Curie-Web/lstm_chem/utils/smiles_tokenizer2.py

import numpy as np


class SmilesTokenizer(object):
    def __init__(self):
        atoms = [
            'Al', 'As', 'B', 'Br', 'C', 'Cl', 'F', 'H', 'I', 'K', 'Li', 'N',
            'Na', 'O', 'P', 'S', 'Se', 'Si', 'Te'
        ]
        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
        table_len = len(self.table)

        self.table_2_chars = list(filter(lambda x: len(x) == 2, self.table))
        self.table_1_chars = list(filter(lambda x: len(x) == 1, self.table))

        self.one_hot_dict = {}
        for i, symbol in enumerate(self.table):
            vec = np.zeros(table_len, dtype=np.float32)
            vec[i] = 1
            self.one_hot_dict[symbol] = vec

    def tokenize(self, smiles):
        smiles = smiles + ' '
        N = len(smiles)
        token = []
        i = 0
        while (i < N):
            c1 = smiles[i]
            c2 = smiles[i:i + 2]

            if c2 in self.table_2_chars:
                token.append(c2)
                i += 2
                continue

            if c1 in self.table_1_chars:
                token.append(c1)
                i += 1
                continue

            i += 1

        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