Computational Molecular Biology 2025, Vol.15, No.6, 265-272 http://bioscipublisher.com/index.php/cmb 265 Review Article Open Access Advances in Computational Vaccinology: From Antigen Discovery to Immune Simulation ShiyingYu Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China Corresponding author: shiying.yu@cuixi.org Computational Molecular Biology, 2025, Vol.15, No.6 doi: 10.5376/cmb.2025.15.0026 Received: 01 Sep., 2025 Accepted: 11 Oct., 2025 Published: 02 Nov., 2025 Copyright © 2025 Yu, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Yu S.Y., 2025, Advances in computational vaccinology: from antigen discovery to immune simulation, Computational Molecular Biology, 15(6): 265-272 (doi: 10.5376/cmb.2025.15.0026) Abstract Computational vaccinology, as an emerging interdisciplinary subject integrating bioinformatics, immunology and systems biology, is profoundly transforming the vaccine research and development process. This study systematically reviews the key advancements in the field of computational vaccinology, covering theoretical foundations, core technologies, and practical application scenarios. It examines the background of the shift in vaccine development from traditional methods to computational strategies, and introduces genomic-based antigen screening methods (reverse vaccinology), epitope prediction algorithms, and the application of structural bioinformatics in antigen design The integrated application of immunoinformatics tools and databases was explored, especially the value of multi-omics data in refined antigen analysis. The practical value of computational vaccinology was demonstrated through multiple actual cases (such as AI-assisted COVID-19 vaccine development, multi-epitope vaccine design for tuberculosis and malaria, as well as tumor neoantigen prediction and clinical transformation). This study reveals the crucial role of computational vaccinology in enhancing the efficiency of vaccine development, reducing costs, responding to emerging infectious diseases, and achieving personalized immunization strategies. At the same time, it provides theoretical basis and technical prospects for the future construction of AI-driven automated vaccine platforms. Keywords Computational vaccinology; Reverse vaccinology; Epitope prediction; Immune simulation; Vaccine design 1 Introduction How were vaccines made in the past? Most of the time, it relies on experience - detoxification, inactivation, and then trying bit by bit (Li et al., 2024). Although these methods are indeed effective, the process is slow, the cost is considerable, and they are often inadequate in dealing with new pathogens (He and Wang, 2024). In recent years, computational vaccinology has gradually come to the fore, not because it is "high-end and sophisticated", but because it is indeed more practical in saving time and costs. After the integration of genomic, proteomic and immune data, antigen screening is no longer a blind exploration. New methods such as reverse vaccinology and immunoinformatics have begun to provide precise targets, especially in dealing with infectious diseases and cancers, and have been proven to have obvious advantages (Basmenj et al., 2025). However, no matter how good the tools are, it still depends on how they are used. The computing platforms that many researchers rely on nowadays are no longer merely analytical tools; they are more like the "experimental front desk" for vaccine development. Platforms like iVAX package epitope localization, antigen construction, and immune simulation, and also come with data visualization and resource databases. As long as the models are reasonable and the data are reliable, they can even preliminarily determine which antigens have potential before the vaccines enter the laboratory (Moise et al., 2015). Of course, computational models are not omnipotent, especially when it comes to new variant strains. Whether the algorithm can keep up is a question. However, from the overall trend, the progress of AI algorithms and structural modeling has indeed greatly compressed the time of the prediction work that originally took several years to complete. Therefore, computational vaccinology is regarded as the "standard tool" for the next stage of vaccine research and development (Nag et al., 2025; Tang et al., 2025). This study reviews the latest advancements in the field of computational vaccinology, with a focus on the entire process from antigen discovery to immune simulation. It also explores the challenges and future development
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