def test_rmsd_confs():
methane1 = Conformer(name='methane1',
charge=0,
mult=1,
atoms=[
Atom('C', -1.38718, 0.38899, 0.00000),
Atom('H', -0.27778, 0.38899, -0.00000),
Atom('H', -1.75698, 1.06232, 0.80041),
Atom('H', -1.75698, -0.64084, 0.18291),
Atom('H', -1.75698, 0.74551, -0.98332)
])
methane2 = Conformer(name='methane2',
charge=0,
mult=1,
atoms=[
Atom('C', -1.38718, 0.38899, 0.00000),
Atom('H', -0.43400, 0.50158, -0.55637),
Atom('H', -2.23299, 0.69379, -0.64998),
Atom('H', -1.36561, 1.03128, 0.90431),
Atom('H', -1.51612, -0.67068, 0.30205)
])
# Methane but rotated should have an RMSD ~ 0 Angstroms
assert not conf_is_unique_rmsd(
conf=methane2, conf_list=[methane1], rmsd_tol=0.1)
def _generate_conformers(self, n_confs=None):
"""Generate conformers at the TS """
from autode.conformers.conformer import Conformer
from autode.conformers.conf_gen import get_simanl_atoms
from autode.conformers.conformers import conf_is_unique_rmsd
n_confs = Config.num_conformers if n_confs is None else n_confs
self.conformers = []
distance_consts = get_distance_constraints(self)
with Pool(processes=Config.n_cores) as pool:
results = [
pool.apply_async(get_simanl_atoms, (self, distance_consts, i))
for i in range(n_confs)
]
conf_atoms_list = [res.get(timeout=None) for res in results]
for i, atoms in enumerate(conf_atoms_list):
conf = Conformer(name=f'{self.name}_conf{i}',
charge=self.charge,
mult=self.mult,
atoms=atoms,
dist_consts=distance_consts)
# If the conformer is unique on an RMSD threshold
if conf_is_unique_rmsd(conf, self.conformers):
conf.solvent = self.solvent
conf.graph = deepcopy(self.graph)
self.conformers.append(conf)
logger.info(f'Generated {len(self.conformers)} conformer(s)')
return None
def _generate_conformers(self, n_confs=None):
"""
Use a simulated annealing approach to generate conformers for this
molecule.
Keyword Arguments:
n_confs (int): Number of conformers requested if None default to
autode.Config.num_conformers
"""
n_confs = n_confs if n_confs is not None else Config.num_conformers
self.conformers = []
if self.smiles is not None and self.rdkit_conf_gen_is_fine:
logger.info(f'Using RDKit to gen conformers. {n_confs} requested')
method = AllChem.ETKDGv2()
method.pruneRmsThresh = Config.rmsd_threshold
method.numThreads = Config.n_cores
logger.info(
'Running conformation generation with RDKit... running')
conf_ids = list(
AllChem.EmbedMultipleConfs(self.rdkit_mol_obj,
numConfs=n_confs,
params=method))
logger.info(' ... done')
conf_atoms_list = [
get_atoms_from_rdkit_mol_object(self.rdkit_mol_obj, conf_id)
for conf_id in conf_ids
]
else:
logger.info('Using simulated annealing to generate conformers')
with Pool(processes=Config.n_cores) as pool:
results = [
pool.apply_async(get_simanl_atoms, (self, None, i))
for i in range(n_confs)
]
conf_atoms_list = [res.get(timeout=None) for res in results]
for i, atoms in enumerate(conf_atoms_list):
conf = Conformer(name=f'{self.name}_conf{i}',
charge=self.charge,
mult=self.mult,
atoms=atoms)
# If the conformer is unique on an RMSD threshold
if conf_is_unique_rmsd(conf, self.conformers):
conf.solvent = self.solvent
self.conformers.append(conf)
logger.info(f'Generated {len(self.conformers)} unique conformer(s)')
return None
def test_conf_class():
h2_conf = Conformer(
name='h2_conf',
charge=0,
mult=1,
atoms=[Atom('H', 0.0, 0.0, 0.0),
Atom('H', 0.0, 0.0, 0.7)])
assert hasattr(h2_conf, 'optimise')
assert hasattr(h2_conf, 'dist_consts')
assert h2_conf.n_atoms == 2
assert h2_conf.energy is None
assert h2_conf.dist_consts is None
h2_conf.optimise(method=orca)
assert h2_conf.energy == -1.160780546661
assert h2_conf.atoms is not None
assert h2_conf.n_atoms == 2
# Check that if the conformer calculation does not complete successfully
# then don't raise an exception for a conformer
h2_conf_broken = Conformer(
name='h2_conf_broken',
charge=0,
mult=1,
atoms=[Atom('H', 0.0, 0.0, 0.0),
Atom('H', 0.0, 0.0, 0.7)])
h2_conf_broken.optimise(method=orca)
assert h2_conf_broken.atoms is None
assert h2_conf_broken.n_atoms == 0
def test_unique_confs():
conf1 = Conformer()
conf2 = Conformer()
conf3 = Conformer()
# Set two energies the same and leave one as none..
conf1.energy = 1
conf2.energy = 1
unique_confs = get_unique_confs(conformers=[conf1, conf2, conf3])
assert len(unique_confs) == 1
assert type(unique_confs[0]) is Conformer
assert unique_confs[0].energy == 1
def test_conf_class():
os.chdir(os.path.join(here, 'data', 'conformers'))
h2_conf = Conformer(
name='h2_conf',
charge=0,
mult=1,
atoms=[Atom('H', 0.0, 0.0, 0.0),
Atom('H', 0.0, 0.0, 0.7)])
assert hasattr(h2_conf, 'optimise')
assert hasattr(h2_conf, 'dist_consts')
assert h2_conf.n_atoms == 2
assert h2_conf.energy is None
assert h2_conf.dist_consts is None
h2_conf.optimise(method=orca)
assert h2_conf.energy == -1.160780546661
assert h2_conf.atoms is not None
assert h2_conf.n_atoms == 2
# Check that if the conformer calculation does not complete successfully then
# don't raise an exception for a conformer
h2_conf_broken = Conformer(
name='h2_conf_broken',
charge=0,
mult=1,
atoms=[Atom('H', 0.0, 0.0, 0.0),
Atom('H', 0.0, 0.0, 0.7)])
h2_conf_broken.optimise(method=orca)
assert h2_conf_broken.atoms is None
assert h2_conf_broken.n_atoms == 0
os.remove('h2_conf_opt_orca.inp')
os.remove('h2_conf_broken_opt_orca.inp')
os.chdir(here)
def test_unique_confs_none():
conf1 = Conformer()
conf1.energy = 0.1
# Conformer with energy just below the threshold
conf2 = Conformer()
conf2.energy = 0.1 + (0.9 / Constants.ha2kJmol)
unique_confs = get_unique_confs(conformers=[conf1, conf2],
energy_threshold_kj=1)
assert len(unique_confs) == 1
# If the energy is above the threshold there should be two unique
# conformers
conf2.energy += 0.2 / Constants.ha2kJmol
unique_confs = get_unique_confs(conformers=[conf1, conf2],
energy_threshold_kj=1)
assert len(unique_confs) == 2