Vaccine development using artificial intelligence and machine learning: A review

• Published in: International journal of biological macromolecules Vol. 282; no. Pt 1; p. 136643
• Main Authors: Asediya, Varun S., Anjaria, Pranav A., Mathakiya, Rafiyuddin A., Koringa, Prakash G., Nayak, Jitendrakumar B., Bisht, Deepanker, Fulmali, Devansh, Patel, Vishal A., Desai, Dhruv N.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-12-2024
• Subjects: Adjuvant discovery; COVID-19 vaccine; Epitope prediction; Immunogenicity; Machine learning models; Molecular design and synthesis prediction; Protein structure prediction; Vaccine design optimization; Vaccine supply chain optimization; Adjuvant discovery; Immunogenicity; Epitope prediction; Molecular design and synthesis prediction; AI; COVID-19 vaccine; Protein structure prediction; Machine learning models; Vaccine supply chain optimization; Vaccine design optimization
• ISSN: 0141-8130; 1879-0003; 1879-0003
• DOI: 10.1016/j.ijbiomac.2024.136643

The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.
•A comprehensive review on use of Artificial Intelligence (AI) and Machine Learning in vaccine design, its challenges, and future trends.•We discussed most recent available resources that can assist in vaccine design to lead future of vaccinology.•We pointed out methods and different model available to use for vaccine design and its implication.•We emphasize on challenges and ethical issue with future directions of AI in vaccinology.