def test18GetBestRMSAndConjugatedGroups(self):
mol = Chem.MolFromSmiles(
"CCC(=O)[O-] |(-1.11,0.08,-0.29;0.08,-0.18,0.58;1.34,0.03,-0.16;1.74,1.22,-0.32;2.06,-1.04,-0.66)|"
)
qry = Chem.MolFromSmiles(
"CCC([O-])=O |(-1.11,0.08,-0.29;0.08,-0.18,0.58;1.34,0.03,-0.16;1.74,1.22,-0.32;2.06,-1.04,-0.66)|"
)
rmsd = rdMolAlign.GetBestRMS(qry, mol)
self.failUnlessAlmostEqual(rmsd, 0, 3)
rmsd = rdMolAlign.GetBestRMS(qry,
mol,
symmetrizeConjugatedTerminalGroups=False)
self.failUnlessAlmostEqual(rmsd, 0.747, 3)
def rmsd_frag_mol(gen_mol, ref_mol, start_pt):
try:
# Delete linker - Gen mol
du = Chem.MolFromSmiles('*')
clean_frag = Chem.RemoveHs(
AllChem.ReplaceSubstructs(Chem.MolFromSmiles(start_pt), du,
Chem.MolFromSmiles('[H]'), True)[0])
fragmented_mol = get_frags(gen_mol, clean_frag, start_pt)
if fragmented_mol is not None:
# Delete linker - Ref mol
clean_frag_ref = Chem.RemoveHs(
AllChem.ReplaceSubstructs(Chem.MolFromSmiles(start_pt), du,
Chem.MolFromSmiles('[H]'), True)[0])
fragmented_mol_ref = get_frags(ref_mol, clean_frag_ref, start_pt)
if fragmented_mol_ref is not None:
# Sanitize
Chem.SanitizeMol(fragmented_mol)
Chem.SanitizeMol(fragmented_mol_ref)
# Align
pyO3A = rdMolAlign.GetO3A(fragmented_mol,
fragmented_mol_ref).Align()
rms = rdMolAlign.GetBestRMS(fragmented_mol, fragmented_mol_ref)
return rms #score
except:
return 100 # Dummy RMSD
def test17GetBestRMS(self):
sdf = os.path.join(RDConfig.RDBaseDir, 'Code', 'GraphMol', 'MolAlign',
'test_data', 'probe_mol.sdf')
molS = Chem.SDMolSupplier(sdf, True, False)
mol1 = molS[1]
mol2 = molS[2]
# AlignMol() would return this for the rms: 2.50561
# But the best rms is: 2.43449
rmsd = rdMolAlign.GetBestRMS(mol1, mol2)
self.failUnlessAlmostEqual(rmsd, 2.43449209)
def compare_conformers(reference, result, rmsd_cutoff):
"""
For different methods, match the conformer minima to those of the reference
method. Ex. Conf G of reference method matches with conf R of method 2.
Parameters
----------
in_dict : OrderedDict
dictionary from input file, where key is method and value is dictionary
first entry should be reference method
in sub-dictionary, keys are 'sdfile' and 'sdtag'
rmsd_cutoff : float
cutoff above which two structures are considered diff conformers
Returns
-------
mol_dict : dict of dicts
mol_dict['mol_name']['energies'] =
[[file1_conf1_E file1_conf2_E] [file2_conf1_E file2_conf2_E]]
An analogous structure is followed for mol_dict['mol_name']['indices'].
"""
conformer_match = reference.copy()
for mid in tqdm(reference.molecule_index.unique(),
desc='Matching conformers'):
ref_confs = reference.loc[reference.molecule_index == mid]
query_confs = result.loc[result.molecule_index == mid]
rms_matrix = {i: {} for i, ref_row in ref_confs.iterrows()}
for i, ref_row in ref_confs.iterrows():
for j, query_row in query_confs.iterrows():
rmsd = rdMolAlign.GetBestRMS(ref_row['mol'].to_rdkit(),
query_row['mol'].to_rdkit())
rms_matrix[i][j] = rmsd
for ref, rms_list in rms_matrix.items():
conf = min(rms_list, key=rms_list.get)
conformer_match.loc[ref, 'ff_mol_name'] = conf
conformer_match.loc[ref, 'rmsd'] = rms_list[conf]
return conformer_match
def opt_conf(i, rmsd_cutoff):
"""
A helper function to optimize the geometry of a conformer.
Only for use within this parent function
"""
conformer = conformers[i]
calculator = conformer.ase_molecule.get_calculator()
labels = []
for bond in conformer.get_bonds():
labels.append(bond.atom_indices)
if isinstance(conformer, TS):
label = conformer.reaction_label
ind1 = conformer.rmg_molecule.get_labeled_atoms("*1")[0].sorting_label
ind2 = conformer.rmg_molecule.get_labeled_atoms("*3")[0].sorting_label
labels.append([ind1, ind2])
type = 'ts'
else:
label = conformer.smiles
type = 'species'
if isinstance(calc, FileIOCalculator):
if calculator.directory:
directory = calculator.directory
else:
directory = 'conformer_logs'
calculator.label = "{}_{}".format(conformer.smiles, i)
calculator.directory = os.path.join(directory, label,'{}_{}'.format(conformer.smiles, i))
if not os.path.exists(calculator.directory):
try:
os.makedirs(calculator.directory)
except OSError:
logging.info("An error occured when creating {}".format(calculator.directory))
calculator.atoms = conformer.ase_molecule
conformer.ase_molecule.set_calculator(calculator)
opt = BFGS(conformer.ase_molecule, logfile=None)
if type == 'species':
if isinstance(i,int):
c = FixBondLengths(labels)
conformer.ase_molecule.set_constraint(c)
try:
opt.run(steps=1e6)
except RuntimeError:
logging.info("Optimization failed...we will use the unconverged geometry")
pass
if str(i) == 'ref':
conformer.update_coords_from("ase")
try:
rmg_mol = Molecule()
rmg_mol.from_xyz(
conformer.ase_molecule.arrays["numbers"],
conformer.ase_molecule.arrays["positions"]
)
if not rmg_mol.is_isomorphic(reference_mol):
logging.info("{}_{} is not isomorphic with reference mol".format(conformer,str(i)))
return False
except AtomTypeError:
logging.info("Could not create a RMG Molecule from optimized conformer coordinates...assuming not isomorphic")
return False
if type == 'ts':
c = FixBondLengths(labels)
conformer.ase_molecule.set_constraint(c)
try:
opt.run(fmax=0.20, steps=1e6)
except RuntimeError:
logging.info("Optimization failed...we will use the unconverged geometry")
pass
conformer.update_coords_from("ase")
energy = get_energy(conformer)
conformer.energy = energy
if len(return_dict)>0:
conformer_copy = conformer.copy()
for index,post in return_dict.items():
conf_copy = conformer.copy()
conf_copy.ase_molecule.positions = post
conf_copy.update_coords_from("ase")
rmsd = rdMolAlign.GetBestRMS(conformer_copy.rdkit_molecule,conf_copy.rdkit_molecule)
if rmsd <= rmsd_cutoff:
return True
if str(i) != 'ref':
return_dict[i] = conformer.ase_molecule.get_positions()
return True
def systematic_search(conformer,
delta=float(120),
energy_cutoff = 10.0, #kcal/mol
rmsd_cutoff = 0.5, #angstroms
cistrans = True,
chiral_centers = True,
multiplicity = False,
):
"""
Perfoms a systematic conformer analysis of a `Conformer` or a `TS` object
Variables:
- conformer (`Conformer` or `TS`): a `Conformer` or `TS` object of interest
- delta (int or float): a number between 0 and 180 or how many conformers to generate per dihedral
- cistrans (bool): indication of if one wants to consider cistrans bonds
- chiral_centers (bool): indication of if one wants to consider chiral centers bonds
Returns:
- confs (list): a list of unique `Conformer` objects within 1 kcal/mol of the lowest energy conformer determined
"""
rmsd_cutoff_options = {
'loose' : 1.0,
'default': 0.5,
'tight': 0.1
}
energy_cutoff_options = {
'high' : 50.0,
'default' : 10.0,
'low' : 5.0
}
if isinstance(rmsd_cutoff,str):
rmsd_cutoff = rmsd_cutoff.lower()
assert rmsd_cutoff in rmsd_cutoff_options.keys(), 'rmsd_cutoff options are loose, default, and tight'
rmsd_cutoff = rmsd_cutoff_options[rmsd_cutoff]
if isinstance(energy_cutoff,str):
energy_cutoff = energy_cutoff.lower()
assert energy_cutoff in energy_cutoff_options.keys(), 'energy_cutoff options are low, default, and high'
energy_cutoff = energy_cutoff_options[energy_cutoff]
if not isinstance(conformer, TS):
reference_mol = conformer.rmg_molecule.copy(deep=True)
reference_mol = reference_mol.to_single_bonds()
manager = Manager()
return_dict = manager.dict()
pool = multiprocessing.Pool()
def opt_conf(i, rmsd_cutoff):
"""
A helper function to optimize the geometry of a conformer.
Only for use within this parent function
"""
conformer = conformers[i]
calculator = conformer.ase_molecule.get_calculator()
labels = []
for bond in conformer.get_bonds():
labels.append(bond.atom_indices)
if isinstance(conformer, TS):
label = conformer.reaction_label
ind1 = conformer.rmg_molecule.get_labeled_atoms("*1")[0].sorting_label
ind2 = conformer.rmg_molecule.get_labeled_atoms("*3")[0].sorting_label
labels.append([ind1, ind2])
type = 'ts'
else:
label = conformer.smiles
type = 'species'
if isinstance(calc, FileIOCalculator):
if calculator.directory:
directory = calculator.directory
else:
directory = 'conformer_logs'
calculator.label = "{}_{}".format(conformer.smiles, i)
calculator.directory = os.path.join(directory, label,'{}_{}'.format(conformer.smiles, i))
if not os.path.exists(calculator.directory):
try:
os.makedirs(calculator.directory)
except OSError:
logging.info("An error occured when creating {}".format(calculator.directory))
calculator.atoms = conformer.ase_molecule
conformer.ase_molecule.set_calculator(calculator)
opt = BFGS(conformer.ase_molecule, logfile=None)
if type == 'species':
if isinstance(i,int):
c = FixBondLengths(labels)
conformer.ase_molecule.set_constraint(c)
try:
opt.run(steps=1e6)
except RuntimeError:
logging.info("Optimization failed...we will use the unconverged geometry")
pass
if str(i) == 'ref':
conformer.update_coords_from("ase")
try:
rmg_mol = Molecule()
rmg_mol.from_xyz(
conformer.ase_molecule.arrays["numbers"],
conformer.ase_molecule.arrays["positions"]
)
if not rmg_mol.is_isomorphic(reference_mol):
logging.info("{}_{} is not isomorphic with reference mol".format(conformer,str(i)))
return False
except AtomTypeError:
logging.info("Could not create a RMG Molecule from optimized conformer coordinates...assuming not isomorphic")
return False
if type == 'ts':
c = FixBondLengths(labels)
conformer.ase_molecule.set_constraint(c)
try:
opt.run(fmax=0.20, steps=1e6)
except RuntimeError:
logging.info("Optimization failed...we will use the unconverged geometry")
pass
conformer.update_coords_from("ase")
energy = get_energy(conformer)
conformer.energy = energy
if len(return_dict)>0:
conformer_copy = conformer.copy()
for index,post in return_dict.items():
conf_copy = conformer.copy()
conf_copy.ase_molecule.positions = post
conf_copy.update_coords_from("ase")
rmsd = rdMolAlign.GetBestRMS(conformer_copy.rdkit_molecule,conf_copy.rdkit_molecule)
if rmsd <= rmsd_cutoff:
return True
if str(i) != 'ref':
return_dict[i] = conformer.ase_molecule.get_positions()
return True
#if not isinstance(conformer,TS):
# calc = conformer.ase_molecule.get_calculator()
# reference_conformer = conformer.copy()
# if opt_conf(reference_conformer, calc, 'ref', rmsd_cutoff):
# conformer = reference_conformer
combos = find_all_combos(
conformer,
delta=delta,
cistrans=cistrans,
chiral_centers=chiral_centers)
if len(combos) == 0:
logging.info(
"This species has no torsions, cistrans bonds, or chiral centers")
logging.info("Returning origional conformer")
return [conformer]
_, torsions = find_terminal_torsions(conformer)
calc = conformer.ase_molecule.get_calculator()
if isinstance(calc, FileIOCalculator):
logging.info("The calculator generates input and output files.")
results = []
global conformers
conformers = {}
combinations = {}
logging.info("There are {} possible conformers to investigate...".format(len(combos)))
for index, combo in enumerate(combos):
combinations[index] = combo
torsions, cistrans, chiral_centers = combo
copy_conf = conformer.copy()
for i, torsion in enumerate(torsions):
tor = copy_conf.torsions[i]
i, j, k, l = tor.atom_indices
mask = tor.mask
copy_conf.ase_molecule.set_dihedral(
a1=i,
a2=j,
a3=k,
a4=l,
angle=torsion,
mask=mask
)
copy_conf.update_coords()
for i, e_z in enumerate(cistrans):
ct = copy_conf.cistrans[i]
copy_conf.set_cistrans(ct.index, e_z)
for i, s_r in enumerate(chiral_centers):
center = copy_conf.chiral_centers[i]
copy_conf.set_chirality(center.index, s_r)
copy_conf.update_coords_from("ase")
copy_conf.ase_molecule.set_calculator(calc)
conformers[index] = copy_conf
processes = []
for i, conf in list(conformers.items()):
p = Process(target=opt_conf, args=(i, rmsd_cutoff))
processes.append(p)
active_processes = []
for process in processes:
if len(active_processes) < multiprocessing.cpu_count():
process.start()
active_processes.append(process)
continue
else:
one_done = False
while not one_done:
for i, p in enumerate(active_processes):
if not p.is_alive():
one_done = True
break
process.start()
active_processes[i] = process
complete = np.zeros_like(active_processes, dtype=bool)
while not np.all(complete):
for i, p in enumerate(active_processes):
if not p.is_alive():
complete[i] = True
energies = []
for positions in list(return_dict.values()):
conf = conformer.copy()
conf.ase_molecule.positions = positions
conf.ase_molecule.set_calculator(calc)
energy = conf.ase_molecule.get_potential_energy()
conf.update_coords_from("ase")
energies.append((conf,energy))
df = pd.DataFrame(energies,columns=["conformer","energy"])
df = df[df.energy < df.energy.min() + (energy_cutoff * units.kcal / units.mol /
units.eV)].sort_values("energy").reset_index(drop=True)
redundant = []
conformer_copies = [conf.copy() for conf in df.conformer]
for i,j in itertools.combinations(range(len(df.conformer)),2):
copy_1 = conformer_copies[i].rdkit_molecule
copy_2 = conformer_copies[j].rdkit_molecule
rmsd = rdMolAlign.GetBestRMS(copy_1,copy_2)
if rmsd <= rmsd_cutoff:
redundant.append(j)
redundant = list(set(redundant))
df.drop(df.index[redundant], inplace=True)
if multiplicity and conformer.rmg_molecule.multiplicity > 2:
rads = conformer.rmg_molecule.get_radical_count()
if rads % 2 == 0:
multiplicities = range(1,rads+2,2)
else:
multiplicities = range(2,rads+2,2)
else:
multiplicities = [conformer.rmg_molecule.multiplicity]
confs = []
i = 0
for conf in df.conformer:
if multiplicity:
for mult in multiplicities:
conf_copy = conf.copy()
conf_copy.index = i
conf_copy.rmg_molecule.multiplicity = mult
confs.append(conf_copy)
i += 1
else:
conf.index = i
confs.append(conf)
i += 1
logging.info("We have identified {} unique, low-energy conformers for {}".format(
len(confs), conformer))
return confs
def calc_rmsd(reference, result):
for i, row in tqdm(reference.iterrows(), desc='Calculating RMSD'):
result.loc[i, 'rmsd'] = rdMolAlign.GetBestRMS(
row['mol'].to_rdkit(), result.loc[i, 'mol'].to_rdkit())
