text: # -*- coding: utf-8 -*-
  # Author: TDC Team
  # License: MIT
  
  
  """This file contains all metadata of datasets in TDC.
  
  Attributes:
      adme_dataset_names (list): all adme dataset names
      admet_benchmark (dict): a dictionary with key the TDC task and value a list of dataset names
      admet_metrics (dict): a dictionary with key the dataset name and value the recommended metric
      admet_splits (dict): a dictionary with key the dataset name and value the recommended split
      antibodyaff_dataset_names (list): all antibody_aff dataset names
      benchmark2id (dict): benchmark names to dataverse download ID
      benchmark2type (dict): benchmark names to file type in download format
      benchmark_names (dict): a dictionary mapping benchmark group name to each benchmark group dataset names
      bm_metric_names (dict): a dictionary mapping benchmark group name to each benchmark group metric names
      bm_split_names (dict): a dictionary mapping benchmark group name to each benchmark group split names
      catalyst_dataset_names (list): all catalyst dataset names
      category_names (dict): mapping from ML problem (1st tier) to all tasks
      crisproutcome_dataset_names (list): all crispr outcome dataset names
      dataset_list (list): total list of dataset names in TDC
      dataset_names (dict): mapping from task name to list of dataset names
      ddi_dataset_names (list): all ddi dataset names
      develop_dataset_names (list): all develop dataset names
      distribution_oracles (list): all distribution learning oracles, i.e. molecule evaluators
      docking_benchmark (dict): docking benchmark target names
      docking_target_info (dict): docking benchmark target pockets info
      download_oracle_names (list): oracle names that require downloading predictors
      drugres_dataset_names (list): all drugres dataset names
      drugsyn_benchmark (dict): drugcombo benchmark group targets
      drugsyn_dataset_names (list): all drugsyn dataset names
      drugsyn_metrics (dict): a dictionary with key the dataset name and value the recommended metric
      drugsyn_splits (dict):  a dictionary with key the dataset name and value the recommended split
      dti_dataset_names (list): all dti dataset names
      dti_dg_benchmark (dict): dti_dg benchmark group dataset names
      dti_dg_metrics (dict): a dictionary with key the dataset name and value the recommended metric
      dti_dg_splits (dict):  a dictionary with key the dataset name and value the recommended split
      epitope_dataset_names (list): all epitope dataset names
      evaluator_name (list): list of evaluator names
      forwardsyn_dataset_names (list): all reaction dataset names
      gda_dataset_names (list): all gda dataset names
      generation_datasets (list): all generation dataset names
      guacamol_oracle (list): list of oracles from guacamol
      hts_dataset_names (list): all hts dataset names
      meta_oracle_name (list): list of all meta oracle names
      molgenpaired_dataset_names (list): all molgenpaired dataset names
      mti_dataset_names (list): all mti dataset names
      name2id (dict): mapping from dataset names to dataverse id
      name2stats (dict): mapping from dataset names to statistics
      name2type (dict): mapping from dataset names to downloaded file format
      oracle2id (dict): mapping from oracle names to dataverse id
      oracle2type (dict): mapping from oracle names to downloaded file format
      receptor2id (dict): mapping from receptor id to dataverse id 
      oracle_names (list): list of all oracle names
      paired_dataset_names (list): all paired dataset names
      paratope_dataset_names (list): all paratope dataset names
      peptidemhc_dataset_names (list): all peptidemhc dataset names
      ppi_dataset_names (list): all ppi dataset names
      property_names (list): a list of oracles that correspond to some molecular properties
      qm_dataset_names (list): all qm dataset names
      retrosyn_dataset_names (list): all retrosyn dataset names
      sdf_file_names (list): list of sdf file names
      single_molecule_dataset_names (list): all molgen dataset names
      synthetic_oracle_name (list): all oracle names for synthesis
      test_multi_pred_dataset_names (list): test multi pred task name
      test_single_pred_dataset_names (list): test single pred task name
      toxicity_dataset_names (list): all toxicity dataset names
      trivial_oracle_names (list): a list of oracle names for trivial oracles
      yield_dataset_names (list): all yield dataset names
  """
  ####################################
  # test cases
  test_single_pred_dataset_names = ['test_single_pred']
  test_multi_pred_dataset_names = ['test_multi_pred']
  
  # single_pred prediction
  
  toxicity_dataset_names = ['tox21', 'clintox', 'herg', 'dili', 'skin_reaction', 'ames', 'carcinogens_lagunin', 'ld50_zhu']
  
  adme_dataset_names = ['lipophilicity_astrazeneca',
   'solubility_aqsoldb',
   'hydrationfreeenergy_freesolv',
   'caco2_wang',
   'hia_hou',
   'pgp_broccatelli',
   'bioavailability_ma',
   'vdss_lombardo',
   'cyp2c19_veith',
   'cyp2d6_veith',
   'cyp3a4_veith',
   'cyp1a2_veith',
   'cyp2c9_veith',
   'cyp2c9_substrate_carbonmangels', 
   'cyp2d6_substrate_carbonmangels',
   'cyp3a4_substrate_carbonmangels', 
   'bbb_martins',
   'ppbr_az',
   'half_life_obach',
   'clearance_hepatocyte_az',
   'clearance_microsome_az']
  
  hts_dataset_names = ['hiv', 
  'sarscov2_3clpro_diamond', 
  'sarscov2_vitro_touret']
  
  qm_dataset_names = ['qm7b', 'qm8', 'qm9']
  
  epitope_dataset_names = ['iedb_jespersen', 'pdb_jespersen']
  
  paratope_dataset_names = ['sabdab_liberis']
  
  develop_dataset_names = ['tap', 'sabdab_chen']
  
  ####################################
  # multi_pred prediction
  
  dti_dataset_names = ['davis',
   'kiba',
   'bindingdb_kd',
   'bindingdb_ic50',
   'bindingdb_ki',
   'bindingdb_patent']
  
  ppi_dataset_names = ['huri']
  
  peptidemhc_dataset_names = ['mhc2_iedb_jensen', 'mhc1_iedb-imgt_nielsen']
  
  ddi_dataset_names = ['drugbank', 'twosides']
  
  mti_dataset_names = ['mirtarbase']
  
  gda_dataset_names = ['disgenet']
  
  crisproutcome_dataset_names = ['leenay']
  
  drugres_dataset_names = ['gdsc1', 'gdsc2']
  
  drugsyn_dataset_names = ['oncopolypharmacology', 'drugcomb']
  
  antibodyaff_dataset_names = ['protein_sabdab']
  
  yield_dataset_names = ['uspto_yields', 'buchwald-hartwig']
  
  catalyst_dataset_names = ['uspto_catalyst']
  
  
  ####################################
  # generation
  
  
  retrosyn_dataset_names = ['uspto50k', 'uspto']
  
  forwardsyn_dataset_names = ['uspto']
  
  single_molecule_dataset_names = ['zinc', 'moses', 'chembl']
  
  paired_dataset_names = ['uspto50k', 'uspto']
  
  
  ####################################
  # resource
  
  compound_library_names = ['drugbank_drugs', 'chembl_drugs', 'broad_repurposing_hub', 'antivirals']
  biokg_library_names = ['hetionet']
  
  ####################################
  # oracles
  
  #### evaluator for distribution learning, the input of __call__ is list of smiles
  distribution_oracles = ['novelty', 'diversity', 'uniqueness', 'validity', 'fcd_distance', 'kl_divergence']  
  
  
  property_names = ['drd2', 'qed', 'logp', 'sa', 'gsk3b', 'jnk3',]
  
  evaluator_name = ['roc-auc', 'f1', 'pr-auc', 'precision', 'recall', \
  				  'accuracy', 'mse', 'rmse', 'mae', 'r2', 'micro-f1', 'macro-f1', \
  				  'kappa', 'avg-roc-auc', 'rp@k', 'pr@k', 'pcc', 'spearman']
  
  evaluator_name.extend(distribution_oracles)
  
  guacamol_oracle = ['rediscovery', 'similarity', 'median', 'isomers', 'mpo', 'hop', \
  				   'celecoxib_rediscovery', 'troglitazone_rediscovery', 'thiothixene_rediscovery', \
  				   'aripiprazole_similarity', 'albuterol_similarity', 'mestranol_similarity', 
  				   'isomers_c7h8n2o2', 'isomers_c9h10n2o2pf2cl', \
  				   'osimertinib_mpo', 'fexofenadine_mpo', 'ranolazine_mpo', 'perindopril_mpo', \
  				   'amlodipine_mpo', 'sitagliptin_mpo', 'zaleplon_mpo', \
  				   'median1', 'median2', \
  				   'valsartan_smarts', 'deco_hop', 'scaffold_hop']
  
  
  ####################################
  # Benchmark Datasets
  
  admet_benchmark = {'ADME': ['caco2_wang', 
  							'hia_hou',
  							'pgp_broccatelli', 
  							'bioavailability_ma',
  							'lipophilicity_astrazeneca',
  							'solubility_aqsoldb',
  							'bbb_martins',
  							'ppbr_az',
  							'vdss_lombardo',
  							'cyp2d6_veith',
  							'cyp3a4_veith',
  							'cyp2c9_veith',
  							'cyp2d6_substrate_carbonmangels',
  							'cyp3a4_substrate_carbonmangels',
  							'cyp2c9_substrate_carbonmangels',
  							'half_life_obach',
  							'clearance_microsome_az',
  							'clearance_hepatocyte_az'],
  					'Tox':['herg',
  							'ames',
  							'dili',
  							'ld50_zhu']
  					}
  
  drugsyn_benchmark = {'Synergy': ['drugcomb_css',
                                   'drugcomb_hsa',
                                   'drugcomb_loewe',
                                   'drugcomb_bliss',
                                   'drugcomb_zip'
                                   ]}
  
  dti_dg_benchmark = {'DTI': ['bindingdb_patent']} 
  
  docking_benchmark = {'Targets': ['drd3', '1iep', '2rgp', '3eml', '3ny8', '4rlu', '4unn', '5mo4', '7l11']}
  
  docking_target_info = {'drd3': {'center': (9, 22.5, 26), 'size': (15, 15, 15)},
  					   '1iep': {'center': (15.61389189189189, 53.38013513513513, 15.454837837837842), 'size': (15, 15, 15)},
  					   '2rgp': {'center': (16.292121212121213, 34.87081818181819, 92.0353030303030), 'size': (15, 15, 15)},
  					   '3eml': {'center': (-9.063639999999998, -7.1446, 55.86259999999999), 'size': (15, 15, 15)},
  					   '3ny8': {'center': (2.2488, 4.68495, 51.39820000000001), 'size': (15, 15, 15)},
  					   '4rlu': {'center': (-0.7359999999999999, 22.75547368421052, -31.2368947368421), 'size': (15, 15, 15)},
  					   '4unn': {'center': (5.684346153846153, 18.191769230769232, -7.37157692307692), 'size': (15, 15, 15)},
  					   '5mo4': {'center': (-44.901709677419355, 20.490354838709674, 8.483354838709678), 'size': (15, 15, 15)},
  					   '7l11': {'center': (-21.814812500000006, -4.216062499999999, -27.983781250000), 'size': (15, 15, 15)}
  						}
  
  ####################################
  
  #### Benchmark Metrics
  admet_metrics = {'caco2_wang': 'mae',
  				'hia_hou': 'roc-auc',
  				'pgp_broccatelli': 'roc-auc', 
  				'bioavailability_ma': 'roc-auc',
  				'lipophilicity_astrazeneca': 'mae',
  				'solubility_aqsoldb': 'mae',
  				'bbb_martins': 'roc-auc',
  				'ppbr_az': 'mae',
  				'vdss_lombardo': 'spearman',
  				'cyp2c9_veith': 'pr-auc',
  				'cyp2d6_veith': 'pr-auc',
  				'cyp3a4_veith': 'pr-auc',
  				'cyp2c9_substrate_carbonmangels': 'pr-auc',
  				'cyp3a4_substrate_carbonmangels': 'roc-auc',
  				'cyp2d6_substrate_carbonmangels': 'pr-auc',
  				'half_life_obach': 'spearman',
  				'clearance_hepatocyte_az': 'spearman',
  				'clearance_microsome_az': 'spearman',
  				'ld50_zhu': 'mae',
  				'herg': 'roc-auc',
  				'ames': 'roc-auc',
  				'dili': 'roc-auc'
  				}
  
  drugsyn_metrics = {'drugcomb_css': 'mae',
                     'drugcomb_hsa':'mae',
                    'drugcomb_loewe':'mae',
                    'drugcomb_bliss':'mae',
                    'drugcomb_zip':'mae',
                    'drugcomb_css_brain':'mae',
                    'drugcomb_css_ovary':'mae',
                    'drugcomb_css_lung':'mae',
                    'drugcomb_css_skin':'mae',
                    'drugcomb_css_hematopoietic_lymphoid':'mae',
                    'drugcomb_css_breast':'mae',
                    'drugcomb_css_prostate':'mae',
                    'drugcomb_css_kidney':'mae',
                    'drugcomb_css_colon':'mae',
                     }
  
  dti_dg_metrics = {'bindingdb_patent': 'pcc'}
  
  #### Benchmark Splits
  admet_splits = {'caco2_wang': 'scaffold',
  				'hia_hou': 'scaffold',
  				'pgp_broccatelli': 'scaffold', 
  				'bioavailability_ma': 'scaffold',
  				'lipophilicity_astrazeneca': 'scaffold',
  				'solubility_aqsoldb': 'scaffold',
  				'bbb_martins': 'scaffold',
  				'ppbr_az': 'scaffold',
  				'vdss_lombardo': 'scaffold',
  				'cyp2c9_veith': 'scaffold',
  				'cyp2d6_veith': 'scaffold',
  				'cyp3a4_veith': 'scaffold',
  				'cyp2c9_substrate_carbonmangels': 'scaffold',
  				'cyp3a4_substrate_carbonmangels': 'scaffold',
  				'cyp2d6_substrate_carbonmangels': 'scaffold',
  				'half_life_obach': 'scaffold',
  				'clearance_hepatocyte_az': 'scaffold',
  				'clearance_microsome_az': 'scaffold',
  				'ld50_zhu': 'scaffold',
  				'herg': 'scaffold',
  				'ames': 'scaffold',
  				'dili': 'scaffold'
  				}
  
  drugsyn_splits = {'drugcomb_css': 'combination',
                      'drugcomb_hsa': 'combination',
                      'drugcomb_loewe': 'combination',
                      'drugcomb_bliss': 'combination',
                      'drugcomb_zip': 'combination'
                      }
  
  dti_dg_splits = {'bindingdb_patent': 'group'}
  
  ####################################
  
  # evaluator for single molecule, the input of __call__ is a single smiles OR list of smiles
  download_oracle_names = ['drd2', 'gsk3b', 'jnk3', 'fpscores', 'cyp3a4_veith']
  trivial_oracle_names = ['qed', 'logp', 'sa'] + guacamol_oracle
  synthetic_oracle_name = ['askcos', 'ibm_rxn']
  download_receptor_oracle_name = ['1iep_docking', '2rgp_docking', '3eml_docking', '3ny8_docking', '4rlu_docking',
  								 '4unn_docking', '5mo4_docking', '7l11_docking', 'drd3_docking']
  
  
  
  meta_oracle_name = ['isomer_meta', 'rediscovery_meta', 'similarity_meta', 'median_meta', 'docking_score', 'molecule_one_synthesis']
  
  oracle_names = download_oracle_names + trivial_oracle_names + distribution_oracles + synthetic_oracle_name + meta_oracle_name + download_receptor_oracle_name 
  
  molgenpaired_dataset_names = ['qed', 'drd2', 'logp']
  
  generation_datasets = retrosyn_dataset_names + forwardsyn_dataset_names + molgenpaired_dataset_names 
  # generation
  ####################################
  
  category_names = {'single_pred': ["Tox",
  									"ADME",
  									"HTS",
  									"Epitope",
  									"Develop",
  									"QM",
  									"Paratope",
  									"Yields",
  									"CRISPROutcome"],
  				'multi_pred': ["DTI",
  								"PPI",
  								"DDI",
  								"PeptideMHC",
  								"DrugRes",
  								"AntibodyAff",
  								"DrugSyn",
  								"MTI",
  								"GDA",
  								"Catalyst"],
  				'generation': ["RetroSyn",
  								"Reaction",
  								"MolGen"
  								]
  				}
  
  def get_task2category():
  	task2category = {}
  	for i, j in category_names.items():
  		for x in j:
  			task2category[x] = i
  	return task2category
  
  dataset_names = {"Toxicity": toxicity_dataset_names, 
  				"ADME": adme_dataset_names, 
  				"HTS": hts_dataset_names, 
  				"DTI": dti_dataset_names, 
  				"PPI": ppi_dataset_names, 
  				"DDI": ddi_dataset_names,
  				"RetroSyn": retrosyn_dataset_names,
  				"Reaction": forwardsyn_dataset_names, 
  				"MolGen": single_molecule_dataset_names,
  				"PeptideMHC": peptidemhc_dataset_names,
  				"Epitope": epitope_dataset_names,
  				"Develop": develop_dataset_names,
  				"DrugRes": drugres_dataset_names,
  				"QM": qm_dataset_names,
  				"AntibodyAff": antibodyaff_dataset_names,
  				"DrugSyn": drugsyn_dataset_names,
  				"MTI": mti_dataset_names,
  				"GDA": gda_dataset_names,
  				"Paratope": paratope_dataset_names, 
  				"Yields": yield_dataset_names, 
  				"Catalyst": catalyst_dataset_names, 
  				"CRISPROutcome": crisproutcome_dataset_names,
  				"test_single_pred": test_single_pred_dataset_names,
  				"test_multi_pred": test_multi_pred_dataset_names
  				}
  
  benchmark_names = {"admet_group": admet_benchmark,
                     "drugcombo_group": drugsyn_benchmark,
                     "docking_group": docking_benchmark,
                     "dti_dg_group": dti_dg_benchmark}
  
  bm_metric_names = {"admet_group": admet_metrics,
                    "drugcombo_group": drugsyn_metrics,
                    "dti_dg_group": dti_dg_metrics}
  
  bm_split_names = {"admet_group": admet_splits,
                    "drugcombo_group": drugsyn_splits,
                    "dti_dg_group": dti_dg_splits}
  
  dataset_list = []
  for i in dataset_names.keys():
      dataset_list = dataset_list + [i.lower() for i in dataset_names[i]]
  
  name2type = {'toxcast': 'tab',
   'tox21': 'tab',
   'clintox': 'tab',
   'lipophilicity_astrazeneca': 'tab',
   'solubility_aqsoldb': 'tab',
   'hydrationfreeenergy_freesolv': 'tab',
   'caco2_wang': 'tab',
   'hia_hou': 'tab',
   'pgp_broccatelli': 'tab',
   'f20_edrug3d': 'tab',
   'f30_edrug3d': 'tab',
   'bioavailability_ma': 'tab',
   'vd_edrug3d': 'tab',
   'cyp2c19_veith': 'tab',
   'cyp2d6_veith': 'tab',
   'cyp3a4_veith': 'tab',
   'cyp1a2_veith': 'tab',
   'cyp2c9_veith': 'tab',
   'cyp2c9_substrate_carbonmangels': 'tab', 
   'cyp2d6_substrate_carbonmangels': 'tab',
   'cyp3a4_substrate_carbonmangels': 'tab', 
   'carcinogens_lagunin': 'tab', 
   'halflife_edrug3d': 'tab',
   'clearance_edrug3d': 'tab',
   'bbb_adenot': 'tab',
   'bbb_martins': 'tab',
   'ppbr_ma': 'tab',
   'ppbr_edrug3d': 'tab',
   'hiv': 'tab',
   'sarscov2_3clpro_diamond': 'tab',
   'sarscov2_vitro_touret': 'tab',
   'davis': 'tab',
   'kiba': 'tab',
   'bindingdb_kd': 'tab',
   'bindingdb_ic50': 'csv',
   'bindingdb_ki': 'csv',
   'bindingdb_patent': 'csv',
   'huri': 'tab',
   'drugbank': 'tab',
   'twosides': 'csv',
   'mhc1_iedb-imgt_nielsen': 'tab',
   'mhc2_iedb_jensen': 'tab',
   'uspto': 'csv',
   'uspto50k': 'tab',
   'zinc': 'tab', 
   'moses': 'tab',
   'chembl': 'tab',
   'qed': 'tab', 
   'drd2': 'tab', 
   'logp': 'tab',
   'drugcomb':'pkl',
   'gdsc1': 'pkl',
   'gdsc2': 'pkl',
   'iedb_jespersen': 'pkl',
   'pdb_jespersen': 'pkl',
   'qm7b': 'pkl',
   'qm8': 'pkl',
   'qm9': 'pkl',
   'tap': 'tab',
   'sabdab_chen': 'tab',
   'protein_sabdab': 'csv',
   'oncopolypharmacology': 'pkl',
   'mirtarbase': 'csv',
   'disgenet': 'csv',
   'sabdab_liberis': 'pkl',
   'uspto_yields': 'pkl', 
   'uspto_catalyst': 'csv',
   'buchwald-hartwig': 'pkl',
   'hetionet':'tab', 
   'herg': 'tab',
   'dili': 'tab',
   'ppbr_az': 'tab',
   'ames': 'tab',
   'skin_reaction': 'tab',
   'drugbank_drugs': 'csv', 
   'clearance_microsome_az': 'tab',
   'clearance_hepatocyte_az': 'tab',
   'half_life_obach': 'tab',
   'ld50_zhu': 'tab',
   'vdss_lombardo': 'tab',
   'leenay':'tab',
   'test_single_pred': 'tab',
   'test_multi_pred': 'tab',
   'gdsc_gene_symbols': 'tab'}
  
  name2id = {'bbb_adenot': 4259565,
   'bbb_martins': 4259566,
   'bindingdb_ic50': 4291560,
   'bindingdb_kd': 4291555,
   'bindingdb_ki': 4291556,
   'bindingdb_patent': 4724851,
   'bioavailability_ma': 4259567,
   'caco2_wang': 4259569,
   'clearance_edrug3d': 4259571,
   'clintox': 4259572,
   'cyp1a2_veith': 4259573,
   'cyp2c19_veith': 4259576,
   'cyp2c9_veith': 4259577,
   'cyp2d6_veith': 4259580,
   'cyp3a4_veith': 4259582,
   'cyp2c9_substrate_carbonmangels': 4259584,
   'cyp2d6_substrate_carbonmangels': 4259578,
   'cyp3a4_substrate_carbonmangels': 4259581, 
   'carcinogens_lagunin': 4259570,
   'davis': 5219748,
   'drugbank': 4139573,
   'drugcomb': 4215720,
   'f20_edrug3d': 4259586,
   'f30_edrug3d': 4259589,
   'halflife_edrug3d': 4259587,
   'hia_hou': 4259591,
   'hiv': 4259593,
   'huri': 4139567,
   'hydrationfreeenergy_freesolv': 4259594,
   'kiba': 5255037,
   'lipophilicity_astrazeneca': 4259595,
   'pgp_broccatelli': 4259597,
   'ppbr_edrug3d': 4259600,
   'ppbr_ma': 4259603,
   'sarscov2_3clpro_diamond': 4259606,
   'sarscov2_vitro_touret': 4259607,
   'solubility_aqsoldb': 4259610,
   'tox21': 4259612,
   'toxcast': 4259613,
   'twosides': 4139574,
   'vd_edrug3d': 4259618,
   'mhc1_iedb-imgt_nielsen': 4167073,
   'mhc2_iedb_jensen': 4167074,
   'zinc': 4170963,
   'moses': 4170962,
   'chembl': 4170965,
   'qed': 4170959, 
   'drd2': 4170957, 
   'logp': 4170961, 
   'gdsc1': 4165726,
   'gdsc2': 4165727,
   'iedb_jespersen': 4165725, 
   'pdb_jespersen': 4165724,
   'qm7b': 4167096,
   'qm8': 4167110,
   'qm9': 4167112,
   'tap': 4167113,
   'sabdab_chen': 4167164,
   'protein_sabdab': 4167357,
   'oncopolypharmacology': 4167358,
   'mirtarbase': 4167359,
   'disgenet': 4168282,
   'sabdab_liberis': 4168425,
   'uspto50k': 4171823,
   'buchwald-hartwig': 4186955,
   'uspto_yields': 4186956, 
   'uspto_catalyst': 4171574,
   'uspto': 4171642, 
   'hetionet': 4201734,
   'herg': 4259588,
   'dili': 4259585,
   'ppbr_az': 4259599,
   'ames': 4259564,
   'skin_reaction': 4259609,
   'clearance_microsome_az': 4266186,
   'clearance_hepatocyte_az': 4266187,
   'ld50_zhu': 4267146,
   'half_life_obach': 4266799,
   'vdss_lombardo': 4267387,
   'leenay':4279966,
   'test_single_pred': 4832455,
   'test_multi_pred': 4832456,
   'gdsc_gene_symbols': 5255026}
  
  oracle2type = {'drd2': 'pkl', 
  			   'jnk3': 'pkl', 
  			   'gsk3b': 'pkl',
  			   'fpscores': 'pkl', 
  			   'cyp3a4_veith': 'pkl', 
  			   }
  
  oracle2id = {'drd2': 4178625,
  			 'gsk3b': 4170295,
  			 'jnk3': 4170293,
  			 'fpscores': 4170416, 
  			 'cyp3a4_veith': 4411249, 
  			}
  
  benchmark2type = {'admet_group': 'zip',
                    'drugcombo_group': 'zip',
                    'docking_group': 'zip',
                    'dti_dg_group': 'zip'}
  
  benchmark2id = {'admet_group': 4426004,
                  'drugcombo_group':  4426002,
                  'docking_group': 4554082,
                  'dti_dg_group': 4742443}
  
  receptor2id = {'drd3': 5137901, 
  			   '1iep': 5137914, 
  			   '2rgp': 5137916, 
  			   '3eml': 5137919, 
  			   '3ny8': 5137915, 
  			   '4rlu': 5137918, 
  			   '4unn': 5137917, 
  			   '5mo4': 5137920, 
  			   '7l11': 5137921, }
  
  
  sdf_file_names = {
  	'grambow': ['Product', 'Reactant', 'TS']
  }
  
  name2stats = {
  	'caco2_wang': 906,
  	'hia_hou': 578,
  	'pgp_broccatelli': 1212,
  	'bioavailability_ma': 640,
  	'lipophilicity_astrazeneca': 4200,
  	'solubility_aqsoldb': 9982,
  	'bbb_martins': 1975,
  	'ppbr_az': 1797,
  	'vdss_lombardo': 1130,
  	'cyp2c19_veith': 12092,
  	'cyp2d6_veith': 13130,
  	'cyp3a4_veith': 12328,
  	'cyp1a2_veith': 12579,
  	'cyp2c9_veith': 12092,
  	'cyp2c9_substrate_carbonmangels': 666,
  	'cyp2d6_substrate_carbonmangels': 664,
  	'cyp3a4_substrate_carbonmangels': 667,
  	'half_life_obach': 667,
  	'clearance_hepatocyte_az': 1020,
  	'clearance_microsome_az': 1102,
  	'ld50_zhu': 7385,
  	'herg': 648,
  	'ames': 7255,
  	'dili': 475,
  	'skin_reaction': 404,
  	'carcinogens_lagunin':278,
  	'tox21': 7831,
  	'clintox': 1484,
  	'sarscov2_vitro_touret': 1480,
  	'sarscov2_3clpro_diamond': 879,
  	'hiv': 41127,
  	'qm7b': 7211,
  	'qm8': 21786,
  	'qm9': 133885,
  	'uspto_yields': 853638,
  	'buchwald-hartwig': 55370,
  	'sabdab_liberis': 1023,
  	'iedb_jespersen': 3159,
  	'pdb_jespersen': 447,
  	'tap': 242,
  	'sabdab_chen': 2409,
  	'leenay': 1521,
  	'bindingdb_kd': 52284,
  	'bindingdb_ki': 375032,
  	'bindingdb_ic50': 991486,
  	'bindingdb_patent': 243344,
  	'davis': 27621,
  	'kiba': 118036,
  	'drugbank': 191808,
  	'twosides': 4649441,
  	'huri': 51813,
  	'disgenet': 52476,
  	'gdsc1': 177310,
  	'gdsc2': 92703,
  	'drugcomb': 297098,
  	'oncopolypharmacology': 23052,
  	'mhc1_iedb-imgt_nielsen': 185985,
  	'mhc2_iedb_jensen': 134281,
  	'protein_sabdab': 493,
  	'mirtarbase': 400082,
  	'uspto_catalyst': 721799,
  	'moses': 1936962,
  	'zinc': 249455,
  	'chembl': 1961462,
  	'uspto50k': 50036,
  	'uspto': 1939253
  }
  