load_bbbp#
- skfp.datasets.moleculenet.load_bbbp(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False, force_update: bool = False) DataFrame | tuple[list[str], ndarray]#
Load the BBBP (Blood-Brain Barrier Penetration) dataset.
The task is to predict blood-brain barrier penetration (barrier permeability) of small drug-like molecules [1] [2].
Tasks
1
Task type
classification
Total samples
2039
Recommended split
scaffold
Recommended metric
AUROC
- 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.moleculenet import load_bbbp >>> dataset = load_bbbp() >>> dataset (['[Cl].CC(C)NCC(O)COc1cccc2ccccc12', ..., '[N+](=NCC(=O)N[C@@H]([C@H](O)C1=CC=C([N+]([O-])=O)C=C1)CO)=[N-]'], array([1, 1, 1, ..., 1, 1, 1]))
>>> dataset = load_bbbp(as_frame=True) >>> dataset.head() SMILES label 0 [Cl].CC(C)NCC(O)COc1cccc2ccccc12 1 1 C(=O)(OC(C)(C)C)CCCc1ccc(cc1)N(CCCl)CCCl 1 2 c12c3c(N4CCN(C)CC4)c(F)cc1c(c(C(O)=O)cn2C(C)CO... 1 3 C1CCN(CC1)Cc1cccc(c1)OCCCNC(=O)C 1 4 Cc1onc(c2ccccc2Cl)c1C(=O)N[C@H]3[C@H]4SC(C)(C)... 1