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使用规范回归模型预测工程纳米颗粒的细胞毒性:一种in silico方法
A Valeriano1, F Bondaug1,2, I Ebardo1,2
1Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines.
SAR and QSAR in environmental research
|August 8, 2023
概括
由于它们的工业用途,预测纳米粒子毒性至关重要. 这项研究开发了准确的规范回归模型,以预测工程纳米粒子 (NP) 细胞毒性,确定关键预测因素.
科学领域:
- 环境科学 环境科学
- 毒理学 毒理学 毒理学
- 计算化学计算化学
背景情况:
- 人工制造的纳米粒子 (NP) 被广泛使用,但它们的物理化学修饰可以诱导毒性.
- 了解NP毒性对于安全应用和风险评估至关重要.
- 预测模型可以帮助评估NP安全概况.
研究的目的:
- 开发和验证规范化的回归模型,用于预测工程NP细胞毒性.
- 确定影响NP毒性的关键物理化学描述因素.
- 评估预测模型在独立数据集上的概括能力.
主要方法:
- 编制了2010-2022年工程NP细胞毒性的数据集.
- 使用列表式删除和kNN归算处理缺失的数据,创建两个数据集.
- 构建和验证的,LASSO和弹性网调节回归模型.
主要成果:
- 模型在两个数据集的内部和外部验证中获得了高F1分数,从91.81%到93.63%不等.
- 开发的模型表现出对未见数据的强烈概括性.
- 细胞类型,材料,细胞源,细胞组织,合成方法和外套/功能组被确定为NP细胞毒性的关键预测因素.
结论:
- 正规化的回归模型准确地预测工程NP细胞毒性.
- 这些模型是强大的,并且对新数据进行了很好的概括.
- 确定了关键描述符,可以指导更安全的纳米材料的设计.
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