The aim of this work was the calibration and validation of mathematical models based on a quantitative structure–activity relationship approach to discriminate sweet, tasteless and bitter molecules. The sweet-tasteless and the sweet-bitter datasets included 566 and 508 compounds, respectively. A total of 3763 conformation-independent Dragon molecular descriptors were calculated and subsequently reduced through both unsupervised reduction and supervised selection coupled with the k-nearest neighbors classification technique. A model based on nine descriptors was retained as the optimal one for sweet and tasteless molecules, while a model based on four descriptors was calibrated for the sweetness-bitterness dataset. Models were properly validated through cross-validation and external test sets. The applicability domain of models was investigated, and the interpretation of the role of the molecular descriptors in classifying sweet and non-sweet tastes was evaluated. The classification and the performance of the models presented in this paper are simple but accurate. They are based on a relatively small number of descriptors and a straightforward classification approach. The results presented here indicate that the proposed models can be used to accurately select new compounds as potential sweetener candidates.

Rojas, C., Ballabio, D., Consonni, V., Tripaldi, P., Mauri, A., Todeschini, R. (2016). Quantitative structure–activity relationships to predict sweet and non-sweet tastes. THEORETICAL CHEMISTRY ACCOUNTS, 135(3), 1-13 [10.1007/s00214-016-1812-1].

Quantitative structure–activity relationships to predict sweet and non-sweet tastes

BALLABIO, DAVIDE
Secondo
;
CONSONNI, VIVIANA;MAURI, ANDREA
Penultimo
;
TODESCHINI, ROBERTO
Ultimo
2016

Abstract

The aim of this work was the calibration and validation of mathematical models based on a quantitative structure–activity relationship approach to discriminate sweet, tasteless and bitter molecules. The sweet-tasteless and the sweet-bitter datasets included 566 and 508 compounds, respectively. A total of 3763 conformation-independent Dragon molecular descriptors were calculated and subsequently reduced through both unsupervised reduction and supervised selection coupled with the k-nearest neighbors classification technique. A model based on nine descriptors was retained as the optimal one for sweet and tasteless molecules, while a model based on four descriptors was calibrated for the sweetness-bitterness dataset. Models were properly validated through cross-validation and external test sets. The applicability domain of models was investigated, and the interpretation of the role of the molecular descriptors in classifying sweet and non-sweet tastes was evaluated. The classification and the performance of the models presented in this paper are simple but accurate. They are based on a relatively small number of descriptors and a straightforward classification approach. The results presented here indicate that the proposed models can be used to accurately select new compounds as potential sweetener candidates.
Articolo in rivista - Articolo scientifico
Classification; k-Nearest neighbors; QSAR; Sweetness;
QSAR; k-Nearest neighbors; Classification; Sweetness; molecular descriptors
English
2016
135
3
1
13
66
reserved
Rojas, C., Ballabio, D., Consonni, V., Tripaldi, P., Mauri, A., Todeschini, R. (2016). Quantitative structure–activity relationships to predict sweet and non-sweet tastes. THEORETICAL CHEMISTRY ACCOUNTS, 135(3), 1-13 [10.1007/s00214-016-1812-1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/105093
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