def save_model(self, request, obj, form, change):
if form.cleaned_data['pubchem_id']:
comp = Compound.from_cid(form.cleaned_data['pubchem_id'])
obj.pubchem_name = comp.synonyms[0]
else:
obj.pubchem_name = None
obj.save()
def do_search_api():
args = reqparse.RequestParser(). \
add_argument("Molecular", type=str). \
add_argument("Type", type=str, default='similarity'). \
add_argument("Cid", type=int). \
parse_args()
molecular_name = args['Molecular']
search_type = args['Type']
cid_num = args['Cid']
if search_type == 'similarity':
table_name = SIM_TABLE
elif search_type == 'substructure':
table_name = SUB_TABLE
elif search_type == 'superstructure':
table_name = SUPER_TABLE
top_k = NUM
if cid_num:
from pubchempy import get_compounds, Compound
comp = Compound.from_cid(cid_num)
molecular_name = comp.isomeric_smiles
if not molecular_name:
return "no molecular"
if molecular_name:
try:
res_smi = do_search(table_name, molecular_name, top_k)
except:
return "There has no results, please input the correct molecular and ensure the table has data."
re = {}
re["Smiles"] = res_smi
return jsonify(re), 200
return "not found", 400
def get_smiles(idx, names=None, cids=None, sids=None, binding_db=None):
try:
cid = int(cids[idx])
smiles_string = binding_db.loc[binding_db['PubChem CID'] ==
cid]["Ligand SMILES"].values[0]
except:
try:
sid = int(sids[idx])
smiles_string = binding_db.loc[binding_db['PubChem SID'] ==
sid]["Ligand SMILES"].values[0]
except:
try:
cid = int(cids[idx])
smiles_string = str(
Compound.from_cid(int(cid)).isomeric_smiles)
except:
try:
chembl_id = Substance.from_sid(sids[idx]).source_id
print(chembl_id)
try:
compounds = CompoundResource()
c = compounds.get(chembl_id)
smiles_string = c["smiles"]
except:
smiles_string = binding_db.loc[
binding_db["ChEMBL ID of Ligand"] ==
chembl_id]["Ligand SMILES"].values[0]
except:
try:
name = names[idx]
cs = get_compounds(name, 'name')
smiles_string = cs[0].isomeric_smiles
except:
smiles_string = np.nan
return (smiles_string)
def from_id(cid):
"""This is a pass through to pubchempy.Compound.from_cid(cid)
"""
# sometimes cid comes in as a float, it should be an int
if isinstance(cid,float):
cid = int(cid)
return Compound.from_cid(cid)
def scrape_pubchem_for_inchi():
from pubchempy import BadRequestError, Compound, NotFoundError
for m in Molecule.objects.filter(
pubchem_id__isnull=False).order_by('pubchem_id'):
if m.inchi_code == "":
try:
c = Compound.from_cid(m.pubchem_id)
m.inchi_code = c.inchi
m.save()
except (BadRequestError, NotFoundError):
logging.error('Invalid PubChem CID: {}'.format(m.pubchem_id))
def __call__(self, representation):
"""Create `pubchempy.Compound` object from passed CID representation.
Parameters
----------
representation : int
CID integer as per `pubchempy` doccumentation for `from_cid`.
Returns
-------
compound : pubchempy.Compound
Pubchempy represenation of molecule, accessed from the PubChem database.
"""
compound = Compound.from_cid(representation) # calls PubChem API
time.sleep(self.crawl_delay) # ensure good scraping practise
return compound
def decagon_preprocess(cfg, cid_dict):
in_file = cfg['DATASET']['COMBO_PATH']
out_file = cfg['DATASET']['PROCESSED_PATH']
# get list of labels that have more interactions than NUM_MIN_INTERACTION
label_list, counter_dict = filter_interactions(in_file, cfg)
unknown_dict = {}
with open(in_file, 'r') as csv_in:
with open(out_file, 'w') as csv_out:
csv_reader = csv.reader(csv_in, delimiter=',')
next(csv_reader, None) # ignore Header
csv_writer = csv.writer(csv_out)
total = sum(1 for row in open(in_file))
for line_idx, line in enumerate(csv_reader):
printProgress(
line_idx + 1, total, '| Processing {} pairs...'.format(
cfg['DATASET']['DATA_NAME']), ' ', 1, 50)
stitch1, stitch2, side_effect, _ = line # _ : string name for side effects
if side_effect in label_list:
row_string = []
for stitch in [stitch1, stitch2]:
smiles = None
try:
smiles = cid_dict[stitch]
except KeyError:
# Key doesn't exists in dictionary
if stitch in unknown_dict:
smiles = unknown_dict[
stitch] # if already retrieved from pubchempy
else:
if stitch1[3] == '1' or stitch2[3] == '1':
print("Pair has stereo : [%s,%s]" %
(stitch1, stitch2)) # ?
print(">>> Adding {} to dictionary...".format(
stitch))
smiles = Compound.from_cid(
int(stitch[4:])
).canonical_smiles # Obtain smiles from pubchempy
unknown_dict[
stitch] = smiles # update unknown dicts for future reference
row_string.append(smiles)
row_string.append(side_effect)
csv_writer.writerow(row_string)
def main(argv):
"""Main entry point of program.
"""
# Parse arguments
parser = argparse.ArgumentParser(description=
'Retrieves compounds from PubChem using either a list of CID''s or'
' identifier and type, e.g. Glucose name .\n',
formatter_class=RawTextHelpFormatter)
parser.add_argument('cid', metavar='cid', nargs='+',action='store',
help='CIDs to fetch.')
# parser.add_argument('output_dir', metavar='output_dir', action='store',
# help='Output directory to place csvs into.')
parser.add_argument('-s', nargs=2, metavar=('identifier', 'type'), action='store', dest='search',
help='Identifier and type to search for: e.g. -s Glucose name\n'
' Types supported: cid, name, smiles, sdf, inchi, inchikey, formula.\n')
args = parser.parse_args()
cid = args.cid
#output_dir = args.output_dir
search = args.search
c = Compound.from_cid(cid, as_dataframe=True)
pprint(c.record)
print "Synonyms", ";".join(c.synonyms)
print c.iupac_name
# alternate name
# metabolic_network_id
print c.cid
# chebi
# kegg
# bigg
# HMDB
# DrubBank
print c.inchi
print c.inchikey
print c.canonical_smiles
# protein associations
print c.exact_mass
print c.molecular_weight
print c.molecular_formula
print c.charge
def save_model(self, request, obj, form, change):
if form.cleaned_data['chebi_id']:
chebi_id = form.cleaned_data['chebi_id']
chebi_comb = ChebiEntity('CHEBI:' + str(chebi_id))
parent_id = chebi_comb.get_parent_id()
if parent_id:
s = re.findall(r'\d+', parent_id)
chebi_id = int(s[0])
chebi_comb = ChebiEntity('CHEBI:' + str(chebi_id))
obj.chebi_id = chebi_id
obj.chebi_name = chebi_comb.get_name()
else:
obj.chebi_name = None
if form.cleaned_data['pubchem_id']:
comp = Compound.from_cid(form.cleaned_data['pubchem_id'])
obj.pubchem_name = comp.synonyms[0]
else:
obj.pubchem_name = None
obj.save()
from numpy import random
from pubchempy import get_compounds, Compound
# comp = Compound.from_cid(6602565)
# print(comp.isomeric_smiles)
# comps = get_compounds('Aspirin', 'name')
# print(comps[0].xlogp)
# # 1.2
import csv
with open('inhibitor_all.csv', 'r') as inhibitor_all:
reader = csv.reader(inhibitor_all, delimiter=',', quotechar='"')
# next(reader, 5) # skip the headers
Actives = [compound[2] for compound in reader if compound[3]=="Active"]
with open('inhibitor_active.csv', 'w') as inhibitor_active:
for active in Actives:
comp = Compound.from_cid(active)
print(comp.isomeric_smiles)
writer = csv.writer(inhibitor_active, delimiter=',')
# writer.writerow(["Active", "mol_id", "smiles"]) # write header
writer.writerow([1, active, comp.isomeric_smiles])
print(len(Active))
# break
# with open('inhibitor_all.csv', 'r') as inhibitor_all:
# reader = csv.reader(inhibitor_all, delimiter=',', quotechar='"')
# # next(reader, 5) # skip the headers
# Inactive = [compound[2] for compound in reader if compound[3]=="Inactive"]
# for x in xrange(12):
# Inactive = random.choice(Inactive, 3880)
# print(len(Inactive))
#! /home/caleb/.anaconda3/bin/python
import sys
from pubchempy import get_compounds, Compound
arg = sys.argv[0:]
arg.pop(0)
for cid in arg:
cid = int(cid)
pc = Compound.from_cid(cid)
iupac_name = pc.iupac_name
if iupac_name == None:
iupac_name = ''
names = pc.synonyms
smi = pc.isomeric_smiles
inchi_val = pc.inchi
inchikey = pc.inchikey
mw = pc.molecular_weight
formula = pc.molecular_formula
CAS = ''
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(names)
print(s)
#4 78-96-6 C3H9NO 75.10966 CC(CN)O C3H9NO/c1-3(5)2-4/h3,5H,2,4H2,1H3 HXKKHQJGJAFBHI-UHFFFAOYSA-N 1-azanylpropan-2-ol 1-amino-2-propanol
import pickle
from rdkit.Chem import MolFromSmiles
from pubchempy import Compound
# ----------------------------------------------------------------------------------------------------------------------
with open('tests/files/features.pkl', 'rb') as f:
REF_FEATURES = pickle.load(f)
STD_FEATURES = REF_FEATURES['standard']
MAP4_FEATURES = REF_FEATURES['map4']
PUB_FEATURES = REF_FEATURES['pubchem']
# ----------------------------------------------------------------------------------------------------------------------
SMILES = 'O=C1C(=Cc2ccccc2)CCCC1=Cc1ccccc1'
MOL = MolFromSmiles(SMILES)
assert MOL, 'Error creating the testing mol object.'
# ----------------------------------------------------------------------------------------------------------------------
cid = 5090
PUB_MOL = Compound.from_cid(cid)
assert isinstance(
PUB_MOL, Compound), 'Error creating the testing pubchem Compound object.'
# ----------------------------------------------------------------------------------------------------------------------
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %%
import pubchempy as pcp
import pandas
from pubchempy import Compound, get_compounds
import logging
logging.basicConfig(level=logging.DEBUG)
#Fetch a compound with cid
#c = pcp.Compound.from_cid(5090)
c = Compound.from_cid(1423)
cs1 = get_compounds('Aspirin', 'name')
cs2 = get_compounds('C1=CC2=C(C3=C(C=CC=N3)C=C2)N=C1', 'smiles')
# %%
#To get 3d information about compounds
cs1 = pcp.get_compounds('Aspirin', 'name', record_type='3d')
cs1
# %%
c.to_dict()
# %%
#Fetch a list of compounds based on cids and print their metadata (schema)
cs_list = []
blocksize = 10
import pandas as pd
from pubchempy import Compound
df = pd.read_csv('AID_1706_datatable_all.csv')
print(df.head())
df['SMILES'] = [
Compound.from_cid(cid).isomeric_smiles for cid in df['PUBCHEM_CID']
]
df.to_csv('AID_1706_datatable_all.csv', index=False)
print(df.head())
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
"pd_idx": pd_idx,
"pd_weight": pd_weight
}
directory = os.path.abspath(os.getcwd()) + "/output/pkl"
if not os.path.exists(directory):
os.makedirs(directory)
with open(directory + "/{}.pkl".format(fig_name), "wb") as f:
pickle.dump(out, f)
#########################################
from pubchempy import Compound
import pandas as pd
cids = [
int(data.drug_idx_to_id[i][3:]) for i in range(len(data.drug_idx_to_id))
]
drugs = [Compound.from_cid(int(cid)) for cid in cids]
drug_ids = pd.DataFrame([[
data.drug_id_to_idx['CID{}'.format(d.cid)], d.cid,
'NA' if len(d.synonyms) == 0 else d.synonyms[0], d.iupac_name
] for d in drugs],
columns=["drug_idx", 'CID', 'synonym', 'iupac_name'])
drug_ids.to_csv('./index-map/drug-map.csv', index=False)
from goatools.test_data.genes_NCBI_9606_ProteinCoding import GENEID2NT as GeneID2nt_hum
geneid2symbol = {v.GeneID: v.Symbol for k, v in GeneID2nt_hum.items()}
genes = [
int(data.prot_idx_to_id[i][6:]) for i in range(len(data.prot_idx_to_id))
]
gene_ids = pd.DataFrame([[
data.prot_id_to_idx['GeneID{}'.format(gene)], gene,
def main():
st.title("Bioinformatics App")
st.set_option('deprecation.showfileUploaderEncoding', False)
activity = [
'Intro', 'SequenceAnalysis', 'DotPlot', 'ProteinSearch',
"MoleculeVisualizer", "ChemicalSearch"
]
choice = st.sidebar.selectbox("Select Activity", activity)
if choice == 'Intro':
st.subheader("Intro")
st.write(
""" This is a bioinformatics web app made with Python and Streamlit. Use the left panel dropdown to choose the various features to use."""
)
image = Image.open("overviewpicture.png")
st.image(image, use_column_width=True)
elif choice == "SequenceAnalysis":
st.subheader("DNA Sequence Analysis")
seq_file = st.file_uploader("Upload FASTA File", type=["fasta", "fa"])
if seq_file is not None:
dna_record = SeqIO.read(seq_file, "fasta")
# st.write(dna_record)
dna_seq = dna_record.seq
details = st.radio("Details", ("Description", "Sequence"))
if details == "Description":
st.write(dna_record.description)
elif details == "Sequence":
st.write(dna_record.seq)
# Nucleotide Frequencies
st.subheader("Nucleotide Frequency")
dna_freq = Counter(dna_seq)
st.write(dna_freq)
adenine_color = st.beta_color_picker("Adenine Color")
thymine_color = st.beta_color_picker("thymine Color")
guanine_color = st.beta_color_picker("Guanine Color")
cytosil_color = st.beta_color_picker("cytosil Color")
if st.button("Plot Freq"):
barlist = plt.bar(dna_freq.keys(), dna_freq.values())
barlist[2].set_color(adenine_color)
barlist[3].set_color(thymine_color)
barlist[1].set_color(guanine_color)
barlist[0].set_color(cytosil_color)
st.pyplot()
st.subheader("DNA Composition")
gc_score = utils.gc_content(str(dna_seq))
at_score = utils.at_content(str(dna_seq))
st.json({"GC Content": gc_score, "AT Content": at_score})
# Nucleotide Count
nt_count = st.text_input("Enter Nucleotide Here",
"Type Nucleotide Alphabet")
st.write("Number of {} Nucleotide is ::{}".format(
(nt_count),
str(dna_seq).count(nt_count)))
# Protein Synthesis
st.subheader("Protein Synthesis")
p1 = dna_seq.translate()
aa_freq = Counter(str(p1))
if st.checkbox("Transcription"):
st.write(dna_seq.transcribe())
elif st.checkbox("Translation"):
st.write(dna_seq.translate())
elif st.checkbox("Complement"):
st.write(dna_seq.complement())
elif st.checkbox("AA Frequency"):
st.write(aa_freq)
elif st.checkbox("Plot AA Frequency"):
aa_color = st.beta_color_picker("Pick An Amino Acid Color")
# barlist = plt.bar(aa_freq.keys(),aa_freq.values(),color=aa_color)
# barlist[2].set_color(aa_color)
plt.bar(aa_freq.keys(), aa_freq.values(), color=aa_color)
st.pyplot()
elif st.checkbox("Full Amino Acid Name"):
aa_name = str(p1).replace("*", "")
aa3 = utils.convert_1to3(aa_name)
st.write(aa_name)
st.write("=====================")
st.write(aa3)
st.write("=====================")
st.write(utils.get_acid_name(aa3))
elif choice == "ProteinSearch":
st.subheader("Search for Papers Related to a Protein")
st.write(""" Try entering ACE2 and coronavirus!""")
ace2 = st.text_input("Query Protein")
disease = st.text_input(
"Query Specifier (more specific thing to narrow down papers with)")
if ace2 and disease is not None:
protein = req.get(
'https://www.ebi.ac.uk/proteins/api/proteins?offset=0&size=10&gene='
+ ace2 + '&organism=h**o%20sapiens',
headers={'Accept': "application/json"})
for i, v in enumerate(protein.json()[0]['references']):
counter = 1
try:
title = protein.json(
)[0]['references'][i]['citation']['title']
if counter == 10:
break
if title.find(disease) != -1:
st.write(title)
counter += 1
except:
pass
elif choice == "DotPlot":
st.subheader("Generate Dot Plot For Two Sequences")
seq_file1 = st.file_uploader("Upload 1st FASTA File",
type=["fasta", "fa"])
seq_file2 = st.file_uploader("Upload 2nd FASTA File",
type=["fasta", "fa"])
if seq_file1 and seq_file2 is not None:
dna_record1 = SeqIO.read(seq_file1, "fasta")
dna_record2 = SeqIO.read(seq_file2, "fasta")
# st.write(dna_record)
dna_seq1 = dna_record1.seq
dna_seq2 = dna_record2.seq
details = st.radio("Details", ("Description", "Sequence"))
if details == "Description":
st.write(dna_record1.description)
st.write("=====================")
st.write(dna_record2.description)
elif details == "Sequence":
st.write(dna_record1.seq)
st.write("=====================")
st.write(dna_record2.seq)
cus_limit = st.number_input("Select Max number of Nucleotide", 10,
200, 50)
if st.button("Dot Plot"):
st.write(
"Comparing the first {} Nucleotide of the Two Sequences".
format(cus_limit))
dotplotx(dna_seq1[0:cus_limit], dna_seq2[0:cus_limit])
st.pyplot()
elif choice == "MoleculeVisualizer":
st.subheader(
"Look at a molecule! Pre-loaded example is the Covid-19 Spike Protein. Thank you to: https://github.com/napoles-uach/streamlit_3dmol"
)
component_3dmol()
elif choice == "ChemicalSearch":
st.title(
"Search for chemicals and get info. Pre-loaded example: imatinib")
user_compound = st.text_input("Enter compound name", 'imatinib')
if user_compound is not None:
results = pcp.get_compounds(user_compound, 'name')
for compound in results:
st.write('Compound ID: ' + str(compound.cid))
st.write('SMILES: ' + compound.isomeric_smiles)
vioxx = Compound.from_cid(compound.cid)
st.write('Molecular Formula: ' + vioxx.molecular_formula)
st.write('Molecular Weight: ' + str(vioxx.molecular_weight))
st.write('IUPAC Name: ' + vioxx.iupac_name)
st.write('xlogp value: ' + str(vioxx.xlogp))
def find_pubchem_from_ids(pubchem=None, CASRN=None, inchi=None, inchikey=None,
smiles=None, use_cache=True):
'''Cached query of pubchem database, based on one of many identifiers.
Parameters
----------
pubchem : int, optional
PubChem ID; prefered lookup, [-]
CASRN : str, optional
CAS number, [-]
inchi : str, optional
InChI identification string as given in Common Chemistry (there can be multiple
valid InChI strings for a compound), [-]
inchikey : str, optional
InChI key identification string (meant to be unique to a compound), [-]
smiles : str, optional
SMILES identification string, [-]
use_cache : bool, optional
Whether or not to use the cache, [-]
Returns
-------
cid : intoxidane
PubChem ID, [-]
iupac_name : str
IUPAC name as given in pubchem, [-]
MW : float
Molecular weight, [g/mol]
InChI : str
InChI identification string as given in Common Chemistry (there can be multiple
valid InChI strings for a compound), [-]
InChI_key : str
InChI key identification string (meant to be unique to a compound), [-]
smiles : str
SMILES identification string, [-]
formula : str
Formula, [-]
synonyms : list[str]
List of synonyms of the compound, [-]
Examples
--------
>>> find_pubchem_from_ids(pubchem=962)[0]
962
>>> find_pubchem_from_ids(pubchem=962)[1]
'oxidane'
>>> find_pubchem_from_ids(pubchem=962)[2]
18.015
>>> find_pubchem_from_ids(pubchem=962)[3]
'InChI=1S/H2O/h1H2'
>>> find_pubchem_from_ids(pubchem=962)[4]
'XLYOFNOQVPJJNP-UHFFFAOYSA-N'
>>> find_pubchem_from_ids(pubchem=962)[5]
'O'
>>> find_pubchem_from_ids(pubchem=962)[6]
'H2O'
>>> len(find_pubchem_from_ids(pubchem=962)[7]) > 100
True
>>> find_pubchem_from_ids(CASRN="53850-36-5")[0]
56951715
>>> find_pubchem_from_ids(CASRN="54084-70-7") # Nihonium is missing
[None, None, None, None, None, None, None, None]
try to use rdkit here to check the correct inchikey is found.
>>> find_pubchem_from_ids(inchi='InChI=1S/Cl', inchikey="ZAMOUSCENKQFHK-UHFFFAOYSA-N")[0]
5360523
>>> find_pubchem_from_ids(inchi='InChI=1S/H2O/h1H2', inchikey="XLYOFNOQVPJJNP-UHFFFAOYSA-N")[0]
962
>>> find_pubchem_from_ids(inchi='InChI=1S/I2/c1-2')[0:5]
[807, 'molecular iodine', 253.8089, 'InChI=1S/I2/c1-2', 'PNDPGZBMCMUPRI-UHFFFAOYSA-N']
>>> find_pubchem_from_ids(inchi='InChI=1S/H2/h1H')[0:5]
[783, 'molecular hydrogen', 2.016, 'InChI=1S/H2/h1H', 'UFHFLCQGNIYNRP-UHFFFAOYSA-N']
'''
abort = False
key = (pubchem, CASRN, inchi, inchikey, smiles)
hash_key = deterministic_hash(str(key))
key_file = os.path.join(pubchem_cache_dir, hash_key)
if os.path.exists(key_file) and use_cache:
f = open(key_file, 'r')
json_data = json.loads(f.read())
f.close()
return json_data
if pubchem is not None:
compound = Compound.from_cid(pubchem)
cid = compound.cid
else:
if inchikey is not None:
# Dup for chlorine atomic here
# find_pubchem_from_ids(inchikey='ZAMOUSCENKQFHK-UHFFFAOYSA-N')[0]
compounds = get_compounds(inchikey, 'inchikey')
elif inchi is not None:
# chlorine search "InChI=1S/Cl" finds HCl
compounds = get_compounds(inchi, 'inchi')
elif smiles is not None:
compounds = get_compounds(smiles, 'smiles')
elif CASRN is not None:
compounds = get_compounds(CASRN, 'name')
# maybe sort by ID in the future
if not compounds:
abort = True
cid = None
if not abort:
compound = compounds[0]
cid = compound.cid
if cid is None:
abort = True
if abort:
cid, iupac_name, mw, inchi_val, inchikey, smi, formula, names = [None]*8
else:
iupac_name = compound.iupac_name
mw = float(compound.molecular_weight)
smi = compound.canonical_smiles
inchi_val = compound.inchi
inchikey = compound.inchikey
formula = compound.molecular_formula
names = compound.synonyms
ans = (cid, iupac_name, mw, inchi_val, inchikey, smi, formula, names)
f = open(key_file, 'w')
json.dump(ans, f)
f.close()
return ans
