Machine learning enables precision engineering of therapeutic nanozymes with enhanced catalytic performance

Artificial nanozymes avoid the intrinsic fragility of natural enzymes, offering excellent environmental robustness and tunable catalytic activities for precision nanomedicine. However, the catalytic performance of nanozymes is determined by multidimensional physicochemical parameters, including size, morphology, composition, and facet exposure. Traditional trial-and-error approaches struggle to effectively navigate the vast chemical space, leaving complex structure-activity relationships largely obscured. To overcome this barrier, machine learning (ML) is rapidly transforming nanozyme engineering from empirically-driven exploration into a predictive, data-centric science. This review systematically summarizes the latest progress in the closed-loop design of therapeutic nanozymes via ML. Rather than just listing algorithms for practical ML, we systematically demonstrated how data-driven workflows accelerate high-throughput virtual screening. Crucially, we highlight the essential role of explainable ML (e.g., SHAP analysis) in interpreting the algorithmic black box to reveal the underlying physicochemical mechanisms governing catalysis. Bridging computation and clinical application, we also comprehensively examine the deployment of AI-tailored nanozymes in precision therapy, including tumor management, inflammatory microenvironment remodeling, tissue regeneration, and intelligent in vitro diagnostics. Finally, by addressing potential challenges, such as data standardization and the in vitro- to- in vivo translational gap, we constructed a strategic outline toward autonomous, intelligent, and personalized catalytic medicine.