Curation

detect_streoisomer_activity_cliff

detect_streoisomer_activity_cliff(dataset: pd.DataFrame, stereoisomer_id_col: str, y_cols: List[str], threshold: float = 2.0, prefix: str = 'AC_') -> pd.DataFrame

Detect activity cliff among stereoisomers based on classification label or pre-defined threshold for continuous values.

Parameters:

Name	Type	Description	Default
dataset	DataFrame	Dataframe	required
stereoisomer_id_col	str	Column which identifies the stereoisomers	required
y_cols	List[str]	List of columns for bioactivities	required
threshold	float	Threshold to identify the activity cliff. Currently, the difference of zscores between isomers are used for identification.	2.0
prefix	str	Prefix for the adding columns	'AC_'

deduplicate

deduplicate(dataset: pd.DataFrame, deduplicate_on: Optional[Union[str, List[str]]] = None, y_cols: Optional[Union[str, List[str]]] = None, keep: Literal['first', 'last'] = 'first', method: Literal['mean', 'median'] = 'median') -> pd.DataFrame

Deduplicate a dataframe.

If deduplicate_on specifies a subset of all columns in the dataset and y_cols specifies a set of non-overlapping columns, data will be grouped by deduplicate_on and the y_cols will be aggregated to a single value per group according to method.

Parameters:

Name	Type	Description	Default
dataset	DataFrame	The dataset to deduplicate.	required
deduplicate_on	Optional[Union[str, List[str]]]	A subset of the columns to deduplicate on (can be default).	None
y_cols	Optional[Union[str, List[str]]]	The columns to aggregate.	None
keep	Literal['first', 'last']	Whether to keep the first or last copy of the duplicates.	'first'
method	Literal['mean', 'median']	The method to aggregate the data.	'median'

discretize

discretize(X: np.ndarray, thresholds: Union[np.ndarray, list], inplace: bool = False, allow_nan: bool = True, label_order: Literal['ascending', 'descending'] = 'ascending') -> np.ndarray

Thresholding of array-like or scipy.sparse matrix into binary or multiclass labels.

Parameters:

Name	Type	Description	Default
X		The data to discretize, element by element. scipy.sparse matrices should be in CSR or CSC format to avoid an un-necessary copy.	required
thresholds	Union[ndarray, list]	Interval boundaries that include the right bin edge.	required
inplace	bool	Set to True to perform inplace discretization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR / CSC matrix and if axis is 1).	False
allow_nan	bool	Set to True to allow nans in the array for discretization. Otherwise, an error will be raised instead.	True
label_order	Literal['ascending', 'descending']	The continuous values are discretized to labels 0, 1, 2, .., N with respect to given threshold bins [threshold_1, threshold_2,.., threshould_n]. When set to 'ascending', the class label is in ascending order with the threshold bins that 0 represents negative class or lower class, while 1, 2, 3 are for higher classes. When set to 'descending' the class label is in ascending order with the threshold bins. Sometimes the positive labels are on the left side of provided threshold. E.g. For binarization with threshold [0.5], the positive label is defined byX < 0.5. In this case, label_order should be descending.	'ascending'

Returns:

Name	Type	Description
X_tr	ndarray	The transformed data.

curate_molecules

curate_molecules(mols: List[Union[str, dm.Mol]], progress: bool = True, remove_stereo: bool = False, fix_mol: bool = True, count_stereoisomers: bool = True, count_stereocenters: bool = True, **parallelized_kwargs) -> Tuple

Curate a list of molecules.

Parameters:

Name	Type	Description	Default
mols	List[Union[str, Mol]]	List of molecules.	required
progress	bool	Whether show curation progress.	True
fix_mol	bool	Whether fix the error in molecule.	True
remove_stereo	bool	Whether remove stereo chemistry information from molecule.	False
count_stereoisomers	bool	Whether count the number of stereoisomers of molecule.	True
count_stereocenters	bool	Whether count the number of stereocenters of molecule.	True

Returns:

Name	Type	Description
mol_dict	Tuple	Dictionary of molecule and additional metadata
num_invalid	Tuple	Number of invßßalid molecules

detect_outliers

detect_outliers(X: np.ndarray, method: OutlierDetectionMethod = 'zscore', **kwargs: Any)

Functional interface for detecting outliers

Parameters:

Name	Type	Description	Default
X	ndarray	The observations that we want to classify as inliers or outliers.	required
method	OutlierDetectionMethod	The method to use for outlier detection.	'zscore'
**kwargs	Any	Keyword arguments for the outlier detection method.	{}