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<i>K</i>_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 (<i>r</i>² = 0.93 and RMSE = 0.41–0.43 for CAEs; <i>r</i>² = 0.90–0.93 and RMSE = 0.38–0.46 for lactones) and external validation (<i>r</i>² = 0.93 and RMSE = 0.43–0.45 for CAEs; <i>r</i>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).

农药、药品及其他有机污染物进入环境后常会发生水解反应，因此需通过实测或估算得到的水解速率，评估其环境持久性。本研究采用直观的多元线性回归（Multiple Linear Regression, MLR）方法，构建了用于预测羧酸酯（carboxylic acid esters, CAEs）与内酯（lactones）碱催化水解速率常数的稳健定量构效关系（Quantitative Structure-Activity Relationship, QSAR）模型。我们对多种独立描述符组合进行了探索，最终得到4个核心模型（内酯与羧酸酯模型各2个），其中羧酸酯QSAR模型与内酯QSAR模型分别包含15和11个参数。核心显著描述符包括pKₐ、电负性、电荷密度及空间参数。模型性能通过药物理论与化学信息学实验室的DTC-QSAR工具进行评估，结果显示其在内部验证与外部验证中均具备高精度：羧酸酯模型的内部验证决定系数r²=0.93，均方根误差RMSE为0.41~0.43；内酯模型的内部验证r²为0.90~0.93，RMSE为0.38~0.46。外部验证方面，羧酸酯模型的r²=0.93，RMSE为0.43~0.45；内酯模型的r²为0.94~0.98，RMSE为0.33~0.41。所构建的模型仅需低成本计算资源，且相较于现有水解速率预测模型（HYDROWIN与SPARC），性能得到了显著提升。