Small, volume 19, issue 48

Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods

Kim Pavel ¹

Serov Nikita ¹

Falchevskaya Aleksandra ¹

Shabalkin Ilia ¹

Dmitrenko Andrei ¹

Kladko Daniil ¹

Vinogradov Vladimir ¹

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International Institute “Solution Chemistry of Advanced Materials and Technologies” ITMO University St. Petersburg 191002 Russian Federation |

Publication type: Journal Article

Publication date: 2023-08-10

Wiley

Journal: Small

Quartile SCImago

Quartile WOS

Impact factor: 13.3

ISSN: 16136810, 16136829

DOI: 10.1002/smll.202303522

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General Chemistry

Biotechnology

General Materials Science

Biomaterials

Abstract

Magnetic nanoparticles are a prospective class of materials for use in biomedicine as agents for magnetic resonance imagining (MRI) and hyperthermia treatment. However, synthesis of nanoparticles with high efficacy is resource‐intensive experimental work. In turn, the use of machine learning (ML) methods is becoming useful in materials design and serves as a great approach to designing nanomagnets for biomedicine. In this work, for the first time, an ML‐based approach is developed for the prediction of main parameters of material efficacy, i.e., specific absorption rate (SAR) for hyperthermia and r₁/r₂ relaxivities in MRI, with parameters of nanoparticles as well as experimental conditions as descriptors. For that, a unique database with more than 980 magnetic nanoparticles collected from scientific articles is assembled. Using this data, several tree‐based ensemble models are trained to predict SAR, r₁ and r₂ relaxivity. After hyperparameter optimization, models reach performances of R² = 0.86, R² = 0.78, and R² = 0.75, respectively. Testing the models on samples unseen during the training shows no performance drops. Finally, DiMag, an open access resource created to guide synthesis of novel nanosized magnets for MRI and hyperthermia treatment with machine learning and boost development of new biomedical agents, is developed.

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Journal of Magnetic Resonance Imaging	Journal of Magnetic Resonance Imaging, 1, 100% Journal of Magnetic Resonance Imaging 1 publication, 100%
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Wiley	Wiley, 1, 100% Wiley 1 publication, 100%
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Kim P. et al. Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods // Small. 2023. Vol. 19. No. 48.

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Kim P., Serov N., Falchevskaya A., Shabalkin I., Dmitrenko A., Kladko D., Vinogradov V. Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods // Small. 2023. Vol. 19. No. 48.

RIS |

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TY - JOUR

DO - 10.1002/smll.202303522

UR - https://doi.org/10.1002%2Fsmll.202303522

TI - Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods

T2 - Small

AU - Kim, Pavel

AU - Serov, Nikita

AU - Falchevskaya, Aleksandra

AU - Shabalkin, Ilia

AU - Dmitrenko, Andrei

AU - Kladko, Daniil

AU - Vinogradov, Vladimir

PY - 2023

DA - 2023/08/10 00:00:00

PB - Wiley

IS - 48

VL - 19

SN - 1613-6810

SN - 1613-6829

ER -

BibTex

Cite this

BibTex Copy

@article{2023_Kim,

author = {Pavel Kim and Nikita Serov and Aleksandra Falchevskaya and Ilia Shabalkin and Andrei Dmitrenko and Daniil Kladko and Vladimir Vinogradov},

title = {Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods},

journal = {Small},

year = {2023},

volume = {19},

publisher = {Wiley},

month = {aug},

url = {https://doi.org/10.1002%2Fsmll.202303522},

number = {48},

doi = {10.1002/smll.202303522}

}

Found error?

Publisher

Wiley

Journal

Small

Quartile SCImago

Quartile WOS

Impact factor

13.3

ISSN

16136810 (Print)
16136829 (Electronic)

Labs

Center for Artificial Intelligence in Chemistry

Profiles