load_chembl238_ki#

skfp.datasets.moleculeace.load_chembl238_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 ChEMBL238 Ki dataset.

The task is to predict the inhibitor constant (Ki) of molecules against the Sodium-dependent dopamine transporter target [1] [2].

Tasks

1

Task type

regression

Total samples

1052

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_chembl238_ki
>>> dataset = load_chembl238_ki()
>>> dataset  
(['CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C([C@@H](O)c2ccc(Cl)c(Cl)c2)C1, ..., 'C[C@H]1CN(CC[S+](O)C(c2ccc(F)cc2)c2ccc(F)cc2)C[C@@H](C)N1CC(O)Cc1ccccc1'], \
array([-3.617, ..., -0.873]))

>>> dataset = load_chembl238_ki(as_frame=True)
>>> dataset.head() 
                                              SMILES        Ki
0  CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C([C@@H](O)c2ccc(Cl... -3.617000
1  CN1CCC(O)(c2ccc(Cl)c(Cl)c2)C(C(=O)c2ccc(Cl)c(C... -1.037426
2      Cc1ccc(C2OC(=O)OC3(c4ccc(C)cc4)CCN(C)CC23)cc1 -3.913284
3       Cc1ccc([C@H](O)C2CN(C)CCC2(O)c2ccc(C)cc2)cc1 -4.027350
4        CN1CCC(O)(c2ccc(F)cc2)C(C(=O)c2ccc(F)cc2)C1 -3.755875