load_bace#
- skfp.datasets.moleculenet.load_bace(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False) DataFrame | tuple[list[str]] | ndarray#
Load and return the BACE dataset.
The task is to predict binding results for a set of inhibitors of human β-secretase 1 (BACE-1) [1] [2].
Tasks
1
Task type
classification
Total samples
1513
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.
- 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_bace >>> dataset = load_bace() >>> dataset (['O1CC[C@@H](NC(=O)[C@@H](Cc2cc3cc(ccc3nc2N)-c2ccccc2C)C)CC1(C)C', ..., 'Clc1cc2nc(n(c2cc1)CCCC(=O)NCC1CC1)N'], array([1, 1, 1, ..., 0, 0, 0]))
>>> dataset = load_bace(as_frame=True) >>> dataset.head() SMILES label 0 O1CC[C@@H](NC(=O)[C@@H](Cc2cc3cc(ccc3nc2N)-c2c... 1 1 Fc1cc(cc(F)c1)C[C@H](NC(=O)[C@@H](N1CC[C@](NC(... 1 2 S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)N[C@H](... 1 3 S1(=O)(=O)C[C@@H](Cc2cc(O[C@H](COCC)C(F)(F)F)c... 1 4 S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)N[C@H](... 1