load_chembl4203_ki#
- skfp.datasets.moleculeace.load_chembl4203_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 ChEMBL4203 Ki dataset.
The task is to predict the inhibitor constant (Ki) of molecules against the Dual specificity protein kinase clk4 target [1] [2].
Tasks
1
Task type
regression
Total samples
731
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_frameargument, 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_chembl4203_ki >>> dataset = load_chembl4203_ki() >>> dataset (['O=c1[nH]cnc2c1sc1c(Cl)ccc(Cl)c12, ..., 'O=C(c1cccc(-c2cnc3[nH]ccc3c2)c1)N1CCOCC1'], \ array([-1.977, ..., -3.8]))
>>> dataset = load_chembl4203_ki(as_frame=True) >>> dataset.head() SMILES Ki 0 O=c1[nH]cnc2c1sc1c(Cl)ccc(Cl)c12 -1.976808 1 Nc1ncnc2onc(-c3ccc(NC(=O)Nc4cccc(C(F)(F)F)c4)c... -2.400002 2 O=c1[nH]cnc2c(-c3ccccc3)c(C(F)(F)F)sc12 -3.299999 3 O=C1Nc2ccccc2Nc2cc(-c3ccncc3F)ccc21 -1.400020 4 Cc1cc(N2CCOCC2)cc2[nH]c(-c3c(NCC(O)c4cccc(Cl)c... -1.700011