VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds

Fritz, Franziska; Preißner, Robert; Banerjee, Priyanka

doi:10.1093/nar/gkab292

Cited by 35 publications

(26 citation statements)

References 30 publications

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“…Furthermore, sourness is also one of the important organoleptic properties of natural compounds. The supernatural 3.0 database compounds were used as input structure for the taste prediction (sweet, bitter and sour) using the VirtualTaste web server ( 27 )⁠ which is based on the machine learning methods described in the published paper ( 28 )⁠. Almost, 170 265 compounds were predicted to be bitter, and 31 803 compounds were predicted to be sweet, with a confidence score of at least 0.7 (see Statistics tab on the webserver).…”

Section: Methodsmentioning

confidence: 99%

SuperNatural 3.0—a database of natural products and natural product-based derivatives

Gallo

Kemmler

Goede

et al. 2022

Nucleic Acids Research

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Natural products (NPs) are single chemical compounds, substances or mixtures produced by a living organism - found in nature. Evolutionarily, NPs have been used as healing agents since thousands of years and still today continue to be the most important source of new potential therapeutic preparations. Natural products have played a key role in modern drug discovery for several diseases. Furthermore, following consumers’ increasing demand for natural food ingredients, many efforts have been made to discover natural low-calorie sweeteners in recent years. SuperNatural 3.0 is a freely available database of natural products and derivatives. The updated version contains 449 058 natural compounds along with their structural and physicochemical information. Additionally, information on pathways, mechanism of action, toxicity, vendor information if available, drug-like chemical space prediction for several diseases as antiviral, antibacterial, antimalarial, anticancer, and target specific cells like the central nervous system (CNS) are also provided for the natural compounds. The updated version of the database also provides a valuable pool of natural compounds in which potential highly sweet compounds are expected to be found. The possible taste profile of the natural compounds was predicted using our published VirtualTaste models. The SuperNatural 3.0 database is freely available via http://bioinf-applied.charite.de/supernatural_3, without any login or registration.

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Section: Methodsmentioning

confidence: 99%

SuperNatural 3.0—a database of natural products and natural product-based derivatives

Gallo

Kemmler

Goede

et al. 2022

Nucleic Acids Research

Self Cite

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“…61 Fritz et al implemented ML models to predict three different taste end points, including sweet, bitter, and sour, which achieved an overall accuracy of 90% by 10-fold cross-validation. 62 Chacko et al developed ML models for predicting odor characters using several ML algorithms, such as RF, gradient boosting, and SVM, and 196 two-dimensional RDKit molecular descriptors as the models' inputs. 63 In addition to traditional features, such as physicochemical properties and molecular fingerprints, features extracted from mass spectra have also been used for ML modeling.…”

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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“…FlavorDB, an online database developed by the Center for Computational Biology from Indraprastha Institute of Information Technology Delhi (IIIT Delhi) in India, was created to integrate the multidimensional aspects of flavor molecules and demonstrate their molecular features, flavor profiles, and natural source details. Unlike other flavor databases, such as BitterDB 46 and VirtualTaste 47 (including the data from the previous SuperSweet database 48 ), which mainly focus on the particular aspect of flavor, FlavorDB collects all kinds of references to compile a comprehensive flavor database. The flavor molecule data originated from resources such as Fenaroli’s handbook of flavor ingredients, the FooDB online database, the arXiv preprint service, and a literature survey.…”

Section: Big Data Sourcesmentioning

confidence: 99%

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

2023

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Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence (AI) is becoming popular in every field, AI methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with AI concepts and learning methods have emerged in the past few years. This review illustrates several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduce some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases are given as examples, demonstrating their applications in food pairing, food–drug interactions, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and AI will play an essential role in food science and food chemistry.

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VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds

Cited by 35 publications

References 30 publications

SuperNatural 3.0—a database of natural products and natural product-based derivatives

SuperNatural 3.0—a database of natural products and natural product-based derivatives

Data-Driven Elucidation of Flavor Chemistry

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

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