Ivan Khokhlov, Leonid Legashev, Irina Bolodurina, Alexander Shukhman, Daniil Shoshin, Svetlana Kolesnik
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Prediction of Dynamic Toxicity of Nanoparticles Using Machine Learning.
Predicting the toxicity of nanoparticles plays an important role in biomedical nanotechnologies, in particular in the creation of new drugs. Safety analysis of nanoparticles can identify potentially harmful effects on living organisms and the environment. Advanced machine learning models are used to predict the toxicity of nanoparticles in a nutrient solution. In this article, we performed a comparative analysis of the current state of research in the field of nanoparticle toxicity analysis using machine learning methods; we trained a regression model for predicting the quantitative toxicity of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of MSE = 2.19 and RMSE = 1.48; we trained a multi-class classification model for predicting the toxicity class of nanoparticles depending on their concentration in the nutrient solution at a fixed point in time with the achieved metrics values of Accuracy = 0.9756, Recall = 0.9623, F1-Score = 0.9640, and Log Loss = 0.1855. As a result of the analysis, we concluded the good predictive ability of the trained models. The optimal dosages for the nanoparticles under study were determined as follows: ZnO = 9.5 × 10-5 mg/mL; Fe3O4 = 0.1 mg/mL; SiO2 = 1 mg/mL. The most significant features of predictive models are the diameter of the nanoparticle and the nanoparticle concentration in the nutrient solution.
ToxicsChemical Engineering-Chemical Health and Safety
CiteScore
4.50
自引率
10.90%
发文量
681
审稿时长
6 weeks
期刊介绍:
The Journal accepts papers describing work that furthers our understanding of the exposure, effects, and risks of chemicals and materials in humans and the natural environment as well as approaches to assess and/or manage the toxicological and ecotoxicological risks of chemicals and materials. The journal covers a wide range of toxic substances, including metals, pesticides, pharmaceuticals, biocides, nanomaterials, and polymers such as micro- and mesoplastics. Toxics accepts papers covering:
The occurrence, transport, and fate of chemicals and materials in different systems (e.g., food, air, water, soil);
Exposure of humans and the environment to toxic chemicals and materials as well as modelling and experimental approaches for characterizing the exposure in, e.g., water, air, soil, food, and consumer products;
Uptake, metabolism, and effects of chemicals and materials in a wide range of systems including in-vitro toxicological assays, aquatic and terrestrial organisms and ecosystems, model mammalian systems, and humans;
Approaches to assess the risks of chemicals and materials to humans and the environment;
Methodologies to eliminate or reduce the exposure of humans and the environment to toxic chemicals and materials.