load_ppbr_az

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

Load the PPBR (Plasma Protein Binding Rate) AstraZeneca dataset.

The task is to predict human plasma protein binding rate (PPBR) [1] [2]. PPBR is expressed as the percentage of a drug bound to plasma proteins in the blood. This rate strongly affects the drug delivery efficiency. The less bound a drug is, the more efficiently it can traverse and diffuse to the site of actions.

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

Tasks	1
Task type	regression
Total samples	1614
Recommended split	scaffold
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 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]

AstraZeneca. “Experimental in vitro Dmpk and physicochemical data on a set of publicly disclosed compounds” (2016)

[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