AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine

Drug-target interactions (DTIs) are fundamental to drug discovery, development, and repositioning. However, experimental methods for DTI identification are often constrained by high costs, time demands, and scalability issues, prompting a shift toward computational approaches. This review systematically explores recent advancements in computational DTI prediction, encompassing ligand-based, target-based, network-based, machine learning (ML), deep learning (DL), and hybrid multi-omics models. Ligand-based techniques, such as QSAR and pharmacophore modeling, offer structure-activity insights but require known ligands. Target-based methods rely on molecular docking and binding site prediction, yet often suffer from incomplete or unknown protein structures. Network-based strategies utilize bipartite and heterogeneous graphs integrated with protein-protein interaction (PPI) networks to infer novel DTIs. ML and DL methods especially graph neural networks and Transformer-based models have significantly improved prediction accuracy by leveraging chemical, biological, and omics features. Notably, hybrid models that integrate genomics, transcriptomics, proteomics, and interactomics data offer a systems biology perspective, enabling context-specific and personalized predictions. Key databases, like DrugBank, ChEMBL, and Binding DB, and computational tools such as Deep Purpose, NeoDTI, and FusionDTI, exemplify the latest advances in DTI prediction. Validation strategies are discussed through contemporary case studies. While substantial progress has been made, key challenges remain, including data sparsity, model interpretability, and generalization. Looking forward, emerging trends such as federated learning, AlphaFold-based docking, and quantum simulations are poised to further transform the field. This review emphasizes the importance of interdisciplinary integration and ethical frameworks, charting a roadmap for future DTI research and its translational applications in precision medicine.