Machine learning facilitating the rational design of nanozymes-Reference-Cited by-同舟云学术

Machine learning facilitating the rational design of nanozymes

Published:2023 Issue:28 Volume:11 Page:6466-6477
ISSN:2050-750X
Container-title:Journal of Materials Chemistry B
language:en
Short-container-title:J. Mater. Chem. B

Author:

Li Yucong¹²,Zhang Ruofei¹,Yan Xiyun¹²³,Fan Kelong¹²³^ORCID

Affiliation:

1. CAS Engineering Laboratory for Nanozyme, Key Laboratory of Protein and Peptide Pharmaceutical, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China

2. University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100408, China

3. Nanozyme Medical Center, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450052, China

Abstract

Nanozymes face challenges in creating high-performance variants quickly. Machine learning shows promise in addressing this obstacle by predicting the activity, selectivity, catalytic mechanisms, optimal structures, and other features of nanozymes.

Funder

National Natural Science Foundation of China

Youth Innovation Promotion Association of the Chinese Academy of Sciences

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Biomedical Engineering,General Chemistry,General Medicine

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TB/D3TB00842H

Reference80 articles.

1. Industrial Applications of Enzymes: Recent Advances, Techniques, and Outlooks

2. Nanomaterials with enzyme-like characteristics (nanozymes): next-generation artificial enzymes