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通过基于分子特征的机器学习,推进淡水生态中的慢性毒性风险评估

Lang Lei¹, Liangmao Zhang¹, Zhibang Han¹

¹Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China.

Environmental pollution (Barking, Essex : 1987)

|December 10, 2023

概括

机器学习 (ML) 使用分子数据预测慢性水生毒性. XGBoost模型准确评估污染物风险,确定SlogP和暴露时间作为环境安全的关键因素.

科学领域:

环境科学环境科学
毒理学毒理学毒理学
计算化学的计算化学

背景情况:

越来越多的化学品生产带来了生态风险.
传统的毒性评估是缓慢和劳动密集的,特别是对于慢性影响.
需要快速,准确的方法来评估慢性生态危害.

研究的目的:

开发机器学习 (ML) 定量结构-活性关系 (QSAR) 模型来预测慢性水生毒性.
为了利用污染物的分子特征 (描述器,指纹,图表).
为慢性生态风险评估建立一个强大且可行的方法.

主要方法:

使用机器学习 (ML),特别是XGBoost (XGB),用于QSAR模型开发.
利用分子描述符来预测水生生物的慢性毒性.
使用核密度估计和双A数据验证的模型准确性.

主要成果:

XGBoost 模型实现了高性能 (R2 = 0.78; RMSE = 0.77).
对10-3至102 mg/L的污染物度进行准确的预测.
确定了SlogP和暴露时间作为影响慢性毒性的主要因素.

结论:

开发了一种可靠的基于ML的QSAR模型,用于预测慢性水生毒性.
该方法是稳固的,可行的,适用于各种污染物.
促进在环境风险评估和管理中更多地使用ML.

关键词:

生态风险生态风险图表注意力网络图表注意力网络分子描述符分子描述符分子指纹的分子指纹.一种物种的敏感度分布.在XGBoost上使用.

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