load_cyp2c9_veith

skfp.datasets.tdc.adme.load_cyp2c9_veith(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False) → DataFrame | tuple[list[str]] | ndarray

Load the CYP2C9 subset of CYP P450 Veith dataset.

The CYP P450 genes are involved in the formation and breakdown (metabolism) of various molecules and chemicals within cells. The task is to predict CYP2C9 inhibition. CYP P450 2C9 plays a major role in the oxidation of both xenobiotic and endogenous compounds [1] [2].

All CYP P450 Veith subsets:

• load_cyp1a2_veith()

• load_cyp2c9_veith()

• load_cyp2c19_veith()

• load_cyp2d6_veith()

• load_cyp3a4_veith()

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

Tasks	1
Task type	classification
Total samples	12092
Recommended split	scaffold
Recommended metric	AUPRC

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]

Veith, Henrike et al. “Comprehensive Characterization of Cytochrome P450 Isozyme Selectivity across Chemical Libraries” Nature Biotechnology vol. 27,11 (2009): 1050-5.

[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