In-silico prediction of sweetness using structure-activity relationship models

Goel, Anukrati; Gajula, Kishore; Gupta, Rakesh; Rai, Beena

doi:10.1016/j.foodchem.2018.01.111

Cited by 25 publications

(13 citation statements)

References 28 publications

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“…Levansucrase binding to sucrose was beneficial to its activity in a wide range of pH (Lee et al, 2018). In addition, simulating the binding of sweeteners and their receptors provided an alternative direction for the design of new sweeteners (Goel et al, 2018). Pentagalloyl-glucose (PGG) inhibited the activity of alpha-amylase and can be used as a food additive to maintain normal glycaemic levels (Yang et al, 2014;Kato-Schwartz et al, 2018).…”

Section: Carbohydratesmentioning

confidence: 99%

Recent developments in molecular docking technology applied in food science: a review

Tao

Huang

Wang

et al. 2019

Int J of Food Sci Tech

150

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Molecular docking is a theoretical simulation method based on bioinformatics, which studies the interaction between molecules (such as ligands and receptors), and predicts their binding modes and affinity via a computer platform. This technology acts as a promising mean in medicinal chemistry such as structurebased rational drug design, which is accepted by researchers in the scientific community. During recent years, various fundamental studies involving biomolecular interaction in the food matrix have gradually emerged. The remarkable advantages of molecular docking such as predicting experiments are attracting increasing attention for its application potential in various fields. This review presents the theory and software development of molecular docking, and emphasises its application in the field of food science, including nutritional components and food safety. Moreover, the operational mechanisms of molecular docking are further summarised in this review.

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Section: Carbohydratesmentioning

confidence: 99%

Recent developments in molecular docking technology applied in food science: a review

Tao

Huang

Wang

et al. 2019

Int J of Food Sci Tech

150

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“…In the same year, Chéron et al [42] used RF to predict either sweetness, bitterness and toxicity properties. In 2018, Goel et al [43] developed QSAR models based on Genetic Function Approximation and ANNs analysis to predict the sweetness of molecules. A RF-based binary classifier to predict the bitterness and sweetness of chemical compounds was implemented by Banerjee et al [44].…”

Section: Sweetness Predictionmentioning

confidence: 99%

Artificial Intelligence in Biological Activity Prediction

Correia

Resende

Baptista

et al. 2019

Practical Applications of Computational Biology and Bioinformatics, 13th International Conference

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Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.

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“…The discovery of the human sweet taste receptor succeeded in the early 2000s ( Li et al, 2002 , Montmayeur et al, 2001 , Nelson et al, 2001 ), providing a base for advanced prediction models. Lately, there have been several studies describing the prediction of sweetness for example by quantitative structure activity relationship (QSAR) models ( Chéron et al, 2017 , Goel et al, 2018 , Yang et al, 2011 ) or by machine learning methods ( Zhong, Chong, Nie, Yan, & Yuan, 2013 ). In addition, in silico methods based on one of the binding sites were applied, for example molecular docking and homology models.…”

Section: Introductionmentioning

confidence: 99%

Structure-dependent effects of sweet and sweet taste affecting compounds on their sensorial properties

Karl

Wendelin²,

Lutsch

et al. 2020

Food Chemistry: X

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A reduction in sugar consumption is desirable from a health point of view. However, the sensory profiles of alternative sweet tasting compounds differ from sucrose regarding their temporal profile and undesired side tastes, reducing consumers’ acceptance. The present study describes a sensory characterization of a variety of sweet and sweet taste affecting compounds followed by a comparison of similarity to sucrose and a multivariate regression analysis to investigate structural determinants and possible interactions for the temporal profile of the sweetness and side-tastes. The results of the present study suggest a pivotal role for the number of ketones, aromatic rings, double bonds and the M LogP in the temporal profile of sweet and sweet taste affecting compounds. Furthermore, interactions between aggregated physicochemical descriptors demonstrate the complexity of the sensory response, which should be considered in future models to predict a comprehensive sensory profile of sweet and sweet taste affecting compounds.

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In-silico prediction of sweetness using structure-activity relationship models

Cited by 25 publications

References 28 publications

Recent developments in molecular docking technology applied in food science: a review

Recent developments in molecular docking technology applied in food science: a review

Artificial Intelligence in Biological Activity Prediction

Structure-dependent effects of sweet and sweet taste affecting compounds on their sensorial properties

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