Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds
Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds
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DOI：
10.1021/acsestwater.2c00193
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Kai Zhang;Huichun Zhang
中科院分区：
文献类型：
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作者：
Kai Zhang;Huichun Zhang
环境化学反应经常被研究用于各种目的;然而，准确地模拟反应动力学或反应途径仍然具有挑战性。现有的研究大多采用传统的定量构效关系（QSAR）或反应模板方法来模拟反应动力学或反应途径;然而，这些方法通常需要大量的特征工程或人工提取反应模板。最近，机器学习（ML）已经成为一种很有前途的化学反应建模工具，因为ML模型可以表现良好，并且在使用不同的化学表示方面非常强大。本文首先对化学反应的传统建模方法和ML建模方法进行了简要比较，然后简要讨论了环境有机反应建模的现状和未来需求。然后讨论了反应动力学和途径的数据收集和数据清洗技术。然后，我们总结了常用的化学表征和特征选择技术的优点和局限性。接下来，我们批判性地回顾了一般ML模型的评估和解释过程，并提出了一个三步评估过程，即与一般指标、基线模型和现有模型的比较。最后，我们探索了小数据集的ML建模方法，包括迁移学习和主动学习，这些方法已成功应用于许多其他领域，用于未来环境化学反应的建模。
Environmental chemical reactions have been frequently investigated for various purposes; however, it remains challenging to accurately model either the reaction kinetics or reaction pathways. Existing studies mostly model reaction kinetics with traditional quantitative structure–activity relationships (QSARs) or reaction pathways with reaction template methods; however, these approaches generally require extensive feature engineering or manual extraction of reaction templates. Recently, machine learning (ML) has become a promising tool for modeling chemical reactions as ML models can perform well and are powerful in using diverse chemical representations. This Review starts with a concise comparison of traditional and ML modeling approaches for chemical reactions, followed by a brief discussion of the status of and future needs in modeling environmental organic reactions. Data collection and data cleaning techniques for reaction kinetics and pathways are then discussed. We then summarize the advantages and limitations of commonly used chemical representations and feature selection techniques. Next, we critically review general ML model evaluation and interpretation processes and propose a three-step evaluation process, that is, comparisons with general metrics, baseline models, and existing models. Lastly, we explore ML modeling approaches for small data sets, including transfer learning and active learning, which have been successfully employed in many other fields, for future modeling of environmental chemical reactions.