Quantitative structure-property relationship (QSPR) model for predicting acidities of ketones

Yunyun Yuan, Philip D. Mosier, Yan Zhang

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Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, USA.

DOI: 10.4236/jbpc.2012.31007                

Abstract

Ketones are one of the most common functional groups, and ketone-containing compounds are essential in both the nature and the chemical sciences. As such, the acidities (pK_a) of ketones provide valuable information for scientists to screen for biological activities, to determine physical properties or to study reaction mechanisms. Direct measurements of pK_a of ketones are not readily available due to their extremely weak acidity. Hence, a quantitative structure-property relationship (QSPR) model that can predict the acidities of ketones and their acidity order is highly desirable. The establishment of an acidity scale in dimethyl sulfoxide (DMSO) solution by Bordwell et al. made such an effort possible. By utilizing the pK_a values of forty-eight ketones determined in DMSO as the training set, a QSPR model for predicting acidities of ketones was built by stepwise multiple linear regression analysis. The established model showed statistical significance and predictive power (r² = 0.91, q² = 0.86, s = 1.42). Moreover, the QSPR model also gave reasonable acidity predictions for five ketones in an external prediction set that were not included in the model generation phase (r² = 0.92, s = 1.618). Overall, the reported QSPR model for predicting acidities of ketones provides a useful tool for both biologists and chemists in understanding the biophysical properties and reaction rates of different classes of ketones.

Keywords

QSPR; Acidity; Ketones; Linear Regression

Conflicts of Interest

The authors declare no conflicts of interest.

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