liuganghuggingface/moldata-clearance

Hepatocyte clearance

Hepatocyte clearance. DeepChem/MoleculeNet release.

Part of the Molecular Datasets collection.

Get started

from datasets import load_dataset
dataset = load_dataset("liuganghuggingface/moldata-clearance", "000_clearance")

Where the data comes from

• Original data: published with the original study
• Redistributed by: MoleculeNet (DeepChem) (dataset page)

Tables

Config	Splits	Rows	Targets
000_clearance	full	837	target (regression; units not specified)

data/ contains rows with readable molecular structures; identical observations are collapsed. observations/ contains all source rows.

Source label columns are text, including values such as <10. SMILES_original retains the input representation; SMILES_canonical provides a canonical representation. structure_key groups molecular components, and structure_status records parsing outcomes.

Data quality

• experiment: Labels are measured in a lab.
• many sources: Compiled from several labs or papers, whose protocols differ. Independent IC50 values for the same compound and target typically differ by a factor of about 5 (Kalliokoski et al. 2013).
• all structures readable: All 837 SMILES were read with RDKit.
• 14.6% undefined stereo: 14.6% of molecules have a stereocenter whose configuration is not given, so mirror-image forms cannot be told apart.
• 1 of 14 stereoisomer sets differs: Found 14 groups of molecules that differ only in stereochemistry. In 1, the values are 10-fold apart (log-scale labels), more than one standard deviation apart, or in different classes. That can be a real effect or a mistake in the source; a model that ignores stereochemistry treats these molecules as the same.
• 3.2% PAINS alerts: 3.2% of molecules contain a PAINS substructure, which often looks active in screening assays without real activity.
• 837 labeled molecules: 837 molecules have a label. Results on fewer than 1,000 molecules vary a lot between splits.
• no source split: Make your own split. A random split puts near-identical molecules in both training and test sets, so results look better than they will on new compounds. Splitting by scaffold, or by date when dates are given, gives a more realistic estimate.

Dataset notes

• Species, conditions, and units have incomplete source documentation. AstraZeneca clearance endpoints are separate datasets.

License

Dataset-wide license: unspecified. Original source.

Citation

Cite the original study and data source:

• DeepChem clearance dataset documentation
• Wu et al. MoleculeNet: a benchmark for molecular machine learning (2018)
• Official DeepChem loader and task definitions