Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation

Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and characterizing diverse molecular structures, thereby limiting their predictive accuracy for the removal of various ECs. This study uses embedded molecular structure vectors generated by a graph neural network (GNN), combined with functional group prompts, as inputs to a feedforward neural network. A data set of 28 ECs and 542 data points, representing diverse molecular structures and physiochemical properties, was built to predict the residual rate of ECs (REC) in ozonation oxidation. Compared to traditional QSAR models, the GNN-based molecular structure embedded methods significantly improve prediction accuracy. The resulting KANO-EC model achieved an R² of 0.97 for REC, demonstrating its ability to capture complex structural features. Moreover, KANO-EC maintains exceptional interpretability, elucidating key functional groups (e.g., carbonyls, hydroxyls, aromatic rings, and amines) involved in the oxidation mechanism. This study presents the KANO-EC model as a novel approach for predicting the ozonation removal efficiency of current and potential ECs. The model also provides valuable insights for developing efficient control strategies for ensuring the long-term safety and sustainability of drinking water supplies.