A New QSPR Study on Relative Sweetness - Rojas Villa, Cristian Xavier (2016)
The aim of this work was to develop predictive structure-property relationships (QSPR) of natural andsynthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composedof 233 sweeteners collected from diverse sources in the literature, which was divided into training(163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of3763 non-conformational Dragon molecular descriptors were calculated which were simultaneouslyanalyzed through multivariable linear regression analysis coupled with the replacement methodvariable subset selection technique. The established six-parameter model was validated throughthe cross-validation techniques, together with Y-randomization and applicability domain analysis.The results for the training set and the test set showed that the non-conformational descriptors offerrelevant information for modeling the RS of a compound. Thus, this model can be used to predictthe sweetness of both un-evaluated and un-synthesized sweeteners.