Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

计算机辅助发现疫苗靶标已成为理性疫苗设计的基石。在本文中，我将讨论机器学习 （ML） 如何为理性疫苗设计中的关键计算步骤提供信息和指导，这些步骤涉及 B 细胞和 T 细胞表位的识别以及保护的相关性。我提供了 ML 模型的示例，以及为其构建的数据类型和预测。我认为，可解释的 ML 有可能通过帮助阐明疫苗诱导的免疫反应背后的分子过程，改善免疫原的鉴定，也可以作为科学发现的工具。我概述了需要解决的数据可用性和方法开发方面的限制和挑战，以弥合 ML 预测的进步与其在疫苗设计中的转化应用之间的差距。