An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines

C. X. Xue, R. S. Zhang, H. X. Liu, X. J. Yao, M. C. Liu, Z. D. Hu, B. T. Fan

研究成果: Article同行評審

51 引文 斯高帕斯(Scopus)

摘要

The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as inputs for the SVM model. The root-mean-square (rms) errors in BDE predictions for the training, test, and overall data sets were 3.808, 3.320, and 3.713 BDE units (kJ mo-1), respectively. The results obtained by Gaussian-kernel SVM were much better than those obtained by multiple linear regression, radial basis function neural networks, linear-kernel SVM, and other QSPR approaches.

原文English
頁(從 - 到)669-677
頁數9
期刊Journal of Chemical Information and Computer Sciences
44
發行號2
DOIs
出版狀態Published - 3月 2004
對外發佈

指紋

深入研究「An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines」主題。共同形成了獨特的指紋。

引用此