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Food Chem Toxicol. 2018 Feb;112:507-517. doi: 10.1016/j.fct.2017.08.008. Epub 2017 Aug 9.

Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform.

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Institute of Bioorganic Chemistry & Petrochemistry, National Academy of Science of Ukraine, 1 Murmanska Street, 02660, Kyiv, Ukraine.
Moscow State University, Chemistry Department, 1 Leninskie Gory, bldg. 3, 119991, Moscow, Russia.
Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany; BIGCHEM, GmbH, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany.
Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300, RA Leiden, The Netherlands; National Institute of Public Health and the Environment, Center for Safety of Substances and Products, PO Box 1, 3720, BA Bilthoven, The Netherlands. Electronic address:
Moscow State University, Chemistry Department, 1 Leninskie Gory, bldg. 3, 119991, Moscow, Russia; N.D. Zelinsky Institute of Organic Chemistry, RAS, 47 Leninsky Prospect, 119991, Moscow, Russia.


Inorganic 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 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.


Nanoparticles; Nanotoxicology; OCHEM; QSPR; Toxicity

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