In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach

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

doi:10.1016/j.foodchem.2020.128538

Cited by 18 publications

(12 citation statements)

References 37 publications

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“…The understanding of ligand binding to T1R1/T1R3 at molecular level currently relies on computational methods including mathematical modelling and molecular docking due to the lack of experimental structural information on T1R1/T1R3 [10,28]. These techniques were also applied for finding potential sweet molecules, expediting drug discovery, identification and characterization of bitter peptides, and investigating cognation of bitter compounds with bitter taste receptors [26,[29][30][31]. To obtain a more comprehensive understanding of the molecular recognition mechanisms of known umami peptides, the binding modes of 205 umami peptides to preprint (which was not certified by peer review) is the author/funder.…”

Section: The Rule Of Umami Peptides-bind Sites Of T1r1mentioning

confidence: 99%

In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach

et al. 2022

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Section: The Rule Of Umami Peptides-bind Sites Of T1r1mentioning

confidence: 99%

In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach

et al. 2022

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“…Several screening pipelines combining ML and MS to identify novel flavor molecules have been developed to achieve more accurate prediction. For example, Goel et al designed a framework comprising QSAR models and molecular docking for identifying possible sweeteners from natural molecules . Xiu et al developed an in silico pipeline to identify novel umami-tasting molecules in batches from SWEET-DB and BitterDB databases via principal component analysis, QSAR modeling, molecular docking, and electronic tongue analysis .…”

Section: Screening and Designing Of Flavor Molecules Based On Computa...mentioning

confidence: 99%

Data-Driven Elucidation of Flavor Chemistry

Kou

Shi

Gao

et al. 2023

J. Agric. Food Chem.

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Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

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“…Both the datasets include tastants from multiple repositories and earlier works on data-based modeling, the details of which can be found in the respective papers [18,25]. ChemTastesDB has 2944 verified tastants, both organic and inorganic, belonging to nine classes, including the five basic tastes and four additional categories, namely tasteless, multitaste, non-sweet, and miscellaneous.…”

Section: Datasetmentioning

confidence: 99%

“…A comprehensive discussion on databases and ML approaches related to tastants can be found in the recent review by Malavolta, Pallante, and co-workers [24]. An integrated data and structure-based modeling framework, combining structure-property relationship, sweet/bitter classification, and molecular docking, was also proposed to screen potential sweeteners [25].…”

Section: Introductionmentioning

confidence: 99%

Classification of Tastants: A Deep Learning Based Approach

Dutta

Jain

Gupta

et al. 2022

Preprint

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Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules— the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1938 bitter, 2079 sweet, and 98 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques, such that the classwise metrics for all the three taste classes are optimized. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations and connect them to the physics of tastant-receptor binding. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.

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In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach

Cited by 18 publications

References 37 publications

In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach

In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach

Data-Driven Elucidation of Flavor Chemistry

Classification of Tastants: A Deep Learning Based Approach

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