ABSTRACT
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 pKa, 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).
Acknowledgements
The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. This research was supported in part by an appointment to the Internship/Research Participation Program at the Center for Environmental Measurement and Modeling administered by the Oak Ridge Institute for Science and Education through Interagency Agreement No. DW-922983301-01 between the U.S. Department of Energy and the U.S. EPA.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The hydrolysis rate constant data used to develop and evaluate the developed QSAR models has been made available in the Supporting Information and will also be posted on https://data.gov/. The DTC-QSAR tool was used to validate the models described herein. DTC-QSAR was developed by the Drug Theoretic and Cheminformatics (DTC) Laboratory within the Department of Pharmaceutical Technology of Jadavpur University, and the software tool is available at https://dtclab.webs.com/software-tools.
Supplementary material
Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2023.2188608