综述开发生物相容催化纳米材料（NMs）以针对癌症活性氧自由基（ROS）为目标，为癌症提供了一种替代的化疗策略。与传统的化疗策略相比，这种基于催化纳米材料的策略可以利用材料的纳米尺度和催化效应，因此有望克服传统策略通常面临的问题，例如高剂量、低靶向性、严重毒性和副作用以及易于产生药物耐受性。因此，相关研究已经成为结合材料、化学、生物学和医学的前沿跨学科领域中最热门的话题之一。到目前为止，已经报告了许多潜在的用于癌症催化疗法的纳米材料。尽管取得了一些进展，但底层化学生物学机制和原理，即支配ROS靶向催化和随后的癌症治疗功能的NM机制和原理仍然不清楚。因此，尽管许多无机NM已经被合成，其结构信息已经存储在公开可用的数据库中，但是从这些库中计算设计或筛选出具有所需医学功能的适当候选者仍然具有挑战性。在这个账户中，我们提出了催化信号转导理论来弥合无机NM的催化活性和医学功能知识之间的差距。通过密度泛函理论计算，研究了无机NM在催化细胞ROS转化中的活性的原子尺度机制，包括H2O2的活化、H2O2的催化消除、O2的活化和O2•-的催化消除。基于这些机制，发展了一些催化的动力学方程和预测模型，使得可以系统地、定量地描述NMs的表面结构和催化活性之间的关系。催化信号转导理论假设NMs的催化活性主导其癌症催化疗法的治疗活性，并且催化活性的顺序主要决定了同一系列NMs的治疗活性的顺序。根据这个理论，预测模型已经实施到计算机程序中，借助机器学习实现了对癌症催化疗法候选NMs的高通量计算筛选。结果揭示了无机NMs如何通过催化细胞ROS的化学转化诱导癌细胞死亡的机制和规律。为理论上设计和筛选癌症治疗候选NMs提供了理论工具。要使催化信号转导成为调控生命的普遍方法，现在的挑战是实现催化NMs的底物选择性以及对其与整个生物系统相互作用的系统性和深入的理解。

ConspectusDeveloping biocompatible catalytic nanomaterials (NMs) to target cancer reactive oxygen species (ROS) has provided an alternative chemotherapy strategy for cancer. Compared to the traditional chemotherapy strategy, this catalytic NM-based strategy can take advantage of the nanoscale and catalytic effects of materials and thus is promising to conquer the troubles from which traditional strategies usually suffer, e.g., high dosage, low targeting, severe toxicity and side effects, and susceptibility to drug resistance. Therefore, the corresponding research has been one of the hottest topics in the frontier interdisciplinary field combining materials, chemistry, biology, and medicine. So far, many NMs have been reported to have potential in cancer catalytic therapy. Despite the progress, the chemicobiological mechanisms and principles, which underlie ROS-targeted catalysis and subsequent cancer therapeutic functions of NMs have remained elusive. Therefore, although numerous inorganic NMs have been synthesized with their structural information deposited in publicly available databases, it is still challenging to computationally design or screen the appropriate candidates with the desired medical functions from the libraries.In this Account, catalytic signal transduction theory has been proposed to bridge the gap between the knowledge of catalytic activities and medical functions of inorganic NMs. The atomistic-level mechanisms responsible for the activities of inorganic NMs in catalyzing cellular ROS conversions, namely, H2O2 activation, H2O2 dismutation, O2 activation, and O2•- dismutation, have been studied by density functional theory calculations. On the basis of the mechanisms, the kinetic equations and prediction models for some of the catalyses have been developed, making it possible to systematically and quantitatively describe the relationships between the surface structures and catalytic activities of NMs. The catalytic signal transduction theory assumes that the catalytic activities of NMs dominate their therapeutic activities in cancer catalytic therapy and that the order of catalytic activities mainly determines the order of therapeutic activities for NMs of the same series. According to this theory, the prediction models have been implemented into computer programs with the aid of machine learning to realize high-throughput computational screening of candidate NMs toward cancer catalytic therapy.The results have revealed mechanisms and rules on how inorganic NMs induce cancer cell death by catalyzing the chemical conversions of cellular ROS. They provided theoretical tools for the in silico design and screening of candidate NMs for cancer therapy. To make catalytic signal transduction a general approach of tuning life with catalytic NMs, the challenge now remains to achieve substrate selectivity for catalytic NMs and a systematic and deep understanding of their interactions with entire biosystems.