title: pKa prediction for acidic phosphorus-containing compounds using multiple linear regression with computational descriptors.
abstract: Ninety-six acidic phosphorus-containing molecules with pKa 1.88 to 6.26 were collected and divided into training and test sets by random sampling. Structural parameters were obtained by density functional theory calculation of the molecules. The relationship between the experimental pKa values and structural parameters was obtained by multiple linear regression fitting for the training set, and tested with the test set; the R(2) values were 0.974 and 0.966 for the training and test sets, respectively. This regression equation, which quantitatively describes the influence of structural parameters on pKa , and can be used to predict pKa values of similar structures, is significant for the design of new acidic phosphorus-containing extractants. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Key Laboratory of Organofluorine Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Donghai
authors.initials: D
authors.lastname: Yu
authors.affiliation: Key Laboratory of Organofluorine Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Ruobing
authors.initials: R
authors.lastname: Du
authors.affiliation: Key Laboratory of Organofluorine Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Ji-Chang
authors.initials: JC
authors.lastname: Xiao
keywords: acidic phosphorus-containing compounds; computational descriptors; multiple linear regression; pKa prediction