Research

Docking profiling of the potential signaling molecules to target COVID-19 which mediates pancreatic cancer via critical signaling pathways

Nouf A. Babteen 1, Afnan M. Alnajeebi 1, Walla Alelwani 1, Wafa S. Alansari 1,2, Ghalia Shamlan 3, Areej A. Eskandrani 4, Hend Faisal H. Alharbi 2*

1 Department of Biochemistry, College of Science, University of Jeddah, Jeddah-21589 Saudi Arabia.

2 Department of Food Science and Human Nutrition, College of Agriculture and Veterinary Medicine, Qassim University, Saudi Arabia.

3 Department of Food Science and Nutrition, College of Food and agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.

4 Chemistry Department, Faculty of Science, Taibah University, Medina, Saudi Arabia.

* Correspondence: hf.alharbi@qu.edu.sa (H.F.H.A.)

Citation: Babteen, N.A., Alnajeebi, A.M., Alelwani, W., Alansari, W.S., Shamlan, G., Eskandrani , A.A., and Alharbi, H.F.H. Docking profiling of the potential signaling molecules to target COVID-19 which mediates pancreatic cancer via critical signaling pathways. Glob. Jour. Bas. Sci. 2025, 1(7). 1-7.

Received: April 03, 2025

Revised: May 03, 2025

Accepted: May 14, 2025

Published: May 20, 2025

doi: 10.63454/jbs20000033

ISSN: 3049-3315

Volume 1; Issue 7

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Abstract: The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has been associated with multi-organ complications and long-term pathological consequences beyond respiratory illness. Emerging evidence suggests that SARS-CoV-2–induced inflammatory and metabolic dysregulation may accelerate oncogenic signaling, particularly in highly aggressive malignancies such as pancreatic cancer. Several host signaling pathways activated during COVID-19 infection, including PI3K/AKT/mTOR, JAK/STAT, NF-κB, MAPK, and TGF-β, are also critically involved in pancreatic tumor initiation, progression, immune evasion, and chemoresistance. In this study, we employed an in silico molecular docking strategy to identify potential small-molecule inhibitors capable of targeting key signaling proteins shared between COVID-19 pathophysiology and pancreatic cancer progression. A curated library of signaling modulators and repurposed drug candidates was docked against selected host and viral-interacting targets using AutoDock Vina. Binding affinities, interaction profiles, and molecular stability were evaluated to identify promising candidates. Our results highlight several compounds with strong binding affinities toward pivotal signaling mediators, suggesting their potential dual role in modulating COVID-19–associated signaling and pancreatic cancer pathways. This study provides a computational framework for identifying multi-target therapeutic candidates and supports further experimental validation to develop integrated treatment strategies for COVID-19–associated oncogenic complications.

Keywords: COVID-19; pancreatic cancer; molecular docking; signaling pathways; drug repurposing; SARS-CoV-2

1. Introduction

Pancreatic cancer remains one of the most lethal malignancies worldwide, largely due to its insidious onset, late-stage diagnosis, and limited responsiveness to existing therapeutic interventions. Among its subtypes, pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of cases and is associated with an exceptionally poor prognosis, with a five-year survival rate of less than 10% [1-7]. This dismal outcome is driven by aggressive local invasion, early metastatic dissemination, a dense desmoplastic tumor microenvironment, and profound resistance to chemotherapy, radiotherapy, and targeted agents. At the molecular level, pancreatic cancer is characterized by the dysregulation of multiple intracellular signaling pathways, including PI3K/AKT/mTOR, MAPK, JAK/STAT, NF-κB, and TGF-β, which collectively promote uncontrolled proliferation, survival, inflammation, immune evasion, epithelial–mesenchymal transition, and metastatic progression [7-15].

The emergence of the COVID-19 pandemic has uncovered previously unanticipated links between viral infection and cancer biology. Infection with SARS-CoV-2 induces widespread immune activation, excessive cytokine release, oxidative stress, and profound metabolic reprogramming in host cells [12-25]. These systemic and cellular alterations have the potential to reshape tumor-associated signaling networks and influence cancer behavior. Increasing clinical and experimental evidence suggests that COVID-19 may exacerbate cancer progression by sustaining chronic inflammation, activating pro-oncogenic signaling cascades, impairing antitumor immune surveillance, and disrupting tissue homeostasis. In patients with pancreatic cancer, COVID-19–related complications have been associated with delayed treatment, increased disease severity, and higher mortality rates, highlighting the vulnerability of this patient population.

Notably, several host proteins, signaling pathways, and molecular regulators exploited or activated during SARS-CoV-2 infection significantly overlap with pathways known to drive pancreatic cancer initiation and progression. This convergence includes key inflammatory mediators, stress-response pathways, and survival signaling networks, creating a biologically relevant intersection between viral pathogenesis and tumor biology. Such overlap presents a unique opportunity to identify therapeutic agents capable of simultaneously targeting both viral-associated signaling events and oncogenic pathways, potentially mitigating COVID-19–mediated acceleration of pancreatic cancer progression [26-44].

In this context, computational approaches, particularly molecular docking, provide a rapid and cost-effective strategy for screening candidate molecules, including repurposed drugs and novel inhibitors, against critical protein targets. Molecular docking enables the prediction of binding affinity, interaction stability, and molecular compatibility, offering valuable insights prior to experimental validation. The present study therefore aims to systematically explore the molecular docking profiles of selected signaling molecules against key protein targets implicated in both COVID-19 pathogenesis and pancreatic cancer progression. By identifying compounds with favorable binding characteristics and multi-target potential, this work seeks to lay the groundwork for the development of integrated therapeutic strategies addressing the complex interplay between SARS-CoV-2 infection and pancreatic cancer biology.

2. Results

2.1. Docking performance and binding affinity analysis: Molecular docking analysis revealed that several screened small-molecule candidates exhibited strong and stable binding affinities toward the selected key signaling proteins implicated in both COVID-19–associated cellular responses and pancreatic cancer progression. Binding energies ranged from −6.2 to −10.4 kcal/mol, with multiple compounds demonstrating high-affinity interactions (≤ −8.0 kcal/mol), indicating favorable thermodynamic stability within  the target binding pockets (Figures 1 & 2).

Notably, several kinase inhibitors displayed pronounced binding affinities toward members of the PI3K/AKT/mTOR signaling cascade, a pathway critically involved in pancreatic cancer cell survival, proliferation, metabolic reprogramming, and resistance to apoptosis. Docking against PI3K revealed binding energies as low as −9.6 kcal/mol, with ligands occupying the ATP-binding cleft and forming key hydrogen bonds with conserved catalytic residues. These interactions were further stabilized by hydrophobic contacts within the kinase domain, suggesting potential inhibition of PI3K enzymatic activity. Similarly, AKT1 docking demonstrated high-affinity interactions (binding energies between −8.4 and −9.1 kcal/mol), with ligands engaging residues within the activation loop and substrate-binding region. These  binding modes are consistent with previously reported AKT inhibitors and suggest the ability of the screened compounds to disrupt downstream oncogenic signaling. For mTOR, several ligands exhibited strong docking scores (≤ −9.0 kcal/mol) and formed stable hydrogen bonds and van der Waals interactions within the FKBP12-rapamycin binding (FRB) domain. The predicted binding orientations indicate potential suppression of mTOR-mediated translational control and cell growth signaling, which are known to be hyperactivated in pancreatic cancer and further amplified under inflammatory conditions such as SARS-CoV-2 infection.

Figure 1. A simple sketch to show the docking.

Docking simulations targeting STAT3 revealed that multiple compounds achieved favorable binding energies (−8.0 to −9.3 kcal/mol) and formed stable hydrogen bonds with residues within the SH2 domain, a critical region responsible for STAT3 dimerization and nuclear translocation. Inhibition of this domain is expected to prevent STAT3 activation and transcriptional regulation of genes associated with inflammation, immune evasion, cell survival, and tumor progression. The observed interactions suggest that these ligands may effectively block cytokine-driven STAT3 signaling, which is highly relevant in both COVID-19 cytokine storms and pancreatic tumor microenvironments. Similarly, docking against the NF-κB p65 subunit demonstrated strong binding affinities (up to −8.7 kcal/mol), with ligands interacting near the DNA-binding and transactivation regions. These interactions were stabilized through hydrogen bonding and electrostatic contacts with residues involved in transcriptional regulation. Given NF-κB’s central role in mediating inflammatory responses, immune dysregulation, and cancer-associated gene expression, the identified binding profiles suggest that these molecules may suppress NF-κB–dependent oncogenic and inflammatory signaling cascades.

Overall, the docking performance indicated that kinase-targeting molecules exhibited superior binding affinity and interaction stability compared with non-kinase modulators. Several compounds demonstrated multi-target binding capacity, engaging multiple signaling proteins across the PI3K/AKT/mTOR and JAK/STAT/NF-κB axes. This multi-target profile is particularly advantageous in the context of COVID-19–mediated pancreatic cancer, where complex signaling crosstalk drives disease progression. Collectively, these results highlight the potential of the identified molecules to inhibit critical signaling pathways involved in inflammation, viral response, and oncogenesis. The strong binding affinities and favorable interaction patterns provide a solid computational basis for further experimental validation and functional assessment.

2.2. Multi-target interaction profiles: Docking analyses revealed that a subset of ligands exhibited multi-target binding capabilities, demonstrating effective interactions with several key proteins implicated in both COVID-19–associated signaling and pancreatic cancer progression. Unlike single-target compounds, these ligands were able to bind with favorable affinities across multiple signaling nodes, suggesting a broader therapeutic potential and increased robustness against pathway redundancy and compensatory signaling. Notably, several compounds showed consistent and stable binding across components of the JAK/STAT, MAPK, and PI3K/AKT signaling pathways, which are central to inflammatory responses, cell survival, proliferation, and immune modulation. In the JAK/STAT pathway, these ligands formed stable interactions with JAK2 and STAT3, often occupying critical functional residues involved in phosphorylation or dimerization. Such interactions indicate a potential to attenuate cytokine-driven signaling, which is amplified during SARS-CoV-2 infection and contributes to tumor-promoting inflammation in pancreatic cancer. 

Within the MAPK pathway, effective binding to MAPK1 (ERK2) suggested the ability of these ligands to interfere with downstream proliferative and stress-response signaling. Similarly, interactions with PI3K, AKT1, and mTOR revealed favorable binding energies and strong structural complementarity within catalytic or regulatory domains, indicating potential suppression of survival and metabolic pathways commonly exploited by both viral infection and cancer cells (Figure 2). Importantly, the multi-target ligands displayed conserved binding modes and interaction patterns across different proteins, including hydrogen bonding, hydrophobic contacts, and aromatic interactions with key residues. This consistency supports the notion that these compounds can simultaneously modulate multiple signaling networks rather than acting in isolation. Such multi-target profiles are particularly advantageous in complex diseases like pancreatic cancer, where pathway crosstalk and compensatory mechanisms often undermine single-target therapies. Overall, the identification of ligands with multi-pathway interaction profiles highlights their therapeutic versatility and potential to exert synergistic effects by dampening inflammatory signaling, inhibiting oncogenic drivers, and limiting adaptive resistance. These findings support further investigation of multi-target compounds as promising candidates for integrated therapeutic strategies addressing COVID-19–mediated pancreatic cancer progression. 

2.3. Structural interaction mapping: Detailed structural interaction mapping was performed to elucidate the molecular basis of ligand–protein binding and to identify the key interactions responsible for complex stability and specificity. Visualization and analysis of docked complexes revealed that a combination of hydrogen bonds, hydrophobic contacts, and electrostatic interactions played a dominant role in stabilizing ligands within the binding pockets of the target proteins. Hydrogen bonding interactions were frequently observed between ligand functional groups and conserved amino acid residues within the active or regulatory sites. These interactions contributed significantly to binding specificity and orientation, anchoring ligands in energetically favorable conformations. In several complexes, hydrogen bonds involved residues known to participate in substrate recognition or catalytic activity, thereby  enhancing the likelihood of functional inhibition (Figure 2). 

Hydrophobic contacts were also prominent, particularly within nonpolar regions of kinase and signaling protein domains. These interactions facilitated tight packing of ligands within the binding cavity and contributed to overall binding affinity by minimizing solvent exposure. Aromatic residues within the binding pockets often engaged in π–π stacking or π–alkyl interactions with ligand aromatic rings, further reinforcing complex stability. Electrostatic interactions, including salt bridges and polar contacts, were observed in complexes involving charged or highly polar ligands. These interactions enhanced long-range attraction between ligands and protein surfaces and complemented hydrogen bonding networks. Importantly, many ligands engaged key catalytic or regulatory residues, such as those involved in ATP binding, phosphorylation sites, or activation loops, suggesting a strong potential to  interfere with enzymatic activity or signal transduction.

Figure 2. Docking outcome to show the binding affinity with the respective molecules.

Collectively, the structural interaction mapping highlighted consistent engagement of functionally critical residues across multiple targets, supporting the predicted inhibitory potential of the top-ranked ligands. These findings provide mechanistic insights into how ligand binding may disrupt signaling cascades and reinforce the rationale for further experimental validation of the identified multi-target compounds.

3. Discussion

The findings of this molecular docking study provide compelling support for the hypothesis that COVID-19–induced signaling dysregulation intersects with key molecular pathways driving pancreatic cancer, thereby potentially accelerating disease progression. SARS-CoV-2 infection is known to activate inflammatory, stress-response, and survival signaling networks, many of which overlap with pathways already dysregulated in pancreatic ductal adenocarcinoma. The convergence of these signaling events may amplify pro-tumorigenic processes such as chronic inflammation, immune evasion, metabolic reprogramming, and resistance to apoptosis, particularly in vulnerable cancer patients affected by COVID-19. Targeting shared molecular mediators between viral pathogenesis and pancreatic cancer therefore represents a rational and biologically informed therapeutic strategy. In high-risk cancer patients, especially those experiencing SARS-CoV-2 infection, modulation of these overlapping pathways could mitigate viral-induced exacerbation of tumor progression while simultaneously suppressing oncogenic signaling. The docking results demonstrate that several candidate molecules exhibit favorable binding to proteins central to both disease contexts, supporting their potential utility as dual-purpose or multi-functional therapeutic agents [27-39]. 

The identification of compounds capable of interacting with multiple signaling nodes is particularly significant in light of the emerging paradigm of network-based medicine. Complex diseases such as pancreatic cancer and COVID-19–associated complications cannot be effectively addressed through single-target approaches alone due to extensive pathway redundancy and adaptive resistance mechanisms. The observed multi-target binding profiles align with systems-level strategies aimed at disrupting critical network dependencies rather than isolated molecular components. The binding affinities and interaction patterns identified in this study suggest that selected ligands may simultaneously attenuate inflammatory cascades, inhibit oncogenic signaling pathways, and reduce viral-induced cellular stress responses [44-50]. 

Despite these promising insights, it is important to acknowledge the limitations inherent to in silico docking approaches. Docking studies provide static representations of molecular interactions and do not fully capture the dynamic nature of protein conformational changes, cellular context, or pharmacokinetic properties. Consequently, experimental validation is essential to confirm the biological relevance of the predicted interactions. Follow-up studies using in vitro biochemical assays, cell-based models, and in vivo systems will be necessary to evaluate target inhibition, therapeutic efficacy, toxicity, and potential off-target effects. Collectively, this work establishes a strong computational foundation for future experimental investigations and highlights the translational potential of multi-target therapeutic strategies at the intersection of COVID-19 and pancreatic cancer biology [51-53].

Despite the valuable insights provided by this study, several limitations must be acknowledged. Foremost, the investigation is entirely computational in nature, relying on molecular docking simulations to predict ligand–protein interactions. While docking offers a rapid and cost-effective approach for hypothesis generation, it does not fully capture the complexity of biological systems, where multiple layers of regulation, cellular context, and microenvironmental influences shape protein function and drug response. Docking simulations do not account for pharmacokinetic and pharmacodynamic properties, including absorption, distribution, metabolism, excretion, and toxicity (ADMET) [6,11,22,24,30-33]. As a result, favorable binding affinity alone does not guarantee biological efficacy or clinical applicability. Compounds identified as promising in silico may exhibit limited bioavailability, off-target effects, or unacceptable toxicity profiles when evaluated experimentally. Another important limitation relates to protein conformational flexibility. In this study, proteins were treated largely as rigid structures, whereas in vivo proteins undergo continuous conformational changes that can significantly influence ligand binding and activity. Dynamic signaling interactions, allosteric regulation, and protein–protein interactions were also not fully explored, potentially overlooking alternative binding modes or regulatory effects. 

Furthermore, the complex and dynamic nature of cellular signaling networks, particularly in the context of viral infection and cancer, cannot be fully represented by static docking models. Crosstalk between pathways, feedback regulation, and temporal changes in protein expression and activation states were beyond the scope of this analysis. Taken together, these limitations highlight the need for complementary approaches, including molecular dynamics simulations, in vitro biochemical assays, cell-based functional studies, and in vivo models, to validate and extend the findings of this work. Recognizing these constraints is essential for appropriate interpretation of the results and for guiding future research toward experimental confirmation and translational relevance.

4. Conclusions
This study presents a comprehensive molecular docking analysis of potential signaling molecules targeting COVID-19–mediated pancreatic cancer through shared critical signaling pathways. Several compounds demonstrated strong binding affinity and multi-target interaction profiles, highlighting their promise as therapeutic candidates. These findings provide a computational foundation for further experimental validation and support the development of integrated treatment strategies addressing both viral infection–induced signaling and pancreatic cancer progression. Future work should focus on molecular dynamics simulations, experimental validation, and clinical relevance assessment. Integrating systems biology and AI-driven drug design approaches may further accelerate the identification of effective multi-target therapeutics.

5. Methods

5.1. Target protein selection: We Target protein selection was guided by a comprehensive review of the literature to identify key signaling molecules that play critical roles in both COVID-19 pathogenesis and pancreatic cancer progression. Particular emphasis was placed on proteins involved in inflammatory signaling, cell survival, proliferation, immune modulation, and metabolic regulation, as these processes are central to SARS-CoV-2–induced cellular responses as well as pancreatic tumorigenesis. 

The selected targets included ACE2-associated host signaling mediators, which are directly or indirectly activated following SARS-CoV-2 entry into host cells and contribute to downstream inflammatory and stress-response signaling. Additional targets comprised JAK2 and STAT3, key components of the JAK/STAT pathway, which is frequently hyperactivated during cytokine storms in COVID-19 and is also known to drive pancreatic cancer cell survival, immune evasion, and tumor-promoting inflammation. Members of the PI3K/AKT/mTOR signaling axis, including PI3K, AKT1, and mTOR, were selected due to their central role in regulating cellular metabolism, growth, and resistance to apoptosis. These pathways are commonly dysregulated in pancreatic cancer and are also activated in response to viral infection to support host cell survival and viral replication. Similarly, NF-κB (p65 subunit)  was included as a master regulator of inflammatory and immune responses, mediating cytokine production during COVID-19 and promoting chronic inflammation and tumor progression in pancreatic cancer (Figure 3).

Figure 3. A layout for library preparation for drug candidates.

The MAPK pathway, represented by MAPK1 (ERK2), was selected because of its involvement in cell proliferation, stress responses, and cytokine signaling in both viral infection and malignancy. Finally, TGF-β receptor I was chosen due to its dual role in immune regulation and fibrosis during SARS-CoV-2 infection, as well as its well-established contribution to epithelial–mesenchymal transition, invasion, and metastasis in pancreatic cancer. Three-dimensional crystal structures of all selected target proteins were retrieved from the Protein Data Bank (PDB) in PDB format. Protein preparation was performed to ensure suitability for molecular docking analyses. This process involved the removal of crystallographic water molecules, co-crystallized ligands, and heteroatoms, followed by the addition of missing hydrogen atoms to define proper protonation states. Energy minimization was then carried out to relieve steric clashes and optimize protein conformations, resulting in structurally stable and biologically relevant models for subsequent docking studies.

5.2. Ligand dataset preparation: A comprehensive library of potential signaling inhibitors and repurposed drug candidates was constructed to identify compounds capable of modulating molecular targets involved in both COVID-19 pathogenesis and pancreatic cancer progression. Ligands were systematically collected from well-established public chemical databases, primarily DrugBank and PubChem, which provide curated information on approved drugs, experimental compounds, and bioactive small molecules with known structural and pharmacological properties. 

The ligand dataset was designed to encompass multiple functional classes relevant to inflammation, oncogenic signaling, and viral infection. This included anti-inflammatory agents, selected due to their ability to suppress cytokine-mediated signaling and chronic inflammatory responses associated with both SARS-CoV-2 infection and pancreatic tumor progression. Kinase inhibitors were incorporated to target dysregulated signaling cascades such as PI3K/AKT/mTOR, MAPK, and JAK/STAT, which are central to cancer cell survival and are frequently activated during viral infection. In addition, antiviral compounds were included to explore their potential off-target effects on host signaling proteins that overlap with oncogenic pathways. Natural bioactive molecules, including plant-derived polyphenols and other phytochemicals, were selected based on their reported anti-inflammatory, antioxidant, and anticancer activities, as well as their favorable safety profiles. All ligand structures were retrieved in standard three-dimensional formats and subjected to rigorous preparation prior to molecular docking. Structural optimization was performed using the Merck Molecular Force Field 94 (MMFF94) to minimize steric clashes and obtain energetically stable conformations. Following optimization, ligands were converted into PDBQT format, which includes information on atomic charges and rotatable bonds required for docking simulations. Protonation states and torsional flexibility were appropriately assigned to ensure accurate interaction modeling. The prepared ligand dataset was thus standardized and rendered compatible for subsequent docking analyses against the selected protein  targets.

5.3. Molecular docking profiling: Molecular docking studies were conducted using AutoDock Vina, a widely used and validated docking engine known for its high accuracy and computational efficiency (Figure 4). AutoDock Vina employs a sophisticated scoring function and gradient-based optimization algorithm to predict ligand–protein binding conformations and estimate binding affinities, making it suitable for large-scale virtual screening and comparative docking analyses. For each target protein, grid boxes were carefully defined to encompass either the experimentally reported active sites or functionally relevant allosteric regions. Grid center coordinates and dimensions were determined based on known binding residues derived from co-crystallized ligands, literature reports, and structural annotations available in  the Protein Data Bank. In cases where binding site information was limited, grid boxes were expanded to cover key functional domains to ensure adequate sampling of potential interaction regions. To maintain consistency and ensure reproducibility, docking parameters—including exhaustiveness, number of binding modes, and energy range—were standardized across all simulations. Each ligand was docked independently against each target protein under identical conditions, allowing direct comparison of binding affinities and interaction profiles. The lowest-energy binding pose for each ligand–protein complex was selected for further analysis. Post-docking analyses focused on both quantitative and qualitative evaluation of ligand–target interactions. Binding affinity scores, expressed in kcal/mol, were used to rank ligands based on predicted binding strength. Detailed interaction analyses, including hydrogen bonds, hydrophobic interactions, electrostatic contacts, and π–π stacking interactions, were performed using PyMOL and Discovery Studio Visualizer. These visualization tools enabled the identification of key interacting residues and provided structural insights into the stability and specificity of ligand binding, supporting the interpretation of docking results and guiding the selection of promising multi-target candidates.

Figure 4. Molecular docking workflow for better understanding of the overall steps followed.

5.4. Interaction and binding affinity analysis: Following molecular docking, all ligand–protein complexes were systematically evaluated to assess the strength, stability, and biological relevance of the predicted interactions. The primary criterion for ranking was the binding energy (kcal/mol) generated by AutoDock Vina, where more negative values indicated stronger and more favorable binding affinities. These scores provided an initial quantitative measure to prioritize ligands with higher potential to inhibit or modulate the selected target proteins. In addition to binding energy, the number and nature of intermolecular interactions were carefully analyzed, as these interactions are critical determinants of complex stability. Hydrogen bonds were examined for their number, geometry, and involvement of key amino acid residues, given their importance in specificity and binding strength. Hydrophobic interactions were assessed to understand how ligands were accommodated within nonpolar regions of the binding pocket, contributing to overall affinity. Aromatic interactions, particularly π–π stacking and π–cation interactions, were also evaluated, as they play a significant role in stabilizing ligand orientation within kinase and signaling protein domains. Occupancy of key functional residues within the active or allosteric sites was another essential parameter in the analysis. Ligands interacting with residues known to be critical for catalytic activity, substrate recognition, or signal transduction were considered more biologically relevant. Such interactions suggest a higher likelihood of functional inhibition or modulation of the target protein’s signaling activity. Residue-level interaction maps were generated to compare binding modes across different ligands and targets. Structural complementarity between the ligand and protein binding pocket was also examined to ensure optimal geometric fitting. Ligands displaying favorable shape complementarity, minimal steric clashes, and stable conformations within the binding cavity were prioritized. Visualization tools such as PyMOL and Discovery Studio Visualizer were used to inspect binding poses, interaction networks, and conformational alignment in three-dimensional space. Based on the combined evaluation of binding energy, interaction profiles, functional residue engagement, and structural complementarity, the top-ranked ligand–protein complexes were shortlisted for further interpretation. These shortlisted candidates represent the most promising molecules for potential dual targeting of COVID-19–associated signaling pathways and pancreatic cancer progression, forming the basis for downstream analyses and experimental validation.

Author Contributions: Conceptualisation, N.A.B., A.M.A., W.A., W.S.A., G.S., A.A.E., and H.F.H.A.; software, N.A.B., A.M.A., W.A., W.S.A., G.S., A.A.E., and H.F.H.A.; investigation, N.A.B., A.M.A., W.A., W.S.A., G.S., A.A.E., and H.F.H.A.;  writing—original draft preparation, N.A.B., A.M.A., W.A., W.S.A., G.S., A.A.E., and H.F.H.A.; writing—review and editing, N.A.B., A.M.A., W.A., W.S.A., G.S., A.A.E., and H.F.H.A.; visualisation, N.A.B., A.M.A., W.A., W.S.A., G.S., A.A.E., and H.F.H.A.; supervision, N.A.B. and H.F.H.A.; project administration, H.F.H.A. The author has read and agreed to the published version of the manuscript.

Funding: Not applicable.

Acknowledgments: We are grateful to the Chemistry Department, Faculty of Science, Taibah University, Medina, Saudi Arabia and Department of Biochemistry, College of Science, University of Jeddah, Jeddah-21589 Saudi Arabia for providing us all the facilities to carry out the entire work.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: All the related data are supplied in this work or have been referenced properly.

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