Computational studies of sweet-tasting molecules

Abstract

Quantitative structure-activity relationships (QSARs) are developed for two separate families of sweet-tasting molecules for which sweetness values relative to sucrose (RS) have been measured. For these two families of sucrose and guanidine derivatives, the molecules were divided into training and test sets. Linear multiple regression equations have been generated to relate separately log(RS) to two types of parameters, namely molecular descriptors and energies derived via molecular field analysis (MFA). The parameters used in the development of linear multiple regression equations were selected by the genetic algorithm. The equations obtained show high predictive quality, which is confirmed by statistical parameters obtained with the test sets.The data for these two families were then combined with data from two other families previously studied, namely the sulfamates and isovanillates, to make a set of 149 compounds. These molecules were also studied by QSAR methods. The generated equations show remarkable predictive power, and the quality of the results suggest that the mechanism of sweet taste receptor is similar and, therefore, that there could well be only one receptor site for sweet taste, particularly for the four sweet taste families considered in this work.

aug-MIA-QSPR study of guanidine derivative sweeteners

“…80 %) and test (ca. 20 %) set compounds, according to that reported earlier [14], for comparison. The aug-MIA-QSPR method was based on the treatment of images to generate descriptors (pixels = binaries) and on the subsequent correlation of these independent variables with the log(RS) values.…”

“…While biological activities have been widely modeled using three-dimensional molecular field fit methods, such as CoMFA [12] and CoMSIA [13], sweetness has been found to correlate well with 2D connectivity terms, suggesting that 3D conformations are not a necessary condition to build a predictive QSPR model [14]. In line with this, a quantitative structure-activity relationship (QSAR) method based on multivariate image analysis (MIA), named MIA-QSAR, has shown to be accurate in estimating bioactivities of a variety of drug-like compounds [15][16][17][18][19][20][21][22]; also, it has been successfully used to model other physical properties, such as NMR chemical shifts [23], electrophoretic profiles [24] and boiling points [25].…”

Sweetness power QSARs by PRECLAV software

“…1, Table 1) Dependent property: Log(RS), the values are taken from literature 16,17 Training set: 33 molecules (Table 1, normal font) Testing set: 8 molecules (Table 1, The three molecules in the testing set having the smallest observed values of Log(RS) have been labeled "un-recommended for synthesis". Two molecules having the highest observed values of Log(RS) have been labeled "recommended for synthesis" and another one has been labeled "uncertain".…”

“…2, Table 2) Dependent property: Log(RS), the values are taken from literature 16 Training set: 33 molecules ( The three testing set molecules having the highest values of Log(RS) have been labeled "recommended for synthesis". The molecule having the smallest Log(RS) value has been labeled "un-recommended for synthesis".…”

“…The importance of the size of the molecule is stressed too -using the "moment of inertia C" descriptor -by the QSAR study on guanidines, performed with a very different training set by Katrizky et al 33 There have been synthesized some guanidines where the R 4 chemical group (see Figure 2) contains a polycyclic system (naphthyl, indanyl, adamantyl, 1,3-benzodioxolil etc.) 16 . We have performed numerous other QSAR studies using PRECLAV (that are not included here) with a database including both the molecules from Table 2 and several guanidines with a polycyclic system.…”