def eval_function(text):
global i, num_valid, all_smiles
i += 1
generated = ''.join(text)
try:
decoded = DeepSMILESLanguageModelUtils.decode(generated,
start='<s>',
end='</s>')
smiles = DeepSMILESLanguageModelUtils.sanitize(decoded)
except Exception:
log_best(i, all_smiles, num_valid, logger)
return -1.0
num_valid += 1
# synthetic accessibility score is a number between 1 (easy to make) and 10 (very difficult to make)
sascore = sascorer.calculateScore(Chem.MolFromSmiles(smiles)) / 10.
# cycle score, squashed between 0 and 1
cyclescore = cycle_scorer.score(smiles)
cyclescore = cyclescore / (1 + cyclescore)
distance_score = distance_scorer.score(smiles)
score = (0.75 * distance_score) + (0.15 *
(1 - sascore)) + (0.10 *
(1 - cyclescore))
all_smiles[smiles] = (score, generated)
logger.debug("%s, %s" % (smiles, str(score)))
log_best(i, all_smiles, num_valid, logger)
return score
def eval_function(text):
global simulations, all_unique, elapsed, current_best_score, current_best_smiles, beats_current
if elapsed >= time_limit or len(seen) == max_gen:
# if elapsed >= time_limit or simulations == max_sims:
raise StopTreeSearch()
simulations += 1
generated = ''.join(text)
try:
decoded = DeepSMILESLanguageModelUtils.decode(generated,
start='<s>',
end='</s>')
smiles = DeepSMILESLanguageModelUtils.sanitize(decoded)
mol = Chem.MolFromSmiles(smiles)
if mol is None: raise Exception
except Exception:
elapsed = time.time() - start
return -1.0
# synthetic accessibility score is a number between 1 (easy to make) and 10 (very difficult to make)
sascore = sascorer.calculateScore(mol) / 10.
# cycle score, squashed between 0 and 1
cyclescore = cycle_scorer.score_mol(mol)
cyclescore = cyclescore / (1 + cyclescore)
distance_score = distance_scorer.score_mol(mol)
weighted_score = (0.75 * distance_score) + (0.15 * (1 - sascore)) + (
0.10 * (1 - cyclescore))
if current_best_score is None or beats_current(distance_score):
current_best_score = distance_score
current_best_smiles = smiles
if distance_score == 1.0:
logger.info("FOUND!")
# ret_score = -1.0 if smiles in seen else weighted_score
ret_score = -1.0 if smiles in all_unique else weighted_score
# rescale score from [0,1] to [-1,1]
ret_score = (ret_score * 2) + (-1) if ret_score >= 0. else ret_score
all_unique[smiles] = (distance_score, generated)
all_valid.append((smiles, distance_score))
seen[smiles] = distance_score
elapsed = time.time() - start
return ret_score
def eval_function(text):
global simulations, num_valid, all_smiles, elapsed
if elapsed >= TIME_PER_ITERATION:
raise StopTreeSearch()
simulations += 1
generated = ''.join(text)
try:
decoded = DeepSMILESLanguageModelUtils.decode(generated,
start='<s>',
end='</s>')
smiles = DeepSMILESLanguageModelUtils.sanitize(decoded)
mol = Chem.MolFromSmiles(smiles)
if mol is None: raise Exception
except Exception:
elapsed = time.time() - start
return -1.0
num_valid += 1
if smiles in seen:
score = -1.0
else:
# synthetic accessibility score is a number between 1 (easy to make) and 10 (very difficult to make)
sascore = sascorer.calculateScore(mol) / 10.
# cycle score, squashed between 0 and 1
cyclescore = cycle_scorer.score_mol(mol)
cyclescore = cyclescore / (1 + cyclescore)
distance_score = distance_scorer.score_mol(mol)
score = (0.75 *
distance_score) + (0.15 *
(1 - sascore)) + (0.10 *
(1 - cyclescore))
seen.add(smiles)
all_smiles[smiles] = (score, generated)
if distance_score == 1.0:
logger.info("FOUND!")
# rescale score from [0,1] to [-1,1]
ret_score = (score * 2) + (-1) if score >= 0. else score
elapsed = time.time() - start
return ret_score
start = time.time()
logger.info("beginning search...")
for i in range(num_attempts):
try:
generated = lm.generate(num_chars=100, text_seed='<s>')
decoded = DeepSMILESLanguageModelUtils.decode(generated,
start='<s>',
end='</s>')
sanitized = DeepSMILESLanguageModelUtils.sanitize(decoded)
num_valid += 1
# synthetic accessibility score is a number between 1 (easy to make) and 10 (very difficult to make)
sascore = sascorer.calculateScore(
Chem.MolFromSmiles(sanitized)) / 10.
# cycle score, squashed between 0 and 1
cyclescore = cycle_scorer.score(sanitized)
cyclescore = cyclescore / (1 + cyclescore)
distance_score = distance_scorer.score(sanitized)
score = (0.75 *
distance_score) + (0.15 *
(1 - sascore)) + (0.10 *
(1 - cyclescore))
all_smiles[sanitized] = (score, generated)
if current_best_score is None or beats_current(score):
current_best_score = score
current_best_smiles = sanitized
start = time.time()
elapsed = time.time() - start
while elapsed < TIME_PER_ITERATION:
simulations += 1
try:
generated = lm.generate(num_chars=100, text_seed='<s>')
decoded = DeepSMILESLanguageModelUtils.decode(generated, start='<s>', end='</s>')
sanitized = DeepSMILESLanguageModelUtils.sanitize(decoded)
mol = Chem.MolFromSmiles(sanitized)
num_valid += 1
# synthetic accessibility score is a number between 1 (easy to make) and 10 (very difficult to make)
sascore = sascorer.calculateScore(mol) / 10.
# cycle score, squashed between 0 and 1
cyclescore = cycle_scorer.score_mol(mol)
cyclescore = cyclescore / (1 + cyclescore)
distance_score = distance_scorer.score_mol(mol)
score = (0.75 * distance_score) + (0.15 * (1 - sascore)) + (0.10 * (1 - cyclescore))
all_smiles[sanitized] = (score, generated)
seen.add(sanitized)
if distance_score == 1.0:
logger.info("FOUND!")
if current_best_score is None or beats_current(score):
current_best_score = score
current_best_smiles = sanitized
