Compound_manipulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import RDConfig\n",
    "import os\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import Draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f64c885a620>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Suppl = Chem.SDMolSupplier('80-selected-cqr-conf.sdf')\n",
    "uni = []\n",
    "uni_Stru = []\n",
    "\n",
    "for x in Suppl:\n",
    "    if x is not None:\n",
    "        Name = x.GetProp('_Name')\n",
    "        if Name not in uni:\n",
    "            uni.append(Name)\n",
    "            uni_Stru.append(x)\n",
    "            \n",
    "    \n",
    "Mol_S = [x for x in uni_Stru]\n",
    "Mol_S[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conformers and Conformation Generation\n",
    "\n",
    "import copy\n",
    "tm = copy.deepcopy(Mol_S[0])\n",
    "tm.RemoveAllConformers()\n",
    "tm.GetNumConformers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cids=AllChem.EmbedMultipleConfs(tm,pruneRmsThresh=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the conformations up using the built-in UFF implementation:\n",
    "for cid in cids: AllChem.UFFOptimizeMolecule(tm,confId=cid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:cadd-py36]",
   "language": "python",
   "name": "conda-env-cadd-py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}