Abstract
Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors-such as imatinib and osimertinib-has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative solutions to these obstacles across the drug discovery pipeline. This review explores how AI/ML methods, including deep learning, graph neural networks, and generative models, are revolutionizing the design, optimization, and repurposing of kinase inhibitors. We detail applications in target identification, virtual screening, structure-activity relationship modeling, resistance prediction, and clinical trial design. Representative case studies-such as AI-optimized BTK and EGFR inhibitors-highlight real-world impact. We also examine current limitations, including data sparsity, model interpretability, and translational gaps between in silico and experimental results. Finally, we discuss emerging directions such as federated learning, personalized kinase inhibitors, and AI-enabled combination therapies. By integrating computational innovation with medicinal chemistry, AI/ML holds immense promise to accelerate and refine the next generation of kinase-targeted therapeutics.