code: def load_kinase(
      shard_size=2000, featurizer=None, split=None, reload=True
  ):
      """Loads Kinase datasets, does not do train/test split
  
      The Kinase dataset is an in-house dataset from Merck that was first introduced in the following paper:
      Ramsundar, Bharath, et al. "Is multitask deep learning practical for pharma?." Journal of chemical information and modeling 57.8 (2017): 2068-2076.
  
      It contains 2500 Merck in-house compounds that were measured
      for IC50 of inhibition on 99 protein kinases. Unlike most of
      the other datasets featured in MoleculeNet, the Kinase
      collection does not have structures for the compounds tested
      since they were proprietary Merck compounds. However, the
      collection does feature pre-computed descriptors for these
      compounds.
  
      Note that the original train/valid/test split from the source
      data was preserved here, so this function doesn't allow for
      alternate modes of splitting. Similarly, since the source data
      came pre-featurized, it is not possible to apply alternative
      featurizations.
  
      Parameters
      ----------
      shard_size: int, optional
          Size of the DiskDataset shards to write on disk
      featurizer: optional
          Ignored since featurization pre-computed
      split: optional
          Ignored since split pre-computed
      reload: bool, optional
          Whether to automatically re-load from disk
      """
  
      KINASE_tasks = [
          "T_00013",
          "T_00014",
          "T_00015",
          "T_00016",
          "T_00017",
          "T_00018",
          "T_00019",
          "T_00020",
          "T_00021",
          "T_00022",
          "T_00023",
          "T_00024",
          "T_00025",
          "T_00026",
          "T_00027",
          "T_00028",
          "T_00029",
          "T_00030",
          "T_00031",
          "T_00032",
          "T_00033",
          "T_00034",
          "T_00035",
          "T_00036",
          "T_00037",
          "T_00038",
          "T_00039",
          "T_00040",
          "T_00041",
          "T_00042",
          "T_00043",
          "T_00044",
          "T_00045",
          "T_00046",
          "T_00047",
          "T_00048",
          "T_00049",
          "T_00050",
          "T_00051",
          "T_00052",
          "T_00053",
          "T_00054",
          "T_00055",
          "T_00056",
          "T_00057",
          "T_00058",
          "T_00059",
          "T_00060",
          "T_00061",
          "T_00062",
          "T_00063",
          "T_00064",
          "T_00065",
          "T_00066",
          "T_00067",
          "T_00068",
          "T_00069",
          "T_00070",
          "T_00071",
          "T_00072",
          "T_00073",
          "T_00074",
          "T_00075",
          "T_00076",
          "T_00077",
          "T_00078",
          "T_00079",
          "T_00080",
          "T_00081",
          "T_00082",
          "T_00083",
          "T_00084",
          "T_00085",
          "T_00086",
          "T_00087",
          "T_00088",
          "T_00089",
          "T_00090",
          "T_00091",
          "T_00092",
          "T_00093",
          "T_00094",
          "T_00095",
          "T_00096",
          "T_00097",
          "T_00098",
          "T_00099",
          "T_00100",
          "T_00101",
          "T_00102",
          "T_00103",
          "T_00104",
          "T_00105",
          "T_00106",
          "T_00107",
          "T_00108",
          "T_00109",
          "T_00110",
          "T_00111",
      ]
  
      data_dir = deepchem.utils.data_utils.get_data_dir()
      data_dir = os.path.join(data_dir, "kinase")
  
      if not os.path.exists(data_dir):
          os.mkdir(data_dir)
  
      train_dir = os.path.join(data_dir, "train_dir")
      valid_dir = os.path.join(data_dir, "valid_dir")
      test_dir = os.path.join(data_dir, "test_dir")
  
      if (
          os.path.exists(train_dir)
          and os.path.exists(valid_dir)
          and os.path.exists(test_dir)
      ):
          logger.info("Reloading existing datasets")
          train_dataset = deepchem.data.DiskDataset(train_dir)
          valid_dataset = deepchem.data.DiskDataset(valid_dir)
          test_dataset = deepchem.data.DiskDataset(test_dir)
  
      else:
          logger.info("Featurizing datasets")
          train_dataset, valid_dataset, test_dataset = gen_kinase(
              KINASE_tasks=KINASE_tasks,
              train_dir=train_dir,
              valid_dir=valid_dir,
              test_dir=test_dir,
              data_dir=data_dir,
              shard_size=shard_size,
          )
  
      transformers = get_transformers(train_dataset)
  
      return (
          KINASE_tasks,
          (train_dataset, valid_dataset, test_dataset),
          transformers,
      )
  
file_path: deepchem/deepchem/molnet/load_function/kinase_datasets.py
imports: import os
  import logging
  import time
  import deepchem
  from deepchem.molnet.load_function.kaggle_features import merck_descriptors
  from deepchem.utils import remove_missing_entries
label_desc: Use image node_id 3 for calling a global function with example usage: load_kinase(shard_size, featurizer, split, reload) and returns: KINASE_tasks,  train_dataset,  valid_dataset,  test_dataset,  transformers