load_chembl264_ki#

skfp.datasets.moleculeace.load_chembl264_ki(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False, force_update: bool = False) → DataFrame | tuple[list[str], ndarray]#

Load the ChEMBL264 Ki dataset.

The task is to predict the inhibitor constant (Ki) of molecules against the Histamine h3 receptor target [1] [2].

Tasks

1

Task type

regression

Total samples

2862

Recommended split

activity_cliff

Recommended metric

RMSE
Parameters:

  • data_dir ({None, str, path-like}, default=None) – Path to the root data directory. If None, currently set scikit-learn directory is used, by default $HOME/scikit_learn_data.

  • as_frame (bool, default=False) – If True, returns the raw DataFrame with columns: “SMILES”, “label”. Otherwise, returns SMILES as list of strings, and labels as a NumPy array (1D integer binary vector).

  • verbose (bool, default=False) – If True, progress bar will be shown for downloading or loading files.

  • force_update (bool, default=False) – If True, always re-download the dataset from HuggingFace Hub, even if it is already present locally. If False, the dataset is downloaded only if it is not yet available locally.

Returns:

data – Depending on the as_frame argument, one of: - Pandas DataFrame with columns: “SMILES”, “label” - tuple of: list of strings (SMILES), NumPy array (labels)

Return type:

pd.DataFrame or tuple(list[str], np.ndarray)

References

Examples

>>> from skfp.datasets.moleculeace import load_chembl264_ki
>>> dataset = load_chembl264_ki()
>>> dataset  
(['CC(=O)c1ccc(OCCCc2c[nH]cn2)cc1, ..., 'CC(C)(C)c1ccc(OCCCCCCN2CCCCCC2)cc1'], \
array([-1.94, ..., -2.919]))

>>> dataset = load_chembl264_ki(as_frame=True)
>>> dataset.head() 
                           SMILES        Ki
0  CC(=O)c1ccc(OCCCc2c[nH]cn2)cc1 -1.939519
1       c1ccc(COCCCc2c[nH]cn2)cc1 -0.415974
2   CC(=O)c1ccc(SCCc2c[nH]cn2)cc1 -0.041393
3        c1ccc(OCCCc2c[nH]cn2)cc1 -1.431364
4  CC(=O)c1ccc(SCCCc2c[nH]cn2)cc1 -1.255273