Machine learning (ML) and deep learning (DL) in vaccine target selection, design, development and characterization.

Area of Science:

• Biotechnology
• Computational Biology
• Immunology

Background:

• Vaccination is a critical public health intervention saving millions of lives annually.
• Traditional vaccine design is often time-consuming and resource-intensive.
• Advancements in computational tools offer new avenues for accelerating vaccine development.

Purpose of the Study:

• To review the application of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in rational vaccine design.
• To illustrate the role of AI/ML/DL in key stages of vaccine development, including target selection, design, and characterization.
• To highlight the potential of these technologies in addressing global infectious disease challenges.

Main Methods:

• Review of existing literature on AI, ML, and DL applications in vaccine design.
• Description of various ML and DL models used in vaccine development.
• Illustration of computational tools for specific vaccine design steps.

Main Results:

• AI/ML/DL significantly aid in rational vaccine design, covering target selection, antigenicity prediction, immunogen structure design, and immune system modeling.
• These technologies facilitate in silico clinical trials, vaccine safety assessment, and omics data integration.
• The review details specific ML/DL models and computational tools employed in these processes.

Conclusions:

• AI, ML, and DL are foundational to modern rational vaccine design, offering faster, safer, and more effective vaccine candidates.
• These technologies play a crucial role in optimizing vaccine formulation and predicting safety and efficacy.
• Despite data limitations, AI/ML/DL advancements promise to tackle major infectious disease threats globally.