def to_sql(self, sess: Session):
source_id = add_dataset(sess, self.origin)
add_props(sess, self.properties)
mols = Molecules(sess)
df = self.to_df()
row_count, _ = df.shape
for _, row in tqdm(df.iterrows(), total=row_count, unit=' row'):
mols.add(source_id, row.smiles, row.to_dict())
mols.commit()
def to_sql(self, sess: Session):
source_id = add_dataset(sess, self.origin)
add_props(sess, self.properties)
mols = Molecules(sess)
for row in tqdm(self._generate(),
total=len(new_client.molecule),
unit=' row'):
smiles, props = row
mols.add(source_id, smiles, props)
def to_sql(self, sess: Session):
source_id = add_dataset(sess, self.origin)
add_props(sess, self.props)
mols = Molecules(sess)
mols.add(source_id, 'CN1CCC[C@H]1c2cccnc2', {'Tag': 'Test'},
PartitionCategory.Unspecific)
mols.add(source_id,
'O1C=C[C@H]([C@H]1O2)c3c2cc(OC)c4c3OC(=O)C5=C4CCC(=O)5', {
'Tag': ['Test1', 'Test2'],
'NR-AR': 1.0
}, PartitionCategory.Verify)
mols.commit()
def to_sql(self, sess: Session):
source_id = add_dataset(sess, self.origin)
add_props(sess, self.properties)
mols = Molecules(sess)
df = self.to_df()
row_count, _ = df.shape
for _, row in tqdm(df.iterrows(), total=row_count, unit=' row'):
if row.SPLIT == 'train':
partition = PartitionCategory.Train
elif row.SPLIT == 'test':
partition = PartitionCategory.Test
else:
partition = PartitionCategory.Unspecific
mols.add(source_id, row.SMILES, {'Tag': 'MOSES'}, partition)
mols.commit()