Advances in Personalized Cancer Vaccine Development: AI Applications from Neoantigen Discovery to mRNA Formulation

Personalized cancer vaccines are a promising immunotherapy targeting patient-specific tumor neoantigens, yet their design and efficacy remain challenging. Recent advances in artificial intelligence (AI) provide powerful tools to enhance multiple stages of cancer vaccine development. This review systematically evaluates AI applications in personalized cancer vaccine research over the past five years, focusing on four key areas: neoantigen discovery, codon optimization, untranslated region (UTR) sequence generation, and mRNA vaccine design. We examine AI model architectures (e.g., neural networks), datasets (from omics to high-throughput assays), and outcomes in improving vaccine development. In neoantigen discovery, machine learning and deep learning models integrate peptide–MHC binding, antigen processing, and T cell receptor recognition to enhance immunogenic neoantigen identification. For sequence optimization, deep learning models for codon and UTR design improve protein expression and mRNA stability beyond traditional methods. AI-driven strategies also optimize mRNA vaccine constructs and formulations, including secondary structures and nanoparticle delivery systems. We discuss how these AI approaches converge to streamline effective personalized vaccine development, while addressing challenges such as data scarcity, tumor heterogeneity, and model interpretability. By leveraging AI innovations, the future of personalized cancer immunotherapy may see unprecedented improvements in both design efficiency and clinical effectiveness.