def generate_structures(vae, smi, char_to_index, limit=1e4, write=False):
rdkit_mols = []
temps = []
iterations = []
iteration = limit_counter = 0
while True:
iteration += 1
limit_counter += 1
t = random.random()*2
candidate = decode_smiles(vae, smi, char_to_index, temp=t).split(" ")[0]
try:
sampled = Chem.MolFromSmiles(candidate)
cation = Chem.AddHs(sampled)
Chem.EmbedMolecule(cation, Chem.ETKDG())
Chem.UFFOptimizeMolecule(cation)
cation = Chem.RemoveHs(cation)
candidate = Chem.MolToSmiles(cation)
if candidate not in rdkit_mols:
temps.append(t)
iterations.append(iteration)
rdkit_mols.append(candidate)
limit_counter = 0
df = pd.DataFrame([rdkit_mols,temps,iterations]).T
df.columns = ['smiles', 'temperature', 'iteration']
print(df)
except:
pass
if limit_counter > limit:
break
if write:
df = pd.DataFrame([rdkit_mols,temps,iterations]).T
df.columns = ['smiles', 'temperature', 'iteration']
pd.DataFrame.to_csv(df, path_or_buf='{}.csv'.format(write), index=False)
return df
def get_descriptors(smiles):
"""
Get a dictionary of RDKit descriptors from a SMILES string.
Parameters
----------
smiles : str
The SMILES string of the chemical of interest
Returns
-------
descriptors : dict
A collection of molecular descriptors
Notes: Developed with RDKit 2019.03.4, although doc pages listed 2019.03.1
"""
mol = Chem.MolFromSmiles(smiles)
mol = Chem.AddHs(mol)
Chem.EmbedMolecule(mol, Chem.ETKDG())
descriptors = {}
# Starting with simple descriptors:
# https://www.rdkit.org/docs/source/rdkit.Chem.Descriptors.html
# Molecular weight
descriptors['molwt'] = Descriptors.ExactMolWt(mol)
# Partial charge metrics
descriptors['max_abs_partial_charge'] = Descriptors.MaxAbsPartialCharge(mol)
descriptors['max_partial_charge'] = Descriptors.MaxPartialCharge(mol)
descriptors['min_abs_partial_charge'] = Descriptors.MinAbsPartialCharge(mol)
descriptors['min_partial_charge'] = Descriptors.MinPartialCharge(mol)
# Basic electron counts
descriptors['num_radical_electrons'] = Descriptors.NumRadicalElectrons(mol)
descriptors['num_valence_electrons'] = Descriptors.NumValenceElectrons(mol)
# 3-D descriptors
# https://www.rdkit.org/docs/source/rdkit.Chem.Descriptors3D.html
# Calculating these should produce the same result, according to some basic tests
# descriptors['asphericity'] = rdMolDescriptors.CalcAsphericity(mol)
# descriptors['eccentricity'] = rdMolDescriptors.CalcEccentricity(mol)
descriptors['asphericity'] = Descriptors3D.Asphericity(mol)
descriptors['eccentricity'] = Descriptors3D.Eccentricity(mol)
descriptors['inertial_shape_factor'] = Descriptors3D.InertialShapeFactor(mol)
descriptors['radius_of_gyration'] = Descriptors3D.RadiusOfGyration(mol)
descriptors['spherocity_index'] = Descriptors3D.SpherocityIndex(mol)
# Graph descriptors
# https://www.rdkit.org/docs/source/rdkit.Chem.GraphDescriptors.html
descriptors['balaban_j'] = GraphDescriptors.BalabanJ(mol)
descriptors['bertz_ct'] = GraphDescriptors.BertzCT(mol)
descriptors['chi0'] = GraphDescriptors.Chi0(mol)
descriptors['chi0n'] = GraphDescriptors.Chi0n(mol)
descriptors['chi0v'] = GraphDescriptors.Chi0v(mol)
descriptors['chi1'] = GraphDescriptors.Chi1(mol)
descriptors['chi1n'] = GraphDescriptors.Chi1n(mol)
descriptors['chi1v'] = GraphDescriptors.Chi1v(mol)
descriptors['chi2n'] = GraphDescriptors.Chi2n(mol)
descriptors['chi2v'] = GraphDescriptors.Chi2v(mol)
descriptors['chi3n'] = GraphDescriptors.Chi3n(mol)
descriptors['chi3v'] = GraphDescriptors.Chi3v(mol)
descriptors['chi4n'] = GraphDescriptors.Chi4n(mol)
descriptors['chi4v'] = GraphDescriptors.Chi4v(mol)
descriptors['hall_kier_alpha'] = GraphDescriptors.HallKierAlpha(mol)
descriptors['kappa1'] = GraphDescriptors.Kappa1(mol)
descriptors['kappa2'] = GraphDescriptors.Kappa2(mol)
descriptors['kappa3'] = GraphDescriptors.Kappa3(mol)
# Predicted properties from Wildman and Crippen
descriptors['log_p'] = Descriptors.MolLogP(mol)
descriptors['refractivity'] = Descriptors.MolMR(mol)
return descriptors
def summ_search(mol,
name,
args,
log,
dup_data,
dup_data_idx,
coord_Map=None,
alg_Map=None,
mol_template=None):
'''embeds core conformers, then optimizes and filters based on RMSD. Finally the rotatable torsions are systematically rotated'''
sdwriter = Chem.SDWriter(name + '_' + 'rdkit' + args.output)
Chem.SanitizeMol(mol)
mol = Chem.AddHs(mol)
mol.SetProp("_Name", name)
# detects and applies auto-detection of initial number of conformers
if args.sample == 'auto':
initial_confs = int(auto_sampling(args.auto_sample, mol, log))
else:
initial_confs = int(args.sample)
#
dup_data.at[dup_data_idx, 'Molecule'] = name
dup_data.at[dup_data_idx, 'RDKIT-Initial-samples'] = initial_confs
if args.nodihedrals == False:
rotmatches = getDihedralMatches(mol, args.heavyonly, log)
else:
rotmatches = []
if len(rotmatches) > args.max_torsions:
log.write("x Too many torsions (%d). Skipping %s" %
(len(rotmatches), (name + args.output)))
status = -1
else:
if coord_Map == None and alg_Map == None and mol_template == None:
if args.etkdg:
ps = Chem.ETKDG()
ps.randomSeed = args.seed
ps.ignoreSmoothingFailures = True
ps.numThreads = 0
cids = rdDistGeom.EmbedMultipleConfs(mol,
initial_confs,
params=ps)
else:
cids = rdDistGeom.EmbedMultipleConfs(
mol,
initial_confs,
ignoreSmoothingFailures=True,
randomSeed=args.seed,
numThreads=0)
if len(cids) == 0 or len(cids) == 1 and initial_confs != 1:
log.write(
"o conformers initially sampled with random coordinates")
cids = rdDistGeom.EmbedMultipleConfs(
mol,
initial_confs,
randomSeed=args.seed,
useRandomCoords=True,
boxSizeMult=10.0,
ignoreSmoothingFailures=True,
numZeroFail=1000,
numThreads=0)
if args.verbose:
log.write("o " + str(len(cids)) +
" conformers initially sampled")
# case of embed for templates
else:
if args.etkdg:
ps = Chem.ETKDG()
ps.randomSeed = args.seed
ps.coordMap = coord_Map
ps.ignoreSmoothingFailures = True
ps.numThreads = 0
cids = rdDistGeom.EmbedMultipleConfs(mol,
initial_confs,
params=ps)
else:
cids = rdDistGeom.EmbedMultipleConfs(
mol,
initial_confs,
randomSeed=args.seed,
ignoreSmoothingFailures=True,
coordMap=coord_Map,
numThreads=0)
if len(cids) == 0 or len(cids) == 1 and initial_confs != 1:
log.write(
"o conformers initially sampled with random coordinates")
cids = rdDistGeom.EmbedMultipleConfs(
mol,
initial_confs,
randomSeed=args.seed,
useRandomCoords=True,
boxSizeMult=10.0,
numZeroFail=1000,
ignoreSmoothingFailures=True,
coordMap=coord_Map,
numThreads=0)
if args.verbose:
log.write("o " + str(len(cids)) +
" conformers initially sampled")
#energy minimize all to get more realistic results
#identify the atoms and decide Force Field
for atom in mol.GetAtoms():
if atom.GetAtomicNum() > 36: #upto Kr for MMFF, if not use UFF
args.ff = "UFF"
#log.write("UFF is used because there are atoms that MMFF doesn't recognise")
if args.verbose:
log.write("o Optimizing " + str(len(cids)) +
" initial conformers with" + args.ff)
if args.verbose:
if args.nodihedrals == False:
log.write("o Found " + str(len(rotmatches)) +
" rotatable torsions")
# for [a,b,c,d] in rotmatches:
# log.write(' '+mol.GetAtomWithIdx(a).GetSymbol()+str(a+1)+ mol.GetAtomWithIdx(b).GetSymbol()+str(b+1)+ mol.GetAtomWithIdx(c).GetSymbol()+str(c+1)+mol.GetAtomWithIdx(d).GetSymbol()+str(d+1))
else:
log.write("o Systematic torsion rotation is set to OFF")
cenergy, outmols = [], []
bar = IncrementalBar('o Minimizing', max=len(cids))
for i, conf in enumerate(cids):
if coord_Map == None and alg_Map == None and mol_template == None:
if args.ff == "MMFF":
GetFF = Chem.MMFFGetMoleculeForceField(
mol, Chem.MMFFGetMoleculeProperties(mol), confId=conf)
elif args.ff == "UFF":
GetFF = Chem.UFFGetMoleculeForceField(mol, confId=conf)
else:
log.write(' Force field {} not supported!'.format(
args.ff))
sys.exit()
GetFF.Initialize()
converged = GetFF.Minimize(maxIts=args.opt_steps_RDKit)
energy = GetFF.CalcEnergy()
cenergy.append(GetFF.CalcEnergy())
#if args.verbose:
# log.write("- conformer", (i+1), "optimized: ", args.ff, "energy", GetFF.CalcEnergy())
#id template realign before doing calculations
else:
num_atom_match = mol.GetSubstructMatch(mol_template)
# Force field parameters
if args.ff == "MMFF":
GetFF = lambda mol, confId=conf: Chem.MMFFGetMoleculeForceField(
mol, Chem.MMFFGetMoleculeProperties(mol), confId=conf)
elif args.ff == "UFF":
GetFF = lambda mol, confId=conf: Chem.UFFGetMoleculeForceField(
mol, confId=conf)
else:
log.write(' Force field {} not supported!'.format(
options.ff))
sys.exit()
getForceField = GetFF
# clean up the conformation
ff_temp = getForceField(mol, confId=conf)
for k, idxI in enumerate(num_atom_match):
for l in range(k + 1, len(num_atom_match)):
idxJ = num_atom_match[l]
d = coord_Map[idxI].Distance(coord_Map[idxJ])
ff_temp.AddDistanceConstraint(idxI, idxJ, d, d, 10000)
ff_temp.Initialize()
#reassignned n from 4 to 10 for better embed and minimzation
n = 10
more = ff_temp.Minimize()
while more and n:
more = ff_temp.Minimize()
n -= 1
energy = ff_temp.CalcEnergy()
# rotate the embedded conformation onto the core_mol:
rms = rdMolAlign.AlignMol(mol,
mol_template,
prbCid=conf,
atomMap=alg_Map,
reflect=True,
maxIters=100)
# elif len(num_atom_match) == 5:
# ff_temp = GetFF(mol, confId=conf)
# conf_temp = mol_template.GetConformer()
# for k in range(mol_template.GetNumAtoms()):
# p = conf_temp.GetAtomPosition(k)
# q = mol.GetConformer(conf).GetAtomPosition(k)
# pIdx = ff_temp.AddExtraPoint(p.x, p.y, p.z, fixed=True) - 1
# ff_temp.AddDistanceConstraint(pIdx, num_atom_match[k], 0, 0, 10000)
# ff_temp.Initialize()
# n = 10
# more = ff_temp.Minimize(energyTol=1e-6, forceTol=1e-5)
# while more and n:
# more = ff_temp.Minimize(energyTol=1e-6, forceTol=1e-5)
# n -= 1
# # realign
# energy = ff_temp.CalcEnergy()
# rms = rdMolAlign.AlignMol(mol, mol_template,prbCid=conf, atomMap=alg_Map,reflect=True,maxIters=50)
cenergy.append(energy)
# outmols is gonna be a list containing "initial_confs" mol objects with "initial_confs"
# conformers. We do this to SetProp (Name and Energy) to the different conformers
# and log.write in the SDF file. At the end, since all the mol objects has the same
# conformers, but the energies are different, we can log.write conformers to SDF files
# with the energies of the parent mol objects. We measured the computing time and
# it's the same as using only 1 parent mol object with 10 conformers, but we couldn'temp
# SetProp correctly
pmol = PropertyMol.PropertyMol(mol)
outmols.append(pmol)
bar.next()
bar.finish()
for i, cid in enumerate(cids):
outmols[cid].SetProp('_Name', name + ' conformer ' + str(i + 1))
outmols[cid].SetProp('Energy', cenergy[cid])
cids = list(range(len(outmols)))
sortedcids = sorted(cids, key=lambda cid: cenergy[cid])
log.write("\n\no Filters after intial embedding of " +
str(initial_confs) + " conformers")
selectedcids, selectedcids_initial, eng_dup, eng_rms_dup = [], [], -1, -1
bar = IncrementalBar('o Filtering based on energy (pre-filter)',
max=len(sortedcids))
for i, conf in enumerate(sortedcids):
# This keeps track of whether or not your conformer is unique
excluded_conf = False
# include the first conformer in the list to start the filtering process
if i == 0:
selectedcids_initial.append(conf)
# check rmsd
for seenconf in selectedcids_initial:
E_diff = abs(cenergy[conf] - cenergy[seenconf]) # in kcal/mol
if E_diff < args.initial_energy_threshold:
eng_dup += 1
excluded_conf = True
break
if excluded_conf == False:
if conf not in selectedcids_initial:
selectedcids_initial.append(conf)
bar.next()
bar.finish()
if args.verbose == True:
log.write("o " + str(eng_dup) +
" Duplicates removed pre-energy filter (E < " +
str(args.initial_energy_threshold) + " kcal/mol )")
#reduce to unique set
if args.verbose:
log.write("o Removing duplicate conformers ( RMSD < " +
str(args.rms_threshold) + " and E difference < " +
str(args.energy_threshold) + " kcal/mol)")
bar = IncrementalBar('o Filtering based on energy and rms',
max=len(selectedcids_initial))
#check rmsd
for i, conf in enumerate(selectedcids_initial):
#set torsions to same value
for m in rotmatches:
rdMolTransforms.SetDihedralDeg(
outmols[conf].GetConformer(conf), *m, 180.0)
# This keeps track of whether or not your conformer is unique
excluded_conf = False
# include the first conformer in the list to start the filtering process
if i == 0:
selectedcids.append(conf)
# check rmsd
for seenconf in selectedcids:
E_diff = abs(cenergy[conf] - cenergy[seenconf]) # in kcal/mol
if E_diff < args.energy_threshold:
rms = get_conf_RMS(outmols[conf], outmols[conf], seenconf,
conf, args.heavyonly,
args.max_matches_RMSD, log)
if rms < args.rms_threshold:
excluded_conf = True
eng_rms_dup += 1
break
if excluded_conf == False:
if conf not in selectedcids:
selectedcids.append(conf)
bar.next()
bar.finish()
# unique_mols, unique_energies = [],[]
# for id in selectedcids:
# unique_mols.append(outmols[id])
# unique_energies.append(cenergy[id])
# log.write(unique_mols[0:2].GetConformers()[0].GetPositions())
if args.verbose == True:
log.write("o " + str(eng_rms_dup) +
" Duplicates removed (RMSD < " +
str(args.rms_threshold) + " / E < " +
str(args.energy_threshold) + " kcal/mol) after rotation")
if args.verbose:
log.write("o " + str(len(selectedcids)) +
" unique (ignoring torsions) starting conformers remain")
dup_data.at[dup_data_idx, 'RDKit-energy-duplicates'] = eng_dup
dup_data.at[dup_data_idx,
'RDKit-RMS-and-energy-duplicates'] = eng_rms_dup
dup_data.at[dup_data_idx,
'RDKIT-Unique-conformers'] = len(selectedcids)
# now exhaustively drive torsions of selected conformers
n_confs = int(len(selectedcids) * (360 / args.degree)**len(rotmatches))
if args.verbose and len(rotmatches) != 0:
log.write("\n\no Systematic generation of " + str(n_confs) +
" confomers")
bar = IncrementalBar(
'o Generating conformations based on dihedral rotation',
max=len(selectedcids))
else:
bar = IncrementalBar('o Generating conformations',
max=len(selectedcids))
total = 0
for conf in selectedcids:
#log.write(outmols[conf])
total += genConformer_r(outmols[conf], conf, 0, rotmatches,
args.degree, sdwriter, args,
outmols[conf].GetProp('_Name'), log)
bar.next()
bar.finish()
if args.verbose and len(rotmatches) != 0:
log.write("o %d total conformations generated" % total)
status = 1
sdwriter.close()
#getting the energy from and mols after rotations
if len(rotmatches) != 0:
rdmols = Chem.SDMolSupplier(name + '_' + 'rdkit' + args.output,
removeHs=False)
if rdmols is None:
log.write("Could not open " + name + args.output)
sys.exit(-1)
bar = IncrementalBar(
'o Filtering based on energy and rms after rotation of dihedrals',
max=len(rdmols))
sdwriter = Chem.SDWriter(name + '_' + 'rdkit' + '_' + 'rotated' +
args.output)
rd_count = 0
rd_selectedcids, rd_dup_energy, rd_dup_rms_eng = [], -1, 0
for i in range(len(rdmols)):
# This keeps track of whether or not your conformer is unique
excluded_conf = False
# include the first conformer in the list to start the filtering process
if rd_count == 0:
rd_selectedcids.append(i)
if args.metal_complex == True:
for atom in rdmols[i].GetAtoms():
if atom.GetSymbol() == 'I' and (
len(atom.GetBonds()) == 6
or len(atom.GetBonds()) == 5
or len(atom.GetBonds()) == 4
or len(atom.GetBonds()) == 3
or len(atom.GetBonds()) == 2):
for el in elementspt:
if el.symbol == args.metal:
atomic_number = el.number
atom.SetAtomicNum(atomic_number)
sdwriter.write(rdmols[i])
# Only the first ID gets included
rd_count = 1
# check rmsd
for j in rd_selectedcids:
if abs(
float(rdmols[i].GetProp('Energy')) -
float(rdmols[j].GetProp('Energy'))
) < args.initial_energy_threshold: # comparison in kcal/mol
excluded_conf = True
rd_dup_energy += 1
break
if abs(
float(rdmols[i].GetProp('Energy')) -
float(rdmols[j].GetProp('Energy'))
) < args.energy_threshold: # in kcal/mol
rms = get_conf_RMS(rdmols[i], rdmols[j], -1, -1,
args.heavyonly, args.max_matches_RMSD,
log)
if rms < args.rms_threshold:
excluded_conf = True
rd_dup_rms_eng += 1
break
if excluded_conf == False:
if args.metal_complex == True:
for atom in rdmols[i].GetAtoms():
if atom.GetSymbol() == 'I' and (
len(atom.GetBonds()) == 6
or len(atom.GetBonds()) == 5
or len(atom.GetBonds()) == 4
or len(atom.GetBonds()) == 3
or len(atom.GetBonds()) == 2):
for el in elementspt:
if el.symbol == args.metal:
atomic_number = el.number
atom.SetAtomicNum(atomic_number)
sdwriter.write(rdmols[i])
if i not in rd_selectedcids:
rd_selectedcids.append(i)
bar.next()
bar.finish()
sdwriter.close()
if args.verbose == True:
log.write("o " + str(rd_dup_energy) +
" Duplicates removed initial energy ( E < " +
str(args.initial_energy_threshold) + " kcal/mol )")
if args.verbose == True:
log.write("o " + str(rd_dup_rms_eng) +
" Duplicates removed (RMSD < " +
str(args.rms_threshold) + " / E < " +
str(args.energy_threshold) + " kcal/mol) after rotation")
if args.verbose == True:
log.write("o " + str(len(rd_selectedcids)) +
" unique (after torsions) conformers remain")
#filtering process after rotations
dup_data.at[dup_data_idx, 'RDKIT-Rotated-conformers'] = total
dup_data.at[dup_data_idx,
'RDKIT-Rotated-Unique-conformers'] = len(rd_selectedcids)
return status
