review · Journal of Microbiological Methods
Traditional vaccine development is expensive, slow, and inefficient, but artificial intelligence offers tools to accelerate this process. Machine learning and deep learning algorithms analyse genomic data, protein structures, and immune system interactions to prioritise antigens, predict antigenic epitopes, and evaluate immunogenicity for laboratory testing. These computational methods also support the rational design of immunogens and the discovery of safer, more effective adjuvants. Despite these capabilities, widespread implementation faces obstacles, including data heterogeneity, limited model interpretability, and regulatory hurdles. Combining artificial intelligence with tools such as synthetic biology and single-cell omics can further improve precision and scalability. Realising the broader benefits of artificial intelligence in producing safe vaccines against infectious diseases requires regulatory harmonisation alongside interdisciplinary collaboration.
Infectious diseases place a substantial burden on global public health, requiring faster and more reliable ways to produce vaccines. Understanding how artificial intelligence supports epitope prediction, antigen selection, and adjuvant discovery helps researchers and healthcare strategists accelerate development cycles. Addressing computational and regulatory challenges can ultimately streamline the creation of safer, more effective preventative treatments against emerging and existing infectious threats.
The review focuses on digital tools for vaccine developers and pharmaceutical researchers, specifically for antigen prioritisation, immunogen design, and adjuvant selection. Because the text evaluates computational methodologies alongside persistent bottlenecks such as data heterogeneity and regulatory uncertainty, the approach remains at an exploratory and pre-clinical stage rather than offering an immediate off-the-shelf product. Commercial adoption will depend on resolving model interpretability and establishing harmonised regulatory standards.
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Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.
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DOI: 10.1016/j.mimet.2024.106998
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