Hydrolysis Reaction Rate Prediction Using Machine Learning: WaterDRoP.

To enable sustainable chemical design, there is a need for the capability to predict the degradation potential of proposed structures not yet produced and for which experimental data are unavailable. Hydrolysis is a key process impacting contaminant fate, especially in aqueous and biological systems. This work develops WaterDRoP (Water Degradation Rate of Pollutants), a machine learning model to predict the rate of hydrolysis from chemical structure in environmentally relevant settings (pH 7 and 25°C). The two-stage model classifies a compound as stable (half-life > 1 year) or unstable (half-life ≤ 1 year) and estimates the numeric half-life of unstable compounds. Each stage is a pretrained neural network fine-tuned using 808 experimental hydrolysis rates collected from reports and databases. WaterDRoP compares favorably to existing models for hydrolysis rate prediction (EPI Suite, Hydrolysis QSAR, QSAR Toolbox) in terms of applicability, stability classification (F1 score), and rate prediction of unstable compounds (RMSE, MAE, R2). Atom-level attribution scores obtained through Shapley Additive Explanations (SHAP) analysis, illustrating the substructures identified by the model as most relevant for anticipating hydrolysis, were compared against proposed hydrolysis mechanisms from the literature. This in silico hydrolysis rate estimation tool and curated training data set are made openly available.