load_chembl2971_ki#

skfp.datasets.moleculeace.load_chembl2971_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 ChEMBL2971 Ki dataset.

The task is to predict the inhibitor constant (Ki) of molecules against the Tyrosine-protein kinase jak2 target [1] [2].

Tasks

1

Task type

regression

Total samples

976

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_chembl2971_ki
>>> dataset = load_chembl2971_ki()
>>> dataset  
(['NC(=O)Nc1sc(-c2ccc(F)cc2)cc1C(N)=O, ..., 'Cc1cc(Nc2nc(N[C@@H](C)c3ccc(F)cc3)c(C#N)cc2F)n[nH]1'], \
array([-0.699, ..., 0.3468]))

>>> dataset = load_chembl2971_ki(as_frame=True)
>>> dataset.head() 
                                              SMILES        Ki
0                 NC(=O)Nc1sc(-c2ccc(F)cc2)cc1C(N)=O -0.698970
1  O[C@H]1CC[C@H](Nc2ccc3nnc(-c4cccc(C(F)(F)F)c4)... -3.380211
2                        c1ccc(-c2ncnc3[nH]ccc23)cc1 -2.683947
3                      Clc1cnc2[nH]cc(-c3ccccc3)c2c1 -2.414973
4                    CCC1Nc2ccccc2-c2ccnc3[nH]cc1c23 -3.230449