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The challenges of developing drugs and vaccines against SARS-CoV-2 is intensified by the constant evolution of the spike protein. This study focuses on using Artificial Intelligence, specifically Generative Adversarial Networks (GANs), to simulate escape sequences, and utilize these sequences to improve the escape prediction model. Our novel GAN model generates synthetic spike protein sequences with potentially higher infectivity and transmissibility. This approach showed a promising increase in prediction accuracy, with improvements noted across various datasets. Such advancements could revolutionize our ability to anticipate future mutations, aiding in the creation of more effective treatments and preventive measures against COVID-19 and its variants. Our findings underscore the potential of AI in addressing challenges posed by fast-evolving pathogens.

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Synthetic Generation of Escape Sequences for Escape Prediction of SARS-CoV-2

  • 会议论文

  • 发表日期：

    2025年

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  • Prem Singh Bist,
  • Hilal Tayara,
  • Kil To Chong

摘要

The challenges of developing drugs and vaccines against SARS-CoV-2 is intensified by the constant evolution of the spike protein. This study focuses on using Artificial Intelligence, specifically Generative Adversarial Networks (GANs), to simulate escape sequences, and utilize these sequences to improve the escape prediction model. Our novel GAN model generates synthetic spike protein sequences with potentially higher infectivity and transmissibility. This approach showed a promising increase in prediction accuracy, with improvements noted across various datasets. Such advancements could revolutionize our ability to anticipate future mutations, aiding in the creation of more effective treatments and preventive measures against COVID-19 and its variants. Our findings underscore the potential of AI in addressing challenges posed by fast-evolving pathogens.