'pySiRC': Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants

摘要

We developed a web application structured in a machine learning and molecular fingerprint algorithm for the automatic calculation of the reaction rate constant of the oxidative processes of organic pollutants by ~·OH and SO_4~(·-) radicals in the aqueous phase-the pySiRC platform. The model development followed the OECD principles: internal and external validation, applicability domain, and mechanistic interpretation. Three machine learning algorithms combined with molecular fingerprints were evaluated, and all the models resulted in high goodness-of-fit for the training set with R~2 > 0.931 for the ~·OH radical and R~2 > 0.916 for the SO_4~(·-) radical and good predictive capacity for the test set with R_(ext)~2=Q_(ext)~2 values in the range of 0.639-0.823 and 0.767-0.824 for the ~·OH and SO_4~(·-) radicals. The model was interpreted using the SHAP (SHapley Additive explanations) method: the results showed that the model developed made the prediction based on a reasonable understanding of how electron-withdrawing and -donating groups interfere with the reactivity of the ~·OH and SO_4~(·-) radicals. We hope that our models and web interface can stimulate and expand the application and interpretation of kinetic research on contaminants in water treatment units based on advanced oxidative technologies.