Artificial intelligence-based epitope discovery of Mpox virus: Rational vaccine design

Abstract

The monkeypox virus (MPXV) has emerged as a significant public health concern, prompting urgent efforts to develop a vaccine. The need for effective preventive measures against this zoonotic orthopoxvirus is underscored by recent global outbreaks. Traditional vaccine development methods can be both costly and time-consuming; however, advancements in genomic and proteomic data processing, particularly through artificial intelligence (AI), offer new avenues for rapid epitope discovery and vaccine design. This advancement is expected to lead to quicker responses during epidemics. Moreover, a revolutionary approach to enhancing vaccine efficacy and safety is the integration of AI-driven epitope discovery with MPXV vaccine development. By utilizing machine learning techniques, researchers can analyze large datasets, including genomic and proteomic information, to identify putative epitopes that trigger immune responses. This AI-driven process not only accelerates the identification of optimal vaccine targets but also predicts how these epitopes interact with the immune system, facilitating the creation of vaccines that effectively elicit strong and durable immune responses. Additionally, researchers can model and simulate immune interactions using AI to refine epitope selection on the basis of predicted immunogenicity and stability, ensuring well-optimized vaccine candidates. Ultimately, this integration streamlines the MPXV vaccine development process, enabling prompt responses to outbreaks and enhancing public health preparedness against this infectious disease. Hence, this review aimed to evaluate the impact of AI on the development of effective vaccines against MPXV. This review highlights how this technology can enhance vaccine design, optimize immune responses, and improve public health preparedness in response to MPXV outbreaks.