README.md

---
size_categories:
- 10K<n<100K
task_categories:
- text-classification
dataset_info:
  features:
  - name: Canonicalized SMILES
    dtype: string
  - name: Canonicalized Taste
    dtype: string
  - name: Original Labels
    dtype: string
  - name: Source
    dtype: string
  - name: standardised_smiles
    dtype: string
  splits:
  - name: train
    num_bytes: 1509222
    num_examples: 10522
  - name: validation
    num_bytes: 322576
    num_examples: 2255
  - name: test
    num_bytes: 324546
    num_examples: 2255
  download_size: 766216
  dataset_size: 2156344
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
tags:
- chemistry
---

# FartDB

<!-- Provide a quick summary of the dataset. -->

Composite dataset of 15,032 molecules and their taste (sweet, bitter, umami, sour, undefined). 

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
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

<!-- Provide the basic links for the dataset. -->

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

<!-- Address questions around how the dataset is intended to be used. -->

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

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

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

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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

<!-- Motivation for the creation of this dataset. -->

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

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

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

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

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?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Dataset Card Authors [optional]

[More Information Needed]

## Dataset Card Contact

[More Information Needed]