def test_seed_produces_same_conformers(self):
import rdkit.Chem
from rdkit.Chem import AllChem
from e3fp.conformer.util import (
mol_from_smiles,
mol_to_standardised_mol,
)
from e3fp.conformer.generate import generate_conformers
ntrials = 10
confgen_params = {'num_conf': 1, 'seed': 42}
smiles = "C" * 20 # long flexible molecule
mol = mol_from_smiles(smiles, "tmp")
mols = [
generate_conformers(mol, **confgen_params)[0]
for i in range(ntrials)
]
fail = False
for i in range(ntrials):
for j in range(i + 1, ntrials):
rms = AllChem.GetBestRMS(mols[i], mols[j])
if rms > 1e-2:
fail = True
break
self.assertFalse(fail)
def run(smiles_file,
bits=1024,
radius=2,
use_chiral=False,
out_file="molecules.csv.bz2",
log=None):
setup_logging(log)
smiles_dict = smiles_to_dict(smiles_file)
mol_list_dict = {}
for name, smiles in smiles_dict.iteritems():
try:
mol = mol_from_smiles(smiles, name)
logging.info("Generating fingerprint for {}".format(name))
fp = fprint2d_from_mol(mol,
bits=bits,
radius=radius,
use_chiral=use_chiral)
logging.info("Generated fingerprint for {}".format(name))
mol_list_dict.setdefault(name,
[]).append(fprint_to_native_tuple(fp))
except Exception:
logging.warning("Fingerprinting {} failed.".format(name))
fp_type = get_fprint2d_fptype(bits=bits,
radius=radius,
use_chiral=use_chiral)
lists_dicts_to_molecules(out_file, smiles_dict, mol_list_dict, fp_type)
def get_canonical_smiles(smiles_dict):
canonical_smiles_dict = {}
for i, (mol_name, smiles) in enumerate(smiles_dict.items()):
try:
mol = mol_from_smiles(smiles, mol_name, standardise=True)
canon_smiles = MolToSmiles(mol, isomericSmiles=True)
canonical_smiles_dict[mol_name] = canon_smiles
except:
canonical_smiles_dict[mol_name] = smiles
return canonical_smiles_dict
def main(sdf_dir,
smiles_file,
num_mols=10000,
first=3,
out_props_file="random_mols_props.txt",
out_smiles_file="random_mols.csv.bz2"):
mol_names = set()
if os.path.isfile(out_smiles_file):
logging.info("Loading existing random molecules.")
smiles_dict = smiles_to_dict(out_smiles_file)
mol_names.update(set(smiles_dict))
out_sdf_files_dict = {k: get_sdf_file(sdf_dir, k) for k in mol_names}
else:
logging.info("Loading SMILES file.")
smiles_dict = smiles_to_dict(smiles_file)
remaining_mol_names = set(smiles_dict.keys())
out_smiles_dict = {}
out_sdf_files_dict = {}
logging.info("Picking random molecules.")
while len(mol_names) < num_mols:
print(len(mol_names))
proto_name = random.choice(smiles_dict.keys())
if proto_name not in remaining_mol_names:
continue
remaining_mol_names.remove(proto_name)
sdf_file = get_sdf_file(sdf_dir, proto_name)
if not os.path.isfile(sdf_file):
continue
mol_names.add(proto_name)
out_smiles_dict[proto_name] = smiles_dict[proto_name]
out_sdf_files_dict[proto_name] = sdf_file
if len(mol_names) % 100 == 0:
logging.info(len(mol_names))
dict_to_smiles(out_smiles_file, out_smiles_dict)
mol_names = sorted(mol_names)
logging.info("Computing mol properties.")
mol_props = {}
for name, smiles in smiles_dict.items():
mol = mol_from_smiles(smiles, name)
nheavy = mol.GetNumHeavyAtoms()
nrot = AllChem.CalcNumRotatableBonds(mol)
mol_props[name] = (nheavy, nrot)
with open(out_props_file, "w") as f:
f.write("mol_name\tnheavy\tnrot\n")
for mol_name in mol_names:
nheavy, nrot = mol_props[mol_name]
f.write("{}\t{:d}\t{:d}\n".format(mol_name, nheavy, nrot))
def test_standardisation(self):
import rdkit.Chem
from e3fp.conformer.util import (
mol_from_smiles,
mol_to_standardised_mol,
)
smiles = "C[N-]c1cccc[n+]1C"
mol = mol_from_smiles(smiles, "tmp")
self.assertEqual(rdkit.Chem.MolToSmiles(mol), smiles)
mol = mol_to_standardised_mol(mol)
self.assertEqual(rdkit.Chem.MolToSmiles(mol), "CN=c1ccccn1C")
def run(
mol2=None,
smiles=None,
standardise=STANDARDISE_DEF,
num_conf=NUM_CONF_DEF,
first=FIRST_DEF,
pool_multiplier=POOL_MULTIPLIER_DEF,
rmsd_cutoff=RMSD_CUTOFF_DEF,
max_energy_diff=MAX_ENERGY_DIFF_DEF,
forcefield=FORCEFIELD_DEF,
seed=SEED_DEF,
params=None,
prioritize=False,
out_dir=OUTDIR_DEF,
compress=COMPRESS_DEF,
overwrite=False,
values_file=None,
log=None,
num_proc=None,
parallel_mode=None,
verbose=False,
):
"""Run conformer generation."""
setup_logging(log, verbose=verbose)
if params is not None:
params = read_params(params)
standardise = get_value(params, "preprocessing", "standardise", bool)
num_conf = get_value(params, "conformer_generation", "num_conf", int)
first = get_value(params, "conformer_generation", "first", int)
pool_multiplier = get_value(params, "conformer_generation",
"pool_multiplier", int)
rmsd_cutoff = get_value(params, "conformer_generation", "rmsd_cutoff",
float)
max_energy_diff = get_value(params, "conformer_generation",
"max_energy_diff", float)
forcefield = get_value(params, "conformer_generation", "forcefield")
seed = get_value(params, "conformer_generation", "seed", int)
# check args
if forcefield not in FORCEFIELD_CHOICES:
raise ValueError(
"Specified forcefield {} is not in valid options {!r}".format(
forcefield, FORCEFIELD_CHOICES))
para = Parallelizer(num_proc=num_proc, parallel_mode=parallel_mode)
# Check to make sure args make sense
if mol2 is None and smiles is None:
if para.is_master():
parser.print_usage()
logging.error("Please provide mol2 file or a SMILES file.")
sys.exit()
if mol2 is not None and smiles is not None:
if para.is_master():
parser.print_usage()
logging.error("Please provide only a mol2 file OR a SMILES file.")
sys.exit()
if num_proc and num_proc < 1:
if para.is_master():
parser.print_usage()
logging.error(
"Please provide more than one processor with `--num_proc`.")
sys.exit()
# Set up input type
if mol2 is not None:
in_type = "mol2"
elif smiles is not None:
in_type = "smiles"
if para.is_master():
if in_type == "mol2":
logging.info("Input type: mol2 file(s)")
logging.info("Input file number: {:d}".format(len(mol2)))
mol_iter = (mol_from_mol2(_mol2_file,
_name,
standardise=standardise)
for _mol2_file, _name in mol2_generator(*mol2))
else:
logging.info("Input type: Detected SMILES file(s)")
logging.info("Input file number: {:d}".format(len(smiles)))
mol_iter = (mol_from_smiles(_smiles,
_name,
standardise=standardise)
for _smiles, _name in smiles_generator(*smiles))
if prioritize:
logging.info(("Prioritizing mols with low rotatable bond number"
" and molecular weight first."))
mols_with_properties = [(
AllChem.CalcNumRotatableBonds(mol),
AllChem.CalcExactMolWt(mol),
mol,
) for mol in mol_iter if mol is not None]
data_iterator = make_data_iterator(
(x[-1] for x in sorted(mols_with_properties)))
else:
data_iterator = make_data_iterator(
(x for x in mol_iter if x is not None))
# Set up parallel-specific options
logging.info("Parallel Type: {}".format(para.parallel_mode))
# Set other options
touch_dir(out_dir)
if not num_conf:
num_conf = -1
logging.info("Out Directory: {}".format(out_dir))
logging.info("Overwrite Existing Files: {}".format(overwrite))
if values_file is not None:
if os.path.exists(values_file) and overwrite is not True:
value_args = (values_file, "a")
logging.info("Values file: {} (append)".format((values_file)))
else:
value_args = (values_file, "w")
logging.info("Values file: {} (new file)".format(
(values_file)))
if num_conf is None or num_conf == -1:
logging.info("Target Conformer Number: auto")
else:
logging.info("Target Conformer Number: {:d}".format(num_conf))
if first is None or first == -1:
logging.info("First Conformers Number: all")
else:
logging.info("First Conformers Number: {:d}".format(first))
logging.info("Pool Multiplier: {:d}".format(pool_multiplier))
logging.info("RMSD Cutoff: {:.4g}".format(rmsd_cutoff))
if max_energy_diff is None:
logging.info("Maximum Energy Difference: None")
else:
logging.info("Maximum Energy Difference: {:.4g} kcal".format(
max_energy_diff))
logging.info("Forcefield: {}".format(forcefield.upper()))
if seed != -1:
logging.info("Seed: {:d}".format(seed))
logging.info("Starting.")
else:
data_iterator = iter([])
gen_conf_kwargs = {
"out_dir": out_dir,
"num_conf": num_conf,
"rmsd_cutoff": rmsd_cutoff,
"max_energy_diff": max_energy_diff,
"forcefield": forcefield,
"pool_multiplier": pool_multiplier,
"first": first,
"seed": seed,
"save": True,
"overwrite": overwrite,
"compress": compress,
}
run_kwargs = {"kwargs": gen_conf_kwargs}
results_iterator = para.run_gen(generate_conformers, data_iterator,
**run_kwargs)
if para.is_master() and values_file is not None:
hdf5_buffer = HDF5Buffer(*value_args)
for result, data in results_iterator:
if (para.is_master() and values_file is not None
and result is not False):
values_to_hdf5(hdf5_buffer, result)
if para.is_master() and values_file is not None:
hdf5_buffer.flush()
hdf5_buffer.close()