load_mlm#

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

Load the MLM dataset from the ASAP Discovery-OpenADMET challenge.

The task is to predict the mouse liver microsomal stability (MLM), i.e. intrinsic clearance in uL/min/mg, of antiviral compounds targeting SARS-CoV-2 and MERS-CoV main protease (Mpro) [1] [2] [3].

Tasks

1

Task type

regression

Total samples

425

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_mlm
>>> dataset = load_mlm()
>>> dataset  
(['CC(C)NC(=O)[C@H](NC1=CC=C2CNCC2=C1)C1=CC(Cl)=CC2=C1N=C(C1=CC=CC=C1)N2', ..., 'CC1=NC=C(CN2C[C@@]3(C(=O)N(C4=CN=CC5=CC=CC=C45)C[C@@H]3C)C3=CC(Cl)=CC=C32)C(=O)N1'], \
array([ 11., ..., 259.]))