open access resource
for quantitative prediction
of nanozyme catalytic activity


ML models




unique samples


data sources


catalytic activities

About DiZyme

The DiZyme 2.0 - web-resource for rational design of nanozymes using machine learning algorithms.
It contains a unique expandable database of nanozymes with links to original articles, an interactive clickable tool for its visualization, and a machine learning models for various levels of user requests (explorative, detailed and customised) capable of predicting multiple catalytic activity represented as the Michaelis-Menten (Km, mM) constant with R2 0.75 and the maximum reaction rate (Vmax, mM/s) with R2 0.77.

Nanozymes are defined as “nanomaterials with enzyme-like characteristics”. Among the currently existing nanozymes, the most common are nanozymes with peroxidase and oxidase activities. Other, more complex hydrolase, catalase, phosphatase, laccase, and superoxide dismutase activities start to appear but are much less presented in the literature.
Due to the high stability, long storage time and stability under various conditions nanozymes have been extensively exploited in cancer theranostics, environmental protection, cytoprotection, biosensing, and other applications, and of major attention is the ability to regulate the catalytic activity of nanomaterials by changing its composition, shape, size, crystal structure, as well as surface chemistry.


Citing new DiZyme AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity

Julia Razlivina, Andrei Dmitrenko, Vladimir Vinogradov — J. Phys. Chem. Lett. 2024, 15, 22, 5804–5813.

doi: 10.1021/acs.jpclett.4c00959

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Citing DiZyme DiZyme: Open-Access Expandable Resource for Quantitative Prediction of Nanozyme Catalytic Activity

Julia Razlivina, Nikita Serov, Olga Shapovalova, Vladimir Vinogradov — Small, 2022, Vol. 18, Issue 12, p. 2105673.

doi: 10.1002/smll.202105673

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