Emmanuel Nkansah¹, Micheal Abimbola Oladosu²*, Moses Adondua Abah³, Abimbola Mary Oluwajembola², Fwangmun Ezekiel Gushit⁴, Olaide Ayokunmi Oladosu⁵, Adesola Esther Adeneye⁶ and Bukola Oluwaseyi Olufosoye⁷

¹Department of Accounting, Economics and Finance, School of Business, La Sierra University, Riverside, CA, USA
²Department of Chemical Sciences, Faculty of Science, Anchor University, Ayobo, Ipaja, Lagos, Nigeria
³Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University of Wukari, Wukari, Taraba State, Nigeria
⁴Department of Public Health, Faculty of Health Science, Ahmadu Bello University, Zaria, Kaduna State, Nigeria
⁵Department of Computer Science, Faculty of Science and Technology, Babcock University, Ilishan, Nigeria
⁶Department of Biological Sciences, Faculty of Science, Anchor University, Ayobo, Ipaja, Lagos, Nigeria
⁷Department of Medical Microbiology, Faculty of Medical Laboratory Sciences, Ambrose Alli University, Ekpoma, Edo State, Nigeria

*Corresponding Author: Micheal Abimbola Oladosu, Department of Chemical Sciences, Faculty of Science, Anchor University, Ayobo, Ipaja, Lagos, Nigeria.

Received: February 02, 2026; Published: February 28, 2026

Abstract

The integration of artificial intelligence (AI) into vaccine development has revolutionised the traditional paradigm of vaccinology, significantly accelerating the timeline from pathogen identification to clinical deployment. This review examines the transformative role of AI technologies, including machine learning, deep learning, and neural networks, in various stages of vaccine design and optimisation for infectious diseases. We conducted a comprehensive literature search of PubMed, Scopus, Web of Science, and Google Scholar databases for publications from 2020 to 2025 using keywords: artificial intelligence, machine learning, deep learning, vaccine design, epitope prediction, infectious diseases, and immunoinformatics. Studies focusing on AI applications in vaccine development pipeline stages were included. We explore AI applications in antigen selection, epitope prediction, immunogen design, adjuvant identification, clinical trial optimisation, and manufacturing processes. Recent advances in graph neural networks, transformer- based architectures, and generative models have enhanced prediction accuracy and enabled the discovery of previously overlooked immunogenic epitopes. Despite these remarkable achievements, challenges persist in data quality, model interpretability, regulatory frameworks, and equitable global implementation. This review synthesises current evidence from 2020-2025 and provides insights into future directions for AI-driven vaccine development against emerging infectious threats.

Keywords: Artificial Intelligence; Machine Learning; Vaccine Design; Epitope Prediction; Infectious Diseases; Immunoinformatics

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Citation

Citation: Micheal Abimbola Oladosu. et al. “Artificial Intelligence in Vaccine Design and Optimisation for Infectious Diseases: A Review". Acta Scientific Microbiology 9.3 (2026): 02-10.

Copyright

Copyright: © 2026 Micheal Abimbola Oladosu. et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.