load_vdss_lombardo

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

Load the Volume of Distribution at Steady State dataset.

The task is to predict the volume of distribution at steady state (VDss) that measures the degree of drug’s concentration in body tissue compared to concentration in blood [1] [2].

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

Tasks	1
Task type	regression
Total samples	1130
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]

Lombardo, Franco, and Yankang Jing. “In Silico Prediction of Volume of Distribution in Humans. Extensive Data Set and the Exploration of Linear and Nonlinear Methods Coupled with Molecular Interaction Fields Descriptors” Journal of Chemical Information and Modeling 56.10 (2016): 2042-2052.

[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