ChemTastesDB: A curated database of molecular tastants

Rojas, Cristián Guerra; Ballabio, Davide; Sarmiento, Karen Pacheco; Jaramillo, Elisa Pacheco; Mendoza, M; García, Fernando

doi:10.1016/j.fochms.2022.100090

Cited by 17 publications

(26 citation statements)

References 32 publications

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“…A total of 2942 molecular tastants are collected from ChemTastesDB 26 . Each molecule is classified into one of the five basic tastes (sweet, bitter, umami, sour and salty), as well as into four non-basic classes, such as tasteless, non-sweet, multitaste and miscellaneous.…”

Section: Data Of Sweet and Non-sweet Molecule Entitiesmentioning

confidence: 99%

Deep electron cloud-activity and field-activity relationships

Yang

2023

Preprint

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Chemists have been pursuing the general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure-activity relationship (QSAR) models have limited application domains, e.g., they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This paper attempts to develop a new QSAR method that could theoretically predict various properties of molecules with diverse structures. The proposed deep electron cloud-activity relationships (DECAR) and deep field-activity relationships (DFAR) methods consist of three essentials: (1) A large number of molecule entities with activity data as training objects and responses; (2) three-dimensional electron cloud density (ECD) or related field data by the accurate density functional theory methods as input descriptors; (3) a deep learning model that is sufficiently flexible and powerful to learn the large data described above. DECAR and DFAR are used to distinguish 977 sweet and 1965 non-sweet molecules (with 6-fold data augmentation) and the classification performance is demonstrated to be significantly better than the traditional least squares support vector machine (LS-SVM) models using traditional descriptors. DECAR and DFAR would provide a feasible and promising way to establish a widely applicable, cumulative, and shareable artificial intelligence-driven QSAR system. They will promote the development of an interactive platform to collect and share the accurate ECD and field data of millions of molecules with annotated activities. With enough input data, we envision the appearance of hundreds of deep networks trained for various molecular activities. Finally, we could anticipate a single DECAR or DFAR network to learn and infer various properties of interest for chemical molecules, which will become an open and shared learning and inference tool for chemists.

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Section: Data Of Sweet and Non-sweet Molecule Entitiesmentioning

confidence: 99%

Deep electron cloud-activity and field-activity relationships

Yang

2023

Preprint

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“…A collection of bitter, sweet, and umami molecules was curated using information from the ChemTastesDB database [29], and the datasets made available by Tuwani and co-workers [21].…”

Section: Datasetmentioning

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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“…A total of 2942 molecular tastants are collected from ChemTastesDB 24 . Each molecule is classified into one of the five basic tastes (sweet, bitter, umami sour and salty), as well as into other nonbasic classes, such as tasteless, nonsweet, multitaste and miscellaneous.…”

Section: Datamentioning

confidence: 99%

Is theoretical chemistry entering a new era of big data learning and reasoning: deep electron cloud-activity and field-activity relationships

Yang

2022

Preprint

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Chemists have been pursuing general mathematical laws to explain the properties of chemical molecules for a long time. However, the traditional quantitative structure-activity relationship (QSAR) models only provide small pieces of knowledge due to their poor generalization performance and limited application domain. This paper attempts to find a path to realize aunified QSAR that can theoretically predict ANY properties of ANY molecules. A framework combining deep learning, accurate quantum chemistry data and a large number of molecularentities is proposed, and its feasibility is studied. The proposed methods are called deep electron cloud-activity relationships (DECAR) and deep field-activity relationships (DFAR), which consist of three essentials: (1) thousands of molecule entities and activities (even reaching the limit of the current computing resources) as input objects and activity responses; (2) three-dimensional electron cloud density or related field data obtained by accurate density functional theory (DFT) methods as input features; and (3) a deep learning model with three-dimensional convolution layers that can learn these large data. The DECAR and DFAR methods are used to distinguish 977 sweet and 1965 nonsweet molecules, and the classification performance of deep learning models is demonstrated to be significantly better than that oftraditional least squares support vector machine (LS-SVM) models. This work will enable the establishment of an interactive international platform for collecting and sharing the accurate electronic cloud and field data of millions of molecules with annotated activities. We envision the development of hundreds of networks trained for various molecular activities using a large number of molecules, which will become open and shared learning and inference tools for chemists all over the world. These networks will make the best use of our existing knowledge of molecular activity to infer the properties of an increasing number ofunknown molecules and will promote our prediction and understanding of molecular properties with unprecedented strength.

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ChemTastesDB: A curated database of molecular tastants

Cited by 17 publications

References 32 publications

Deep electron cloud-activity and field-activity relationships

Deep electron cloud-activity and field-activity relationships

Classification of Tastants: A Deep Learning Based Approach

Is theoretical chemistry entering a new era of big data learning and reasoning: deep electron cloud-activity and field-activity relationships

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