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机器学习辅助的分子结构嵌入用于准确预测新出现的污染物通过臭氧化氧化去除

Jiapeng Yue^1,2, Hongjiao Pang¹, Renke Wei^1,2

¹State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.

Environmental science & technology

|May 1, 2025

概括

一个新的图形神经网络 (GNN) 模型,KANO-EC,准确地预测了在臭氧化过程中新出现的污染物 (EC) 的去除. 这种方法通过确定有效降解污染物的关键分子特征来提高饮用水的安全性.

科学领域:

环境化学环境化学
水处理技术水处理技术
计算化学计算化学

背景情况:

臭氧有效地去除饮用水中的新出现的污染物 (EC).
传统的定量结构-活性关系 (QSAR) 模型与多样化的分子结构作斗争,限制了EC去除预测的准确性.
准确预测EC去除对于确保安全饮用水供应至关重要.

研究的目的:

开发一种新型模型,用于预测臭氧化过程中新出现污染物的残留率 (REC).
提高饮用水处理中EC去除预测模型的准确性和可解释性.
确定影响臭氧EC降解的关键分子特征.

主要方法:

利用图形神经网络 (GNN) 来生成嵌入式分子结构向量.
集成的GNN向量与函数组提示作为输入用于前神经网络.
开发并验证了KANO-EC模型,使用28个EC和542个数据点的数据集.

主要成果:

基于GNN的KANO-EC模型实现了REC的高预测精度 (R2 = 0.97),超过了传统的QSAR模型.
卡诺-EC 证明了极好的解释性,在臭氧化中识别了关键的功能组 (例如碳基,基,氨基).
该模型有效地捕捉了影响EC去除效率的复杂分子结构特征.

结论:

卡诺-EC模型提供了一种新且准确的方法来预测ECs的臭氧化去除效率.
这种预测能力有助于评估饮用水中的当前和潜在的EC.
来自KANO-EC的见解支持开发可持续饮用水安全的有效策略.

关键词:

在QSAR中使用QSAR.注意力机制注意力机制饮用水处理饮用水处理新兴污染物是新出现的污染物.机器学习是机器学习.分子结构嵌入嵌入的分子结构.臭氧化是一种臭氧化.

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