57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
import numpy as np
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class SmilesTokenizer(object):
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def __init__(self):
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atoms = [
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'Al', 'As', 'B', 'Br', 'C', 'Cl', 'F', 'H', 'I', 'K', 'Li', 'N',
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'Na', 'O', 'P', 'S', 'Se', 'Si', 'Te'
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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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table_len = len(self.table)
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self.table_2_chars = list(filter(lambda x: len(x) == 2, self.table))
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self.table_1_chars = list(filter(lambda x: len(x) == 1, 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(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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smiles = smiles + ' '
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N = len(smiles)
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token = []
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i = 0
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while (i < N):
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c1 = smiles[i]
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c2 = smiles[i:i + 2]
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if c2 in self.table_2_chars:
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token.append(c2)
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i += 2
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continue
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if c1 in self.table_1_chars:
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token.append(c1)
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i += 1
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continue
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i += 1
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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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