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预测自由基对有机污染物的反应,使用易于访问的分子描述符

Guoyang Zhang¹, Qiang Zhu², Hongcen Zheng¹

¹State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing, 210023, China.

|November 11, 2023

概括

机器学习模型有效地使用分子指纹预测水生污染物的反应性. 这种方法准确地预测了有机化合物的激进反应,有助于预测环境命运.

科学领域:

环境化学环境化学
计算化学计算化学
化学工程是化学工程的重要组成部分.

背景情况:

由于大数据的可用性,机器学习 (ML) 越来越多地用于预测水生污染物的行为.
ML识别各种自由基之间的反应性差异的能力在很大程度上仍未被探索.
了解激进反应对于预测有机污染物的环境命运至关重要.

研究的目的:

评估将ML算法与分子指纹相结合的有效性,以预测自由基的反应性.
评估ML在区分各种基因对有机化合物的反应模式方面的能力.
开发有机污染物与特定自由基的反应速率常数的预测模型.

主要方法:

编制了211种有机污染物及其与碳酸盐基 (CO3•−) 的速率常数的数据集.
使用线性回归和ML算法 (随机森林) 开发了预测模型.
分子指纹 (MACCS,摩根指纹) 和电子结构特征 (EHOMO,ELUMO,能量差距) 被用作输入描述符.

主要成果:

使用ML和线性回归模型实现了令人满意的预测性能.
使用MACCS指纹的随机森林ML模型产生了0.787.78的RMSEtest.
使用电子能量水平的线性回归导致RMSEtest的0.641.
模型解释表明,CO3•−的反应性特征与SO4•−的反应性比与OH的反应性更接近.

结论:

将ML算法与可访问的分子指纹相结合是准确预测有机化合物激进反应的强大策略.
这种方法可以提高对环境污染物的命运的预测.
这项研究表明,ML有潜力学习和区分激进反应模式.

关键词:

炭酸碳酸盐的根源是什么他们的指纹是指纹.机器学习机器学习分子描述器分子描述器量化结构与活动的关系.速率常数预测的方法

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