Small, volume 19, issue 19, pages 2207106
Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning
Shirokii Nikolai
1
,
Din Yevgeniya
1
,
Petrov Ilya
1
,
Seregin Yurii
1
,
Sirotenko Sofia
1
,
Razlivina Julia
1
,
Serov Nikita
2
,
Publication type: Journal Article
Publication date: 2023-02-11
General Chemistry
Biotechnology
General Materials Science
Biomaterials
Abstract
Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q2 = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.
Citations by journals
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1
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Pharmaceutics
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Pharmaceutics
1 publication, 10%
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Journal of Nanoparticle Research
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Journal of Nanoparticle Research
1 publication, 10%
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Frontiers in Immunology
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Frontiers in Immunology
1 publication, 10%
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Small
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Small
1 publication, 10%
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Journal of Drug Delivery Science and Technology
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Journal of Drug Delivery Science and Technology
1 publication, 10%
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Materials Advances
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Materials Advances
1 publication, 10%
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Environmental Science: Nano
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Environmental Science: Nano
1 publication, 10%
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Computational and Structural Biotechnology Journal
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Computational and Structural Biotechnology Journal
1 publication, 10%
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Advanced Materials
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Advanced Materials
1 publication, 10%
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Nanomaterials
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Nanomaterials
1 publication, 10%
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1
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Citations by publishers
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1
2
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
2 publications, 20%
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Wiley
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Wiley
2 publications, 20%
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Elsevier
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Elsevier
2 publications, 20%
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Royal Society of Chemistry (RSC)
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Royal Society of Chemistry (RSC)
2 publications, 20%
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Springer Nature
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Springer Nature
1 publication, 10%
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Frontiers Media S.A.
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Frontiers Media S.A.
1 publication, 10%
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1
2
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Shirokii N. et al. Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning // Small. 2023. Vol. 19. No. 19. p. 2207106.
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Shirokii N., Din Y., Petrov I., Seregin Y., Sirotenko S., Razlivina J., Serov N., Vinogradov V. Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning // Small. 2023. Vol. 19. No. 19. p. 2207106.
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TY - JOUR
DO - 10.1002/smll.202207106
UR - https://doi.org/10.1002%2Fsmll.202207106
TI - Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning
T2 - Small
AU - Shirokii, Nikolai
AU - Din, Yevgeniya
AU - Petrov, Ilya
AU - Seregin, Yurii
AU - Sirotenko, Sofia
AU - Razlivina, Julia
AU - Serov, Nikita
AU - Vinogradov, Vladimir
PY - 2023
DA - 2023/02/11 00:00:00
PB - Wiley
SP - 2207106
IS - 19
VL - 19
SN - 1613-6810
SN - 1613-6829
ER -
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@article{2023_Shirokii,
author = {Nikolai Shirokii and Yevgeniya Din and Ilya Petrov and Yurii Seregin and Sofia Sirotenko and Julia Razlivina and Nikita Serov and Vladimir Vinogradov},
title = {Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning},
journal = {Small},
year = {2023},
volume = {19},
publisher = {Wiley},
month = {feb},
url = {https://doi.org/10.1002%2Fsmll.202207106},
number = {19},
pages = {2207106},
doi = {10.1002/smll.202207106}
}
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Shirokii, Nikolai, et al. “Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning.” Small, vol. 19, no. 19, Feb. 2023, p. 2207106. https://doi.org/10.1002%2Fsmll.202207106.