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Nanotoxicology data for in silico tools: a literature review
Furxhi, Irini; Murphy, Finbarr; Mullins, Martin; Arvanitis, Athanasios; Poland, Craig A.
The full text of this article will not be available in ULIR until the embargo expires on the 26/02/2021 The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.
Keyword(s): nanoparticles; nanotoxicology; machine learning; in silico
Publication Date:
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Citation(s): 720851
Nanotoxicology; 14 (5), pp. 612-637
Publisher(s): Taylor and Francis
First Indexed: 2020-02-29 07:16:55 Last Updated: 2020-08-01 07:22:50