AI and Machine Learning in Vaccine and Immunotherapy Development

The development of vaccines and immunotherapies against infectious diseases and cancers has been a cornerstone of medical science in the past century. Subunit vaccines, which offer more specific B- and T-cell responses, have proven to be safer and more effective compared to whole-inactivated or attenuated-pathogen-based vaccines. However, the traditional development process was often time-consuming and costly, relying heavily on trial-and-error experimentation and extensive in vivo testing.

Today, artificial intelligence (AI) and deep learning (DL) are transforming vaccine and immunotherapeutic research. These technologies provide predictive frameworks that support rapid, data-driven decision-making, integrate computational models, systems vaccinology, and multi-omics data, and help better phenotype, differentiate, and classify patients' diseases and cancers. By integrating host characteristics, AI and DL enable the development of tailored vaccines and immunotherapeutics, refining the selection of B- and T-cell antigen/epitope targets to enhance efficacy and durability of immune protection.

One of the key applications of AI and DL is the potential replacement of animal preclinical testing with computational-based models. This shift is being actively pursued by organizations such as the United States NIH and FDA, aiming to make the development process more ethical and efficient. AI and DL also improve clinical trials by enabling real-time modeling for immune-bridging, predicting patients' immune responses, safety, and protective efficacy to vaccines and immunotherapeutics.

In this review, we explore the past and current applications of AI and DL as time- and resource-efficient strategies. We also discuss future challenges in implementing these technologies, which are poised to facilitate the rapid development of precision and personalized vaccines and immunotherapeutics for infectious diseases and cancers.

Artificial intelligence and machine learning are not just optimizing the existing processes but are pushing the boundaries of what is possible in medical research. By leveraging advanced computational models, researchers can gain a deeper understanding of immune regulation, immune evasion, and regulatory pathways. This knowledge is crucial for developing more effective and durable vaccines and immunotherapeutics.

In the future, AI and DL will continue to play a pivotal role in transforming the landscape of vaccine and immunotherapy development. The integration of these technologies will lead to more efficient, cost-effective, and personalized treatments, ultimately improving patient outcomes and public health.