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Pesticides, pharmaceuticals, and other organic contaminants often undergo hydrolysis when released into the environment; therefore, measured or estimated hydrolysis rates are needed to assess their environmental persistence. An intuitive multiple linear regression (MLR) approach was used to develop robust QSARs for predicting base-catalyzed rate constants of carboxylic acid esters (CAEs) and lactones. We explored various combinations of independent descriptors, resulting in four primary models (two for lactones and two for CAEs), with a total of 15 and 11 parameters included in the CAE and lactone QSAR models, respectively. The most significant descriptors include p Ka , electronegativity, charge density, and steric parameters. Model performance is assessed using Drug Theoretics and Cheminformatics Laboratory’s DTC-QSAR tool, demonstrating high accuracy for both internal validation ( r2  = 0.93 and RMSE = 0.41–0.43 for CAEs;  r2  = 0.90–0.93 and RMSE = 0.38–0.46 for lactones) and external validation ( r2  = 0.93 and RMSE = 0.43–0.45 for CAEs;  r2  = 0.94–0.98 and RMSE = 0.33–0.41 for lactones). The developed models require only low-cost computational resources and have substantially improved performance compared to existing hydrolysis rate prediction models (HYDROWIN and SPARC).

A multiple linear regression approach to the estimation of carboxylic acid ester and lactone alkaline hydrolysis rate constants