Androgenetic alopecia (AGA) is a common form of hair loss, which is mainly caused by oxidative stress induced dysregulation of hair follicles (HF). Herein, a highly efficient manganese thiophosphite (MnPS 3 ) based superoxide dismutase (SOD) mimic was discovered using machine learning (ML) tools. Remarkably, the IC 50 of MnPS 3 is 3.61 μg•mL −1 , up to 12-fold lower than most reported SOD-like nanozymes. Moreover, a MnPS 3 microneedle patch (MnMNP) was constructed to treat AGA that could diffuse into the deep skin where HFs exist and remove excess reactive oxygen species. Compared with the widely used minoxidil, MnMNP exhibits higher ability on hair regeneration, even at a reduced frequency of application. This study not only provides a general guideline for the accelerated discovery of SOD-like nanozymes by ML techniques, but also shows a great potential as a next generation approach for rational design of nanozymes.

Wound infection is arguably the most common, and potentially the most devastating, complication of the wound healing process. The ideal treatment strategy has to eliminate bacteria, alleviate inflammation, and promote wound healing and skin formation. Herein, a multifunctional heterostructure is designed consisting of ultrasmall platinum–ruthenium nanoalloys and porous graphitic carbon nitride C3N5 nanosheets (denoted as PtRu/C3N5), which concurrently possesses piezoelectric enhanced oxidase ‐mimic nanozyme activity and photocatalytic hydrogen gas production capacity. Moreover, these hybrid nanotherapeutics are integrated in natural hyaluronic acid microneedles, which exhibit almost 100% broad‐spectrum antibacterial efficacy against multiple bacterial strains in vitro and in vivo within 10 min ultrasound treatment, and effectively inhibit inflammation reactions after 1 h visible light irradiation, promising for accelerating the cutaneous wound healing in the bacterial infected mice. This study highlights a competitive strategy for development of all‐in‐one antibacterial and anti‐inflammatory therapies.

Nanozymes are promising new-generation antibacterial agents owing to their low cost, high stability, broadspectrum activity, and minimal antimicrobial resistance. However, the inherent low catalytic activity of nanozymes tends to limit their antibacterial efficacy. Herein, a heterostructure of zinc oxide nanorod@graphdiyne nanosheets (ZnO@GDY NR) with unparallel piezocatalytic enzyme mimic activity is reported, which concurrently possesses intrinsic peroxidase-like activity and strong piezoelectric responses and effectively promotes the decomposition of hydrogen peroxide (H 2 O 2 ) and generation of reactive oxygen species under ultrasound irradiation. Moreover, this piezocatalytic nanozyme exhibits almost 100% antibacterial efficacy against multidrug-resistant pathogens involving methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa in vitro and in vivo. In addition, a piezoelectric activatable nanozyme-based skin patch is developed for rapid skin wound disinfections with satisfactory hemocompatibility and cytocompatibility. This work not only sheds light on the development of an innovative piezoelectric activatable nanozyme-based skin patch for rapid wound disinfection but also provides new insights on the engineering of piezocatalytic nanozymes for nanozyme antibacterial therapy.

Nanozymes constitute an emerging class of nanomaterials with enzyme-like characteristics. Over the past 15 years, more than 1200 nanozymes have been developed, and they have demonstrated promising potentials in broad applications. With the diversification and complexity of its applications, traditional empirical and trial-and-error design strategies no longer meet the requirements for efficient nanozyme design. Thanks to the rapid development of computational chemistry and artificial intelligence technologies, first-principles methods and machine-learning algorithms are gradually being adopted as a more efficient and easier means to assist nanozyme design. This review focuses on the potential elementary reaction mechanisms in the rational design of nanozymes, including peroxidase (POD)-, oxidase (OXD)-, catalase (CAT)-, superoxide dismutase (SOD)-, and hydrolase (HYL)-like nanozymes. The activity descriptors are introduced, with the aim of providing further guidelines for nanozyme active material screening. The computing- and data-driven approaches are thoroughly reviewed to give a proposal on how to proceed with the next-generation paradigm rational design. At the end of this review, personal perspectives on the prospects and challenges of the rational design of nanozymes are put forward, hoping to promote the further development of nanozymes toward superior application performance in the future.

Treatment of diabetic foot ulcers (DFU) needs to reduce inflammation, relieve hypoxia, lower blood glucose, promote angiogenesis, and eliminate pathogenic bacteria, but the therapeutic efficacy is greatly limited by the diversity and synergy of drug functions as well as the DFU microenvironment itself. Herein, an ultrasoundaugmented multienzyme-like nanozyme hydrogel spray was developed using hyaluronic acid encapsulated L-arginine and ultrasmall gold nanoparticles and Cu 1.6 O nanoparticles coloaded phosphorus doped graphitic carbon nitride nanosheets (ACPCAH). This nanozyme hydrogel spray possesses five types of enzyme-like activities, including superoxide dismutase (SOD)-, catalase (CAT)-, glucose oxidase (GOx)-, peroxidase (POD)-, and nitric oxide synthase (NOS)-like activities. The kinetics and reaction mechanism of the sonodynamic/sonothermal synergistic enhancement of the SOD-CAT-GOx-POD/NOS cascade reaction of ACPCAH are fully investigated. Both in vitro and in vivo tests demonstrate that this nanozyme hydrogel spray can be activated by the DFU microenvironment to reduce inflammation, relieve hypoxia, lower blood glucose, promote angiogenesis, and eliminate pathogenic bacteria, thus accelerating diabetic wound healing effectively. This study highlights a competitive approach based on multienzyme-like nanozymes for the development of all-in-one DFU therapies.

Improving the catalytic activity and broadening the scope of nanozymes are prerequisites to supplement or even supersede natural enzymes. However, the discovery of nanozymes is mostly relied on serendipity with limited fine-tunings of chemical composition, which is often incomprehensive and fragmented. Machine learning (ML) is a promising solution to predict the nanozyme performance and thus accelerate the nanozyme development. Herein, a thorough investigation of the peroxidase (POD) mimic reaction catalyzed by nonmetal atom doped graphdiyne (GDY) is presented and two doped GDYs (B-GDY and N-GDY) with best performance were screened out. Specifically, the extreme gradient boosting (XGB) algorithm can mine the connection between the model parameters and maximum energy barrier (R 2 > 78%) or maximum energy consuming step (accuracy > 65%) from the data set constructed by all the nonmetal atom doped GDYs, which provides a method to effectively reduce 20% calculations. In addition, six nonmetal atom doped GDYs with different expected properties were synthesized, and their activity trends in three related experiments were consistent with the predicted results. This study demonstrates that ML can be a useful tool to assist density functional theory (DFT) computational screening and can serve as a guide for optimizing nanozyme performance, depending on doping strategy.

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Nanozymes are nanomaterials with enzyme-like activities, which are promising alternatives for natural enzymes because of their features of excellent performance, controllable activity, and environmental resistance. Improving the catalytic activity and broadening the scope of nanozymes are prerequisites to supplementing or even superseding enzymes. However, the discovery of nanozymes is mostly relied on serendipity with limited fine-tunings of chemical composition, which is often incomprehensive and fragmented so that severely impedes the development of novel nanozymes. In this work, a comprehensive investigation of the peroxidase mimic reaction catalyzed by nonmetallic doped-graphdiyne (GDY) is presented in terms of the dopant atom electronegativity, atomic radius, elemental doping sites, hydrogen peroxide adsorption sites, and bandgaps, covering all nonmetallic elements and enabling the screening of potential nanozymes. The density functional theory (DFT) calculations carefully explore the properties of nanozymes by analyzing two key steps of Gibbs’s free energy of H2O2 decomposition reaction. The machine learning (ML) technique AdaBoost integration model is innovatively utilized based on a fuzzy model for data predicting and it can predict the nanozyme performance with high regression accuracy (R2 >93%) in test set. Thereafter, six different predicted performances were synthesized are synthesized and confirmed in accordance with their excellent kinetics parameters experimentally. From three independent perspectives (DFT, ML, and experimental validation), this work proposes pivotal guidelines for nanozyme design and shows great potential as a general approach for future nanozyme discovery.