Applied immunoinformatics in modern vaccine design: a comprehensive review of available computational tools

The rapid evolution of computational biology has transformed vaccine design from a largely empirical process into a rational, data-driven discipline. At the center of this transformation lies immunoinformatics, which integrates immunology, molecular biology, and data science to accelerate the identification, evaluation, and optimization of vaccine candidates. By enabling the systematic transition from genomic data to viable immunotherapeutic candidates, these approaches offer an unprecedented capacity to shorten development timelines, reduce costs, and broaden population coverage compared to traditional trial-and-error vaccine pipelines. This review provides a state-of-the-art overview of the currently available immunoinformatic tool that underpins modern vaccine design. From identification of conserved and functionally essential pathogen targets, advances in structural prediction, epitope mapping, safety assessment, and systems-level analyses of host-pathogen interactions, immunoinformatics allows researchers to design vaccines with enhanced success rate. Special emphasis is placed on the shift from purely predictive models toward generative frameworks, where artificial intelligence and deep learning now support not only the evaluation, but also the rational engineering of optimized vaccine candidates. For this review, we systematically examined more than 250 computational tools and algorithms spanning every stage of the vaccine discovery pipeline. From this extensive survey, we distilled and discussed the approaches that currently demonstrate the highest performance and practical usability, thereby providing researchers with a curated overview of some the most reliable resources available.