smiles_and_selfies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "broken-humidity",
   "metadata": {},
   "outputs": [],
   "source": [
    "import selfies as sf\n",
    "from rdkit import Chem\n",
    "\n",
    "benzene = 'c1ccccc1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "modified-woman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[C][=C][C][=C][C][=C][Ring1][Branch1_2]'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_selfies = sf.encoder(benzene) \n",
    "encoded_selfies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "heard-marketplace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C1=CC=CC=C1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded_smiles = sf.decoder(encoded_selfies) \n",
    "decoded_smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dangerous-testing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7fe189887e90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolFromSmiles(decoded_smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "italian-doctrine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c1ccccc1'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolToSmiles(Chem.MolFromSmiles(decoded_smiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "christian-prototype",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "can_smiles = Chem.CanonSmiles(benzene)\n",
    "can_decoded_smiles = Chem.CanonSmiles(decoded_smiles)\n",
    "can_smiles == can_decoded_smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eastern-occasions",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': 1,\n",
       " 'F': 1,\n",
       " 'Cl': 1,\n",
       " 'Br': 1,\n",
       " 'I': 1,\n",
       " 'O': 2,\n",
       " 'O+1': 3,\n",
       " 'O-1': 1,\n",
       " 'N': 3,\n",
       " 'N+1': 4,\n",
       " 'N-1': 2,\n",
       " 'C': 4,\n",
       " 'C+1': 5,\n",
       " 'C-1': 3,\n",
       " 'S': 6,\n",
       " 'S+1': 7,\n",
       " 'S-1': 5,\n",
       " 'P': 7,\n",
       " 'P+1': 8,\n",
       " 'P-1': 6,\n",
       " '?': 8}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_constraints = sf.get_semantic_constraints()\n",
    "default_constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "accredited-connecticut",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H': 1,\n",
       " 'F': 1,\n",
       " 'Cl': 1,\n",
       " 'Br': 1,\n",
       " 'I': 1,\n",
       " 'O': 2,\n",
       " 'O+1': 3,\n",
       " 'O-1': 1,\n",
       " 'N': 3,\n",
       " 'N+1': 4,\n",
       " 'N-1': 2,\n",
       " 'C': 4,\n",
       " 'C+1': 5,\n",
       " 'C-1': 3,\n",
       " 'S': 2,\n",
       " 'S+1': 7,\n",
       " 'S-1': 5,\n",
       " 'P': 7,\n",
       " 'P+1': 8,\n",
       " 'P-1': 6,\n",
       " '?': 8,\n",
       " 'Li': 1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#自定义语义限制\n",
    "new_constraints = default_constraints\n",
    "new_constraints['Li'] = 1\n",
    "new_constraints['S'] = 2\n",
    "\n",
    "sf.set_semantic_constraints(new_constraints)\n",
    "sf.get_semantic_constraints()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "compact-lighting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[C]', '[=C]', '[C]', '[=C]', '[C]', '[=C]', '[Ring1]', '[Branch1_2]']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols_benzene = list(sf.split_selfies(encoded_selfies))\n",
    "symbols_benzene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "protecting-mixture",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit ERROR: [16:50:44] SMILES Parse Error: syntax error while parsing: NNC(=O)CNC(=O)\\C=N\\#N\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: Failed parsing SMILES 'NNC(=O)CNC(=O)\\C=N\\#N' for input: 'NNC(=O)CNC(=O)\\C=N\\#N'\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: syntax error while parsing: O=C1NC(=O)\\C(=N/#N)\\C=N1\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: Failed parsing SMILES 'O=C1NC(=O)\\C(=N/#N)\\C=N1' for input: 'O=C1NC(=O)\\C(=N/#N)\\C=N1'\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: syntax error while parsing: NC(=O)CNC(=O)\\C=N\\#N\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: Failed parsing SMILES 'NC(=O)CNC(=O)\\C=N\\#N' for input: 'NC(=O)CNC(=O)\\C=N\\#N'\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: syntax error while parsing: CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: Failed parsing SMILES 'CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O' for input: 'CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O'\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: syntax error while parsing: NC(COC(=O)\\C=N/#N)C(=O)O\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: Failed parsing SMILES 'NC(COC(=O)\\C=N/#N)C(=O)O' for input: 'NC(COC(=O)\\C=N/#N)C(=O)O'\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: syntax error while parsing: CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O\n",
      "RDKit ERROR: [16:50:44] SMILES Parse Error: Failed parsing SMILES 'CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O' for input: 'CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O'\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: syntax error while parsing: NNC(=O)CNC(=O)\\C=N\\#N\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: Failed parsing SMILES 'NNC(=O)CNC(=O)\\C=N\\#N' for input: 'NNC(=O)CNC(=O)\\C=N\\#N'\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: syntax error while parsing: O=C1NC(=O)\\C(=N/#N)\\C=N1\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: Failed parsing SMILES 'O=C1NC(=O)\\C(=N/#N)\\C=N1' for input: 'O=C1NC(=O)\\C(=N/#N)\\C=N1'\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: syntax error while parsing: NC(=O)CNC(=O)\\C=N\\#N\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: Failed parsing SMILES 'NC(=O)CNC(=O)\\C=N\\#N' for input: 'NC(=O)CNC(=O)\\C=N\\#N'\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: syntax error while parsing: CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: Failed parsing SMILES 'CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O' for input: 'CCCCN(CC(O)C1=C\\C(=N/#N)\\C(=O)C=C1)N=O'\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: syntax error while parsing: NC(COC(=O)\\C=N/#N)C(=O)O\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: Failed parsing SMILES 'NC(COC(=O)\\C=N/#N)C(=O)O' for input: 'NC(COC(=O)\\C=N/#N)C(=O)O'\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: syntax error while parsing: CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O\n",
      "RDKit ERROR: [16:50:45] SMILES Parse Error: Failed parsing SMILES 'CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O' for input: 'CCN(CC(O)C1=CC(=O)\\C(=N\\#N)\\C=C1)N=O'\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import urllib.request \n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, Draw, Descriptors, PandasTools\n",
    "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/onecoinbuybus/Database_chemoinformatics/master/smiles_cas_N6512.smi'\n",
    "urllib.request.urlretrieve(url, 'ames.txt') \n",
    "df = pd.read_csv('ames.txt',header=None, sep='\\t') \n",
    "df.columns = ['smiles', 'CAS_NO', 'activity']\n",
    "PandasTools.AddMoleculeColumnToFrame(frame=df, smilesCol='smiles')\n",
    "\n",
    "none_list=[]\n",
    "for i in range(df.shape[0]):\n",
    "    if Chem.MolFromSmiles(df['smiles'][i]) is None:\n",
    "        none_list.append(i)\n",
    "\n",
    "df=df.drop(none_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "embedded-invention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[C][C][=C][C][=C][C][Branch1_2][Branch1_1][=C][Ring1][Branch1_2][C][N+expl][Branch1_2][C][=O][O-expl]'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_selfies = sf.encoder('Cc1cccc(c1C)[N+](=O)[O-]') \n",
    "encoded_selfies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "comic-bible",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_selfies = []\n",
    "for i in df['smiles'].tolist():\n",
    "    encoded_selfies.append(sf.encoder(i[:-1])+'[nop]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "enormous-cause",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[O][=C][C][=C][C][=C][C][=C][Ring1][Branch1_2][C][Branch1_2][C][=O][C][=C][Ring1][O][C][=C][C][=C][Ring1][Branch1_2][NHexpl][C][=C][C][Branch1_2][C][=O][C][=C][C][=C][C][=C][Ring1][Branch1_2][C][Branch1_2][C][=O][C][Ring1][N][=C][NHexpl][C][=C][C][Branch1_2][C][=O][C][=C][C][=C][C][=C][Ring1][Branch1_2][C][Branch1_2][C][=O][C][Ring1][N][=C][C][=C][Ring1][S][C][Ring2][Ring1][Ring1][=C][Ring2][Ring2][Ring2][Ring2][Ring1][P][nop]'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_selfies[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "horizontal-plaintiff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[#C]', '[#N]', '[#O+expl]', '[/Br]', '[/C]', '[/Cl]', '[/F]', '[/N]', '[/O]', '[/S]', '[=C]', '[=N+expl]', '[=N-expl]', '[=N]', '[=O+expl]', '[=O]', '[=P]', '[=S+expl]', '[=S]', '[B]', '[Br]', '[Branch1_1]', '[Branch1_2]', '[Branch1_3]', '[Branch2_1]', '[Branch2_2]', '[Branch2_3]', '[C-expl]', '[C@@Hexpl]', '[C@@expl]', '[C@Hexpl]', '[C@expl]', '[CH-expl]', '[CH2-expl]', '[C]', '[Cl]', '[Expl/Ring1]', '[Expl/Ring2]', '[Expl=Ring1]', '[Expl=Ring2]', '[Expl\\\\Ring1]', '[Expl\\\\Ring2]', '[F]', '[I]', '[N+expl]', '[N-expl]', '[N@+expl]', '[N@@+expl]', '[NHexpl]', '[N]', '[O-expl]', '[O]', '[P+expl]', '[P@@expl]', '[P@expl]', '[P]', '[Ring1]', '[Ring2]', '[S+expl]', '[S]', '[Seexpl]', '[Siexpl]', '[\\\\Br]', '[\\\\C@@Hexpl]', '[\\\\C@Hexpl]', '[\\\\C]', '[\\\\Cl]', '[\\\\F]', '[\\\\N+expl]', '[\\\\N]', '[\\\\O]', '[\\\\S]', '[nop]']\n"
     ]
    }
   ],
   "source": [
    "alphabet = sf.get_alphabet_from_selfies(encoded_selfies)\n",
    "alphabet.add('[nop]')  # '[nop]' is a special padding symbol\n",
    "alphabet = list(sorted(alphabet))\n",
    "print(alphabet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "standing-bundle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "348"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pad_to_len = max(sf.len_selfies(s) for s in encoded_selfies)  \n",
    "symbol_to_idx = {s: i for i, s in enumerate(alphabet)}\n",
    "pad_to_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "greenhouse-treasury",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[O][=C][C][=C][C][=C][C][=C][Ring1][Branch1_2][C][Branch1_2][C][=O][C][=C][Ring1][O][C][=C][C][=C][Ring1][Branch1_2][NHexpl][C][=C][C][Branch1_2][C][=O][C][=C][C][=C][C][=C][Ring1][Branch1_2][C][Branch1_2][C][=O][C][Ring1][N][=C][NHexpl][C][=C][C][Branch1_2][C][=O][C][=C][C][=C][C][=C][Ring1][Branch1_2][C][Branch1_2][C][=O][C][Ring1][N][=C][C][=C][Ring1][S][C][Ring2][Ring1][Ring1][=C][Ring2][Ring2][Ring2][Ring2][Ring1][P][nop]'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_selfies[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "upset-seating",
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol_to_int = dict((c, i) for i, c in enumerate(alphabet))\n",
    "\n",
    "integer_encoded_all=[]\n",
    "for i in encoded_selfies:\n",
    "    symbol_list = sf.split_selfies(i)\n",
    "    integer_encoded = [symbol_to_int[symbol] for symbol in symbol_list]\n",
    "    integer_encoded_all.append(integer_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "tough-indonesian",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize_sequences(sequences, dimension):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1. \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "infinite-crystal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[51,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 56,\n",
       " 22,\n",
       " 34,\n",
       " 22,\n",
       " 34,\n",
       " 15,\n",
       " 34,\n",
       " 10,\n",
       " 56,\n",
       " 51,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 56,\n",
       " 22,\n",
       " 48,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 22,\n",
       " 34,\n",
       " 15,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 56,\n",
       " 22,\n",
       " 34,\n",
       " 22,\n",
       " 34,\n",
       " 15,\n",
       " 34,\n",
       " 56,\n",
       " 49,\n",
       " 10,\n",
       " 48,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 22,\n",
       " 34,\n",
       " 15,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 56,\n",
       " 22,\n",
       " 34,\n",
       " 22,\n",
       " 34,\n",
       " 15,\n",
       " 34,\n",
       " 56,\n",
       " 49,\n",
       " 10,\n",
       " 34,\n",
       " 10,\n",
       " 56,\n",
       " 59,\n",
       " 34,\n",
       " 57,\n",
       " 56,\n",
       " 56,\n",
       " 10,\n",
       " 57,\n",
       " 57,\n",
       " 57,\n",
       " 57,\n",
       " 56,\n",
       " 55,\n",
       " 72]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integer_encoded_all[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "satisfied-engineering",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = vectorize_sequences(integer_encoded_all, len(alphabet))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "soviet-pickup",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0.,\n",
       "       0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.,\n",
       "       1., 0., 0., 0., 1., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 1.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "classified-model",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['activity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "formed-marijuana",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "green-qatar",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=66)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "parallel-stack",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "exclusive-worship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "transsexual-talent",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "vocational-browse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6675204918032787"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "accurate-earth",
   "metadata": {},
   "outputs": [],
   "source": [
    "# smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "precise-account",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'O=C1c2ccccc2C(=O)c3c1ccc4c3[nH]c5c6C(=O)c7ccccc7C(=O)c6c8[nH]c9c%10C(=O)c%11ccccc%11C(=O)c%10ccc9c8c45 '"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['smiles'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "spectacular-accountability",
   "metadata": {},
   "outputs": [],
   "source": [
    "volcabulary = set(\"\".join(list(df.smiles)))\n",
    "volcabulary.update({'<go>', '<eos>'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "certified-second",
   "metadata": {},
   "outputs": [],
   "source": [
    "volcabulary.remove(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "attached-variance",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = dict((c,i) for i,c in enumerate(volcabulary))\n",
    "docs_swap= {v: k for k, v in docs.items()}\n",
    "embed = max([len(smile) for smile in df['smiles']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "given-ceramic",
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_smiles=[x.replace(' ', '') for x in df['smiles']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "isolated-mills",
   "metadata": {},
   "outputs": [],
   "source": [
    "def smi2id(smiles,vocalbulary):\n",
    "    sequence_id=[]\n",
    "    for i in range(len(smiles)):\n",
    "        smi_id=[]\n",
    "        for j in range(len(smiles[i])):\n",
    "            smi_id.append(vocalbulary.index(smiles[i][j]))\n",
    "        sequence_id.append(smi_id)\n",
    "    return sequence_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "living-writing",
   "metadata": {},
   "outputs": [],
   "source": [
    "int_encoded_all_smi=[]\n",
    "for i in smiles_smiles:\n",
    "    int_encoded=np.array(smi2id(i,list(volcabulary))).ravel().tolist()\n",
    "    int_encoded_all_smi.append(int_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "meaningful-chase",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "parental-average",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_smi=vectorize_sequences(int_encoded_all_smi,embed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "selective-excess",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6506, 490)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_smi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "rocky-postcard",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x_smi, y, test_size=0.3, random_state=66)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "quality-owner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7085040983606558"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "accuracy_score(y_test,clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "billion-calibration",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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