Artificial Intelligence Driven Framework for the Design and Development of Next-Generation Avian Viral Vaccines

Area of Science:

• Veterinary Medicine
• Immunology
• Bioinformatics

Background:

• Avian viral pathogens pose significant threats to poultry health and food security.
• Traditional vaccine development methods are often slow, expensive, and struggle with pathogen diversity.

Purpose of the Study:

• To develop an artificial intelligence (AI)-driven pipeline for designing, modeling, and optimizing multi-epitope vaccines against key avian viruses.
• To demonstrate the potential of AI in accelerating veterinary vaccinology.

Main Methods:

• Utilized machine learning and deep learning for epitope prediction, antigenicity assessment, and structural modeling (AlphaFold2).
• Selected B-cell and T-cell epitopes based on binding affinity, conservation, and immunogenicity.
• Incorporated adjuvants and linkers for enhanced construct stability and immune response; performed in silico immune simulations.

Main Results:

• AI pipeline successfully designed multi-epitope vaccines targeting avian RNA and DNA viruses (H5N1, NDV, IBV, IBDV, CAV, FPV).
• In silico simulations predicted strong humoral and cellular immune responses, including cytokine production and memory cell activation.
• Identified challenges including data quality, model interpretability, and ethical considerations.

Conclusions:

• AI offers a transformative and scalable approach for rapid, data-driven vaccine development in veterinary vaccinology.
• This AI-driven pipeline can significantly improve responses to emerging avian diseases and enhance global poultry health.