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

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results highlights the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging to design new nanozymes based on ML approaches. Alternatively, ML has been employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, we summarize recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity. This article is protected by copyright. All rights reserved.