metadata

size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
dataset_info:
  features:
    - name: Canonicalized SMILES
      dtype: string
    - name: Canonicalized Flavor
      dtype: string
    - name: Original Labels
      dtype: string
    - name: Source
      dtype: string
  splits:
    - name: train
      num_bytes: 1237660
      num_examples: 13634
    - name: test
      num_bytes: 263642
      num_examples: 2922
    - name: validation
      num_bytes: 266452
      num_examples: 2922
  download_size: 568637
  dataset_size: 1767754
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*
tags:
  - chemistry

FartDB

Composite dataset of 19,478 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: [More Information Needed]

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

Scifinder: https://scifinder.cas.org (Accessed May 5th 2024)

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.

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Dataset Structure

The first two columns contain: "Canonicalized SMILES" as canonicalized by RDKit and the "Canonicalized Flavor " (sweet, sour, umami, bitter, undefined). "Original Labels": Some databases were labelled by professional tasters or contain labels that had to be canonicalized into one of the four flavor categories defined by us (e.g. "honey" --> "sweet"). These original labels are given for data transparency. "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.

[More Information Needed]

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.

Who are the source data producers?

[More Information Needed]

Annotations [optional]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Dataset Card Authors [optional]

[More Information Needed]

Dataset Card Contact

[More Information Needed]