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

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 K a , 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 ( r 2  = 0.93 and RMSE = 0.41-0.43 for CAEs; r 2  = 0.90-0.93 and RMSE = 0.38-0.46 for lactones) and external validation ( r 2  = 0.93 and RMSE = 0.43-0.45 for CAEs; r 2  = 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).