load_bioavailability_ma

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

Load the Bioavailability dataset.

The task is to predict the oral bioavailability. It is defined as “the rate and extent to which the active ingredient or active moiety is absorbed from a drug product and becomes available at the site of action” [1] [2].

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

Tasks	1
Task type	classification
Total samples	640
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]

Ma, Chang-Ying, et al. “Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA–CG–SVM method” Journal of Pharmaceutical and Biomedical Analysis 47.4-5 (2008): 677-682.

[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