load_sarscov2_3clpro_diamond#

skfp.datasets.tdc.hts.load_sarscov2_3clpro_diamond(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False) → DataFrame | tuple[list[str]] | ndarray#

Load the SARS-CoV-2 3CL Protease Diamond dataset.

XChem crystallographic fragment screen against SARS-CoV-2 main protease at high resolution [1] [2]. The task is to predict molecules’ activity against SARS-CoV-2 3CL Protease.

Tasks

1

Task type

classification

Total samples

880

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_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