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<p>During the global pandemic of coronavirus disease 2019 (COVID-19), mRNA vaccines have demonstrated great potential. In 2023, mRNA won the Nobel Prize in Physiology or Medicine. Currently, the global research and development of mRNA technology is accelerating. It is urgently necessary to use patent analysis to clarify the technological competitive landscape, providing a basis for technological innovation and industrial development in this field. This study is based on the Derwent patent database and employs social network analysis and patent quality assessment methods to conduct a quantitative analysis of mRNA therapeutic patents over the past 27&#xa0;years. Deep learning and machine learning methods are used to predict future core technologies and patentees. The study found that mRNA drugs are currently primarily used in the fields of infectious diseases and cancer. Delivery technology remains one of the critical challenges, while targeted drug research and vector technology will be one of the key future directions for the field. Meanwhile, New organizations have developed novel delivery technologies to break through the patent thickets established by giant companies. The global landscape of mRNA therapy is undergoing a multifaceted developmental pattern, and the monopoly of giant companies is being challenged. The patent landscape of mRNA therapies constructed based on deep learning methods in this study can not only serve as a knowledge tool for comprehensive integration and use, but also inspire the development of efficient production methods for mRNA therapies.</p>

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Patent analysis of mRNA therapy using deep learning

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Yuanqi Cai,
  • Xuejing Zhang,
  • Xiaoming Zhang,
  • Jianxiong Ren,
  • Pingping Wang,
  • Jinyu Cong,
  • Xiang Li,
  • Huali Zuo,
  • Benzheng Wei,
  • Kunmeng Liu

摘要

During the global pandemic of coronavirus disease 2019 (COVID-19), mRNA vaccines have demonstrated great potential. In 2023, mRNA won the Nobel Prize in Physiology or Medicine. Currently, the global research and development of mRNA technology is accelerating. It is urgently necessary to use patent analysis to clarify the technological competitive landscape, providing a basis for technological innovation and industrial development in this field. This study is based on the Derwent patent database and employs social network analysis and patent quality assessment methods to conduct a quantitative analysis of mRNA therapeutic patents over the past 27 years. Deep learning and machine learning methods are used to predict future core technologies and patentees. The study found that mRNA drugs are currently primarily used in the fields of infectious diseases and cancer. Delivery technology remains one of the critical challenges, while targeted drug research and vector technology will be one of the key future directions for the field. Meanwhile, New organizations have developed novel delivery technologies to break through the patent thickets established by giant companies. The global landscape of mRNA therapy is undergoing a multifaceted developmental pattern, and the monopoly of giant companies is being challenged. The patent landscape of mRNA therapies constructed based on deep learning methods in this study can not only serve as a knowledge tool for comprehensive integration and use, but also inspire the development of efficient production methods for mRNA therapies.