应用机器学习方法来预测持久有机污染物的空气半衰期
Ying Zhang1, Liangxu Xie1, Dawei Zhang1
1Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Molecules (Basel, Switzerland)
|November 25, 2023
概括
本研究开发了定量结构-活性关系 (QSAR) 模型,以预测持久有机污染物 (POP) 的空气半衰期. 机器学习方法可以准确预测POPs.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 持久性有机污染物 (POP) 是广泛存在的环境污染物,具有显著的生物积累潜力.
- 了解POPs的环境命运,特别是空气半衰期,对于风险评估至关重要.
- 定量结构-活性关系 (QSAR) 研究提供了一种预测方法来评估污染物行为.
研究的目的:
- 开发和验证QSAR模型,用于预测POP的平均和最大空气半衰期.
- 确定影响POPs大气持久性的关键分子描述因素.
- 评估不同的机器学习算法在POPs的QSAR建模中的性能.
主要方法:
- 使用了五个分子描述符:HOMO_Energy_DMol3,Dipole_Z,SAscore_Fragments,SC_3_P和SIC.
- 使用部分最小平方 (PLS),多重线性回归 (MLR) 和遗传函数近似 (GFA) 构建QSAR模型.
- 使用确定系数 (R^2) 和相对误差 (RE) 评估模型性能.
主要成果:
- 所有开发的QSAR模型都显示了平均和最大空气半衰期的高预测精度.
- MLR模型实现了最高的确定系数 (R^2 > 0.939) 和最低的平均空气半衰期的相对误差.
- PLS模型在最大空气半衰期预测方面表现强 (R^2 = 0.915).
结论:
- 该研究成功建立了强大的QSAR模型,用于预测POPs的空气半衰期.
- 已识别的分子描述符是影响POPs在大气中的持久性的重要因素.
- 经过验证的模型具有良好的预测和外推能力,可以在其定义的域内对POP进行预测和外推.
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