From e80badd278f54e8cde64243aa56687f8e6e32cc2 Mon Sep 17 00:00:00 2001
From: Navan Chauhan <navanchauhan@gmail.com>
Date: Sat, 1 Aug 2020 15:34:22 +0530
Subject: added alpha model

---
 lstm_chem/utils/smiles_tokenizer2.py | 56 ++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 lstm_chem/utils/smiles_tokenizer2.py

(limited to 'lstm_chem/utils/smiles_tokenizer2.py')

diff --git a/lstm_chem/utils/smiles_tokenizer2.py b/lstm_chem/utils/smiles_tokenizer2.py
new file mode 100644
index 0000000..29575ba
--- /dev/null
+++ b/lstm_chem/utils/smiles_tokenizer2.py
@@ -0,0 +1,56 @@
+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
-- 
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