Artificial intelligence-guided nanozyme engineering for chronic wound healing: from rational design to precision therapeutics

Abstract

Chronic non-healing wounds remain a major clinical challenge, with limited therapeutic efficacy and a huge burden on patients. Nanozymes have emerged as promising bioactive platforms for wound repair by modulating reactive oxygen species, immune responses, angiogenesis, and antimicrobial defense. However, traditional nanozyme development primarily relies on empirical approaches, resulting in limited functional specificity and suboptimal therapeutic outcomes. Recent advances in artificial intelligence (AI) have provided promising alternatives to overcome these limitations and accelerate the rational engineering of nanozymes. This review proposes a pathology-informed design framework that links wound-specific therapeutic needs to nanozyme screening, function optimization, and iterative refinement. We synthesize current progress in AI-enabled material discovery and structure–activity prediction, function-oriented and multiobjective optimization, development of stimuli-responsive systems, and elucidation of biological mechanisms. We further examine how these approaches support pathology-guided functional matching, coordination of multifunctional interventions, treatment monitoring, and closed-loop feedback optimization. Key challenges include heterogeneous and biased datasets, limited model generalizability, weak correlation between in vitro catalytic metrics and in vivo efficacy, insufficient wound-relevant endpoints, and incomplete long-term safety assessments. Future work should strengthen the links among material properties, catalytic behavior, and tissue-repair outcomes, while incorporating pathological conditions, delivery systems, and therapeutic feedback into model optimization.