预测农药植物角质层-水分配系数的LSER模型

LSER model for predicting cuticle-water partition coefficients of pesticides

植物角质层-水分配系数(Kcw)对评价农药的渗透及残留具有重要意义。通过实验方法难以高效且经济地测定多种农药的Kcw值,因此有必要发展一种快速预测农药Kcw值的模型。作者收集了23种农药在不同植物中的64个log Kcw实测值,构建了预测农药log Kcw值的线性溶解能关系(liner solvation energy relationship,LSER)模型。所建模型具有良好的拟合度(R^2adj,tra=0.79,RMSEtra=0.38)、稳健性(Q^2BOOT=0.78)和预测能力(Q^2ext=0.81)。该模型可用于预测含有-X(Cl,F,I)、>N-C(O)-NH2、-OCH2COOH、-NH-、-NH2、>C=O、-O-C(O)-NH-、-CN、-S-及-S(O)(O)-等官能团的农药的log Kcw值。机理分析结果表明:疏水相互作用(平均相对贡献率为48%)和n/π电子对相互作用(平均相对贡献率9%)对农药进入植物角质层为正贡献,而氢键受体相互作用(平均相对贡献率26%)和极性相互作用(平均相对贡献率17%)对农药进入角质层为负贡献。本研究构建的pp-LFER模型可用于预测新农药的log Kcw值,并且有助于理解农药在植物角质层与水相间分配的相互作用机制。

Partition coefficients between plant cuticles and water(Kcw)are important for investigating penetration and residual of pesticides in plant.Experimental determination of Kcw values for diverse pesticides was unrealistic.Therefore,it is necessary to develop an effective model for the prediction of Kcw values.In this work,64 log Kcw values for 23 pesticides from previous literatures were collected,and a linear solvation energy relationship(LSER)model for the prediction of log Kcw was developed.This model exhibits good goodness-of-fit(R^2adj,tra=0.79,RMSEtra=0.38),robustness(Q^2BOOT=0.78)and external prediction performance(Q^2ext=0.81).The developed model is appropriate for various pesticides with functional groups such as-X(Cl,F,I),>N-C(O)-NH2,-OCH2 COOH,-NH-,-NH2,>C=O,-O-C(O)-NH-,-CN,-S-,-S(O)(O)-.Mechanism analysis indicated that hydrophobic interactions(average relative contribution of 48%)and n/π-electron pairs interactions(average relative contribution of 9%)contributed to the increase of partition,while hydrogen bond accepting ability(average relative contribution of 26%)and polarizability(average relative contribution of 17%)had negative contribution to the partition of pesticides on plant cuticles.The pp-LFER model developed in this study can be used to predict log Kcw values of new pesticides,and revealed the partition mechanisms of pesticides between plant cuticles and water.