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<p>Integrating AI into epitope prediction is transforming vaccine design by delivering unprecedented accuracy, speed, and efficiency. This review synthesizes recent breakthroughs particularly CNNs, transformers, and GNNs highlighting experimentally validated models like MUNIS and GraphBepi that reveal previously overlooked epitopes. By benchmarking AI tools against traditional methods, we identify structural data integration as pivotal, offering practical strategies to translate computational predictions into actionable experimental workflows for next-generation vaccines.</p><p></p>

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AI-driven epitope prediction: a systematic review, comparative analysis, and practical guide for vaccine development

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月30日

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  • Francisca Villanueva-Flores,
  • Javier I. Sanchez-Villamil,
  • Igor Garcia-Atutxa

摘要

Integrating AI into epitope prediction is transforming vaccine design by delivering unprecedented accuracy, speed, and efficiency. This review synthesizes recent breakthroughs particularly CNNs, transformers, and GNNs highlighting experimentally validated models like MUNIS and GraphBepi that reveal previously overlooked epitopes. By benchmarking AI tools against traditional methods, we identify structural data integration as pivotal, offering practical strategies to translate computational predictions into actionable experimental workflows for next-generation vaccines.