the intrinsic characteristics of nano materials, nanozymes have potential widespread applications within the fields of biosensing, [2] antibacterials, [3] environ mental pollution, [4] and disease therapy. [5] Since our discovery of ferromagnetic nanoparticles with intrinsic peroxidase like activity in 2007, [6] there have been thousands of publications that reported on enzymemimicking activities of nanoma terials, which involve at least six classes of enzymemimicry. [7] According to the litera ture, different nanomaterials can intrinsi cally possess the same enzymemimicking activities, [8] and certain types of nano materials tend to exhibit differential enzymelike catalytic activities. [9] The het erogeneous results reveal the complexity and diversity of nanozymes in terms of catalytic capacity. [10] Indeed, the particle property relationship of nanozymes is complicated, with a current lack of fun damental understanding. Furthermore, the synthesis of nanozymes with desired characteristics are generally determined by trial and error, and based on intui tion and experience, which are timeconsuming, laborious and resourceintensive.As a branch of artificial intelligence, machine learning aims to develop computational algorithms to infer mathematical An abundant number of nanomaterials have been discovered to possess enzyme-like catalytic activity, termed nanozymes. It is identified that a variety of internal and external factors influence the catalytic activity of nanozymes. However, there is a lack of essential methodologies to uncover the hidden mechanisms between nanozyme features and enzyme-like activity. Here, a data-driven approach is demonstrated that utilizes machine-learning algorithms to understand particle-property relationships, allowing for classification and quantitative predictions of enzyme-like activity exhibited by nanozymes. High consistency between predicted outputs and the observations is confirmed by accuracy (90.6%) and R 2 (up to 0.80). Furthermore, sensitive analysis of the models reveals the central roles of transition metals in determining nanozyme activity. As an example, the models are successfully applied to predict or design desirable nanozymes by uncovering the hidden relationship between different periods of transition metals and their enzyme-like performance. This study offers a promising strategy to develop nanozymes with desirable catalytic activity and demonstrates the potential of machine learning within the field of material science.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202201736.

Restoration of sufficient blood supply for the treatment of ischemia remains a significant scientific and clinical challenge. Here, a cell‐like nanoparticle delivery technology is introduced that is capable of recapitulating multiple cell functions for the spatiotemporal triggering of vascular regeneration. Specifically, a copper‐containing protein is successfully prepared using a recombinant protein scaffold based on a de novo design strategy, which facilitates the timely release of nitric oxide and improved accumulation of particles within ischemic tissues. Through closely mimicking physiological cues, the authors demonstrate the benefits of bioactive factors secreted from hypoxic stem cells on promoting angiogenesis. Following this cell‐mimicking manner, artificial hybrid nanosized cells (Hynocell) are constructed by integrating the hypoxic stem cell secretome into nanoparticles with surface coatings of cell membranes fused with copper‐containing protein. The Hynocell, hybridized with different cell‐derived components, provides synergistic effects on targeting ischemic tissues and promoting vascular regeneration in acute hindlimb ischemia and acute myocardial infarction models. This study offers new insights into the utilization of nanotechnology to potentiate the development of cell‐free therapeutics.

The emergence of nanomedicine has provided a promising strategy to greatly enhance the therapeutic efficacy of O2‐dependent photodynamic therapy (PDT). However, plasma‐derived protein corona and/or discontinuous O2 supply substantially limit their tumor delivery efficiency and therapeutic outcomes. Herein, protein corona cloaking‐based cascade nanozymes are developed using genetically engineered human ferritin heavy chain nanocages (FTn) as unique pre‐coated protein corona and cascade nanozymes as steady O2 suppliers. Specifically, FTn is coated onto mesoporous silica nanoparticles (MSNs) to form FTn‐based protein corona, providing active targeting of tumor cells by binding with its receptor. In situ synthesis of ultra‐small Au nanoparticles in MSNs, and biomimetic incorporation of Ru nanoclusters into FTn inner cavity showed glucose oxidase‐like activity and catalase‐like activity, respectively. The two nanozymes are incorporated into MSNs nanoplatform to induce cascade and circular catalytic reactions by consuming glucose and H2O2 within the tumor microenvironment. Compared to MSNs alone, the FTn‐based protein corona is capable of efficiently diminishing plasma‐derived protein corona formation to prolong blood circulation time and improving in vitro tumor cell uptake and in vivo tumor accumulation, thereby providing significantly enhanced therapeutic benefits of PDT by combining with the continuously produced O2 of cascade nanozymes.

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