Free variable selection QSPR study to predict 19F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods
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- 作者：Nasser Goudarzi goudarzi@shahroodut.ac.ir" class="auth_mail" title="E-mail the corresponding author goudarzi10@gmail.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Quantitative structure&ndash ; property relationship (QSPR) ; Random forest ; Radial basis function-partial least square (RBF-PLS) ; 19F chemical shift ; Fluorinated organic compounds (FOCs)
- 刊名：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
- 出版年：2016
- 出版时间：5 April 2016
- 年：2016
- 卷：158
- 期：Complete
- 页码：60-64
- 全文大小：478 K
文摘
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We developed QSPR model based on RF to predict the 19F chemical shift values of some fluorinated organic compounds.
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The number of trees (nt) and the number of randomly selected variables to split each node (m) affect the RF prediction power.
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RF model is not required to make a variable selection in advance, and does not suffer from the overfitting problem.
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The performances of the RF model were superior to the RBF-PLS.
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To the best of our knowledge, there have been no reports yet on the use of RF method to predict the chemical shift.