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Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
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Machine learning (ML) and deep learning (DL) in vaccine target selection, design, development and characterization.

Manojit Bhattacharya1, Srijan Chatterjee2, Arpita Das3

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Summary

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are revolutionizing rational vaccine design. These computational tools accelerate vaccine development, from target selection to clinical trials, improving global health outcomes.

Keywords:
Machine learningartificial intelligencedeep learningdiseasesvaccine

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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.