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  Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system

Regonia, P., Olorocisimo, J., De los Reyes, F., Ikeda, K., & Pelicano, C. M. (2022). Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system. NanoImpact, 28: 100442. doi:10.1016/j.impact.2022.100442.

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Regonia, P.R., Autor
Olorocisimo, J.P., Autor
De los Reyes, F., Autor
Ikeda, K., Autor
Pelicano, Christian Mark1, Autor           
Affiliations:
1Markus Antonietti, Kolloidchemie, Max Planck Institute of Colloids and Interfaces, Max Planck Society, ou_1863321              

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Schlagwörter: Risk assessment; multi-metallic alloy nanoparticles; TiO2; nanotoxicity; machine learning; kernel ridge regression; random forest regression; quantitative structure-toxicity relationship
 Zusammenfassung: Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO2 hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO2). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO2 system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMEST = 0.0954, MAET = 0.0811, RT2 = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMEST = 0.1244, MAET = 0.1106, RT2 = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.

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Sprache(n): eng - English
 Datum: 2022-11-262022
 Publikationsstatus: Erschienen
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 Identifikatoren: DOI: 10.1016/j.impact.2022.100442
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Titel: NanoImpact
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Amsterdam : Elsevier
Seiten: - Band / Heft: 28 Artikelnummer: 100442 Start- / Endseite: - Identifikator: ISSN: 2452-0748