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

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 SO4•- 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 R2 > 0.931 for the •OH radical and R2 > 0.916 for the SO4•- radical and good predictive capacity for the test set with Rext2 = Qext2 values in the range of 0.639-0.823 and 0.767-0.824 for the •OH and SO4•- 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 SO4•- 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.