class TestSmilesGenerator(SmilesTests):
"""
Test SmilesGenerator.
"""
def setUp(self):
"""
Set up tests.
"""
super(TestSmilesGenerator, self).setUp()
self.engine = SmilesGenerator()
def test_get_smiles(self):
"""
Test SmilesGenerator.get_smiles.
"""
for mol in self.mols:
smiles = self.engine.get_smiles(mol)
new = Chem.MolFromSmiles(smiles)
assert new.GetNumAtoms() == mol.GetNumAtoms()
def test_get_smiles_3d(self):
"""
Test SmilesGenerator.get_smiles with stereochemistry assigned from 3D
coordinates.
"""
# generate conformers for ibuprofen
engine = conformers.ConformerGenerator()
mol = engine.generate_conformers(self.mols[1])
assert mol.GetNumConformers() > 0
# check that chirality has not yet been assigned
smiles = self.engine.get_smiles(mol)
assert '@' not in smiles # check for absence of chirality marker
chiral_types = [
Chem.ChiralType.CHI_TETRAHEDRAL_CW,
Chem.ChiralType.CHI_TETRAHEDRAL_CCW
]
chiral = False
for atom in mol.GetAtoms():
if atom.GetChiralTag() in chiral_types:
chiral = True
assert not chiral
# generate SMILES
self.engine = SmilesGenerator(assign_stereo_from_3d=True)
smiles = self.engine.get_smiles(mol)
assert '@' in smiles # check for chirality marker
new = Chem.MolFromSmiles(smiles)
assert new.GetNumAtoms() == self.mols[1].GetNumAtoms()
# check that chirality was assigned to ibuprofen
chiral = False
for atom in mol.GetAtoms():
if atom.GetChiralTag() in chiral_types:
chiral = True
assert chiral
class TestSmilesGenerator(SmilesTests):
"""
Test SmilesGenerator.
"""
def setUp(self):
"""
Set up tests.
"""
super(TestSmilesGenerator, self).setUp()
self.engine = SmilesGenerator()
def test_get_smiles(self):
"""
Test SmilesGenerator.get_smiles.
"""
for mol in self.mols:
smiles = self.engine.get_smiles(mol)
new = Chem.MolFromSmiles(smiles)
assert new.GetNumAtoms() == mol.GetNumAtoms()
def test_get_smiles_3d(self):
"""
Test SmilesGenerator.get_smiles with stereochemistry assigned from 3D
coordinates.
"""
# generate conformers for ibuprofen
engine = conformers.ConformerGenerator()
mol = engine.generate_conformers(self.mols[1])
assert mol.GetNumConformers() > 0
# check that chirality has not yet been assigned
smiles = self.engine.get_smiles(mol)
assert '@' not in smiles # check for absence of chirality marker
chiral_types = [Chem.ChiralType.CHI_TETRAHEDRAL_CW,
Chem.ChiralType.CHI_TETRAHEDRAL_CCW]
chiral = False
for atom in mol.GetAtoms():
if atom.GetChiralTag() in chiral_types:
chiral = True
assert not chiral
# generate SMILES
self.engine = SmilesGenerator(assign_stereo_from_3d=True)
smiles = self.engine.get_smiles(mol)
assert '@' in smiles # check for chirality marker
new = Chem.MolFromSmiles(smiles)
assert new.GetNumAtoms() == self.mols[1].GetNumAtoms()
# check that chirality was assigned to ibuprofen
chiral = False
for atom in mol.GetAtoms():
if atom.GetChiralTag() in chiral_types:
chiral = True
assert chiral
def read_targets(self):
"""
Get labels for molecules from SD data fields matching dataset names.
Returns
-------
data : dict
Nested dictionary containing SMILES and targets for compounds in
each dataset. Keyed by data->dataset->SMILES->target, where target
is a list.
"""
engine = SmilesGenerator()
data = {dataset: {} for dataset in self.dataset_names}
skipped = []
for mol in self.read_data():
smiles = engine.get_smiles(mol)
for prop in list(mol.GetPropNames()):
if prop in data:
score = int(mol.GetProp(prop))
if smiles not in data[prop]:
data[prop][smiles] = []
data[prop][smiles].append(score)
else: # skip irrelevant SD fields
if prop not in skipped:
skipped.append(prop)
continue
print 'Skipped properties:\n{}'.format('\n'.join(skipped))
return data
def read_targets(self):
"""
Get labels for molecules from SD data fields matching dataset names.
Returns
-------
data : dict
Nested dictionary containing SMILES and targets for compounds in
each dataset. Keyed by data->dataset->SMILES->target, where target
is a list.
"""
engine = SmilesGenerator()
data = {dataset: {} for dataset in self.dataset_names}
skipped = []
for mol in self.read_data():
smiles = engine.get_smiles(mol)
for prop in list(mol.GetPropNames()):
if prop in data:
score = int(mol.GetProp(prop))
if smiles not in data[prop]:
data[prop][smiles] = []
data[prop][smiles].append(score)
else: # skip irrelevant SD fields
if prop not in skipped:
skipped.append(prop)
continue
print 'Skipped properties:\n{}'.format('\n'.join(skipped))
return data
def main(featurizer_class,
input_filename,
output_filename,
target_filename=None,
featurizer_kwargs=None,
parallel=False,
client_kwargs=None,
view_flags=None,
compression_level=3,
smiles_hydrogens=False,
include_smiles=False,
scaffolds=False,
chiral_scaffolds=False,
mol_id_prefix=None):
"""
Featurize molecules in input_filename using the given featurizer.
Parameters
----------
featurizer_class : Featurizer
Featurizer class.
input_filename : str
Filename containing molecules to be featurized.
output_filename : str
Output filename. Should end with .pkl or .pkl.gz.
target_filename : str, optional
Pickle containing target values. Should either be array_like or a dict
containing 'names' and 'y' keys, corresponding to molecule names and
target values.
featurizer_kwargs : dict, optional
Keyword arguments passed to featurizer.
parallel : bool, optional
Whether to train subtrainers in parallel using IPython.parallel
(default False).
client_kwargs : dict, optional
Keyword arguments for IPython.parallel Client.
view_flags : dict, optional
Flags for IPython.parallel LoadBalancedView.
compression_level : int, optional (default 3)
Compression level (0-9) to use with joblib.dump.
smiles_hydrogens : bool, optional (default False)
Whether to keep hydrogens when generating SMILES.
include_smiles : bool, optional (default False)
Include SMILES in output.
scaffolds : bool, optional (default False)
Whether to include scaffolds in output.
chiral_scaffods : bool, optional (default False)
Whether to include chirality in scaffolds.
mol_id_prefix : str, optional
Prefix for molecule IDs.
"""
mols, mol_ids = read_mols(input_filename, mol_id_prefix=mol_id_prefix)
# get targets
data = {}
if target_filename is not None:
targets = read_pickle(target_filename)
if isinstance(targets, dict):
mol_indices, target_indices = collate_mols(mols, mol_ids,
targets['y'],
targets['mol_id'])
mols = mols[mol_indices]
mol_ids = mol_ids[mol_indices]
targets = np.asarray(targets['y'])[target_indices]
else:
assert len(targets) == len(mols)
data['y'] = targets
# featurize molecules
print "Featurizing molecules..."
if featurizer_kwargs is None:
featurizer_kwargs = {}
featurizer = featurizer_class(**featurizer_kwargs)
features = featurizer.featurize(mols, parallel, client_kwargs, view_flags)
# fill in data container
print "Saving results..."
data['mol_id'] = mol_ids
data['features'] = features
# sanity checks
assert data['features'].shape[0] == len(mols), (
"Features do not match molecules.")
assert data['mol_id'].shape[0] == len(mols), (
"Molecule IDs do not match molecules.")
# smiles, scaffolds, args
if include_smiles:
smiles = SmilesGenerator(remove_hydrogens=(not smiles_hydrogens))
data['smiles'] = np.asarray([smiles.get_smiles(mol) for mol in mols])
if scaffolds:
data['scaffolds'] = get_scaffolds(mols, chiral_scaffolds)
# construct a DataFrame
try:
if data['features'].ndim > 1:
# numpy arrays will be "summarized" when written as strings
# use str(row.tolist())[1:-1] to remove the surrounding brackets
# remove commas (keeping spaces) to avoid conflicts with csv
if (output_filename.endswith('.csv')
or output_filename.endswith('.csv.gz')):
data['features'] = [
str(row.tolist())[1:-1].replace(', ', ' ')
for row in data['features']
]
else:
data['features'] = [row for row in data['features']]
except AttributeError:
pass
df = pd.DataFrame(data)
# write output file
write_output_file(df, output_filename, compression_level)
class TestClassificationTargets(unittest.TestCase):
"""
Tests for classification_targets.py.
"""
def setUp(self):
"""
Set up tests.
"""
smiles = [
'CC(=O)OC1=CC=CC=C1C(=O)O', 'CC(C)CC1=CC=C(C=C1)C(C)C(=O)O',
'CC1=CC=C(C=C1)C2=CC(=NN2C3=CC=C(C=C3)S(=O)(=O)N)C(F)(F)F'
]
names = ['aspirin', 'ibuprofen', 'celecoxib']
self.y = [0, 1, 0]
self.mols = []
for s, n in zip(smiles, names):
mol = Chem.MolFromSmiles(s)
mol.SetProp('_Name', n)
self.mols.append(mol)
# write active and decoy files
self.temp_dir = tempfile.mkdtemp()
_, self.active_filename = tempfile.mkstemp(dir=self.temp_dir,
suffix='.smi')
_, self.decoy_filename = tempfile.mkstemp(dir=self.temp_dir,
suffix='.smi')
active = open(self.active_filename, 'wb')
decoy = open(self.decoy_filename, 'wb')
for this_smiles, name, y in zip(smiles, names, self.y):
data = '{}\t{}\n'.format(this_smiles, name)
if y:
active.write(data)
else:
decoy.write(data)
active.close()
decoy.close()
_, self.output_filename = tempfile.mkstemp(dir=self.temp_dir)
# get SMILES
self.engine = SmilesGenerator()
self.smiles = [self.engine.get_smiles(mol) for mol in self.mols]
def tearDown(self):
"""
Clean up tests.
"""
shutil.rmtree(self.temp_dir)
def check_output(self, input_args):
"""
Check main output.
Parameters
----------
input_args : list
Command-line arguments.
"""
args = parse_args(input_args)
main(args.actives, args.decoys, args.output, args.stereo_from_3d)
data = read_pickle(self.output_filename)
for smiles, target in zip(data['smiles'], data['targets']):
assert smiles in self.smiles
assert target == self.y[self.smiles.index(smiles)]
return data['smiles'], data['targets']
def test_defaults(self):
"""
Test main with default parameters.
"""
args = [
'-a', self.active_filename, '-d', self.decoy_filename, '-o',
self.output_filename
]
self.check_output(args)
def test_stereo_to_3d(self):
"""
Test main with --stereo-to-3d.
"""
# generate conformers for ibuprofen
engine = conformers.ConformerGenerator()
self.mols[1] = engine.generate_conformers(self.mols[1])
assert self.mols[1].GetNumConformers() > 0
# rewrite actives file with 3D coordinates
_, self.active_filename = tempfile.mkstemp(dir=self.temp_dir,
suffix='.sdf')
with serial.MolWriter().open(self.active_filename) as writer:
for mol, y in zip(self.mols, self.y):
if y:
writer.write([self.mols[1]])
# check for absence of chirality using default arguments
smiles, targets = self.check_output([
'-a', self.active_filename, '-d', self.decoy_filename, '-o',
self.output_filename
])
chiral = False
for this_smiles in smiles:
if '@' in this_smiles:
chiral = True
assert not chiral
# update reference SMILES
self.engine = SmilesGenerator(assign_stereo_from_3d=True)
self.smiles[1] = self.engine.get_smiles(self.mols[1])
# check for presence of chiraliy using --stereo-from-3d
smiles, targets = self.check_output([
'-a', self.active_filename, '-d', self.decoy_filename, '-o',
self.output_filename, '--stereo-from-3d'
])
chiral = False
for this_smiles in smiles:
if '@' in this_smiles:
chiral = True
assert chiral
def main(featurizer_class, input_filename, output_filename,
target_filename=None, featurizer_kwargs=None, parallel=False,
client_kwargs=None, view_flags=None, compression_level=3,
smiles_hydrogens=False, include_smiles=False, scaffolds=False,
chiral_scaffolds=False, mol_id_prefix=None):
"""
Featurize molecules in input_filename using the given featurizer.
Parameters
----------
featurizer_class : Featurizer
Featurizer class.
input_filename : str
Filename containing molecules to be featurized.
output_filename : str
Output filename. Should end with .pkl or .pkl.gz.
target_filename : str, optional
Pickle containing target values. Should either be array_like or a dict
containing 'names' and 'y' keys, corresponding to molecule names and
target values.
featurizer_kwargs : dict, optional
Keyword arguments passed to featurizer.
parallel : bool, optional
Whether to train subtrainers in parallel using IPython.parallel
(default False).
client_kwargs : dict, optional
Keyword arguments for IPython.parallel Client.
view_flags : dict, optional
Flags for IPython.parallel LoadBalancedView.
compression_level : int, optional (default 3)
Compression level (0-9) to use with joblib.dump.
smiles_hydrogens : bool, optional (default False)
Whether to keep hydrogens when generating SMILES.
include_smiles : bool, optional (default False)
Include SMILES in output.
scaffolds : bool, optional (default False)
Whether to include scaffolds in output.
chiral_scaffods : bool, optional (default False)
Whether to include chirality in scaffolds.
mol_id_prefix : str, optional
Prefix for molecule IDs.
"""
mols, mol_ids = read_mols(input_filename, mol_id_prefix=mol_id_prefix)
# get targets
data = {}
if target_filename is not None:
targets = read_pickle(target_filename)
if isinstance(targets, dict):
mol_indices, target_indices = collate_mols(
mols, mol_ids, targets['y'], targets['mol_id'])
mols = mols[mol_indices]
mol_ids = mol_ids[mol_indices]
targets = np.asarray(targets['y'])[target_indices]
else:
assert len(targets) == len(mols)
data['y'] = targets
# featurize molecules
print "Featurizing molecules..."
if featurizer_kwargs is None:
featurizer_kwargs = {}
featurizer = featurizer_class(**featurizer_kwargs)
features = featurizer.featurize(mols, parallel, client_kwargs, view_flags)
# fill in data container
print "Saving results..."
data['mol_id'] = mol_ids
data['features'] = features
# sanity checks
assert data['features'].shape[0] == len(mols), (
"Features do not match molecules.")
assert data['mol_id'].shape[0] == len(mols), (
"Molecule IDs do not match molecules.")
# smiles, scaffolds, args
if include_smiles:
smiles = SmilesGenerator(remove_hydrogens=(not smiles_hydrogens))
data['smiles'] = np.asarray([smiles.get_smiles(mol) for mol in mols])
if scaffolds:
data['scaffolds'] = get_scaffolds(mols, chiral_scaffolds)
# construct a DataFrame
try:
if data['features'].ndim > 1:
# numpy arrays will be "summarized" when written as strings
# use str(row.tolist())[1:-1] to remove the surrounding brackets
# remove commas (keeping spaces) to avoid conflicts with csv
if (output_filename.endswith('.csv')
or output_filename.endswith('.csv.gz')):
data['features'] = [str(row.tolist())[1:-1].replace(', ', ' ')
for row in data['features']]
else:
data['features'] = [row for row in data['features']]
except AttributeError:
pass
df = pd.DataFrame(data)
# write output file
write_output_file(df, output_filename, compression_level)
class Dragon(object):
"""
Wrapper for dragon6shell.
Parameters
----------
subset : str, optional (default '2d')
Descriptor subset.
kwargs : dict, optional
Keyword arguments for SmilesGenerator.
"""
def __init__(self, subset="2d", **kwargs):
self.subset = subset
self.initialized = False
self.config_filename, self.smiles_engine = None, None
self.smiles_engine_kwargs = kwargs
def initialize(self):
"""
Initialize.
This is not part of __init__ because it breaks IPython.parallel.
"""
fd, self.config_filename = tempfile.mkstemp()
os.close(fd)
with open(self.config_filename, "wb") as f:
f.write(self.get_config())
self.smiles_engine = SmilesGenerator(**self.smiles_engine_kwargs)
self.initialized = True
def __del__(self):
"""
Cleanup.
"""
if self.config_filename is not None:
os.unlink(self.config_filename)
def get_config(self):
"""
Get configuration file.
"""
if self.subset == "2d":
return """<?xml version="1.0" encoding="utf-8"?>
<DRAGON version="6.0.36" script_version="1" generation_date="2014/11/17">
<OPTIONS>
<CheckUpdates value="true"/>
<SaveLayout value="true"/>
<ShowWorksheet value="false"/>
<Decimal_Separator value="."/>
<Missing_String value="NaN"/>
<DefaultMolFormat value="1"/>
<HelpBrowser value="/usr/bin/xdg-open"/>
<RejectUnusualValence value="false"/>
<Add2DHydrogens value="false"/>
<MaxSRforAllCircuit value="19"/>
<MaxSR value="35"/>
<MaxSRDetour value="30"/>
<MaxAtomWalkPath value="2000"/>
<LogPathWalk value="true"/>
<LogEdge value="true"/>
<Weights>
<weight name="Mass"/>
<weight name="VdWVolume"/>
<weight name="Electronegativity"/>
<weight name="Polarizability"/>
<weight name="Ionization"/>
<weight name="I-State"/>
</Weights>
<SaveOnlyData value="false"/>
<SaveLabelsOnSeparateFile value="false"/>
<SaveFormatBlock value="%b - %n.txt"/>
<SaveFormatSubBlock value="%b-%s - %n - %m.txt"/>
<SaveExcludeMisVal value="false"/>
<SaveExcludeAllMisVal value="false"/>
<SaveExcludeConst value="false"/>
<SaveExcludeNearConst value="false"/>
<SaveExcludeStdDev value="false"/>
<SaveStdDevThreshold value="0.0001"/>
<SaveExcludeCorrelated value="false"/>
<SaveCorrThreshold value="0.95"/>
<SaveExclusionOptionsToVariables value="false"/>
<SaveExcludeMisMolecules value="false"/>
<SaveExcludeRejectedMolecules value="false"/>
</OPTIONS>
<DESCRIPTORS>
<block id="1" SelectAll="true"/>
<block id="2" SelectAll="true"/>
<block id="3" SelectAll="true"/>
<block id="4" SelectAll="true"/>
<block id="5" SelectAll="true"/>
<block id="6" SelectAll="true"/>
<block id="7" SelectAll="true"/>
<block id="8" SelectAll="true"/>
<block id="9" SelectAll="true"/>
<block id="10" SelectAll="true"/>
<block id="11" SelectAll="true"/>
<block id="12" SelectAll="true"/>
<block id="21" SelectAll="true"/>
<block id="22" SelectAll="true"/>
<block id="23" SelectAll="true"/>
<block id="24" SelectAll="true"/>
<block id="25" SelectAll="true"/>
<block id="28" SelectAll="true"/>
<block id="29" SelectAll="true"/>
</DESCRIPTORS>
<MOLFILES>
<molInput value="stdin"/>
<molInputFormat value="SMILES"/>
</MOLFILES>
<OUTPUT>
<SaveStdOut value="true"/>
<SaveProject value="false"/>
<SaveFile value="false"/>
<logMode value="stderr"/>
</OUTPUT>
</DRAGON>
"""
else:
raise NotImplementedError
def get_descriptors(self, mols):
"""
Parameters
----------
mols : array_like
Molecules.
"""
if not self.initialized:
self.initialize()
smiles = [self.smiles_engine.get_smiles(mol) for mol in mols]
args = ["dragon6shell", "-s", self.config_filename]
p = subprocess.Popen(args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = p.communicate("\n".join(smiles))
if not stdout:
raise RuntimeError(stderr)
data, names = self.parse_descriptors(stdout)
# adjust for skipped molecules
# descriptors are in same order as smiles
missing = np.setdiff1d(smiles, names)
features = np.zeros(len(smiles), dtype=object)
idx = 0 # index into calculated features
for i, this_smiles in enumerate(smiles):
if this_smiles in missing:
features[i] = None
else:
assert this_smiles == names[idx] # confirm match
features[i] = data[idx]
idx += 1
assert len(features) == len(mols)
return features
def parse_descriptors(self, string):
"""
Parse Dragon descriptors.
Parameters
----------
string : str
Output from dragon6shell.
"""
df = pd.read_table(StringIO(string))
if self.subset == "2d":
del df["nHBonds"], df["Psi_e_1d"], df["Psi_e_1s"]
# extract names
names = df["NAME"].values
# delete No. and NAME columns
del df["No."], df["NAME"]
return np.asarray(df, dtype=float), names
class MoleculeDatabase(object):
"""
Molecule database.
Molecules are keyed by SMILES.
Parameters
----------
kwargs : dict, optional
Keyword arguments for SmilesMap.
"""
def __init__(self, **kwargs):
self.engine = SmilesGenerator(**kwargs)
self.smiles = set()
def __len__(self):
return len(self.smiles)
def __iter__(self):
for smiles in self.smiles:
yield smiles
def __contains__(self, item):
return item in self.smiles
def add_mol(self, mol):
"""
Add a molecule to the database.
Parameters
----------
mol : RDKit Mol
Molecule.
"""
self.smiles.add(self.engine.get_smiles(mol))
def load(self, filename):
"""
Load an existing database.
Parameters
----------
filename : str
Existing database filename.
"""
if filename.endswith('.gz'):
f = gzip.open(filename)
else:
f = open(filename)
for line in f:
smiles = line.strip()
mol = Chem.MolFromSmiles(smiles) # sanity check
if mol is None:
raise ValueError(
'Database is unreadable: "{}".'.format(smiles))
self.smiles.add(smiles)
f.close()
def save(self, filename):
"""
Save the database to disk.
Parameters
----------
filename : str
Filename.
"""
if filename.endswith('.gz'):
f = gzip.open(filename, 'wb')
else:
f = open(filename, 'wb')
for smiles in self.smiles:
f.write('{}\n'.format(smiles))
f.close()
class TestClassificationTargets(unittest.TestCase):
"""
Tests for classification_targets.py.
"""
def setUp(self):
"""
Set up tests.
"""
smiles = ['CC(=O)OC1=CC=CC=C1C(=O)O', 'CC(C)CC1=CC=C(C=C1)C(C)C(=O)O',
'CC1=CC=C(C=C1)C2=CC(=NN2C3=CC=C(C=C3)S(=O)(=O)N)C(F)(F)F']
names = ['aspirin', 'ibuprofen', 'celecoxib']
self.y = [0, 1, 0]
self.mols = []
for s, n in zip(smiles, names):
mol = Chem.MolFromSmiles(s)
mol.SetProp('_Name', n)
self.mols.append(mol)
# write active and decoy files
self.temp_dir = tempfile.mkdtemp()
_, self.active_filename = tempfile.mkstemp(dir=self.temp_dir,
suffix='.smi')
_, self.decoy_filename = tempfile.mkstemp(dir=self.temp_dir,
suffix='.smi')
active = open(self.active_filename, 'wb')
decoy = open(self.decoy_filename, 'wb')
for this_smiles, name, y in zip(smiles, names, self.y):
data = '{}\t{}\n'.format(this_smiles, name)
if y:
active.write(data)
else:
decoy.write(data)
active.close()
decoy.close()
_, self.output_filename = tempfile.mkstemp(dir=self.temp_dir)
# get SMILES
self.engine = SmilesGenerator()
self.smiles = [self.engine.get_smiles(mol) for mol in self.mols]
def tearDown(self):
"""
Clean up tests.
"""
shutil.rmtree(self.temp_dir)
def check_output(self, input_args):
"""
Check main output.
Parameters
----------
input_args : list
Command-line arguments.
"""
args = parse_args(input_args)
main(args.actives, args.decoys, args.output, args.stereo_from_3d)
data = read_pickle(self.output_filename)
for smiles, target in zip(data['smiles'], data['targets']):
assert smiles in self.smiles
assert target == self.y[self.smiles.index(smiles)]
return data['smiles'], data['targets']
def test_defaults(self):
"""
Test main with default parameters.
"""
args = ['-a', self.active_filename, '-d', self.decoy_filename, '-o',
self.output_filename]
self.check_output(args)
def test_stereo_to_3d(self):
"""
Test main with --stereo-to-3d.
"""
# generate conformers for ibuprofen
engine = conformers.ConformerGenerator()
self.mols[1] = engine.generate_conformers(self.mols[1])
assert self.mols[1].GetNumConformers() > 0
# rewrite actives file with 3D coordinates
_, self.active_filename = tempfile.mkstemp(dir=self.temp_dir,
suffix='.sdf')
with serial.MolWriter().open(self.active_filename) as writer:
for mol, y in zip(self.mols, self.y):
if y:
writer.write([self.mols[1]])
# check for absence of chirality using default arguments
smiles, targets = self.check_output(
['-a', self.active_filename, '-d', self.decoy_filename, '-o',
self.output_filename])
chiral = False
for this_smiles in smiles:
if '@' in this_smiles:
chiral = True
assert not chiral
# update reference SMILES
self.engine = SmilesGenerator(assign_stereo_from_3d=True)
self.smiles[1] = self.engine.get_smiles(self.mols[1])
# check for presence of chiraliy using --stereo-from-3d
smiles, targets = self.check_output(
['-a', self.active_filename, '-d', self.decoy_filename, '-o',
self.output_filename, '--stereo-from-3d'])
chiral = False
for this_smiles in smiles:
if '@' in this_smiles:
chiral = True
assert chiral
def main(featurizer_class, input_filename, output_filename,
target_filename=None, featurizer_kwargs=None, parallel=False,
client_kwargs=None, view_flags=None, compression_level=3,
smiles_hydrogens=False, names=False, scaffolds=False,
chiral_scaffolds=False):
"""
Featurize molecules in input_filename using the given featurizer.
Parameters
----------
featurizer_class : Featurizer
Featurizer class.
input_filename : str
Filename containing molecules to be featurized.
output_filename : str
Output filename. Should end with .pkl or .pkl.gz.
target_filename : str, optional
Pickle containing target values. Should either be array_like or a dict
containing 'names' and 'y' keys, corresponding to molecule names and
target values.
featurizer_kwargs : dict, optional
Keyword arguments passed to featurizer.
parallel : bool, optional
Whether to train subtrainers in parallel using IPython.parallel
(default False).
client_kwargs : dict, optional
Keyword arguments for IPython.parallel Client.
view_flags : dict, optional
Flags for IPython.parallel LoadBalancedView.
compression_level : int, optional (default 3)
Compression level (0-9) to use with joblib.dump.
smiles_hydrogens : bool, optional (default False)
Whether to keep hydrogens when generating SMILES.
names : bool, optional (default False)
Whether to include molecule names in output.
scaffolds : bool, optional (default False)
Whether to include scaffolds in output.
chiral_scaffods : bool, optional (default False)
Whether to include chirality in scaffolds.
"""
mols, mol_names = read_mols(input_filename)
# get targets
data = {}
if target_filename is not None:
targets = read_pickle(target_filename)
if isinstance(targets, dict):
mol_indices, target_indices = collate_mols(
mols, mol_names, targets['y'], targets['names'])
mols = mols[mol_indices]
mol_names = mol_names[mol_indices]
targets = np.asarray(targets['y'])[target_indices]
else:
assert len(targets) == len(mols)
data['y'] = targets
# featurize molecules
print "Featurizing molecules..."
if featurizer_kwargs is None:
featurizer_kwargs = {}
featurizer = featurizer_class(**featurizer_kwargs)
features = featurizer.featurize(mols, parallel, client_kwargs, view_flags)
# fill in data container
print "Saving results..."
data['features'] = features
# calculate SMILES
smiles = SmilesGenerator(remove_hydrogens=(not smiles_hydrogens))
data['smiles'] = np.asarray([smiles.get_smiles(mol) for mol in mols])
# sanity checks
assert data['features'].shape[0] == len(mols), (
"Features do not match molecules.")
assert data['smiles'].shape[0] == len(mols), (
"SMILES do not match molecules.")
# names, scaffolds, args
if names:
data['names'] = mol_names
if scaffolds:
data['scaffolds'] = get_scaffolds(mols, chiral_scaffolds)
data['args'] = {'featurizer_class': featurizer_class.__name__,
'input_filename': input_filename,
'target_filename': target_filename,
'featurizer_kwargs': featurizer_kwargs,
'chiral_scaffolds': chiral_scaffolds}
# write output file
write_output_file(data, output_filename, compression_level)