Machine Learning–Driven Screening of High‐Activity Antitumor Nanozymes Using an Ensemble Enzymatic Oracle System

ABSTRACT Nanozymes, by mimicking the catalytic sites of natural enzymes, have emerged as effective substitutes for traditional natural enzymes. However, the relationship between the physicochemical properties and activity of nanozymes is complex and nonlinear. Traditional laboratory screening and theoretical calculations struggle to handle large‐scale samples and multidimensional parameters, resulting in inefficiency and high costs in the pursuit of high performance nanozymes. Efficiently screening and designing nanozymes with desirable properties remain a significant challenge. Machine learning (ML) technology can capture the complex nonlinear relationship between the physicochemical properties and activity of nanozymes, allowing for the simultaneous processing of multidimensional input variables, thereby achieving more accurate and efficient performance prediction. This study adopts a data‐driven approach to propose an ensemble enzymatic activity prediction model, utilizing ML algorithms to understand the particle–activity relationship, thereby enabling the efficient screening of nanozymes with high peroxidase (POD)‐like activity. The ensemble model integrates five sub‐models, significantly improving prediction accuracy and enhancing generalization compared to single models, achieving an accuracy of 82.4%. External experimental validation results indicate that the model's activity screening outcomes for four nanozymes outside the dataset are consistent with the results of enzymatic activity assays. Further in vitro and in vivo experiments substantiate the effectiveness of the model‐selected nanozymes with POD activity in tumor treatment. This study offers a promising strategy for screening antitumor nanozymes with the desired POD activity and demonstrates the potential of ML in the field of materials science.