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

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported; no meta-analytic or pooled effect estimates provided

• Primary Outcome: Narrative synthesis of AI applications in kinase structural biology, drug discovery, and signalling pathway analysis — no primary quantitative outcome reported

• Nnt Or Sensitivity: Not applicable to this review type; no diagnostic, therapeutic, or prognostic statistics are presented

• Confidence Interval: Not reported

Clinical Application

The computational tools described (AlphaFold, molecular docking platforms, ML frameworks) are increasingly accessible via open-source platforms and cloud computing. However, integration into clinical drug development pipelines requires substantial bioinformatics infrastructure, experimental validation capacity, and regulatory expertise that is beyond the scope of most clinical settings Australia has a well-developed kinase inhibitor prescribing landscape through the PBS, including imatinib, dasatinib, erlotinib, gefitinib, osimertinib, alectinib, crizotinib, ibrutinib, and multiple others listed for haematological and solid malignancies. The TGA has approved numerous kinase inhibitors consistent with international regulatory decisions. RACGP and COSA guidelines support molecular testing to guide kinase inhibitor selection. The computational advances described in this review have potential long-term relevance to Australian precision oncology programs, particularly through institutions such as the Peter MacCallum Cancer Centre, WEHI, and the Garvan Institute, which conduct translational kinase research. However, this review does not provide evidence that would directly change current Australian prescribing practice or PBS listing decisions. Researchers and clinician-scientists working in kinase-driven oncology, signal transduction biology, and computational drug discovery. Indirect relevance to oncologists managing patients with kinase-driven malignancies (e.g., CML, NSCLC with EGFR/ALK mutations, HER2-positive breast cancer, RET-altered cancers)

Abstract

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.

References

1. 1.Gomari, M. M., Ntwali, P. M., Choupani, E., & Ashayeri, N. (2026). Harnessing AI to decode protein kinases: Structural, functional, and therapeutic design perspectives. New Biotechnology. Advance online publication. https://doi.org/10.1016/j.nbt.2026.02.010