Show simple item record

dc.contributor.authorKovalishyn, Vasyl
dc.contributor.authorAbramenko, Natalia
dc.contributor.authorKopernyk, Iryna
dc.contributor.authorCharochkina, Larysa
dc.contributor.authorMetelytsia, Larysa
dc.contributor.authorTetko, Igor V
dc.contributor.authorPeijnenburg, Willie
dc.contributor.authorKustov, Leonid
dc.date.accessioned2018-01-09T12:41:54Z
dc.date.available2018-01-09T12:41:54Z
dc.date.issued2017-08-09
dc.identifier.citationModelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform. 2017 Food Chem. Toxicol.en
dc.identifier.issn1873-6351
dc.identifier.pmid28802948
dc.identifier.doi10.1016/j.fct.2017.08.008
dc.identifier.urihttp://hdl.handle.net/10029/621068
dc.description.abstractInorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure-Property Relationships for nanomaterials (nano-QSPRs). Importantly, development of such models requires careful collection and curation of data. This article overviews freely available nano-QSPR models, which were developed using the Online Chemical Modeling Environment (OCHEM). Multiple data on toxicity of nanoparticles to different living organisms were collected from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape, surface charge and information about the biological test species were used as descriptors for developing QSPR models. QSPR methodologies used Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.58-0.80 for regression models and balanced accuracies of 65-88% for classification models. These results matched the predictions for the test sets used to develop the models. The proposed nano-QSPR models and uploaded data are freely available online at http://ochem.eu/article/103451 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.
dc.language.isoenen
dc.rightsArchived with thanks to Food and chemical toxicology : an international journal published for the British Industrial Biological Research Associationen
dc.titleModelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform.en
dc.typeArticleen
dc.identifier.journalFood Chem Toxicol 2017, advance online publication (ahead of print)en
html.description.abstractInorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure-Property Relationships for nanomaterials (nano-QSPRs). Importantly, development of such models requires careful collection and curation of data. This article overviews freely available nano-QSPR models, which were developed using the Online Chemical Modeling Environment (OCHEM). Multiple data on toxicity of nanoparticles to different living organisms were collected from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape, surface charge and information about the biological test species were used as descriptors for developing QSPR models. QSPR methodologies used Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.58-0.80 for regression models and balanced accuracies of 65-88% for classification models. These results matched the predictions for the test sets used to develop the models. The proposed nano-QSPR models and uploaded data are freely available online at http://ochem.eu/article/103451 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.


Files in this item

Thumbnail
Name:
Publisher version

This item appears in the following Collection(s)

Show simple item record