Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery

Graph-based deep learning has emerged as a powerful framework for modeling drug-target interactions (DTIs), enabling the integration of molecular, structural, and systems-level information within a unified representation. In this review, we survey graph-based DTI models across biomedical network-, sequence/hybrid-, and structure-based paradigms, which form a continuum from large-scale association inference to structure-resolved interaction modeling with increasing mechanistic specificity. Beyond architectural advances, we introduce an output-driven perspective in which models are evaluated according to how well their predictions align with the informational and decision-making requirements of different stages of the drug discovery pipeline. Within this framework, attention mechanisms and semi-supervised learning are discussed as key developments that enhance feature prioritization and data efficiency in data-limited settings. We further examine how model outputs support applications ranging from target identification and drug repurposing to structure-guided lead optimization. Finally, we analyze key benchmarking challenges, including data leakage, sequence redundancy, and structural bias, and discuss emerging directions such as multimodal integration and the use of predicted protein structures. Together, this review provides a unified perspective on the design, evaluation, and translational application of graph-based DTI models.