drugforge.alchemy.predict.get_data_from_femap

drugforge.alchemy.predict.get_data_from_femap(fe_map: FEMap, ligands: list, assay_units: str | None = None, reference_dataset: str | None = None, cdd_protocol: str | None = None) → tuple[DataFrame, DataFrame]

Given a cinnabar FEMap add the experimental reference data and generate and return: 1. a Pandas DataFrame that has all absolute predictions and measurements (DG in kcal/mol) and (pIC50) 2. a pd DF that has all relative predictions and measurements (DDG in kcal/mol) and PIC50

Args:

fe_map: The cinnabar FEMap which has all calculated edges present and the absolute estimates. ligands: The list of asap ligands which are part of the network. assay_units: The units of the experimental data, which should be extracted from the reference dataset. reference_dataset: The name of the cdd csv file which contains the experimental data. cdd_protocol: The name of the CDD protocol from which we should extract experimental data.

Returns:

An absolute and relative free energy prediction dataframe.