Source code for tdc.single_pred.adme

# -*- coding: utf-8 -*-
# Author: TDC Team
# License: MIT

import sys
import warnings


from . import single_pred_dataset
from ..utils import print_sys, fuzzy_search, property_dataset_load
from ..metadata import dataset_names

[docs]class ADME(single_pred_dataset.DataLoader): """Data loader class to load datasets in ADME task. More info: Args: name (str): the dataset name. path (str, optional): The path to save the data file, defaults to './data' label_name (str, optional): For multi-label dataset, specify the label name, defaults to None print_stats (bool, optional): Whether to print basic statistics of the dataset, defaults to False convert_format (str, optional): Automatic conversion of SMILES to other molecular formats in MolConvert class. Stored as separate column in dataframe, defaults to None """ def __init__( self, name, path="./data", label_name=None, print_stats=False, convert_format=None, ): """Create ADME dataloader object.""" super().__init__( name, path, label_name, print_stats, dataset_names=dataset_names["ADME"], convert_format=convert_format, ) = fuzzy_search(, dataset_names["ADME"]) if == "ppbr_az": import pandas as pd import os self.ppbr_df = pd.read_csv( os.path.join(self.path, + ".tab"), sep="\t" ) df = self.ppbr_df[self.ppbr_df.Species == "Homo sapiens"] self.entity1 = df.Drug.values self.y = df.Y.values self.entity1_idx = df.Drug_ID.values if print_stats: self.print_stats() print("Done!", flush=True, file=sys.stderr)
[docs] def get_approved_set(self): import pandas as pd if not in ["pampa_ncats"]: raise ValueError("This function is only available for PAMPA_NCATS dataset") entity1, y, entity1_idx = property_dataset_load( "approved_pampa_ncats", self.path, None, dataset_names["ADME"] ) return pd.DataFrame({"Drug_ID": entity1_idx, "Drug": entity1, "Y": y})
[docs] def get_other_species(self, species=None): if not in ["ppbr_az"]: raise ValueError( "This function is only available for assays with species label, including PPBR" ) if species == "all": return self.ppbr_df if species in self.ppbr_df.Species.unique(): return self.ppbr_df[self.ppbr_df.Species == species].reset_index(drop=True) else: raise ValueError( "You can only specify the following set of species name: 'Canis lupus familiaris', 'Cavia porcellus', 'Homo sapiens', 'Mus musculus', 'Rattus norvegicus', 'all'" )
[docs] def harmonize(self, mode=None): """Removing duplicated experimental readouts.""" if mode not in ["max", "min", "remove_all"]: raise ValueError( "Please specify 'mode' of removal, currently supported 'max'/'min'/'remove_all'!" ) if mode == "max": df_ = self.get_data() df = ( df_.sort_values("Y", ascending=True) .drop_duplicates("Drug") .reset_index(drop=True) ) elif mode == "min": df_ = self.get_data() df = ( df_.sort_values("Y", ascending=False) .drop_duplicates("Drug") .reset_index(drop=True) ) elif mode == "remove_all": df_ = self.get_data() df = df_.drop_duplicates("Drug", keep=False).reset_index(drop=True) self.entity1 = df.Drug.values self.y = df.Y.values self.entity1_idx = df.Drug_ID.values return df