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<p>This study introduces a machine learning framework to predict effective antiviral combinations for influenza A. It identifies Pimodivir with Epinephrine or L-Adrenaline as synergistic agents, confirmed by experiments demonstrating increased binding affinity and viral suppression. Multiple synergy scoring methods validate these drug combinations’ potential, offering a strategic pathway for designing rational combination therapies against influenza and other RNA viruses.</p>

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Predictive modeling & mechanistic validation of synergistic pimodivir combinations for anti-influenza therapy via PB2cap affinity boost

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Peng Luo,
  • Kexin Li,
  • Yubin Xie,
  • Kun Huang,
  • Zhenzhi Qin,
  • Jian-Piao Cai,
  • Yu Fu,
  • Jianli Cao,
  • Sihang Cao,
  • Ziyao Zhou,
  • Zi-Wei Ye,
  • Shuofeng Yuan

摘要

This study introduces a machine learning framework to predict effective antiviral combinations for influenza A. It identifies Pimodivir with Epinephrine or L-Adrenaline as synergistic agents, confirmed by experiments demonstrating increased binding affinity and viral suppression. Multiple synergy scoring methods validate these drug combinations’ potential, offering a strategic pathway for designing rational combination therapies against influenza and other RNA viruses.