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---
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 -->
<!-- This section describes suitable use cases for the dataset. -->
<!-- [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
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. -->
<!-- [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
<!-- 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. -->
<!-- [More Information Needed]
<!-- ## More Information [optional]
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<!-- ## Dataset Card Authors [optional]
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## Dataset Card Contact
[@eachanjohnson](https://huggingface.co/eachanjohnson) |