liuganghuggingface/moldata-moleculeace_chembl2147_ki

MoleculeACE CHEMBL2147 Ki

Activity-cliff prediction benchmark for CHEMBL2147 using KI activity labels from ChEMBL. Includes the original train/test assignments and activity-cliff annotations.

Part of the Molecular Datasets collection.

Get started

from datasets import load_dataset
dataset = load_dataset("liuganghuggingface/moldata-moleculeace_chembl2147_ki", "000_CHEMBL2147_Ki")

Where the data comes from

• Original data: https://github.com/molML/MoleculeACE
• Original study: Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
• Built on: ChEMBL

Tables

Config	Splits	Rows	Targets
000_CHEMBL2147_Ki	train, test	1,456	y [pEC50/pKi] (regression; -log10(M))

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.
• all structures readable: All 1,456 SMILES were read with RDKit.
• 16.6% undefined stereo: 16.6% of molecules have a stereocenter whose configuration is not given, so mirror-image forms cannot be told apart.
• 2.1% PAINS alerts: 2.1% of molecules contain a PAINS substructure, which often looks active in screening assays without real activity.
• 1,456 labeled molecules: 1,456 molecules have a label.
• source split: train / test: The source divides the data into train (1,162 rows), test (294 rows). Use this division to compare with published results.

Dataset notes

• Activity-cliff labels depend on the original similarity and potency thresholds.
• Targets pooled from ChEMBL can contain heterogeneous assays and measurement conditions.
• The dataset includes its original training and test assignments.

License

MIT (benchmark repository); underlying ChEMBL data CC BY-SA 3.0

Citation

Cite the original study and data source:

• Exposing the Limitations of Molecular Machine Learning with Activity Cliffs