Strain-level prediction of phage-host interactions (PHIs) is essential for developing targeted phage therapies. Traditional empirical and homology-based methods often lack the resolution and scalability needed for precision applications. Recently, a new generation of artificial intelligence-driven models has emerged leveraging genomic information to infer PHIs at strain-level resolution. Here, we review recent advances in strain-level PHI prediction, spanning biologically grounded feature-based models, hybrid representation-learning frameworks, phylogeny-agnostic machine learning approaches, and end-to-end deep learning architectures. We examine how these modelling strategies navigate shared structural constraints arising from sparse and imbalanced outcome data, assay-dependent labels, infection complexity, and limited generalization. We further analyse how evaluation design, negative definition, and train-test splitting strategies shape apparent strain-level performance, and why inappropriate benchmarking can inflate claims of biological resolution. Framing these issues in the context of clinical phage therapy, we examine how current strain-level PHI prediction frameworks perform under the biological, experimental, and data constraints characteristic of real-world therapeutic settings. Finally, we outline pragmatic pathways toward more robust, interpretable, and clinically translatable PHI prediction systems.
Malajczuk et al. (Thu,) studied this question.