def compute_one_inchi(mol):
molfrom = Chem.MolFromSmiles(mol)
if molfrom == None:
return ((None, None, None))
ini = inchi.MolToInchi(molfrom)
inikey = inchi.InchiToInchiKey(ini)
hsh = hashlib.md5(ini.encode('utf-8')).hexdigest()
return ((hsh, inikey, ini))
def set_computable(self):
mol = tool_chemical.read_string("mol", self._mol)
# molecular_formula = Descriptors.rdMolDescriptors.CalcMolFormula(mol)
# molecular_weight = Descriptors.ExactMolWt(mol)
self._smiles = Chem.MolToSmiles(mol, isomericSmiles=False)
self._inchi = inchi.MolToInchi(mol)
self._inchikey = inchi.MolToInchiKey(mol)
self._molecular_formula = Chem.CalcMolFormula(mol)
self._molecular_weight = Chem.CalcExactMolWt(mol)
def convert(input, input_mod='smi'):
"""
convert SMILES into other molecular identifier
:param input: SMILES
:param input_mod: 'smi'
:return: str(molecular formula), str(inchi), str(inchikey)
"""
mol = read_string(input_mod, input)
molecular_formula = Descriptors.rdMolDescriptors.CalcMolFormula(mol)
molecular_inchi = inchi.MolToInchi(mol)
molecular_inchikey = inchi.MolToInchiKey(mol)
return molecular_formula, molecular_inchi, molecular_inchikey
def set_computables_from_mol(self, mol):
try: # warning comes up in pycharm (bug of pycharm)
self.molecular_formula = Descriptors.rdMolDescriptors.CalcMolFormula(
mol)
self.molecular_weight = Descriptors.ExactMolWt(mol)
self.inchi = inchi.MolToInchi(mol)
self.inchikey = inchi.MolToInchiKey(mol)
self.smiles = Chem.MolToSmiles(mol, isomericSmiles=False)
except Exception as e:
raise SpectrumError("Error occurred while computing properties" +
e.args) from e
assert self.molecular_formula is not None, "molecular-formula can't be None"
assert self.molecular_weight is not None, "molecular-weight can't be None"
assert self.inchi is not None, "inchi can't be None"
assert self.inchikey is not None, "inchikey can't be None"
assert self.smiles is not None, "smiles can't be None"
def fill_base_test(cursor):
df = pd.read_csv("toxicity_85832.csv")
#df = df.drop("Unnamed: 0", axis=1)
names_of_columns = list(df.columns)
smiles = list(df["SMILES"])
df = df.drop("SMILES", axis=1)
toxic_vals = np.array(df.values)
#molecules
canonize_smiles = [_canonize_mixture(smile) for smile in smiles]
inchi_smiles = [
inchi.MolToInchi(Chem.MolFromSmiles(smile))
for smile in canonize_smiles
]
inchikey = [
inchi.MolToInchiKey(Chem.MolFromSmiles(smile))
for smile in canonize_smiles
]
ids = [x for x in range(len(canonize_smiles))]
ziped_vals = zip(inchikey, inchi_smiles, canonize_smiles)
cursor.executemany(
"""insert into 'molecules' (inchi_key,inchi,canonical_smiles) values (?,?,?)""",
ziped_vals)
#tasks
descr_tasks = [
randomStringwithDigitsAndSymbols(random.randint(1, 30))
for i in range(20)
]
cursor.executemany("""insert into 'tasks' (descr) values (?)""",
zip(descr_tasks))
#tasks_running
completed = [random.randint(0, 1) for i in range(1000)]
id_tasks = [random.randint(1, len(descr_tasks)) for i in range(1000)]
id_molecules = [random.randint(1, len(smiles)) for i in range(1000)]
zip_tasks_running = zip(id_tasks, id_molecules, completed)
cursor.executemany(
"""insert into 'tasks_running' (id_task, id_molecule, completed) values (?,?,?)""",
zip_tasks_running)
#descriptors
name_of_descr = [
randomStringwithDigitsAndSymbols(random.randint(1, 30))
for i in range(10)
]
name_of_version = [
randomStringwithDigitsAndSymbols(random.randint(1, 30))
for i in range(10)
]
ziped_versions = zip(name_of_descr, name_of_version)
cursor.executemany(
"""insert into 'descriptors' (descriptor, version) values (?,?)""",
ziped_versions)
cursor.execute(
"""insert into 'descriptors' (descriptor, version) values (?,?)""",
("mordred", "0.315"))
#descriptor_values
id_descriptor = [11 for i in range(len(smiles))]
id_molecule = [x + 1 for x in range(len(smiles))]
id_tasks = [
random.randint(1, len(descr_tasks)) for i in range(len(smiles))
]
valid = [random.randint(0, 1) for i in range(len(smiles))]
value = func(canonize_smiles)
ziped_descr_vals = zip(id_molecule, id_descriptor, id_tasks, valid, value)
cursor.executemany(
"""insert into 'descriptors_values' (id_molecule, id_descriptor, id_task, valid, value) values (?,?,?,?,?)""",
ziped_descr_vals)
#endpoints
features = names_of_columns[1:]
descriptions = [feature.split('_')[1] for feature in features]
types = ['_'.join(feature.split('_')[2:]) for feature in features]
ziped_endpoints = zip(descriptions, types)
cursor.executemany("""insert into 'endpoints' (desc, type) values (?,?)""",
ziped_endpoints)
#experimnetal data
ids_molecules = []
ids_endpoints = []
values_endpoints = []
for i in range(len(toxic_vals[:, 0])):
for j in range(len(toxic_vals[0, :])):
if (~np.isnan(toxic_vals[i, j])):
ids_molecules.append(i + 1)
ids_endpoints.append(j + 1)
values_endpoints.append(toxic_vals[i, j])
ziped_experimental_data = zip(ids_molecules, ids_endpoints,
values_endpoints)
cursor.executemany(
"""insert into 'experimental_data' (id_molecule, id_endpoint, value) values (?,?,?)""",
ziped_experimental_data)
return cursor
def calculate_inchi(smile):
return inchi.MolToInchi(Chem.MolFromSmiles(smile))
def loadSDF(sdfPath):
# Create images
#generateImages(sdfPath)
# Create a molecule supplier
suppl = Chem.SDMolSupplier(sdfPath)
# Filter empty entries
sdf = [x for x in suppl if x is not None]
# For each molecule in supplier
for mol in sdf:
data = {}
try:
data['fCharge'] = mol.GetProp('Charge')
except:
data['fCharge'] = Chem.GetFormalCharge(mol)
try:
data['name'] = mol.GetProp('DATABASE_ID')
except:
data['name'] = 'unkown'
try:
data['molMass'] = mol.GetProp('Total Molweight')
except:
data['molMass'] = Descriptors.ExactMolWt(mol)
try:
data['cLogP'] = mol.GetProp('cLogP')
except:
data['cLogP'] = Crippen.MolLogP(mol) # não sei se ta certo
try:
data['cLogS'] = mol.GetProp('cLogS')
except:
data['cLogS'] = 0.0
try:
data['tpsa'] = mol.GetProp('Polar Surface Area')
except:
data['tpsa'] = rdMolDescriptors.CalcTPSA(mol)
try:
data['totalSurfaceArea'] = mol.GetProp('Total Surface Area')
except:
data['totalSurfaceArea'] = rdMolDescriptors.CalcTPSA(mol)
try:
data['hbondAcceptors'] = mol.GetProp('H-Acceptors')
except:
data['hbondAcceptors'] = rdMolDescriptors.CalcNumHBA(mol)
try:
data['hbondDonnors'] = mol.GetProp('H-Donors')
except:
data['hbondDonnors'] = rdMolDescriptors.CalcNumHBD(mol)
try:
data['rotable'] = mol.GetProp('Rotatable Bonds')
except:
data['rotable'] = rdMolDescriptors.CalcNumRotatableBonds(mol)
try:
data['mutagenic'] = mol.GetProp('Mutagenic')
except:
data['mutagenic'] = 'Unknown'
try:
data['tumorigenic'] = mol.GetProp('Tumorigenic')
except:
data['tumorigenic'] = 'Unknown'
try:
data['irritant'] = mol.GetProp('Irritant')
except:
data['irritant'] = 'Unkown'
try:
data['smiles'] = mol.GetProp('SMILES')
except:
data['smiles'] = Chem.MolToSmiles(mol)
try:
data['InChI'] = mol.GetProp('INCHI_IDENTIFIER')
except:
data['InChI'] = inchi.MolToInchi(mol)
try:
data['inchiKey'] = mol.GetProp('INCHI_KEY')
except:
data['inchiKey'] = inchi.MolToInchiKey(mol)
try:
data['nonHAtoms'] = mol.GetProp('Non-H Atoms')
except:
data['nonHAtoms'] = -1 # Não sei calcular
try:
data['numAtoms'] = mol.GetProp('numAtoms')
except:
data['numAtoms'] = mol.GetNumAtoms()
try:
data['stereoCenters'] = mol.GetProp('Stereo Centers')
except:
data['stereoCenters'] = mol.GetNumAtoms()
try:
data['provider'] = mol.GetProp('DATABASE_NAME')
except:
print("Nenhum fornecedor encontrado, o campo é obrigatório!")
continue
tmp = AllChem.Compute2DCoords(mol) # Compute its coordinates
Draw.MolToFile(mol,
os.path.join(settings.FILES_DIR, f'molImages/' + data["inchiKey"] + '.png'),
size=(300,300),
kekulize=True,
wedgeBonds=True,
fitImage=True) # Save it
Draw.MolToFile(mol,
os.path.join(settings.FILES_DIR, f'molThumbs/' + data["inchiKey"] + '.png'),
size=(150,150),
kekulize=True,
wedgeBonds=True,
fitImage=True)
feedDatabase(data)
if Compounds.objects.filter(inChIKey=data['inchiKey']).exists():
if not Compounds.objects.filter(provider=['provider']).exists():
feedDatabase(data)
print("feed1")
# append no sdf da base de dados
a = 1
else:
print("continue123")
continue
else:
a = 1
feedDatabase(data)
print("feed2")
'''except:
def parse_f(f):
names = ['']
cid = -1
CAS = f.split('/')[1] if '/' in f else f
CAS = CAS.split('.')[0]
if CAS in ignored_CASs:
return None
failed_mol = False
try:
if CAS in syn_data:
d = syn_data[CAS]
if 'pubchem' in d:
raise Exception(
'Pubchem specified, not trying to use the mol file')
elif 'formula' in d:
raise Exception(
'Formula specified, not trying to use the mol file')
try:
mol = Chem.MolFromMolFile(f)
assert mol is not None
except:
print('Cannot read %s' % f)
1 / 0
try:
inchi_val = inchi.MolToInchi(mol)
except:
print('BAILING ON %s' % f)
1 / 0
mol = inchi.MolFromInchi(inchi_val) # Works better for ions
if mol is None:
print('BAILING ON reconversion to mol %s' % f)
1 / 0
except:
failed_mol = True
if CAS in syn_data:
d = syn_data[CAS]
if 'pubchem' in d:
if str(d['pubchem']) in mycache:
cid, iupac_name, names, mw, smi, inchi_val, inchikey, formula = mycache[
str(d['pubchem'])]
else:
pc = Compound.from_cid(d['pubchem'])
cid = pc.cid
iupac_name = pc.iupac_name
names = pc.synonyms
mw = pc.molecular_weight
smi = pc.canonical_smiles
inchi_val = pc.inchi
inchikey = pc.inchikey
formula = pc.molecular_formula
mycache[str(d['pubchem'])] = (cid, iupac_name, names, mw,
smi, inchi_val, inchikey,
formula)
else:
cid = -1
names = d['synonyms'] if 'synonyms' in d else ['']
mw = float(d['MW'])
smi = d['smiles'] if 'smiles' in d else ''
formula = d['formula'] if 'formula' in d else ''
inchi_val = d['inchi'] if 'inchi' in d else ''
inchikey = d['inchikey'] if 'inchikey' in d else ''
iupac_name = ''
else:
print('FAILED on %s and no custom data was available either' % CAS)
return None
if not failed_mol:
smi = Chem.MolToSmiles(mol, True)
inchi_val = inchi.MolToInchi(mol)
inchikey = inchi.InchiToInchiKey(inchi_val)
mw = Descriptors.MolWt(mol)
# for i in mol.GetAtoms():
# if i.GetIsotope():
# mw = Descriptors.ExactMolWt(mol)
# break
formula = CalcMolFormula(mol, True, True)
iupac_name = ''
try:
if not failed_mol:
if str(inchikey) in mycache:
cid, iupac_name, names = mycache[str(inchikey)]
else:
try:
pc = get_compounds(inchikey, 'inchikey')[0]
cid = pc.cid
iupac_name = pc.iupac_name
names = pc.synonyms
mycache[str(inchikey)] = (cid, iupac_name, names)
except:
mycache[str(inchikey)] = (-1, '', [''])
except:
cid = -1
iupac_name = ''
names = ['']
other_CAS = []
if CAS in pdf_data:
d = pdf_data[CAS]
name = d['Name']
if 'Other Names' in d:
syns = d['Other Names']
else:
syns = []
if not iupac_name:
iupac_name = name
else:
syns.insert(0, name)
if 'Deleted CAS' in d:
other_CAS.extend(d['Deleted CAS'])
if 'Alternate CAS' in d:
other_CAS.extend(d['Alternate CAS'])
syns = [i for i in syns if i not in dup_names]
names = syns + [i for i in names if i not in all_names] + other_CAS
actual_names = []
for name in names:
if name in all_user_names:
# If the name is in the user db, only add it if it corresponds to this CAS number
if CAS in syn_data and 'synonyms' in syn_data[
CAS] and name in syn_data[CAS]['synonyms']:
actual_names.append(name)
else:
# Discard it otherwise
pass
else:
# If the name is not in the user db we're all good
actual_names.append(name)
if CAS in syn_data and 'synonyms' in syn_data[CAS]:
# If the user has any syns for this cas number, add those names if the name hasn't already been aded
for n in syn_data[CAS]['synonyms']:
if n not in actual_names:
actual_names.append(n)
actual_names = [i for i in actual_names if i]
if inchi_val is not None:
inchi_val = inchi_val.replace('InChI=1S/', '')
formula = serialize_formula(formula)
s = '%d\t%s\t%s\t%g\t%s\t%s\t%s\t%s\t' % (cid, CAS, formula, mw, smi,
inchi_val, inchikey, iupac_name)
s += '\t'.join(actual_names)
print(s)
return None
except Exception:
print("Not able to remove stereochemistry. Chembl.")
try:
mol = standardise.run(mol)
except standardise.StandardiseException as e:
logging.warn(e.message)
try:
mol = s.standardize(mol)
except Exception:
print("Not able to standardize. Chembl.")
try:
mol = s.tautomer_parent(mol, skip_standardize=True)
except Exception:
print("Not able to make tautomer parent. Chembl.")
mol = s.stereo_parent(mol, skip_standardize=True)
chembl_help[lig][0] = inchi.MolToInchi(mol)
#BDB preparing
bdb_help = []
list_help = []
conn = psycopg2.connect('dbname=bdb user=data host=/tmp/')
curs = conn.cursor()
curs.execute(
"select bdb_2_ligand_inchi, bdb_2_ligand_inchi from bdb_base B FULL OUTER JOIN bdb_extend E ON B.bdb_0_bindingdb_reactant_set_id = E.bdb_0_bindingdb_reactant_set_id where bdb_40_uniprot__swissprot__entry_name_of_target_chain IN ('THA_HUMAN', 'THB_HUMAN', 'RARA_HUMAN', 'RARB_HUMAN', 'RARG_HUMAN', 'PPARA_HUMAN', 'PPARD_HUMAN', 'PPARG_HUMAN', 'NR1D1_HUMAN', 'NR1D2_HUMAN', 'RORA_HUMAN', 'RORB_HUMAN', 'RORG_HUMAN', 'NR1H2_HUMAN', 'NR1H3_HUMAN', 'NR1H4_HUMAN', 'VDR_HUMAN', 'NR1I2_HUMAN', 'NR1I3_HUMAN', 'HNF4A_HUMAN', 'HNF4G_HUMAN', 'RXRA_HUMAN', 'RXRB_HUMAN', 'RXRG_HUMAN', 'NR2C1_HUMAN', 'NR2C2_HUMAN', 'NR2E1_HUMAN', 'NR2E3_HUMAN', 'COT1_HUMAN', 'COT2_HUMAN', 'NR2F6_HUMAN', 'ESR1_HUMAN', 'ESR2_HUMAN', 'ERR1_HUMAN', 'ERR2_HUMAN', 'ERR3_HUMAN', 'GCR_HUMAN', 'MCR_HUMAN', 'PRGR_HUMAN', 'ANDR_HUMAN', 'NR4A1_HUMAN', 'NR4A2_HUMAN', 'NR4A3_HUMAN', 'STF1_HUMAN', 'NR5A2_HUMAN', 'NR6A1_HUMAN', 'NR0B1_HUMAN', 'NR0B2_HUMAN') OR bdb_41_uniprot__swissprot__primary_id_of_target_chain IN ('P10827', 'P10828', 'P10276', 'P10826', 'P13631', 'Q07869', 'Q03181', 'P37231', 'P20393', 'Q14995', 'P35398', 'Q92753', 'P51449', 'P55055', 'Q13133', 'Q96RI1', 'P11473', 'O75469', 'Q14994', 'P41235', 'Q14541', 'P19793', 'P28702', 'P48443', 'P13056', 'P49116', 'Q9Y466', 'Q9Y5X4', 'P10589', 'P24468', 'P10588', 'P03372', 'Q92731', 'P11474', 'O95718', 'P62508', 'P04150', 'P08235', 'P06401', 'P10275', 'P22736', 'P43354', 'Q92570', 'Q13285', 'O00482', 'Q15406', 'P51843', 'Q15466');"
)
bdb = curs.fetchall()
#BDB duplicates
i = 0
while i < len(bdb):
delete = False
