README.md

---
license: cc-by-4.0
task_categories:
- text-classification
- zero-shot-classification
- text2text-generation
- translation
tags:
- chemistry
- SMILES
- docking
pretty_name: 'Ultra-large docking: AmpC 96M (Lyu J, Wang S, Balius T, Singh I, Nature 2019)'
size_categories: 
- '10M<n<100M'
configs:
- config_name: main_data
  data_files: "chunked/ampc-dock_*.csv.gz"
  sep: ","
  default: true
---
# Ultra-large docking data: AmpC 96M compounds

These data are from John J. Irwin, Bryan L. Roth, and Brian K. Shoichet's labs. They published it as:

> [!NOTE]  
> Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, O'Meara MJ, Che T, Algaa E, Tolmachova K, Tolmachev AA, Shoichet BK, Roth BL, Irwin JJ. 
Ultra-large library docking for discovering new chemotypes. Nature. 2019 Feb;566(7743):224-229. doi: [10.1038/s41586-019-0917-9](https://doi.org/10.1038/s41586-019-0917-9). 
Epub 2019 Feb 6. PMID: [30728502](https://pubmed.ncbi.nlm.nih.gov/30728502/); PMCID: [PMC6383769](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383769/).
> 

## Dataset Details

The compounds are represented as SMILES strings, and are annotated with ZINC IDs and DOCKscore. For convenience we have added molecuar weight, 
Crippen cLogP, and topological surface area as calculated by RDKit (using [schemist](https://github.com/scbirlab/schemist)).


### Dataset Description

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

The authors of doi: [10.1038/s41586-019-0917-9](https://doi.org/10.1038/s41586-019-0917-9) carried out a massive dockign campaign to
see if increasing the numerb of compounds in virtual libraries would increase the number of docking hits that represent new active
chemical scaffolds that validate in the wet lab.

They docked libraries of ~100 million molecules to AmpC, a $\beta$-lactamase, and the D_4 dopamine receptor. This dataset contains the 
compounds and DOCKscores for AmpC. We removed compounds with anomalous DOCKscores, and used [schemist](https://github.com/scbirlab/schemist)
to add molecuar weight, Crippen cLogP, and topological surface area.

<!-- - - **Curated by:** @eachanjohnson -->
<!-- - - **Funded by [optional]:** [The Francis Crick Institute] -->
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- **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/)

### Dataset Sources

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- **Repository:** FigShare doi: [0.6084/m9.figshare.7359626.v2](https://doi.org/10.6084/m9.figshare.7359626.v2)
- **Paper:** doi: [10.1038/s41586-019-0917-9](https://doi.org/10.1038/s41586-019-0917-9)
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<!-- ## Uses

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### Direct Use

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- Chemical property prediction

<!-- ### Out-of-Scope Use -->

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<!-- ## Dataset Structure  -->

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<!-- ## Dataset Creation

<!-- ### Curation Rationale

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### Source Data

Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, O'Meara MJ, Che T, Algaa E, Tolmachova K, Tolmachev AA, Shoichet BK, Roth BL, Irwin JJ. 
Ultra-large library docking for discovering new chemotypes. Nature. 2019 Feb;566(7743):224-229. doi: [10.1038/s41586-019-0917-9](https://doi.org/10.1038/s41586-019-0917-9). 
Epub 2019 Feb 6. PMID: [30728502](https://pubmed.ncbi.nlm.nih.gov/30728502/); PMCID: [PMC6383769](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383769/).

<!-- #### Data Collection and Processing

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#### Who are the source data producers?

Jiankun Lyu†, Sheng Wang†, Trent E. Balius†, Isha Singh†, Anat Levit, Yurii S. Moroz, Matthew J. O’Meara, Tao Che, Enkhjargal Algaa, Kateryna Tolmachova,
Andrey A. Tolmachev, Brian K. Shoichet*, Bryan L. Roth*, and John J. Irwin*

†These authors contributed equally.
*Corresponding authors.

### Annotations

We used [schemist](https://github.com/scbirlab/schemist) (which in turn uses RDKit)
to add molecuar weight, Crippen cLogP, and topological surface area.

<!-- #### 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. -->

<!-- #### Who are the annotators?  -->

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

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<!-- #### Personal and Sensitive Information  -->

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<!-- ## Bias, Risks, and Limitations  -->

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

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<!-- ### Recommendations  -->

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

## Citation

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

**BibTeX:**

```
@article{10.1038/s41586-019-0917-9, 
year = {2019}, 
title = {{Ultra-large library docking for discovering new chemotypes}}, 
author = {Lyu, Jiankun and Wang, Sheng and Balius, Trent E. and Singh, Isha and Levit, Anat and Moroz, Yurii S. and O’Meara, Matthew J. and Che, Tao and Algaa, Enkhjargal and Tolmachova, Kateryna and Tolmachev, Andrey A. and Shoichet, Brian K. and Roth, Bryan L. and Irwin, John J.}, 
journal = {Nature}, 
issn = {0028-0836}, 
doi = {10.1038/s41586-019-0917-9}, 
pmid = {30728502}, 
pmcid = {PMC6383769}, 
url = {https://www.ncbi.nlm.nih.gov/pubmed/30728502}, 
abstract = {{Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor. Using a make-on-demand library that contains hundreds-of-millions of molecules, structure-based docking was used to identify compounds that, after synthesis and testing, are shown to interact with AmpC β-lactamase and the D4 dopamine receptor with high affinity.}}, 
pages = {224--229}, 
number = {7743}, 
volume = {566}, 
keywords = {}
}
```

**APA:**

Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O'Meara, M. J., Che, T., Algaa, E., Tolmachova, K., Tolmachev, A. A., Shoichet, B. K., 
Roth, B. L., & Irwin, J. J. (2019). Ultra-large library docking for discovering new chemotypes. Nature, 566(7743), 224–229. 
https://doi.org/10.1038/s41586-019-0917-9

<!-- ## Glossary [optional]

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## Dataset Card Authors [optional]

@eachanjohnson

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