def run(self):
from toolbox.molecule import Molecule
self.semaphore.acquire()
start_time = time.time()
logging.debug("SMILES: " + self.smiles)
diss_table = Molecule._GetDissociationTable(self.smiles,
fmt='smiles',
mid_pH=default_pH,
min_pKa=0,
max_pKa=14,
T=default_T)
logging.debug("Min charge: %d" % diss_table.min_charge)
logging.debug("Min nH: %d" % diss_table.min_nH)
elapsed_time = time.time() - start_time
self.db_lock.acquire()
db = SqliteDatabase(self.options.db_file)
kegg = Kegg.getInstance()
name = kegg.cid2name(self.cid)
if diss_table is not None:
for row in diss_table.ToDatabaseRow():
db.Insert(self.options.table_name, [self.cid, name] + row)
else:
db.Insert(self.options.table_name, [self.cid, name] + [None] * 10)
del db
self.db_lock.release()
logging.info("Completed C%05d, elapsed time = %.1f sec" %
(self.cid, elapsed_time))
self.semaphore.release()
def run(self):
from toolbox.molecule import Molecule
self.semaphore.acquire()
start_time = time.time()
logging.debug("SMILES: " + self.smiles)
diss_table = Molecule._GetDissociationTable(self.smiles, fmt='smiles',
mid_pH=default_pH, min_pKa=0, max_pKa=14, T=default_T)
logging.debug("Min charge: %d" % diss_table.min_charge)
logging.debug("Min nH: %d" % diss_table.min_nH)
elapsed_time = time.time() - start_time
self.db_lock.acquire()
db = SqliteDatabase(self.options.db_file)
kegg = Kegg.getInstance()
name = kegg.cid2name(self.cid)
if diss_table is not None:
for row in diss_table.ToDatabaseRow():
db.Insert(self.options.table_name, [self.cid, name] + row)
else:
db.Insert(self.options.table_name, [self.cid, name] + [None] * 10)
del db
self.db_lock.release()
logging.info("Completed C%05d, elapsed time = %.1f sec" %
(self.cid, elapsed_time))
self.semaphore.release()
def CalculateThermo():
parser = MakeOpts()
options, _ = parser.parse_args(sys.argv)
pH, I, pMg, T = options.pH, options.I, options.pMg, options.T
db = SqliteDatabase('../res/gibbs.sqlite')
G = GroupContribution(db=db)
G.init()
ignore_protonations = False
list_of_mols = []
if options.smiles:
list_of_mols.append({'id':options.smiles, 'mol':options.smiles,
'format':'smiles'})
elif options.inchi:
list_of_mols.append({'id':options.inchi, 'mol':options.inchi,
'format':'inchi'})
elif options.csv_input_filename:
for row in csv.DictReader(open(options.csv_input_filename, 'r')):
if "InChI" in row:
list_of_mols.append({'id':row["ID"], 'mol':row["InChI"],
'format':'inchi'})
elif "smiles" in row:
list_of_mols.append({'id':row["ID"], 'mol':row["smiles"],
'format':'smiles'})
else:
raise Exception("There must be one molecular ID column: InChI or smiles")
else:
parser.error("must use either -s or -c option")
if options.biochemical:
print ("Calculating biochemical formation energies for %s compounds"
" at pH = %.1f, I = %.2f, pMg = %.1f, T = %.2f" %
(len(list_of_mols), pH, I, pMg, T))
else:
print ("Calculating chemical formation energies for %s compounds" %
len(list_of_mols))
rowdicts = []
for mol_dict in list_of_mols:
mol_id = mol_dict['id']
diss_table = Molecule._GetDissociationTable(mol_dict['mol'],
fmt=mol_dict['format'])
try:
mol = diss_table.GetMostAbundantMol(pH, I, pMg, T) or \
diss_table.GetAnyMol()
if mol is None:
raise Exception("Cannot convert input string to Molecule: " +
mol_dict['mol'])
decomposition = G.Mol2Decomposition(mol,
ignore_protonations=ignore_protonations)
groupvec = decomposition.AsVector()
dG0 = G.groupvec2val(groupvec)
nH = decomposition.Hydrogens()
nMg = decomposition.Magnesiums()
diss_table.SetFormationEnergyByNumHydrogens(dG0, nH, nMg)
pmap = diss_table.GetPseudoisomerMap()
if options.biochemical:
dG0_prime = pmap.Transform(pH, pMg, I, T)
rowdicts.append({'ID':mol_id, 'pH':pH, 'I':I, 'pMg':pMg,
'dG0\'':"%.1f" % dG0_prime, 'groupvec':str(groupvec)})
else:
for p_nH, p_z, p_nMg, p_dG0 in pmap.ToMatrix():
rowdicts.append({'ID':mol_id, 'nH':p_nH, 'charge':p_z, 'nMg':p_nMg,
'dG0':"%.1f" % p_dG0, 'groupvec':str(groupvec)})
except GroupDecompositionError:
rowdicts.append({'ID':mol_id, 'error':"cannot decompose"})
except GroupMissingTrainDataError:
rowdicts.append({'ID':mol_id, 'groupvec':str(groupvec),
'error':"missing training data"})
if options.csv_output_filename is not None:
out_fp = open(options.csv_output_filename, 'w')
print "writing results to %s ... " % options.csv_output_filename
else:
out_fp = sys.stdout
if options.biochemical:
titles = ['ID', 'error', 'pH', 'I', 'pMg', 'dG0\'', 'groupvec']
else:
titles = ['ID', 'error', 'nH', 'nMg', 'charge', 'dG0', 'groupvec']
csv_writer = csv.DictWriter(out_fp, titles)
csv_writer.writeheader()
csv_writer.writerows(rowdicts)
def CalculateThermo():
parser = MakeOpts()
options, _ = parser.parse_args(sys.argv)
pH, I, pMg, T = options.pH, options.I, options.pMg, options.T
db = SqliteDatabase('../res/gibbs.sqlite')
G = GroupContribution(db=db)
G.init()
ignore_protonations = False
list_of_mols = []
if options.smiles:
list_of_mols.append({
'id': options.smiles,
'mol': options.smiles,
'format': 'smiles'
})
elif options.inchi:
list_of_mols.append({
'id': options.inchi,
'mol': options.inchi,
'format': 'inchi'
})
elif options.csv_input_filename:
for row in csv.DictReader(open(options.csv_input_filename, 'r')):
if "InChI" in row:
list_of_mols.append({
'id': row["ID"],
'mol': row["InChI"],
'format': 'inchi'
})
elif "smiles" in row:
list_of_mols.append({
'id': row["ID"],
'mol': row["smiles"],
'format': 'smiles'
})
else:
raise Exception(
"There must be one molecular ID column: InChI or smiles")
else:
parser.error("must use either -s or -c option")
if options.biochemical:
print(
"Calculating biochemical formation energies for %s compounds"
" at pH = %.1f, I = %.2f, pMg = %.1f, T = %.2f" %
(len(list_of_mols), pH, I, pMg, T))
else:
print("Calculating chemical formation energies for %s compounds" %
len(list_of_mols))
rowdicts = []
for mol_dict in list_of_mols:
mol_id = mol_dict['id']
diss_table = Molecule._GetDissociationTable(mol_dict['mol'],
fmt=mol_dict['format'])
try:
mol = diss_table.GetMostAbundantMol(pH, I, pMg, T) or \
diss_table.GetAnyMol()
if mol is None:
raise Exception("Cannot convert input string to Molecule: " +
mol_dict['mol'])
decomposition = G.Mol2Decomposition(
mol, ignore_protonations=ignore_protonations)
groupvec = decomposition.AsVector()
dG0 = G.groupvec2val(groupvec)
nH = decomposition.Hydrogens()
nMg = decomposition.Magnesiums()
diss_table.SetFormationEnergyByNumHydrogens(dG0, nH, nMg)
pmap = diss_table.GetPseudoisomerMap()
if options.biochemical:
dG0_prime = pmap.Transform(pH, pMg, I, T)
rowdicts.append({
'ID': mol_id,
'pH': pH,
'I': I,
'pMg': pMg,
'dG0\'': "%.1f" % dG0_prime,
'groupvec': str(groupvec)
})
else:
for p_nH, p_z, p_nMg, p_dG0 in pmap.ToMatrix():
rowdicts.append({
'ID': mol_id,
'nH': p_nH,
'charge': p_z,
'nMg': p_nMg,
'dG0': "%.1f" % p_dG0,
'groupvec': str(groupvec)
})
except GroupDecompositionError:
rowdicts.append({'ID': mol_id, 'error': "cannot decompose"})
except GroupMissingTrainDataError:
rowdicts.append({
'ID': mol_id,
'groupvec': str(groupvec),
'error': "missing training data"
})
if options.csv_output_filename is not None:
out_fp = open(options.csv_output_filename, 'w')
print "writing results to %s ... " % options.csv_output_filename
else:
out_fp = sys.stdout
if options.biochemical:
titles = ['ID', 'error', 'pH', 'I', 'pMg', 'dG0\'', 'groupvec']
else:
titles = ['ID', 'error', 'nH', 'nMg', 'charge', 'dG0', 'groupvec']
csv_writer = csv.DictWriter(out_fp, titles)
csv_writer.writeheader()
csv_writer.writerows(rowdicts)