Deep learning in next-generation vaccine development for infectious diseases.

Area of Science:

• Computational biology and bioinformatics
• Vaccinology and immunology
• Artificial intelligence in medicine

Background:

• The genomic era has transformed vaccine development, with computer science playing a pivotal role.
• Computer-aided epitope selection is fundamental to rational vaccine design.
• Artificial intelligence (AI), particularly deep learning (DL), is rapidly advancing vaccine development strategies.

Purpose of the Study:

• To review the developmental history of modern vaccine design strategies.
• To discuss the integration of immunoinformatics and DL models in vaccine development.
• To highlight recent advancements and tools in DL-assisted epitope mapping and vaccine construct development.

Main Methods:

• Review of immunoinformatics and DL models for identifying T cell and B cell epitopes.
• Analysis of vaccine construct development strategies, including linkers and adjuvants.
• Examination of bioinformatics and immunoinformatics tools for vaccine construct characterization.

Main Results:

• DL-based strategies are significantly accelerating epitope prediction and vaccine design.
• Various DL-based tools are available for epitope mapping and vaccine construct development.
• Integrated frameworks connecting bioinformatics and DL approaches are crucial for efficient vaccine development.

Conclusions:

• DL-assisted vaccine development offers rapid and cost-effective solutions.
• These computational approaches are reshaping the landscape of next-generation vaccine design.
• Continued integration of AI and bioinformatics will drive future innovations in vaccinology.