class DeepSmilesEncoder(FunctionApplier):
def __init__(self, astype=Series, branches=True, rings=True):
super().__init__(self.encode)
self.encoder = DeepSmilesConverter(branches, rings)
def encode(self, smiles):
k = self.function_kwargs
return self.encoder.encode(smiles)
class ChemgramsGoalDirectedGenerator(GoalDirectedGenerator):
def __init__(self, num_iterations, keep_top_n, time_per_iteration_minutes):
self.num_iterations = num_iterations
self.keep_top_n = keep_top_n
self.time_per_iteration_minutes = time_per_iteration_minutes
self.lm = None
env = os.environ.copy()
env["PATH"] = "/Users/luis/kenlm/build/bin:" + env["PATH"]
self.lm_trainer = KenLMTrainer(env)
self.converter = Converter(rings=True, branches=True)
def generate_optimized_molecules(self,
scoring_function,
number_molecules,
starting_population=None):
self.new_model_dir()
vocab = get_arpa_vocab(
'../resources/chembl_25_deepsmiles_klm_10gram_200503.arpa')
self.lm = KenLMDeepSMILESLanguageModel(
'../resources/chembl_25_deepsmiles_klm_10gram_200503.klm', vocab)
print("generating %s samples..." % number_molecules)
smiles_and_scores = []
TIME_PER_ITERATION = self.time_per_iteration_minutes * 60 # in seconds
found = False
for n in range(1, self.num_iterations + 1):
print("iteration %s" % n)
num_valid = 0
start = time.time()
elapsed = time.time() - start
while elapsed < TIME_PER_ITERATION:
try:
generated = self.lm.generate(num_chars=100,
text_seed='<s>')
decoded = DeepSMILESLanguageModelUtils.decode(generated,
start='<s>',
end='</s>')
smiles = DeepSMILESLanguageModelUtils.sanitize(decoded)
score = scoring_function.score(smiles)
num_valid += 1
smiles_and_scores.append((smiles, score))
if score == 1.0:
found = True
break
except Exception:
pass
elapsed = time.time() - start
print("num valid: %s" % num_valid)
if found:
break
self.retrain(n, self.keep_top_n, smiles_and_scores)
return [
pair[0] for pair in list(
reversed(sorted(smiles_and_scores, key=lambda p: p[1])))
[:number_molecules]
]
def new_model_dir(self):
print(
"deleting any existing molexit directory, and creating a new one..."
)
path = Path("../models/molexit/")
if os.path.exists(path) and os.path.isdir(path):
shutil.rmtree(path)
path.mkdir(parents=True, exist_ok=True)
def retrain(self, n, keep_top_n, smiles_and_scores):
print("writing dataset...")
name = 'molexit-%d' % n
dataset = '../models/molexit/%s.txt' % name
dataset_scores = []
with open(dataset, 'w') as f:
for smi, score in list(
reversed(sorted(smiles_and_scores,
key=lambda p: p[1])))[:keep_top_n]:
dsmi = self.converter.encode(
pybel.readstring("smi", smi.strip()).write("can").strip())
tok = DeepSMILESTokenizer(dsmi)
tokens = tok.get_tokens()
f.write(' '.join([t.value for t in tokens]))
f.write("\n")
dataset_scores.append(score)
print('dataset: size: %s, mean score: %s, max score: %s' %
(len(dataset_scores), np.mean(dataset_scores),
np.max(dataset_scores)))
print('training new LM...')
self.lm_trainer.train(10, dataset, '../models/molexit', name)
vocab = get_arpa_vocab('../models/molexit/%s.arpa' % name)
self.lm = KenLMDeepSMILESLanguageModel(
'../models/molexit/%s.klm' % name, vocab)