AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development

Area of Science:

• Immunology and Computational Biology
• Vaccine Development and Bioinformatics

Background:

• Traditional epitope prediction methods face limitations in accuracy and efficiency.
• Artificial intelligence (AI) offers transformative potential for enhancing vaccine design.

Purpose of the Study:

• To review recent advancements in AI-driven epitope prediction for vaccine design.
• To highlight key AI models and their experimental validation.
• To provide strategies for integrating computational predictions into experimental workflows.

Main Methods:

• Synthesis of recent literature on AI applications in epitope prediction.
• Focus on Convolutional Neural Networks (CNNs), transformers, and Graph Neural Networks (GNNs).
• Benchmarking AI tools against traditional prediction methods.

Main Results:

• AI models demonstrate unprecedented accuracy, speed, and efficiency in epitope prediction.
• Experimentally validated models like MUNIS and GraphBepi identify novel epitopes.
• Integration of structural data is crucial for improving prediction accuracy.

Conclusions:

• AI, particularly CNNs, transformers, and GNNs, significantly advances epitope prediction for vaccine design.
• Structural data integration is pivotal for translating AI predictions into effective experimental strategies.
• This review offers practical insights for developing next-generation vaccines using AI.