This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.

Rojas, C., Todeschini, R., Ballabio, D., Mauri, A., Consonni, V., Tripaldi, P., et al. (2017). A QSTR-based expert system to predict sweetness of molecules. FRONTIERS IN CHEMISTRY, 5(JUL) [10.3389/fchem.2017.00053].

A QSTR-based expert system to predict sweetness of molecules

TODESCHINI, ROBERTO
Secondo
;
BALLABIO, DAVIDE;MAURI, ANDREA;CONSONNI, VIVIANA;GRISONI, FRANCESCA
Ultimo
2017

Abstract

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.
Articolo in rivista - Articolo scientifico
Classification; Expert system; Molecular descriptors; QSAR; Sweetness;
Classification, Expert system, Molecular descriptors, QSAR, Sweetness
English
2017
5
JUL
53
open
Rojas, C., Todeschini, R., Ballabio, D., Mauri, A., Consonni, V., Tripaldi, P., et al. (2017). A QSTR-based expert system to predict sweetness of molecules. FRONTIERS IN CHEMISTRY, 5(JUL) [10.3389/fchem.2017.00053].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/164811
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