load_pic50_mers_cov#

skfp.datasets.asap.load_pic50_mers_cov(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False, force_update: bool = False) → DataFrame | tuple[list[str], ndarray]#

Load the pIC50 MERS-CoV dataset from the ASAP Discovery-OpenADMET challenge.

The task is to predict the pIC50 (negative log10 of IC50 in uM) of antiviral compounds against MERS-CoV, measured by fluorescence dose-response assay [1] [2] [3].

Tasks

1

Task type

regression

Total samples

1198

Recommended split

time

Recommended metric

MAE
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 float 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.asap import load_pic50_mers_cov
>>> dataset = load_pic50_mers_cov()
>>> dataset  
(['COC[C@]1(C)C(=O)N(C2=CN=CC3=CC=CC=C23)C(=O)N1C', ..., 'COC1=CC=CC=C1[C@H]1C[C@H](C)CCN1C(=O)CC1=CN=CC2=CC=CC=C12'], \
array([4.19, ..., 5.47]))