{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem.Draw import rdMolDraw2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "modmols = []\n",
    "patt = Chem.MolFromSmarts('ClccccF')\n",
    "\n",
    "smi = 'c1cc(F)ccc1Cl'\n",
    "molist = [Chem.MolFromSmiles(smi)]\n",
    "\n",
    "for mol in molist:\n",
    "    hit_ats = list(mol.GetSubstructMatch(patt))\n",
    "    hit_bonds = []\n",
    "    for bond in patt.GetBonds():\n",
    "        aid1 = hit_ats[bond.GetBeginAtomIdx()]\n",
    "        aid2 = hit_ats[bond.GetEndAtomIdx()]\n",
    "        hit_bonds.append(mol.GetBondBetweenAtoms(aid1,aid2).GetIdx())\n",
    "    d = rdMolDraw2D.MolDraw2DSVG(500, 500) # or MolDraw2DCairo to get PNGs\n",
    "    rdMolDraw2D.PrepareAndDrawMolecule(d, mol, highlightAtoms=hit_ats,\n",
    "                                       highlightBonds=hit_bonds)\n",
    "    modmols.append(mol)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 6, 5, 4, 2, 3]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hit_ats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x23549334d50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modmols[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'int' object has no attribute '__iter__'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-b101eb3dc708>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mDraw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMolsToGridImage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodmols\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mhighlightAtomLists\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhit_ats\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py\u001b[0m in \u001b[0;36mShowMols\u001b[1;34m(mols, maxMols, **kwargs)\u001b[0m\n\u001b[0;32m    191\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mprop\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mprop\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mprop\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mmaxMols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m   \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrawOptions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdrawOptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'useSVG'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mSVG\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\rdkit\\Chem\\Draw\\__init__.py\u001b[0m in \u001b[0;36mMolsToGridImage\u001b[1;34m(mols, molsPerRow, subImgSize, legends, highlightAtomLists, highlightBondLists, useSVG, **kwargs)\u001b[0m\n\u001b[0;32m    577\u001b[0m     return _MolsToGridImage(mols, molsPerRow=molsPerRow, subImgSize=subImgSize, legends=legends,\n\u001b[0;32m    578\u001b[0m                             \u001b[0mhighlightAtomLists\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhighlightAtomLists\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m                             highlightBondLists=highlightBondLists, **kwargs)\n\u001b[0m\u001b[0;32m    580\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\rdkit\\Chem\\Draw\\__init__.py\u001b[0m in \u001b[0;36m_MolsToGridImage\u001b[1;34m(mols, molsPerRow, subImgSize, legends, highlightAtomLists, highlightBondLists, drawOptions, **kwargs)\u001b[0m\n\u001b[0;32m    523\u001b[0m           \u001b[1;32mdel\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m     d2d.DrawMolecules(list(mols), legends=legends, highlightAtoms=highlightAtomLists,\n\u001b[1;32m--> 525\u001b[1;33m                       highlightBonds=highlightBondLists, **kwargs)\n\u001b[0m\u001b[0;32m    526\u001b[0m     \u001b[0md2d\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFinishDrawing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m     \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_drawerToImage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md2d\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute '__iter__'"
     ]
    }
   ],
   "source": [
    "Draw.MolsToGridImage(modmols,highlightAtomLists=hit_ats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}