load_clearance_microsome_az#

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

Load the microsome subset of Clearance AstraZeneca dataset.

The task is to predict drug clearance. It is defined as the volume of plasma cleared of a drug over a specified time period and it measures the rate at which the active drug is removed from the body [1] [2] [3]. Many studies [2] show various clearance outcomes of experiments performed with human hepatocytes (HHEP) and human liver microsomes (HLM) which are two main in vitro systems used in metabolic stability and inhibition studies. This subset od the Clearance dataset includes measurements from microsome studies.

This dataset is a part of “excretion” subset of ADME tasks.

Tasks

1

Task type

regression

Total samples

1102

Recommended split

scaffold

Recommended metric

Spearman correlation
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