def predict():
client = Algorithmia.client('simtds2YG9Ed/wd5xucmvHy+U8G1')
algo = client.algo('Dmitry_BV/predictor/1.1.2')
algo.set_options(timeout=100) # optional
# cgr = "C12(C(CCC1C3C(C4(C(C(C3)=C)=CC(C[->=]C4)=O)CC#C)CC2)=O)C.[O-]" # Test input
smi = request.args.get('post')
beamSize = request.args.get('beamSize')
model = request.args.get('model')
input_query = {"reaction": smi, 'beamWidth': int(beamSize), "model": model}
answers = algo.pipe(input_query).result["product"]
# If CGR string received, performing decomposition;
# Generating 2 SMILES strings (reactants and products) and 2 SVG images for them
result_dict = {}
if model == "cgr":
decomposed = smiles(answers)
decomposed_smiles, svg_list = decompose_cgr(decomposed)
img = get_svg(decomposed)
result_dict['decomposed_smiles'] = decomposed_smiles
result_dict['decomposed_svg'] = svg_list
elif model == "smiles":
answers = smi + ">>" + answers
img = get_svg(smiles(answers))
result_dict['prediction'] = answers
result_dict['reaction'] = img
return json.dumps(result_dict)
def convert(dct):
out = {}
for k, (tuples, value) in dct.items():
k = smiles(k)
k.canonicalize()
new_mols = []
for prob, mols in tuples:
synth = []
for mol in mols:
new_mol = smiles(mol)
new_mol.canonicalize()
synth.append(new_mol)
new_mols.append((prob, tuple(synth)))
out[k] = (tuple(new_mols), value)
return out
def get_smiles():
"""
Get SMILES string for IUPAC names
Web translation API: SYSTRAN.io
"""
smiles_list = []
string = request.args.get('post')
string = string.replace(",", ".")
string = string.split(".")
source_lang = request.args.get('lang')
url = "https://systran-systran-platform-for-language-processing-v1.p.rapidapi.com/translation/text/translate"
headers = {
'x-rapidapi-key':
"28bb9cac94msh0d21dd8efbc884ep1172f0jsndc7797830a0e",
'x-rapidapi-host':
"systran-systran-platform-for-language-processing-v1.p.rapidapi.com"
}
for i in range(len(string)):
if source_lang != "en":
# If source lang. is not English, perform translation
querystring = {
"source": source_lang,
"target": "en",
"input": string[i]
}
response = requests.request("GET",
url,
headers=headers,
params=querystring)
response = json.loads(response.text)
string[i] = response['outputs'][0]['output'].lower().lstrip(
" ").lstrip("the")
try:
smi = str(
smiles(get_compounds(string[i], "name")[0].canonical_smiles))
smiles_list.append(smi)
except IndexError:
continue
smiles_list = ".".join(smiles_list)
img = get_svg(smiles(smiles_list))
return json.dumps({
'smiles': smiles_list,
'img': img
})
def test_good_smiles():
for t, v in zip('CN C-N C=N C#N C:N cn'.split(), (1, 1, 2, 3, 4, 4)):
t = smiles(t)
assert isinstance(t, MoleculeContainer)
assert t.bond(1, 2).order == v
t = smiles('C.N ')
assert isinstance(t, MoleculeContainer)
assert not t.has_bond(1, 2)
for t, v1, v2 in zip(
'C[->.]N C[->=]N C[=>#]N C[#>:]N C[:>.]N [O->0][.>-][Na+>0]'.split(
), (1, 1, 2, 3, 4, None), (None, 2, 3, 4, None, 1)):
t = smiles(t)
assert isinstance(t, CGRContainer)
assert t.bond(1, 2).order == v1
assert t.bond(1, 2).p_order == v2
for t in '[Fe] [13C] [C+4] [C:14] [C--:12] [C@@H] [C@H] [OH2] [OH3+]'.split(
):
assert isinstance(smiles(t), MoleculeContainer)
for t in 'C(C)C C(-C)C C(C)-C'.split():
assert smiles(t).bonds_count == 2
for t, v in zip(
'C1CC1 C-1CC-1 C%10CC%10 C-%11CC-%11 C12CC1C2 C-1-2CC-1C-2'.split(
), (1, 1, 1, 1, 2, 2)):
assert smiles(t).rings_count == v
for t in (
'[C+>-] [C++>--] [C-3>+2] [C0>+] [C-->0] [C*>^] [C^>*] [C+>-*>^] [C-3>+2^>*] '
'[C0>-*>^] [C+2>0^>*] [n+>0]').split():
assert isinstance(smiles(t), CGRContainer)
def image():
post = request.args.get('post')
post = smiles(post)
result_dict = {'img': get_svg(post)}
if isinstance(post, CGRContainer):
decomposed_smiles, svg_list = decompose_cgr(post)
result_dict['decomposed_smiles'] = decomposed_smiles
result_dict['decomposed_svg'] = svg_list
return json.dumps(result_dict)
def __new__(cls, molecule, *args, **kwargs):
if cls.__bb__ is None:
from .rules import rules
bb = [smiles(x.strip()) for x in TextIOWrapper(resource_stream(__name__, 'data/building_blocks.smiles'))]
for b in bb: # recalculate canonic forms. prevent errors when CGRtools rules set changes.
b.canonicalize()
cls.__bb__ = frozenset(str(b) for b in bb)
cls.__reactors__ = tuple((1., Reactor(x, delete_atoms=True, automorphism_filter=False, one_shot=False))
for x in rules)
return super().__new__(cls, *args, **kwargs)
def run(index, smi=None, **kwargs):
"""
perform calculations for one reaction
:param index:
index of the reaction, to keep tracking initial order of tasks
:param smi:
Reaction SMILES
:param crest_speed:
speed and precision of CREST calculations, possible options :
- quick
- squick
- mquick (default for this project)
:param dft:
define to perform
:return:
ReactionComponents named tuple
"""
start = time()
print(smi)
if smi:
reaction = smiles(smi)
else:
# TO DO: accept RDF as alternative
raise NotImplemented
# TO DO: add check for reaction container
if reaction:
if not reaction.reactants:
return ReactionComponents(index, smi, None, None, None, 'problem: with reactants', spent_time(start))
elif not reaction.products:
return ReactionComponents(index, smi, None, None, None, 'problem: with products', spent_time(start))
else:
reactants = best_conformers(reaction.reactants, **kwargs)
if not reactants:
return ReactionComponents(index, smi, None, None, None,
'anomaly terminated calculations for all of reactants', spent_time(start))
elif any(isinstance(x, FailReport) for x in reactants):
return ReactionComponents(index, smi, reactants, None, None,
'anomaly terminated calculations for one of reactants', spent_time(start))
products = best_conformers(reaction.products, **kwargs)
if not products:
ReactionComponents(index, smi, None, None, None,
'anomaly terminated calculations for all of products', spent_time(start))
elif any(isinstance(x, FailReport) for x in products):
return ReactionComponents(index, smi, reactants, products, None,
'anomaly terminated calculations for one of products', spent_time(start))
try:
energy_dif = sum([x.min_energy for x in products]) - sum([x.min_energy for x in reactants])
except TypeError:
return ReactionComponents(index, smi, reactants, products,
None, 'min energy read error', spent_time(start))
return ReactionComponents(index, smi, reactants, products,
energy_dif, 'terminated normally', spent_time(start))
else:
return ReactionComponents(index, smi, None, None, None, 'problem: reaction smiles empty or incorrect',
spent_time(start))
def convert(dct):
out = {}
for k, (tuples, value) in dct.items():
k = smiles(k)
k.canonicalize()
new_mols = []
for prob, mols in tuples:
synth = []
for mol in mols:
new_mol = smiles(mol)
new_mol.canonicalize()
synth.append(new_mol)
rxn = ReactionContainer((k,), synth)
ext_center = set(rxn.extended_centers_list[0])
qk = k.substructure(ext_center.intersection(k), as_query=True)
qsynth = [m.substructure(ext_center.intersection(m), as_query=True) for m in synth]
template = ReactionContainer((qk,), qsynth)
new_mols.append((prob, Reactor(template), set(synth)))
out[k] = (tuple(new_mols), value)
return out
from ThetaSynthesis.synthon import RolloutSynthon
from pickle import dump
data = []
target = None
reactions = set()
for line in open('test.smiles', 'r'):
line = line.strip()
if line == '$$$$':
data.append((target, reactions))
target = None
reactions = set()
elif line.startswith('#'):
continue
elif target is None:
target = smiles(line)
target.canonicalize()
else:
r = smiles(line)
r.canonicalize()
reactions.add(r)
for num in [.01, .1, 1., 10., 100.]:
results = []
for target, reactions in data:
found = []
tree = RetroTree(target,
synthon_class=RolloutSynthon,
size=10000,
c_puct=num)
for node in tree:
Test synthon for Acetaminophen.
"""
__slots__ = ()
def __iter__(self):
for prob, molecules in data[self._molecule][0]:
yield prob, tuple(type(self)(mol) for mol in molecules)
def __bool__(self):
return self._molecule in building_blocks
def __float__(self):
return data[self._molecule][1]
building_blocks = {smiles('Oc1ccccc1')}
pre_data = {
'CC(=O)Nc1ccc(O)cc1': (((.25, ('Oc1ccc(N)cc1',),),
(.15, ('C(Nc1ccc(OC)cc1)(C)=O',)),
(.2, ('ON=C(C)c1ccc(O)cc1',)),
(.25, ('Oc1ccc(O)cc1',)),
(.15, ('C(Nc1ccc(OC2OCCCC2)cc1)(C)=O',))), 1.),
'Oc1ccc(N)cc1': (((.3, ('O=N(=O)c1ccc(O)cc1',)),
(.4, ('c1(N)ccc(OC)cc1',)),
(.3, ('c1(O)ccc(F)cc1',))), 1.),
'O=N(=O)c1ccc(O)cc1': (((.35, ('Oc1ccccc1',)),
(.35, ('N(c1ccc(N)cc1)(=O)=O',)),
(.3, ('c1(N(=O)=O)cc(c(cc1)O)Br',))), 1.),
'Oc1ccccc1': ((), 1.),
'C(Nc1ccc(OC)cc1)(C)=O': ((), -1.),
'Oc1ccc(O)cc1': ((), 0.),
def test_invalid_ring_closure_ignoring():
data = 'C1CC1C1CC1\nC=1CC1'
with StringIO(data) as f, SMILESRead(f, ignore=True, store_log=True) as r:
for t, v in zip_longest(r, ('C1CC1C2CC2', 'C1=CC1')):
assert t == smiles(v)
def load_nicklaus_tautomers(*,
return_X_y=False,
as_frame=False,
as_regression=False):
"""Load and return Nicklaus's tautomers dataset (Regression and Classification).
================== ==============
Samples total 5960
Samples Regression 2824
Data molecules, type: MoleculeContainer
Targets real ratio 0.0 - 1.0, type: float (Regression)
Classes 5
================== ==============
Molecules has .meta attribute which returns dict with additional data:
structure_id: row in original file
tautomer_id: id of structure of tautomer in row
additive.{n}: solvent name. {n} started from 1 id of solvent. in mixtures will be presented more additive keys.
e.g. additive.2, additive.3 ...
amount.{n}: amount of additive.
prevalence (optional): Qualitative category of tautomer reported in the publication.
temperature (optional) in Kelvin
pH (optional)
For Regression: The numeric proportion of tautomer based on its quantitative ratio and qualitative prevalence.
For Classification: Quantitative ratio of tautomer compared to other tautomers.
Numeric classification of qualitative prevalence:
0: Not observed
1: Less favored, less stable, minor, observed
2: Equally, favored, major, in equilibrium, preferred, similar spectra
3: More favored, more stable, predominant, strongly favored
4: Exclusively observed, only observed, only tautomer, identical tautomer
Numeric classification of quantitative amount of tautomers:
0: ratio = 0.0 - 0.0099
1: ratio = 0.01 - 0.30
2: ratio = 0.31 - 0.69
3: ratio = 0.70 - 0.99
4: ratio = 1
Parameters
----------
return_X_y : bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object.
as_frame : bool, default=False
If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric). The target is
a pandas DataFrame or Series depending on the number of target columns.
If `return_X_y` is True, then (`data`, `target`) will be pandas
DataFrames or Series as described below.
as_regression : bool, default=False
If True, returns regression subset instead of classes
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : ndarray of shape (n, )
The data array.
target : ndarray of shape (n, )
The regression or classification target.
feature_names: list
The name of the dataset ('Tautomers').
target_names: list
The name of target (['ratio or category']).
frame: DataFrame of shape (n, 2)
Only present when `as_frame=True`. DataFrame with `data` and
`target`.
(data, target) : tuple if ``return_X_y`` is True
"""
with resource_stream('CIMtools.datasets',
'data/tautomer_database_release_3a.xlsx') as s:
data = read_excel(s,
na_values='nul',
true_values=['yes'],
false_values=['no'])
parsed = []
for record, mol in data.iterrows():
meta = {}
n = m = 0
c = mol['Size']
sol = mol['Solvent']
if mol['Solvent_Mixture']:
for n, sol in enumerate(sol.split(','), 1):
meta[f'additive.{n}'] = sol.strip()
sol_prop = mol['Solvent_Proportion']
if isinstance(sol_prop, str):
sol_prop = [float(x) for x in sol_prop.split(':')]
sum_prop = sum(sol_prop)
for m, prop in enumerate(sol_prop, 1):
meta[f'amount.{m}'] = prop / sum_prop
if n != m:
raise ValueError
elif not isnan(sol_prop):
raise ValueError
else:
meta['additive.1'] = sol
meta['amount.1'] = 1.
temp = mol['Temperature']
if not isinstance(temp, str) and not isnan(temp):
meta['temperature'] = temp
ph = mol['pH']
if not isinstance(ph, str) and not isnan(ph):
meta['pH'] = ph
tmp = []
for n in range(1, c + 1):
s = smiles(mol[f'SMILES_{n}'])
s.kekule()
s.standardize(fix_stereo=False)
if s.check_valence(): # skip invalid records
break
s.implicify_hydrogens(fix_stereo=False)
s.thiele()
s.meta['structure_id'] = record + 1
s.meta['tautomer_id'] = n
s.meta['category'] = mol[f'Prevalence_Category_{n}']
rat = mol[f'Quantitative_ratio_{n}']
if not isinstance(rat, str) and not isnan(rat):
s.meta['ratio'] = rat
prev = mol[f'Qualitative_prevalence_{n}']
if isinstance(prev, str):
s.meta['prevalence'] = prev.lower()
s.meta.update(meta)
tmp.append(s)
else:
parsed.extend(tmp)
if as_regression:
data = array([x for x in parsed if 'ratio' in x.meta])
target = array([x.meta['ratio'] for x in data])
target_names = ['ratio']
else:
data = array(parsed)
target = array([x.meta['category'] for x in data], dtype=int)
target_names = ['category']
feature_names = ['Tautomers']
if as_frame:
frame, data, target = _convert_data_dataframe(
caller_name='load_nicklaus_tautomers',
data=data,
target=target,
feature_names=feature_names,
target_names=target_names)
else:
frame = None
if return_X_y:
return data, target
return Bunch(data=data,
target=target,
frame=frame,
target_names=target_names,
feature_names=feature_names)
