ChemTastesDB is a database that includes curated information of 2944 molecular tastants. ChemTastesDB constitutes a useful tool for the scientific community to expand the information of molecular tastants, which could assist in the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR) studies for taste prediction by means of diverse machine learning approaches.

Molecules are sequestered to one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other non-basic classes such as: tasteless, non-sweet, multitaste and miscellaneous. ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. Moreover, the molecular structure in the HyperChem (.hin) format of each chemical is provided.

ChemTastesDB is a database that includes curated information of 2944 molecular tastants. ChemTastesDB constitutes a useful tool for the scientific community to expand the information of molecular tastants, which could assist in the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR) studies for taste prediction by means of diverse machine learning approaches.

Molecules are labelled in one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, multitaste and miscellaneous). ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. Moreover, the molecular structure in the HyperChem (.hin) format of each chemical is provided.

This is version 1.2 of the ChemTastesDB.

What's new in version 1.2:

Chemical information (for instance, name, PubChem CID or CAS number) for some tastants has been included.

The database is freeware and may be used if proper reference is given to the authors. Preferably refer to the following paper: Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & García, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090. https://doi.org/10.1016/j.fochms.2022.100090.

ChemTastesDB is a database that includes curated information of 4075 molecular tastants. ChemTastesDB is distributed to the scientific community to expand the information of molecular tastants, which could assist the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR/QSPR) studies for taste prediction. Examples of QSPR approaches for the prediction of molecular taste are given in the following publication: Rojas, C., Abril-González, M., Ballabio, D. & García, F. (2025). ChemTastesPredictor: An ensemble of machine learning classifiers to predict the taste of molecular tastants (Under review).

The 4075 molecular tastants are categorized into one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, non-bitter, multitaste and miscellaneous). The molecules are categorized into following ten classes: sweet (1313), bitter (1615), umami (220), sour (49), salty (16), multitaste (179), tasteless (232), non-sweet (304), non-bitter (28), and miscellaneous (119).

ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. In addition, the molecular structure in the HyperChem (.hin) format of each compound is provided.

This is version 2.0 of the ChemTastesDB. In this new version, 1131 newly curated compounds were added. These new molecules were retrieved from 52 new bibliographic references.