load_chembl287_ki#

skfp.datasets.moleculeace.load_chembl287_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 ChEMBL287 Ki dataset.

The task is to predict the inhibitor constant (Ki) of molecules against the Sigma non-opioid intracellular receptor 1 target [1] [2].

Tasks

1

Task type

regression

Total samples

1328

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_chembl287_ki
>>> dataset = load_chembl287_ki()
>>> dataset  
(['O=S1(=O)c2ccccc2CCC12CCN(Cc1ccccc1)CC2, ..., 'Cc1[nH]c2cc(C(F)(F)F)ccc2c(=O)c1CN(C)Cc1ccccc1'], \
array([-1.301, ..., -1.949]))

>>> dataset = load_chembl287_ki(as_frame=True)
>>> dataset.head() 
                                              SMILES        Ki
0             O=S1(=O)c2ccccc2CCC12CCN(Cc1ccccc1)CC2 -1.301030
1   COc1ccc(N2C[C@H](CN3CCC(O)(c4ccsc4)CC3)OC2=O)cc1 -1.531479
2  COc1ccc(N2C[C@H](CN3CCC(O)(c4ccc5c(c4)OCO5)CC3... -1.278754
3         CNC(=O)CC1Cc2ccccc2C2(CCN(Cc3ccccc3)CC2)O1 -2.230449
4                OCC1OC2(CCN(Cc3ccccc3)CC2)c2ccccc21 -0.752816