def rdkit_mmff94_xyz(smiles, **kwargs):
"""
Returns the string of the XYZ file obtained performing the MMFF94 molecular mechanics optimization of the given
SMILES using RDKit.
Writing temporary files in $MM_WORKING_DIR if defined or otherwise in /tmp
:param smiles: input_SMILES
:param max_iterations: max number of iterations (default 500)
:return : XYZ string of optimized geometry, success (whether the MM optimization was successful and the smiles has
stayed identical after optimization)
"""
working_dir = os.environ[
"MM_WORKING_DIR"] if "MM_WORKING_DIR" in os.environ else "/tmp"
# Converting the molecule to RDKit object
mol = MolFromSmiles(smiles)
smi_canon = MolToSmiles(MolFromSmiles(smiles))
# Setting paths
filename_smiles = str(os.getpid()) + "_" + smi_to_filename(smi_canon)
xyz_path = join(working_dir, filename_smiles + '.xyz')
post_MM_smi_path = join(working_dir, filename_smiles + '.smi')
# Computing geometry
try:
# Adding implicit hydrogens
mol = AddHs(mol)
# MM optimization
EmbedMolecule(mol)
value = MMFFOptimizeMolecule(mol, maxIters=kwargs["max_iterations"])
# Success if returned value is null
success_RDKIT_output = value == 0
# Computing XYZ from optimized molecule
xyz_str = MolToXYZBlock(mol)
# Writing optimized XYZ to file
with open(xyz_path, "w") as f:
f.writelines(xyz_str)
# Success if the optimization has converged and the post MM smiles is identical the pre MM smiles
success = success_RDKIT_output and check_identical_geometries(
xyz_path, smi_canon, post_MM_smi_path)
except Exception as e:
success = False
xyz_str = None
finally:
# Removing files
remove_files([post_MM_smi_path, xyz_path])
return xyz_str, success
def _optimize(self, mol):
return MMFFOptimizeMolecule(mol)
smiles = 'CN1COCN(C1=N[N+](=O)[O-])CC2=CN=C(S2)Cl'
# ファイル名を決める
t = datetime.datetime.fromtimestamp(time.time())
psi4.set_output_file("{}_{}{}{}_{}{}.log".format(smiles, t.year, t.month,
t.day, t.hour, t.minute))
# SMILES から三次元構造を発生させて、粗3D構造最適化
mol = Chem.MolFromSmiles(smiles)
mol = Chem.AddHs(mol)
params = ETKDGv3()
params.randomSeed = 1
EmbedMolecule(mol, params)
# MMFF（Merck Molecular Force Field） で構造最適化する
MMFFOptimizeMolecule(mol)
#UFF（Universal Force Field）普遍力場で構造最適化したい場合は
#UFFOptimizeMolecule(mol)
conf = mol.GetConformer()
# Psi4 に入力可能な形式に変換する。
# 電荷とスピン多重度を設定（下は、電荷０、スピン多重度1)
mol_input = "0 1"
#各々の原子の座標をXYZフォーマットで記述
for atom in mol.GetAtoms():
mol_input += "\n " + atom.GetSymbol() + " " + str(conf.GetAtomPosition(atom.GetIdx()).x)\
+ " " + str(conf.GetAtomPosition(atom.GetIdx()).y)\
+ " " + str(conf.GetAtomPosition(atom.GetIdx()).z)
batch = batch.to(args.device)
pos_s = simple_generate_batch(model_cnf,
batch,
num_samples=1,
embedder=embedder)[0] # (1, BN, 3)
batch.pos = pos_s[0]
batch.to('cpu')
batch_list = batch.to_data_list()
all_data_list += batch_list
grouped_data = split_dataset_by_smiles(all_data_list)
for smiles in grouped_data:
for data in grouped_data[smiles]:
rdmol = data['rdmol']
rdmol = set_rdmol_positions_(rdmol, data.pos.cpu())
gen_rdmols.append(rdmol)
# Optimize using MMFF
opt_rdmols = []
if args.mmff:
for mol in tqdm(gen_rdmols, desc='MMFF Optimize'):
opt_mol = deepcopy(mol)
MMFFOptimizeMolecule(opt_mol)
opt_rdmols.append(opt_mol)
gen_rdmols = opt_rdmols
# Save
logger.info('Saving to: %s' % args.out)
with open(args.out, 'wb') as f:
pickle.dump(gen_rdmols, f)