ArticleAdvanced materials (Deerfield Beach, Fla.)2026
Machine Learning-Optimized Single-Atom Catalysts Enable Microenvironment-Adaptive Chemodynamic-Bioorthogonal Cancer Therapy.
Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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-N
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