Structural and anharmonic vibrational spectroscopic analysis of artificial sweetener alitame: A DFT study for molecular basis of sweet taste

Altunayar‐Unsalan, Cisem; Ünsalan, Ozan

doi:10.1016/j.molstruc.2021.131157

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…The distances between atoms are the average of distances calculated for 50 different optimised conformations of the molecule (distances between AH and B atoms), with the standard deviation between parenthesis. Notably, the highlighted substructure of alitame covers the adjacent NH and C=O groups, supporting Altunayar-Unsalan and Unsalan theory[9].…”

supporting

confidence: 65%

“…Notably, for the first time, a recent computational investigation on alitame structure hypothesised that the dihedral angles of −19.64º and 7.03º between two single NH groups and two adjacent C=O groups are associated with sweetness [9]. Our analysis shows that bit 1565 is associated with regions related to the two dihedral angles.…”

Section: E Feature Explainabilitymentioning

confidence: 57%

“…Insights into sweeteners' molecular structures may undoubtedly help sustain and strengthen the molecular basis of sweetness, providing a valuable platform to design novel molecules with a sweet taste [9]. In this sense, the last decades were marked with the development of (Quantitative) Structure-Activity Relationship (QSAR) models for sweetness prediction.…”

Section: Introductionmentioning

confidence: 99%

“…The four structures of cyclamate, alitame, 1-n-propoxy-2-amino-4nitrobenzene and β-D-Glucopyranose and their relative sweetness to sucrose are presented here. Moreover, the bits associated with the AH-B-(X)[4],[5] and the Altunayar-Unsalan and Unsalan[9] dihedral angles theories are highlighted and the impact in model prediction is shown between parenthesis. The distances between atoms are the average of distances calculated for 50 different optimised conformations of the molecule (distances between AH and B atoms), with the standard deviation between parenthesis.…”

mentioning

confidence: 99%

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Development of Deep Learning approaches to predict relationships between chemical structures and sweetness

Capela

Correia

Pereira

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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The non-caloric sweeteners market is catching up with the market of conventionally used sugars due to the benefits of preventing obesity, tooth decay and other health problems. Developing strategies for designing easier-to-produce novel molecules with a sweet taste and less toxicity are up-todate motivations for the food industry. In this sense, Machine Learning (ML) approaches have been reported as cutting-edge technologies to guide the design of new molecules towards specific objectives, including sweet taste.The largest known dataset of sweet molecules is here provided. The dataset contains fully integrated 9541 sweeteners and 1141 bitterants from FooDB, FlavorDB and literature. This robust dataset allowed the development of standard Machine and Deep Learning pipelines towards conceiving Structure-Activity Relationships (SAR) between molecules and sweetness.In this work, we showcase that Textual Convolutional Neural Networks (TextCNN), Graph Convolutional Networks (GCN), and Deep Neural Networks (DNNs) outperformed most of traditional "shallow" learning approaches. These Deep Learning (DL) models produced platforms to guide the design of new sweeteners and repurposing existing compounds.Sixty million compounds from PubChem were evaluated using these models. Herein, we deliver a dataset of 67724 compounds that present high probabilities of being sweet. Quick searches in literature allowed us to find 13 molecules reported as potent sweetening agents, revealing that our approach is suitable for finding new sweeteners, valuable to expand food chemistry databases, repurposing existing chemicals and designing novel molecules with a sweet taste.

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supporting

confidence: 65%

Section: E Feature Explainabilitymentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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