DOI: 10.1016/j.atmosenv.2013.12.018
Scopus记录号: 2-s2.0-84896837776
论文题名: Integrated QSPR models to predict the soil sorption coefficient for a large diverse set of compounds by using different modeling methods
作者: Shao Y ; , Liu J ; , Wang M ; , Shi L ; , Yao X ; , Gramatica P
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 88 起始页码: 212
结束页码: 218
语种: 英语
英文关键词: Genetic algorithms
; Local lazy regression
; LS-SVM
; OECD principles
; QSPR
; Soil sorption coefficient
Scopus关键词: Local lazy regression
; LS-SVM
; OECD principles
; QSPR
; Soil sorption coefficients
; Forecasting
; Genetic algorithms
; Linear regression
; Lunar surface analysis
; Organic compounds
; Soils
; Support vector machines
; Sorption
; data set
; environmental assessment
; environmental risk
; error analysis
; genetic algorithm
; integrated approach
; numerical model
; OECD
; physicochemical property
; prediction
; sorption
; article
; environmental parameters
; genetic algorithm
; least squares support vector machine
; local lazy regression
; multiple linear regression analysis
; prediction
; priority journal
; quantitative structure property relation
; regression analysis
; soil sorption coefficient
; support vector machine
; training
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: The soil sorption coefficient (Koc) is a key physicochemical parameter to assess the environmental risk of organic compounds. To predict soil sorption coefficient in a more effective and economical way, here, quantitative structure-property relationship (QSPR) models were developed based on a large diverse dataset including 964 non-ionic organic compounds. Multiple linear regression (MLR), local lazy regression (LLR) and least squares support vector machine (LS-SVM) were utilized to develop QSPR models based on the four most relevant theoretical molecular descriptors selected by genetic algorithms-variable subset selection (GA-VSS) procedure. The QSPR development strictly followed the OECD principles for QSPR model validation, thus great attentions were paid to internal and external validations, applicability domain and mechanistic interpretation. The obtained results indicate that the LS-SVM model performed better than the MLR and the LLR models. For best LS-SVM model, the correlation coefficients (R2) for the training set was 0.913 and concordance correlation coefficient (CCC) for the prediction set was 0.917. The root-mean square errors (RMSE) were 0.330 and 0.426, respectively. The results of internal and external validations together with applicability domain analysis indicate that the QSPR models proposed in our work are predictive and could provide a useful tool for prediction soil sorption coefficient of new compounds. © 2013 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80688
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Chemistry, Lanzhou University, Lanzhou 730000, China; Nanjing Institute of Environment Sciences, Ministry of Environment Protection, Nanjing 210042, China; Nanjing University of Technology College of Biotechnology and Pharmaceutical Engineering, Nanjing University of Technology, Nanjing 210009, China; QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Via Dunant 3, Varese 21100, Italy
Recommended Citation:
Shao Y,, Liu J,, Wang M,et al. Integrated QSPR models to predict the soil sorption coefficient for a large diverse set of compounds by using different modeling methods[J]. Atmospheric Environment,2014-01-01,88