open access resource
for quantitative prediction
of nanozyme catalytic activity
open access resource
for quantitative prediction
of nanozyme catalytic activity
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.
Julia Razlivina, Andrei Dmitrenko, Vladimir Vinogradov — J. Phys. Chem. Lett. 2024, 15, 22, 5804–5813.
doi: 10.1021/acs.jpclett.4c00959
read moreJulia Razlivina, Nikita Serov, Olga Shapovalova, Vladimir Vinogradov — Small, 2022, Vol. 18, Issue 12, p. 2105673.
doi: 10.1002/smll.202105673
read more