Machine Learning-Optimized Single-Atom Catalysts Enable Microenvironment-Adaptive Chemodynamic-Bioorthogonal Cancer Therapy.

Chemodynamic therapy (CDT), which harnesses endogenous chemical energy within the tumor microenvironment (TME), has shown high potential for precise cancer treatment. However, its efficacy is often limited by the mildly acidic and reductive nature of the TME that compromises catalyst stability and activity. Developing catalysts capable of maintaining robust performance under such physiological constraints remains a key challenge. Herein, we report a programmable dual-catalytic platform that integrates machine learning-guided design with atomic-level precision. Through predictive modeling, we establish quantitative structure-performance relationships that guided the rational synthesis of iron single-atoms (Fe-N5 SAs). The Fe-N5 SAs demonstrate exceptional chemodynamic reactivity and environmental stability within the complex TME, efficiently converting endogenous hydrogen peroxide into hydroxyl radicals for precise tumor ablation. Moreover, Fe-N5 SAs exhibit potent bioorthogonal catalytic activity, enabling in situ prodrug activation and localized synthesis of doxorubicin under physiological conditions. This synergistic CDT-bioorthogonal dual-catalytic mechanism achieves tumor-selective, stimulus-free, and combinatorial therapy, markedly enhancing overall antitumor efficacy. This study establishes a machine learning-guided framework for single-atom catalyst design and expands the frontiers of metal catalysis in biomedical applications.