Rational Design Strategies for Nanozymes

Abstract

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.

Keywords: computing-driven; data-driven; database; descriptor; high-throughput screening; machine learning; mechanism; nanozyme; rational design.