load_chembl4616_ec50#

skfp.datasets.moleculeace.load_chembl4616_ec50(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False, force_update: bool = False) → DataFrame | tuple[list[str], ndarray]#

Load the ChEMBL4616 EC50 dataset.

The task is to predict the half maximal effective concentration (EC50) of molecules against the Growth hormone secretagogue receptor type 1 target [1] [2].

Tasks

1

Task type

regression

Total samples

682

Recommended split

activity_cliff

Recommended metric

RMSE
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.

  • force_update (bool, default=False) – If True, always re-download the dataset from HuggingFace Hub, even if it is already present locally. If False, the dataset is downloaded only if it is not yet available locally.

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

Examples

>>> from skfp.datasets.moleculeace import load_chembl4616_ec50
>>> dataset = load_chembl4616_ec50()
>>> dataset  
(['CCCCCCCC(=O)OC[C@H](NC(=O)CN)C(=O)N[C@@H](CO)C(=O)N[C@@H](Cc1ccccc1)C(=O)O, ..., 'CC(=O)N1CCC[C@H](NC(=O)[C@H]2CN(S(=O)(=O)c3ccccc3)C[C@@H]2NC(=O)c2cc(-c3ccccc3Cl)on2)C1'], \
array([-1.857, ..., -2.111]))

>>> dataset = load_chembl4616_ec50(as_frame=True)
>>> dataset.head() 
                                              SMILES      EC50
0  CCCCCCCC(=O)OC[C@H](NC(=O)CN)C(=O)N[C@@H](CO)C... -1.857332
1  CCCCCCCC(=O)OC[C@H](NC(=O)CNC(=O)[C@@H](N)CCCN... -0.147985
2  CC(C)(N)C(=O)N[C@H](COCc1ccccc1)C(=O)N1CCC2(CC...  0.072578
3  NC(=O)CN(CCc1ccccc1)C(=O)[C@@H](Cc1ccc2ccccc2c...  0.468521
4  CC(C)N(CCNC(=O)C1c2ccc(Oc3cccc(F)c3)cc2CCN1C(=... -0.633468