Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1002/qsar.200860065
Title (Primary) Estimation of soil organic carbon normalized sorption coefficient (Koc) using least squares-support vector machine
Author Wang, B.; Chen, J.; Li, X.; Wang, Y.; Chen, L.; Zhu, M.; Yu, H.; Kühne, R. ORCID logo ; Schüürmann, G.
Source Titel QSAR & Combinatorial Science
Year 2009
Department OEC
Volume 28
Issue 5
Page From 561
Page To 567
Language englisch
Keywords LS-SVM; Soil organic carbon normalized sorption coefficient; Parameters determination; Adaptive random search technique; QSAR
Abstract Least squares-support vector machine (LS-SVM) was used to derive a quantitative structure-activity relationship (QSAR) model for predicting the soil sorption coefficient normalized to organic carbon, Koc, from 24 fragment-specific increments and four further molecular descriptors, employing a training set of 571 organic compounds and three external validation sets. The combinational parameters of LS-SVM were optimized by adaptive random search technique (ARST). ARST could search the optimal combinational parameters of LS-SVM from the solution space in a simple and quick way. The developed LS-SVM model was compared with the model established by multiple linear regression (MLR) analysis using the same data sets. Generally, the LS-SVM model performed slightly better than the MLR model with respect to goodness-of-fit, predictivity, and applicability domain (AD). The ADs of the LS-SVM and MLR models were described on the basis of leverages and standardized residuals. Both the LS-SVM and MLR models had wide ADs within a given reliability (standardized residual<3 SE units), but the LS-SVM model was superior for compounds with high leverages.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=691
Wang, B., Chen, J., Li, X., Wang, Y., Chen, L., Zhu, M., Yu, H., Kühne, R., Schüürmann, G. (2009):
Estimation of soil organic carbon normalized sorption coefficient (Koc) using least squares-support vector machine
QSAR Comb. Sci. 28 (5), 561 - 567 10.1002/qsar.200860065