Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction
Peptide-based cancer vaccines represent a promising immunotherapeutic strategy aimed at inducing tumor-specific immune responses through the targeting of tumor-associated antigens and neoantigens. Recent advances in next-generation sequencing and immunogenomics have accelerated the identification of candidate neoantigens; however, the development of effective peptide vaccines remains limited by challenges related to antigen selection, HLA polymorphism, antigen processing, and variability in immunogenicity. Artificial intelligence (AI), including machine learning and deep learning approaches, has emerged as a transformative tool capable of addressing these limitations through large-scale integration and analysis of genomic, transcriptomic, proteomic, and immunological data. In this review, we summarize the current role of AI across the peptide cancer vaccine development pipeline, from neoantigen discovery and epitope prioritization to prediction of peptide–HLA binding, antigen presentation, and T-cell receptor recognition. We discuss the application of modern computational frameworks, including pan-allelic prediction models, transformer-based architectures, immunopeptidomics-informed learning, and multi-modal AI systems integrating tumor and immune microenvironment data. Furthermore, we examine the clinical translation of personalized neoantigen vaccines, including their combination with immune checkpoint inhibitors and their emerging role in aggressive malignancies such as glioblastoma. Despite substantial progress, significant challenges remain, including high false-positive prediction rates, limited diversity of training datasets, biological complexity of immunogenicity, and regulatory and manufacturing barriers associated with individualized therapies. Continued integration of AI-driven prediction tools with experimental validation and translational immunology will be essential for the development of clinically effective and scalable precision cancer vaccines.