old_contents: import datetime
  from django.db import models
  from chembl_core_model.models import *
  from chembl_core_db.db.customFields import BlobField, ChemblCharField
  from chembl_core_db.db.customManagers import CompoundMolsManager
  from chembl_core_db.db.models.abstractModel import ChemblCoreAbstractModel
  from chembl_core_db.db.models.abstractModel import ChemblModelMetaClass
  from django.utils import six
  
  
  class TargetPredictions(six.with_metaclass(ChemblModelMetaClass, ChemblCoreAbstractModel)):
  
      pred_id = ChemblPositiveIntegerField(primary_key=True, length=11, blank=False, null=False, help_text=u'Prediction unique ID')
      parent_molregno =  ChemblPositiveIntegerField(length=11, null=False, help_text=u'')
      chembl_id = ChemblCharField(max_length=20, null=False, help_text=u'ChEMBL identifier for this compound (for use on web interface etc)')
      tid = ChemblPositiveIntegerField(length=11, null=False, help_text=u'Unique ID for the target')
      target_chembl_id = ChemblCharField(max_length=20, help_text=u'ChEMBL identifier for this target (for use on web interface etc)')
      target_accession = ChemblCharField(max_length=20, help_text=u'Accession for the sequence in the source database from which it was taken (e.g., UniProt accession for proteins).')
      probability = models.DecimalField(max_digits=20, decimal_places=18, help_text=u'Probability of binding for this molecule-target pair')
      in_training = ChemblCharField(max_length=20, help_text=u'Mark if molecule is in training')
      value = ChemblPositiveIntegerField(length=5, null=False, help_text=u'1uM or 10uM')
  
      class Meta:
          db_table = 'target_predictions'
  
new_contents: import datetime
  from django.db import models
  from chembl_core_model.models import *
  from chembl_core_db.db.customFields import BlobField, ChemblCharField
  from chembl_core_db.db.customManagers import CompoundMolsManager
  from chembl_core_db.db.models.abstractModel import ChemblCoreAbstractModel
  from chembl_core_db.db.models.abstractModel import ChemblModelMetaClass
  from django.utils import six
  
  
  class TargetPredictions(six.with_metaclass(ChemblModelMetaClass, ChemblCoreAbstractModel)):
  
      pred_id = ChemblPositiveIntegerField(primary_key=True, length=11, blank=False, null=False, help_text=u'Prediction unique ID')
      parent_molregno =  ChemblPositiveIntegerField(length=11, null=False, help_text=u'')
      chembl_id = ChemblCharField(max_length=20, null=False, help_text=u'ChEMBL identifier for this compound (for use on web interface etc)')
      tid = ChemblPositiveIntegerField(length=11, null=False, help_text=u'Unique ID for the target')
      target_chembl_id = ChemblCharField(max_length=20, help_text=u'ChEMBL identifier for this target (for use on web interface etc)')
      target_accession = ChemblCharField(max_length=20, help_text=u'Accession for the sequence in the source database from which it was taken (e.g., UniProt accession for proteins).')
      probability = models.DecimalField(max_digits=12, decimal_places=11, help_text=u'Probability of binding for this molecule-target pair')
      in_training = ChemblCharField(max_length=20, help_text=u'Mark if molecule is in training')
      value = ChemblPositiveIntegerField(length=5, null=False, help_text=u'1uM or 10uM')
  
      class Meta:
          db_table = 'target_predictions'
subject: fix predictions model