Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes.

Area of Science:

• Biochemistry
• Materials Science
• Chemical Engineering

Background:

• Nanozymes, nanomaterials with enzyme-like biomimicry, offer broad applications in various scientific fields.
• Inconsistent catalytic capacity among nanozymes presents a significant challenge in their development and application.
• Machine learning (ML) shows potential for new material discovery but faces hurdles in nanozyme design.

Purpose of the Study:

• To summarize recent advancements in ML for designing catalytic materials and engineered enzymes.
• To explore how ML can overcome challenges in nanozyme research and development.
• To highlight ML-driven strategies for enhancing nanozyme design, focusing on catalytic activity and substrate selectivity.

Main Methods:

• Review of recent progress in ML applications for materials and enzyme design.
• Analysis of ML algorithms successfully employed in catalytic material and enzyme engineering.
• Identification of emergent ML strategies applicable to nanozyme research.

Main Results:

• ML offers promising strategies to accelerate nanozyme research and development.
• ML can help overcome time-consuming and laborious experimental testing in nanozyme discovery.
• Successful ML applications in catalytic materials and enzymes provide a foundation for nanozyme design.

Conclusions:

• Incorporating ML algorithms can facilitate the guided development of next-generation nanozymes.
• ML aids in optimizing the intelligent design and application of nanozymes.
• ML-driven approaches can enhance substrate selectivity and catalytic activity in nanozyme design.