用机器学习方法开发水生环境中抗生素降解动力学参数的预测模型
Meijuan Zhang1, Tong Xu1, Yueli Lan1
1Pollution Prevention Biotechnology Laboratory of Hebei Province, College of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China. xut@hebust.edu.cn.
这项研究开发了机器学习模型,以预测水中的抗生素降解率. 这些模型准确地评估了这些新出现的污染物的环境持久性,有助于对生态风险进行评估.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 生态毒理学 生态毒理学
背景情况:
- 抗生素是水生环境中新出现的污染物,构成生态风险.
- 由于缺乏水解 (kH) 和基降解 (kOH) 速率常数,因此无法准确评估抗生素的环境持久性.
研究的目的:
- 开发抗生素kH和kOH值的预测模型.
- 为了解决抗生素环境持久性评估的数据缺口.
- 为了深入了解驱动抗生素降解的分子机制.
主要方法:
- 使用多个线性回归和机器学习算法 (随机森林,SVM,XGBoost).
- 基于69 kH和80 kOH值的数据集开发了模型.
- 采用XGBoost进行数据归算,并开发了一种用于同时预测kH和kOH的多任务模型.
- 应用沙普利添加式解释 (SHAP) 来获得机械学的洞察力.
主要成果:
- XGBoost模型在预测降解速率常数方面表现出最佳的性能.
- 成功归纳缺失的 kH 和 kOH 数据.
- 开发了一种高性能多任务模型,用于同时预测.
- 通过SHAP分析确定了影响降解速率的关键分子描述因素.
结论:
- 开发的模型有助于准确预测抗生素降解率 (kH和kOH).
- 这种方法增强了对水生环境中的结构降解关系的理解.
- 对评估新出现的污染物的环境持久性和生态风险作出重大贡献.
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