转移学习使环境微型和纳米塑料对细胞毒性的可靠预测成为可能
Kunpeng Chen1, Keyuan Li2, Alexa Canchola1
1Department of Environmental Sciences, University of California, Riverside, CA 92521, United States.
Journal of hazardous materials
|July 27, 2025
概括
一个新的转移学习模型准确地预测了微型和纳米塑料 (MNP) 的细胞毒性,克服了数据限制. 这种方法有助于评估这些新出现的环境污染物带来的风险.
科学领域:
- 环境毒理学环境毒理学
- 计算化学是一种计算化学.
- 纳米材料科学是一门学科.
背景情况:
- 微塑料和纳米塑料 (MNP) 是普遍存在的环境污染物,具有潜在的人类健康风险.
- 评估MNP毒性受到稀缺的实验数据和传统机器学习模型的局限性所阻碍.
- 开发可靠的MNP毒性预测模型对于风险评估至关重要.
研究的目的:
- 开发一个强大的定量结构-活性关系 (QSAR) 模型来预测MNP诱导的细胞毒性.
- 利用转移学习来提高模型性能,使用有限的MNP特定数据.
- 确定影响MNP细胞毒性的关键因素.
主要方法:
- 开发了一个基于转移学习的QSAR模型,使用三层神经网络.
- 预先在一个大型纳米粒子数据集上训练模型,并在三个小型MNP数据集上微调模型.
- 包含实验注释 (剂量,暴露时间) 和量子化学描述符.
主要成果:
- 转移学习QSAR模型表现出高的预测性能 (例如,ROC-AUC = 0.88,平衡精度 = 0.83).
- 该模型在预测MNP细胞毒性方面明显优于传统方法.
- 表面功能和剂量被确定为细胞毒性的关键预测因素.
结论:
- 转移学习对于开发具有有限数据的新兴污染物的预测毒理学模型是有效的.
- 开发的模型为评估MNP健康风险提供了可靠的工具.
- 纳米塑料可能表现出比微塑料更快的细胞毒性诱导,这需要进一步调查.
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