intro_to_rdkit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8ae86a3-aed4-4c43-86fd-a714e74a2fec",
   "metadata": {},
   "source": [
    "# Intro to RDKit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6704d7-5766-4ee8-8767-1ff33c9b420b",
   "metadata": {},
   "source": [
    "https://www.rdkit.org/docs/GettingStartedInPython.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b32199b-59af-4c29-8cd0-bb6978a912af",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Basic I/O"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616821d3-feed-4502-866e-12fd30d6abd1",
   "metadata": {},
   "source": [
    "The majority of the basic molecular functionality is found in module `rdkit.Chem`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ccf2885-3f29-4a07-be00-67e0f18bfd49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d4ab4e8-cd5c-4297-bcfa-ea3ec42d0088",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Chem.MolFromSmiles('Cc1ccccc1') # m: rdkit.Chem.rdchem.Mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e03b16f-612a-4c8b-b609-237ac80f863b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa40fa520>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "523f3bec-728c-4ad9-93da-78b863d0b0f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<rdkit.Chem.rdchem.Mol object at 0x2b3fa40fa520>\n"
     ]
    }
   ],
   "source": [
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6448c77-19c4-439b-9d5b-a24b338d3407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rdkit.Chem.rdchem.Mol"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(m) # check the instance of 'm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87b2b8ab-aaa2-4f5c-bc20-04d8697db331",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit ERROR: [21:55:08] SMILES Parse Error: unclosed ring for input: 'C1CCCCC2'\n",
      "[21:55:08] SMILES Parse Error: unclosed ring for input: 'C1CCCCC2'\n"
     ]
    }
   ],
   "source": [
    "m2 = Chem.MolFromSmiles(\"C1CCCCC2\") # wrong SMILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "819eefde-b515-49bb-ac6b-cb067f1df5ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1685c9b8-8169-47e1-8dfd-c88ace4566fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_drugs= []\n",
    "with open(\"data/drugs.smi\", \"r\") as fin : # fin : file pointer\n",
    "    for l in fin : # read line by line\n",
    "        smi = l.strip() #string.strip() delete any space, \\n, \\t on the most front and back of string\n",
    "        m = Chem.MolFromSmiles(smi)\n",
    "        all_drugs.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa1f19b6-5a3d-4752-adef-f0584bcada33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0x2b3fa41a1640>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a16a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1700>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1820>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a17c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a18e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1880>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1760>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1940>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a19a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1a00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1a60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1b80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1b20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1ac0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1ca0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1c40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1be0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1dc0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1d60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1d00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1e20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1ee0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1e80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2040>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1fa0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a1f40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a20a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2100>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2160>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2280>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2220>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a21c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a22e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2340>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a23a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2400>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2460>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a24c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a2520>, <rdkit.Chem.rdchem.Mol object at 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0x2b3fa41a34c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3460>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a35e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3580>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3640>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a36a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3700>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3820>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a37c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3760>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3880>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a39a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3940>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a38e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3a00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3a60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3ac0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3b80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3b20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a3be0>, <rdkit.Chem.rdchem.Mol object at 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0x2b3fa41a4a00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4be0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4c40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4ca0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4d00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4d60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4dc0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4ee0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4e80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4e20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4fa0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a4f40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5100>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a50a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5040>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5160>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a51c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5220>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5280>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a52e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5340>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a53a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5400>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5520>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a54c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5460>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5580>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a55e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5700>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a56a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5640>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5760>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a57c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5820>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5880>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a58e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5a00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a59a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5940>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5a60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5ac0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5be0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5b80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5b20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5c40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5ca0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5d00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5d60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5dc0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5e20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5e80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5fa0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5f40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a5ee0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6040>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a60a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6100>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6160>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a61c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6220>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6280>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a62e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6340>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a63a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6400>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6520>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a64c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a65e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6580>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6460>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6640>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a66a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6700>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6820>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a67c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6760>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6880>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a68e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6940>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a69a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6a00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6ac0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6a60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6b80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6b20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6be0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6c40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6ca0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6dc0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6d60>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6d00>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6e20>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6f40>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6ee0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6e80>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a6fa0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7040>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a70a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7100>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7160>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a71c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7220>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7280>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a72e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7400>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a73a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7340>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7460>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a74c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7520>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7580>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a76a0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7640>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a75e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7700>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7760>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a77c0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7820>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7880>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a78e0>, <rdkit.Chem.rdchem.Mol object at 0x2b3fa41a7940>]\n"
     ]
    }
   ],
   "source": [
    "print(all_drugs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6362ae85-4d0d-40c7-a2eb-4079b59b7976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_drugs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5608a0-5b24-4bdb-aae0-ad152979ba4a",
   "metadata": {},
   "source": [
    "Reading sets of molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6063bd54-465d-4879-8d1f-483d46640848",
   "metadata": {},
   "outputs": [],
   "source": [
    "suppl = Chem.SDMolSupplier('data/drugs.sdf') # read multiple molecules from sdf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "055c0ad8-10b4-4699-b7f6-d4a02090bbb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rdkit.Chem.rdmolfiles.SDMolSupplier"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(suppl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fed7c2f1-2cfe-4293-b272-6be72924ac07",
   "metadata": {},
   "outputs": [],
   "source": [
    "for mol in suppl:\n",
    "    print(mol.GetNumAtoms()) # mol : variable, GetNumAtoms() : method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c93d612-cb45-4662-a30b-98e8a98aac82",
   "metadata": {
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    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mol No. 0 : 25 atmos.\n",
      "Mol No. 1 : 11 atmos.\n",
      "Mol No. 2 : 13 atmos.\n",
      "Mol No. 3 : 19 atmos.\n",
      "Mol No. 4 : 36 atmos.\n",
      "Mol No. 5 : 10 atmos.\n",
      "Mol No. 6 : 13 atmos.\n",
      "Mol No. 7 : 16 atmos.\n",
      "Mol No. 8 : 21 atmos.\n",
      "Mol No. 9 : 21 atmos.\n",
      "Mol No. 10 : 25 atmos.\n",
      "Mol No. 11 : 26 atmos.\n",
      "Mol No. 12 : 7 atmos.\n",
      "Mol No. 13 : 30 atmos.\n",
      "Mol No. 14 : 23 atmos.\n",
      "Mol No. 15 : 19 atmos.\n",
      "Mol No. 16 : 20 atmos.\n",
      "Mol No. 17 : 10 atmos.\n",
      "Mol No. 18 : 18 atmos.\n",
      "Mol No. 19 : 19 atmos.\n",
      "Mol No. 20 : 17 atmos.\n",
      "Mol No. 21 : 17 atmos.\n",
      "Mol No. 22 : 20 atmos.\n",
      "Mol No. 23 : 17 atmos.\n",
      "Mol No. 24 : 20 atmos.\n",
      "Mol No. 25 : 13 atmos.\n",
      "Mol No. 26 : 15 atmos.\n",
      "Mol No. 27 : 29 atmos.\n",
      "Mol No. 28 : 16 atmos.\n",
      "Mol No. 29 : 31 atmos.\n",
      "Mol No. 30 : 20 atmos.\n",
      "Mol No. 31 : 19 atmos.\n",
      "Mol No. 32 : 16 atmos.\n",
      "Mol No. 33 : 25 atmos.\n",
      "Mol No. 34 : 16 atmos.\n",
      "Mol No. 35 : 20 atmos.\n",
      "Mol No. 36 : 21 atmos.\n",
      "Mol No. 37 : 24 atmos.\n",
      "Mol No. 38 : 10 atmos.\n",
      "Mol No. 39 : 23 atmos.\n",
      "Mol No. 40 : 26 atmos.\n",
      "Mol No. 41 : 44 atmos.\n",
      "Mol No. 42 : 25 atmos.\n",
      "Mol No. 43 : 21 atmos.\n",
      "Mol No. 44 : 9 atmos.\n",
      "Mol No. 45 : 17 atmos.\n",
      "Mol No. 46 : 24 atmos.\n",
      "Mol No. 47 : 16 atmos.\n",
      "Mol No. 48 : 21 atmos.\n",
      "Mol No. 49 : 19 atmos.\n",
      "Mol No. 50 : 21 atmos.\n",
      "Mol No. 51 : 19 atmos.\n",
      "Mol No. 52 : 26 atmos.\n",
      "Mol No. 53 : 18 atmos.\n",
      "Mol No. 54 : 30 atmos.\n",
      "Mol No. 55 : 12 atmos.\n",
      "Mol No. 56 : 23 atmos.\n",
      "Mol No. 57 : 12 atmos.\n",
      "Mol No. 58 : 11 atmos.\n",
      "Mol No. 59 : 14 atmos.\n",
      "Mol No. 60 : 20 atmos.\n",
      "Mol No. 61 : 32 atmos.\n",
      "Mol No. 62 : 16 atmos.\n",
      "Mol No. 63 : 17 atmos.\n",
      "Mol No. 64 : 23 atmos.\n",
      "Mol No. 65 : 13 atmos.\n",
      "Mol No. 66 : 28 atmos.\n",
      "Mol No. 67 : 19 atmos.\n",
      "Mol No. 68 : 26 atmos.\n",
      "Mol No. 69 : 12 atmos.\n",
      "Mol No. 70 : 35 atmos.\n",
      "Mol No. 71 : 20 atmos.\n",
      "Mol No. 72 : 28 atmos.\n",
      "Mol No. 73 : 39 atmos.\n",
      "Mol No. 74 : 13 atmos.\n",
      "Mol No. 75 : 14 atmos.\n",
      "Mol No. 76 : 20 atmos.\n",
      "Mol No. 77 : 25 atmos.\n",
      "Mol No. 78 : 27 atmos.\n",
      "Mol No. 79 : 21 atmos.\n",
      "Mol No. 80 : 23 atmos.\n",
      "Mol No. 81 : 30 atmos.\n",
      "Mol No. 82 : 19 atmos.\n",
      "Mol No. 83 : 16 atmos.\n",
      "Mol No. 84 : 36 atmos.\n",
      "Mol No. 85 : 13 atmos.\n",
      "Mol No. 86 : 13 atmos.\n",
      "Mol No. 87 : 18 atmos.\n",
      "Mol No. 88 : 28 atmos.\n",
      "Mol No. 89 : 24 atmos.\n",
      "Mol No. 90 : 12 atmos.\n",
      "Mol No. 91 : 32 atmos.\n",
      "Mol No. 92 : 34 atmos.\n",
      "Mol No. 93 : 31 atmos.\n",
      "Mol No. 94 : 20 atmos.\n",
      "Mol No. 95 : 17 atmos.\n",
      "Mol No. 96 : 33 atmos.\n",
      "Mol No. 97 : 18 atmos.\n",
      "Mol No. 98 : 20 atmos.\n",
      "Mol No. 99 : 22 atmos.\n",
      "Mol No. 100 : 33 atmos.\n",
      "Mol No. 101 : 24 atmos.\n",
      "Mol No. 102 : 27 atmos.\n",
      "Mol No. 103 : 199 atmos.\n",
      "Mol No. 104 : 28 atmos.\n",
      "Mol No. 105 : 12 atmos.\n",
      "Mol No. 106 : 25 atmos.\n",
      "Mol No. 107 : 22 atmos.\n",
      "Mol No. 108 : 40 atmos.\n",
      "Mol No. 109 : 10 atmos.\n",
      "Mol No. 110 : 34 atmos.\n",
      "Mol No. 111 : 28 atmos.\n",
      "Mol No. 112 : 22 atmos.\n",
      "Mol No. 113 : 30 atmos.\n",
      "Mol No. 114 : 30 atmos.\n",
      "Mol No. 115 : 22 atmos.\n",
      "Mol No. 116 : 10 atmos.\n",
      "Mol No. 117 : 30 atmos.\n",
      "Mol No. 118 : 26 atmos.\n",
      "Mol No. 119 : 14 atmos.\n",
      "Mol No. 120 : 20 atmos.\n",
      "Mol No. 121 : 21 atmos.\n",
      "Mol No. 122 : 24 atmos.\n",
      "Mol No. 123 : 20 atmos.\n",
      "Mol No. 124 : 22 atmos.\n",
      "Mol No. 125 : 27 atmos.\n",
      "Mol No. 126 : 19 atmos.\n",
      "Mol No. 127 : 41 atmos.\n",
      "Mol No. 128 : 18 atmos.\n",
      "Mol No. 129 : 24 atmos.\n",
      "Mol No. 130 : 28 atmos.\n",
      "Mol No. 131 : 43 atmos.\n",
      "Mol No. 132 : 14 atmos.\n",
      "Mol No. 133 : 32 atmos.\n",
      "Mol No. 134 : 29 atmos.\n",
      "Mol No. 135 : 16 atmos.\n",
      "Mol No. 136 : 19 atmos.\n",
      "Mol No. 137 : 29 atmos.\n",
      "Mol No. 138 : 30 atmos.\n",
      "Mol No. 139 : 10 atmos.\n",
      "Mol No. 140 : 36 atmos.\n",
      "Mol No. 141 : 19 atmos.\n",
      "Mol No. 142 : 34 atmos.\n",
      "Mol No. 143 : 22 atmos.\n",
      "Mol No. 144 : 27 atmos.\n",
      "Mol No. 145 : 34 atmos.\n",
      "Mol No. 146 : 31 atmos.\n",
      "Mol No. 147 : 22 atmos.\n",
      "Mol No. 148 : 37 atmos.\n",
      "Mol No. 149 : 35 atmos.\n",
      "Mol No. 150 : 18 atmos.\n",
      "Mol No. 151 : 16 atmos.\n",
      "Mol No. 152 : 33 atmos.\n",
      "Mol No. 153 : 46 atmos.\n",
      "Mol No. 154 : 13 atmos.\n",
      "Mol No. 155 : 18 atmos.\n",
      "Mol No. 156 : 24 atmos.\n",
      "Mol No. 157 : 22 atmos.\n",
      "Mol No. 158 : 21 atmos.\n",
      "Mol No. 159 : 28 atmos.\n",
      "Mol No. 160 : 26 atmos.\n",
      "Mol No. 161 : 22 atmos.\n",
      "Mol No. 162 : 31 atmos.\n",
      "Mol No. 163 : 39 atmos.\n",
      "Mol No. 164 : 19 atmos.\n",
      "Mol No. 165 : 25 atmos.\n",
      "Mol No. 166 : 19 atmos.\n",
      "Mol No. 167 : 32 atmos.\n",
      "Mol No. 168 : 27 atmos.\n",
      "Mol No. 169 : 22 atmos.\n",
      "Mol No. 170 : 15 atmos.\n",
      "Mol No. 171 : 51 atmos.\n",
      "Mol No. 172 : 15 atmos.\n",
      "Mol No. 173 : 20 atmos.\n",
      "Mol No. 174 : 26 atmos.\n",
      "Mol No. 175 : 12 atmos.\n",
      "Mol No. 176 : 9 atmos.\n",
      "Mol No. 177 : 4 atmos.\n",
      "Mol No. 178 : 34 atmos.\n",
      "Mol No. 179 : 22 atmos.\n",
      "Mol No. 180 : 14 atmos.\n",
      "Mol No. 181 : 34 atmos.\n",
      "Mol No. 182 : 31 atmos.\n",
      "Mol No. 183 : 31 atmos.\n",
      "Mol No. 184 : 17 atmos.\n",
      "Mol No. 185 : 35 atmos.\n",
      "Mol No. 186 : 36 atmos.\n",
      "Mol No. 187 : 45 atmos.\n",
      "Mol No. 188 : 24 atmos.\n",
      "Mol No. 189 : 19 atmos.\n",
      "Mol No. 190 : 25 atmos.\n",
      "Mol No. 191 : 39 atmos.\n",
      "Mol No. 192 : 32 atmos.\n",
      "Mol No. 193 : 27 atmos.\n",
      "Mol No. 194 : 29 atmos.\n",
      "Mol No. 195 : 32 atmos.\n",
      "Mol No. 196 : 26 atmos.\n",
      "Mol No. 197 : 21 atmos.\n",
      "Mol No. 198 : 22 atmos.\n",
      "Mol No. 199 : 17 atmos.\n",
      "Mol No. 200 : 23 atmos.\n",
      "Mol No. 201 : 50 atmos.\n",
      "Mol No. 202 : 17 atmos.\n",
      "Mol No. 203 : 41 atmos.\n",
      "Mol No. 204 : 22 atmos.\n",
      "Mol No. 205 : 32 atmos.\n",
      "Mol No. 206 : 33 atmos.\n",
      "Mol No. 207 : 23 atmos.\n",
      "Mol No. 208 : 12 atmos.\n",
      "Mol No. 209 : 11 atmos.\n",
      "Mol No. 210 : 28 atmos.\n",
      "Mol No. 211 : 23 atmos.\n",
      "Mol No. 212 : 17 atmos.\n",
      "Mol No. 213 : 45 atmos.\n",
      "Mol No. 214 : 32 atmos.\n",
      "Mol No. 215 : 21 atmos.\n",
      "Mol No. 216 : 19 atmos.\n",
      "Mol No. 217 : 28 atmos.\n",
      "Mol No. 218 : 25 atmos.\n",
      "Mol No. 219 : 19 atmos.\n",
      "Mol No. 220 : 33 atmos.\n",
      "Mol No. 221 : 35 atmos.\n",
      "Mol No. 222 : 26 atmos.\n",
      "Mol No. 223 : 15 atmos.\n",
      "Mol No. 224 : 30 atmos.\n",
      "Mol No. 225 : 28 atmos.\n",
      "Mol No. 226 : 10 atmos.\n",
      "Mol No. 227 : 15 atmos.\n",
      "Mol No. 228 : 11 atmos.\n",
      "Mol No. 229 : 19 atmos.\n",
      "Mol No. 230 : 16 atmos.\n",
      "Mol No. 231 : 22 atmos.\n",
      "Mol No. 232 : 54 atmos.\n",
      "Mol No. 233 : 22 atmos.\n",
      "Mol No. 234 : 15 atmos.\n",
      "Mol No. 235 : 9 atmos.\n",
      "Mol No. 236 : 15 atmos.\n",
      "Mol No. 237 : 40 atmos.\n",
      "Mol No. 238 : 21 atmos.\n",
      "Mol No. 239 : 13 atmos.\n",
      "Mol No. 240 : 32 atmos.\n",
      "Mol No. 241 : 20 atmos.\n",
      "Mol No. 242 : 21 atmos.\n",
      "Mol No. 243 : 16 atmos.\n",
      "Mol No. 244 : 26 atmos.\n",
      "Mol No. 245 : 18 atmos.\n",
      "Mol No. 246 : 37 atmos.\n",
      "Mol No. 247 : 26 atmos.\n",
      "Mol No. 248 : 19 atmos.\n",
      "Mol No. 249 : 17 atmos.\n",
      "Mol No. 250 : 35 atmos.\n",
      "Mol No. 251 : 31 atmos.\n",
      "Mol No. 252 : 11 atmos.\n",
      "Mol No. 253 : 28 atmos.\n",
      "Mol No. 254 : 31 atmos.\n",
      "Mol No. 255 : 23 atmos.\n",
      "Mol No. 256 : 22 atmos.\n",
      "Mol No. 257 : 65 atmos.\n",
      "Mol No. 258 : 14 atmos.\n",
      "Mol No. 259 : 22 atmos.\n",
      "Mol No. 260 : 32 atmos.\n",
      "Mol No. 261 : 40 atmos.\n",
      "Mol No. 262 : 19 atmos.\n",
      "Mol No. 263 : 22 atmos.\n",
      "Mol No. 264 : 26 atmos.\n",
      "Mol No. 265 : 23 atmos.\n",
      "Mol No. 266 : 22 atmos.\n",
      "Mol No. 267 : 13 atmos.\n",
      "Mol No. 268 : 10 atmos.\n",
      "Mol No. 269 : 28 atmos.\n",
      "Mol No. 270 : 15 atmos.\n",
      "Mol No. 271 : 36 atmos.\n",
      "Mol No. 272 : 21 atmos.\n",
      "Mol No. 273 : 12 atmos.\n",
      "Mol No. 274 : 17 atmos.\n",
      "Mol No. 275 : 46 atmos.\n",
      "Mol No. 276 : 18 atmos.\n",
      "Mol No. 277 : 14 atmos.\n",
      "Mol No. 278 : 22 atmos.\n",
      "Mol No. 279 : 13 atmos.\n",
      "Mol No. 280 : 21 atmos.\n",
      "Mol No. 281 : 27 atmos.\n",
      "Mol No. 282 : 10 atmos.\n",
      "Mol No. 283 : 24 atmos.\n",
      "Mol No. 284 : 27 atmos.\n",
      "Mol No. 285 : 36 atmos.\n",
      "Mol No. 286 : 23 atmos.\n",
      "Mol No. 287 : 22 atmos.\n",
      "Mol No. 288 : 29 atmos.\n",
      "Mol No. 289 : 23 atmos.\n",
      "Mol No. 290 : 16 atmos.\n",
      "Mol No. 291 : 25 atmos.\n",
      "Mol No. 292 : 31 atmos.\n",
      "Mol No. 293 : 16 atmos.\n",
      "Mol No. 294 : 29 atmos.\n",
      "Mol No. 295 : 23 atmos.\n",
      "Mol No. 296 : 32 atmos.\n",
      "Mol No. 297 : 22 atmos.\n",
      "Mol No. 298 : 18 atmos.\n",
      "Mol No. 299 : 24 atmos.\n",
      "Mol No. 300 : 19 atmos.\n",
      "Mol No. 301 : 14 atmos.\n",
      "Mol No. 302 : 20 atmos.\n",
      "Mol No. 303 : 28 atmos.\n",
      "Mol No. 304 : 35 atmos.\n",
      "Mol No. 305 : 65 atmos.\n",
      "Mol No. 306 : 22 atmos.\n",
      "Mol No. 307 : 23 atmos.\n",
      "Mol No. 308 : 14 atmos.\n",
      "Mol No. 309 : 21 atmos.\n",
      "Mol No. 310 : 17 atmos.\n",
      "Mol No. 311 : 27 atmos.\n",
      "Mol No. 312 : 25 atmos.\n",
      "Mol No. 313 : 29 atmos.\n",
      "Mol No. 314 : 30 atmos.\n",
      "Mol No. 315 : 23 atmos.\n",
      "Mol No. 316 : 41 atmos.\n",
      "Mol No. 317 : 37 atmos.\n",
      "Mol No. 318 : 19 atmos.\n",
      "Mol No. 319 : 15 atmos.\n",
      "Mol No. 320 : 29 atmos.\n",
      "Mol No. 321 : 51 atmos.\n",
      "Mol No. 322 : 18 atmos.\n",
      "Mol No. 323 : 19 atmos.\n",
      "Mol No. 324 : 16 atmos.\n",
      "Mol No. 325 : 39 atmos.\n",
      "Mol No. 326 : 31 atmos.\n",
      "Mol No. 327 : 30 atmos.\n",
      "Mol No. 328 : 20 atmos.\n",
      "Mol No. 329 : 14 atmos.\n",
      "Mol No. 330 : 29 atmos.\n",
      "Mol No. 331 : 51 atmos.\n",
      "Mol No. 332 : 27 atmos.\n",
      "Mol No. 333 : 29 atmos.\n",
      "Mol No. 334 : 22 atmos.\n",
      "Mol No. 335 : 10 atmos.\n",
      "Mol No. 336 : 31 atmos.\n",
      "Mol No. 337 : 20 atmos.\n",
      "Mol No. 338 : 21 atmos.\n",
      "Mol No. 339 : 42 atmos.\n",
      "Mol No. 340 : 16 atmos.\n",
      "Mol No. 341 : 16 atmos.\n",
      "Mol No. 342 : 17 atmos.\n",
      "Mol No. 343 : 23 atmos.\n",
      "Mol No. 344 : 37 atmos.\n",
      "Mol No. 345 : 14 atmos.\n",
      "Mol No. 346 : 39 atmos.\n",
      "Mol No. 347 : 22 atmos.\n",
      "Mol No. 348 : 80 atmos.\n",
      "Mol No. 349 : 27 atmos.\n",
      "Mol No. 350 : 17 atmos.\n",
      "Mol No. 351 : 22 atmos.\n",
      "Mol No. 352 : 23 atmos.\n",
      "Mol No. 353 : 26 atmos.\n",
      "Mol No. 354 : 33 atmos.\n",
      "Mol No. 355 : 14 atmos.\n",
      "Mol No. 356 : 23 atmos.\n",
      "Mol No. 357 : 11 atmos.\n",
      "Mol No. 358 : 17 atmos.\n",
      "Mol No. 359 : 19 atmos.\n",
      "Mol No. 360 : 16 atmos.\n",
      "Mol No. 361 : 42 atmos.\n",
      "Mol No. 362 : 18 atmos.\n",
      "Mol No. 363 : 22 atmos.\n",
      "Mol No. 364 : 32 atmos.\n",
      "Mol No. 365 : 16 atmos.\n",
      "Mol No. 366 : 20 atmos.\n",
      "Mol No. 367 : 33 atmos.\n",
      "Mol No. 368 : 31 atmos.\n",
      "Mol No. 369 : 28 atmos.\n",
      "Mol No. 370 : 21 atmos.\n",
      "Mol No. 371 : 23 atmos.\n",
      "Mol No. 372 : 7 atmos.\n",
      "Mol No. 373 : 32 atmos.\n",
      "Mol No. 374 : 41 atmos.\n",
      "Mol No. 375 : 34 atmos.\n",
      "Mol No. 376 : 15 atmos.\n",
      "Mol No. 377 : 33 atmos.\n",
      "Mol No. 378 : 19 atmos.\n",
      "Mol No. 379 : 19 atmos.\n",
      "Mol No. 380 : 32 atmos.\n",
      "Mol No. 381 : 21 atmos.\n",
      "Mol No. 382 : 19 atmos.\n",
      "Mol No. 383 : 30 atmos.\n",
      "Mol No. 384 : 13 atmos.\n",
      "Mol No. 385 : 31 atmos.\n",
      "Mol No. 386 : 20 atmos.\n",
      "Mol No. 387 : 26 atmos.\n",
      "Mol No. 388 : 29 atmos.\n",
      "Mol No. 389 : 30 atmos.\n",
      "Mol No. 390 : 27 atmos.\n",
      "Mol No. 391 : 11 atmos.\n",
      "Mol No. 392 : 17 atmos.\n",
      "Mol No. 393 : 42 atmos.\n",
      "Mol No. 394 : 19 atmos.\n",
      "Mol No. 395 : 20 atmos.\n",
      "Mol No. 396 : 24 atmos.\n",
      "Mol No. 397 : 24 atmos.\n",
      "Mol No. 398 : 24 atmos.\n",
      "Mol No. 399 : 30 atmos.\n",
      "Mol No. 400 : 65 atmos.\n",
      "Mol No. 401 : 17 atmos.\n",
      "Mol No. 402 : 18 atmos.\n",
      "Mol No. 403 : 35 atmos.\n",
      "Mol No. 404 : 23 atmos.\n",
      "Mol No. 405 : 40 atmos.\n",
      "Mol No. 406 : 14 atmos.\n",
      "Mol No. 407 : 18 atmos.\n",
      "Mol No. 408 : 40 atmos.\n",
      "Mol No. 409 : 23 atmos.\n",
      "Mol No. 410 : 6 atmos.\n",
      "Mol No. 411 : 29 atmos.\n",
      "Mol No. 412 : 10 atmos.\n",
      "Mol No. 413 : 39 atmos.\n",
      "Mol No. 414 : 11 atmos.\n",
      "Mol No. 415 : 8 atmos.\n",
      "Mol No. 416 : 24 atmos.\n",
      "Mol No. 417 : 25 atmos.\n",
      "Mol No. 418 : 20 atmos.\n",
      "Mol No. 419 : 16 atmos.\n",
      "Mol No. 420 : 29 atmos.\n",
      "Mol No. 421 : 23 atmos.\n",
      "Mol No. 422 : 17 atmos.\n",
      "Mol No. 423 : 19 atmos.\n",
      "Mol No. 424 : 14 atmos.\n",
      "Mol No. 425 : 24 atmos.\n",
      "Mol No. 426 : 37 atmos.\n",
      "Mol No. 427 : 19 atmos.\n",
      "Mol No. 428 : 9 atmos.\n",
      "Mol No. 429 : 21 atmos.\n",
      "Mol No. 430 : 24 atmos.\n",
      "Mol No. 431 : 31 atmos.\n",
      "Mol No. 432 : 21 atmos.\n",
      "Mol No. 433 : 19 atmos.\n",
      "Mol No. 434 : 31 atmos.\n",
      "Mol No. 435 : 23 atmos.\n",
      "Mol No. 436 : 29 atmos.\n",
      "Mol No. 437 : 31 atmos.\n",
      "Mol No. 438 : 26 atmos.\n",
      "Mol No. 439 : 37 atmos.\n",
      "Mol No. 440 : 33 atmos.\n",
      "Mol No. 441 : 15 atmos.\n",
      "Mol No. 442 : 11 atmos.\n",
      "Mol No. 443 : 9 atmos.\n",
      "Mol No. 444 : 28 atmos.\n",
      "Mol No. 445 : 50 atmos.\n",
      "Mol No. 446 : 31 atmos.\n",
      "Mol No. 447 : 14 atmos.\n",
      "Mol No. 448 : 27 atmos.\n",
      "Mol No. 449 : 22 atmos.\n",
      "Mol No. 450 : 23 atmos.\n",
      "Mol No. 451 : 35 atmos.\n",
      "Mol No. 452 : 28 atmos.\n",
      "Mol No. 453 : 23 atmos.\n",
      "Mol No. 454 : 26 atmos.\n",
      "Mol No. 455 : 28 atmos.\n",
      "Mol No. 456 : 19 atmos.\n",
      "Mol No. 457 : 16 atmos.\n",
      "Mol No. 458 : 38 atmos.\n",
      "Mol No. 459 : 30 atmos.\n",
      "Mol No. 460 : 13 atmos.\n",
      "Mol No. 461 : 27 atmos.\n",
      "Mol No. 462 : 33 atmos.\n",
      "Mol No. 463 : 18 atmos.\n",
      "Mol No. 464 : 37 atmos.\n",
      "Mol No. 465 : 30 atmos.\n",
      "Mol No. 466 : 24 atmos.\n",
      "Mol No. 467 : 36 atmos.\n",
      "Mol No. 468 : 25 atmos.\n",
      "Mol No. 469 : 32 atmos.\n",
      "Mol No. 470 : 37 atmos.\n",
      "Mol No. 471 : 31 atmos.\n",
      "Mol No. 472 : 21 atmos.\n",
      "Mol No. 473 : 14 atmos.\n",
      "Mol No. 474 : 23 atmos.\n",
      "Mol No. 475 : 16 atmos.\n",
      "Mol No. 476 : 10 atmos.\n",
      "Mol No. 477 : 12 atmos.\n",
      "Mol No. 478 : 33 atmos.\n",
      "Mol No. 479 : 36 atmos.\n",
      "Mol No. 480 : 28 atmos.\n",
      "Mol No. 481 : 31 atmos.\n",
      "Mol No. 482 : 35 atmos.\n",
      "Mol No. 483 : 11 atmos.\n",
      "Mol No. 484 : 13 atmos.\n",
      "Mol No. 485 : 22 atmos.\n",
      "Mol No. 486 : 33 atmos.\n",
      "Mol No. 487 : 23 atmos.\n",
      "Mol No. 488 : 21 atmos.\n",
      "Mol No. 489 : 9 atmos.\n",
      "Mol No. 490 : 7 atmos.\n",
      "Mol No. 491 : 23 atmos.\n",
      "Mol No. 492 : 21 atmos.\n",
      "Mol No. 493 : 24 atmos.\n",
      "Mol No. 494 : 22 atmos.\n",
      "Mol No. 495 : 20 atmos.\n",
      "Mol No. 496 : 40 atmos.\n",
      "Mol No. 497 : 26 atmos.\n",
      "Mol No. 498 : 22 atmos.\n",
      "Mol No. 499 : 16 atmos.\n",
      "Mol No. 500 : 24 atmos.\n",
      "Mol No. 501 : 21 atmos.\n",
      "Mol No. 502 : 23 atmos.\n",
      "Mol No. 503 : 29 atmos.\n",
      "Mol No. 504 : 27 atmos.\n",
      "Mol No. 505 : 32 atmos.\n",
      "Mol No. 506 : 28 atmos.\n",
      "Mol No. 507 : 29 atmos.\n",
      "Mol No. 508 : 29 atmos.\n",
      "Mol No. 509 : 11 atmos.\n",
      "Mol No. 510 : 11 atmos.\n",
      "Mol No. 511 : 33 atmos.\n",
      "Mol No. 512 : 9 atmos.\n",
      "Mol No. 513 : 40 atmos.\n",
      "Mol No. 514 : 19 atmos.\n",
      "Mol No. 515 : 25 atmos.\n",
      "Mol No. 516 : 18 atmos.\n",
      "Mol No. 517 : 22 atmos.\n",
      "Mol No. 518 : 30 atmos.\n",
      "Mol No. 519 : 27 atmos.\n",
      "Mol No. 520 : 25 atmos.\n",
      "Mol No. 521 : 12 atmos.\n",
      "Mol No. 522 : 23 atmos.\n",
      "Mol No. 523 : 5 atmos.\n",
      "Mol No. 524 : 23 atmos.\n",
      "Mol No. 525 : 33 atmos.\n",
      "Mol No. 526 : 25 atmos.\n",
      "Mol No. 527 : 25 atmos.\n",
      "Mol No. 528 : 37 atmos.\n",
      "Mol No. 529 : 7 atmos.\n",
      "Mol No. 530 : 23 atmos.\n",
      "Mol No. 531 : 19 atmos.\n",
      "Mol No. 532 : 35 atmos.\n",
      "Mol No. 533 : 41 atmos.\n",
      "Mol No. 534 : 5 atmos.\n",
      "Mol No. 535 : 25 atmos.\n",
      "Mol No. 536 : 17 atmos.\n",
      "Mol No. 537 : 10 atmos.\n",
      "Mol No. 538 : 41 atmos.\n",
      "Mol No. 539 : 13 atmos.\n",
      "Mol No. 540 : 21 atmos.\n",
      "Mol No. 541 : 25 atmos.\n",
      "Mol No. 542 : 13 atmos.\n",
      "Mol No. 543 : 21 atmos.\n",
      "Mol No. 544 : 28 atmos.\n",
      "Mol No. 545 : 28 atmos.\n",
      "Mol No. 546 : 15 atmos.\n",
      "Mol No. 547 : 19 atmos.\n",
      "Mol No. 548 : 28 atmos.\n",
      "Mol No. 549 : 61 atmos.\n",
      "Mol No. 550 : 21 atmos.\n",
      "Mol No. 551 : 22 atmos.\n",
      "Mol No. 552 : 33 atmos.\n",
      "Mol No. 553 : 17 atmos.\n",
      "Mol No. 554 : 20 atmos.\n",
      "Mol No. 555 : 10 atmos.\n",
      "Mol No. 556 : 21 atmos.\n",
      "Mol No. 557 : 30 atmos.\n",
      "Mol No. 558 : 20 atmos.\n",
      "Mol No. 559 : 33 atmos.\n",
      "Mol No. 560 : 30 atmos.\n",
      "Mol No. 561 : 23 atmos.\n",
      "Mol No. 562 : 25 atmos.\n",
      "Mol No. 563 : 23 atmos.\n",
      "Mol No. 564 : 29 atmos.\n",
      "Mol No. 565 : 42 atmos.\n",
      "Mol No. 566 : 4 atmos.\n",
      "Mol No. 567 : 25 atmos.\n",
      "Mol No. 568 : 14 atmos.\n",
      "Mol No. 569 : 16 atmos.\n",
      "Mol No. 570 : 15 atmos.\n",
      "Mol No. 571 : 12 atmos.\n",
      "Mol No. 572 : 15 atmos.\n",
      "Mol No. 573 : 17 atmos.\n",
      "Mol No. 574 : 12 atmos.\n",
      "Mol No. 575 : 10 atmos.\n",
      "Mol No. 576 : 21 atmos.\n",
      "Mol No. 577 : 17 atmos.\n",
      "Mol No. 578 : 11 atmos.\n",
      "Mol No. 579 : 18 atmos.\n",
      "Mol No. 580 : 20 atmos.\n",
      "Mol No. 581 : 25 atmos.\n",
      "Mol No. 582 : 22 atmos.\n",
      "Mol No. 583 : 30 atmos.\n",
      "Mol No. 584 : 21 atmos.\n",
      "Mol No. 585 : 42 atmos.\n",
      "Mol No. 586 : 49 atmos.\n",
      "Mol No. 587 : 26 atmos.\n",
      "Mol No. 588 : 21 atmos.\n",
      "Mol No. 589 : 14 atmos.\n",
      "Mol No. 590 : 19 atmos.\n",
      "Mol No. 591 : 9 atmos.\n",
      "Mol No. 592 : 19 atmos.\n",
      "Mol No. 593 : 21 atmos.\n",
      "Mol No. 594 : 16 atmos.\n",
      "Mol No. 595 : 16 atmos.\n",
      "Mol No. 596 : 36 atmos.\n",
      "Mol No. 597 : 28 atmos.\n",
      "Mol No. 598 : 35 atmos.\n",
      "Mol No. 599 : 17 atmos.\n",
      "Mol No. 600 : 19 atmos.\n",
      "Mol No. 601 : 20 atmos.\n",
      "Mol No. 602 : 16 atmos.\n",
      "Mol No. 603 : 26 atmos.\n",
      "Mol No. 604 : 24 atmos.\n",
      "Mol No. 605 : 29 atmos.\n",
      "Mol No. 606 : 34 atmos.\n",
      "Mol No. 607 : 50 atmos.\n",
      "Mol No. 608 : 33 atmos.\n",
      "Mol No. 609 : 15 atmos.\n",
      "Mol No. 610 : 30 atmos.\n",
      "Mol No. 611 : 28 atmos.\n",
      "Mol No. 612 : 7 atmos.\n",
      "Mol No. 613 : 9 atmos.\n",
      "Mol No. 614 : 17 atmos.\n",
      "Mol No. 615 : 22 atmos.\n",
      "Mol No. 616 : 54 atmos.\n",
      "Mol No. 617 : 16 atmos.\n",
      "Mol No. 618 : 14 atmos.\n",
      "Mol No. 619 : 27 atmos.\n",
      "Mol No. 620 : 27 atmos.\n",
      "Mol No. 621 : 16 atmos.\n",
      "Mol No. 622 : 10 atmos.\n",
      "Mol No. 623 : 39 atmos.\n",
      "Mol No. 624 : 23 atmos.\n",
      "Mol No. 625 : 18 atmos.\n",
      "Mol No. 626 : 21 atmos.\n",
      "Mol No. 627 : 31 atmos.\n",
      "Mol No. 628 : 11 atmos.\n",
      "Mol No. 629 : 23 atmos.\n",
      "Mol No. 630 : 10 atmos.\n",
      "Mol No. 631 : 8 atmos.\n",
      "Mol No. 632 : 10 atmos.\n",
      "Mol No. 633 : 17 atmos.\n",
      "Mol No. 634 : 27 atmos.\n",
      "Mol No. 635 : 25 atmos.\n",
      "Mol No. 636 : 34 atmos.\n",
      "Mol No. 637 : 25 atmos.\n",
      "Mol No. 638 : 19 atmos.\n",
      "Mol No. 639 : 32 atmos.\n",
      "Mol No. 640 : 26 atmos.\n",
      "Mol No. 641 : 37 atmos.\n",
      "Mol No. 642 : 21 atmos.\n",
      "Mol No. 643 : 17 atmos.\n",
      "Mol No. 644 : 16 atmos.\n",
      "Mol No. 645 : 25 atmos.\n",
      "Mol No. 646 : 31 atmos.\n",
      "Mol No. 647 : 30 atmos.\n",
      "Mol No. 648 : 35 atmos.\n",
      "Mol No. 649 : 19 atmos.\n",
      "Mol No. 650 : 21 atmos.\n",
      "Mol No. 651 : 23 atmos.\n",
      "Mol No. 652 : 11 atmos.\n",
      "Mol No. 653 : 26 atmos.\n",
      "Mol No. 654 : 31 atmos.\n",
      "Mol No. 655 : 21 atmos.\n",
      "Mol No. 656 : 65 atmos.\n",
      "Mol No. 657 : 13 atmos.\n",
      "Mol No. 658 : 33 atmos.\n",
      "Mol No. 659 : 21 atmos.\n",
      "Mol No. 660 : 19 atmos.\n",
      "Mol No. 661 : 21 atmos.\n",
      "Mol No. 662 : 18 atmos.\n",
      "Mol No. 663 : 28 atmos.\n",
      "Mol No. 664 : 33 atmos.\n",
      "Mol No. 665 : 26 atmos.\n",
      "Mol No. 666 : 21 atmos.\n",
      "Mol No. 667 : 13 atmos.\n",
      "Mol No. 668 : 41 atmos.\n",
      "Mol No. 669 : 14 atmos.\n",
      "Mol No. 670 : 33 atmos.\n",
      "Mol No. 671 : 17 atmos.\n",
      "Mol No. 672 : 22 atmos.\n",
      "Mol No. 673 : 25 atmos.\n",
      "Mol No. 674 : 27 atmos.\n",
      "Mol No. 675 : 35 atmos.\n",
      "Mol No. 676 : 26 atmos.\n",
      "Mol No. 677 : 30 atmos.\n",
      "Mol No. 678 : 31 atmos.\n",
      "Mol No. 679 : 23 atmos.\n",
      "Mol No. 680 : 34 atmos.\n",
      "Mol No. 681 : 49 atmos.\n",
      "Mol No. 682 : 35 atmos.\n",
      "Mol No. 683 : 18 atmos.\n",
      "Mol No. 684 : 19 atmos.\n",
      "Mol No. 685 : 25 atmos.\n",
      "Mol No. 686 : 15 atmos.\n",
      "Mol No. 687 : 29 atmos.\n",
      "Mol No. 688 : 28 atmos.\n",
      "Mol No. 689 : 10 atmos.\n",
      "Mol No. 690 : 21 atmos.\n",
      "Mol No. 691 : 17 atmos.\n",
      "Mol No. 692 : 34 atmos.\n",
      "Mol No. 693 : 17 atmos.\n",
      "Mol No. 694 : 33 atmos.\n",
      "Mol No. 695 : 12 atmos.\n",
      "Mol No. 696 : 21 atmos.\n",
      "Mol No. 697 : 19 atmos.\n",
      "Mol No. 698 : 40 atmos.\n",
      "Mol No. 699 : 26 atmos.\n",
      "Mol No. 700 : 11 atmos.\n",
      "Mol No. 701 : 5 atmos.\n",
      "Mol No. 702 : 20 atmos.\n",
      "Mol No. 703 : 41 atmos.\n",
      "Mol No. 704 : 4 atmos.\n",
      "Mol No. 705 : 15 atmos.\n",
      "Mol No. 706 : 14 atmos.\n",
      "Mol No. 707 : 11 atmos.\n",
      "Mol No. 708 : 29 atmos.\n",
      "Mol No. 709 : 25 atmos.\n",
      "Mol No. 710 : 27 atmos.\n",
      "Mol No. 711 : 13 atmos.\n",
      "Mol No. 712 : 17 atmos.\n",
      "Mol No. 713 : 19 atmos.\n",
      "Mol No. 714 : 29 atmos.\n",
      "Mol No. 715 : 16 atmos.\n",
      "Mol No. 716 : 37 atmos.\n",
      "Mol No. 717 : 21 atmos.\n",
      "Mol No. 718 : 34 atmos.\n",
      "Mol No. 719 : 17 atmos.\n",
      "Mol No. 720 : 10 atmos.\n",
      "Mol No. 721 : 20 atmos.\n",
      "Mol No. 722 : 26 atmos.\n",
      "Mol No. 723 : 25 atmos.\n",
      "Mol No. 724 : 22 atmos.\n"
     ]
    }
   ],
   "source": [
    "# print with index\n",
    "idx = 0 # index\n",
    "for m in suppl :\n",
    "    num_atom = m.GetNumAtoms()\n",
    "    print(f\"Mol No. {idx} : {num_atom} atmos.\")\n",
    "    idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c0807dc9-45a7-4285-a4fd-4f818752b787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa55715e0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suppl[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d44c8f2c-c944-4f78-a8ba-303085e99878",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list comprehension\n",
    "large_mole = [mol for mol in suppl if mol.GetNumAtoms() >= 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aef7b4bb-2d3b-47a2-96c2-5b5187e61970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa5571e20>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_mole[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45296220-a31c-42f6-bdc7-506124a3053d",
   "metadata": {},
   "source": [
    "Writing molecules\n",
    "1) SMILES\n",
    "2) 3D coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a683fa5e-4e5b-4f7e-ae31-f92080f8555f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CC(C)c1nc(CN(C)C(=O)N[C@@H](CCN2CCOCC2)C(=O)N[C@H](CC[C@H](Cc2ccccc2)NC(=O)OCc2cncs2)Cc2ccccc2)cs1\n"
     ]
    }
   ],
   "source": [
    "print(Chem.MolToSmiles(large_mole[-2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b77789e6-9a2d-4ed8-a127-331eaf65f221",
   "metadata": {},
   "source": [
    "@ : counter-clockwise  \n",
    "@@ : clockwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c3a1d71-3eb1-4c92-915c-f4cdeda8f74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CC(C)c1nc(CN(C)C(=O)NC(CCN2CCOCC2)C(=O)NC(CCC(Cc2ccccc2)NC(=O)OCc2cncs2)Cc2ccccc2)cs1'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolToSmiles(large_mole[-2], isomericSmiles=False, kekuleSmiles=False) #without chriality\n",
    "#ref : https://www.rdkit.org/docs/source/rdkit.Chem.rdmolfiles.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9282484-fa6e-4b44-817e-4e579adc3ec0",
   "metadata": {},
   "source": [
    "Kekule form of the SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a9dcefa9-5bc1-436c-a61b-d3afcda7d403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C1=CC=NC=C1'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3 = Chem.MolFromSmiles('C1=CC=CN=C1')\n",
    "Chem.Kekulize(m3) #Kekulize the molecule\n",
    "Chem.MolToSmiles(m3,kekuleSmiles=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "790ed733-f195-4553-9015-0b79d9bf7a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     RDKit          2D\n",
      "\n",
      "  6  6  0  0  0  0  0  0  0  0999 V2000\n",
      "    1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.7500    1.2990    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.7500    1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  2  0\n",
      "  2  3  1  0\n",
      "  3  4  2  0\n",
      "  4  5  1  0\n",
      "  5  6  2  0\n",
      "  6  1  1  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# MDL Mol blocks\n",
    "print(Chem.MolToMolBlock(m3)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "10a812b2-e6ee-490e-8549-8442ec63e153",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyrimidine\n",
      "     RDKit          2D\n",
      "\n",
      "  6  6  0  0  0  0  0  0  0  0999 V2000\n",
      "    1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.7500    1.2990    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.7500    1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  2  0\n",
      "  2  3  1  0\n",
      "  3  4  2  0\n",
      "  4  5  1  0\n",
      "  5  6  2  0\n",
      "  6  1  1  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "m3.SetProp(\"_Name\",\"pyrimidine\") #SetProp : set property\n",
    "print(Chem.MolToMolBlock(m3)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7bbe5d0c-2e1d-4989-9d6c-ec83495ceb52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AddConformer',\n",
       " 'ClearComputedProps',\n",
       " 'ClearProp',\n",
       " 'Debug',\n",
       " 'GetAromaticAtoms',\n",
       " 'GetAtomWithIdx',\n",
       " 'GetAtoms',\n",
       " 'GetAtomsMatchingQuery',\n",
       " 'GetBondBetweenAtoms',\n",
       " 'GetBondWithIdx',\n",
       " 'GetBonds',\n",
       " 'GetBoolProp',\n",
       " 'GetConformer',\n",
       " 'GetConformers',\n",
       " 'GetDoubleProp',\n",
       " 'GetIntProp',\n",
       " 'GetNumAtoms',\n",
       " 'GetNumBonds',\n",
       " 'GetNumConformers',\n",
       " 'GetNumHeavyAtoms',\n",
       " 'GetProp',\n",
       " 'GetPropNames',\n",
       " 'GetPropsAsDict',\n",
       " 'GetRingInfo',\n",
       " 'GetStereoGroups',\n",
       " 'GetSubstructMatch',\n",
       " 'GetSubstructMatches',\n",
       " 'GetUnsignedProp',\n",
       " 'HasProp',\n",
       " 'HasSubstructMatch',\n",
       " 'NeedsUpdatePropertyCache',\n",
       " 'RemoveAllConformers',\n",
       " 'RemoveConformer',\n",
       " 'SetBoolProp',\n",
       " 'SetDoubleProp',\n",
       " 'SetIntProp',\n",
       " 'SetProp',\n",
       " 'SetUnsignedProp',\n",
       " 'ToBinary',\n",
       " 'UpdatePropertyCache',\n",
       " '__DebugMol',\n",
       " '__GetSubstructMatch',\n",
       " '__GetSubstructMatches',\n",
       " '__class__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getinitargs__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__instance_size__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__safe_for_unpickling__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_repr_html_',\n",
       " '_repr_png_',\n",
       " '_repr_svg_']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the method of instance 'm3'\n",
    "dir(m3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b9eeda09-a101-4b66-bfd9-0f3b01b05944",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add hydrogen\n",
    "m3_H = Chem.AddHs(m3) # warning! this function returns a new molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c952814c-78c3-4af7-a4e6-d3becca63cc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyrimidine\n",
      "     RDKit          2D\n",
      "\n",
      " 11 11  0  0  0  0  0  0  0  0999 V2000\n",
      "    1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.7500    1.2990    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.7500    1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.5000   -2.5981    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.5000   -2.5981    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.5000    2.5981    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  2  0\n",
      "  2  3  1  0\n",
      "  3  4  2  0\n",
      "  4  5  1  0\n",
      "  5  6  2  0\n",
      "  6  1  1  0\n",
      "  1  7  1  0\n",
      "  2  8  1  0\n",
      "  3  9  1  0\n",
      "  4 10  1  0\n",
      "  6 11  1  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(Chem.MolToMolBlock(m3_H))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0fb55e47-9053-4242-a7a9-4fde0a82676a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa558f820>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3_H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f23a1589-e28a-46a7-9b78-a06a39f946b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "m4 = large_mole[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "324cadd8-3510-4afb-bc1e-7c2df31a7c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "m4_H = Chem.AddHs(m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1f87b33-d089-4b9c-a383-b54f357793c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa558fca0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4_H\n",
    "#something is wrong in H atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2e2f7ae6-d435-4fe3-acc4-92cad7d3e3bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     RDKit          2D\n",
      "\n",
      "107111  0  0  1  0  0  0  0  0999 V2000\n",
      "    0.9013    1.8841    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.0933    1.7796    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.6810    2.5886    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.5000    0.8660    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.4781    0.6581    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.5827   -0.3364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.4487   -0.8364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.4487   -1.8364    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.5827   -2.3364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.3147   -2.3364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.3147   -3.3364    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.1808   -1.8364    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.0468   -2.3364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.9128   -2.8364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.9128   -3.8364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.7788   -4.3364    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.6449   -3.8364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.5109   -4.3364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.5109   -5.3364    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.6449   -5.8364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.7788   -5.3364    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.5468   -1.4704    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.5468   -1.4704    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.0468   -0.6044    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.5468    0.2617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.0468    1.1277    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.0468    1.1277    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.5468    1.9937    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.0468    2.8597    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.5468    3.7258    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.5468    3.7258    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.0468    4.5918    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.5468    5.4578    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.5468    5.4578    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.0468    4.5918    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.4128    1.4937    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -9.2788    1.9937    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -9.2788    2.9937    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -10.1449    1.4937    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -11.0109    1.9937    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -11.0109    2.9937    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -11.8199    3.5815    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -11.5109    4.5326    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -10.5109    4.5326    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -10.2019    3.5815    0.0000 S   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.6808    0.7617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.6808    1.7617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.5468    2.2617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.5468    3.2617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.6808    3.7617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.8147    3.2617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.8147    2.2617    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.6691   -0.7431    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000   -0.0000    0.0000 S   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    0.0000    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  1  0\n",
      "  2  3  1  0\n",
      "  2  4  1  0\n",
      "  4 54  1  0\n",
      "  4  5  2  0\n",
      "  5  6  1  0\n",
      "  6  7  1  0\n",
      "  6 53  2  0\n",
      "  7  8  1  0\n",
      "  8  9  1  0\n",
      "  8 10  1  0\n",
      " 10 11  2  0\n",
      " 10 12  1  0\n",
      " 12 13  1  0\n",
      " 13 14  1  0\n",
      " 13 22  1  0\n",
      " 14 15  1  0\n",
      " 15 16  1  0\n",
      " 16 21  1  0\n",
      " 16 17  1  0\n",
      " 17 18  1  0\n",
      " 18 19  1  0\n",
      " 19 20  1  0\n",
      " 20 21  1  0\n",
      " 22 23  2  0\n",
      " 22 24  1  0\n",
      " 24 25  1  0\n",
      " 25 26  1  0\n",
      " 25 46  1  0\n",
      " 26 27  1  0\n",
      " 27 28  1  0\n",
      " 28 29  1  0\n",
      " 28 36  1  0\n",
      " 29 30  1  0\n",
      " 30 35  1  0\n",
      " 30 31  2  0\n",
      " 31 32  1  0\n",
      " 32 33  2  0\n",
      " 33 34  1  0\n",
      " 34 35  2  0\n",
      " 36 37  1  0\n",
      " 37 38  2  0\n",
      " 37 39  1  0\n",
      " 39 40  1  0\n",
      " 40 41  1  0\n",
      " 41 45  1  0\n",
      " 41 42  2  0\n",
      " 42 43  1  0\n",
      " 43 44  2  0\n",
      " 44 45  1  0\n",
      " 46 47  1  0\n",
      " 47 52  1  0\n",
      " 47 48  2  0\n",
      " 48 49  1  0\n",
      " 49 50  2  0\n",
      " 50 51  1  0\n",
      " 51 52  2  0\n",
      " 53 54  1  0\n",
      "  1 55  1  0\n",
      "  1 56  1  0\n",
      "  1 57  1  0\n",
      "  2 58  1  0\n",
      "  3 59  1  0\n",
      "  3 60  1  0\n",
      "  3 61  1  0\n",
      "  7 62  1  0\n",
      "  7 63  1  0\n",
      "  9 64  1  0\n",
      "  9 65  1  0\n",
      "  9 66  1  0\n",
      " 12 67  1  0\n",
      " 13 68  1  6\n",
      " 14 69  1  0\n",
      " 14 70  1  0\n",
      " 15 71  1  0\n",
      " 15 72  1  0\n",
      " 17 73  1  0\n",
      " 17 74  1  0\n",
      " 18 75  1  0\n",
      " 18 76  1  0\n",
      " 20 77  1  0\n",
      " 20 78  1  0\n",
      " 21 79  1  0\n",
      " 21 80  1  0\n",
      " 24 81  1  0\n",
      " 25 82  1  1\n",
      " 26 83  1  0\n",
      " 26 84  1  0\n",
      " 27 85  1  0\n",
      " 27 86  1  0\n",
      " 28 87  1  6\n",
      " 29 88  1  0\n",
      " 29 89  1  0\n",
      " 31 90  1  0\n",
      " 32 91  1  0\n",
      " 33 92  1  0\n",
      " 34 93  1  0\n",
      " 35 94  1  0\n",
      " 36 95  1  0\n",
      " 40 96  1  0\n",
      " 40 97  1  0\n",
      " 42 98  1  0\n",
      " 44 99  1  0\n",
      " 46100  1  0\n",
      " 46101  1  0\n",
      " 48102  1  0\n",
      " 49103  1  0\n",
      " 50104  1  0\n",
      " 51105  1  0\n",
      " 52106  1  0\n",
      " 53107  1  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(Chem.MolToMolBlock(m4_H))\n",
    "# the positions of H atoms are wrong in large molecule. We need to compute 2D coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "da411111-4568-4ffa-b5f3-ad9516f3eb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import AllChem\n",
    "#Chem contains the primary function\n",
    "#Allchem contains the all function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "03cab00a-7301-4d9a-965e-a41519363e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.Compute2DCoords(m4_H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1ca44d0b-3243-426d-8e5d-8229e03b04b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     RDKit          2D\n",
      "\n",
      "107111  0  0  1  0  0  0  0  0999 V2000\n",
      "    9.3830    4.4440    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.7233    3.0969    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    7.3762    3.7565    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.0636    1.7497    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    6.5860    1.4916    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    6.3749    0.0065    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.0494   -0.6956    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.7238   -1.3977    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.6691   -2.8967    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.4530   -0.6009    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.6085    0.3709    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.1275   -1.3030    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.1433   -0.5061    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.6536    0.7647    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.4504    2.0356    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.2473    3.3064    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.7463    3.2516    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.5432    4.5225    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.8411    5.8480    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.3421    5.9027    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.5452    4.6319    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.9402   -1.7769    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.1399   -1.5849    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.2381   -3.1024    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.0350   -4.3732    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.3058   -3.5764    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.5766   -2.7795    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.8474   -1.9826    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.1182   -1.1857    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "   -9.9854   -0.2940    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.7146   -1.0909    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.0505   -0.7118    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.7527    0.6138    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.9558    1.8846    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "   -8.0736    5.7104    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.9962    4.6667    0.0000 S   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.2358   -5.1701    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "    4.0483   -7.5608    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.1030   -6.0618    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.8322   -5.2649    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    7.7221   -0.6532    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.7657    0.4242    0.0000 S   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "   10.7301    3.7843    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.6409    4.9276    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "    1.2117   -2.2835    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "    0.0920    2.6716    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "    2.6563    7.3694    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "   -2.7797   -1.5087    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
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      "   -8.9148    3.3748    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -11.2015    2.0019    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -11.3109   -0.9962    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.7693   -2.5899    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.5561   -0.5834    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.6282    2.6961    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.4098    1.6335    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  -10.1847    2.4533    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.8625    7.1955    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.1817   -4.0059    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.5611   -6.4409    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.1264   -8.1681    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.6680   -9.7619    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.3191   -8.3577    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.4285   -5.3597    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.8869   -3.7659    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    7.9802   -2.1308    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  1  0\n",
      "  2  3  1  0\n",
      "  2  4  1  0\n",
      "  4 54  1  0\n",
      "  4  5  2  0\n",
      "  5  6  1  0\n",
      "  6  7  1  0\n",
      "  6 53  2  0\n",
      "  7  8  1  0\n",
      "  8  9  1  0\n",
      "  8 10  1  0\n",
      " 10 11  2  0\n",
      " 10 12  1  0\n",
      " 12 13  1  0\n",
      " 13 14  1  0\n",
      " 13 22  1  0\n",
      " 14 15  1  0\n",
      " 15 16  1  0\n",
      " 16 21  1  0\n",
      " 16 17  1  0\n",
      " 17 18  1  0\n",
      " 18 19  1  0\n",
      " 19 20  1  0\n",
      " 20 21  1  0\n",
      " 22 23  2  0\n",
      " 22 24  1  0\n",
      " 24 25  1  0\n",
      " 25 26  1  0\n",
      " 25 46  1  0\n",
      " 26 27  1  0\n",
      " 27 28  1  0\n",
      " 28 29  1  0\n",
      " 28 36  1  0\n",
      " 29 30  1  0\n",
      " 30 35  1  0\n",
      " 30 31  2  0\n",
      " 31 32  1  0\n",
      " 32 33  2  0\n",
      " 33 34  1  0\n",
      " 34 35  2  0\n",
      " 36 37  1  0\n",
      " 37 38  2  0\n",
      " 37 39  1  0\n",
      " 39 40  1  0\n",
      " 40 41  1  0\n",
      " 41 45  1  0\n",
      " 41 42  2  0\n",
      " 42 43  1  0\n",
      " 43 44  2  0\n",
      " 44 45  1  0\n",
      " 46 47  1  0\n",
      " 47 52  1  0\n",
      " 47 48  2  0\n",
      " 48 49  1  0\n",
      " 49 50  2  0\n",
      " 50 51  1  0\n",
      " 51 52  2  0\n",
      " 53 54  1  0\n",
      "  1 55  1  0\n",
      "  1 56  1  0\n",
      "  1 57  1  0\n",
      "  2 58  1  0\n",
      "  3 59  1  0\n",
      "  3 60  1  0\n",
      "  3 61  1  0\n",
      "  7 62  1  0\n",
      "  7 63  1  0\n",
      "  9 64  1  0\n",
      "  9 65  1  0\n",
      "  9 66  1  0\n",
      " 12 67  1  0\n",
      " 13 68  1  1\n",
      " 14 69  1  0\n",
      " 14 70  1  0\n",
      " 15 71  1  0\n",
      " 15 72  1  0\n",
      " 17 73  1  0\n",
      " 17 74  1  0\n",
      " 18 75  1  0\n",
      " 18 76  1  0\n",
      " 20 77  1  0\n",
      " 20 78  1  0\n",
      " 21 79  1  0\n",
      " 21 80  1  0\n",
      " 24 81  1  0\n",
      " 25 82  1  6\n",
      " 26 83  1  0\n",
      " 26 84  1  0\n",
      " 27 85  1  0\n",
      " 27 86  1  0\n",
      " 28 87  1  1\n",
      " 29 88  1  0\n",
      " 29 89  1  0\n",
      " 31 90  1  0\n",
      " 32 91  1  0\n",
      " 33 92  1  0\n",
      " 34 93  1  0\n",
      " 35 94  1  0\n",
      " 36 95  1  0\n",
      " 40 96  1  0\n",
      " 40 97  1  0\n",
      " 42 98  1  0\n",
      " 44 99  1  0\n",
      " 46100  1  0\n",
      " 46101  1  0\n",
      " 48102  1  0\n",
      " 49103  1  0\n",
      " 50104  1  0\n",
      " 51105  1  0\n",
      " 52106  1  0\n",
      " 53107  1  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(Chem.MolToMolBlock(m4_H))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "18af585a-5725-46da-97df-58f120faa5f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JzMw0MDAwNjYOCAjYsmVLW/uff/55+fLlAwcOzMnJweU8+h6enhAUBJaW4OEBGhpQUwNcLtTWgrT07bKyp01NTSwW68SJE7W1tdieQqHMnDnT1dV14cKFMjIynX3c8+fPzczMZGRkcnNz2zZe5fP5kZGRvr6+ycnJdDqdw+EwGAwvLy98HpTQ65HoyizhPdDY2Dhr1iwAGDRokCANqKKiQl9fv3///u0O4fP5OLzat29fh/fHJY7q6+tLcXl9ScBms1t9EEFZGbKxQTduIIRQQYGojSZcxikhIcHd3V0QG2ppaTEYjNzc3OLiYmwm+CCCJUuWAIC7u7sIG8HOHqZfv34BAQG1tb2ysBahJURGezfNzc2fffYZAOjo6OTk5LT8CmcyCksCxwWKNDQ0mEym6EfgbeKkpCScKSURBPvj4uxZr1iBANBnn3Vg1tDQAABycnL4j0wm8/jx46NHj8Yah6fkYj40MzOTSqXS6fTXr1+LMIuPjwcAFRWV69evC9LslZWVGQyGiDOjhA8fUpqkV1JdXZ2XlwcA6enp5eXlWlpa9+7dMzIyamkzYcIEAEhISGj3DjY2NrNmzaqsrNy/f7/oZ2VlZR09ehSfXJQUzc3NDx48ePDgQbs97FoSHQ0XLoCcHBw+3ME9S0tLAQCvYwKAsrLymjVr0tLSkpKSnJ2dhw0bpqioeP78+fPnz3OEd2BGCGH3pk6d6ujoKDgd3y6+vr4AsH79+oULFz569Ain2dfW1gYFBenr63t5eaWnp+MXzM/P78B7wocEkdFeyatXr+7fvw8AcXFxP/zwQ0xMDD652BIso4mJicJuEhgYSKFQDhw4gAWlFdXV1V5eXr6+vkZGRg4ODra2tu/1DToHQojH4+Hi8IsWLQoLC2tX2lisZjc3hBBs3w7//p3SDvittdrUZB0/fnxoaGhGRoasrKylpaWlpaWUlNCd2D179rx582b06NFmZmaHRSp3cnLynTt3FBUV161bh69MmzYtKirq/v37s2fPrqurCwoK8vLywjFyh7/bCB8UREZ7K5GRkf7+/lFRUQoKCm01FABwqQth0Sg2mD9/fn19fUBAQMvrDQ0NAQEBhoaGQUFBe/fu5fP5ysrKeBlRUsjKys6YMWPGjBkFBQW//vrrypUrhw4devTo0cbGxpZmfn67GhqsFy4s9fTs+J7CZBRDo9FoNJq+vr6+vr7ogtZPnz5NTEysra0VvTG1Y8cOhNC6detwtr+AmTNn3rt378GDB/b29qNGjTp58mRwcHBJSUnHL0D4cJDokgKhi6SkpPzwww8IoQMHDgjrclFZWUmhUOTl5dueohHw7NkzKpWK95cRQmw2OzQ0VLDRbGVllZiYKGgQJMGmlQUFBfhDbm7uuXPnTExMsIe4GFJFRQVCKCsri06nU6nUhw8finNP3AHJyclJmEFdXV2rD20JDAy8cOHCH3/80aq9UitSU1MpFIqCgoLobbotW7a8efOmpqamwwOvhA8KEo1+0NTX1x84cAAA7t279/DhQ8F1GRkZHB4qKioKm3Kqq6sbGRk1Nja+ePFC2P1NTU2XL1/OZrN37dp1+fJlExMTFxeXt2/fWlhYREVF3b1719zcXHC0UYJdRvT09PAHQ0PDlStXZmRkRERETJ48uaKiwtfX18DAwN3d3d7evrm52dXVFdcT6ZCysjJosTbaFsHevegEz+nTp8+bN09DQ0OEDU4pdXV1FfE4jLKysoqKCo1GE21G+LCQtI4TRMFkMjdt2sRkMi9evHjnzp3ODj8xeXISwF8TJ6I2ZYQwHA7n6tWrNBpNIJEmJibXrl3j8/m4phGTycwQsyfJtm3Izw+tW4f+w7yo+/fvW1tbY8+lpKQUFRVLOmzj946zZ8/OmDHj/Pnz3XHgzZs3zc3N6F1aWLs8f/6cSqXKysoWFRWJvpsg8CctQnsXREY/aJhM5rx5844cObJ69erff/9dWHW4dql99YpPoSAABFDj6yuYknM4nKSkpMDAQFtbW1VVVaxBKioqcnJyoaGhghUAnGP08uXLQ4cOdfywlBR07BhCCL16hXbu7ORbdpe7d+/KyMjgCM7IyOiPP/7ocMjevXtxPeYNGzb0tHu4irPolFJCr4YcBv3QMTc3X7du3Y0bN65fvx4WFvbdd99t2bJFdD0RFot1/PjxkN27LyOE0yADAgLio6Lmzp0bGxsbHx/fcmfGxMTEwsIiPDy8qalp1KhReImAy+W+fPnS39+/srJSX9BoVAQ1NYBntZqaUFPTnfcFgLKysrKyMlNT0+Tk5CFDhuCWHiI4ffo0m82eOnVqc3NzUlKStbW1ra1tSEgIbpjcLvgFmUxmTbe9bZeKior09PSMjIyEhITLly/LyMhs3ry5Jx5E+CCQtI4TRMFms3FryYKCgpUrV+Kpt6mpqbBdFB6PFx4ejvueA4D1hAn5Ghp3BwzAoivYSjY0NHR2dj537lxhYSEe6O3tDQDTp0/Pysqyt7e3s7PrRDQaHY3q6pCDA8rORrt2ofv3u/nW4mygCcCVmXBBEA6Hc+jQIbyUqaqqeujQIWGz44CAgFOnTl27dm3JkiVd9vPkSeTnhxBCXl4oIQGdPo02bEBWVmjmzIqW/8U0NTU1NTXx3J/QJyEy2puIiYnBbdcoFIq9vT3eoRZw9+5dwVnDkSNHbtiwYcyYMVu3bj1//ryvr6+8vDwA0Gi01atXt916rqmpwZ2F1Gg0AFBQUMCFil+9enXu3DlRPoWFIQD0v/+h7Gz0yy/o2bPuv2ZKSsqiRYv8/Pw+/fRT0TLa2NiIKzO1PNWal5f3ySef4J/DJ598gldCWpVbDggIwCeORNcPFc2RI8jFBWVno/XrkZwcXj5BAEhNja+srDxlypSVK1f6+/sPHToUAI4c6ZX9qwniQGS0l8FmswMDA/GkXktLC2tcXFycoJevvr7+unXrBIca586di0NOV1dXBoOBFxB1dHSuXbsmuGdlZSWDwRggLX0YIJlCcXF27nAzRDDy75YdXl5IWRkdP/5e3rFlNHrs2LFVq1ZVVla2a/ndd98BgJmZWduz9hEREbq6up6enjhybyWXeXl5uHsHLgXdNY4cQS9eIBcXtH49+uortHQpcnNDHh5o9Wr05ZdrtbW1AWDz5s03btwAgH79+pHj830VIqO9kufPn1taWmKhFKwADhw4kMFgzJw5E/9RV1c3NDQ0Pz8fTyfxifvU1FR8ugkAbG1ts7OzAwMD8UaTHIVSIS+PAFB4uLh+/O9/CABZWqIRIxAA2rXrvbxddnb2jRs3EELnz5/HK7P9+/cPCwtrZZaeni4tLU2lUoU1SmpoaLhw4YKLi4u/v3+HzZG6wJEjKC8PRUai6dMRQigz85+AdPz47wFASUnJw8MDITR58mQACA8/+N59IHwIEBntrfD5/HPnzikpKRkYGCgpKTEYDMFMVlNT88CBA227ZmI4HM7+/fsFaaeCyW9qaioKDUUAaNgwJDxjX0BMdPSjmTORoiJauxYBoOHDhaVVdYecnJy5c+diJ2fMmCEIHnk8HtYm0TvgFy5caDcafS/Ex6P9+1FMDMIr1VwuGj8eLV2Kdu9Gv//+V15eniA7IjEx9skTs9RUFQ6nQtQdCb0TIqO9G3t7ewA4dOhQeXk5PrLJYDBqajruU//q1auRI0dqaGgMHjz4zz///Psqh4OGDUMUCrp7V/RwFotlbGwMALs9PZljxyIKBb1rTdwThIeH9+vXDwBkZWV9fHxYLBauRa+trS36ZePi4nA7I7xK8H6JjEQAaN48sYxfvrRJSoLXrze9dzcIEofIaO9m+fLlAIAnvLdv3y4rKxNn1LNnz549e4ZTcAICAv71XXQ0unULBQSgQ4dQQ4OwO+zYsQMAhg0bZmlpKUWl3tu6tXvv0TFVVVXOzs44V8HQ0BDH0devX39f93/58iVCiMfjdZgbIODQIQSAvv1WLOPGxqdJSdSUFNnm5sIuO0n4MCGHQXs3TU1NAIAr2FtbW+OQrUNsbGxGjRpVWVkpGPsPM2bAL7+AuzssWAA+PvDHH/D77xAVBYmJzSkpeXl5BQUFaWlpAQEBFApl4cKFcXFxGv36jd248f2/279RU1MLDQ3FXd7wBpGSktLjx48fPHjwXu6P6zM1NTWdPHlSzCG5uQAAQ4aIZSwnZ6amZsfnNzOZkV10kfChQtLvezdYRnEyU2dH4Vpzrcc2N4OKCsjLg4EB1NfDt99CXh7+Bg0bZvTypcBw7NixWHEOHjyIk6X+A6ZPn+7n57d48WIAwMXlgoKChg8fvnjxYlNTUzMzs5EjR545c8bR0fGfMRUV8PIlmJmByKPxJSUlBw8eZLPZ4jvz118AAEOHimuvoxMwYMDG2tro4uKdmprO0tIDxH8W4UOGRKO9G3weqbP9lFrKaOuxdDrU1WEjoFJZc+a8nTULrKxKR4+uHDLE0NBw0KBBampqdDo9MzOTx+PNmzevm71/Hz9+nJubCwAXLlzo0LixsXHTpk0AsGfPnri4OHd39wEDBmRnZ+/evTssLAxXG0lJSUGCDmPZ2eDrC83N4OYGTKaIO/fv39/FxWXVqlXie15aOmHEiAlDh74V055OH0yhyCPE1tbeLi3dfoE+Qm+ERKO9my5EowghFotFoVBw5NXOWEdH2LoVEILvviviciMiIjZs2PDb6dPjx4/PHTMGm/B4PDMzsxcvXnS/nDNeizQyMnr8+DFe6hXBtm3b8vPzzc3N3d3daTTatGnTDhw4EBsbe+XKFV1d3QsXLsTHxyclJf0z4No12LQJ9PWhuRnu34eFC4XdmUqlysvLI4R4PB6XyxVRqhnD5XIzMp7yeDwDA1G1nVohK2uMELuwcO3AgbukpNTFH0j4kCHRaO+mC9EoTjuXlZUVOtbSEvz8wN8f9PUBIC4u7siRI/fu3WtpQqPRdu3aBQABAQG4YHt3OHfunL+//8OHDw0MDLS0tGJiYto1S09PP3r0KI1GCw0NFZSSo9Fos2bNOnbs2PDhw5cvX+7t7W1ubv7PGDk5wO41NIDInxLe+r93796VK1fOnj3boc+vXr3icDh6enp0Oj02Nra4uLjDIXV10QDcgQN3qqjY1tb+0aE9obdAZLR303KLSUwE6inmWAsLC0dHR8EpKQGLFi2ysLDg8dDZs1mdc7oNDg4OW7ZsKSkpKSgoKCsrmzt37okTJ1rZ8Pl8FxcXDoezfv16Qdf4lhgZGeFWSNOnT/+nNOry5XDoEBw+DFFRMHt2h540NTW9evXKx8enVV39tmRmZgKAoaFhc3PzsmXLDAwMVq5ciVcn2qW6+spff8396y/bt2996+vjVFQ+6dAZQq9BonkChO6CK9WLe3YTIYTQ69evAUBHRwcfZ3ry5IkI45ycnAMHDiCEfvjhh9TU1FbfRkfnKCjUq6mh7vS1fPToUU5OTnFxcauy0C4uLi2PeB48eBAA9PT0RNSib4fFi9GwYWjePHTxojjm4rSe5vP54eHhgwYN0tXVlZeX37Rp04IFC3BxaxkZGWdn57b1DJnM35OT6UlJ8Pbtjk44T+glEBnt3eBznFVVVZWVlStWrBDWTrkl+fn5ampq48ePNzU1BYD09HQRxmfPnp0yZQou5NyugZXV30fquw/ef2/J9OnTcSZsUVERLpeHD4l2An39v49neniIOULQerrdxP5bt26NHTsWuycoZa+hoeHu7r506VK8oiotLW1vb4/T/hFC5eV3sYa+efM+fkyEDw8io72YGzdu4CXC+/fvu7m5AYCCgsLevXtFNF9qCa6N1Kq7fSs+++wzADh58qQwg8RERKEgOTkkskO7uKSmptrb27dsoaGrq5uYmLhgwQIA+PLLLzt1t8b6+oIpU+rV1RGdjkJCxB84a9YsANi2bVvLi48fP8bX4V29Ag6HExcXN/vdWoGSktLq1au/+uorLKbLli1DCMXFxenp9Xv0aGxhISnb3GchMtoriY6OxifKMVQq1d7eHldZBwAzM7NHjx6JGNG3xe8AABsbSURBVF5VVeXj46OoqKiiotJu3TxMTU0NnU6n0WiiG7EtXoz09JDoPnIvXrz45ZdfEEJnzpwR9KcTRnZ29urVqwWlqXGZVGVlZVyvXnySk5MBQE5OToZGe9KZs6pPnjyhUCiCliTPnz+3s7PDaw7q6uqBgYGNjY0t7ePi4gQZC4qKig4ODsuXL3/+/HlcXByuXeDpuQ4hiTUEJPQ0REZ7Gc+ePbOzs8P/YzU1Nf39/f38/AR187Zs2YKbLVOpVGdnZyaT2Wp4Q0PD7t278VIAhULBK3oGBgaRkZFtnxUWFgYAs2fPFu1SaSlyckKPH6OmJrRnD0II1dai7Gz06FHR9evXQ0JCdu7ceezYsSNHjtTU1AQEBIiOfwW8ffuWwWAoKSnhabKpqenrTka82H8cG5aXl3dq7Oeffw4Ajo6Ozs7OtHc1WBkMRnV1tbAhcXFx8+bNw381CgoKK1euxD9nBwcH0lupb0NktNeQn5/v7OyMhU9RUZHBYAhU8uXLl3PmzBGsJ65evVpaWhoAhgwZItilabd5cmpqKm5nDwC2tratdGrhwoUAcAw3WRKJhwdycUG1tcjBAdHpf69GTpp0WRAvL126dNmyZUePHl28eLGYMopxdXUFACxkKioqx48fF1OS7t69O2rUKDxKVlY2NDS0Uw2i79+/T6FQcARKp9PXr18vOiQXkJqaikNXKpVKpVKXLVvG5XLFfy6hN0Jk9IMG1yUqKipavXo1jqrk5OQ2b97cqu49elc3T1NTE9usWbNm0qRJu3fvRu92loe8O/uNmycLBvJ4vNDQUBz0qaioHDp0CP+3r6urk5OTo1Kp4qQBbNyIEhNRUBBycUEASFERGRkhO7u0zz//3MnJaevWrcHBwadOnUIIHTx4UHwZTUxMpNFoUlJSt27dmj9/Pvb/m29OiNwVQw8f5k6dOlWwiInFFADmzp0rzqPr6uoCAwOVlZVxFDxkyJBXr16J6bCA5ORkXDylC2MJvQ4iox80a9euRQhlZWV5eHhISUnZ29uL3ouvrKwUlEEaNWpUXFzc3bt3BTvLxsbG4eHh7QZlRUVFgo3ycePGJSUlXbx4EQAsLS3F8XPjRoQQ+u475OWF/r1s+I9j2dnZCKHnz5+LWQSew+Hg3CMGg4GvXLlyZeLEmbKyTVJSyN0dtV3RzchAdnZIVZWvqjpYQ0MjMDAQF10V1NmbMyfYx0doWdTGRnTsWLG6ujpe8bC2tpaRkaFSqWlpaeI43ApcP/v333/vwlhC74LI6AeNlZWVn5+fh4fH0aNHxd9guXPnjpGREdYCrIz6+vpnzpzpcHYZERExaNAgvJ6Io1dxuitv2ICwVXU1EpmE2jn27t2LPa+vrxdcZDKRmxuiUhEAMjREYWF/q3ZUFFq27O/rysro8OH0VmJdVla2fv0OGo0HgExNW2+IcTjo3DlkYIAAkKnp6qlTp8bGxiKE1q1bBwALFy7sgv947N69e7swltC7IDL6QSOIRo8ePdqpgY2Nja6urqqqqkpKSoKgTByYTObatWupVCpeGTx//rxo+4gIBICUlJCQbkldpKCgAE+Kb9682fbblBRkbo6kpNDixcjfHyGEPv8cASAZGeTsjEpKhN723j00dCgCQFQqWrsW4VL6+/b9fREAmZuje/f+iXLLysrwcoewVqwiwOWvVq5c2dmBhF4HkdEPmvDwcIRQaWlpTExMZ8cmJCQAgLm5eReei9NFMcuWLSsRokwNDWjwYASADh8WdbcTJ04ghAoKCm7duvX3peJiJLLhMF4JXbp0qTADDgfFxiJPTxQYiF68QB4eyN8fdZRJhRBCbDYKDETq6sjJCX37LeLzkZMTkpdHw4ej8HDUdsFj69at4i9utCQ+Ph4Axo4d29mBhF4HkdE+Cy7wMR23W+sM0dHRFAqFTqd///33onu+b97MxRGc6NUCHFNnZGScOHEC8flo7Vp06hT69luUno5Wr0br1qHjxxtiYoqLi7F9eHg43u/qcHfL0xOxWMjVVfwzSn9TW4s8PdH9++jHH9H69ejJE6Hdp2pra/Fppdu3b3fyEbUUCkVWVpbs1Pd5iIz2WfChRmtr606NYrFYI0aMAAA/Pz/0757vU6dOzcjIEFgmJydraY2ytMxLTu7gnrNnz/bz83N3dz9x4gR6+hQFByOEUH09Wr8eycri6fSrKVMAQE1NzcLCAstWaGhoh97i3iW3b6PNmzv1lggh5OmJEEIbNyJn5w4s8Srt6NGjO5v+aWBgAACCU6GEvgqR0T7LtWvXurA9sn37dgAYPnw4q8V+Nu75jhOAGAxGU1MTj8ebNGkSAHhiNRLJv6LR1FR04gRCCDU2olWrkLs7cnND9vb3HRxaltCfOnWqmJq1bBnS1UVtqqZ0zNmzqLYWFRejDnfRGhsbdXV1tbW1xW/ThMFLE5cvX+60c4ReBSnb3GfpQkXnly9f7tmzh0Kh/PDDD4KzmAAwf/786dOnb9++PTg4OCgo6MqVK3Pnzn38+LGenh7ubScMhNDevXsnTpwIAJqammPGjAEzMzh+HK5fhwcPwMQEPD2xZZa19dixY/X09IqKih4/frxt2zZ80KBDZGWT9PVflJdbAgwW/00BYOFCUFYGFRWoqenAUk5O7rfffouKijI0NMzLy8vKyvr000/FecS4ceNw3minHCP0PiSt44Se4tSpUwCwatUqMe35fD6usuEsfJb78OFDXBcKp1JduHBB9D3PnTsHACNGjPhXaMnloowMVF2N4uPRsmVo3DgkL7/w3ekAnLa5YsUKMd1euXIlAJw5c0ZMewFv3yIANGCAuPa4031aWpqIQi2t2PquYaq3t3dn3SP0Ikg02mfpbEXn8PDwP//8U0tLKzAwUJjNlClToqOjTUxMcNcjd3d3Npvt4ODQqlQoprKyEvdw3rJly79CSxoNRo4EAJg8GXCBFYQOFRS4ZGW9ePGCw+H4+Pj8/PPPnp6eY971LBFGc3PzqFGjsrOzy8rKkpOTx48fL+bLAkBTEwCA+MH68+fP/f39S0pKzMzMxBxSVVVVWloKANXV1eI7Ruh1kOr3fRbxJ/VPnjwBACsrqw0bNhw9elREm08+n+/o6Ig1dNKkSZWVlY6OjjNmzMCl4FuxadOmsrKymTNnrlixou0Ts7KyAACHq0Ch6BsY2NjYbNy4kcFguLq68vl8H58dHXrO5XIrKiqGDRtWU1NTXl7eoX1LcHl78fsGjBw50tvb28nJSfxHNDU13bx58+bNm1wut1O+EXoXREb7JuXl5RMmTDh37ty4ceOam5tFG+NKSBUVFYaGhoLyUe3CYDBu3rwJALNmzXr06FF4eHj//v3j4uJGjx7t5eXV8kExMTHnzp2j0+khISFtY9XCwkKsxYmJiW2f4u3tbWnpl5V16eHDjt/00aNH/v7+OEeqU7DZb2fM2DtmzK9i2uOMhQEDBogf8yooKDg5OTk5OeFKMYQ+i6RXFQg9QkxMDE7d37FjBy4gL4KWR05FmP0dOQIAgCCRvrq62t3dHc/ZhwwZcvfuXdQma6ot4eHhTk5Ofn5+kydPbtfAxwcBoGnTOnjN+vp6fOL+5s2bnT293uW8WvFJSkrCHxITE3vuKQSJQ2S0bxITE7N06VI/P785c+Z0KKPiHDl9+PChYO9+xIgRreqbxMXFmZiYAACFQrG3t8et5FtlTbUkPDwcn8vCj25LXR3q3x8BoPbqoP4Dm83G6pmZmYlLn2Di4+NbfWhL1/JqCYS2kEl9n+WLL77w9va2tLTs0BKX15ORkcHV4dpSWFj4xRdfCObsDAaj1Tx92rRpKSkpvr6+dDo9LCxs//79bbOmWtLc3MxkMgFAUNGuFYqKwGAAAIjMpwJpaWkbGxsAMDY2HjZsmOD6zz//LPiQmpqal5dXWFhYXV3dst9nh1twcXFxVVVVAPDrr+JO/AkfJ2Snvm9Cp9OxQCgpKbVsbdQuOPdz8ODBgwe3n3q5d+9evOMMAAMHDvz666/bfeL27duXLVtmYWHR3Nzc2Ni4devWkJAQY2PjtsYXL168devW+fPnly9fLsyrb7+FZ8/AxgYA4PFjsLAAwW4/k8nEHe5qa2vblX42m+3v748tx48fj/6duSkvL0+n0/FCRGxsrIWFhYqKirS0tKKioqysrJycnIKCgoyMDIVC0dHRUVdXj4qKwhWsCYT2kXQ4TPig4XA4b968wcWKMIGBgSLsf/jhBwBQVlbW0NAAADk5OX9//5Z9khFC1dXVMjIyNBqtw9WGhAQ0ejQqK0ObNqHMTJSbi16/LquqqlqzZg02wLmcbRFcX7NmzZgxYwwNDXV1ddXU1GRlZVv+4xcWLGNcXV09PT39/PzmzZsn7s+L8FFColGCKJhM5smTJ319ffPy8i5evDh+/HgXFxdhxhUVFV5eXgAQEhJiY2Pj5eV16tQpb2/vc+fOhYSECNpqRkREsNlsKysrXEpZNKtXQ2AgUKnwzTfw5AmYmNi+eJGwcOHC3377DQCEJSEIWh9ra2unpqa2+rahoYHNZh85cmTHjh1Llizx9PSsra1ls9kNDQ1NTU0sFqu+vp7D4TQ0NHzzzTeGhobFxcVi/KgIHy9ERgkdkJycHBISUlVVdfbsWVzRXRgbNmyoqKiwtrbGs/7Q0NDly5e7urpmZmbOmTNnxYoVBw8e1NDQuHr1KgC07UrfLhoaQKfD2bOgpwfl5aCioqympiYjI4PLSwtbr9i2bVurDy1RUFBQUFDAkamBgYGgG1Urbty4gft6CtqQEAjtQraYCB1gYmJiZ2c3ZcoU0WbR0dEXLlyQk5M7duyY4OL06dOfPn0aGBgoIyMTFhY2cuTIkydP3rlzh0ql4tbzImhuhspKUFSE//0PlJXh4kXIzYX4+LtVVVX9+vUbPXr06NGjcX+qrtHhFtOCBQu0tLQAQEQATiAAkVFCh8jJyWloaODCo8Jobm52dXVFCPn4+OD+JQJwUaiUlBRLS8vS0lIXFxcWizVhwgRtbW3Rz929GxYvhspKoFLh1i1oGXf6+PjgD7geVYdwOJyCggIAKC8vxxkC0PnDsgSCMIiMEkShoaGB58WLFi2aPn26MLOdO3dmZ2ebmppu3LixXQMTE5OYmJgff/xRSkpKRUUlNTV1x44dbDZb2A1fvoQ9e6CpCf6tyX8jWFQVZ3UVAKqrq3/66ScA+PPPP9PT0/HFLlTAIhDahcgooQPwxBl3XW/1FY/Hw6fFDQ0NNTU1T548KeLUI4VCqays5HK5XC6XzWb7+vpaWFjg4/ytQAjWrAEWC1avhmnTRPlWVFRUWFgIAElJSaLPrT969OjgwYO3bt0SXME5pCQaJXQfIqOErvPw4cPr168DwJs3b5KTkyfjck1CKCws9PX1BYBLly7dv39/+PDhT58+nTx58sqVK3GWu4ALF24WFaVpaYHwUlN/k5GR8fTpUwC4evUqi8USYWlubu7i4mJlZSW4Qib1hPcFkVFCt8jMzIyNjS0oKOhQj9zc3Orr67/++mtbW9uZM2empqb6+PhIS0vjrSc86QaA8vJyDw/H3NwJJ06kCS819Q/h4eH+/v64f5wIpKWl5eXlZWRkBFfIpJ7wviAySugWlHeINgsPD79586aKisq+ffvwFTk5uR07diQnJ0+ZMqWkpMTBwcHW1ragoGDjxo2VlZVz5sxZtKiDYqOYJUuWeHt7i04k6N+/v46Ojo2NjYyMDD4dm5GRsXbt2vDw8JbCSiB0DSKjhG5hbGxsaWmpp6cnwqa2thZvPe3Zs2fgwIEtvzI1NY2Liztx4oSqqmpkZOSIESPaZk0J49GjRzIyMjiFQFNTU3TTkYyMjD/++OPVq1f4j7du3Ro6dKidnd3t27c7fBCBIBqSfk/oOuPGjcMbO87OziKKPXt5eRUVFU2cOLHdmsdUKtXV1XXhwoXfffddWFiYsrKygoJCRUWFUbub9O9ITU21srIaNWpUVFQUAHi+6+kkjLYbSsHBwSoqKrm5uaIHEggdQqJRQtdRVFRUVVUFAG1tbWGZ8Hw+v6amRkZG5vTp0yICxgEDBvz000937txRV1cvLi6eOnWqh4dHXV1du8aVlZWLFy9ubGw0NjYWndAqoO1KqJubm7e3t2ixJhDEgcgooacICwvLycmhUqlGRkbPnz8fifsvCef27dvm5uYvXrzw8fGh0WiHDx82Nja+cuVKKzMOh/Pll1/m5+dPnjw5NDRUTGdaRaOqqqpY93EJFQKhOxAZJfQUdXV15eXlFRUVpaWlQ941/hRBQUGBgoIC3npKTEycNGnS27dv7ezs5s+fj5NDMe7u7tHR0dra2pcvXxZdoqklrdKbnJ2d8Sqt6AbRBII4EBkl9CB37ty5evXqmzdvRJuVlJRwOBwXFxfBvrmZmdnDhw9DQ0OVlZV/++03ExOToKAgHo939uzZkJAQWVnZX3/9VUdHR3xPSHoToecgMkroQZYsWeLi4mJoaCjaLDg4GPdEanmRSqU6Ozu/ePFi0aJFDQ0NXl5eo0aNwlVCjh07JqwskzDImSVCz0FklNBTWFlZ4YDRwcFBmA2WTj8/v/Pnz7ebfKqjo3Pt2rWbN28OGjQoMzOTx+ONHTtWdPvSdp9SWVkJwgvrieDUqVN4N3/Lli2dHUv4SCAySugphg0bhjt8TJgwQZjNsWPHli5dWlJSInrH3NbW1traGgD4fH5qaqqZmVlkZKSYbjx8+HDGjBk5OTnq6upLly7FCVLi09TU9ObNm8LCwvLy8k4NJHw8EBklSIbjx48DwKpVq5hM5q5du0QbJyQk4OpQly5dsrCwePXqla2t7fz581+/fi1iVFJS0rx586ZNmxYXF9evXz8FBYX8/Px58+Y5OTm1OsXfLg0NDYcPH+bxeAkJCTExMRUVFZ16QcJHhGR7mBA+WgStlYX1UxLA4XDGjBkDAN7e3gghHo8XGhqqpKQEACoqKocOHeJyua2GZGVl2dnZ4VUCJSUlBoNRW1ubn5/v6emJi96rq6uHhoa2ahMtgM1mh4aG4oqoX375JW7d3KGfhI8WIqMEybB27Vp/f39/f/+lS5eKtgwICACAIUOGNDY2Ci4WFRV9+eWXOBQYN25cUlISvv769WtnZ2ecEyovL+/u7l5aWoq/WrBggbS0tIuLy5w5c/DAGTNmZGZmtnwWj8cLCwsTdEidNGnSxYsX6+rqEEKpqanv8/0JfQgiowTJIGY0+urVK9wQ6c6dO22/jYiIwMf5paSknJ2dN2zYgPfipaWlnZ2di4qKBJZsNtvJyQnHp8OHD9+xYwcu+SwnJ+fj48NisRBCd+/eHTt2LBbQESNGhIeHCwtXCYSWEBklSIYbN27gDxERESLMPvvsMwCwt7cXZsBkMt3c3KhUKt6Fp1AodnZ2L1++bNf4wYMH+DAVhUJZsmTJkiVLsLAOHToUrxsAgIGBwdmzZ9suFBAIwiAySvhwSUpKolKpGhoaHXa0//XXXwFAWVm5w6l3c3Pzzp078Qppv379fH19R4wYISMjIy0trampGRgY2NTU9P7egPBRQHbqCR8i165dA4Bx48YFBAScPn26w55Lw4YNAwAdHZ2ampqoqCicbN8uMjIy27Zty8jImDt3bnl5+aBBg6Kjo9lstpSUVH5+PoPBwApLIIgPkVHCh8j9+/cBACH05s2bDlsxQ4tDSo6OjnPnzi0rKxNtb2Rk9Mcff9y8efObb75BCAGAkpKSmMWiCIRWkHqjhA+RsrIyf39/9O/joSIQVB4R/9AnhUKxtbUFctye0G2IjBI+RPr37+/t7c3n8z08PMSxF0hhFxrVkeP2hG5CJvWEDxEzMzMAoFAo+EOHCKSwC6ElaRFK6CZERgkfIqtXrwYACoXSbt+RtmAppNPpXC5XWlpaWCl+EWOJjBK6DJFRQl8ASyEuV9pZQSRro4RuQmSU0BfAk3ppaWnovIyStVFCNyFbTIS+gGBSP2rUKNxlr7NjiYwSugyRUUJfoKioCADk5OTS09PFsefxeD/99FNaWtrhw4fJpJ7QTYiMEno35eXl+/fvP3bsmIGBwalTpwwMDNzc3ERUuUcIXb9+fevWrZmZmRQK5ZtvvomLiwOADhtGEQhCkexZVAKhy1RVVXl5eeEokkajDR06FP+TnjRp0tOnT9sd8uDBA0tLS2ymr6/v4eGBSzppaWlBe3XzCARxIDJK6H00NDQEBgaqqalhQbSyssK62bJunru7O64TiklOTp47dy6219bW3rRpk0BPBw0a9L///U9TUxMA5OTk/Pz8mpubJfdyhN4HkVFCL6C0tBRLW15eXmhoKG4xDwBTp06Ni4traVlfX89gMPCkfvDgwbdu3cLXQ0NDAUBJSWnVqlWLFi3C9fE0NDQCAwNxNeiqqipnZ2dB3bx79+79969J6KUQGSX0Ag4ePJiTk4MQWrVqlaBNXlRUlDD7lJQUQR89W1vb169fczgcBoPh4OCAFVZBQYHBYNTU1LQaGBsbO2LECACgUCj29vYVFRU9+2KEPgEFiV39gUCQFIcOHWpsbOzXr19ERMT8+fM1NTUFEaUwuFzu4cOHfXx8GhoaVFRUJkyYEBsby2az6XS6q6vr999/379//3YHslis3bt3BwUFsdlsCwuLw4cPT5o06fTp08uWLSNJUYR2Ien3hN7B3LlzFyxYoK2t7ezs/MUXX4jWUACQkpLy9PTMzs7+4osvmEzms2fPuFyunZ3d8+fPDx06JExDAUBWVnbnzp1paWkzZsxYunQp7quck5PD5XLf8ysR+gok4YnQO1BXV+/fvz8+7ik+Ojo6V69eHT9+fEpKyqlTp8Q8oQ8AI0aMiI6OvnPnTlhYWHp6enx8fOddJnwsEBkl9ALWrl2Lq43s37+/C8NxUpSxsXEXxi5ZsmT+/Pn19fVdGEv4SCAySugF4MPyAECn07swvMun5g0NDXk8HgDMnj27a48mfAwQGSX0fbp8an7IkCH4gyDnlEBoC9liIvR9cDRKTs0Teggio4S+D6nhROhRiIwS+j6khhOhRyEySuj7kGiU0KOQLSZCH4fH440cObKxsbFTDZoIBPEh0Sihj7Nr1660tLSMjIyAgABJ+0Lom5Dfz4Q+TmlpaXV1dXNzc1VVlaR9IfRNiIwS+jhVVVUXL14kJ+IJPQeZ1BP6OP369fv222+dnZ0l7Qihz0JklNDHsbe3BwBpaemvvvpK0r4Q+iak3iiBQCB0CxKNEggEQrcgMkogEAjdgsgogUAgdAsiowQCgdAtiIwSCARCt/g/YzCX3b/ZIAAAAAW7elRYdHJka2l0UEtMIHJka2l0IDIwMjEuMDkuMgAAeJzt0ntQVFUYAPDvXnYXkPcbdpfl8hAWFhZYXqK4e64YoZgCCphGXgxzw3wgmuXkIGYzzZBkhJqiTFYYaaARIIy6e2+CSM6gMoyDGWphMSJmKGliaLvfopROY/3XH92ZO+d3v3POd75zzr1hqL0IpscBxp9lpnel6d1ISRjO1Iqsxtrxb05paq1E1pb20fdYP23NjMUfzbf021haU78e+00Jg0ygHk0cT8CYBzwetsEw/Xj4KenpJ9I/EX9YP4cTTRv9B/jTKn9d/mH7t9nHj20s4Mw9Zca/wsNsFJhboP5v/9utHVBA0UBZgVUcZyXS0yIxJ5aAOFZPS6zB2gasbRnbCUra1o6xswd7B7D3AAdHcHQCJ3dwcgZnF3BxBVc3cHMHD08l7eHFeHmDtw94R4CPFKQykMlBFghyX/AN4HwVelrhx/kxeprx5/wD9HRgEBM0UUkHBTPBIRCiBGU4pwzV06FhXJhKT6vCuQg1qGM4daSejozioqL1dLSG08To6dg4DuIBEgAmAZUI9GSgpwCdBJKpINGCjQ5sCNiwYDcN7JNFlMN0cHgGHFPA8VlwTgXnGeAyE1zSwG0WuD0H7rPBfQ54pYN3hoj2yQSfuSCdB9IskGWLKHkOyOeD4nnwWwDMQvB/AQJyIfBFCFkEIRyE5oFqMUS8BBH5ELkEol6G6KWg0UPMKxBbAO5isVVcrFgkcXZxdXN3kij8GP8AX3GoMlwVJomMitbEqL0cxaZrgLFnGc94stPjyoj5o67cjj2tSUGX2A4Sr2Wr0LtFFKu+fUln9ha+i7x5zYCOeHCKXDzUPQUTVewlBe4ao9mZc1cQ2dV6dE9XAdHkJ/Jmz7gvJ8vP+mA8+WYwGRo4pjW7NGOrbnf7PoyTVldDYRmFbp4TomtoS8a1SE2L7rUUGuvJ7HYixZVz0XeOryRiWTraffH7xLp5K7qpey3pCDiCdvnRg0j6jqINHW26TZMq0D1frjKmFfbhWleHbfj7vd+go88UGULLUrDmgvWbjTuHStHacjee6stHp/bn86d/iEYrFZV8Ytsozq09eZw/17oDrSka5Ntd2g1mD9cP8D3SMtzLsTpbIUk2jPa9ohCyk3ahk1r9hM7S7Th+ZIWdkPNJKeZxqirhZ8fHozs8PuQFP18cz6k28LcSh9CVDif5A/4qdNbbF/neKQ/QjqPD/OqiebhfjnUVNLfz0IcXewptyR+hsyMoofhIE3rg8iV+W9Y29PQNhdr6wmrc45HzvO7UkAF9oqdFV3X1Z/SrrJpciBUJZr9eWkJGh4Yw3pj2FlGNCOi8aXHEdX0N2lZ2hxgSQnAvbxB7dlf4efwHuuoYlslowXUP1qlYolmNnsZOYPvr9qD3MP7sOyMydPV1nhzK2ILOvX6OLIqRotfM/pUU3y9H18tLSTXrj+cQZN9MqhZQWENu5lJy7fN3LXeaV01qryShY/omkOa+OHSmtFI3GOSKrqQbjbVXKrDO5SKFUdvXjjmpxh7dSLOlfifrYHLCWIPx+QuPTk27F4Y13Fj4KWmCcLSYLCUddy7jGC/2MLkbV4fx89BI6jwt91LTWqRT/9KJ3g9hZE3XNfT2+M3amf7r0F9N7tVqljSgU7u0upxvU7HOzvUDRsXuBvSq8hReLK9En7lw2zjxNyn62K1SfrCyBO3ZH82XygS8C7aqgd+XkI7xz75v4NU31+K+TqT28msSfC3nk8rzhrv9WP/Ix45Czu85WAOXGCPkKsByR3djhXqrYszpPcdeSBcm4tz52wP5imwPjH8gbeDztljOZFuZROj2PGv5b4cDhIO0L8Zn7bzHR7M/obPYHbr3ojZinnV7nYwJ8k70reWU9otDNP57N/kw0holR4/G7Sd2LWL0uqZaUrjpAI7/7utJpFhUiG7KfUBSF9lYzuEP1Jj9RBc6HpgAAAiselRYdE1PTCByZGtpdCAyMDIxLjA5LjIAAHicfVnLbiU3Dt37K+oHXBApPsRFFunuJB0MYgNJZ/5h9vP/mEOprKpOmLEDxJeXRR2+DqnqlyN/fv/yr//899g//OXl5Tja//kvIo5/99bay29H/nF8+umXX9+Oz99+/PQh+fz+59u3Pw5qfhARnmoHfa/947f33z4kdHw+4uyDI+yQU0Ra46Odbf4c+xyG3jide4fNfrawoVTodej52d3I8OF0Ne1W6Mm016wb20GnS3h+/Tc9Pd4OO3VYawo9CVKrzjXYM5wrAxHKrw2qhZ5DT88m0c2P13ZaqLEUigMH93R4AP8rnT3cexSKAYv9NAuiOF75HGEulSuE3Bx8ivbGbZ7d2tAq2AjxOzQNX8/gdW/Re6XIQEknMfweE2VjHF9pZmJwJknvPD3XBvslzswN0GlXy+/dxLtWigpF5ESbiANv66plUZABJp8MO10QrJ55r6qCfAbTxToc7ycrIY+V4oCinCqduecfzGJVGpGTdxgaQtQGMj/Es9KLAl/Z6cLUDIrR2KmKOdP0WkUZxuW0TiNGpcgr5CGNEVNkx91GVJGED+9ZO9QjZCZSh0TZECjWt7SKwgQbHK/9pMbCVc458wNbrUsMFLGgkDp0K9XsHwDoTUejtKpuSGelmjlaCmjyfMg9xMoIZJZwLIIunRJKDNZRlQhnG73aSTRQJqlKQy2qlCbHHa8gmRHNOGPRxxitikCnS7VLl+QPVAHVRmeyBnpOk7mAtHkrkfbZSXEGqjgYmgw2Kau5y6U5VFrMpKEWylbquo53Eo1ZKy1acJWqns2EoDZtKpJWnchrAL7i78qm2UZG/R80RxZgP0PVPZ0aQ+pE9WwoJCpbczKx2ZBRAZW2UhraUfgZUnhPVfDlSpOpjsjx0ilUyrHBK6SEgFpqgkyt5FDpGSaMtgiCHJzfBnvV0CIr9g0sZ8kRTg0VVWnq8cf0KIwxjtD7+Imq8MQmh3JXTNbjVU/yRlwxmfiik2YG89AEfzcqfR8X3VKgsRAuMWtU4ozJZc6YCz1dY+NoVeS1TR5tyKBr2gTle5SDmKYmyCZQ9QhCM/IySsrz9NGZ3NMjBoe3qpZ0bQvAiWKeE1F73Z4qiDzaw9QlLQkLlZQDyvqKSQuP2FC/SCbmslZpV1ua3lGUnlNngKSqZKpDE9SAmgNNyBnsVvszrsO9CZgJs747lf2GbvyKfQXBbC2HCLxhLveaNhVhRpAgWASGUTEIuuErYmmGivOciIFqrrwBsK/4vovzjAtKI1oVSutQRACVsr/B8xhj2L4qTYEmdiDC9shzzgTyVHWl6bSJ3BHchc2AdS1hZnp49g2SknuVcCs3Ncv0gIahJ9Mmj65ltVvmB9wqhN+Eh/DXg8MyP1mQhCjl4MDWaSUde5unG8bBRCesXO6JTtMh0DuhMLGqEWCWFjNBEAcFnsEa6KRlo/lKEGGH5qw2G46FqFL8yE9HaHLbVuzl5dErPc6gtVyC8jowSmdsKppiSehJw8hTKxV9OhNY0LAtWXLWoFJxzPDAIpKSVwgM/3KN9pgWsUcwCBWrHcquzOFoUxErYhtJ64btp2SCQVPRsZnD69yosPRzVeiDVwENJA/RB7cZQJb1OzI5azuz3M3nFoQoVaqyrILWFY+hgrGE9nJLHZmgtchp69cakEktVDNFazvznEE5Y4bVAcgkLWfgWKIGNfwDgHGpKmMDzjxgppfjf8wWwrAkpb68wmWQqgBEW6oY0YOyQMEOvSynyFRhoAUoe2T2ceNzr+IfK1W5npG2eT0B0NL96JdqpxZJIS0nPJ6qdGUBcDQ8fIFXWFmi5KXQlQDVXCrnlWv0uuXDrqgytl/JNsD55boQvs7HHBiUeypuSlqHapEdrnxDEPd18+Sym2OmytFKuEV1NF+uX1pRTq5SSXegsByvWX9Ncx2pVGlxKKgzWwT5Ra+gqUvdxXnENltwzAnRy7tx64sobKCzcsNzI1wESlWZLIWdAtt0Wu24tJTVAoRTVdCCSCZS0RWLWHnnbmsyjYHLXM9mwVby8dbhr6o+h3LgTjViXfBwqajG909vX757L7Pe1Hx6f/tyv6nJX75fyODD0e/3LvlR7tcr+av7s67P99sSfDjsfilCEPn96kPwUbftwNd+jMd7DIJOPN5W5Gd6vpXgqbXfO2VTT8mGj3sApSLtUxAWmoobNLbgKeH7qUSaj25PQFc0H93OUMLFo7xP57YktD3kGUwY205xxjONbbd4Yg48/bh6Uxp7WMZKlo/y9iLfZk3J9oITMx7ljZkTcz66MXMsiWzJTDiM8cbcZ/pBZBtznzUw8PTjbjol/bYzMYMpn1dNOeb/HzfKJdl+4eIoeWDfke9ZFDiw3xUXKcHwv4su6y5f8GzLkpjzTc72QrI2YL5vLyQxp2R7IYk5zT8vXjTNb4SSmGFe7rP8kmzMkphxoGzMkphxoNyNMXtFHnY0Maf5jRmULWlet2WMkSmR7YXOjsOB2wudPRc44XHjkDxQtxeamHHgw3JihvlH6856hrnHlWAKdo5tUYNuwLbIQTc6mzGGqcfGngJ7LuZTsKGZza63jcx8CXYwLcsYO91GarEEG6m3JbiJhlbX24bqvHocxj7//MsP+3jvq9NtQ3ZZEn9uq7P3/X7KLslG7b463TdsH5dk4/ZYve8b+GiXZCMftHrfb5bkS7KjPGb3IUgb85AlGc8FbrLB2JiHrd6HvYzAxjl8McC4JeOSbOQjFieMjTzaJdnIgxYnwN53EQ5ezDA2/uiXZOOPWSbI4XOpSVqA1cfuQskDsWMeiRw8EPcESeQgntjIIxYzxM3kra0mj5vKG10iv0UJG00dj3mUuNHDcY+kJquJqX034i/ZPZdagpccXveMyX84ycaF3VuWDug0fMvShWSLdvswZ6Pm9LudmPMRLQ3Dz2n/nO35+ePfcPD3y/8A5/DS9eRJEFMAAATMelRYdFNNSUxFUyByZGtpdCAyMDIxLjA5LjIAAHicfVZLax5XDP0rXTowHq7eUkwhkE26cboPWRSvAs2DJsv8+B5pnGLfNsX483c10rnS0ZHG7968f6BPDzfv3rx/8XDzev7Ox9vXN7++fXE/39+9fvXq/fXoiccD/wj71yf35xPXJ1+fYN0/PsM9/229f3rdhvPj9q//ZP1z3/79xD9J6J7/2/72qZn/L4WfEfCVfvl+cxtnOqsfcVIZ/t7d5rlCLA47g5YULHVKFcGwVnKMgXSVH3KaLDdYaJ2UGgefasIdFKebZcFHgNw+fpZJ5EFnlS6CxU7nZEWUl5PM5QBOgo+LGE0UG7kf63RPTYZJzzC+TCThnbQA2yIQl6mW47RsmR63C3XQ5cWnmS9rmyXwLzDkrXLcIq3kCUVaqsIHUNlEJwkiPGwnSvO6nIxR1zoZxUxeKJAMZdw2Cz7ZxylZy/tGycwVl01UDLkSLaoJBLNTdRILDQ/Ik7y5WrFyLEUaw2do5HShwH7Bh5dWI1N/JQOfCynZ5ZOm1PczSBe7vIRWta1Auw14kFp1fatW8WXyQi5NWtaY0G7wyG2KKI1hTyCW42KbczjGsxg+beWIg09ZIPaKl6tdtBipX+zT5H4WZ3uBPCYdyxKtccIlfJlSKOO4WrQaCoVJrnYbUHTyDh2HCqHPNgkaPW4ov3GBEeFZkxdB2NNVS63xIhWBxlshy4d4OpXw042uNR1E4zh02JLFNRaGxrST52ySZw4WN46jt600iBxfGkdiVSclZyD3SUkqEA6Le9EwnOWPFlIdDsraYCd5Xi5l1BOLMmI9BinjUvhAEDJVeJk3dYjX4Jarc1UXhvlG/BAOYqC1O29pFVzWcoUPhBhIka+JccMga5sqW7ZD32rht1Q6t4XOKbJry0KjBdKMUWZ2pRCTQF7ewsS+gK5bqSrTf+yYlENPRbHzVHlWUBFd7iGr4yN7MgHoWBQdUBzeSWERUG+kwORp14LOIAKI5MxtCPJrQyGwOsTd5k6r3nfwsOyJU0yxt4pQsfWWCcTN5PTCiR56CAa3cTceQwLKu3TjhkW8LZ0ZFLORgpVSrz1It2FQXFFhKfXeC8LYwYdxhfRwg7gYaahj9chsP5u2S9eBkAx7FIZww4ZF54s6QA13PdwbvRvM3iSCU4z7XW8sRwIYwxZMQ6QS9WLHYu0kBJPXx1oc1IkWoNBqPxXL99Iw5FR4W4jXdAqX2lzq0qsMQ2LJ3GsqIdwBQVbYboD12RXtI3CqnuJORGaisoXTe2ShHcM1KF7oC7Zj539dtty14zAciy+yCe7YqciwLZhg9pn9nEGRThoa7uHMkzv5rsJzIarQWsKbDdwt7VcbcPCCiCEL9fSSzBPiaE1rr5feM5AWZDA+yj3ayEcMGTVyCqPXsLDrXJXZywabCDNy7XosXW5huiPkxfHHt88ff//r85eX6/zw9bePX/788PDh20kv9fkxnh/z+ZFkO2/utPkzbectnm07b3hcz8+y4Qlv560a2fBlw9PtuW3k2HafbffZdp/teBsfvsX7Fu9bvPt23vG2emLLPza+Y+/2hh97+ze83PjIDT83/Nzxtvxrw68Nvzb82vBrw6+ND/zjsxt2Ba/nlH//G+1q54VVaxAYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa558fca0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4_H"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67138f97-e078-4cc8-8d53-8dbdc371ab69",
   "metadata": {},
   "source": [
    "To get 3D coordinate of molecules is difficult. For example, consider cis and trans isomer. It would take cis, trans, gauch form. Finding the 3D coordinate with lowest energy is difficult. So, rdkit use the approximation to speed up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bfe71119-61c5-420d-bd1a-acb769f53069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.EmbedMolecule(m4_H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c1ffea28-2011-41b1-8d8d-b7483cb7e5fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     RDKit          3D\n",
      "\n",
      "107111  0  0  1  0  0  0  0  0999 V2000\n",
      "    9.5633    3.5235   -0.7668 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    9.0410    2.2581   -0.1615 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.1850    2.6113    1.0351 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.1220    1.5664   -1.1425 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    7.0142    2.0303   -1.7002 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    6.3506    1.2869   -2.5224 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.0732    1.7471   -3.2011 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.0400    1.7706   -2.1956 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.7724    3.0097   -1.5009 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.3010    0.5984   -1.8995 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.5367   -0.4815   -2.5022 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.2831    0.6269   -0.9151 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.5109   -0.5072   -0.5530 C   0  0  1  0  0  0  0  0  0  0  0  0\n",
      "    1.7093   -0.9239    0.9100 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.1384   -1.2749    1.1852 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.4155   -1.6907    2.5213 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.8755   -1.7851    2.6609 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.3197   -3.2330    2.5410 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.6501   -3.7902    1.4606 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.3302   -4.0183    1.7779 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.8055   -2.9734    2.7688 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0280   -0.3226   -0.7066 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.3790    0.7480   -1.2159 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.8900   -1.2780   -0.3084 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.3708   -1.0118   -0.4988 C   0  0  2  0  0  0  0  0  0  0  0  0\n",
      "   -2.8466   -0.6986    0.8534 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.1693   -0.3074    1.2953 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.4035   -1.0562    1.2522 C   0  0  2  0  0  0  0  0  0  0  0  0\n",
      "   -6.1111   -1.4945    0.0548 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.5623   -0.4025   -0.8423 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.8232    0.1144   -0.6401 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.2998    1.1239   -1.4268 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.5376    1.6521   -2.4404 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.2752    1.1334   -2.6401 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.7850    0.1006   -1.8366 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.3937   -0.6130    2.2571 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.6658    0.7469    2.4481 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.1198    1.6435    1.7976 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.5963    1.1940    3.4130 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.8498    2.5743    3.5701 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.5649    3.2717    3.9241 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.9378    3.3600    5.1396 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.8318    4.0233    5.2008 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.3345    4.5932    4.1113 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.4988    4.1693    2.9243 S   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.8085   -2.2451   -1.2005 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.0693   -2.4379   -2.4929 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.9313   -3.1924   -2.4968 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.2021   -3.3932   -3.6658 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.6556   -2.8130   -4.8176 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.7974   -2.0598   -4.8002 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.5260   -1.8564   -3.6446 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    6.8410    0.0280   -2.7789 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.2426   -0.0022   -1.8072 S   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   10.5057    3.3785   -1.3351 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    9.6780    4.3173    0.0263 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.8042    3.9278   -1.4600 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    9.8679    1.5709    0.1224 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    7.1985    2.9717    0.6756 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    8.6756    3.2897    1.7296 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    7.9481    1.6678    1.6127 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.8627    1.1123   -4.0613 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.2161    2.7854   -3.5739 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    4.6981    3.6546   -1.5636 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.5801    2.8945   -0.4262 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.9765    3.5641   -2.0452 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.0643    1.5381   -0.3986 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.7961   -1.4230   -1.1304 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.0378   -1.7433    1.1727 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.3886   -0.0595    1.5601 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.4060   -2.0722    0.4484 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.7674   -0.3915    0.9461 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.2085   -1.3104    3.5874 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.3590   -1.2371    1.8100 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    5.1906   -3.8028    3.4871 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    6.4186   -3.1706    2.2799 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    3.2400   -5.0222    2.2577 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.6997   -4.0846    0.8654 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.7235   -2.9020    2.6231 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    2.9997   -3.3544    3.8068 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.6311   -2.1675    0.1239 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.4835   -0.1189   -1.1312 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.0527    0.0320    1.3097 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.5680   -1.6130    1.5555 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.0369   -0.0011    2.4170 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.3370    0.7788    0.8808 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.0802   -2.0841    1.7641 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.0845   -2.0025    0.4829 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.7181   -2.3116   -0.5351 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.4786   -0.2694    0.1536 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -9.2968    1.5111   -1.2401 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -7.8876    2.4627   -3.0927 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -5.6773    1.5461   -3.4352 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -4.7699   -0.2600   -2.0484 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.9211   -1.3180    2.8573 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.5862    2.6908    4.4042 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -8.2861    3.0448    2.6527 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -6.3839    2.8680    6.0318 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.4072    5.1867    4.0123 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.8607   -2.3178   -1.4719 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.5919   -3.1685   -0.5864 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.5230   -3.6862   -1.6273 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.6912   -3.9919   -3.6368 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.1161   -2.9440   -5.7576 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.1480   -1.6029   -5.7184 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -3.4055   -1.2586   -3.7157 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    6.4172   -0.7469   -3.4473 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  1  0\n",
      "  2  3  1  0\n",
      "  2  4  1  0\n",
      "  4 54  1  0\n",
      "  4  5  2  0\n",
      "  5  6  1  0\n",
      "  6  7  1  0\n",
      "  6 53  2  0\n",
      "  7  8  1  0\n",
      "  8  9  1  0\n",
      "  8 10  1  0\n",
      " 10 11  2  0\n",
      " 10 12  1  0\n",
      " 12 13  1  0\n",
      " 13 14  1  0\n",
      " 13 22  1  0\n",
      " 14 15  1  0\n",
      " 15 16  1  0\n",
      " 16 21  1  0\n",
      " 16 17  1  0\n",
      " 17 18  1  0\n",
      " 18 19  1  0\n",
      " 19 20  1  0\n",
      " 20 21  1  0\n",
      " 22 23  2  0\n",
      " 22 24  1  0\n",
      " 24 25  1  0\n",
      " 25 26  1  0\n",
      " 25 46  1  0\n",
      " 26 27  1  0\n",
      " 27 28  1  0\n",
      " 28 29  1  0\n",
      " 28 36  1  0\n",
      " 29 30  1  0\n",
      " 30 35  1  0\n",
      " 30 31  2  0\n",
      " 31 32  1  0\n",
      " 32 33  2  0\n",
      " 33 34  1  0\n",
      " 34 35  2  0\n",
      " 36 37  1  0\n",
      " 37 38  2  0\n",
      " 37 39  1  0\n",
      " 39 40  1  0\n",
      " 40 41  1  0\n",
      " 41 45  1  0\n",
      " 41 42  2  0\n",
      " 42 43  1  0\n",
      " 43 44  2  0\n",
      " 44 45  1  0\n",
      " 46 47  1  0\n",
      " 47 52  1  0\n",
      " 47 48  2  0\n",
      " 48 49  1  0\n",
      " 49 50  2  0\n",
      " 50 51  1  0\n",
      " 51 52  2  0\n",
      " 53 54  1  0\n",
      "  1 55  1  0\n",
      "  1 56  1  0\n",
      "  1 57  1  0\n",
      "  2 58  1  0\n",
      "  3 59  1  0\n",
      "  3 60  1  0\n",
      "  3 61  1  0\n",
      "  7 62  1  0\n",
      "  7 63  1  0\n",
      "  9 64  1  0\n",
      "  9 65  1  0\n",
      "  9 66  1  0\n",
      " 12 67  1  0\n",
      " 13 68  1  6\n",
      " 14 69  1  0\n",
      " 14 70  1  0\n",
      " 15 71  1  0\n",
      " 15 72  1  0\n",
      " 17 73  1  0\n",
      " 17 74  1  0\n",
      " 18 75  1  0\n",
      " 18 76  1  0\n",
      " 20 77  1  0\n",
      " 20 78  1  0\n",
      " 21 79  1  0\n",
      " 21 80  1  0\n",
      " 24 81  1  0\n",
      " 25 82  1  6\n",
      " 26 83  1  0\n",
      " 26 84  1  0\n",
      " 27 85  1  0\n",
      " 27 86  1  0\n",
      " 28 87  1  1\n",
      " 29 88  1  0\n",
      " 29 89  1  0\n",
      " 31 90  1  0\n",
      " 32 91  1  0\n",
      " 33 92  1  0\n",
      " 34 93  1  0\n",
      " 35 94  1  0\n",
      " 36 95  1  0\n",
      " 40 96  1  0\n",
      " 40 97  1  0\n",
      " 42 98  1  0\n",
      " 44 99  1  0\n",
      " 46100  1  0\n",
      " 46101  1  0\n",
      " 48102  1  0\n",
      " 49103  1  0\n",
      " 50104  1  0\n",
      " 51105  1  0\n",
      " 52106  1  0\n",
      " 53107  1  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(Chem.MolToMolBlock(m4_H))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c9911d3f-2356-417c-bd27-aa76e88381a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa558fca0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4_H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "65fd6b09-3701-457b-99d9-b45b0a5749cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa5d4f580>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove H\n",
    "m4_noH = Chem.RemoveHs(m4_H)\n",
    "m4_noH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2325acb3-29f0-4301-bd9e-8230d1e21dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x2b3fa5d4f580>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to convert 3D into 2D\n",
    "AllChem.Compute2DCoords(m4_noH)\n",
    "m4_noH"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4c73ce-4612-45e3-9183-2ee7037d5efc",
   "metadata": {},
   "source": [
    "# Fingerprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5a7330f5-b554-4573-b155-355dbb856b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600\n"
     ]
    }
   ],
   "source": [
    "molecules = []\n",
    "for smi in open(\"data/drugs.smi\").readlines():\n",
    "    m = Chem.MolFromSmiles(smi)\n",
    "    m2 = Chem.AddHs(m)\n",
    "    molecules.append(m2)\n",
    "print(len(molecules))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "543f4082-bb69-435a-9223-a890adf06378",
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = molecules[0]\n",
    "m2 = molecules[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "79ae9d9b-e0e1-4d5f-8a94-4dfd2a4c059a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import AllChem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "db9da181-e6f2-41f2-ae76-2ccce51c3f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp1 = AllChem.GetMorganFingerprint(m1,2) # iteration\n",
    "fp2 = AllChem.GetMorganFingerprint(m2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1132a234-ed4d-4f6d-b870-de2350c7e62c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<rdkit.DataStructs.cDataStructs.UIntSparseIntVect object at 0x2b3fa5d70b20>\n"
     ]
    }
   ],
   "source": [
    "print(fp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f760d695-d5e1-4429-8609-d0572d2cb7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GetLength',\n",
       " 'GetNonzeroElements',\n",
       " 'GetTotalVal',\n",
       " 'ToBinary',\n",
       " 'ToList',\n",
       " 'UpdateFromSequence',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getinitargs__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__idiv__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__instance_size__',\n",
       " '__isub__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__or__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__safe_for_unpickling__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__weakref__']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(fp1) # to check what method we can use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "af76e886-a6a7-486f-820e-6af7301271b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10565946: 1,\n",
       " 36538691: 1,\n",
       " 56091288: 7,\n",
       " 231329144: 1,\n",
       " 295224923: 1,\n",
       " 490247508: 6,\n",
       " 618185734: 1,\n",
       " 631553871: 1,\n",
       " 658782932: 1,\n",
       " 772606658: 1,\n",
       " 829315160: 2,\n",
       " 864942730: 1,\n",
       " 886246489: 1,\n",
       " 1101907775: 1,\n",
       " 1319199979: 1,\n",
       " 1461310226: 1,\n",
       " 1665999679: 1,\n",
       " 1957005222: 1,\n",
       " 2013031783: 1,\n",
       " 2041434490: 2,\n",
       " 2092489639: 1,\n",
       " 2277419761: 1,\n",
       " 2912088145: 1,\n",
       " 2976816164: 1,\n",
       " 3067762898: 1,\n",
       " 3210252437: 1,\n",
       " 3217380708: 13,\n",
       " 3265829077: 1,\n",
       " 3352400832: 1,\n",
       " 3430949351: 1,\n",
       " 3602415581: 1,\n",
       " 3612926680: 1,\n",
       " 3624363645: 1,\n",
       " 3632350815: 1,\n",
       " 3650455129: 1,\n",
       " 3798139026: 1,\n",
       " 3838343967: 1,\n",
       " 3974650111: 1,\n",
       " 3980805843: 1,\n",
       " 4240369074: 1,\n",
       " 4277593716: 10}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp1.GetNonzeroElements() #fragment list and the number of fragment in a molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "01f6d3a8-3d42-4d31-80d6-5c5b556cdcc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp1_bit = AllChem.GetMorganFingerprintAsBitVect(m1, 2, nBits=1024) # to convert into 1024 binary\n",
    "fp2_bit = AllChem.GetMorganFingerprintAsBitVect(m2, 2, nBits=1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3d551573-b397-4ab7-abe9-94b213c3aa47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000001000000000000000000000000000001000000000000000000000000000000000000000000001000000110000000000000000000000000000000000000000000000000000000010010000000000000000000000000000000000000000000000000000000000001001000000000000000000000100000000000000000001000000000000000000000000000000000000000000000000000000000010000000010000000000000000100000000000000010010000000000000000101000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000101000100000000000000000000100000000100000000000010000000000000100000000000000000000000000000000000000000100000000000000001100010000000000000000000000001000000000000000000000000000000001000000000000100000000000000000000000000000001000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000110000000000000000000000001000000000000000000000000000010000000000000000000000000000000000\n"
     ]
    }
   ],
   "source": [
    "fp1_str = fp1_bit.ToBitString() # print as a string of length 1024\n",
    "print(fp1_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ad29fed9-f935-4bb3-a89c-b2e5e9cf510f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000000000000001000001000000000000001000000000000000000000000000100000000000000000000000100000000000000000000000001000000000010000000000000100000010000000000000000000000010000000000000000000000000000000000001000000000000100010100000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000100000000000000010000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000010000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000100000000000000000000000000100000000000000000000000000000000000100000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000100000000000000000000000000000000010000000000000000000000001000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100\n"
     ]
    }
   ],
   "source": [
    "fp2_str = fp2_bit.ToBitString() # print as a string\n",
    "print(fp2_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40500951-ef9c-4d50-b80c-dea7e4f3f5f8",
   "metadata": {},
   "source": [
    "## To compute similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bdd0092a-22ac-42fd-8956-fc0d51f43e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import DataStructs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7afd8442-4386-46a2-be24-33659d60e4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute the Dice similarity between fp1 and fp2\n",
    "DataStructs.DiceSimilarity(fp1, fp2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "172af941-4e87-439b-bfb5-33395658405c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16393442622950818"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tanimoto Coefficient\n",
    "DataStructs.FingerprintSimilarity(fp1_bit, fp2_bit) # binary fingerprint\n",
    "    # union : at least 1 in bit vector\n",
    "    # intersection : both of them are 1 in bit vector"
   ]
  }
 ],
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