load_hia_hou

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

Load the Human Intestinal Absorption dataset.

The task is to predict whether a drug is well absorbed via the human intestine. It is relevant for oral drug design [1] [2].

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

Tasks	1
Task type	classification
Total samples	578
Recommended split	scaffold
Recommended metric	AUROC

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]

Hou T et al. “ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification” J Chem Inf Model. 2007;47(1):208-218.

[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