Search Results (464)

Background/Objectives: Gold(I) complexes are promising bioactive agents with anticancer and anti-inflammatory potential. This study evaluated cytochrome P450 (CYP) interactions and in vitro pharmacokinetic properties of two Au(I)–triphenylphosphine complexes bearing 6-alkoxy-9-deazapurine ligands. Methods: Complexes [Au(HL_1,2)(PPh₃)] (HL₁ = 6-isopropyloxy-9-deazapurine, complex 1; HL₂ = 6-benzyloxy-9-deazapurine, complex 2) were investigated. Inhibition of nine human CYP isoforms was assessed in liver microsomes, and kinetics were analyzed using Dixon and Lineweaver–Burk plots. CYP binding was evaluated by UV–Vis difference spectroscopy. ADME properties (chemical/plasma stability, microsomal stability, plasma protein binding, and PAMPA permeability) were determined. Binding thermodynamics were analyzed by ITC. Results: Both complexes weakly inhibited most CYP isoforms, with stronger effects on CYP2C9 and CYP3A4/5. A non-competitive inhibition mechanism was observed, which may be related to the binding of the complexes to the substrate channels of CYP2C9 and CYP3A4, thereby limiting the active site’s accessibility to the substrate, as supported by molecular docking studies. UV–Vis spectra showed type I binding with Kd values of 9.32 µM (1) and 12.64 µM (2). Both compounds showed high chemical and plasma stability (>90%), moderate microsomal stability (~60% after 60 min), high plasma protein binding (~80%), and low passive permeability. Conclusions: Au(I)–triphenylphosphine complexes with 6-alkoxy-9-deazapurine ligands exhibit moderate CYP affinity and defined pharmacokinetic profiles, supporting further preclinical evaluation. Full article

Artificial intelligence has advanced from merely predicting static protein structures to modeling equilibrium conformational ensembles. It now concurrently forecasts structure and binding affinity and actively participates in candidate selection during the initial stages of drug discovery. Foundation models introduced between 2024 and 2026, including BioEmu, AlphaFlow, DiG, Boltz-2, Chai-1, NeuralPLexer, and explicit-solvent prediction systems such as SuperWater, have begun to address issues previously identified as fundamental concerns in earlier critiques of AI in drug discovery. Nevertheless, many of these models are presently accessible only as preprints and require validation through independent peer review. Evidence indicates a shift in the primary bottleneck from representation challenges to validation difficulties. However, this transition remains incomplete and heavily dependent on context. The risks associated with AI-enabled drug discovery are increasingly not solely about the models’ capacity to accurately represent ensembles, but also about whether the evidentiary standards used to validate AI-derived predictions keep pace with the rapidity with which these predictions are generated and employed. This article introduces a four-tier validation framework designed to align the extent of computational and experimental evidence with the translational and regulatory risks associated with various artificial intelligence (AI) applications within the molecular sciences. These applications include machine learning (ML) models that analyze sequences, structures, conformational ensembles, protein–ligand complexes, and molecular dynamics trajectories. Tier 1 addresses the internal reproducibility of ML inference when applied to molecular inputs; Tier 2 pertains to the robustness of molecular-science benchmarks such as CASP, CASF-2016, PoseBusters, and OpenFE; Tier 3 involves prospective experimental validation against biophysical and biochemical measurements; and Tier 4 encompasses clinical and translational calibration within physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) frameworks. This validation hierarchy functions as an explicit conceptual guide, serving as a framework rather than a regulatory requirement. It is firmly grounded in established principles derived from ICH Q8/Q9/Q10, the FDA model-informed drug development (MIDD) approach, the EMA reflection paper on AI in the medicinal product lifecycle, and the EU AI Act. The manuscript further incorporates recent evidence from ensemble-aware AI, prospective docking, free-energy campaigns, and clinical-stage AI-derived candidates. It concludes with specific recommendations pertaining to lifecycle governance, uncertainty reporting, and the adoption of harmonized evidentiary templates for AI/ML applications in the molecular sciences. Full article

Background/Objectives: The rapid expansion of targeted therapies has reshaped oncology by exploiting ligand-receptor interactions (LRI) to improve treatment specificity and patient outcomes. However, the data describing these ligands remain fragmented across multiple sources, limiting accessibility for researchers and clinicians. To address this gap, we developed the OncoSolidDB, the first curated and oncology-focused bioinformatics database dedicated to ligands associated with solid malignancies. Methods: OncoSolidDB integrates and harmonizes data from reliable repositories, including ChEMBL, DrugBank and the Anti-Cancer Fund, consolidating curated structural, chemical, pharmacological, and clinical annotations along with standardized identifiers. Results: The database currently encompasses 243 ligands across 15 major solid tumor types including breast, lung, colorectal, melanoma, prostate, gastric, ovarian, cervical, bladder, esophageal, head and neck, thyroid, pancreatic, renal and liver cancer (Hepatocellular Carcinoma, HCC). Each entry is annotated by standardized identifiers (DrugBank, ChEMBL), approval year, chemical structures (SMILES strings, 2D images), and downloadable protein structure files (PDB format). Temporal coverage spans 1953–2025, enabling exploration of historical trends in oncology drug approvals. The database content is suitable for bioinformatics analysis, molecular docking, virtual screening, ligand-based modeling, and drug repurposing studies. Outputs are available through a freely accessible web interface that supports search browsing by cancer type. Conclusions: By consolidating oncology-specific ligand data into a single, structured platform, OncoSolidDB offers a valuable resource for advancing drug discovery, repurposing strategies, and the rational design of next-generation targeted therapies for solid tumors. OncoSolidDB is accessible via our Bioinformatics Research PortalEinstein. Full article

Ovotransferrin (OVT), a major iron binding glycoprotein in egg white, is increasingly studied as a multifunctional ingredient for food preservation, mineral delivery, and colloidal design. This review critically evaluates how native structure, iron saturation, thermal history, glycation, phosphorylation, fibrillation, and interactions with proteins, polysaccharides, polyphenols, and lipid interfaces influence or determine OVT behavior during processing and gastrointestinal digestion. Rather than defining digestive stability simply as resistance to proteolysis, the review compares how processing routes reshape protease accessibility, peptide release, residual allergenic risk, and the persistence of antimicrobial or antioxidant functions. Particular emphasis is placed on cross-matrix interactions because OVT rarely acts as an isolated purified protein in practical formulations; its performance depends on pH, ionic strength, competing ligands, and the architecture of emulsified, coated, or liquid food systems. The available literature indicates that the most mature application space is multifunctional food system design, including preservation-oriented coatings, Pickering-type emulsions, oleogel-associated systems, and liquid food delivery platforms. Broader industrial applications will require standardized reporting of apo/holo state, processing history, digestion models, real food validation, sensory consequences, and allergenicity. To reduce overinterpretation, the present synthesis prioritizes primary studies and weighs model food or real food validation more heavily than mechanistic or preclinical evidence when discussing application readiness. Overall, OVT should be regarded as a promising but context-dependent protein platform whose value lies in coupling bioactivity with techno-functionality rather than in any single universal health claim. Methodological transparency is further supported by explicit database sources, reproducible search blocks, inclusion/exclusion rules, and a structured quality-appraisal and evidence tiering framework. Full article

Meltable metal–organic frameworks (MOF) are essential for the formation of MOF glasses, which have emerged as a new family of functional materials offering promising potential for applications in gas separation, luminescence, energy storage, and beyond. Herein, the synthesis of iron-based zeolitic imidazolate framework (ZIF) crystals, specifically Fe₃(Im)₆(HIm)₂, where Im is imidazolate, is reported. Upon the substitution of some Im linkers with a secondary ligand, 5,6-dimethylbenzimidazole (dmbIm), it was found that such substitution induces the formation of new phases: one phase exhibits meltability and subsequent glass formation, while another phase [Fe₃(Im)_1.56(dmbIm)_4.44(HIm)₂] is non-meltable. Through structural characterizations, the configuration of the tetrahedral [Fe-linkers] units was revealed to be crucial in determining the meltability of Fe-ZIF. The incorporation of a large secondary ligand hinders the occurrence of melting. This work provides an insight into how ligands affect the accessibility of the liquid state of MOFs, showing a practical strategy for designing meltable MOFs. Full article

Cynodon dactylon (Bermuda grass) is a perennial medicinal grass widely distributed across tropical and subtropical regions and known for its antioxidant and anti-inflammatory properties. The present study aimed to identify bioactive metabolites from the leaves of C. dactylon and evaluate their potential interaction with PTEN-induced kinase 1 (PINK1), a crucial regulator of mitochondrial quality control implicated in neurodegenerative disorders, particularly Parkinson’s disease. GC–MS analysis identified a total of 95 phytochemicals, of which the top 20 metabolites were selected based on retention time and area percentage. These metabolites were subjected to virtual screening using PyRx, with ATP employed as the reference ligand. Among the screened metabolites, 5,8,11-eicosatrienoic acid was the high-affinity compound which predicted a binding affinity of −5.9 kcal/mol and forming two hydrogen bond interactions within the PINK1 active site. The docked complexes were further evaluated through a 100 ns molecular dynamics simulation in replicates that showed stable binding of the protein–ligand complex, as reflected by RMSD values, reduced residue fluctuations and stable radius of gyration and solvent-accessible surface area. These findings suggest that 5,8,11-eicosatrienoic acid from C. dactylon may act as a potential PINK1 modulator for Parkinson’s disease. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

Boron (B) is increasingly recognized as more than a trace dietary element, emerging as a context-dependent organizer of molecular interactions at the host–microbiome interface. B exhibits reversible covalent chemistry driven by Lewis’ acidity and selective affinity for cis-diol-rich biomolecules, enabling dynamic complexation with polyols, glycans, and phenolic ligands that dominate the intestinal mucus environment and shape microbial ecology. We synthesize evidence supporting an architecture-based framework in which B modulates biological function by conditioning the physicochemical context of microbial communication rather than acting as a single-pathway effector. Central to this model is spatial bioavailability, distinguishing plasma-accessible boron from microbiota-accessible boron (MAB), species that persist in the lumen and mucus layer long enough to influence interface-level processes. We propose that insufficient or altered MAB availability may contribute to dysbiosis (DYS) by destabilizing quorum-associated coordination, signal persistence, and mucosal microstructure, thereby promoting barrier dysfunction and inflammaging. Particular attention is given to B-mediated symbiotaxis, a hypothesis-driven concept describing how B-containing molecular assemblies may bias microbial communities toward cooperative, barrier-supportive configurations and reduce ecological volatility. We identify key knowledge gaps and experimental priorities (speciation-aware measurements, signal-centric readouts) necessary to determine when, where, and how B-mediated molecular architecture may counteract DYS and support healthspan. Full article

Galectins are a functionally diverse family of β-galactosyl-binding lectins that are ubiquitously present in animal species, with key roles in development and immune regulation. Recently, galectins have been found to recognize microbial glycosylated moieties, but the detailed mechanisms of their innate immune functions in mucosal epithelia have remained elusive. The zebrafish (Danio rerio) represents an ideal genetically tractable model to address these questions, as the skin, gills, and gut display mucosal surfaces exposed to the environment. In this study, we investigated the range of endogenous and microbial glycans that are recognized by zebrafish galectin Drgal1 present in epidermal mucus, which would be consistent with defense functions against a bacterial challenge. Results revealed that zebrafish galectin isoform Drgal1-L2 can recognize selected bacterial glycans, as well as zebrafish mucus glycans and cell-surface receptors for bacterial adhesins such as fibronectin (K_D = 1.593 × 10⁻⁶ M) and CD147 (K_D = 1.115 × 10⁻⁶ M). Furthermore, preliminary experiments revealed that Drgal1-L2 may hinder bacterial adhesion to epidermal mucus in about 50% at 2.5 μg/mL. Our results suggest that Drgal1-L2 present in epidermal mucus can prevent access of pathogenic bacteria to the epithelial cell surface by alternate or synergic binding to bacterial glycans and to zebrafish mucus components and epithelial receptors for bacterial adhesins. Thus, the present study provides key information for the testing of the abovementioned hypothesis by implementing gene-silencing approaches targeting both zebrafish Drgal1-L2 and its ligands. Full article

Coumarins represent a distinctive class of non-classical carbonic anhydrase inhibitors that interact with the entrance region of the catalytic pocket rather than directly coordinating the catalytic Zn²⁺ ion. In this study, a series of glycosylated coumarins was synthesized through a copper-catalyzed multicomponent reaction involving propargyl glycosides, salicylaldehyde, and tosyl azide, providing efficient access to iminocoumarin-based glycosides derived from natural carbohydrates. The inhibitory activity of the synthesized compounds was evaluated against human carbonic anhydrase isoforms I, II, IX, and XII using a stopped-flow CO₂ hydrase assay. The compounds showed negligible inhibition of the cytosolic isoforms hCA I and hCA II, while displaying moderate activity toward the tumor-associated isoforms hCA IX and hCA XII, with Ki values ranging from 12.9 to 41.8 μM. Among the series, 6-O-(2H-chromene-2-one-3-yl-methyl)-D-galactopyranose (10a) emerged as the most potent inhibitor of hCA IX and XII. Structure–activity relationship analysis indicated that deprotected glycosyl derivatives exhibit improved inhibitory activity compared to protected analogues. To rationalize these observations, molecular docking followed by molecular dynamics simulations and MM-GBSA binding free energy calculations were performed for both anomeric forms of compound 10a. The computational results revealed a clear preference for the β-anomer, particularly in hCA IX and XII, where favorable interactions with catalytic threonine residues and isoform-specific aromatic residues stabilize the ligand within the active-site entrance. These findings provide a molecular explanation for the experimentally observed selectivity and highlight glycosyl coumarins as potential starting points for further optimization toward selective inhibitors of tumor-associated carbonic anhydrases. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells when the spike receptor-binding domain (RBD) engages angiotensin-converting enzyme 2 (ACE2). Cannabinoid scaffolds have recently been reported to bind S1/RBD, block spike-mediated membrane fusion, and modulate host inflammatory pathways, making them attractive candidates for entry inhibition. Here, we applied an integrated computational pipeline to prioritize cannabis-derived compounds as interfacial blockers of the RBD–ACE2 complex across variants. Eleven phytocannabinoids were docked into the wild-type (WT) RBD–ACE2 interface, identifying three cavities, with ligands preferentially occupying pocket 1. Complexes were subjected to triplicate 200 ns all-atom molecular dynamics (MD) simulations for WT, Delta, and Omicron BA.1 RBD–ACE2. Binding energetics were quantified using molecular mechanics/generalized Born surface area (MM/GBSA) and solvated interaction energy (SIE), and per-residue contributions were analyzed together with solvent-accessible surface area (SASA) and residue interaction networks. Among all compounds, cannabidiol (CBD) and cannabinol (CBN) were the only ligands that remained stably bound in pocket 1 for all variants. CBN showed the most favorable ligand–complex binding in WT, whereas CBD preserved favorable binding in Omicron BA.1 despite reduced interface burial, indicating that van der Waals/electrostatic complementarity and solvation, rather than surface coverage alone, govern affinity. Both ligands weakened modeled RBD–ACE2 binding by perturbing hot-spot residues centered on Y505 or N501Y in RBD and E37, A387, and R393 in ACE2. Overall, our results highlight CBD and CBN as tractable, variant-spanning interface disruptors and illustrate how MD-based free-energy calculations can support computational drug discovery against evolving viral protein–protein interfaces. Full article

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

Molecular docking is a foundational technique in computational drug discovery, widely used to generate binding hypotheses, prioritize compounds, and support target-selectivity studies. The continued growth of open-source docking resources, together with improvements in scoring functions, sampling strategies, and hardware acceleration, has substantially lowered barriers to teaching, early-stage hit identification, and reproducible research. Beyond standalone docking engines, the open-source ecosystem now encompasses browser-accessible tools, preparation and analysis utilities, integrative modeling platforms, and AI-augmented methods for pose prediction, rescoring, and virtual screening. These developments have made docking workflows more accessible, customizable, and transparent across diverse research settings. This review examines open-source docking from a workflow-centered perspective, spanning study design, structural-data acquisition, binding-site definition, receptor and ligand preparation, docking execution, and post-docking validation. It further evaluates how open AI methods are being incorporated into these stages to expand structural coverage, improve screening efficiency, and support contemporary structure-based drug design. Collectively, this review outlines a practical and evidence-based framework for the effective use of open-source docking and virtual-screening pipelines in modern drug discovery. Full article

The electrochemical reduction of carbon dioxide (CO₂RR) into value-added chemicals using renewable electricity is a pivotal strategy for achieving a sustainable carbon cycle. However, this process is plagued by intrinsic challenges, including poor product selectivity, competing hydrogen evolution, and catalyst instability. Metal–organic frameworks (MOFs), with their highly designable periodic structures, atomically dispersed active sites, and tunable pore microenvironments, have emerged as a uniquely versatile platform to address these issues. This review articulates a multi-scale design philosophy that enables precise steering of the CO₂RR pathway. We systematically elaborate on hierarchical tuning strategies, beginning with molecular-scale engineering of active sites (metal nodes and organic ligands) to define intrinsic activity and intermediate binding. This is synergistically integrated with the optimization of electronic structure and charge transport to overcome conductivity bottlenecks, meso-scale modulation of crystal morphology and defects to enhance mass transport and site accessibility, and the construction of heterogeneous interfaces for tandem catalysis and synergistic effects. Through this coherent, cross-scale design framework, MOF-based catalysts demonstrate exceptional capability in the precise control of reaction pathways, leading to remarkably selective synthesis of target high-value products, from C₁ compounds (CO, HCOOH, CH₄, CH₃OH) to C₂⁺ species (C₂H₄, C₂H₅OH) and urea. Finally, we outline future directions centered on dynamic mechanistic understanding, electrode engineering for industrial current densities, and stability enhancement, thereby providing a comprehensive material design guideline to advance CO₂RR technology. This work positions MOFs as a quintessential tunable catalytic platform for the sustainable conversion of CO₂. Full article

Excessive macrophage activation is thought to be the primary cause of the cytokine storm that results in severe coronavirus disease 2019 (COVID-19) complications. The underlying mechanisms remain elusive, and more research is needed to find disease-critical genes and develop effective therapies. In this study, we used publicly accessible microarray datasets of cytokine storm in cultured human monocyte-derived macrophages challenged with cytokines, and employed bioinformatics, such as weighted gene co-expression network analysis (WGCNA) and differential expression analysis, to dissect gene expression profiles and identify putative disease-related molecules. Initially, three co-expression modules and related key genes were discovered, which highly correlated to macrophages challenged with cytokines. Then, a preliminary gene expression signature consisting of 203 upregulated and 24 downregulated genes was identified. Next, protein–protein interaction analysis and hub gene identification were used to identify 11 crucial hub genes, namely tripartite motif-containing 21 (TRIM21), interferon regulatory factor 1 (IRF1), guanylate binding protein 1 (GBP1), transporter associated with antigen processing 1 (TAP1), nuclear myosin I (NMI), interleukin 15 receptor subunit alpha (IL15RA), apolipoprotein L1 (APOL1), intercellular adhesion molecule 1 (ICAM-1), protein tyrosine phosphatase non-receptor type 1 (PTPN1), E74-like ETS transcription factor 4 (ELF4) and guanylate binding protein 2 (GBP2). Then, the LINCS L1000 characteristic direction signatures search engine (L1000CDS2) was employed for drug repurposing studies. Dasatinib was predicted to be the leading therapeutic compound to perturb the gene signature of cytokine storm in human macrophages. Connectivity Map results suggested that dasatinib may normalize ICAM-1 expression. In addition, the results of molecular docking studies and molecular dynamics simulation revealed that dasatinib may spontaneously interact with ICAM-1 via several key residues and form a relatively stable protein–ligand complex. Overall, this work, based on an analysis of co-expression correlation networks, gene expression signatures and pivotal genes in human macrophages challenged with cytokines, combined with drug repurposing studies, demonstrated that dasatinib may interact with ICAM-1 and could be a potential candidate for cytokine storm. However, due to the limitations of computational approaches, further experimental validation is necessary. Full article