一个基于机器学习的综合模型,用于预测醇杀菌剂对藻类 (Auxenochlorella pyrenoidosa) 的混合毒性
Li-Tang Qin1, Xue-Fang Tian2, Jun-Yao Zhang2
1College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control, Guilin University of Technology, Guilin 541006, China; Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin 541006, China.
Environment international
|November 29, 2024
概括
机器学习准确地预测了酸杀菌剂混合物的毒性. 结合SVM和RF算法的共识模型显示出最佳性能,有助于对生态风险进行评估.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
背景情况:
- 定量结构-活性关系 (QSAR) 用于混合物毒性预测.
- 准确预测醇真菌杀菌剂混合物的毒性仍然是一个挑战.
- 机器学习 (ML) 为解决这些预测差距提供了一个有希望的策略.
研究的目的:
- 开发和评估ML模型,以预测醇杀菌剂混合物的毒性.
- 为此任务确定最有效的ML算法和建模方法.
- 为对醇真菌杀菌剂混合物的生态风险评估做出贡献.
主要方法:
- 应用了12ML算法 (例如SVM,RF,XGBoost) 来预测225种醇杀菌剂对Auxenochlorella pyrenoidosa的混合毒性.
- 开发了36个单一的ML模型和12个共识模型.
- 使用度添加 (CA),独立作用 (IA) 和分子描述符 (MD) 作为变量.
主要成果:
- 使用CA,IA和MD的模型显示出卓越的预测能力.
- 结合SVM和RF (CM0) 的共识模型获得了最高的准确性 (R2=0.980).
- CM0表现出强大的外部预测能力 (外部R2=0.945,CCC=0.967).
结论:
- ML,特别是共识建模,有效地预测了醇真菌杀菌剂混合物的毒性.
- 开发的模型为评估生态风险提供了有价值的工具.
- 这项研究强调了ML在环境毒理学中的潜力.
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