Harnessing AI to decode protein kinases: Structural, functional, and therapeutic design perspectives

Recent advances in Artificial Intelligence (AI) are reshaping kinase research by uncovering complex regulatory mechanisms and accelerating drug discovery. These advances have enabled the capture of elusive allosteric transitions and transient cryptic pockets by exploring dynamic conformational landscapes, revealing features critical for understanding ligand interactions and enzymatic regulation. AI-driven structure prediction tools, such as AlphaFold (AF)-based models, offer high-resolution insights into kinase conformations and complex assembly, thereby clarifying receptor activation in signaling pathways. Moreover, cutting-edge AI frameworks provide fresh perspectives on substrate prediction, cofactor mapping, and mutation-induced structural changes. Integrative strategies that combine Machine Learning (ML), molecular docking, Molecular Dynamics (MD) simulation, and experimental validation have further streamlined the discovery of novel kinase inhibitors and related studies. Collectively, these AI-enhanced approaches deepen our understanding of kinase-mediated signaling—from developmental processes to oncogenic transformation—and highlight the powerful synergy between computational and experimental research in advancing therapeutic innovations. Artificial Intelligence (AI) is transforming protein kinase research by revealing allostery, cryptic binding sites, and conformational diversity. AI-driven structure prediction, substrate and cofactor mapping, and mutation analysis advance the structure-focused characterization of kinases. • AI enhances kinase allostery insights and reveals cryptic binding sites. • Kinase conformational diversity is efficiently profiled using AI frameworks. • AI enables accurate mapping of kinase substrates and cofactors. • Mutation-driven functional effects can be rapidly predicted through AI analytics. • AI accelerates kinase engineering and inhibitor design strategies.