The article discusses the role of Machine Learning (ML) in vaccine target selection, focusing on the identification of B and T cell epitopes and correlates of protection. ML models are used to predict epitope regions, assess immunogenicity, and understand immune responses. The author highlights the importance of interpretable ML for improving the identification of immunogens and enhancing our understanding of immune processes. Challenges such as data availability and method development are addressed, emphasizing the need for further advancements to bridge the gap between ML predictions and their application in vaccine design. The article also covers various ML architectures, data types, and prediction tasks, providing examples of successful applications in reverse vaccinology and vaccine design. Additionally, it explores the use of graph-based representations and protein language models to improve epitope prediction and interpretability. The article concludes by discussing the broader implications of ML in vaccine design, including the evaluation of structural and functional features of candidate targets and the prediction of epitope-paratope interactions.The article discusses the role of Machine Learning (ML) in vaccine target selection, focusing on the identification of B and T cell epitopes and correlates of protection. ML models are used to predict epitope regions, assess immunogenicity, and understand immune responses. The author highlights the importance of interpretable ML for improving the identification of immunogens and enhancing our understanding of immune processes. Challenges such as data availability and method development are addressed, emphasizing the need for further advancements to bridge the gap between ML predictions and their application in vaccine design. The article also covers various ML architectures, data types, and prediction tasks, providing examples of successful applications in reverse vaccinology and vaccine design. Additionally, it explores the use of graph-based representations and protein language models to improve epitope prediction and interpretability. The article concludes by discussing the broader implications of ML in vaccine design, including the evaluation of structural and functional features of candidate targets and the prediction of epitope-paratope interactions.