load_half_life_obach

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

Load the Half Life Obach dataset.

The task is to predict the half life of a drug. It is defined as a duration for the concentration of the drug in the body to be reduced by half. It measures the duration of actions of a drug [1] [2].

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

Tasks	1
Task type	regression
Total samples	667
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

[1]

Obach, R. Scott, Franco Lombardo, and Nigel J. Waters. “Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds” Drug Metabolism and Disposition 36.7 (2008): 1385-1405.

[2]

Huang, Kexin, et al. “Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development” Proceedings of Neural Information Processing Systems, NeurIPS Datasets and Benchmarks, 2021