预测对per-和多基基物质的临界微粒度
B Creton1, E Barraud1, C Nieto-Draghi1
1Thermodynamics and Molecular Simulation, IFP Energies nouvelles, Rueil-Malmaison, France.
SAR and QSAR in environmental research
|April 9, 2024
概括
量化结构-属性关系 (QSPR) 模型被开发用于预测和多基物质 (PFAS) 的临界菌度 (CMC). 化和混合数据集都为PFAS行为提供了可靠的预测模型.
科学领域:
- 环境化学环境化学
- 计算化学的计算化学
- 物理化学 物理化学
背景情况:
- 和多基基物质 (PFAS) 广泛使用,但对环境造成了担忧.
- 预测它们的行为,如临界细胞度 (CMC),对于风险评估至关重要.
- 现有的PFAS CMC实验数据有限.
研究的目的:
- 开发定量结构与财产关系 (QSPR) 模型,用于预测PFAS CMC.
- 为了比较仅在化物数据上训练的模型与在混合数据集上训练的模型.
- 确定一个强大的模型,具有广泛的应用领域,以补充实验数据.
主要方法:
- 从文献中编制PFAS的实验CMC值.
- 开发了两种类型的支持矢量机 (SVM) 模型:仅用于化化合物的类型 (I),用于所有化合物的类型 (II).
- 将模型与参考数据验证并比较其性能.
主要成果:
- 两种类型 (I) 和类型 (II) SVM模型都表现出强大的预测性能和高可靠性.
- 对比分析证实了这两种建模方法的稳定性.
- 为了实际使用,选择了具有最广泛适用范围的模型.
结论:
- 使用 SVM 的 QSPR 建模对于预测 PFAS CMC 有效.
- 开发的模型可以可靠地估计CMC值,有助于理解PFAS行为.
- 选择的模型增强了现有的实验数据,支持环境和安全评估.
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