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使用机器学习来分类-和多醇基物质的免疫抑制活性

Yuxin Xuan¹, Yulu Wang¹, Rui Li¹

¹College of Public Health, Zhengzhou University, Zhengzhou, P. R. China.

Toxicology mechanisms and methods

|August 6, 2024

概括

机器学习模型准确地预测了和多基物质 (PFAS) 的免疫抑制作用. 这项研究确定了关键的分子特征,有助于评估PFAS对人类健康和环境的风险.

科学领域:

环境毒理学环境毒理学
计算化学计算化学
免疫毒理学免疫毒理学

背景情况:

和多醇基物质 (PFAS) 是持久性有机污染物,具有已知的免疫抑制作用.
评估PFAS免疫抑制对于监管毒理学和风险评估至关重要.
评估PFAS免疫毒性的现有方法在范围和效率上是有限的.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测PFASs的免疫抑制活性.
确定与PFAS免疫抑制作用相关的关键分子描述剂.
建立一个有效评估大量PFAS化合物的框架.

主要方法:

从科学文献中收集了146种PFAS的数据,包括它们的结构和度梯度.
利用Dragon描述符进行结构性表征,并采用特征选择技术 (重要性分析,逐步消除).
开发并比较了三个ML模型:随机森林 (RF),极端梯度增强机 (XGB) 和分类增强机 (CB).

主要成果:

这三种ML模型都表现出了出色的预测性能,RF模型在测试组中平均AUC为0.9720.
特性重要性分析确定了度,SpPosA_X,IVDE,R2s和SIC2作为影响免疫抑制活性的关键分子特征.
适用性领域分析证实了开发模型的可靠预测边界.

结论:

这项研究是ML模型首次用于研究PFAS免疫抑制活性.
鉴定到的分子特征为了解PFAS免疫毒性背后的机制提供了见解.
开发的模型提供了一个强大的工具,可以有效地评估许多PFAS的免疫抑制潜力,从而为环境和健康风险评估提供信息.

关键词:

和多醇基物质是多和多的.极端梯度增强机器极端梯度增强机器免疫抑制是一种免疫抑制.机器学习是机器学习.随机的森林随机的森林

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