Machine Learning‐Engineered Nanozyme System for Synergistic Anti‐Tumor Ferroptosis/Apoptosis Therapy

Abstract Nanozymes with multienzyme‐like activity have sparked significant interest in anti‐tumor therapy via responding to the tumor microenvironment (TME). However, the consequent induction of protective autophagy substantially compromises the therapeutic efficacy. Here, a targeted nanozyme system (Fe‐Arg‐CDs@ZIF‐8/HAD, FZH) is shown, which enhances synergistic anti‐tumor ferroptosis/apoptosis therapy by leveraging machine learning (ML). A novel ML model, termed the sequential backward Tree‐Classifier for Gaussian Process Regression (TCGPR), is proposed to improve data pattern recognition following the divide‐and‐conquer principle. Based on this, a Bayesian optimization algorithm is employed to select candidates from the extensive search space. Leveraging this fresh material discovery framework, a novel strategy for enhancing nanozyme‐based tumor therapy, has been developed. The results reveal that FZH effectively exerts anti‐tumor effects by sequentially responding to the TME, having a cascade reaction to induce ferroptosis. Moreover, the endogenous elevation of high concentration nitric oxide (NO) serves as a direct mechanism for killing tumor cells while concurrently suppressing the protective autophagy induced by oxidative stress (OS), enhancing synergistic ferroptosis/apoptosis therapy. Overall, a novel strategy for improving nanozyme‐based tumor therapy has been proposed, underlying the integration of ML, experiments, and biological applications.