implicate Malaria Therapy Design. Chemical Physics Impact 2024, 9, 100703. https://doi.org/10.1016/j.chphi.2024.100703 . Cui, M.; Sun, H.; Liu, X.; Huang, Z.; Su, Z.; Zheng, Y.; Shen, Y.; Wang, M. Antibacterial Mechanism and Computer Simulation Analysis of a Novel Antimicrobial Peptide Inducing Membrane Disintegration in Gram-Positive Bacteria. Food Bioscience 2025, 71, 107101. https://doi.org/10.1016/j.fbio.2025.107101 . Ancuceanu, R.; Lascu, B. E.; Drăgănescu, D.; Dinu, M. In Silico ADME Methods Used in the Evaluation of Natural Products. Pharmaceutics 2025, 17 (8), 1002. https://doi.org/10.3390/pharmaceutics17081002 . José Angel Santiago-Cruz; Posadas-Mondragón, A.; José Leopoldo Aguilar-Faisal; Ortiz-García, C. I.; Danai Montalvan-Sorrosa; Herrera-González, N. E.; Angélica Pérez-Juárez. In Vitro Evaluation of the Antiviral Effect of Spirulina Maxima (Arthrospira) Alga against Chikungunya Virus. Viruses 2025, 17 (12), 1583–1583. https://doi.org/10.3390/v17121583 . Yong, K. S.; Choi, S. B.; Wahab, H. Docking of Dengue NS2B-NS3 Protease with Murraya koenigii. . https://doi.org/10.2991/iccst-15.2015.14 . Ogundele, A. V.; Das, A. M.; Paz, C. Gallic Acid from Elaeocarpus Floribundus Stem Bark: A Potent Natural Antioxidant with Enzymatic and Pharmacokinetic Validation. Antioxidants 2025, 14 (10), 1161. https://doi.org/10.3390/antiox14101161 . Mamoudou, H.; Abdoulaye, A. H.; Ditchou, N. Y. O.; Olumasai, J. N.; Adissa, R. M. Z. K.; Mune, M. A. M. Computational Investigation of Plectranthus Neochilus Essential Oil Phytochemicals Interaction with Dipeptidyl Peptidase 4: A Potential Avenue for Antidiabetic Drug Discovery. Current Pharmaceutical Analysis 2025, 21 (3), 169–178. https://doi.org/10.1016/j.cpan.2025.03.002 . Ade Arsianti; Azizah, N. N.; Erlina, L. Molecular Docking, ADMET Profiling of Gallic Acid and Its Derivatives (N-Alkyl Gallamide) as Apoptosis Agent of Breast Cancer MCF-7 Cells. F1000Research 2024, 11, 1453–1453. https://doi.org/10.12688/f1000research.127347.3 . Panchal, J.; Prajapati, J.; Dabhi, M.; Patel, A.; Patel, S.; Rawal, R.; Saraf, M.; Goswami, D. Comprehensive Computational Investigation for Ligand Recognition and Binding Dynamics of SdiA: A Degenerate LuxR -Type Receptor in Klebsiella Pneumoniae. Molecular Diversity 2024. https://doi.org/10.1007/s11030-023-10785-6 . McAllister, R. G.; Konermann, L. Challenges in the Interpretation of Protein H/D Exchange Data: A Molecular Dynamics Simulation Perspective. Biochemistry 2015, 54 (16), 2683–2692. https://doi.org/10.1021/acs.biochem.5b00215 . Tiwari, V.; Sowdhamini, R. Structure Modelling of Odorant Receptor from Aedes Aegypti and Identification of Potential Repellent Molecules. Computational and Structural Biotechnology Journal 2023, 21, 2204–2214. https://doi.org/10.1016/j.csbj.2023.03.005 . Zhang, L.; Li, S.; Liu, S.; Wang, Z. Dietary Acrylamide Induces Depression via SIRT3-Mediated Mitochondrial Oxidative Injury: Evidence from Multi-Omics and Mendelian Randomization. Current Issues in Molecular Biology 2025, 47 (10), 836. https://doi.org/10.3390/cimb47100836 . Zhang, L.; Li, S.; Liu, S.; Wang, Z. Dietary Acrylamide Induces Depression via SIRT3-Mediated Mitochondrial Oxidative Injury: Evidence from Multi-Omics and Mendelian Randomization. Current Issues in Molecular Biology 2025, 47 (10), 836. https://doi.org/10.3390/cimb47100836 . Elzupir, A. O.; Almahmoud, S. A. J. Unveiling of Phosphodiesterase-5 Hot Residues Binding to Xanthine Derivatives for Erectile Dysfunction Therapy: A Computational Drug Repurposing Approach. PLOS One 2025, 20 (11), e0336267. https://doi.org/10.1371/journal.pone.0336267 . Tallei, T. E.; Kapantow, N. H.; Nurdjannah Jane Niode; Hessel, S. S.; Maghfirah Savitri; Fatimawali Fatimawali; Kang, S.; Park, M. N.; Muhammad Raihan; Widya Hardiyanti; Firzan Nainu; Kim, B. Integrative in Silico and in Vivo Drosophila Model Studies Reveal the Anti-Inflammatory, Antioxidant, and Anticancer Properties of Red Radish Microgreen Extract. Scientific Reports 2025, 15 (1). https://doi.org/10.1038/s41598-025-02999-5 . Feng, Y.; Jin, C.; Shihao Lv; Zhang, H.; Ren, F.; Wang, J. Molecular Mechanisms and Applications of Polyphenol-Protein Complexes with Antioxidant Properties: A Review. Antioxidants 2023, 12 (8), 1577–1577. https://doi.org/10.3390/antiox12081577 . Haider, N.; Hasan, M. N.; Onyango, J.; Billah, M.; Khan, S.; Papakonstantinou, D.; Paudyal, P.; Asaduzzaman, M. Global Dengue Epidemic Worsens with Record 14 Million Cases and 9,000 Deaths Reported in 2024. International Journal of Infectious Diseases 2025, 158, 107940. https://doi.org/10.1016/j.ijid.2025.107940 . and, V. Dengue: global situation, surveillance and progress – 2024 update. Who.int. https://www.who.int/publications/i/item/who-wer10052-665-678 . Mukherjee, M.; Lihong, N.; David, C. Y. K. Cancer Therapeutics: In-Silico Evaluation of Novel UBR5 Protein Inhibitors. Proceedings in Technology Transfer 2025, 401–411. https://doi.org/10.1007/978-981-96-3770-6_41 . Falah Azeez, Z.; Ali Khaleel, L.; Ali Kadhim Kyhoiesh, H. Synthesis, Biological Evaluation, Molecular Docking Analyses, and ADMET Study of Azo Derivatives Containing 1-Naphthol against MβL-Producing S. Maltophilia. Results in Chemistry 2024, 12, 101864. https://doi.org/10.1016/j.rechem.2024.101864 . Chavan, N. D.; Sarveswari, S.; Vijayakumar, V. Quinoline Derivatives’ Biological Interest for Anti-Malarial and Anti-Cancer Activities: An Overview. RSC Advances 2025, 15 (37), 30576–30604. https://doi.org/10.1039/d5ra00534e . View of Potential Inhibitor of DENV-2 Virus Protease (NS2B-NS3): An In-Silico Studies of Anti-Viral Plants| Journal of Drug Delivery and Therapeutics. Jddtonline.info. https://jddtonline.info/index.php/jddt/article/view/6870/6401 (accessed 2026-05-03). Loaiza-Cano, V.; Monsalve-Escudero, L. M.; Filho, C. da S. M. B.; Martinez-Gutierrez, M.; Sousa, D. P. de. Antiviral Role of Phenolic Compounds against Dengue Virus: A Review. Biomolecules 2020, 11 (1), 11. https://doi.org/10.3390/biom11010011 . Purohit, P.; Barik, D.; Agasti, S.; Panda, M.; Meher, B. R. Evaluation of the Inhibitory Potency of Anti-Dengue Phytocompounds against DENV-2 NS2B-NS3 Protease: Virtual Screening, ADMET Profiling and Molecular Dynamics Simulation Investigations. Journal of biomolecular structure & dynamics 2024, 42 (6), 2990–3009. https://doi.org/10.1080/07391102.2023.2212798 . Frimayanti, N.; Chee, C. F.; Zain, S.; Rahman, N. Abd. Design of New Competitive Dengue Ns2b/Ns3 Protease Inhibitors—a Computational Approach. International Journal of Molecular Sciences 2011, 12 (2), 1089–1100. https://doi.org/10.3390/ijms12021089 . Lim, L.; Dang, M.; Roy, A.; Kang, J.; Song, J. Curcumin Allosterically Inhibits the Dengue NS2B-NS3 Protease by Disrupting Its Active Conformation. ACS Omega 2020, 5 (40), 25677–25686. https://doi.org/10.1021/acsomega.0c00039 . Fouzia Ismat; Tariq, A.; Shaheen, A.; Ullah, R.; Raheem, K.; Muhammad Muddassar; Mahboob, S.; Abbas, W.; Iqbal, M.; Rahman, M. Inhibition of NS2B-NS3 Protease from All Four Serotypes of Dengue Virus by Punicalagin, Punicalin and Ellagic Acid Identified from Punica Granatum. Journal of Biomolecular Structure and Dynamics 2024, 1–16. https://doi.org/10.1080/07391102.2024.2314258 . Rothan, H. A.; Han, H. C.; Ramasamy, T. S.; Othman, S.; Rahman, N. A.; Yusof, R. Inhibition of Dengue NS2B-NS3 Protease and Viral Replication in Vero Cells by Recombinant Retrocyclin-1. BMC Infectious Diseases 2012, 12 (1). https://doi.org/10.1186/1471-2334-12-314 . de Sousa, L. R. F.; Wu, H.; Nebo, L.; Fernandes, J. B.; da Silva, M. F. das G. F.; Kiefer, W.; Kanitz, M.; Bodem, J.; Diederich, W. E.; Schirmeister, T.; Vieira, P. C. Flavonoids as Noncompetitive Inhibitors of Dengue Virus NS2B-NS3 Protease: Inhibition Kinetics and Docking Studies. Bioorganic & Medicinal Chemistry 2015, 23 (3), 466–470. https://doi.org/10.1016/j.bmc.2014.12.015 . Wu, D.; Mao, F.; Ye, Y.; Li, J.; Xu, C.; Luo, X.; Chen, J.; Shen, X. Policresulen, a Novel NS2B/NS3 Protease Inhibitor, Effectively Inhibits the Replication of DENV2 Virus in BHK-21 Cells. Acta Pharmacologica Sinica 2015, 36 (9), 1126–1136. https://doi.org/10.1038/aps.2015.56 . Sarwar, M. W.; Riaz, A.; Dilshad, S. M. R.; Al-Qahtani, A.; Nawaz-Ul-Rehman, M. S.; Mubin, M. Structure Activity Relationship (SAR) and Quantitative Structure Activity Relationship (QSAR) Studies Showed Plant Flavonoids as Potential Inhibitors of Dengue NS2B-NS3 Protease. BMC Structural Biology 2018, 18 (1). https://doi.org/10.1186/s12900-018-0084-5 . Mukhtar, M.; Haris Ahmed Khan; Najam. Exploring the Inhibitory Potential of Nigella Sativa against Dengue Virus NS2B/NS3 Protease and NS5 Polymerase Using Computational Approaches. RSC Advances 2023, 13 (27), 18306–18322. https://doi.org/10.1039/d3ra02613b . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9667555\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":638616619,\"identity\":\"6bce73f6-30cf-4151-b60e-a1361c563e19\",\"order_by\":0,\"name\":\"Joyanti Biswas\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"DGHS\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Joyanti\",\"middleName\":\"\",\"lastName\":\"Biswas\",\"suffix\":\"\"},{\"id\":638616620,\"identity\":\"67f941bf-be6e-4637-930c-837cf4597759\",\"order_by\":1,\"name\":\"Md. Raisul Islam\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"DGHS\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Md.\",\"middleName\":\"Raisul\",\"lastName\":\"Islam\",\"suffix\":\"\"},{\"id\":638616621,\"identity\":\"645d90ab-8998-454d-9187-cfcc9c9263db\",\"order_by\":2,\"name\":\"Sumaiya Nousheen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"DGHS\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sumaiya\",\"middleName\":\"\",\"lastName\":\"Nousheen\",\"suffix\":\"\"},{\"id\":638616622,\"identity\":\"5575bce5-9bdf-49e2-b31e-6bf0dc3250a4\",\"order_by\":3,\"name\":\"Md. Abdul Jalil\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Dinajpur Medical College\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Md.\",\"middleName\":\"Abdul\",\"lastName\":\"Jalil\",\"suffix\":\"\"},{\"id\":638616623,\"identity\":\"63e84a28-6044-4382-817f-cd71ce3aa722\",\"order_by\":4,\"name\":\"Milan Kumar Saha\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"DGHS\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Milan\",\"middleName\":\"Kumar\",\"lastName\":\"Saha\",\"suffix\":\"\"},{\"id\":638616624,\"identity\":\"cbe8943a-02ed-4340-a02e-6348def720e5\",\"order_by\":5,\"name\":\"Kalyan Dhar\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACCQbmBiCVAOHxAtn8DAyMBxKAEmw4tTCiaZEEYogWHHowtRgcAGoB83BokWw/2Cbxc0da4tr2sw8Y3u6wyze+3XzgwMMcCwY++QasWqR5Etske8/kJG47k27AOPdMsuW2O8cSDiRuw+0wOYbENgnetorEbQfSGJh525gNzG7kGODXwv+wTfIvSMv5ZyAt9QbGMwhokZZIbJPmbQM67AbYlsMGBhIEtEjOeNhsLduWZrztxjOGg3PbjhtIQP3Cw8aWgFWLxPnkgzfftiXLbjufxvjgbVu1Af/s5oMPf26rk5NvPoDdGgYGFgkYC6IEyuXBpR4ImD+gWYxH7SgYBaNgFIxIAAChwWEFsbShnwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Politecnico di Milano\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Kalyan\",\"middleName\":\"\",\"lastName\":\"Dhar\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-05-10 04:54:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9667555/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9667555/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":109073836,\"identity\":\"6a3200fc-9c37-45fd-a1d6-751b40591110\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 10:48:39\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":280387,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRoot Mean Square Deviation (RMSD) analysis of the Cα atoms of the structure of the ligand-protein complexes over the 100 ns simulation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9667555/v1/f37d8487cf702e68d4bca435.png\"},{\"id\":109073799,\"identity\":\"4039b197-0c91-41bb-9eb7-2ed244db3ca9\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 10:48:28\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":37951,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRoot Mean Square Fluctuation (RMSF) analysis of the residues of the 2FOM protein upon compound binding over the 100 ns simulation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9667555/v1/bb6bf44214a1c0af33dc61fb.png\"},{\"id\":109074142,\"identity\":\"5ea6339a-ab7f-47e9-85cc-ea653992a402\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 10:50:02\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":100168,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRadius of gyration (Rg) analysis of the backbone structure of the ligand-protein complexes over the 100 ns simulation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9667555/v1/32a5ca9b3cb59221e03e8742.png\"},{\"id\":109074143,\"identity\":\"709ce80b-ee3d-4661-bed5-d77aa8bbf389\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 10:50:02\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":157830,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSolvent-Accessible Surface Area (SASA) analysis of the structure of the ligand-protein complexes over the 100 ns simulation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9667555/v1/577c067d6fc48b529dd6dc05.png\"},{\"id\":109073833,\"identity\":\"11d3a016-b3fc-4220-9ea5-0f684212a433\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 10:48:38\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":80478,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIllustrating the number of hydrogen bonds between solute and solvent extracted from protein ligand complexes during a 100 ns simulation time.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9667555/v1/e881ec5b2ba527b769369b10.png\"},{\"id\":109296618,\"identity\":\"41dfefd5-eb58-41e3-8db7-a410e2715b8f\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 08:48:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":971123,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9667555/v1/22b05305-d062-4434-90d6-6896748df1d5.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"In-Silico Screening and Molecular Dynamics Evaluation of Spirulina Platensis-Derived Compounds as Potential Antiviral Agents Against Dengue Virus NS2b/NS3 Protease\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eDengue represents one of the most significant vector-borne viral diseases globally, with transmission mediated by Aedes aegypti and Aedes albopictus mosquitoes. The causative agent, dengue virus (DENV), encompasses four serotypes (DENV-1 to DENV-4) and a recently identified fifth serotype (DENV-5) discovered in Sarawak, Malaysia in 2013 [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. The epidemiological burden has reached unprecedented levels, with the World Health Organization (WHO) reporting more than 14.2\\u0026nbsp;million dengue cases in 2024, including 7.5\\u0026nbsp;million confirmed cases, over 52,000 severe manifestations, and approximately 10,000 deaths[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. In Bangladesh, dengue fever was first documented in 1964, with subsequent major outbreaks in 2000, 2013, 2019, and 2022. Between January and September 2023, the country recorded 203,406 infections with 989 deaths, representing a case fatality rate of 0.49%, with 96.1% of infections occurring between July and September[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].Despite its clinical significance and increasing prevalence, dengue lacks specific antiviral therapy, with current management limited to supportive care measures. Several drug candidates, including chloroquine, prednisolone, lovastatin, and celgosivir, have entered clinical trials but failed to demonstrate efficacy in reducing viremia or providing significant therapeutic benefit[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. This therapeutic gap, combined with the emergence of drug-resistant viral strains and adverse effects associated with synthetic antivirals, has catalyzed investigations into natural product-based drug discovery approaches. The dengue virus NS2b/NS3 serine protease is a bifunctional enzyme complex consisting of the NS3 catalytic domain and the NS2B cofactor. This complex is essential for viral replication as it catalyzes the post-translational proteolytic processing of the viral polyprotein into functional domains required for viral assembly and replication. The three-dimensional crystal structure of this protease (PDB ID: 2FOM) has been elucidated, enabling structure-based rational drug design approaches[\\u003cspan additionalcitationids=\\\"CR9\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe NS2b/NS3 protease represents a superior therapeutic target compared to other viral enzymes because of its essential role in the viral lifecycle and its evolutionary conservation across DENV serotypes, suggesting potential cross-serotype activity of inhibitors. Spirulina platensis (Arthrospira platensis) is a nutrient-dense cyanobacterium classified as a superfood due to its exceptional concentrations of essential nutrients and bioactive compounds. Beyond its well-established nutritional profile, clinical and laboratory research has documented significant immune-stimulating, anti-inflammatory, and antiviral properties. Calcium spirulan (Ca-SP), an acidic polysaccharide derived from Spirulina platensis, has demonstrated potent inhibitory activity against multiple enveloped viruses[\\u003cspan additionalcitationids=\\\"CR12\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Previous studies have confirmed antiviral efficacy of polysaccharides and methanol extracts of Spirulina against rotavirus, adenovirus, and coxsackievirus. Recent computational screening identified eight Spirulina-derived molecules with promising activity against SARS-CoV-2 using consensus docking methodologies. However, systematic evaluation of Spirulina compounds against dengue virus using integrated molecular docking and molecular dynamics simulation remains unexplored[\\u003cspan additionalcitationids=\\\"CR15\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTraditional drug discovery encompasses a protracted timeline of 10\\u0026ndash;15 years from initial research to commercial availability, with substantial financial investment. Computer-aided drug design has emerged as a transformative approach, significantly accelerating the drug discovery pipeline and reducing development costs. CADD methodologies, particularly molecular docking and molecular dynamics simulations, enable virtual screening of large compound libraries and prediction of binding modes and stability of drug-target complexes under physiological conditions[\\u003cspan additionalcitationids=\\\"CR18 CR19 CR20\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. The integration of structure-based docking with ligand-based design, complemented by molecular dynamics simulation and ADMET profiling, provides a comprehensive framework for rational identification of lead compounds with optimal pharmacokinetic properties[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe absence of an approved dengue antiviral, the failure of multiple clinical-stage synthetic candidates, and the continued epidemiological expansion of all four DENV serotypes collectively constitute an urgent unmet medical need. Natural products have historically furnished a disproportionate fraction of antiviral and antibacterial drugs, owing to their structural complexity and inherent biocompatibility. Spirulina platensis, a globally cultivated and clinically safe organism, has demonstrated broad-spectrum antiviral activity across multiple viral families, yet its constituent compounds have not been systematically evaluated against the dengue NS2b/NS3 protease[\\u003cspan additionalcitationids=\\\"CR25\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Computational screening of this well-characterized phytochemical library against a crystallographically resolved viral target therefore represents a scientifically justified and resource-efficient first step toward dengue antiviral discovery. Furthermore, the integration of MD simulation\\u0026mdash;a methodology rarely applied to spirulina-derived compounds\\u0026mdash;enables validation of docking predictions under physiologically realistic conditions, increasing the translational relevance of computational hits[\\u003cspan additionalcitationids=\\\"CR28\\\" citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Despite growing interest in natural product-based antiviral development and the established therapeutic relevance of the dengue NS2b/NS3 protease, several critical gaps remain in the existing literature:\\u003c/p\\u003e \\u003cp\\u003eNo prior study has combined molecular docking, 100-ns MD simulation, ADMET profiling, and multi-organ toxicity assessment for Spirulina platensis compounds against DENV NS2b/NS3 protease. Most published natural product docking studies against dengue targets report binding affinities without MD validation, failing to account for time-dependent conformational changes that determine real-world binding feasibility. Incomplete compound space coverage: Previous spirulina-SARS-CoV-2 computational studies screened only a subset of compounds [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]; a full library screen against dengue-specific targets has not been performed. Existing studies rarely combine ADMET with multi-organ toxicity profiling using machine-learning models, leaving safety-related attrition risks unaddressed at the computational stage [\\u003cspan additionalcitationids=\\\"CR30 CR31 CR32\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Limited structure-activity relationship data: The physicochemical and structural determinants of spirulina compound binding to the dengue protease active site remain unexplored, impeding rational analog design.\\u003c/p\\u003e \\u003cp\\u003eOur study carries significance at multiple levels: It establishes the first integrated computational framework for evaluating spirulina-derived compounds against DENV NS2b/NS3 protease, generating novel structure-activity insights and validating binding stability through extended MD simulation. Identification of safe, bioavailable lead compounds from a widely consumed, sustainably cultivated organism directly addresses the therapeutic gap in dengue management, which affects hundreds of millions annually [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Noteworthy, The study demonstrates that MD instability can overturn docking-based rankings (sulfoquinovosyldiglyceride), validating the importance of dynamic sampling in computational antiviral campaigns and providing a methodological template for related flaviviral targets[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBy generating ADMET- and toxicity-stratified lead compound tiers, this work directly informs the design of focused experimental follow-up studies, reducing resource expenditure in early-stage antiviral discovery. This work contributes to the growing evidence base supporting marine- and cyanobacterium-derived natural products as underexplored reservoirs of antiviral scaffolds, with potential applications beyond dengue to related flaviviral pathogens including Zika and West Nile virus [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. This study was designed to: (1) screen 19 bioactive compounds from Spirulina platensis against dengue NS2b/NS3 protease by molecular docking; (2) evaluate binding stability of the seven highest-affinity compounds through 100-ns molecular dynamics simulation; (3) characterize ADMET pharmacokinetic profiles and multi-organ toxicity; and (4) generate a rational, tiered lead compound shortlist for experimental validation[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cp\\u003eThe three-dimensional crystal structure of dengue virus NS2b/NS3 protease (PDB ID: 2FOM) was downloaded from the RCSB Protein Data Bank (\\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). The protein structure contained 697 amino acid residues distributed across two chains with missing residues in specific regions. Protein preparation was performed using BIOVIA Discovery Studio (DassaultSyst\\u0026egrave;mes, 2016), employing the following workflow: (1) visualization of the protein structure in three dimensions; (2) deletion of heteroatoms, water molecules, and non-essential ligands; (3) identification and computational addition of missing amino acid residues based on the FASTA sequence; (4) addition of hydrogen atoms using the CHARMM force field; and (5) energy minimization using Swiss PDB Viewer (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://spdbv.unil.ch/\\u003c/span\\u003e\\u003cspan address=\\\"https://spdbv.unil.ch/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The prepared protein was saved in PDB format for subsequent docking studies[\\u003cspan additionalcitationids=\\\"CR42\\\" citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNineteen bioactive compounds previously identified from Spirulina platensis in peer-reviewed literature were selected for this study (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Compound structures were retrieved from the PubChem database (\\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) in Structure Data File (SDF) format. The selected compounds represented diverse chemical classes including fatty acids, polysaccharides, polyphenols, and phytosterols. Ligand preparation involved: (1) conversion of SDF files to three-dimensional PDB format using Online SMILES Translator; (2) visualization in BIOVIA Discovery Studio; (3) addition of polar hydrogen atoms; (4) energy minimization using Swiss PDB Viewer; and (5) conversion to PDBQT format using PyRx. All ligand structures were subject to energy minimization to remove steric clashes and generate conformations suitable for docking[\\u003cspan additionalcitationids=\\\"CR45\\\" citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBioactive compounds from Spirulina platensis selected for computational screening.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCompound\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChemical Class\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCa-SP (Calcium spirulan)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePolysaccharide\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHayashi et al., 2008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSulfoquinovosyldiglyceride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGlycolipid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eChirasuwan et al., 2009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePalmitic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePalmitoleic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnsaturated fatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMyristic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMargaric acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOleic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnsaturated fatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLinoleic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePolyunsaturated fatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCapric acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLauric acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStearic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFatty acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eβ-sitosterol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePhytosterol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003en-heptadecane\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAlkane\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eα-pinene\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMonoterpene\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAkram et al., 2010\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eα-terpineol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMonoterpenol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAkram et al., 2010\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGallic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePolyphenol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHetta et al., 2014\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRutin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFlavonoid glycoside\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNuhu, 2013\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTannin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePolyphenolic compound\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNuhu, 2013\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNa-SP (Sodium spirulan)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePolysaccharide\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLee et al., 2007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eMolecular docking was performed using PyRx (version 0.8), a virtual screening tool integrating AutoDock Vina. The prepared protein (2FOM) was designated as the macromolecule, while all 19 compounds served as ligands. The docking grid box was centered at coordinates: X\\u0026thinsp;=\\u0026thinsp;0.097 \\u0026Aring;, Y\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;17.0563 \\u0026Aring;, Z\\u0026thinsp;=\\u0026thinsp;13.8011 \\u0026Aring;, with dimensions encompassing the entire protein surface to enable blind docking. Blind docking was selected to improve hit enrichment and comprehensively explore the ligand pose space without predetermined assumptions regarding the binding site. AutoDock Vina conducted exhaustive conformational sampling, generating multiple ligand poses for each compound and calculating binding free energies for each pose[\\u003cspan additionalcitationids=\\\"CR48\\\" citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. The workflow encompassed: (1) loading of protein and ligand structures; (2) conversion to PDBQT format; (3) specification of docking parameters; (4) execution of docking simulations; and (5) ranking of results by binding affinity (ΔG in kcal/mol). The seven compounds exhibiting the lowest binding affinities (highest binding strengths) were selected for subsequent analysis.\\u003c/p\\u003e \\u003cp\\u003eVisualization of protein-ligand interactions was performed using PyMOL (version 3.1.4) and BIOVIA Discovery Studio 2021. The PyMOL visualization pipeline included: (1) importation of docked complexes[\\u003cspan additionalcitationids=\\\"CR51\\\" citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]; (2) color coding of protein chains; (3) representation of protein structures as cartoon diagrams or molecular surfaces; (4) identification and visualization of hydrogen bonds; (5) highlighting of hydrophobic interactions; and (6) labeling of key interacting residues with three-letter codes and position identifiers.\\u003c/p\\u003e \\u003cp\\u003eAbsorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties were assessed for the seven top-ranked compounds using Swiss ADME (\\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) and ADMETlab 3.0 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://admetlab3.scbdd.com/\\u003c/span\\u003e\\u003cspan address=\\\"https://admetlab3.scbdd.com/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). ADMET analysis evaluated: (1) molecular weight; (2) water solubility predictions; (3) gastrointestinal absorption (GI absorption classification: high/low); (4) blood-brain barrier (BBB) permeability; (5) Lipinski's Rule of Five compliance (molecular weight\\u0026thinsp;\\u0026lt;\\u0026thinsp;500 Da, LogP\\u0026thinsp;\\u0026lt;\\u0026thinsp;5, hydrogen bond donors\\u0026thinsp;\\u0026lt;\\u0026thinsp;5, hydrogen bond acceptors\\u0026thinsp;\\u0026lt;\\u0026thinsp;10); and (6) oral bioavailability potential. Toxicity profiling assessed organ-specific toxicity including hepatotoxicity, nephrotoxicity, neurotoxicity, respiratory toxicity, and cardiotoxicity using machine learning models trained on experimental toxicity datasets[\\u003cspan additionalcitationids=\\\"CR54 CR55\\\" citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]\\u003c/p\\u003e \\u003cp\\u003eThe molecular dynamics (MD) simulations were performed using the AMBER14 force field for a total duration of 100 nanoseconds, with an integration time step of 2.50 femtoseconds. The system temperature was maintained at 298 K using the Berendsen thermostat, while the pressure was controlled at 1 atm to mimic standard conditions. Physiological pH (7.4) was assumed throughout the simulation. The system was solvated using the TIP3P water model with a density of 0.997 g/cm\\u0026sup3;, and a salt concentration of 0.9% NaCl was applied to replicate biological ionic strength. Trajectory data were recorded at intervals of every 250 picoseconds for subsequent analysis.MD simulations were initiated from the docked poses of the seven lead compounds[\\u003cspan additionalcitationids=\\\"CR58\\\" citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]. The trajectory data generated over the 100 ns simulation were analyzed to compute the following structural and dynamical parameters:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eRoot Mean Square Deviation (RMSD): Calculated for Cα atoms to assess structural stability. RMSD measures the deviation of the complex structure from its initial configuration over simulation time, with values\\u0026thinsp;\\u0026lt;\\u0026thinsp;3.0 \\u0026Aring; generally indicating stable complexes.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eRoot Mean Square Fluctuation (RMSF): Evaluated for individual amino acid residues to quantify local flexibility and residual vibration. RMSF values provide insights into regions of protein flexibility and identify critical residues involved in ligand binding.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eRadius of Gyration (Rg): Computed to assess the compactness and rigidity of the protein backbone. Stable Rg values indicate maintained structural integrity, while increasing Rg suggests protein expansion or unfolding.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eSolvent-Accessible Surface Area (SASA): Calculated to evaluate changes in surface exposure during the simulation. Increased SASA values indicate protein expansion or conformational changes affecting complex stability.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eHydrogen Bond Analysis: Total hydrogen bonds formed between the ligand-protein complex and solvent were monitored throughout the simulation. Persistent hydrogen bonding indicates favorable binding interactions and complex stability.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003cp\\u003eSoftware tools employed included PyRx, BIOVIA Discovery Studio, PyMOL, YASARA, Swiss PDB Viewer, and online analytical platforms (Swiss ADME, ADMETlab 3.0, PubChem, RCSB PDB).\\u003c/p\\u003e\"},{\"header\":\"3. Results and Discussion\",\"content\":\"\\u003cp\\u003eAmong the 19 Spirulina-derived compounds screened, seven exhibited favorable binding interactions with the dengue NS2b/NS3 protease, ranked by binding affinity (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Tannin demonstrated the highest binding affinity (\\u0026minus;\\u0026thinsp;8.9 kcal/mol), followed by Rutin (\\u0026minus;\\u0026thinsp;7.8 kcal/mol), β-sitosterol (\\u0026minus;\\u0026thinsp;7.1 kcal/mol), Sulfoquinovosyldiglyceride(\\u0026minus;\\u0026thinsp;5.9 kcal/mol),Gallic acid(\\u0026minus;\\u0026thinsp;5.7 kcal/mol), α-terpineol (\\u0026minus;\\u0026thinsp;5.3 kcal/mol), and Oleic acid (\\u0026minus;\\u0026thinsp;5.1 kcal/mol). All seven compounds exhibited RMSD values of 0 for both upper and lower bounds, indicating geometrically optimal docking poses with minimal conformational adjustment[\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMolecular docking results for the seven lead compounds against dengue NS2b/NS3 protease.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRank\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCompound\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBinding Affinity (kcal/mol)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRMSD/UB\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eRMSD/LB\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTannin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;8.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRutin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;7.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eβ-sitosterol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;7.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSulfoquinovosyldiglyceride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;5.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGallic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;5.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eα-terpineol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;5.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOleic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;5.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eADMET analysis of the seven lead compounds revealed critical information regarding their pharmacokinetic profiles and drug-likeness (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Gallic acid, oleic acid, α-terpineol, and β-sitosterol demonstrated high GI absorption potential, suggesting favorable intestinal uptake. Molecular weight analysis indicated that all compounds except tannin and rutin complied with Lipinski's Rule of Five, predicting enhanced oral bioavailability. Water solubility profiles ranged from poor (tannin, β-sitosterol, sulfoquinovosyldiglyceride) to soluble (gallic acid, α-terpineol), with intermediate solubility observed for rutin and oleic acid. Blood-brain barrier permeability analysis indicated that only α-terpineol demonstrated potential BBB penetration, while all other compounds showed restricted CNS entry, reducing neurotoxicity risk[\\u003cspan additionalcitationids=\\\"CR63\\\" citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eADMET properties of the seven lead compounds.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCompound\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMW (g/mol)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSolubility\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGI Absorption\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eBBB Penetration\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eLipinski Violation\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTannin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1700.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePoor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRutin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e610.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eβ-sitosterol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e414.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePoor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSulfoquinovosyldiglyceride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e841.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePoor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGallic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e170.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSoluble\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eα-terpineol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e154.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSoluble\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOleic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e282.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eToxicity profiling (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) demonstrated that tannin exhibited the most favorable toxicity profile with \\\"No\\\" classification across hepatotoxicity, nephrotoxicity, and neurotoxicity, and \\\"Medium\\\" risk for respiratory and cardiotoxicity. Rutin showed similar favorable characteristics with \\\"No\\\" hepatotoxicity and neurotoxicity, though medium respiratory and cardiotoxicity risk.β-sitosterol, sulfoquinovosyldiglyceride, and oleic acid exhibited elevated toxicity concerns in specific organ systems, particularly hepatic and renal toxicity. Gallic acid demonstrated medium-risk toxicity across multiple systems, while α-terpineol showed medium neurotoxicity risk.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eToxicity profiling of the seven lead compounds across organ systems.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCompound\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHepatotoxicity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNephrotoxicity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeurotoxicity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eRespiratory Toxicity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCardiotoxicity\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTannin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRutin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eβ-sitosterol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSulfoquinovosyldiglyceride\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGallic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eα-terpineol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOleic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eMolecular dynamics simulations of the seven ligand-protein complexes over 100 ns provided comprehensive insights into binding stability and complex dynamics under physiological conditions.RMSD analysis of Cα atoms revealed critical differences in complex stability patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Six of the seven compounds exhibited overall RMSD trends below 3.3 \\u0026Aring;, indicating stable protein conformations throughout the 100 ns simulation. Oleic acid maintained the most consistent RMSD profile with a horizontal trend, despite minor deviations at specific timepoints (23.5 ns: 3.4 \\u0026Aring;; 27.5 ns: 3.9 \\u0026Aring;; 42.5 ns: 3.5 \\u0026Aring;)[\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNotably, sulfoquinovosyldiglyceride (SQD) exhibited an abnormal increasing trend after 80 ns, with RMSD values progressively rising above 3.3 \\u0026Aring;, indicating loss of structural stability during the final 20 ns of the simulation. This destabilization suggests that SQD, despite moderate initial binding affinity, does not maintain favorable binding interactions under extended physiological conditions[\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eRMSF analysis of individual amino acid residues across the 100 ns simulation revealed residue-specific flexibility patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). For tannin, rutin, β-sitosterol, gallic acid, α-terpineol, and oleic acid, RMSF values demonstrated remarkable consistency, indicating uniform residual flexibility and stable ligand-induced stabilization of protein regions. Initial residues (positions 1\\u0026ndash;54) showed maximum variation across all complexes, with RMSF values ranging from 3.4 to 10.6 \\u0026Aring;, reflecting the inherent flexibility of terminal protein regions[\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn contrast, SQD exhibited anomalous fluctuation patterns in the region spanning residues 513\\u0026ndash;698, with RMSF values ranging abnormally from 7.89 to 12.91 \\u0026Aring;. This pronounced regional fluctuation, substantially exceeding values observed for other complexes, confirms the instability of this compound and suggests unsuccessful stabilization of critical protein regions. Radius of gyration analysis assessed structural compactness and rigidity maintenance throughout the simulation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Most complexes exhibited stable Rg values ranging from 16.8 to 17.1 \\u0026Aring;, demonstrating maintained structural compactness[\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eGallic acid displayed minor Rg elevation between 57.75 and 67.5 ns (17.514 to 17.743 \\u0026Aring;) before stabilizing, suggesting transient conformational adjustment followed by recovery. Importantly, SQD demonstrated continuous and pronounced Rg increase beginning at 81 ns and continuing through the end of the simulation, indicating progressive protein expansion and loss of structural organization, consistent with RMSD and RMSF findings. SASA analysis monitored surface exposure changes during the simulation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The average SASA values for six of the seven complexes ranged consistently from 10,002 to 10,741 Ų, indicating stable surface characteristics. SQD displayed a marked increase in SASA at approximately 60 ns, with values diverging from the stable range and remaining elevated through the end of the simulation[\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis elevation in surface area corresponds with observations from RMSD, RMSF, and Rg analyses, further supporting the conclusion that SQD undergoes deleterious conformational changes affecting complex stability.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eHydrogen Bond Formation\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eHydrogen bonding between the ligand-protein complex and the solvent environment was comprehensively monitored (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAll seven compounds established hydrogen bonds throughout the simulation, reflecting sustained interactions within the binding pocket and with solvent molecules. Tannin and Rutin exhibited the most extensive hydrogen bonding patterns, with tannin establishing 11\\u0026ndash;23 hydrogen bonds and rutin maintaining 16\\u0026ndash;34 hydrogen bonds throughout the simulation. These elevated hydrogen bond counts correlate with their superior binding affinities and suggest multiple favorable intermolecular interactions.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, β-sitosterol and α-terpineol formed fewer hydrogen bonds (1\\u0026ndash;3 bonds), consistent with their lower binding affinities and reduced polar surface areas. The sustained hydrogen bonding observed for tannin and rutin throughout the 100 ns simulation indicates persistent favorable electrostatic interactions that stabilize the protein-ligand complex[\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eDengue remains among the most significant vector-borne viral diseases globally, with alarming epidemiological trends. The WHO's documentation of \\u0026gt;\\u0026thinsp;14.2\\u0026nbsp;million dengue cases in 2024, including\\u0026thinsp;\\u0026gt;\\u0026thinsp;7.5\\u0026nbsp;million confirmed cases and \\u0026gt;\\u0026thinsp;10,000 deaths, underscores the urgent need for effective therapeutic interventions. Bangladesh has experienced recurring outbreaks since 2000, with increasing severity and mortality rates. The absence of clinically approved antiviral therapy, combined with limited vaccine coverage and suboptimal vector control measures, perpetuates disease transmission and morbidity. These epidemiological realities justify the investment in computational drug discovery approaches targeting dengue-specific vulnerabilities[\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe dengue virus NS2b/NS3 serine protease was selected as the pharmacological target based on its essential role in viral replication and proteolytic processing of the viral polyprotein. This protease complex catalyzes the sequential cleavage of the dengue-encoded polyprotein into mature structural and non-structural proteins required for viral genome replication and virion assembly. The evolutionary conservation of this enzyme across all four major dengue serotypes and the fifth serotype suggests that inhibitors developed against this target possess potential for broad-spectrum activity. Previous literature has validated the NS2b/NS3 protease as a productive drug discovery target, with numerous structure-based drug design studies and clinical development efforts aimed at identifying selective protease inhibitors. Spirulina platensis represents a compelling natural product source for antiviral drug discovery. Extensive clinical and laboratory evidence has documented the immunostimulatory, anti-inflammatory, and antiviral properties of spirulina extracts and their constituent bioactive compounds. The documented efficacy against multiple viral pathogens (rotavirus, adenovirus, coxsackievirus, SARS-CoV-2, influenza) provides precedent for evaluating spirulina components against dengue virus. The safety profile established through decades of human consumption and clinical applications, combined with the accessibility and sustainability of spirulina cultivation, positions this organism as an excellent source for lead compound identification. The 19 compounds selected for this study represent the chemical diversity present within spirulina, including polysaccharides, fatty acids, polyphenols, and phytosterols[\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe ranking of compounds by binding affinity identified tannin and rutin as superior lead candidates. The binding affinities of \\u0026minus;\\u0026thinsp;8.9 and \\u0026minus;\\u0026thinsp;7.8 kcal/mol, respectively, indicate favorable thermodynamic binding, with tannin's affinity among the strongest reported for natural products against dengue proteases. These binding energies suggest favorable enthalpic interactions, likely mediated through hydrogen bonding and hydrophobic interactions within the substrate-binding pocket of the NS2b/NS3 protease. The structural features of tannin and rutin supporting favorable binding reflect their polyphenolic nature, with multiple hydroxyl groups capable of hydrogen bond formation with backbone amide and polar side chains of catalytic residues. Rutin's superior performance relative to structurally simpler polyphenols and oleic acid likely derives from its larger size and complex ring structure enabling multiple binding interactions. The modest binding affinities of fatty acids (oleic acid: \\u0026minus;5.1 kcal/mol) and simple aromatics (α-terpineol: \\u0026minus;5.3 kcal/mol) compared to polyphenols reflect their reduced capacity for specific polar interactions.\\u003c/p\\u003e \\u003cp\\u003eADMET analysis revealed important distinctions between compounds regarding their suitability for pharmaceutical development. Gallic acid and α-terpineol exhibited superior characteristics for drug candidates based on zero Lipinski violations and high GI absorption, coupled with favorable water solubility[\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e]. These properties predict efficient intestinal absorption and systemic bioavailability essential for oral formulations. The fact that these compounds maintain favorable ADMET profiles while still exhibiting binding affinities\\u0026thinsp;\\u0026ge;\\u0026thinsp;\\u0026minus;\\u0026thinsp;5.3 kcal/mol suggests their viability for further development.\\u003c/p\\u003e \\u003cp\\u003eTannin and rutin, despite exhibiting the strongest binding affinities, demonstrate elevated molecular weights and Lipinski violations, predicting suboptimal oral bioavailability[\\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e76\\u003c/span\\u003e]. This characteristic does not preclude their development as therapeutic agents but suggests alternative formulation strategies (parenteral administration, topical delivery, sustained-release formulations) or chemical modification to enhance bioavailability. The three Lipinski violations observed for both compounds warrant synthetic modification to reduce molecular weight and hydrogen bonding capacity while maintaining antiviral potency. The toxicity profiles demonstrated that tannin poses minimal risks across most organ systems, with only medium-level respiratory and cardiac toxicity concerns. This favorable safety profile, combined with superior binding affinity, positions tannin as the highest-priority candidate for lead compound optimization and experimental validation[\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe 100 ns molecular dynamics simulations provided critical validation of binding stability predictions derived from static docking poses. This extended sampling of conformational space under physiological conditions revealed that six of seven compounds maintained stable protein-ligand complexes throughout the simulation period[\\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e]. Tannin and rutin, the two compounds with the strongest docking affinities, both demonstrated excellent stability profiles across all assessed parameters (RMSD, RMSF, Rg, SASA). The sustained hydrogen bonding observed for these compounds (23 bonds for tannin; 34 bonds for rutin) indicates persistent favorable interactions that stabilize the complex[\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThese findings suggest that the docking predictions accurately reflected the favorable binding thermodynamics and binding modes of these compounds. Oleic acid maintained the most consistent RMSD trajectory despite its modest binding affinity, suggesting that even less energetically favorable compounds can maintain stable complexes under physiological conditions. This finding underscores the value of MD simulations in identifying compounds that, while exhibiting lower docking scores, may maintain functional binding stability. The marked instability of sulfoquinovosyldiglyceride (SQD) beginning at 80 ns, evidenced by increasing RMSD, RMSF, Rg, and SASA values, indicates that docking affinity alone provides insufficient information regarding practical binding stability.\\u003c/p\\u003e \\u003cp\\u003eDespite its\\u0026thinsp;\\u0026minus;\\u0026thinsp;5.9 kcal/mol binding affinity, SQD's inability to maintain stable interactions under physiological conditions argues against its selection as a lead candidate. This finding illustrates the critical importance of incorporating MD simulations into computational drug discovery workflows, as docking-only approaches would have incorrectly ranked SQD above oleic acid and α-terpineol. Integration of docking affinity, ADMET properties, toxicity profiles, and MD simulation stability enables comprehensive lead compound prioritization:\\u003c/p\\u003e \\u003cp\\u003eTier 1 (Highest Priority):\\u003c/p\\u003e \\u003cp\\u003eTannin: Superior binding affinity (\\u0026minus;\\u0026thinsp;8.9 kcal/mol), excellent MD stability, minimal toxicity profile, but requires bioavailability optimization through chemical modification or alternative formulation strategies.\\u003c/p\\u003e \\u003cp\\u003eTier 2 (High Priority):\\u003c/p\\u003e \\u003cp\\u003eRutin: Strong binding affinity (\\u0026minus;\\u0026thinsp;7.8 kcal/mol), excellent MD stability, favorable toxicity profile, but similar bioavailability limitations requiring optimization.\\u003c/p\\u003e \\u003cp\\u003eGallic acid: Moderate binding affinity (\\u0026minus;\\u0026thinsp;5.7 kcal/mol), favorable ADMET properties with high GI absorption, excellent water solubility, and minimal Lipinski violations, making it suitable for direct advancement to experimental validation.\\u003c/p\\u003e \\u003cp\\u003eTier 3 (Moderate Priority):\\u003c/p\\u003e \\u003cp\\u003eOleic acid: Modest binding affinity but excellent MD stability and favorable ADMET properties.\\u003c/p\\u003e \\u003cp\\u003eα-terpineol: Moderate binding affinity with favorable ADMET characteristics, though medium neurotoxicity risk warrants careful evaluation[\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe superior performance of tannin and rutin compared to simpler polyphenols and non-polar compounds reflects the importance of structural complexity and polar interactions in dengue NS2b/NS3 protease inhibition[\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e]. Polyphenolic compounds, with their multiple aromatic rings and hydroxyl groups, provide a structural framework accommodating multiple hydrogen bonds and π-π interactions with protein residues. The correlation between binding affinity and hydrogen bonding capacity (tannin: 23 H-bonds; rutin: 34 H-bonds) suggests that electrostatic interactions dominate the binding thermodynamics of these compounds. In contrast, fatty acids and monoterpenes, while capable of establishing hydrophobic interactions within the binding pocket, lack the polar groups necessary for extensive hydrogen bonding, resulting in weaker overall binding. This structure-activity relationship provides a rational basis for designing second-generation analogs with enhanced binding properties through incorporation of additional polar moieties or optimization of molecular geometry.\\u003c/p\\u003e \\u003cp\\u003eWhile computational studies provide critical insights into molecular interactions and stability predictions, these approaches cannot definitively establish antiviral efficacy without experimental validation. The current findings identify lead compounds warranting progression to:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eIn vitro cell-based assays assessing dengue virus replication inhibition using authenticated viral strains and standardized plaque reduction or quantitative RT-PCR methodologies.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eEnzymatic assays directly measuring NS2b/NS3 protease inhibition using recombinant protein and synthesized substrate peptides.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eStructural biology studies including X-ray crystallography or cryo-EM to visualize ligand-protein interactions and confirm predicted binding modes.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003ePharmacokinetic studies in appropriate animal models to establish absorption, distribution, metabolism, elimination, and oral bioavailability.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e\"},{\"header\":\"5. Toxicology studies assessing in vivo safety in animal models and potential for off-target effects.\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003e5.Limitations and Contextual Considerations\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis computational study operates within several important limitations. First, molecular docking and MD simulations represent static and semi-dynamic representations of a highly dynamic biological system. The protein and ligands are treated as relatively rigid structures, whereas in vivo binding likely involves conformational adaptation and induced-fit mechanisms not fully captured by these methodologies. Second, blind docking without prior knowledge of the substrate-binding pocket may identify artifactual binding modes at non-functional sites. Although the present study was constrained to the protein surface through grid box configuration, more targeted approaches incorporating known binding site information might refine predictions.\\u003c/p\\u003e \\u003cp\\u003eThird, the ADMET and toxicity predictions rely on machine learning models trained on limited experimental datasets, introducing inherent uncertainty in quantitative predictions. Empirical laboratory testing remains essential to validate these computational estimates. Fourth, only 19 of the 48 known compounds in spirulina could be evaluated due to time and computational constraints, potentially missing additional promising candidates. Finally, the dengue NS2b/NS3 protease structure represents a single serotype (likely derived from serotype 2 based on sequence identity), and binding characteristics may vary across serotypes due to natural genetic variation.\\u003c/p\\u003e \\u003cp\\u003eAs stated earlier that our computational investigation identified tannin and rutin from Spirulina platensis as promising lead compounds against dengue virus NS2B/NS3 protease based on molecular docking, ADMET profiling, toxicity assessment, and molecular dynamics simulation. Tannin demonstrated the strongest binding affinity (\\u0026minus;\\u0026thinsp;8.9 kcal/mol) with minimal organ-system toxicity while maintaining excellent stability throughout 100 ns MD simulation. Previous experimental studies have reported that flavonoids and polyphenolic compounds can effectively inhibit dengue NS2B/NS3 protease, supporting the therapeutic relevance of the present findings [\\u003cspan additionalcitationids=\\\"CR83 CR84 CR85 CR86 CR87\\\" citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e88\\u003c/span\\u003e]\\u003c/p\\u003e\"},{\"header\":\"6. Conclusion\",\"content\":\"\\u003cp\\u003eComputational investigation identified tannin and rutin from Spirulina platensis as promising lead compounds against dengue virus NS2b/NS3 protease based on molecular docking, ADMET profiling, toxicity assessment, and molecular dynamics simulation. Tannin demonstrated the strongest binding affinity (\\u0026minus;\\u0026thinsp;8.9 kcal/mol) with minimal organ-system toxicity, while maintaining excellent stability throughout 100 ns MD simulation. Rutin exhibited comparable stability and favorable toxicity profiles despite slightly lower binding affinity. ADMET analysis identified gallic acid and oleic acid as compounds with superior bioavailability characteristics suitable for direct experimental advancement, while tannin and rutin require bioavailability optimization through chemical modification or alternative formulation strategies.\\u003c/p\\u003e \\u003cp\\u003eThe marked instability of sulfoquinovosyldiglyceride during MD simulations, despite its reasonable docking affinity, underscores the critical importance of dynamical sampling in computational drug discovery. The integration of binding affinity predictions, ADMET properties, toxicity profiles, and MD stability assessment provides a comprehensive framework for rational lead compound identification. These findings establish a strong computational foundation for experimental validation through in vitro cell-based dengue replication assays, enzymatic protease inhibition studies, and in vivo pharmacokinetic and toxicology evaluations. Success of these subsequent experimental studies as stated earlier in the discussion section could generate novel antiviral therapeutic options for dengue treatment and contribute to addressing a significant global health challenge affecting hundreds of millions of individuals annually.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCompeting Interests\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare no competing financial interests.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis research was supported by computational resources provided by the Bangladesh Medical University Department of Pharmacology.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eAuthor contributions are summarized below:\\u0026bull; Dr. Joyanti Biswas (ORCID: 0009-0008-9629-3894), Department of Pharmacology, Bangladesh Medical University \\u0026mdash; Conceptualization, pharmacological interpretation, manuscript review.\\u0026bull; Dr. Md. Raisul Islam (ORCID: 0009-0001-4252-5863), Department of Pharmacology, Sher-E-Bangla Medical College \\u0026mdash; Data interpretation, literature review, manuscript editing.\\u0026bull; Dr. Sumaiya Nousheen (ORCID: 0009-0007-7374-0105), Department of Pharmacology and Therapeutics, Holy Family Red Crescent Medical College \\u0026mdash; ADMET analysis support, manuscript preparation.\\u0026bull; Dr. Md. Abdul Jalil (ORCID: 0009-0001-9237-3140), Department of Pharmacology, Dinajpur Medical College \\u0026mdash; Toxicity analysis, validation, manuscript revision.\\u0026bull; Dr. Milan Kumar Saha, Consultant (Surgery), DGHS, Mohakhali, Dhaka, Bangladesh \\u0026mdash; Clinical interpretation and biomedical significance assessment.\\u0026bull; Dr. Kalyan Dhar, Department of Chemical Engineering, Politecnico di Milano, Italy; Shyamoli Engineering College, University of Dhaka, Bangladesh \\u0026mdash; Corresponding author; molecular docking, molecular dynamics simulations, computational analysis, visualization, and primary manuscript drafting.We appreciate your time and consideration of our manuscript. We believe the interdisciplinary nature and translational relevance of this study will be of interest to the readership of the Journal of Molecular Modeling, and we look forward to your response.Sincerely,Dr. Kalyan Dhar, PhDCorresponding AuthorDepartment of Chemical EngineeringPolitecnico di Milano20133 Milan, ItalyShyamoli Engineering CollegeUniversity of DhakaDhaka-1000, BangladeshEmail: [kalyankumar.dhar@polimi.it](mailto:kalyankumar.dhar@polimi.it)\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eAll datas will be available after acceptance of the MS with the SA GitHub repo: www.github.com/onepartho\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eSchaefer, T. J.; Wolford, R. W. Dengue Fever. Nih.gov. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK430732/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ncbi.nlm.nih.gov/books/NBK430732/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWorld Health Organization. Dengue and severe dengue. World Health Organization. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue\\u003c/span\\u003e\\u003cspan address=\\\"https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang, Z.-S.; Baua, A. D.; Hemdan, M. S.; Assavalapsakul, W.; Wang, W.-H.; Lin, C.-Y.; Chao, D.-Y.; Chen, Y.-H.; Wang, S.-F. Dengue Virus Infection: A Systematic Review of Pathogenesis, Diagnosis and Management. Journal of Infection and Public Health 2025, 18 (12), 102982. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jiph.2025.102982\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jiph.2025.102982\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDisease Outbreak News: Dengue - Global Situation (30 May 2024) - World | ReliefWeb. reliefweb.int. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://reliefweb.int/report/world/disease-outbreak-news-dengue-global-situation-30-may-2024\\u003c/span\\u003e\\u003cspan address=\\\"https://reliefweb.int/report/world/disease-outbreak-news-dengue-global-situation-30-may-2024\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIkponmwosa Jude Ogieuhi; Ahmed, M. M.; Jamil, S.; Olalekan John Okesanya; Bonaventure Michael Ukoaka; Eshun, G.; Jerico Bautista Ogaya; Eliseo, D. Dengue Fever in Bangladesh: Rising Trends, Contributing Factors, and Public Health Implications. Tropical Diseases Travel Medicine and Vaccines 2025, 11 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s40794-025-00251-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s40794-025-00251-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eObi, J. O.; Guti\\u0026eacute;rrez-Barbosa, H.; Chua, J. V.; Deredge, D. J. Current Trends and Limitations in Dengue Antiviral Research. Tropical Medicine and Infectious Disease 2021, 6 (4), 180. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/tropicalmed6040180\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/tropicalmed6040180\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGan, V. C. Dengue: Moving from Current Standard of Care to State-of-The-Art Treatment. Current Treatment Options in Infectious Diseases 2014, 6 (3), 208\\u0026ndash;226. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s40506-014-0025-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s40506-014-0025-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFrimayanti, N.; Chee, C. F.; Zain, S.; Rahman, N. Abd. Design of New Competitive Dengue Ns2b/Ns3 Protease Inhibitors\\u0026mdash;a Computational Approach. International Journal of Molecular Sciences 2011, 12 (2), 1089\\u0026ndash;1100. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/ijms12021089\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms12021089\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLin, Y.-F.; Lai, H.-C.; Lin, C.-S.; Hung, P.-Y.; Kan, J.-Y.; Chiu, S.-W.; Lu, C.-H.; Petrova, S. F.; Baltina, L.; Lin, C.-W. Discovery of Potent Dengue Virus NS2B-NS3 Protease Inhibitors among Glycyrrhizic Acid Conjugates with Amino Acids and Dipeptides Esters. Viruses 2024, 16 (12), 1926. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/v16121926\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/v16121926\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLang, J.; Dutta, S. K.; Leuthold, M. M.; Reichert, L.; K\\u0026uuml;hl, N.; Martina, B.; Klein, C. D. Antiviral Drug Discovery with an Optimized Biochemical Dengue Protease Assay: Improved Predictive Power for Antiviral Efficacy. Antiviral Research 2024, 234, 106053. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.antiviral.2024.106053\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.antiviral.2024.106053\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHarun Norshidah; Chiuan Herng Leow; Kamarulzaman Ezatul Ezleen; Wahab, H. A.; Ramachandran Vignesh; Rasul, A.; Ngit Shin Lai. Assessing the Potential of NS2B/NS3 Protease Inhibitors Biomarker in Curbing Dengue Virus Infections: In Silico vs. in Vitro Approach. Assessing the potential of NS2B/NS3 protease inhibitors biomarker in curbing dengue virus infections: In silico vs. In vitro approach 2023, 13. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fcimb.2023.1061937\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fcimb.2023.1061937\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMader, J.; Gallo, A.; Schommartz, T.; Handke, W.; Nagel, C.-H.; G\\u0026uuml;nther, P.; Brune, W.; Reich, K. Calcium Spirulan Derived from Spirulina Platensis Inhibits Herpes Simplex Virus 1 Attachment to Human Keratinocytes and Protects against Herpes Labialis. Journal of Allergy and Clinical Immunology 2016, 137 (1), 197\\u0026ndash;203.e3. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jaci.2015.07.027\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jaci.2015.07.027\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHayashi, T.; Hayashi, K.; Maeda, M.; Kojima, I. Calcium Spirulan, an Inhibitor of Enveloped Virus Replication, from a Blue-Green AlgaSpirulina Platensis. Journal of Natural Products 1996, 59 (1), 83\\u0026ndash;87. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/np960017o\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/np960017o\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVerani, M.; Manera, C.; Pagani, A.; Banti, M.; Carducci, A.; Gasperin, F.; Cannaos, A.; Di Giuseppe, G.; Palego, L.; Nieri, P.; Federigi, I. In Vitro Evaluation of Virucidal Effect of Polysaccharides Extracted and Purified from Arthrospira Platensis and Dunaliella Salina on Human Adenovirus Type 5 in A549 Cells. Molecules 2026, 31 (6), 1023. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/molecules31061023\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/molecules31061023\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRabaan, A. A.; Kaabi, A.; None Muzaheed; Mubarak Alfaresi; Garout, M.; Alotaibi, N.; Ameen; Alsayyah, A.; Alali, N. A.; Sulaiman, T.; Alotaibi, J.; Alissa, M. Antiviral Actions of Natural Compounds against Dengue Virus RNA Dependent RNA Polymerase: Insights from Molecular Dynamics and Gibbs Free Energy Landscape. Journal of Biomolecular Structure and Dynamics 2024, 1\\u0026ndash;18. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/07391102.2024.2325120\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/07391102.2024.2325120\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592\\u0026ndash;7618. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/cimb46070451\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cimb46070451\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl Khzem, A. H.; Wali, S. M. Drug Repurposing as an Effective Drug Discovery Strategy: A Critical Review. Drug design, development and therapy 2025, 19, 12019\\u0026ndash;12034. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.2147/DDDT.S576701\\u003c/span\\u003e\\u003cspan address=\\\"10.2147/DDDT.S576701\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGourab Ray. The Technological Re-Engineering of Pharmaceutical Research and Development: A Quantitative Analysis of Innovation\\u0026rsquo;s Impact on the Drug Discovery Value Chain. World Journal of Advanced Research and Reviews 2025, 27 (2), 533\\u0026ndash;550. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.30574/wjarr.2025.27.2.2857\\u003c/span\\u003e\\u003cspan address=\\\"10.30574/wjarr.2025.27.2.2857\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNobuaki Yasuo; Ishida, T.; Masakazu Sekijima. Computer Aided Drug Discovery Review for Infectious Diseases with Case Study of Anti-Chagas Project. Parasitology international 2021, 83, 102366\\u0026ndash;102366. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.parint.2021.102366\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.parint.2021.102366\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl Khzem, A. H.; Wali, S. M. Drug Repurposing as an Effective Drug Discovery Strategy: A Critical Review. Drug design, development and therapy 2025, 19, 12019\\u0026ndash;12034. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.2147/DDDT.S576701\\u003c/span\\u003e\\u003cspan address=\\\"10.2147/DDDT.S576701\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFahim, A. M. Advances in Computer-Aided Drug Design: From Molecular Docking to AI-Driven Therapeutic Discovery. ASPET Discovery 2026, 100024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.aspetd.2026.100024\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.aspetd.2026.100024\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFahim, A. M. Advances in Computer-Aided Drug Design: From Molecular Docking to AI-Driven Therapeutic Discovery. ASPET Discovery 2026, 100024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.aspetd.2026.100024\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.aspetd.2026.100024\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChallapa-Mamani, M. R.; Tom\\u0026aacute;s-Alvarado, E.; Espinoza-Baigorria, A.; Le\\u0026oacute;n-Figueroa, D. A.; Sah, R.; Rodr\\u0026iacute;guez-Morales, A. J.; Barboza, J. J. Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania Donovani: An Update and Literature Review. Tropical Medicine and Infectious Disease 2023, 8 (10), 457\\u0026ndash;457. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/tropicalmed8100457\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/tropicalmed8100457\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLim, L.; Dang, M.; Roy, A.; Kang, J.; Song, J. Curcumin Allosterically Inhibits the Dengue NS2B-NS3 Protease by Disrupting Its Active Conformation. ACS Omega 2020, 5 (40), 25677\\u0026ndash;25686. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/acsomega.0c00039\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acsomega.0c00039\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSaqallah, F. G.; Abbas, M. A.; Wahab, H. A. Recent Advances in Natural Products as Potential Inhibitors of Dengue Virus with a Special Emphasis on NS2b/NS3 Protease. Phytochemistry 2022, 202, 113362. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.phytochem.2022.113362\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.phytochem.2022.113362\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJos\\u0026eacute; Angel Santiago-Cruz; Posadas-Mondrag\\u0026oacute;n, A.; Jos\\u0026eacute; Leopoldo Aguilar-Faisal; Ortiz-Garc\\u0026iacute;a, C. I.; Danai Montalvan-Sorrosa; Herrera-Gonz\\u0026aacute;lez, N. E.; Ang\\u0026eacute;lica P\\u0026eacute;rez-Ju\\u0026aacute;rez. In Vitro Evaluation of the Antiviral Effect of Spirulina Maxima (Arthrospira) Alga against Chikungunya Virus. Viruses 2025, 17 (12), 1583\\u0026ndash;1583. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/v17121583\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/v17121583\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTahir ul Qamar, M.; Maryam, A.; Muneer, I.; Xing, F.; Ashfaq, U. A.; Khan, F. A.; Anwar, F.; Geesi, M. H.; Khalid, R. R.; Rauf, S. A.; Siddiqi, A. R. Computational Screening of Medicinal Plant Phytochemicals to Discover Potent Pan-Serotype Inhibitors against Dengue Virus. Scientific Reports 2019, 9 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41598-018-38450-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-018-38450-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHossain, M. S.; Soharth Hasnat; Akter, S.; Mim, M. M.; Tahcin, A.; Hoque, M.; Durjoy Sutradhar; Akter, A.; Namin Rouf Sium; Hossain, S.; Runa Masuma; Sakhawat Hossen Rakib; Islam, M. A.; Islam, T.; Bhattacharya, P.; Hoque, M. N. Computational Identification of Vernonia Cinerea-Derived Phytochemicals as Potential Inhibitors of Nonstructural Protein 1 (NSP1) in Dengue Virus Serotype-2. Frontiers in Pharmacology 2024, 15. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fphar.2024.1465827\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fphar.2024.1465827\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTahir ul Qamar, M.; Maryam, A.; Muneer, I.; Xing, F.; Ashfaq, U. A.; Khan, F. A.; Anwar, F.; Geesi, M. H.; Khalid, R. R.; Rauf, S. A.; Siddiqi, A. R. Computational Screening of Medicinal Plant Phytochemicals to Discover Potent Pan-Serotype Inhibitors against Dengue Virus. Scientific Reports 2019, 9 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41598-018-38450-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-018-38450-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592\\u0026ndash;7618. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/cimb46070451\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cimb46070451\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592\\u0026ndash;7618. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/cimb46070451\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cimb46070451\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYaswanth, M.; Dubey, A.; Tufail, A.; Nath, S.; Janardhan, S.; Maffia, M.; Ragusa, A.; Mishra, V. K. Computational Repurposing of Drugs against Dengue Virus Targeting NS5 and Methyltransferase Proteins. Scientific Reports 2025, 15 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41598-025-09443-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-025-09443-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHossain, M. S.; Soharth Hasnat; Akter, S.; Mim, M. M.; Tahcin, A.; Hoque, M.; Durjoy Sutradhar; Akter, A.; Namin Rouf Sium; Hossain, S.; Runa Masuma; Sakhawat Hossen Rakib; Islam, M. A.; Islam, T.; Bhattacharya, P.; Hoque, M. N. Computational Identification of Vernonia Cinerea-Derived Phytochemicals as Potential Inhibitors of Nonstructural Protein 1 (NSP1) in Dengue Virus Serotype-2. Frontiers in Pharmacology 2024, 15. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fphar.2024.1465827\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fphar.2024.1465827\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePurohit, P.; Sahoo, S.; Panda, M.; Sahoo, P. S.; Meher, B. R. Targeting the DENV NS2B-NS3 Protease with Active Antiviral Phytocompounds: Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulation Studies. Journal of Molecular Modeling 2022, 28 (11). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s00894-022-05355-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00894-022-05355-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePurohit, P.; Sahoo, S.; Panda, M.; Sahoo, P. S.; Meher, B. R. Targeting the DENV NS2B-NS3 Protease with Active Antiviral Phytocompounds: Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulation Studies. Journal of Molecular Modeling 2022, 28 (11). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s00894-022-05355-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00894-022-05355-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCordero, A. M. F.; Gonzales, A. A. Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants. Current Issues in Molecular Biology 2024, 46 (7), 7592\\u0026ndash;7618. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/cimb46070451\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cimb46070451\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFerreira, F. J. N.; Carneiro, A. S. AI-Driven Drug Discovery: A Comprehensive Review. ACS Omega 2025, 10 (23). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/acsomega.5c00549\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acsomega.5c00549\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHartig, M. U.; Appelhaus, J.; Vollenbr\\u0026ouml;ker, M.; Lamprecht, A. In-Situ Forming Polyester Implants for Sustained Intravesical Oxybutynin Release. Pharmaceutics 2025, 17 (11), 1369. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/pharmaceutics17111369\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/pharmaceutics17111369\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSagaya Jansi, R.; Khusro, A.; Agastian, P.; Alfarhan, A.; Al-Dhabi, N. A.; Arasu, M. V.; Rajagopal, R.; Barcelo, D.; Al-Tamimi, A. Emerging Paradigms of Viral Diseases and Paramount Role of Natural Resources as Antiviral Agents. Science of The Total Environment 2021, 759, 143539. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.scitotenv.2020.143539\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.scitotenv.2020.143539\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHartig, M. U.; Appelhaus, J.; Vollenbr\\u0026ouml;ker, M.; Lamprecht, A. In-Situ Forming Polyester Implants for Sustained Intravesical Oxybutynin Release. Pharmaceutics 2025, 17 (11), 1369. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/pharmaceutics17111369\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/pharmaceutics17111369\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eData, P. RCSB PDB \\u0026ndash;\\u0026thinsp;2FOM: Dengue Virus NS2B/NS3 Protease. Rcsb.org. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.rcsb.org/structure/2FOM\\u003c/span\\u003e\\u003cspan address=\\\"https://www.rcsb.org/structure/2FOM\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (accessed 2026-05-03).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eData, P. RCSB PDB \\u0026ndash; 3L6P: Crystal Structure of Dengue Virus 1 NS2B/NS3 protease. Rcsb.org. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.rcsb.org/structure/3L6P\\u003c/span\\u003e\\u003cspan address=\\\"https://www.rcsb.org/structure/3L6P\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (accessed 2026-05-03).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTimiri, A. K.; Selvarasu, S.; Kesherwani, M.; Vijayan, V.; Sinha, B. N.; Devadasan, V.; Jayaprakash, V. Synthesis and Molecular Modelling Studies of Novel Sulphonamide Derivatives as Dengue Virus 2 Protease Inhibitors. Bioorganic Chemistry 2015, 62, 74\\u0026ndash;82. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.bioorg.2015.07.005\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.bioorg.2015.07.005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSp\\u0026iacute;nola, M. P.; Mendes, A. R.; Jos\\u0026eacute; A. M. Prates. Chemical Composition, Bioactivities, and Applications of Spirulina (Limnospira Platensis) in Food, Feed, and Medicine. Foods 2024, 13 (22), 3656\\u0026ndash;3656. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/foods13223656\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/foods13223656\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChaiprasert, A.; Han, P.; Laomettachit, T.; Ruengjitchatchawalya, M. Network Analysis Retrieving Bioactive Compounds from Spirulina (Arthrospira Platensis) and Their Targets Related to Systemic Lupus Erythematosus. PLOS ONE 2024, 19 (8), e0309303. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0309303\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0309303\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMarjanović, B.; Benković, M.; Jurina, T.; Sokač Cvetnić, T.; Valinger, D.; Gajdoš Kljusurić, J.; Jurinjak Tušek, A. Bioactive Compounds from Spirulina Spp.\\u0026mdash;Nutritional Value, Extraction, and Application in Food Industry. Separations 2024, 11 (9), 257. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/separations11090257\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/separations11090257\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCoumar, M. S. Molecular Docking for Computer-Aided Drug Design : Fundamentals, Techniques, Resources and Applications; Academic Press: London, 2021.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization. Arxiv.org. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://arxiv.org/html/2604.21937v1\\u003c/span\\u003e\\u003cspan address=\\\"https://arxiv.org/html/2604.21937v1\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (accessed 2026-05-03).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTang, S.-L.; Sumitra, M. R.; Chen, L.-C.; Liu, F.-C.; Hsu, H.-L.; Kuo, Y.-C.; Ansar, M.; Huang, S.-L.; Lee, S.-Y.; Wang, H.-J.; Lawal, B.; Wu, A. T. H.; Wen, Y.-T.; Huang, H.-S. Machine Learning\\u0026ndash;Driven Discovery of NSC828779 as a Multi-Mechanistic NLRP3 Inflammasome Inhibitor for Inflammatory Diseases. Computers in Biology and Medicine 2025, 197, 111110. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.compbiomed.2025.111110\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.compbiomed.2025.111110\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGarc\\u0026iacute;a-Orteg\\u0026oacute;n, M.; Simm, G. N. C.; Tripp, A. J.; Hern\\u0026aacute;ndez-Lobato, J. M.; Bender, A.; Bacallado, S. DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. Journal of Chemical Information and Modeling 2022, 62 (15), 3486\\u0026ndash;3502. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/acs.jcim.1c01334\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acs.jcim.1c01334\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSeeliger, D.; de Groot, B. L. Ligand Docking and Binding Site Analysis with PyMOL and Autodock/Vina. Journal of Computer-Aided Molecular Design 2010, 24 (5), 417\\u0026ndash;422. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s10822-010-9352-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s10822-010-9352-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eADMETlab 3.0. admetlab3.scbdd.com. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://admetlab3.scbdd.com/\\u003c/span\\u003e\\u003cspan address=\\\"https://admetlab3.scbdd.com/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDaina, A.; Michielin, O.; Zoete, V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Scientific Reports 2017, 7 (1), 1\\u0026ndash;13. \\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\\u003eVelid Unsal; Oner, E.; Reşit Yıldız; Başak Doğru Mert. Comparison of New Secondgeneration H1 Receptor Blockers with Some Molecules; a Study Involving DFT, Molecular Docking, ADMET, Biological Target and Activity. BMC Chemistry 2025, 19 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s13065-024-01371-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13065-024-01371-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIndah Purwaningsih; Iman Permana Maksum; Dadan Sumiarsa; Sriwidodo Sriwidodo. Pharmacokinetics and Toxicity Overview of Active Compounds Berberine, Palmatine, and Jatrorrhizine from Fibraurea Tinctoria Lour: Drug-Likeness, ADMET Prediction, and in Vivo Extract Toxicity Assessment. Journal of Toxicology 2025, 2025 (1), 7251602\\u0026ndash;7251602. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1155/jt/7251602\\u003c/span\\u003e\\u003cspan address=\\\"10.1155/jt/7251602\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSaritha, K.; Alivelu, M.; Mohammad, M. Drug-Likeness Analysis, in Silico ADMET Profiling of Compounds in Kedrostis Foetidissima (Jacq.) Cogn, and Antibacterial Activity of the Plant Extract. In Silico Pharmacology 2024, 12 (2). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s40203-024-00240-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s40203-024-00240-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOluyemi, W. M.; Nwokebu, G.; Adewumi, A. T.; Eze, S. C.; Mbachu, C. C.; Ogueli, E. C.; Nwodo, N.; Soliman, M. E. S.; Mosebi, S. The Characteristic Structural and Functional Dynamics of P. Falciparum DHFR Binding with Pyrimidine Chemotypes Implicate Malaria Therapy Design. Chemical Physics Impact 2024, 9, 100703. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.chphi.2024.100703\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.chphi.2024.100703\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCui, M.; Sun, H.; Liu, X.; Huang, Z.; Su, Z.; Zheng, Y.; Shen, Y.; Wang, M. Antibacterial Mechanism and Computer Simulation Analysis of a Novel Antimicrobial Peptide Inducing Membrane Disintegration in Gram-Positive Bacteria. Food Bioscience 2025, 71, 107101. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.fbio.2025.107101\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.fbio.2025.107101\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAncuceanu, R.; Lascu, B. E.; Drăgănescu, D.; Dinu, M. In Silico ADME Methods Used in the Evaluation of Natural Products. Pharmaceutics 2025, 17 (8), 1002. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/pharmaceutics17081002\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/pharmaceutics17081002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJos\\u0026eacute; Angel Santiago-Cruz; Posadas-Mondrag\\u0026oacute;n, A.; Jos\\u0026eacute; Leopoldo Aguilar-Faisal; Ortiz-Garc\\u0026iacute;a, C. I.; Danai Montalvan-Sorrosa; Herrera-Gonz\\u0026aacute;lez, N. E.; Ang\\u0026eacute;lica P\\u0026eacute;rez-Ju\\u0026aacute;rez. In Vitro Evaluation of the Antiviral Effect of Spirulina Maxima (Arthrospira) Alga against Chikungunya Virus. Viruses 2025, 17 (12), 1583\\u0026ndash;1583. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/v17121583\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/v17121583\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYong, K. S.; Choi, S. B.; Wahab, H. Docking of Dengue NS2B-NS3 Protease with Murraya koenigii. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.atlantis-press.com\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.2991/iccst-15.2015.14\\u003c/span\\u003e\\u003cspan address=\\\"10.2991/iccst-15.2015.14\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOgundele, A. V.; Das, A. M.; Paz, C. Gallic Acid from Elaeocarpus Floribundus Stem Bark: A Potent Natural Antioxidant with Enzymatic and Pharmacokinetic Validation. Antioxidants 2025, 14 (10), 1161. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/antiox14101161\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/antiox14101161\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMamoudou, H.; Abdoulaye, A. H.; Ditchou, N. Y. O.; Olumasai, J. N.; Adissa, R. M. Z. K.; Mune, M. A. M. Computational Investigation of Plectranthus Neochilus Essential Oil Phytochemicals Interaction with Dipeptidyl Peptidase 4: A Potential Avenue for Antidiabetic Drug Discovery. Current Pharmaceutical Analysis 2025, 21 (3), 169\\u0026ndash;178. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cpan.2025.03.002\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cpan.2025.03.002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAde Arsianti; Azizah, N. N.; Erlina, L. Molecular Docking, ADMET Profiling of Gallic Acid and Its Derivatives (N-Alkyl Gallamide) as Apoptosis Agent of Breast Cancer MCF-7 Cells. F1000Research 2024, 11, 1453\\u0026ndash;1453. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.12688/f1000research.127347.3\\u003c/span\\u003e\\u003cspan address=\\\"10.12688/f1000research.127347.3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePanchal, J.; Prajapati, J.; Dabhi, M.; Patel, A.; Patel, S.; Rawal, R.; Saraf, M.; Goswami, D. Comprehensive Computational Investigation for Ligand Recognition and Binding Dynamics of SdiA: A Degenerate LuxR -Type Receptor in Klebsiella Pneumoniae. Molecular Diversity 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s11030-023-10785-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11030-023-10785-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMcAllister, R. G.; Konermann, L. Challenges in the Interpretation of Protein H/D Exchange Data: A Molecular Dynamics Simulation Perspective. Biochemistry 2015, 54 (16), 2683\\u0026ndash;2692. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/acs.biochem.5b00215\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acs.biochem.5b00215\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTiwari, V.; Sowdhamini, R. Structure Modelling of Odorant Receptor from Aedes Aegypti and Identification of Potential Repellent Molecules. Computational and Structural Biotechnology Journal 2023, 21, 2204\\u0026ndash;2214. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.csbj.2023.03.005\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.csbj.2023.03.005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang, L.; Li, S.; Liu, S.; Wang, Z. Dietary Acrylamide Induces Depression via SIRT3-Mediated Mitochondrial Oxidative Injury: Evidence from Multi-Omics and Mendelian Randomization. Current Issues in Molecular Biology 2025, 47 (10), 836. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/cimb47100836\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cimb47100836\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang, L.; Li, S.; Liu, S.; Wang, Z. Dietary Acrylamide Induces Depression via SIRT3-Mediated Mitochondrial Oxidative Injury: Evidence from Multi-Omics and Mendelian Randomization. Current Issues in Molecular Biology 2025, 47 (10), 836. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/cimb47100836\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cimb47100836\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eElzupir, A. O.; Almahmoud, S. A. J. Unveiling of Phosphodiesterase-5 Hot Residues Binding to Xanthine Derivatives for Erectile Dysfunction Therapy: A Computational Drug Repurposing Approach. PLOS One 2025, 20 (11), e0336267. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0336267\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0336267\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTallei, T. E.; Kapantow, N. H.; Nurdjannah Jane Niode; Hessel, S. S.; Maghfirah Savitri; Fatimawali Fatimawali; Kang, S.; Park, M. N.; Muhammad Raihan; Widya Hardiyanti; Firzan Nainu; Kim, B. Integrative in Silico and in Vivo Drosophila Model Studies Reveal the Anti-Inflammatory, Antioxidant, and Anticancer Properties of Red Radish Microgreen Extract. Scientific Reports 2025, 15 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41598-025-02999-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-025-02999-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeng, Y.; Jin, C.; Shihao Lv; Zhang, H.; Ren, F.; Wang, J. Molecular Mechanisms and Applications of Polyphenol-Protein Complexes with Antioxidant Properties: A Review. Antioxidants 2023, 12 (8), 1577\\u0026ndash;1577. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/antiox12081577\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/antiox12081577\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHaider, N.; Hasan, M. N.; Onyango, J.; Billah, M.; Khan, S.; Papakonstantinou, D.; Paudyal, P.; Asaduzzaman, M. Global Dengue Epidemic Worsens with Record 14 Million Cases and 9,000 Deaths Reported in 2024. International Journal of Infectious Diseases 2025, 158, 107940. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ijid.2025.107940\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ijid.2025.107940\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eand, V. Dengue: global situation, surveillance and progress \\u0026ndash; 2024 update. Who.int. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.who.int/publications/i/item/who-wer10052-665-678\\u003c/span\\u003e\\u003cspan address=\\\"https://www.who.int/publications/i/item/who-wer10052-665-678\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMukherjee, M.; Lihong, N.; David, C. Y. K. Cancer Therapeutics: In-Silico Evaluation of Novel UBR5 Protein Inhibitors. Proceedings in Technology Transfer 2025, 401\\u0026ndash;411. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/978-981-96-3770-6_41\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/978-981-96-3770-6_41\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFalah Azeez, Z.; Ali Khaleel, L.; Ali Kadhim Kyhoiesh, H. Synthesis, Biological Evaluation, Molecular Docking Analyses, and ADMET Study of Azo Derivatives Containing 1-Naphthol against MβL-Producing S. Maltophilia. Results in Chemistry 2024, 12, 101864. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.rechem.2024.101864\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.rechem.2024.101864\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChavan, N. D.; Sarveswari, S.; Vijayakumar, V. Quinoline Derivatives\\u0026rsquo; Biological Interest for Anti-Malarial and Anti-Cancer Activities: An Overview. RSC Advances 2025, 15 (37), 30576\\u0026ndash;30604. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1039/d5ra00534e\\u003c/span\\u003e\\u003cspan address=\\\"10.1039/d5ra00534e\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eView of Potential Inhibitor of DENV-2 Virus Protease (NS2B-NS3): An In-Silico Studies of Anti-Viral Plants| Journal of Drug Delivery and Therapeutics. Jddtonline.info. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://jddtonline.info/index.php/jddt/article/view/6870/6401\\u003c/span\\u003e\\u003cspan address=\\\"https://jddtonline.info/index.php/jddt/article/view/6870/6401\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (accessed 2026-05-03).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLoaiza-Cano, V.; Monsalve-Escudero, L. M.; Filho, C. da S. M. B.; Martinez-Gutierrez, M.; Sousa, D. P. de. Antiviral Role of Phenolic Compounds against Dengue Virus: A Review. Biomolecules 2020, 11 (1), 11. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/biom11010011\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/biom11010011\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePurohit, P.; Barik, D.; Agasti, S.; Panda, M.; Meher, B. R. Evaluation of the Inhibitory Potency of Anti-Dengue Phytocompounds against DENV-2 NS2B-NS3 Protease: Virtual Screening, ADMET Profiling and Molecular Dynamics Simulation Investigations. Journal of biomolecular structure \\u0026amp; dynamics 2024, 42 (6), 2990\\u0026ndash;3009. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/07391102.2023.2212798\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/07391102.2023.2212798\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFrimayanti, N.; Chee, C. F.; Zain, S.; Rahman, N. Abd. Design of New Competitive Dengue Ns2b/Ns3 Protease Inhibitors\\u0026mdash;a Computational Approach. International Journal of Molecular Sciences 2011, 12 (2), 1089\\u0026ndash;1100. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/ijms12021089\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms12021089\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLim, L.; Dang, M.; Roy, A.; Kang, J.; Song, J. Curcumin Allosterically Inhibits the Dengue NS2B-NS3 Protease by Disrupting Its Active Conformation. ACS Omega 2020, 5 (40), 25677\\u0026ndash;25686. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/acsomega.0c00039\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acsomega.0c00039\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFouzia Ismat; Tariq, A.; Shaheen, A.; Ullah, R.; Raheem, K.; Muhammad Muddassar; Mahboob, S.; Abbas, W.; Iqbal, M.; Rahman, M. Inhibition of NS2B-NS3 Protease from All Four Serotypes of Dengue Virus by Punicalagin, Punicalin and Ellagic Acid Identified from Punica Granatum. Journal of Biomolecular Structure and Dynamics 2024, 1\\u0026ndash;16. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/07391102.2024.2314258\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/07391102.2024.2314258\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRothan, H. A.; Han, H. C.; Ramasamy, T. S.; Othman, S.; Rahman, N. A.; Yusof, R. Inhibition of Dengue NS2B-NS3 Protease and Viral Replication in Vero Cells by Recombinant Retrocyclin-1. BMC Infectious Diseases 2012, 12 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/1471-2334-12-314\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/1471-2334-12-314\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ede Sousa, L. R. F.; Wu, H.; Nebo, L.; Fernandes, J. B.; da Silva, M. F. das G. F.; Kiefer, W.; Kanitz, M.; Bodem, J.; Diederich, W. E.; Schirmeister, T.; Vieira, P. C. Flavonoids as Noncompetitive Inhibitors of Dengue Virus NS2B-NS3 Protease: Inhibition Kinetics and Docking Studies. Bioorganic \\u0026amp; Medicinal Chemistry 2015, 23 (3), 466\\u0026ndash;470. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.bmc.2014.12.015\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.bmc.2014.12.015\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWu, D.; Mao, F.; Ye, Y.; Li, J.; Xu, C.; Luo, X.; Chen, J.; Shen, X. Policresulen, a Novel NS2B/NS3 Protease Inhibitor, Effectively Inhibits the Replication of DENV2 Virus in BHK-21 Cells. Acta Pharmacologica Sinica 2015, 36 (9), 1126\\u0026ndash;1136. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/aps.2015.56\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/aps.2015.56\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSarwar, M. W.; Riaz, A.; Dilshad, S. M. R.; Al-Qahtani, A.; Nawaz-Ul-Rehman, M. S.; Mubin, M. Structure Activity Relationship (SAR) and Quantitative Structure Activity Relationship (QSAR) Studies Showed Plant Flavonoids as Potential Inhibitors of Dengue NS2B-NS3 Protease. BMC Structural Biology 2018, 18 (1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12900-018-0084-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12900-018-0084-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMukhtar, M.; Haris Ahmed Khan; Najam. Exploring the Inhibitory Potential of Nigella Sativa against Dengue Virus NS2B/NS3 Protease and NS5 Polymerase Using Computational Approaches. RSC Advances 2023, 13 (27), 18306\\u0026ndash;18322. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1039/d3ra02613b\\u003c/span\\u003e\\u003cspan address=\\\"10.1039/d3ra02613b\\\" 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\":\"info@researchsquare.com\",\"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\":\"Dengue virus, molecular docking, molecular dynamics simulation, Spirulina platensis, NS2b/NS3 protease\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9667555/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9667555/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eDengue virus (DENV) remains a global health concern, with no clinically approved antiviral treatment available to date. The NS2b/NS3 serine protease is an essential viral enzyme responsible for polyprotein processing and represents a promising therapeutic target. This computational study employed molecular docking and molecular dynamics (MD) simulations to identify potential antiviral compounds from Spirulina platensis. A total of 19 bioactive compounds isolated from Spirulina platensis were screened against the dengue virus NS2b/NS3 protease (PDB ID: 2FOM) using PyRx virtual screening with AutoDock Vina. The seven compounds with the highest binding affinities were subjected to 100 nanosecond molecular dynamics simulations using the AMBER14 force field at physiological conditions (pH 7.4, 298 K). ADMET analysis and toxicity profiling were performed to evaluate drug-likeness and safety parameters. Tannin (\\u0026minus;\\u0026thinsp;8.9 kcal/mol) and Rutin (\\u0026minus;\\u0026thinsp;7.8 kcal/mol) exhibited the strongest binding affinities and remained stable throughout the simulation. RMSD analysis confirmed complex stability (\\u0026lt;\\u0026thinsp;3.3 \\u0026Aring; for six of seven compounds), while hydrogen bonding patterns revealed sustained interactions between ligands and protein residues. ADMET screening identified gallic acid, oleic acid, alpha-terpineol, and beta-sitosterol as possessing favorable oral bioavailability characteristics. Notably, tannin demonstrated minimal toxicity across major organ systems. Our findings suggest that tannin and rutin from Spirulina platensis are promising lead compounds warranting further experimental validation for dengue antiviral drug development. This study demonstrates the potential of marine-derived natural products in accelerating the discovery of new therapeutic agents against viral infections.\\u003c/p\\u003e\",\"manuscriptTitle\":\"In-Silico Screening and Molecular Dynamics Evaluation of Spirulina Platensis-Derived Compounds as Potential Antiviral Agents Against Dengue Virus NS2b/NS3 Protease\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-12 10:18:01\",\"doi\":\"10.21203/rs.3.rs-9667555/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"ec80041f-f336-4676-8b33-ea4226777c9e\",\"owner\":[],\"postedDate\":\"May 12th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Rejected\",\"date\":\"2026-05-15T06:46:38+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-05-14T07:17:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-05-14T07:16:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Journal of Molecular Modeling\",\"date\":\"2026-05-10T04:41:02+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-15T06:55:30+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-12 10:18:01\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9667555\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9667555\",\"identity\":\"rs-9667555\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}