Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.

Predicting host-pathogen protein-protein interactions (PPIs) is a cornerstone of modern infectious disease research, offering unparalleled insights into the molecular mechanisms underlying infection and immune evasion. Despite its transformative potential, the field faces persistent challenges, including limited experimental data, class imbalance, and the dynamic evolution of pathogens. The current study explores cutting-edge computational approaches that have redefined host-pathogen protein-protein interaction (HP-PPI) prediction. Notably, transfer learning has emerged as a game changer, enabling models to leverage knowledge from well-characterized systems to predict interactions in previously underexplored pathogens. Hybrid and ensemble models have proven highly effective, combining the strengths of diverse algorithms to capture the complexity of biological interactions. Explainable AI tools are now bridging the gap between computational predictions and biological interpretability, offering actionable insights into key interaction drivers. Additionally, the review discusses advanced data integration techniques, such as multi-omics fusion and graph-based learning, which explore new dimensions in HP-PPI research. This synthesis of challenges, solutions, and future perspectives highlights a paradigm shift in computational biology, in which scalable, interpretable, and biologically informed models pave the way for breakthroughs in therapeutic discovery, vaccine development, and precision medicine. Our review sets the stage for future advancements, emphasizing the potential of next-generation technologies to unravel the intricate dance between hosts and pathogens.