基于虚拟化学分析和机器学习的聚对苯二甲酸乙二醇酯纳米塑料在粒径和性质影响下对水生生物毒性的预测

Enyoh Christian Ebere, Chidi Edbert Duru, Qingyue Wang, Senlin Lu
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引用次数: 0

摘要

本研究主要针对聚对苯二甲酸乙二醇酯(PET)的毒性进行了化学分析和预测,考虑了粒径和性能的影响。采用基于多层感知器人工神经网络(MLP ANN)和支持向量机的虚拟化学技术和机器学习方法,研究了不同尺寸的PET(编码为1、4、9、16和25 nm的NP1至NP5)对加利福尼亚电鳐和斑马鱼等水生生物的影响。PET NPs在硅中构建并表征,然后分别停靠在生物体的乙酰胆碱酯酶(TcAChE)和细胞色素P450 (Zf CYP450)上。结果表明,NP1与NP4的结合亲和力从- 7.1 kcal mol-1稳定增加到- 9.9 kcal mol-1,而TcAChE与NP5的结合亲和力则下降(- 8.9 kcal mol-1)。Zf CYP450也有类似的模式,范围从-5.2千卡摩尔-1到-8.1千卡摩尔-1。MLP神经网络的准确率分别为85.9%和77.3%。相比之下,基于TcAChE和Zf CYP450的固有性质,SVM对PET NPs的毒性预测准确率分别为99.5%和99.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Virtual chemical analysis and machine learning-based prediction of polyethylene terephthalate nanoplastics toxicity on aquatic organisms as influenced by particle size and properties
This study focuses on the chemical analysis and prediction of Polyethylene Terephthalate (PET) toxicity, considering the influence of particle size and properties. The effect PET of different sizes (1, 4, 9, 16 and 25 nm coded NP1 to NP5) on aquatic organisms such as Terpedo californica (electric ray fish) and Danio rerio (zebrafish) as model species was evaluated by virtual chemical techniques and machine learning methodology based on Multilayer Perceptrons Artificial Neural Networks (MLP ANN) and Support Vector Machine. The PET NPs was built and characterized in silico and then docked on the acetylcholinesterase (TcAChE) and cytochrome P450 (Zf CYP450) of the organisms, respectively. The results showed that the binding affinities of the NPs increased steadily from – 7.1 kcal mol-1 to – 9.9 kcal mol-1 for NP1 to NP4 and experienced a drop at NP5 (– 8.9 kcal mol-1) for TcAChE. The Zf CYP450 also had a similar pattern ranging from -5.2 kcal mol-1 to -8.1 kcal mol-1. The MLP ANN showed an accuracy of 85.9 % and 77.3 %. In comparison, SVM showed a better PET NPs toxicity prediction with an accuracy of 99.5 % and 99.4% based on the inherent properties of TcAChE and Zf CYP450, respectively.
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