Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds  

作　　者：Ya Wang Weihao Tang Zijun Xiao Wenhao Yang Yue Peng Jingwen Chen Junhua Li 

机构地区：[1]State Key Joint Laboratory of Environment Simulation and Pollution Control,School of Environment,Tsinghua University,Beijing 100084,China [2]Key Laboratory of Industrial Ecology and Environmental Engineering(MOE),School of Environmental Science and Technology,Dalian University of Technology,Linggong Road 2,Dalian 116024,China

出　　处：《Journal of Environmental Sciences》2023年第2期98-104,共7页环境科学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (No.21936005)

摘　　要：Predicting the logarithm of hexadecane/air partition coefficient(L)for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships.Herein,two quantitative structure activity relationship(QSAR)models were developed with 1272 L values for the organic compounds by using multiple linear regression(MLR)and support vector machine(SVM)algorithms.On the basis of the OECD principles,the goodness of fit,robustness and predictive ability for the developed models were evaluated.The SVM model was first developed,and the predictive capability for the SVM model is slightly better than that for the MLR model.The applicability domain(AD)of these two models has been extended to include more kinds of emerging pollutants,i.e.,oraganosilicon compounds.The developed QSAR models can be used for predicting L values of various organic compounds.The van derWaals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound.These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.

关 键 词：L value Quantitative structure-activity RELATIONSHIP Multiple linear regression Support vector machine Oraganosilicon compounds 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] X51[自动化与计算机技术—控制科学与工程]