Computational Molecular Biology 2025, Vol.15, No.6, 265-272 http://bioscipublisher.com/index.php/cmb 270 6.3 Ethical, regulatory, and data privacy concerns related to vaccine AI Not all challenges stem from technical difficulties. Once AI enters the core process of vaccine development, regulatory and ethical issues become unavoidable. Want the public to trust AI models? First, we need to figure out how these models make judgments. But strangely enough, many of the most effective models are also the most "black box" ones. In addition, the use of an individual's genomic and health data can also easily raise privacy concerns. The more such data is used, the higher the requirements for the governance framework will be. If the model also contains data bias, not only will the results be distorted, but it may also amplify the already existing health inequalities. Therefore, from algorithmic fairness to privacy protection and then to regulatory standards, behind the development of AI vaccines is actually a test of a whole set of social mechanisms (El Arab et al., 2025). 7 Future Perspectives and Technological Innovations It is no longer news that AI is increasingly involved in vaccine research and development. But interestingly, the efficiency it has demonstrated in antigen and adjuvant screening has indeed changed many of the old methods that relied on feeling and trial in the past. Tools like convolutional neural networks and recurrent neural networks, which were previously mainly used in image and text processing, are now also being brought in to assist in the design of multi-epitope vaccines. By combining omics data and structural information, they can significantly enhance the accuracy of antigen selection. During the outbreak of COVID-19, AI-driven epitope prediction came into its own, and the pace of research and development significantly accelerated. In terms of adjuvant development, many new strategies have also moved away from the traditional trial-and-error model. By relying on the inference of immune pathways through AI models, the discovery efficiency has been accelerated and the hit rate has been improved. However, when it comes to truly personalized vaccines, we still have to go back to the genetic level. The combination of immunogenomics and computational vaccinology has begun to make "personalized" vaccines possible. As long as the genomic and transcriptomic data of patients can be obtained, the system can predict which new antigens each person may respond to. Combined with the analysis of the immune library, the design can be targeted. This approach is particularly useful in dealing with infectious diseases where the virus mutates rapidly, such as in the direction of cancer vaccines. Some neoantigen vaccines have already shown initial effects in clinical trials. In addition, digital twin simulation and vaccine formulas customized specifically for different populations have made the protection strategies for people with different genetic backgrounds more precise, and both safety and effectiveness are more guaranteed. Of course, a fully automated vaccine design process sounds ideal, but to be truly successful, many technical barriers still need to be overcome. At present, from antigen screening to epitope prediction and then to immune simulation, many steps can indeed be automatically processed. But the question is - can the model understand it? Is the data standard correct? Can the experiment verify whether it can be connected? All of these are still being resolved. Only when the AI process can be integrated with high-throughput experiments and regulatory frameworks can vaccine research and development truly enter the "closed-loop era". By then, the response speed to sudden infectious diseases and the capacity for large-scale production might indeed undergo a qualitative change. Acknowledgments I thank the anonymous reviewers and the editor for their meticulous review of the manuscript, whose constructive comments and valuable suggestions improved the structure of the argument. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Anderson L., Hoyt C., Zucker J., McNaughton A., Teuton J., Karis K., Arokium-Christian N., Warley J., Stromberg Z., Gyori B., and Kumar N., 2025, Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions, Frontiers in Immunology, 16: 1502484.
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