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阐明PFAS与人类氧体增殖器激活受体α结合的关键特征:一种可解释的机器学习方法

Kazuhiro Maeda¹, Masashi Hirano², Taka Hayashi³

¹Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan.

Environmental science & technology

|December 22, 2023

概括

这项研究开发了一种可解释的机器学习模型,用于选与PPARα结合的和多基基物质 (PFAS). 分子大小和静电性质是影响PFAS-PPARα结合的关键因素,这对替代PFAS的安全性有影响.

科学领域:

环境化学环境化学
毒理学毒理学毒理学
计算化学计算化学

背景情况:

-和多醇基物质 (PFAS) 通过结合人类氧酶增殖器激活受体α (PPARα) 来破坏肝脏脂质代谢.
快速查PFAS的PPARα结合对于评估潜在的健康风险至关重要.
对于PFAS选的传统机器学习模型缺乏可解释性,阻碍了对结构-活动关系的理解.

研究的目的:

开发一种新的,可解释的机器学习方法,用于快速选与PPARα结合的PFAS.
确定控制PFAS和PPARα之间的相互作用的关键分子描述符.
评估与替代PFAS化合物相关的潜在风险.

主要方法:

计算了各种PFAS的PPARα-PFAS结合得分和206个分子描述符.
采用分子描述物的系统和客观选择来构建一个预测模型.
使用可解释的机器学习方法来解释绑定机制.

主要成果:

开发了一个高度预测性的机器学习模型,仅使用三个分子描述符:分子大小 (b_single) 和静电特性 (BCUT_PEOE_3,PEOE_VSA_PPOS).
确定了分子大小和静电特性作为PPARα-PFAS结合的关键因素.
观察到具有较高碳含量和基的替代PFAS显示PPARα亲和力增加.

结论:

一个可解释的机器学习模型可以有效地选PFAS的PPARα结合.
分子大小和静电特征是PFAS-PPARα相互作用的重要决定因素.
需要进一步进行生物验证,以确认具有高PPARα亲和度的替代PFAS的毒性.

关键词:

在PFAS中,有很多方法.这是一种PPARα.在QSAR中使用QSAR.这就是 SHAP SHAP 的意思.机器学习是机器学习.分子描述器分子描述器

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