利用机器学习预测纳米粒子的动态毒性

IF 3.9 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Toxics Pub Date : 2024-10-15 DOI:10.3390/toxics12100750
Ivan Khokhlov, Leonid Legashev, Irina Bolodurina, Alexander Shukhman, Daniil Shoshin, Svetlana Kolesnik
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引用次数: 0

摘要

预测纳米粒子的毒性在生物医学纳米技术中发挥着重要作用,尤其是在创造新药物方面。对纳米粒子进行安全分析可以确定其对生物体和环境的潜在有害影响。先进的机器学习模型可用于预测营养液中纳米粒子的毒性。在本文中,我们利用机器学习方法对纳米粒子毒性分析领域的研究现状进行了比较分析;我们训练了一个回归模型,用于预测纳米粒子的定量毒性,该模型取决于纳米粒子在营养液中固定时间点的浓度,所达到的指标值为 MSE = 2.19 和 RMSE = 1.48;我们训练了一个多类分类模型,用于根据纳米粒子在营养液中的浓度预测其在固定时间点的毒性类别,其指标值分别为准确率 = 0.9756、召回率 = 0.9623、F1-分数 = 0.9640 和对数损失 = 0.1855。分析结果表明,训练有素的模型具有良好的预测能力。所研究的纳米粒子的最佳剂量确定如下:ZnO = 9.5 × 10-5 mg/mL;Fe3O4 = 0.1 mg/mL;SiO2 = 1 mg/mL。预测模型的最大特点是纳米粒子的直径和营养液中的纳米粒子浓度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Toxics
Toxics Chemical 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.
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