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QSPR MODELLING FOR PREDICTING TOXICITY OF NANOMATERIALSстатья
- Авторы: KOVALISHYN Vasyl, PEIJNENBURG Willie, KOPERNYK Iryna, ABRAMENKO Natalia, METELYTSIA Larysa1
- Сборник: Proceedings of NANOCON 2015
- Аннотация: Nowadays, nanomaterials are highly integrated into our daily life. However, recent studies have shown obvious toxicity of some nanoparticles to living organisms, and their potentially negative influence on environmental ecosystems. The goal of the present study is to develop efficient QSPR models that allow predicting the ecotoxicological properties and effects of inorganic nanomaterials (metals and oxides) by using the Online Chemical Modeling Environment (OCHEM). Numerical data on toxicity of nanoparticles to different organisms have been taken from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape and information about the biological test species were used as obligatory condition for all properties in OCHEM. 1 5 QSPR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSPR methodologies used Random Forests (WEKA-RF), kNearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a q2=0.69-0.79 for regression models and total accuracies Ac=76- 100% for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 78-100% (for low/high toxicity classifications) and q 2=0.70-0.79 for regressions. The method showed itself to be a potential tool for estimation of toxicity of new nanoparticles at early stages of nanomaterial development.
- Добавил в систему: Абраменко(Головина) Наталия Борисовна