def do_one_smiles2graph(datum):
'''This function takes the reactants and candidate bond changes and generates
all of the candidate products in a format the model expects'''
try:
reaction, cand_bonds, gbonds = datum
r, _, p = reaction.split('>')
n = count(r)
if n <= 2 or n > 100: # for the sake of training, ignore huge examples
return (None, None)
return smiles2graph(r, p, cand_bonds, gbonds, cutoff=cutoff, core_size=core_size)
except Exception as e:
print(e)
sys.stdout.flush()
return None, None
def read_data(coord):
try:
data = []
data_f = open(opts.test_path, 'r')
cand_f = open(opts.cand_path, 'r')
for line in data_f:
r, e = line.strip("\r\n ").split() # reactant smiles, true edits
cand = cand_f.readline() # candidate bond changes from CoreFinder
cand_split = cand.strip("\r\n ").split()
cbonds = [] # list of (x, y, t, v)
for i in range(1, len(cand_split), 2):
x, y, t = cand_split[i].split('-')
x, y = tuple(sorted([int(x) - 1, int(y) - 1]))
# record candidate bond as (atom num, atom num, bond order, likelihood score)
cbonds.append((x, y, float(t), float(cand_split[i + 1])))
data.append((r, cbonds))
data_len = len(data)
for it in range(data_len):
reaction, cand_bonds = data[it]
r = reaction.split('>')[0]
ncore = core_size
while True:
src_tuple, conf = smiles2graph(r,
None,
cand_bonds,
None,
core_size=ncore,
cutoff=MAX_NCAND,
testing=True)
if len(conf) <= MAX_NCAND:
break
ncore -= 1
queue.put((r, conf))
feed_map = {x: y for x, y in zip(_src_holder, src_tuple)}
session.run(enqueue, feed_dict=feed_map)
queue.put((None, None))
except Exception as e:
sys.stderr.write(e)
sys.stderr.flush()
finally:
coord.request_stop()
def do_one_smiles2graph(datum):
try:
reaction, cand_bonds, gbonds = datum
r, _, p = reaction.split('>')
n = count(r)
if n <= 2 or n > 100: # for the sake of training, ignore huge examples
return (None, None)
return smiles2graph(r,
p,
cand_bonds,
gbonds,
cutoff=cutoff,
core_size=core_size)
except Exception as e:
print(e)
sys.stdout.flush()
return None, None
def predict(self, react, top_cand_bonds, top_cand_scores=[]):
'''react: atom mapped reactant smiles
top_cand_bonds: list of strings "ai-aj-bo"'''
cand_bonds = []
if not top_cand_scores:
top_cand_scores = [0.0 for b in top_cand_bonds]
for i, b in enumerate(top_cand_bonds):
x, y, t = b.split('-')
x, y, t = int(float(x)) - 1, int(float(y)) - 1, float(t)
cand_bonds.append((x, y, t, float(top_cand_scores[i])))
while True:
src_tuple, conf = smiles2graph(react,
None,
cand_bonds,
None,
core_size=core_size,
cutoff=MAX_NCAND,
testing=True)
if len(conf) <= MAX_NCAND:
break
ncore -= 1
feed_map = {x: y for x, y in zip(self._src_holder, src_tuple)}
self.session.run(self.enqueue, feed_dict=feed_map)
cur_pred_scores, cur_pred = self.session.run(self.predict_vars)
idxfunc = lambda a: a.GetAtomMapNum()
bond_types = [
Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE,
Chem.rdchem.BondType.TRIPLE, Chem.rdchem.BondType.AROMATIC
]
bond_types_as_double = {0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, 1.5: 4}
# Don't waste predictions on bond changes that aren't actually changes
rmol = Chem.MolFromSmiles(react)
rbonds = {}
for bond in rmol.GetBonds():
a1 = idxfunc(bond.GetBeginAtom())
a2 = idxfunc(bond.GetEndAtom())
t = bond_types.index(bond.GetBondType()) + 1
a1, a2 = min(a1, a2), max(a1, a2)
rbonds[(a1, a2)] = t
outcomes = []
for i in range(len(cur_pred)):
idx = cur_pred[i]
cbonds = []
# Define edits from prediction
for x, y, t, v in conf[idx]:
x, y = x + 1, y + 1
if ((x, y) not in rbonds and t > 0) or (
(x, y) in rbonds and rbonds[(x, y)] != t):
cbonds.append((x, y, bond_types_as_double[t]))
pred_smiles = edit_mol(rmol, cbonds)
outcomes.append((cur_pred_scores[i], '.'.join(set(pred_smiles))))
all_scores = softmax(np.array([x[0] for x in outcomes]))
for i in range(len(outcomes)):
outcomes[i] = (all_scores[i], outcomes[i][1])
return outcomes
