load_rlm_clint#

skfp.datasets.biogen_adme.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 Biogen ADME benchmark.

The task is to predict the log10 of rat liver microsomal intrinsic clearance (RLM CLint, in mL/min/kg) of molecules [1].

Tasks

1

Task type

regression

Total samples

3054

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.biogen_adme import load_rlm_clint
>>> dataset = load_rlm_clint()
>>> dataset  
(['CNc1cc(Nc2cccn(-c3ccccn3)c2=O)nn2c(C(=O)N[C@@H]3C[C@@H]3F)cnc12', ..., 'C[C@@H](CN1CCC(n2c(=O)[nH]c3cc(Br)ccc32)CC1)NC(=O)[C@@H]1C[C@H]1c1ccccc1'], \
array([1.392, ..., 2.759]))