Rational Design Strategies for Nanozymes

Area of Science:

• Nanomaterials Science
• Computational Chemistry
• Artificial Intelligence

Background:

• Nanozymes are nanomaterials with enzyme-like activity, with over 1200 developed in 15 years.
• Traditional empirical design methods are insufficient for complex nanozyme applications.
• Computational and AI technologies offer efficient alternatives for nanozyme design.

Purpose of the Study:

• To review elementary reaction mechanisms for rational nanozyme design.
• To introduce activity descriptors for screening nanozyme active materials.
• To propose next-generation computing- and data-driven design paradigms.

Main Methods:

• Focus on peroxidase (POD), oxidase (OXD), catalase (CAT), superoxide dismutase (SOD), and hydrolase (HYL)-like nanozymes.
• Review of first-principles methods and machine-learning algorithms.
• Analysis of activity descriptors for material screening.

Main Results:

• Identification of key elementary reaction mechanisms in nanozyme catalysis.
• Guidelines for selecting active nanozyme materials using activity descriptors.
• A framework for next-generation rational nanozyme design.

Conclusions:

• Computing- and data-driven approaches are crucial for efficient nanozyme design.
• Further development is needed to enhance nanozyme application performance.
• This review provides perspectives on future prospects and challenges.