Vaccine development using artificial intelligence and machine learning: A review

Area of Science:

• Vaccinology
• Computational Biology
• Bioinformatics

Background:

• Vaccine development is complex, requiring identification of antigens, adjuvants, and delivery systems for efficacy.
• The COVID-19 pandemic highlighted the need for rapid and effective vaccine development.
• Artificial intelligence (AI) offers powerful tools for analyzing large datasets in biological research.

Purpose of the Study:

• To explore the applications of AI in various stages of vaccine development.
• To review how machine learning methods can enhance vaccine design and predict performance.
• To address the challenges and ethical considerations associated with AI in vaccinology.

Main Methods:

• Review of current literature on AI applications in vaccine development.
• Analysis of machine learning techniques for predicting immune responses, discovering adjuvants, and optimizing vaccine parameters.
• Discussion of AI's role in protein structure prediction and supply chain modeling.

Main Results:

• AI can predict immune responses from protein sequences and aid in novel vaccine candidate discovery.
• Machine learning facilitates adjuvant selection, dose optimization, and vaccine supply chain management.
• AI tools can predict protein structures, crucial for antigen design.

Conclusions:

• AI holds significant potential to expedite vaccine development and preparedness for future pandemics.
• Careful consideration of data quality, algorithmic bias, and data privacy is essential for responsible AI implementation.
• Integrating AI into vaccine research requires robust validation of methods and data integrity.