code: def load_pdbbind(
      featurizer: dc.feat.ComplexFeaturizer,
      splitter: Union[dc.splits.Splitter, str, None] = "random",
      transformers: List[Union[TransformerGenerator, str]] = [
          "normalization"
      ],
      reload: bool = True,
      data_dir: Optional[str] = None,
      save_dir: Optional[str] = None,
      pocket: bool = True,
      set_name: str = "core",
      **kwargs,
  ) -> Tuple[
      List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]
  ]:
      """Load PDBBind dataset.
  
      The PDBBind dataset includes experimental binding affinity data
      and structures for 4852 protein-ligand complexes from the "refined set"
      and 12800 complexes from the "general set" in PDBBind v2019 and 193
      complexes from the "core set" in PDBBind v2013.
      The refined set removes data with obvious problems
      in 3D structure, binding data, or other aspects and should therefore
      be a better starting point for docking/scoring studies. Details on
      the criteria used to construct the refined set can be found in [4]_.
      The general set does not include the refined set. The core set is
      a subset of the refined set that is not updated annually.
  
      Random splitting is recommended for this dataset.
  
      The raw dataset contains the columns below:
  
      - "ligand" - SDF of the molecular structure
      - "protein" - PDB of the protein structure
      - "CT_TOX" - Clinical trial results
  
      Parameters
      ----------
      featurizer: ComplexFeaturizer or str
          the complex featurizer to use for processing the data.
          Alternatively you can pass one of the names from
          dc.molnet.featurizers as a shortcut.
      splitter: Splitter or str
          the splitter to use for splitting the data into training, validation, and
          test sets.  Alternatively you can pass one of the names from
          dc.molnet.splitters as a shortcut.  If this is None, all the data
          will be included in a single dataset.
      transformers: list of TransformerGenerators or strings
          the Transformers to apply to the data.  Each one is specified by a
          TransformerGenerator or, as a shortcut, one of the names from
          dc.molnet.transformers.
      reload: bool
          if True, the first call for a particular featurizer and splitter will cache
          the datasets to disk, and subsequent calls will reload the cached datasets.
      data_dir: str
          a directory to save the raw data in
      save_dir: str
          a directory to save the dataset in
      pocket: bool (default True)
          If true, use only the binding pocket for featurization.
      set_name: str (default 'core')
          Name of dataset to download. 'refined', 'general', and 'core' are supported.
  
      Returns
      -------
      tasks, datasets, transformers: tuple
          tasks: list
              Column names corresponding to machine learning target variables.
          datasets: tuple
              train, validation, test splits of data as
              ``deepchem.data.datasets.Dataset`` instances.
          transformers: list
              ``deepchem.trans.transformers.Transformer`` instances applied
              to dataset.
  
      References
      ----------
      .. [1] Liu, Z.H. et al. Acc. Chem. Res. 2017, 50, 302-309. (PDBbind v.2016)
      .. [2] Liu, Z.H. et al. Bioinformatics, 2015, 31, 405-412. (PDBbind v.2014)
      .. [3] Li, Y. et al. J. Chem. Inf. Model., 2014, 54, 1700-1716.(PDBbind v.2013)
      .. [4] Cheng, T.J. et al. J. Chem. Inf. Model., 2009, 49, 1079-1093. (PDBbind v.2009)
      .. [5] Wang, R.X. et al. J. Med. Chem., 2005, 48, 4111-4119. (Original release)
      .. [6] Wang, R.X. et al. J. Med. Chem., 2004, 47, 2977-2980. (Original release)
      """
  
      loader = _PDBBindLoader(
          featurizer,
          splitter,
          transformers,
          PDBBIND_TASKS,
          data_dir,
          save_dir,
          pocket=pocket,
          set_name=set_name,
          **kwargs,
      )
      return loader.load_dataset(loader.name, reload)
  
file_path: deepchem/deepchem/molnet/load_function/pdbbind_datasets.py
imports: import os
  import numpy
  import pandas
  import deepchem
  from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader
  from deepchem.data import Dataset
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 1 for calling a global function with example usage: load_pdbbind(featurizer, splitter, transformers, reload, data_dir, save_dir, pocket, set_name) and returns: loader