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使用可解释机器学习和基于周期表的描述器预测材料的纳米毒性.

Fang Liu^1,2,3, Jimin Zhu¹, Jing Zhang⁴

¹College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

ACS applied materials & interfaces

|December 24, 2025

概括

预测工程纳米材料在不同核心组合中的毒性是具有挑战性的. 本研究介绍了一种使用周期表描述符的机器学习框架,用于准确的跨材料纳米毒性预测,帮助设计更安全的材料.

关键词:

交叉物质关系交叉物质关系基本的属性是元素的属性.纳米生物相互作用的相互作用.纳米描述器的使用方法定量纳米结构 - 活动关系.

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科学领域:

纳米技术纳米技术
材料科学材料科学材料科学
计算毒理学计算毒理学

背景情况:

工程纳米材料的生物效应取决于物理化学性质.
预测各种材料组成中的纳米毒性是一个重大挑战.
现有的模型往往无法对新纳米材料进行概括.

研究的目的:

开发一个可解释的机器学习框架,用于跨材料纳米毒性预测.
使用基于周期表的描述符来提高预测准确度.
建立一个用于更安全的纳米材料设计和风险评估的计算工具.

主要方法:

构建了1206个金属氧化物纳米粒子条目与细胞毒性数据的数据集.
通过材料内部和材料交叉验证评估的机器学习模型.
嵌入的元素描述符 (电子阴性,电离能,原子半径,氧化状态).

主要成果:

元素描述器显著提高了跨材料预测性能 (R2 0.350.65对于未见材料).
实验验证证了NiO和Cr2O3纳米粒子的模型可靠性.
确定了特定于材料和细胞类型的有毒反应和机械洞察力 (氧化应激).

结论:

跨材料纳米毒性预测是可行的使用元素描述符.
开发的框架是可扩展和可解释的纳米材料安全.
这种方法支持工程纳米材料的知情设计和风险评估.