Jiaxuan Hu

This review systematically surveys AI-driven drug-target interaction (DTI) prediction, starting from classical molecular binding theories (lock-and-key, induced fit, conformational selection) and summarizing task settings such as binary interaction classification, binding affinity regression, and multitask prediction with uncertainty assessment; it organizes multimodal representations for drugs and target proteins (molecular sequences, graph structures, 3D conformations, physicochemical properties, biological perturbation profiles, protein sequences and structures, biomedical knowledge networks) together with interaction labels and auxiliary biomedical data, compares representative approaches across orthogonal dimensions including input representation, encoder architecture, interaction-mod

This review systematically surveys AI-driven drug-target interaction (DTI) prediction, starting from classical molecular binding theories (lock-and-key, induced fit, conformational selection) and summarizing task settings such as binary interaction classification, binding affinity regression, and multitask prediction with uncertainty assessment; it organizes multimodal representations for drugs and target proteins (molecular sequences, graph structures, 3D conformations, physicochemical properties, biological perturbation profiles, protein sequences and structures, biomedical knowledge networks) together with interaction labels and auxiliary biomedical data, compares representative approaches across orthogonal dimensions including input representation, encoder architecture, interaction-mod