04_Oligomer_SMILES_to_3D.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "95fab04e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "acd809e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>SMILES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pi1</td>\n",
       "      <td>N1C(=O)C2=CC3=C(C=C2C1=O)C(=O)N(C3=O)C1=CC=C(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p12</td>\n",
       "      <td>N1C(=O)C2=CC3=C(C=C2C1=O)C(=O)N(C3=O)C1=CC=C(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>benzene</td>\n",
       "      <td>c1ccccc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cyclohexane</td>\n",
       "      <td>C1CCCCC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>styrene</td>\n",
       "      <td>C=Cc1ccccc1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name                                             SMILES\n",
       "0          pi1   N1C(=O)C2=CC3=C(C=C2C1=O)C(=O)N(C3=O)C1=CC=C(...\n",
       "1          p12   N1C(=O)C2=CC3=C(C=C2C1=O)C(=O)N(C3=O)C1=CC=C(...\n",
       "2      benzene                                           c1ccccc1\n",
       "3  cyclohexane                                           C1CCCCC1\n",
       "4      styrene                                        C=Cc1ccccc1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./smi.csv', names=['Name','SMILES'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "33ee1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "mols = [Chem.MolFromSmiles(smi) for smi in df.SMILES]\n",
    "hmols = [Chem.AddHs(m) for m in mols]\n",
    "for mol  in hmols:\n",
    "    AllChem.EmbedMolecule(mol,AllChem.ETKDG())\n",
    "    print(AllChem.UFFOptimizeMolecule(mol,1000))\n",
    "    \n",
    "smiles = list(df.SMILES)\n",
    "#sid = list(df.SOURCE_ID)\n",
    "#libs = df[df.columns[0]]\n",
    "writer = Chem.SDWriter('TEST.sdf')\n",
    "\n",
    "for n in range(len(df)):\n",
    "    #hmols[n].SetProp(\"_Library\",\"%s\"%libs[n])\n",
    "    #hmols[n].SetProp(\"_Name\",\"%s\"%sid[n])\n",
    "    #hmols[n].SetProp(\"_SourceID\",\"%s\"%sid[n])\n",
    "    hmols[n].SetProp(\"_SMILES\",\"%s\"%smiles[n])\n",
    "    writer.write(hmols[n])\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "500a180d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f7fe9b6dbc0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mols[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0dae787",
   "metadata": {},
   "source": [
    "H로 passivation 된 PMDA-PDA 의 oligomer 가 Mol object 로 생성되었고,  \n",
    "3D 구조로도 성공적으로 변환이 된다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76125b71",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chemvae",
   "language": "python",
   "name": "chemvae"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}