load_herg_central_inhib#
- skfp.datasets.tdc.tox.load_herg_central_inhib(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False) DataFrame | tuple[list[str]] | ndarray#
Load the inhibition subset of hERG Central dataset.
The task is to predict the inhibition of ether-à-go-go related gene (hERG), crucial for coordination of the heart’s beating [1] [2].
In this subset, the task is predicting the binary inhibition ability, i.e. if percentage inhibition at 10 µM is smaller than -50.
Tasks
1
Task type
classification
Total samples
306893
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_frameargument, 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