Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds

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.