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

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)669-677
Number of pages9
JournalJournal of Chemical Information and Computer Sciences
Volume44
Issue number2
DOIs
Publication statusPublished - Mar 2004
Externally publishedYes

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