Machine learning facilitating the rational design of nanozymes

Li, Yucong; Zhang, Ruofei; Yan, Xiyun

doi:10.1039/d3tb00842h

Cited by 14 publications

(8 citation statements)

References 80 publications

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“…Therefore, our primary emphasis in this phase remains on collecting experimental data and enriching the data set. 79 In addition to the above methods that may improve the specificity of nanozymes, we also recommend that researchers should use quantitative comparisons (k cat /K M or k cat ) when studying the specificity of nanozymes, which can more accurately compare the specificity of nanozymes between different reactions or substrates. 80 Improving the specificity of nanozymes is a very important part in the process of replacing natural enzymes.…”

Section: The Directional Development Of Nanozymesmentioning

confidence: 99%

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Exploring the Specificity of Nanozymes

Fan,

Zhang,

Fan

et al. 2024

ACS Nano

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Nanozymes, nanomaterials exhibiting enzyme-like activities, have emerged as a prominent interdisciplinary field over the past decade. To date, over 1200 different nanomaterials have been identified as nanozymes, covering four catalytic categories: oxidoreductases, hydrolases, isomerases, and lyases. Catalytic activity and specificity are two pivotal benchmarks for evaluating enzymatic performance. Despite substantial progress being made in quantifying and optimizing the catalytic activity of nanozymes, there is still a lack of in-depth research on the catalytic specificity of nanozymes, preventing the formation of consensual knowledge and impeding a more refined and systematic classification of nanozymes. Recently, debates have emerged regarding whether nanozymes could possess catalytic specificity similar to that of enzymes. This Perspective discusses the specificity of nanozymes by referring to the catalytic specificity of enzymes, highlights the specificity gap between nanozymes and enzymes, and concludes by offering our perspective on future research on the specificity of nanozymes.

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Section: The Directional Development Of Nanozymesmentioning

confidence: 99%

“…However, it is important to note that ML in nanozyme research is still emerging and heavily reliant on data sourced from nanozyme databases. Therefore, our primary emphasis in this phase remains on collecting experimental data and enriching the data set …”

Section: The Directional Development Of Nanozymesmentioning

confidence: 99%

Exploring the Specificity of Nanozymes

Fan,

Zhang,

Fan

et al. 2024

ACS Nano

Self Cite

View full text Add to dashboard Cite

show abstract

“…Inspired by this, AI-driven data analysis and performance prediction have become essential tools for assessing material properties and spearheading new material developments. 211,212 In contrast to the traditional, resource-intensive methods of developing nanozymes—relying on trial and error or intuition and experience, which are often inefficient and costly. Using AI algorithms, including machine learning (ML) and deep learning (DL), presents a more effective alternative.…”

Section: Perspectivesmentioning

confidence: 99%

“…Using AI algorithms, including machine learning (ML) and deep learning (DL), presents a more effective alternative. 212 leveraging existing data through artificial intelligence algorithms such as ML and DL as a reliable tool can uncover the hidden relationships between the physicochemical characteristics of nanozymes and their enzyme-like functions. This approach holds tremendous research and practical value, opening new frontiers in material science.…”

Section: Perspectivesmentioning

confidence: 99%