ORIGINAL RESEARCH article
Front. Immunol.
Sec. Vaccines and Molecular Therapeutics
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1654060
This article is part of the Research TopicInnovative Adjuvant Strategies: Enhancing Vaccine Efficacy Through Transdisciplinary ApproachesView all 7 articles
Machine Learning Insights into Vaccine Adjuvants and Immune Outcomes
Provisionally accepted
Yuhyun Ji*
Kavitha BekkariRuchin PatelMohammed ShardarGeoffrey WalfordSammoon KimYaping LiuWillis Read-ButtonKristina TracyColleen BarrKunal Bakshi
Nicole L Sullivan*
Nicholas Murgolo*- Merck Sharp & Dohme Corp, Rahway, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Adjuvants boost the immune response to vaccine antigens, serving as key components in safe and effective vaccines. However, selecting a suitable adjuvant for a new vaccine can be challenging. This is due to the wide variety of adjuvants and the many mechanisms of vaccines they are meant to enhance. Therefore, the adjuvant selection process heavily relies on empirical experiments, which are timeconsuming and resource-intensive. In this study, we introduce a machine learning approach leveraging non-human primate RNA transcriptomic data to predict immunogenic antibody levels after vaccination. Furthermore, analysis of the trained deep learning models enabled the identification of immune response mechanisms that are stimulated by adjuvants. Integration of machine learning has the potential to expedite vaccine adjuvant selection by focusing on evaluating adjuvant candidates with the highest probability of success. This may ultimately facilitate the development of more effective vaccines.
Keywords: machine learning, deep learning, artificial intelligence, adjuvant, Vaccine, RNA transcriptomics, Antibody titers, immune response
Received: 25 Jun 2025; Accepted: 08 Aug 2025.
Copyright: © 2025 Ji, Bekkari, Patel, Shardar, Walford, Kim, Liu, Read-Button, Tracy, Barr, Bakshi, Sullivan and Murgolo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yuhyun Ji, Merck Sharp & Dohme Corp, Rahway, United States
Nicole L Sullivan, Merck Sharp & Dohme Corp, Rahway, United States
Nicholas Murgolo, Merck Sharp & Dohme Corp, Rahway, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.