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, available from the GitHub repositiory. We used 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:
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
- License: MIT
Dataset Sources
- Repository: https://github.com/molecularinformatics/Computational-ADME
- Paper: https://doi.org/10.1021/acs.jcim.3c00160
Uses
Benchmarking chemical property prediction models.
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
- 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, a tool for processing chemical datasets.
Who are the source data producers?
Biogen
Personal and Sensitive Information
None
Citation
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}
}