load_sider

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

Load and return the SIDER (Side Effect Resource) dataset.

The task is to predict adverse drug reactions (ADRs) as drug side effects to 27 system organ classes in MedDRA classification [1] [2]. All tasks are binary.

Tasks	27
Task type	multitask classification
Total samples	1427
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” and 27 label columns, with names corresponding to MedDRA system organ classes (see [1] for details). Otherwise, returns SMILES as list of strings,and labels as a NumPy array (2D integer array).

• 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” and 27 label columns - tuple of: list of strings (SMILES), NumPy array (labels)

Return type:

pd.DataFrame or tuple(list[str], np.ndarray)

References

[1] (1,2)

Han Altae-Tran et al. “Low Data Drug Discovery with One-Shot Learning” ACS Cent. Sci. 2017, 3, 4, 283–293

[2]

Zhenqin Wu et al. “MoleculeNet: a benchmark for molecular machine learning” Chem. Sci., 2018,9, 513-530

Examples

>>> from skfp.datasets.moleculenet import load_sider
>>> dataset = load_sider()
>>> dataset  
(['C(CNCCNCCNCCN)N', ..., 'CCC(=O)C(CC(C)N(C)C)(C1=CC=CC=C1)C2=CC=CC=C2'], array([[1, 1, 0, ..., 1, 1, 0],
   [0, 1, 0, ..., 0, 1, 0],
   [0, 1, 0, ..., 0, 1, 0],
   ...,
   [1, 1, 0, ..., 1, 1, 1],
   [0, 1, 0, ..., 1, 1, 1],
   [1, 1, 0, ..., 1, 1, 1]]))

>>> dataset = load_sider(as_frame=True)
>>> dataset.head() 
                                                  SMILES  ...  Injury, poisoning and procedural complications
0                                    C(CNCCNCCNCCN)N  ...                                               0
1  CC(C)(C)C1=CC(=C(C=C1NC(=O)C2=CNC3=CC=CC=C3C2=...  ...                                               0
2  CC[C@]12CC(=C)[C@H]3[C@H]([C@@H]1CC[C@]2(C#C)O...  ...                                               0
3    CCC12CC(=C)C3C(C1CC[C@]2(C#C)O)CCC4=CC(=O)CCC34  ...                                               1
4             C1C(C2=CC=CC=C2N(C3=CC=CC=C31)C(=O)N)O  ...                                               0
...