Driving Multifunctional Nanomedicine Design for Non‐Inflammatory Tumor Therapy with Integrated Machine Learning and Density Functional Theory

Due to the promotive role of inflammation in tumor progression, designing multifunctional nanomedicines that synergistically combine anti-tumor and anti-inflammatory properties emerges as a promising approach to enhance cancer treatment. However, identifying optimal nano-agents from a vast pool of candidates using traditional trial-and-error methods remains inefficient and lacking in systematic guidance. In this study, this challenge is addressed by integrating photothermal therapy for tumor ablation with catalase-mimicking nanozymes for inflammation mitigation as a model system to explore non-inflammatory tumor treatment strategies. Using interpretable machine learning techniques, experimental data are systematically analyzed to elucidate the relationships between nanomaterial features and functional properties, enabling the precise identification of photothermal agents with robust anti-inflammatory synergistic effects. Through this framework, ruthenium oxide nanoparticles (RuO₂ NPs) are identified as a highly efficient multifunctional candidate. The catalytic properties of RuO₂ NPs are further validated and rationalized through density functional theory calculations. Experimental investigations confirm the remarkable performance of RuO₂ NPs, demonstrating their ability to achieve efficient photothermal tumor ablation at NIR-II biowindow and simultaneously mitigate inflammation by promoting a favorable immune microenvironment. This work highlights the transformative potential of machine learning-driven approaches in the rational design and accelerated discovery of multifunctional nanomaterials.