Artificial intelligence strategies for predicting kinase inhibitor resistance: A comprehensive review of methods, challenges, and future perspectives

Abstract Kinase inhibitors are essential in targeted cancer therapy, yet resistance often emerges through secondary mutations, activation of compensatory signaling pathways, or drug‐efflux mechanisms. Artificial intelligence (AI) provides a workflow‐based strategy rather than a list of unrelated tools for predicting and addressing kinase‐inhibitor resistance. In this review, AI methodologies are systematically classified into machine‐learning frameworks, molecular‐modeling tools, bioinformatics databases, network‐biology resources, and explainability platforms, offering a structured perspective on their interdependence within resistance prediction workflows. Deep learning models demonstrate superior predictive performance compared to traditional approaches, while explainable‐AI (XAI) techniques such as SHAP and LIME enhance interpretability and clinical trust. Integration of multi‐omics data including genomic, proteomic, and transcriptomic profiles further strengthens model robustness and clinical relevance. AI‐driven in silico simulations of kinase drug interactions are also facilitating the design of next‐generation inhibitors. By emphasizing workflow integration and methodological taxonomy, this review highlights how AI can revolutionize resistance prediction and rational drug development, paving the way toward precision medicine in kinase‐targeted therapies.