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Acta Phys. -Chim. Sin.  2009, Vol. 25 Issue (08): 1587-1592    DOI: 10.3866/PKU.WHXB20090752
A Novel QSAR Model Based on Geostatistics and Support Vector Regression
CHEN Yuan, YUAN Zhe-Ming, ZHOU Wei, XIONG Xing-Yao
College of Bio-safety Science and Technology, Hunan AgriculturalUniversity, Changsha 410128, P. R. China|Hunan Provincial Key Laboratory of Crop GermplasmInnovation andUtilization, Hunan AgriculturalUniversity, Changsha 410128, P. R. China
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Based on principal component analysis (PCA), geostatistics (GS) and support vector regression (SVR), a novel individual forecasting method for quantitative structure-activity relationship (QSAR)——Weight-PCA-GS-SVR was proposed. The basic principles were as follows: firstly, dimensions were reduced and redundant information from independent descriptors was eliminated using PCA; secondly, the principal components that have no relationship to activity were removed nonlinearly using SVR; thirdly, weighted distances between samples were calculated by the retained principal components; fourthly, a common range was confirmed using high-dimensional geostatistics; lastly, k nearest neighbors of each test sample were found from the training set with their weighted distances shorter than a common range and then the models were constructed and the individual prediction was found to be feasible using SVR. Weight-PCA-GS-SVR optimized the model along the column direction (descriptor) and row direction (sample), and had all the advantages of SVR. It therefore provides a newway to choose k nearest neighbors in the field as well as being a novel weighted method for determining the retained principal components or the retained descriptors. Predicted results from three data sets all verify that the novel method has the highest prediction precision among all reference models and has a remarkable advantage over reported results. Weight-PCA-GS-SVR, therefore, can be widely used in QSAR and other regression prediction fields.

Key wordsQuantitative structure-activity relationship      Geostatistics      Support vector regression      Principal component analysis      Individual prediction     
Received: 16 March 2009      Published: 26 May 2009
MSC2000:  O641  
Corresponding Authors: YUAN Zhe-Ming     E-mail: zhmyuan@sina. com
Cite this article:

CHEN Yuan, YUAN Zhe-Ming, ZHOU Wei, XIONG Xing-Yao. A Novel QSAR Model Based on Geostatistics and Support Vector Regression. Acta Phys. -Chim. Sin., 2009, 25(08): 1587-1592.

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