assigning_bond_orders

assigning_bond_orders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions\n",
    "DrawingOptions.includeAtomNumbers=False\n",
    "from IPython.display import Image, display, HTML\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_block = 'HETATM    1  C12 UNL     1       0.647  -0.495  26.462  1.00  \\nHETATM    2  C10 UNL     1       0.007  -1.506  27.406  1.00  \\nHETATM    3  C9  UNL     1       0.348  -1.508  28.764  1.00  \\nHETATM    4  C8  UNL     1      -0.243  -2.417  29.644  1.00  \\nHETATM    5  C6  UNL     1      -1.187  -3.351  29.193  1.00  \\nHETATM    6  C5  UNL     1      -1.545  -3.360  27.830  1.00  \\nHETATM    7  C7  UNL     1      -1.809  -4.295  30.187  1.00  \\nHETATM    8  C11 UNL     1      -0.933  -2.444  26.954  1.00  \\nHETATM    9  C4  UNL     1      -2.580  -4.323  27.272  1.00  \\nHETATM   10  N1  UNL     1      -3.974  -3.904  27.556  1.00  \\nHETATM   11  C3  UNL     1      -4.253  -2.457  27.355  1.00  \\nHETATM   12  C2  UNL     1      -4.980  -2.149  26.038  1.00  \\nHETATM   13  C1  UNL     1      -5.002  -0.640  25.795  1.00  \\nHETATM   14  O8  UNL     1      -4.264  -2.736  24.946  1.00  \\nHETATM   15  C28 UNL     1      -4.712  -3.959  24.536  1.00  \\nHETATM   16  C27 UNL     1      -5.162  -5.019  25.327  1.00  \\nHETATM   17  C29 UNL     1      -4.638  -4.137  23.147  1.00  \\nHETATM   18  S2  UNL     1      -5.186  -4.988  27.101  1.00  \\nHETATM   19  C32 UNL     1      -5.560  -6.226  24.724  1.00  \\nHETATM   20  O6  UNL     1      -4.780  -6.303  27.555  1.00  \\nHETATM   21  O7  UNL     1      -6.463  -4.432  27.497  1.00  \\nHETATM   22  C31 UNL     1      -5.504  -6.379  23.340  1.00  \\nHETATM   23  C30 UNL     1      -5.037  -5.334  22.550  1.00  \\nHETATM   24  O1  UNL     1       1.362  -1.231  25.453  1.00  \\nHETATM   25  C13 UNL     1       2.786  -1.161  25.595  1.00  \\nHETATM   26  C14 UNL     1       3.412  -2.502  25.477  1.00  \\nHETATM   27  C15 UNL     1       4.391  -2.936  24.609  1.00  \\nHETATM   28  N2  UNL     1       4.553  -4.249  24.924  1.00  \\nHETATM   29  N4  UNL     1       3.725  -4.619  25.917  1.00  \\nHETATM   30  N5  UNL     1       3.028  -3.549  26.275  1.00  \\nHETATM   31  C16 UNL     1       5.409  -5.216  24.276  1.00  \\nHETATM   32  C17 UNL     1       4.756  -6.056  23.163  1.00  \\nHETATM   33  C18 UNL     1       4.104  -5.209  22.056  1.00  \\nHETATM   34  C19 UNL     1       2.580  -5.250  22.127  1.00  \\nHETATM   35  N3  UNL     1       2.107  -6.645  21.910  1.00  \\nHETATM   36  C20 UNL     1       2.910  -7.694  22.615  1.00  \\nHETATM   37  C21 UNL     1       3.779  -7.102  23.718  1.00  \\nHETATM   38  S1  UNL     1       0.428  -6.892  21.977  1.00  \\nHETATM   39  C22 UNL     1      -0.000  -7.160  20.268  1.00  \\nHETATM   40  O2  UNL     1       0.189  -8.130  22.686  1.00  \\nHETATM   41  O3  UNL     1      -0.175  -5.651  22.424  1.00  \\nHETATM   42  C23 UNL     1      -0.366   0.473  25.776  1.00  \\nHETATM   43  C24 UNL     1      -1.682   0.642  26.560  1.00  \\nHETATM   44  C25 UNL     1       0.215   1.880  25.521  1.00  \\nHETATM   45  C26 UNL     1      -0.694  -0.150  24.420  1.00  \\nHETATM   46  O5  UNL     1      -1.638  -0.881  24.160  1.00  \\nHETATM   47  O4  UNL     1       0.201   0.193  23.470  1.00  \\nHETATM   48  H39 UNL     1       0.425  -0.638  22.968  1.00  \\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HETATM    1  C12 UNL     1       0.647  -0.495  26.462  1.00  \n",
      "HETATM    2  C10 UNL     1       0.007  -1.506  27.406  1.00  \n",
      "HETATM    3  C9  UNL     1       0.348  -1.508  28.764  1.00  \n",
      "HETATM    4  C8  UNL     1      -0.243  -2.417  29.644  1.00  \n",
      "HETATM    5  C6  UNL     1      -1.187  -3.351  29.193  1.00  \n",
      "HETATM    6  C5  UNL     1      -1.545  -3.360  27.830  1.00  \n",
      "HETATM    7  C7  UNL     1      -1.809  -4.295  30.187  1.00  \n",
      "HETATM    8  C11 UNL     1      -0.933  -2.444  26.954  1.00  \n",
      "HETATM    9  C4  UNL     1      -2.580  -4.323  27.272  1.00  \n",
      "HETATM   10  N1  UNL     1      -3.974  -3.904  27.556  1.00  \n",
      "HETATM   11  C3  UNL     1      -4.253  -2.457  27.355  1.00  \n",
      "HETATM   12  C2  UNL     1      -4.980  -2.149  26.038  1.00  \n",
      "HETATM   13  C1  UNL     1      -5.002  -0.640  25.795  1.00  \n",
      "HETATM   14  O8  UNL     1      -4.264  -2.736  24.946  1.00  \n",
      "HETATM   15  C28 UNL     1      -4.712  -3.959  24.536  1.00  \n",
      "HETATM   16  C27 UNL     1      -5.162  -5.019  25.327  1.00  \n",
      "HETATM   17  C29 UNL     1      -4.638  -4.137  23.147  1.00  \n",
      "HETATM   18  S2  UNL     1      -5.186  -4.988  27.101  1.00  \n",
      "HETATM   19  C32 UNL     1      -5.560  -6.226  24.724  1.00  \n",
      "HETATM   20  O6  UNL     1      -4.780  -6.303  27.555  1.00  \n",
      "HETATM   21  O7  UNL     1      -6.463  -4.432  27.497  1.00  \n",
      "HETATM   22  C31 UNL     1      -5.504  -6.379  23.340  1.00  \n",
      "HETATM   23  C30 UNL     1      -5.037  -5.334  22.550  1.00  \n",
      "HETATM   24  O1  UNL     1       1.362  -1.231  25.453  1.00  \n",
      "HETATM   25  C13 UNL     1       2.786  -1.161  25.595  1.00  \n",
      "HETATM   26  C14 UNL     1       3.412  -2.502  25.477  1.00  \n",
      "HETATM   27  C15 UNL     1       4.391  -2.936  24.609  1.00  \n",
      "HETATM   28  N2  UNL     1       4.553  -4.249  24.924  1.00  \n",
      "HETATM   29  N4  UNL     1       3.725  -4.619  25.917  1.00  \n",
      "HETATM   30  N5  UNL     1       3.028  -3.549  26.275  1.00  \n",
      "HETATM   31  C16 UNL     1       5.409  -5.216  24.276  1.00  \n",
      "HETATM   32  C17 UNL     1       4.756  -6.056  23.163  1.00  \n",
      "HETATM   33  C18 UNL     1       4.104  -5.209  22.056  1.00  \n",
      "HETATM   34  C19 UNL     1       2.580  -5.250  22.127  1.00  \n",
      "HETATM   35  N3  UNL     1       2.107  -6.645  21.910  1.00  \n",
      "HETATM   36  C20 UNL     1       2.910  -7.694  22.615  1.00  \n",
      "HETATM   37  C21 UNL     1       3.779  -7.102  23.718  1.00  \n",
      "HETATM   38  S1  UNL     1       0.428  -6.892  21.977  1.00  \n",
      "HETATM   39  C22 UNL     1      -0.000  -7.160  20.268  1.00  \n",
      "HETATM   40  O2  UNL     1       0.189  -8.130  22.686  1.00  \n",
      "HETATM   41  O3  UNL     1      -0.175  -5.651  22.424  1.00  \n",
      "HETATM   42  C23 UNL     1      -0.366   0.473  25.776  1.00  \n",
      "HETATM   43  C24 UNL     1      -1.682   0.642  26.560  1.00  \n",
      "HETATM   44  C25 UNL     1       0.215   1.880  25.521  1.00  \n",
      "HETATM   45  C26 UNL     1      -0.694  -0.150  24.420  1.00  \n",
      "HETATM   46  O5  UNL     1      -1.638  -0.881  24.160  1.00  \n",
      "HETATM   47  O4  UNL     1       0.201   0.193  23.470  1.00  \n",
      "HETATM   48  H39 UNL     1       0.425  -0.638  22.968  1.00  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(pdb_block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_mol = Chem.MolFromPDBBlock(pdb_block, removeHs=False, sanitize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RB6VG+P/444+sXr2ab775pjgEt2zZQt++ffn3v/9tciGYnw8bN8LQoaU7rl27dpiZmUmfoTB6EoSiYoiKgmbNQKvF3Fz3qxJBeO/ePYKDg5k2bRotW7YEdEsqvfXWWyxcuJAxY8aUf1F6sHgx9O5dumNsbW2pVq0aFy9eNEhNQuiLaUzQKMTTrFkD7dvr3i0EWrRQJgjff/99nJ2di0eCbtq0icDAQMLDwxk2bFj5F6QHlpYwcGDZjq1Xr54EoTB68kQoTF9aGuzY8VDbnRJB+M0337B161YuXLiAi4sLZmZmhIaGmmQIHpwELcfC9SK4sx8GT4PdH8G7O3Xb0/ZCQCjcecp56taty6VLlwxerxDPQ54IhenbuBHc3aFdu+KvWrSA33+HzEwoj3EaixYtYvLkyYSFhdGsWTMKCgoYOXIkPXr0ICgoyPAF6Js1aHbAzCj42Knsp2nceDl//CEDZYRxkyAUJu9afDxugYGY3W8WBXjlFVi+HMzLoc1j/vz5zJo1i4iICHx9fQF47733sLW1Nd01/MzB+0PIWg5RDx60C+CHGeC3HDTX4Ha7vz0DANWrV2XXLoNWKsRzkyAUJu38+fM02rSJs2fP4vGX762sYMQIw15bq9UyceJEVq5cya5du3jttdcAOHLkCEuXLiUuLg4bGxvDFmFAqpowYwz4TQZ1H8AKvGfD4jd1TaOjdz/9HPXqgbSMCmMnfYTCpK1evZp27drh4eHx0Pcl9XE9rT+rNAoLCwkODmbdunXs3bu3OAQ1Gg3BwcGMGzfuiWsrmpIaPWF2V8jSlO34unXh+nXIztZvXULokzwRCpOl1WpZt24dISEhj2/UUx9XSTQaDYMHDyYhIYEDBw7QqFGj4m3Tp08nJyeH2bNn6/ei5azDHHgQ490WQ7f7v3e9/0+XzhDR+ennqVcPtFpIToa//GsSwqhIEAqTFR8fz9WrVxkwYMDjG0vq49KDzMxM/Pz8SElJ4eDBg9SuXbt42/Hjx1m0aBG7du2SmVTuc3DQjWVyc1O6EiGeTJpGhclavXo1bdu2xcnJqcTtD/q4wufA1UI4cxLmzoXExLJdLy0tja5du3Lnzh0OHDjwUAjm5eURGBjIiBEjeP3118t2ASP31Vfwv/9b+uMCAuAJ/4mEMAoShMIk3bp1i7Vr13Lo0CFu3LjxxP3+2seVfhdiY6FlS13f1dix8MMPoHmG/q/U1FQ6deqElZUVcXFxuLq6PrR99uzZZGRkMG/evOe8M+NVqxaEhsKZM89+jKH7aoXQB2kaFSbp66+/xsPDA2dnZ06fPk316tUB2LYNWrV6ch9Xj4Nw6xbExOhmZQsI0E1R2q/fWDp2bEXPnj2Lz/VAUlISXbt25eWXX2bTpk2PjQRNTExkwYIFREVF4eDgYOA7V07PnuDrC8HBcPAgqFTPcJAB+2qF0Bd5IhR6dA8+CoCdN3Uf906G0ANw+xD4toFuPaF9V9h88bmuUlhYSHh4OEFBQRw5cgSX+0tMLF6smwrsacvgVasGgYGweTPcvAnbtuXh4KBizpw51KhRA09PT8LCwjh58iSnTp3itddeo23btmzduvWxECwoKCA4OJigoCC6dev2hCtWHIsX654M09Ofbf8iM11frdlyiLpi2NqEKCsJQmFgmfDZAgjcCrt2QNQ0WP0xpJZ99a/IyEju3r2LpaUlDRo0oGnTZsyaBVOnwnffQUljZ57Eygq6dLFi8eLFJCUlkZiYSO/evYmOjqZ58+YMHDgQf39/1qxZg6Wl5WPHFxQU0LNnTxYsWFDm+zEl1avDli08dXmre/fggw/grWWgrfZwX60QxkbWIxR6dA+md4ddDlDTBq6dgXbz4NZGmPgfaGoDXIex4yHwG2hTtmZELy8vmjdvzvHjx2nf3pP09Bls3KgmKgrattXf3dy8eZPc3NyHBsWIZ5OfD2+9Bf/PDNJGwaedYNd7ME0Fuz4HZ6ULFOIv5IlQ6JeFO8xeo+usC+sD1mqolQfnbuu23z0LKY5Qxx6yskp9+jNnzrB//3769etHfHw8bdrso0uXjzh4UL8hCODq6ioh+ARPm5Q74yCYNYRREfCpF2Cu66v9PwlBYYQkCIWB2cOkEFjfH7r1gO4z4a1QqF4EjRuDnx/s2/fMZ/viiy/o3r07v/wSj7u7BR4emfToMU5e1i5v1qCJ0w2CKU1rp1YLEyfC/v0Gq0yIUpNRo0KPqsDMiD8/dv4UHsw+8l0JI1giI2HRIvD2hpdeImfFVNStfDEzs4bp02Hnzod2Lyws5O1ff+WFF+qSsi8WOrvSqNFBVKoqhrslUbIyTsptZgZVqugW+Y2Ph6ZNy7VqIUokT4RCOc2bw6pVcPky+PtzyWo+J07U4erVmRSdPaX7v+WSJcU/mzp2JKxmTdLnjuJKdhG924+XEFTQoxMWYKGblHvbNlgbBvWsSz5u5kwYMgR69NBNvSaE0iQIhfKqV4cZM2jU9Aju7p+Rnr6D9PRIMt2uUNB4O+erTOGs0wRCt6/Hc+Ikoq47kGfvQN26DZSuvNIr66TcixbpngZ9fCA9vYwzeguhJzJqVBilgv5doPebaN74iZvqzzl95P8YOGAoJ/+4yPCBg1h04wZNP/wQBg1SulRRRpmZ8M47sVy79m9iYmKwsrJSuiRRSckToTBKFhauWFi4Y+f2CU55YXy7ajKtXn0J80LdZNt16tRRukTxnOzt4fPPW3H58mWCgoIoKipSuiRRSUkQCiOWQcaNr7jFR3z/fQqH4k8yO2wm5ubmxU8PU6ZMKZ5rdN++fVy7dk3JgkUpubq6EhsbS1xcHKGhoUqXIyopGTUqjJg9Ng6ObFnUhmrVVPj5tyBmxxZat26Bra0tycnJrFu3jo8//phbt27RvXt3xo8fz6effqp04SYpJSWF3377jYKCAjIyMgDIyMigoKCA/Px8MjMzAbh37x6FhYUlfpeXl8e4cePo0qXLM1/Xw8OD77//Hi8vL3x8fCrEgsbCtEgQCiNmhoX9DA4ePsqgQS8SEvIvvgqvxeJFhbBBy549exgxYgQWFhasWrUKjUaDb6uGxF3MwaueDbnJB/jxUgO6dHBHrfStGLnr16/j5+fH+fPnUalUODo6AmBnZ4eVlRXm5uaPfadSqahSRTdqt2bNmlhaWnLnzh169uzJli1b6NWr1zNf387ODjMzM/Ly8vR/c0I8hQShMF7Tp1P42WfMOn6cl5KSyNyyhd8dHak94zIkJ/E/w4bz5ogRFBUVER4eTteub1C/dk3+q3TdJmjLli1kZGRw587zL5L04osvEhgYSEJCAh4eHk/dPyMjg4CAAAIDA/Hy8nru6wtRWjJqVBinP/6A27eJiYlh6dKlbNu2jX79/OnX72WGDp2v26dhQ3BxYefOnXh7e7Nt22bauOYTe9GKqvYWFGbdJqdmN/p2MqYnwnvw0QhouwTedNWt0LHbByaqIPgDyK4GWXnw3groX6/cqurYsSNeXl7MmjXruc+l1WoZMGAAZ8+e5ciRI4+t2PHovv369SMpKYmffvrpb/cVwlBksIwwTh4e8OqrLD92jIaDB7P50iXiNRrqBxwgvdENePXV4iUQWrVqxZQpU/Dx6QHmDtRr34M+ffrQ47WGOJpEm4f+V+gojdTUVA4dOsSA0izb8TfMzMxYuXIleXl5vPvuu3+77yeffMK+fftKXOJKiPIiQSiMVkZGBrt376Zv374sWrSIESN6Y2NTxIULg8nJOV28X9WqVZk3bx4WFiaRelCQAjOG6uZZDd0Omly4agUN769t5NIY6tyDy5nlUk5ERASvvPIKL7/8st7O6eDgwKZNm1i/fj2rVq0qcZ+9e/cya9Ys1q1bR/369fV2bSFKS4JQGK3IyEicnJwoKCggMTGR4cN1owkLCzNISvKnsPDuI0dYUcPTB696uicLde2O+BjjQJnSrNBRDrZt+57+/fvr/bxNmjRhxYoVjBkzhmPHjj20LSUlhYCAAKZPn463t7fery1EaUgfoTBafn5+uLu7k5ycjIuLC/Pnt+Ly5bHF26tU6UbDhjGYmakUrLK0ntBHOMkSRnwAGc6QkQvvfwMDDP+UdPkyNG2aztGjRbz4omEWSBo5ciR79+4lISEBR0dH8vPz6dy5M05OTkRGRmJuLn+PC2VJEArlJSRAp04PfaXVasnOzsba2hqNRoO2VSsyIn24enUW5ub2ODq+iaNjH5yd+2FuLn1LZfXZZ7B+PTzywKZXGo2G9u3bU7NmTbZv3864cePYsWMHv/zyC1WrVjXchYV4RibSqSIqtMJC3dqEe/cWf/Xdli2EhITQu3dvLH79lQXm5hTa/j8aNtyBg4MX5uZG1+BpkiIiICDAsNewtrbmu+++o2XLlgwZMoStW7dy8OBBCUFhNKRNQhgHc3Pd5JP3fzZGR9PJx4cVGzbQd8gQABwde+Ho2KPih+DNm/DBB5Cfb9DLJCXBL79Av34GvQwAdevWJSQkhH379rF06VJatmxp+IsK8YwkCIXRyc7O5ocffgCgXr16tGnT5uEdbh8C3zbQrSe07wqbT8BHAbDzpm773skQeqCcq9YjOztYt073Y0AREdC6te5NFUM7efIkn3zyCcOGDWP48OHs37+fadOmAVBQUEBhYWnWuRdCv6RpVBidy5cv07hxY3bu3MlHH32EmZnZwzs8eOfOvxak7YOgj6GWIqUahq0tvP8+RV/+L2ZBQw02GCg1tXxWsTp//jzdunUjICCAuXPnArrJ0h8E4dy5c8nJyeHNN99ErVbj6elp+KKE+AsJQmF0ateuzalTp1Cr1bz11luQmPjwDiW9c3fuqu7dvOU2cO0MtPMp/8L1adxY/tt9JTXubMLFxTBptXixQU77kCtXrtC1a1e8vLxYsmRJ8fc7d+6kSpUqnDp1iqVLl7J//368vb1ZuXKl4YsS4hHSNCqMjp2dHcuWLWP58uWo1SX0B5b0zl2Dlx55N698a9Y7ewecXQeTmvoxYJrr9N26dYtu3brx4osvsmrVqodek6hSpQpFRUWMHDmSefPmsWjRIt58syuNbbKJu5gDQG7yAaIPppCr1A2ISkOCUBilYcOGEfCk4YyTQmB9f+jWA7rPhLdCoYZZyfuaMDe3CVhbe1BQkKa3cx6cBC3HwvUiuLMfBk+D559m+3H37t2je/fuODk5sXXr1hJXn9dqtYwfPx4PDw927NjBp5+GGaASIZ5OmkaFcUhI0I0YLUlRETRp8udnF0/47vAjO0X8+WvnT6Gz3issdyqVMw0bRur3pNag2QEzo+BjJ/2e+oGcnBx69+5Nfn4+u3fvxs7OrsT9VCoVgwYNws/Pjy+//BJHR0eyizK4eCiG7YkPJk1vYJgihfgLCUKhvNatIT397/dRmdLsMcoqKtLNGHPunO7n7Fm4cgU2bwbMwftDyFoOUUP1f+38/HwGDBjA1atXiY+Px8nJ6anHbNmyBZVKBeTdnzT9jb+sJ6n/GoV4lAShUN6DdwjFI7QUZHzIxSuH0apyKLQIom7dd7C536GRn59Kbu5ZNJpzaDTnyM09x9atE5g+/XU0GnB0hBdegBdfhFde0c1bAKCqCTPGgN9kUPfRX7VFRUUEBQWRmJhIfHw8bm5uz3ScSv7IEQqTIBTCaGWgyTiDRfWV1HVxJufW1+TkZWOjtuXUqRfRaM5hbm6DtfULqNUvoFY3ont3azw9deFXvfqTz1yjJ8zeDdM0+qv2gw8+YM+ePRw4cIC6deuW4Qy6SdNr3P+krt0Rn9r6q0+IJ5G5RoUwKlq02jzMzO4Pey38nbs3/01aVhrman/+8Y9BqFVmZGf/goWFK1ZWtQHlBwpNnTqVZcuWsXfvXlq0aKF0OUKUiowaFcKIXL06i6SkB6Nlb5Fx4yvyHRfSwONbXM2/50bmdQBsbVtiZVWH5w3BwkIICYHffiv7ORYtWsTixYuJjo6WEBQmSYJQCCPi6jqajIz93L37PeCCjYMjdy+04/ezPbmcURNHW1e9Xk+lguvXwdcXMjJKd2xOTg7vvfceERERbNu2jQ4dOui1NiHKizSNCmFkbt5cRmrqHF555b+oVI4Gv15ODrRrBy+9BBs2PNsxR48eJSgoiKKiItasWcOrr75q2CKFMCB5IhTCyLi6jsLKqh6pqR8BkJ39KxrNHwa7no0NbNoEMTEQHv73+xYUFDB//nxee+01OnbsyLFjxyQEhcmTUaNCGB1z6tb9irw83Ut0V6+GYmvbipo1Zxvsii+8ACtWFLF06de0atWyxGWSTp8+TWBgILdv3yY2NhYvLy+D1SNEeZInQiGMkI3N/+Do2BOA7Ozj2NoafhDKgAHmNG16ir59+3L79u3i7wsLC5k/fz4tW7bEw8OD48ePSwiKCkX6CIUwYtpb17hyYCBu3f+DpU1Z3s0rnfz8fDp37oyTkxNRUVFcuHCBYcOGcfbsWcLDw+nTR49v4AthJOSJUAgjZnb0GO5vn8BSXadcrmdpacmGDRv4+eef8fX1pUmTJtSsWZPTp09LCIoKS4JQCGN2/Di0aAGPLk5sQLVr12bdunU4OzuzatUqNm7cSNWqVcvt+kKUN2kaFcKYnTgBaWnQqZPSlQhRYUkQCiGEqNSkaVQIRd2DjwJg503dx72TIfQA3D4Evm2gW09o3xU2X1S0SiEqMnmPUAijkwmffQWBW8G/FqTtg6CPocMKqKH8BNtCVDQShEIorSAFZgyF5TZw7Qy0awu3rKChi267S2Oocw8uZ0INB2VrFaICkqZRIZRm4Q6z18C2bRDWB6zVUCsPzt1/qf3uWUhxhDqyeLEQhiBPhEIYHXuYFAIj+kO4M2TkwvvfSLOoEAYio0aFEEJUatI0KoQQolKTIBRCCFGpSRAKIYSo1CQIhRBCVGoShEIIISo1CUIhhBCVmgShEEKISk2CUAghRKUmQSiEEKJSkyAUQghRqUkQCiGEqNQkCIUQQlRqEoRCCCEqNQlCIYQQldr/B8ZHn5lsK3k2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x15231116cda0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdb_mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x152310623990>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pdb_mol = copy.deepcopy(pdb_mol)\n",
    "AllChem.Compute2DCoords(test_pdb_mol)\n",
    "test_pdb_mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_smiles = \"C[C@@H]1CN(CC2=C(C)C=CC(=C2)C(OCC2=CN(CC3CCN(CC3)S(C)(=O)=O)N=N2)C(C)(C)C(O)=O)S(=O)(=O)C2=C(O1)C=CC=C2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_mol = Chem.MolFromSmiles(ref_smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x152311147ad0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference = Chem.RemoveHs(ref_mol)\n",
    "\n",
    "# sort atoms by atomic number, so that hydrogens are at the end\n",
    "order_by_atomic_num = tuple(zip(*sorted(\n",
    "[(a.GetIdx() if a.GetAtomicNum() > 1 else a.GetIdx() + pdb_mol.GetNumAtoms(), i\n",
    ") for i, a in enumerate(pdb_mol.GetAtoms())])))[1]\n",
    "\n",
    "# Reorder pdb_mol \n",
    "pdb_mol = AllChem.RenumberAtoms(pdb_mol, order_by_atomic_num)\n",
    "\n",
    "# Assign bond orders\n",
    "pdb_mol_assign = AllChem.AssignBondOrdersFromTemplate(reference, Chem.RemoveHs(pdb_mol))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x152310628f30>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pdb_mol_assign = copy.deepcopy(pdb_mol_assign)\n",
    "AllChem.Compute2DCoords(test_pdb_mol_assign)\n",
    "test_pdb_mol_assign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Draw.MolToFile(test_pdb_mol_assign,\"/tmp/test1.png\")\n",
    "display(Image(\"/tmp/test1.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get hydrogens back \n",
    "pdb_mol_bonds = Chem.RWMol(pdb_mol_assign)\n",
    "for a in pdb_mol.GetAtoms():\n",
    "    if (a.GetAtomicNum() == 1):\n",
    "        pdb_mol_bonds.AddAtom(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x1523104d4928>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdb_mol_bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_mol_no_h = Chem.RemoveHs(pdb_mol)\n",
    "isomorphisms = pdb_mol_no_h.GetSubstructMatches(\n",
    "    pdb_mol_no_h, uniquify=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_mol_conf = pdb_mol.GetConformer()\n",
    "pdb_mol_bonds_conf = pdb_mol_bonds.GetConformer()\n",
    "\n",
    "for b in pdb_mol.GetBonds():\n",
    "    if (b.GetBeginAtom().GetAtomicNum() == 1):\n",
    "        hydro = b.GetBeginAtom()\n",
    "    elif (b.GetEndAtom().GetAtomicNum() == 1):\n",
    "        hydro = b.GetEndAtom()\n",
    "    else:\n",
    "        continue\n",
    "    nh = 0\n",
    "    for isomorphism in isomorphisms:\n",
    "        heavy_idx = isomorphism[b.GetOtherAtom(hydro).GetIdx()]\n",
    "        hydro_idx = hydro.GetIdx()\n",
    "        heavy = pdb_mol_bonds.GetAtomWithIdx(heavy_idx)\n",
    "        nh = heavy.GetNumImplicitHs() + heavy.GetNumExplicitHs()\n",
    "        if nh:\n",
    "            break\n",
    "    if (not nh):\n",
    "        raise RuntimeError(\"Could not find isomorphisms that satisfies \"\n",
    "                          \"bond orders and hydrogen topology\")\n",
    "    heavy.SetNoImplicit(True)\n",
    "    heavy.SetNumExplicitHs(nh - 1)\n",
    "    pdb_mol_bonds.AddBond(heavy_idx, hydro_idx, Chem.BondType.SINGLE)\n",
    "    pdb_mol_bonds_conf.SetAtomPosition(hydro_idx, pdb_mol_conf.GetAtomPosition(hydro_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x1523104d4928>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdb_mol_bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x1523104dc6c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pdb_mol_bonds = copy.deepcopy(pdb_mol_bonds)\n",
    "AllChem.Compute2DCoords(test_pdb_mol_bonds)\n",
    "test_pdb_mol_bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rdkit.Chem.rdmolops.SanitizeFlags.SANITIZE_NONE"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.SanitizeMol(pdb_mol_bonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gtn_core",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}