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<p>The resurgence of poxviral diseases, including the global spread of monkeypox, underscores the urgent need for effective antiviral strategies. This study focuses on the structural exploration of Thymidine kinase (TK), a crucial enzyme linked to viral replication in orthopox viruses, to identify potential inhibitors. Leveraging a structure-based drug design approach, known inhibitors of TK were used to generate novel compounds via AI-driven molecular design tools. These candidate molecules underwent rigorous virtual screening, pharmacokinetic evaluations, and molecular docking to assess their efficacy compared to the standard antiviral drug AZT. The intermolecular binding energy was twice estimated, once by the heuristic method of molecular docking and then by the absolute binding free energy derivation from the specified complexes by the neural networking-based algorithm. Molecular dynamics simulations further validated the stability and binding interactions of the proposed compounds with the TK enzyme. Notably, the chosen candidates exhibited superior binding energy and favorable pharmacological profiles, marking them as promising antiviral agents against the vaccinia and monkeypox viruses. This preliminary investigation offers a foundation for subsequent <i>in vitro</i> and <i>in vivo</i> validations to advance therapeutic development.</p> Graphical abstract <p><i>Synopsis</i>. The computational drug discovery workflow for finding new inhibitors of the Vaccinia virus thymidine kinase is presented in this paper. A library of unique chemical entities was created using a REINVENT4 (mol2mol) deep-learning module. This library was combined with known inhibitors to create a virtual screening library. SwissADME was used to filter compounds for drug-likeness and pharmacokinetic characteristics before molecular docking against the thymidine kinase receptor was performed to benchmark both freshly created molecules and conventional medications. Based on docking performance in comparison to reference medications, top-scoring candidates were chosen as lead compounds. Their complex behavior and binding stability were then evaluated using 100 ns molecular dynamics (MD) simulations. In order to find promising antiviral leads, the pipeline combines AI-driven molecule creation, in silico screening, docking, and MD simulations.</p> <p></p>

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Integration of structure-based drug design approach to generate novel thymidine kinase inhibitor against Vaccinia virus

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Hemanga Kumar Das,
  • Deep Prakash Parasar,
  • Sagar Raval,
  • Krishan Kumar,
  • Akan Das

摘要

The resurgence of poxviral diseases, including the global spread of monkeypox, underscores the urgent need for effective antiviral strategies. This study focuses on the structural exploration of Thymidine kinase (TK), a crucial enzyme linked to viral replication in orthopox viruses, to identify potential inhibitors. Leveraging a structure-based drug design approach, known inhibitors of TK were used to generate novel compounds via AI-driven molecular design tools. These candidate molecules underwent rigorous virtual screening, pharmacokinetic evaluations, and molecular docking to assess their efficacy compared to the standard antiviral drug AZT. The intermolecular binding energy was twice estimated, once by the heuristic method of molecular docking and then by the absolute binding free energy derivation from the specified complexes by the neural networking-based algorithm. Molecular dynamics simulations further validated the stability and binding interactions of the proposed compounds with the TK enzyme. Notably, the chosen candidates exhibited superior binding energy and favorable pharmacological profiles, marking them as promising antiviral agents against the vaccinia and monkeypox viruses. This preliminary investigation offers a foundation for subsequent in vitro and in vivo validations to advance therapeutic development.

Graphical abstract

Synopsis. The computational drug discovery workflow for finding new inhibitors of the Vaccinia virus thymidine kinase is presented in this paper. A library of unique chemical entities was created using a REINVENT4 (mol2mol) deep-learning module. This library was combined with known inhibitors to create a virtual screening library. SwissADME was used to filter compounds for drug-likeness and pharmacokinetic characteristics before molecular docking against the thymidine kinase receptor was performed to benchmark both freshly created molecules and conventional medications. Based on docking performance in comparison to reference medications, top-scoring candidates were chosen as lead compounds. Their complex behavior and binding stability were then evaluated using 100 ns molecular dynamics (MD) simulations. In order to find promising antiviral leads, the pipeline combines AI-driven molecule creation, in silico screening, docking, and MD simulations.