README.md

---
license: mit
task_categories:
- text-classification
- text2text-generation
- translation
- zero-shot-classification
tags:
- chemistry
- biology
- SMILES
- benchmark
size_categories:
- 1k<n<10k
pretty_name: 'Biogen ADME (public data)'
configs:
- config_name: full
  data_files: "biogen-adme.csv.gz"
- config_name: scaffold-split
  data_files:
  - split: train
    path: "biogen-adme_train.csv.gz"
  - split: test
    path: "biogen-adme_test.csv.gz"
---
# Biogen ADME dataset (public data)

Data from [Fang et al., Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective](https://doi.org/10.1021/acs.jcim.3c00160), available from the [GitHub repositiory](https://github.com/molecularinformatics/Computational-ADME). We used [schemist](https://github.com/scbirlab/schemist) (which in turn uses RDKit)
to add molecuar weight, Murcko scaffold, Crippen cLogP, and topological surface area, and to generate scaffold splits.

## Dataset Details

From the [original README](https://github.com/molecularinformatics/Computational-ADME):

>
> To benefit the broader computational chemistry community and improve the > quality and diversity of public-domain ADME data sets we have disclosed a > collection of 3521 diverse compounds selected from commercially available > compound libraries (i.e. Enamine, eMolecules, WuXi LabNetwork, Mcule) and > tested them against our internal six ADME in vitro assays described in this > study using the same experimental conditions as of our in-house datasets.
> 

### Dataset Description

- **Curated by:** Biogen
<!-- - **Funded by:** The Francis Crick Institute -->
- **License:** MIT

### Dataset Sources

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

- **Repository:** https://github.com/molecularinformatics/Computational-ADME
- **Paper:** https://doi.org/10.1021/acs.jcim.3c00160
<!-- - **Demo [optional]:** [More Information Needed] -->

## Uses

Benchmarking chemical property prediction models.

<!-- ### Direct Use   -->

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### Out-of-Scope Use

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## Dataset Structure

The train-test splits are generated by scaffold.

The column headings of the data are:

- **SMILES**: Original SMILES string, as in the original data release in the [GitHub repositiory](https://github.com/molecularinformatics/Computational-ADME) 
- **smiles**: Canonicalized SMILES string
- **id**: Numeric structure identifier
- **inchikey**: Unique structure identifier
- **scaffold**: Murcko scaffold
- **mwt**: Molecular weight
- **clogp**: Crippen LogP
- **tpsa**: Calculated topological polar surface area.

The following columns are ADME properties:

- log_hlm: human liver microsomal (HLM) stability (Clint, mL/min/kg)
- log_mdr1_mdck_er: MDR1-MDCK efflux ratio
- log_solubility: solubility at pH 6.8 (ug/mL)
- log_plasma_protein_binding_human: human plasma protein binding (hPPB) percent unbound 
- log_plasma_protein_binding_rat: rat plasma protein binding (rPPB) percent unbound
- log_rlm: rat liver microsomal (RLM) stability (Clint, mL/min/kg)

## Dataset Creation

### Curation Rationale

To make the Biogen ADME dataset readily available with light preprocessing.

#### Data Collection and Processing

Additional properties and scaffold splits were calculated using [schemist](https://github.com/scbirlab/schemist), a tool for processing chemical datasets.

#### Who are the source data producers?

Biogen

#### Personal and Sensitive Information

None

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

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

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<!-- ### Recommendations  -->

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## 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{doi:10.1021/acs.jcim.3c00160,
    author = {Fang, Cheng and Wang, Ye and Grater, Richard and Kapadnis, Sudarshan and Black, Cheryl and Trapa, Patrick and Sciabola, Simone},
    title = {Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion  Prediction: An Industrial Perspective},
    journal = {Journal of Chemical Information and Modeling},
    volume = {63},
    number = {11},
    pages = {3263-3274},
    year = {2023},
    doi = {10.1021/acs.jcim.3c00160},
    note = {PMID: 37216672},
    URL = {https://doi.org/10.1021/acs.jcim.3c00160},
    eprint = {https://doi.org/10.1021/acs.jcim.3c00160}
}
```

<!-- **APA:** -->

<!-- ## Glossary [optional]  -->

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

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<!-- ## Dataset Card Authors [optional]

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## Dataset Card Contact

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