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<p>The rapid emergence of SARS-CoV-2 variants and the reduced efficacy of existing antiviral therapies necessitate the development of new inhibitors targeting essential viral proteins. The SARS-CoV-2 main protease (Mpro), a key enzyme required for viral replication, represents an attractive therapeutic target. In this study, an integrated structure-based computational workflow was employed to identify potential Mpro inhibitors derived from triazine-based nucleoside analogues. A library of 1,843 derivatives was generated through systematic structural modifications and screened using hierarchical virtual screening, molecular docking, MM/GBSA binding free-energy calculations, density functional theory (DFT), and 500 ns molecular dynamics (MD) simulations. Four lead compounds were selected based on their favourable docking scores, binding free energies, and interactions with key catalytic and substrate-binding residues, including His41, Cys145, Glu166, and Gly143. DFT analysis revealed differences in the intrinsic electronic properties of the selected compounds, while MD simulations confirmed the dynamic stability of the protein–ligand complexes throughout the simulation period. Among the identified candidates, compound 300,625,067 demonstrated the most favourable overall binding stability and persistent interactions within the Mpro active site. This study identifies structurally optimized triazine-based nucleoside analogues as potential SARS-CoV-2 Mpro inhibitors and demonstrates the utility of an integrated computational strategy for the rational design and prioritization of antiviral candidates for further experimental validation.</p>

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Designing triazine-based nucleoside analogues as promising SARS-CoV-2 main protease inhibitors: a molecular modeling study

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Madhu Sudan,
  • Sarika Arora,
  • Mohd Saeed,
  • Danveer Singh Yadav

摘要

The rapid emergence of SARS-CoV-2 variants and the reduced efficacy of existing antiviral therapies necessitate the development of new inhibitors targeting essential viral proteins. The SARS-CoV-2 main protease (Mpro), a key enzyme required for viral replication, represents an attractive therapeutic target. In this study, an integrated structure-based computational workflow was employed to identify potential Mpro inhibitors derived from triazine-based nucleoside analogues. A library of 1,843 derivatives was generated through systematic structural modifications and screened using hierarchical virtual screening, molecular docking, MM/GBSA binding free-energy calculations, density functional theory (DFT), and 500 ns molecular dynamics (MD) simulations. Four lead compounds were selected based on their favourable docking scores, binding free energies, and interactions with key catalytic and substrate-binding residues, including His41, Cys145, Glu166, and Gly143. DFT analysis revealed differences in the intrinsic electronic properties of the selected compounds, while MD simulations confirmed the dynamic stability of the protein–ligand complexes throughout the simulation period. Among the identified candidates, compound 300,625,067 demonstrated the most favourable overall binding stability and persistent interactions within the Mpro active site. This study identifies structurally optimized triazine-based nucleoside analogues as potential SARS-CoV-2 Mpro inhibitors and demonstrates the utility of an integrated computational strategy for the rational design and prioritization of antiviral candidates for further experimental validation.