scbirlab/stokes-2020-ai

---
license: cc-by-4.0
task_categories:
- text-classification
tags:
- chemistry
- biology
- antibiotics
- SMILES
size_categories:
- 10K<n<100K
pretty_name: Data from Stokes, 2020
configs:
- config_name: eco
data_files:
- split: train
path: "*-eco0-train.csv.gz"
- split: test
path: "*-eco0-test.csv.gz"
- split: validation
path: "*-eco0-validation.csv.gz"
- config_name: broad-repurposing
data_files:
- split: train
path: "*-broad-repurposing-train.csv.gz"
- split: test
path: "*-broad-repurposing-test.csv.gz"
- split: validation
path: "*-broad-repurposing-validation.csv.gz"
- config_name: zinc-predictions
data_files:
- split: train
path: "*-zinc-predictions-train.csv.gz"
- split: test
path: "*-zinc-predictions-test.csv.gz"
- split: validation
path: "*-zinc-predictions-validation.csv.gz"
---
# stokes-2020-ai

SMILES of compounds used for training and prediction in:

> Stokes, J. M., Yang, K., ..., Collins, J. J. (2020).
> A Deep Learning Approach to Antibiotic Discovery.
> Cell, 180(4), 688–702.e13.
> https://doi.org/10.1016/j.cell.2020.01.021
> PMID: 32084340; PMCID: PMC8349178.

The SMILES strings have been canonicalized, and split into training (70%), validation (15%), and test (15%) sets by Murcko scaffold.
Additional features like molecular weight and topological polar surface area have also been calculated.

## Dataset Details

### Dataset Description

- **Curated by:** [@eachanjohnson](https://huggingface.co/eachanjohnson)
- **Funded by:** The Francis Crick Institute
- **License:** CC-by-4.0

### Dataset Sources

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<!-- - **Repository:** https://doi.org/10.5281/zenodo.8136904 -->
- **Paper** https://doi.org/10.1016/j.cell.2020.01.021
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## Uses

Developing chemistry models.

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### Out-of-Scope Use

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## Dataset Structure

- **SMILES**: SMILES string of compound
- **id**: Numerical almost-unique identifier of compound
- **inchikey**: Unique identifier for compound
- **smiles**: RDKit-canonicalized SMILES string of compound
- **pubchem_name**: Compound name pulled from PubChem
- **pubchem_id**: PubChem compound ID
- **scaffold**: Murcko scaffold of compound
- **mwt**: Molecular weight of compound
- **clogp**: Crippen LogP of compound
- **tpsa**: Topological polar surface area of compound
- **is_train**: In training split
- **is_test**: In test split
- **is_validation**: In validation split

## Dataset Creation

### Curation Rationale

To make available a large dataset of SMILES strings for DOS compounds, as distinct from commonly encountered
virtual libraries from conventional combinatorial chemistry.

#### Data Collection and Processing

Data were processed using [schemist](https://github.com/scbirlab/schemist), a tool for processing chemical datasets.

#### Who are the source data producers?

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M.,
MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M.,
Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M.,
Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J.

#### Personal and Sensitive Information

None.

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## Citation

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**BibTeX:**

```
@article{10.1016/j.cell.2020.01.021,
title = {A Deep Learning Approach to Antibiotic Discovery},
journal = {Cell},
volume = {180},
number = {4},
pages = {688-702.e13},
year = {2020},
issn = {0092-8674},
doi = {https://doi.org/10.1016/j.cell.2020.01.021},
url = {https://www.sciencedirect.com/science/article/pii/S0092867420301021},
author = {Jonathan M. Stokes and Kevin Yang and Kyle Swanson and Wengong Jin and Andres Cubillos-Ruiz and Nina M. Donghia and Craig R. MacNair and Shawn French and Lindsey A. Carfrae and Zohar Bloom-Ackermann and Victoria M. Tran and Anush Chiappino-Pepe and Ahmed H. Badran and Ian W. Andrews and Emma J. Chory and George M. Church and Eric D. Brown and Tommi S. Jaakkola and Regina Barzilay and James J. Collins},
keywords = {antibiotics, antibiotic resistance, antibiotic tolerance, machine learning, drug discovery},
abstract = {Summary
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub—halicin—that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.}
}
```

**APA:**

> Stokes, J. M., Yang, K., ..., Collins, J. J. (2020).
> A Deep Learning Approach to Antibiotic Discovery.
> Cell, 180(4), 688–702.e13.
> https://doi.org/10.1016/j.cell.2020.01.021

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## Dataset Card Contact

[@eachanjohnson](https://huggingface.co/eachanjohnson)