def push(data):
indigo = Indigo()
compound = indigo.loadMolecule(data)
indigo_record = IndigoRecordMolecule(indigo_object=compound)
elasticRepository = elastic_repository_molecule()
elasticRepository.index_record(record=indigo_record)
def search(data):
indigo = Indigo()
compound = indigo.loadMolecule(data)
indigo_record = IndigoRecordMolecule(indigo_object=compound)
elasticRepository = elastic_repository_molecule()
return elasticRepository.filter(exact=indigo_record)
def test_search_empty_fingerprint(
elastic_repository: ElasticRepository,
indigo_fixture: Indigo,
resource_loader: Callable[[str], str],
):
for smile in ["[H][H]", "[H][F]"]:
rec = IndigoRecord(indigo_object=indigo_fixture.loadMolecule(smile),
skip_errors=True)
elastic_repository.index_record(rec)
time.sleep(5)
result = elastic_repository.filter(exact=IndigoRecord(
indigo_object=indigo_fixture.loadMolecule("[H][H]"), skip_errors=True))
assert ("[H][H]" == next(result).as_indigo_object(
indigo_fixture).canonicalSmiles())
with pytest.raises(StopIteration):
next(result).as_indigo_object(indigo_fixture).canonicalSmiles()
def get_compound_id(smiles):
""" returns kegg id for compund with given smiles """
indigo = Indigo()
# convert smiles to standard format
mol = indigo.loadMolecule(smiles)
mol.aromatize()
moi_smiles = mol.canonicalSmiles()
# Get list of possible kegg IDs
url = "http://rest.genome.jp/subcomp/?smiles=%s&cutoff=1.0" % smiles
http_client = HTTPClient()
try:
response = http_client.fetch(url).body
except HTTPError as e:
raise RuntimeError("Error:", str(e))
http_client.close()
subcomp_results = response.split("\n")
subcomp_results.pop()
subcomp_results = ([i.split('\t')[0] for i in subcomp_results])
# get smiles for all compound IDs found
all_smiles = []
uni = UniChem()
mapping = uni.get_mapping("kegg_ligand", "chebi")
ch = ChEBI()
all_smiles = [ch.getCompleteEntity(mapping[x]).smiles
for x in subcomp_results]
# convert smiles to a standard format
for pos, mol in enumerate(all_smiles):
m = indigo.loadMolecule(mol)
m.aromatize()
all_smiles[pos] = m.canonicalSmiles()
# check if smiles matches given and, if so, use that compound ID
# if not, errors out
try:
index = all_smiles.index(moi_smiles)
except:
raise RuntimeError("SMILES unmatchable to: %s" % str(all_smiles))
return subcomp_results[index]
def wrapper():
indigo = Indigo()
opts = request.json.get('options', {})
for key, value in opts.items():
try:
indigo.setOption(key, value)
except IndigoException as e:
pass
mol_text = request.json.get("struct")
try:
mol = indigo.loadMolecule(mol_text)
except IndigoException as e:
return jsonify({'msg': str(e)})
return func(mol)
def get_molfile_v3000(compound: str) -> str:
"""Computes the molfile v3000 from a compound string.
Args:
compound: A molfile (either v2000 or v3000), MRV file, etc.
Returns:
The molfile v3000.
"""
indigo = Indigo()
indigo.setOption("molfile-saving-mode", "3000")
molecule = indigo.loadMolecule(compound)
return molecule.molfile()
def get_inchikey(compound: str) -> str:
"""Computes the InChIKey from a compound string.
Args:
compound: A molfile (either v2000 or v3000), MRV file, SMILES, etc.
Returns:
The InChIKey for the compound.
"""
indigo = Indigo()
indigo_inchi = IndigoInchi(indigo)
molecule = indigo.loadMolecule(compound)
inchi = indigo_inchi.getInchi(molecule)
return indigo_inchi.getInchiKey(inchi)
def smiles(smiles_string):
# Render SMILES to PNG
indigo = Indigo()
renderer = IndigoRenderer(indigo)
mol = indigo.loadMolecule(smiles_string)
mol.layout() # if not called, will be done automatically by the renderer
indigo.setOption("render-output-format", "png")
indigo.setOption("render-image-size", 250, 250)
indigo.setOption("render-background-color", 1.0, 1.0, 1.0)
indigo.setOption("render-coloring", True)
indigo.setOption("aromaticity-model", "generic")
mol.dearomatize()
buf = renderer.renderToBuffer(mol)
buf = io.BytesIO(buf)
return send_file(
buf,
mimetype='image/png',
as_attachment=False,
)
from bingo_elastic.elastic import ElasticRepository
from bingo_elastic.model import helpers
from indigo import Indigo
if __name__ == '__main__':
indigo = Indigo()
start_time = time.time()
repository = ElasticRepository(host="127.0.0.1", port=9200)
# try:
# sdf = helpers.iterate_sdf("../data/pubchem/Compound_000000001_000500000.sdf")
# repository.index_records(sdf)
# except ValueError as e:
# print(e)
#
#
# time.sleep(300)
print(
"--- {} seconds for reading and indexing data ---".format(time.time() -
start_time))
mol = indigo.loadMolecule(
"Cc1ccc2nc(-c3ccc(NC(C4N(C(c5cccs5)=O)CCC4)=O)cc3)sc2c1")
target = IndigoRecord(indigo_object=mol)
alg = SimilarityMatch(target, 0.5)
similar_records = repository.filter(similarity=alg, limit=20)
print(similar_records)
print("--- {} seconds ---".format(time.time() - start_time))
indigo.setOption("fp-ext-enabled", True)
indigo.setOption("fp-ord-qwords", 0) # optional
indigo.setOption("fp-tau-qwords", 0) # optional
indigo.setOption("fp-any-qwords", 0) # optional
molecules = []
for id in range(len(df_descriptors)):
smiles = df_descriptors.ix[id, 'Structure']
descriptors = list(df_descriptors.ix[id])[2:]
normalized = [(f - min_bounds[i]) / (max_bounds[i] - min_bounds[i])
for i, f in enumerate(descriptors)]
fp = pack_normalized_descriptors_to_fingerprint(
normalized, density=0.3, byte_size=fp_size_bytes)
mol = indigo.loadMolecule(smiles)
molecules += [mol]
ext_fp = mol.fingerprintExt(fp, fp_size_bytes)
bingo.insertWithExtFP(mol, ext_fp)
print("Similarity matrix:")
for m1 in molecules:
for m2 in molecules:
similarity = indigo.similarity(m1, m2)
print("%.4f " % similarity, end="")
print()
print("Bingo similarity search:")
query_id = 0 # use the fist molecule as a query for an example
smiles = df_descriptors.ix[query_id, 'Structure']
print("Query: %s" % smiles)
for i, sml in enumerate(smiles):
print("#%2d: %s" % (i, sml))
df_descriptors = []
indigo = Indigo()
# indigo.setOption("similarity-type", "sim")
# indigo.setOption("similarity-type", "chem")
# indigo.setOption("similarity-type", "ECFP2")
indigo.setOption("similarity-type", "ECFP4")
# indigo.setOption("similarity-type", "ECFP6")
# indigo.setOption("similarity-type", "ECFP8")
fingerprints = []
for sml in smiles:
mol = indigo.loadMolecule(sml)
fp = mol.fingerprint("sim")
fingerprints += [fp]
print("*** Fingerprints ***")
for i, fp in enumerate(fingerprints):
str = fp.toString()
density = fp_density(str)
print("#%2d: Density: %f ; FP: %s" % (i, density, str))
print("*** Similarity matrix ***")
for f0 in fingerprints:
for f1 in fingerprints:
similarity = indigo.similarity(f0, f1)
