Covid-19 Research

Research Article

Evaluating the Efficacy of Different SARS-Cov-2 Drug Targets Using the Topo-Geometrical Superposition Algorithm, Molecular Docking and Chemical Reactivity Frameworks

Journal of Biomedical Research & Environmental Sciences article abstract with citation details, DOI, publication dates, subject areas, full text links, and references.

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Open Access
Article Type Research Article
Subject General Science
OCLC JBRES Record
Alejandro Morales-Bayuelo*, Valentina Perez-Quinones, Zvikomborero Zinhumwe and Praveen Mallri
Issue: Volume6-Issue5
Pages: 417-432
Received: 2025-04-01
Accepted: 2025-05-07
Published: 2025-05-10

Abstract

Introduction: The COVID-19 pandemic, an urgent global public health emergency, has resulted in millions of confirmed cases and deaths. Global healthcare systems face significant strain as facilities struggle to manage hospital capacities amid supply shortages. RdRp functions as the essential enzyme for viral replication through respiratory particles. The Food and Drug Administration recognizes Remdesivir as the only approved drug for COVID-19, yet scientists continue to explore additional treatment options.

Methods: The research implemented Glide docking software to perform Standard Precision (SP) and Extra Precision (XP) simulations. The docking grid maintained default parameters for the IFD method, which focused on accommodating different receptor conformations targeting RNA-dependent RNA polymerase's binding pocket. Molecular Quantum Similarity Measure (MQSM) performed Quantum similarity analyses through ZAB quantitative measures that evaluated molecules based on their Density Function (DFs). The molecular quantum similarity (MQS) calculation required two operators: the Dirac delta function and the Coulomb operator. The chemical reactivity analysis involved determining four primary indices, namely chemical potential (µ), hardness (η), softness (S) and electrophilicity (ɷ), to assess ligand stability and electrophilic characteristics alongside reactivity. Fukui functions revealed local chemical reactivity sites of the ligands to provide complete information about their reaction patterns.

Results: The molecular docking analysis showed that ligands formed important bonds with particular residues inside the binding pocket through hydrogen bonds to GLU166 and CYS145. The protein residues develop essential binding interactions that stabilize ligands. The chemical reactivity assessments revealed global reactivity parameters such as each ligand's chemical potential, hardness and softness, and electrophilicity. The analysis with Fukui functions revealed the electrophilic and nucleophilic areas on ligands to understand their reaction behavior and binding process better.

Conclusion: The multi-method research approach integrates molecular docking quantum similarity analysis and chemical reactivity studies and delivers essential details about ligands targeting the SARS-CoV-2 RNA-dependent RNA polymerase. The research findings support crucial advancements in designing drugs for COVID-19 treatment through rational methods.

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© 2025 Morales-Bayuelo A, et al. Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

Morales-Bayuelo A, Pérez-Quiñones V, Zinhumwe Z, Mallri P. Evaluating the Effi cacy of Different SARSCov- 2 Drug Targets Using the Topo-Geometrical Superposition Algorithm, Molecular Docking and Chemical Reactivity Frameworks. J Biomed Res Environ Sci. 2025 May 10; 6(5): 417-432. doi: 10.37871/jbres2099, Article ID: JBRES2099, Available at: https://www. jelsciences.com/articles/jbres2099.pdf

Subject area(s)

Organic Chemistry

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