Quantum chemical and molecular modeling approaches for repurposing anticoagulants against SARS-CoV-2 main protease
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Hassan Nour
Abstract
The SARS-CoV-2 pandemic has posed a global health emergency since 2019, driving continuous efforts to discover effective treatments. While vaccines and recommended medications exist, drug repurposing offers a cost-effective and efficient approach to identify new therapeutic options. Anticoagulants, previously used to mitigate hypercoagulability linked to SARS-CoV-2 infection, present promising candidates for repurposing as antiviral agents. This study combines molecular docking, molecular dynamics simulations, and Density Functional Theory (DFT) analysis to evaluate the inhibitory potential of six anticoagulant drugs against the SARS-CoV-2 main protease (Mpro). Molecular docking identified Warfarin and Fluindione as the most potent candidates, exhibiting binding affinities of −6.9 and −6.8 kcal/mol, respectively. Molecular dynamics simulations confirmed the stability of these drug-protease complexes in an aqueous environment, potentially disrupting viral replication. Additionally, DFT calculations provided insights into the electronic properties governing molecular reactivity and stability. Quantum descriptors, including HOMO-LUMO energy gaps, chemical potential, hardness, and electrophilicity, were evaluated to rationalize the docking interactions. The results indicate that Apixaban, characterized by the highest hardness and stability, exhibited robust electron-donating and accepting properties, while Fluindione demonstrated high electrophilicity, correlating with its reactivity. These findings offer a comprehensive computational framework for repurposing anticoagulants as SARS-CoV-2 inhibitors and warrant further experimental validation.
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Research ethics: This study does not involve any human participants, animal subjects, or clinical trials and therefore does not require Ethical Approval.
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Informed consent: Not applicable, as no human subjects were involved in this study.
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Author contributions: Conceptualization: Hassan Nour, Nouh Mounadi, Samir Chtita; writing – original draft: Hassan Nour, Nouh Mounadi, Roussafi Bouchra; writing – review and editing: Hassan Nour, Nouh Mounadi, Roussafi Bouchra, Abdelkabir Errougui, Mohammed Bouachrine, Samir Chtita; methodology, formal analysis and data curation: Hassan Nour, Nouh Mounadi, Samir Chtita; supervision: Samir Chtita.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare no conflict of interest, financial or otherwise.
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Research funding: The authors reported there is no funding associated with the work featured in this article.
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Data availability: The data are included in the article and Supplementary Materials.
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Clinical Trial Registration Number: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cppm-2025-0098).
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