一个有效的机器学习模型用于预测新兴化学品的急性口服毒性:多域应用和结构-活性关系
SAR and QSAR in environmental research
|July 31, 2025
概括
这项研究开发了一种机器学习模型,用于预测新出现的污染物的急性口腔毒性 (AOT),为动物化学安全测试提供了更快,更安全的替代方案. 该模型准确地对毒性进行分类,有助于设计更绿色的化学品.
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:
- 新出现的污染物带来环境风险,需要进行生物安全评估.
- 急性口服毒性 (AOT) 分类,通常使用LD50,对于全球协调系统 (GHS) 下的化学安全至关重要.
- 动物试验的局限性推动了对机器学习等替代预测方法的需求.
研究的目的:
- 开发和优化用于预测新出现污染物的LD50分类的机器学习模型.
- 使用分子描述符和指纹建立可靠的AOT评估QSAR模型.
- 为早期选更安全的化学化合物提供一个工具.
主要方法:
- 使用了超过6000个已知的AOT值的数据集来训练机器学习模型.
- 采用分子描述符和指纹作为毒性预测特征.
- 应用了Shapley添加式解释 (SHAP) 和信息获取 (IG) 来实现模型的解释性.
主要成果:
- 在LD50分类中实现了>0.86的准确性和>0.84的回忆,超过了以前的模型.
- 在各种新兴污染物类型中证实了模型的稳定性.
- 确定了影响毒性的关键分子描述器 (例如,BCUTp_1h,ATSC1pe,SLogP_VSA4) 和子结构 (例如,P-O,P-S).
结论:
- 开发的机器学习模型有效地预测新出现的污染物的AOT.
- 确定了与大鼠不良影响相关的关键分子特征.
- 该模型支持设计更安全,更绿色的化学品,并增强对毒性机制的理解.
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