Guidelines for Method ComparisonRead the first pre-print from the Small Molecule Steering Committee

Dataset

molecularml/moleculeace-chembl237-ki

Bioassay CHEMBL237 of protein Opioid receptor molecular machine learning with activity cliffs.

Created on: July 24, 2024Dataset size: 83 KBNumber of datapoints: 2,603
Public

Status

Uncertified

This artifact has not been certified by approved reviewers. It may contain issues related to data quality.

Learn more here.

Tags

Activity Cliff
QSAR

Modalities

MOLECULE

Details

README

Background

This dataset comprises collected and curated bioactivity data for the target Opioid receptorfrom ChEMBL assay CHEMBL237, utilized to evaluate the performance of various machine learning algorithms on activity cliffs. We employed classical machine learning methods combined with common molecular descriptors, as well as neural networks based on unstructured molecular data such as molecular graphs or SMILES strings.

Activity cliffs are molecules with small differences in structure but large differences in potency. Activity cliffs play an important role in drug discovery, but the bioactivity of activity cliff compounds are notoriously difficult to predict.

Description of readouts

  • exp_mean [nM]: Inhibition [Inhibitory Constant, Ki]
  • y: Negative of log transform of the bioactivity value.
  • split: Train-test split based on activity cliff.

Data resource: