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