metadata

size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
dataset_info:
  features:
    - name: Canonicalized SMILES
      dtype: string
    - name: Standardized SMILES
      dtype: string
    - name: Canonicalized Taste
      dtype: string
    - name: Source
      dtype: string
  splits:
    - name: full
      num_bytes: 1865867
      num_examples: 15031
    - name: train
      num_bytes: 1304664
      num_examples: 10521
    - name: validation
      num_bytes: 278960
      num_examples: 2255
    - name: test
      num_bytes: 282243
      num_examples: 2255
  download_size: 1225561
  dataset_size: 3731734
configs:
  - config_name: default
    data_files:
      - split: full
        path: data/full-*
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
tags:
  - chemistry

FartDB

Composite dataset of 15,025 molecules and their taste (sweet, bitter, umami, sour, undefined).

Dataset Details

Dataset Description

FartDB is a curated dataset drawn from five data sources: FlavorDB, PlantMolecularTasteDB, ChemTastesDB, Tas2R Agonists DB and Scifinder. A canonicalized SMILES is mapped to one of five flavor categories: sweet, bitter, umami, sour, undefined. Salty molecules are not considered as only a small number of compounds have this taste. The dataset was enriched with other descriptors from PubChem where available and when multiple datasets contained the same SMILES/Taste data point, these duplicates were removed.

Note that a small number (< 5) of canonicalized SMILES in the dataset may not form valid molecules. These may need to be removed before use.

Curated by: Fart Labs
License: MIT

Dataset Sources

FlavorDB: Neelansh Garg†, Apuroop Sethupathy†, Rudraksh Tuwani†, Rakhi NK†, Shubham Dokania†, Arvind Iyer†, Ayushi Gupta†, Shubhra Agrawal†, Navjot Singh†, Shubham Shukla†, Kriti Kathuria†, Rahul Badhwar, Rakesh Kanji, Anupam Jain, Avneet Kaur, Rashmi Nagpal, and Ganesh Bagler*, FlavorDB: A database of flavor molecules, Nucleic Acids Research, gkx957, (2017). †Equal contribution *Corresponding Author https://doi.org/10.1093/nar/gkx957

PlantMolecularTasteDB: Gradinaru Teodora-Cristiana, Madalina Petran, Dorin Dragos, and Marilena Gilca. "PlantMolecularTasteDB: A Database of Taste Active Phytochemicals." Frontiers in Pharmacology 2022; 12:3804. https://doi.org/10.3389/fphar.2021.751712

ChemTastesDB: Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & García, F. (2021). ChemTastesDB: A Curated Database of Molecular Tastants (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5747393

Tas2R Agonists DB: Sebastian Bayer, Ariane Isabell Mayer, Gigliola Borgonovo, Gabriella Morini, Antonella Di Pizio, and Angela Bassoli, Journal of Agricultural and Food Chemistry 2021 69 (46), 13916-13924, https://doi.org/10.1021/acs.jafc.1c05057

IUPAC Digitilized Dissociation Constants: Jonathan Zheng, Olivier Lafontant-Joseph https://doi.org/10.5281/zenodo.7236453

Umami Compounds (manually added): B. Suess, D. Festring, T. Hofmann, 15 - Umami compounds and taste enhancers, Editor(s): J.K. Parker, J.S. Elmore, L. Methven, In Woodhead Publishing Series in Food Science, Technology and Nutrition, Flavour Development, Analysis and Perception in Food and Beverages, Woodhead Publishing, 2015, Pages 331-351, ISBN 9781782421030, https://doi.org/10.1016/B978-1-78242-103-0.00015-1

Uses

This dataset is intended for the training of machine learning models, in particular transformer models trained on SMILES such as ChemBERTa.

Direct Use

This dataset has been used to finetune ChemBERTa to be able to predict the flavor from an arbitrary SMILES input.

Dataset Structure

The first two columns contain: "Canonicalized SMILES" as canonicalized by RDKit and the "Canonicalized Flavor " (sweet, sour, umami, bitter, undefined). "Source": which database this data point derives from

PubChem descriptors: Where available, the dataset was enriched with descriptors accessible through the PubChem API. "PubChemID", "IUPAC Name", "Molecular Formula", "Molecular Weight", "InChI", "InChI Key"

Dataset Creation

Curation Rationale

This dataset contains the majority of all known SMILES to flavor mappings publicly available. In order to use this data for supervised machine learning, both the SMILES and the flavor categories had to be made consistent.

Source Data

All databases contained SMILES or SMILES data was obtained for the molecule from PubChem. The databases were previously curated dataset of tastants with three exceptions: Tas2R Agonists DB lists molecules which bind the human taste receptor; Scifinder was used to search for acidic molecules even if they had not been expressly tested for taste; some umami compounds were added manually from the literature.

Data Collection and Processing

SMILES were canonicalized with RDKit Any rows with empty fields were removed. PubChem properties were enriched after duplicates were removed based on "Canonicalized SMILES" and "Canonicalized Flavor".

FlavorDB: FlavorDB has human generated labels (e.g. "honey", "sweet-like", "tangy") which then had to be mapped onto the 5 canonical flavor categories. ChemTastesDB: Human-generated labels were again canonicalized into the 5 flavor categories. PhytocompoundsDB: Human-generated labels were again canonicalized into the 5 flavor categories. Tas2R Agonists DB: This dataset contains molecules which bind the human bitter receptor. All datapoints were hence labelled as "bitter". Scifinder: A random subset of small molecules (< 500 Da) that are listed with a pKa between 2 and 7 which is a safe range for human tasting. Umami DB: A small dataset of known umami compounds was manually curated from the literature.

Citation

Zimmermann Y, Sieben L, Seng H, Pestlin P, Görlich F. A Chemical Language Model for Molecular Taste Prediction. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-d6n15-v2  This content is a preprint and has not been peer-reviewed.

@unpublished{Zimmermann2024chemical,
    doi = {10.26434/chemrxiv-2024-d6n15-v2},
    publisher = {American Chemical Society (ACS)},
    title = {A Chemical Language Model for Molecular Taste Prediction},
    url = {http://dx.doi.org/10.26434/chemrxiv-2024-d6n15-v2},
    author = {Zimmermann, Yoel and Sieben, Leif and Seng, Henrik and Pestlin, Philipp and G{\"o}rlich, Franz},
    date = {2024-12-11},
    year = {2024},
    month = {12},
    day = {11},
}