@article{GU2026, 
author = {Jiang GU and Yuhang LI},
title = {Artificial intelligence reshaping vaccine development: a new paradigm of efficiency revolution and precision design},
year = {2026},
journal = {Journal of Army Medical University},
volume = {48},
number = {4},
pages = {387-393},
keywords = {artificial intelligence, deep learning, vaccine, adjuvants},
url = {https://www.sciopen.com/article/10.16016/j.2097-0927.202512040},
doi = {10.16016/j.2097-0927.202512040},
abstract = {Artificial intelligence (AI) is systematically reshaping the entire vaccine research and development pipeline through integrating deep learning architectures with multi-omics data. This facilitates a paradigm shift from traditional trial-and-error empirical approaches to a novel rational design framework centered on data-driven and algorithm-generated methodologies. Despite challenges including data heterogeneity, model opacity, algorithmic bias, and lagging regulatory frameworks, AI enables a transformative leap from structural mimicry to functional innovation in vaccine design via deep integration with immunological mechanisms. This review comprehensively analyzes AI applications across key vaccine development stages: target identification, antigen design, adjuvant screening, process optimization, clinical trial design, and vaccine hesitancy mitigation. We particularly summarize recent tools, core architectures, functional features, and representative cases for AI-powered rational vaccine design, providing actionable references for future AI implementation in vaccinology.}
}