From data to immunity: the role of machine learning in advancing malaria vaccine research: a scoping review

Malaria remains a significant global health burden, necessitating the development of effective vaccines. Traditional vaccine development is challenged by the complexity of the Plasmodium parasite and lengthy empirical processes. Machine learning (ML) offers a promising avenue to accelerate and enhance vaccine research.

This review synthesizes recent advances in the application of ML to malaria vaccine research, focusing on immunological signature identification, antigen discovery, and predictive modeling of vaccine efficacy, to highlight its transformative potential.

A targeted literature search was conducted for peer-reviewed articles, reviews, and systematic analyses published between 2017 and 2025. Studies directly addressing ML or AI in malaria vaccine development were included. Data extraction covered ML methodologies, data types, applications, validation strategies, challenges, and limitations. Thematic analysis categorized findings, and a quality assessment ensured methodological rigor.

Thematic analysis identified five key areas: (1) antigen discovery and prioritization using supervised and semi-supervised learning; (2) immune signature identification and efficacy prediction via diverse ML algorithms; (3) computational tool and framework development for data integration; (4) broad reviews of AI/ML applications; and (5) epidemiological modeling for policy support. Most studies were conducted in Europe and North America, often with collaborations in Africa.

ML is transforming malaria vaccine research by accelerating antigen discovery, enabling precise immune profiling, and predicting vaccine efficacy. Addressing data quality, model interpretability, and validation challenges is crucial for realizing the full potential of ML in developing next-generation malaria vaccines.