load_rlm_clint#

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

Load the RLM CLint dataset from the ExpansionRx-OpenADMET challenge.

The task is to predict the rat liver microsomal intrinsic clearance (RLM CLint) of molecules [1] [2].

Note that this dataset was not part of the original challenge. It was provided by the organizers afterward as an additional endpoint. Time train-test split indexes also do not align with other datasets from this benchmark for this reason.

Tasks

1

Task type

regression

Total samples

633

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.expansionrx import load_rlm_clint
>>> dataset = load_rlm_clint()
>>> dataset  
(['c1cc(-c2ccnc(Nc3ccc(CN4CCOCC4)cc3)n2)cnn1', ..., 'CN(C)CCNc1nc(NC(=O)OCCN(C)C)cc2c(-c3cncc(N4CCN(C)CC4)c3)cccc12'], \
array([99.3, ..., 9.3]))