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import copy
import numpy as np
import time
class SmilesTokenizer(object):
def __init__(self):
atoms = [
'Li',
'Na',
'Al',
'Si',
'Cl',
'Sc',
'Zn',
'As',
'Se',
'Br',
'Sn',
'Te',
'Cn',
'H',
'B',
'C',
'N',
'O',
'F',
'P',
'S',
'K',
'V',
'I',
]
special = [
'(', ')', '[', ']', '=', '#', '%', '0', '1', '2', '3', '4', '5',
'6', '7', '8', '9', '+', '-', 'se', 'te', 'c', 'n', 'o', 's'
]
padding = ['G', 'A', 'E']
self.table = sorted(atoms, key=len, reverse=True) + special + padding
self.table_len = len(self.table)
self.one_hot_dict = {}
for i, symbol in enumerate(self.table):
vec = np.zeros(self.table_len, dtype=np.float32)
vec[i] = 1
self.one_hot_dict[symbol] = vec
def tokenize(self, smiles):
N = len(smiles)
i = 0
token = []
timeout = time.time() + 5 # 5 seconds from now
while (i < N):
for j in range(self.table_len):
symbol = self.table[j]
if symbol == smiles[i:i + len(symbol)]:
token.append(symbol)
i += len(symbol)
break
if time.time() > timeout:
break
return token
def one_hot_encode(self, tokenized_smiles):
result = np.array(
[self.one_hot_dict[symbol] for symbol in tokenized_smiles],
dtype=np.float32)
result = result.reshape(1, result.shape[0], result.shape[1])
return result
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