polymer_build_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical demo of bootstrap process for linear polymer structure building and MD setup from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-07 20:53:06.570 [INFO    :        __init__:line 22  ] - Enabling RDKit 2023.09.6 jupyter extensions\n",
      "2025-01-07 20:53:07.301 [INFO    :           utils:line 149 ] - Note: NumExpr detected 20 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 16.\n",
      "2025-01-07 20:53:07.302 [INFO    :           utils:line 162 ] - NumExpr defaulting to 16 threads.\n"
     ]
    }
   ],
   "source": [
    "# Warning suppression and logging\n",
    "import warnings \n",
    "warnings.catch_warnings(record=True)\n",
    "warnings.filterwarnings('ignore', category=UserWarning)\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "\n",
    "import logging\n",
    "from polymerist.genutils.logutils.IOHandlers import LOG_FORMATTER\n",
    "\n",
    "logging.basicConfig(\n",
    "    level=logging.INFO,\n",
    "    format =LOG_FORMATTER._fmt,\n",
    "    datefmt=LOG_FORMATTER.datefmt,\n",
    "    force=True\n",
    ")\n",
    "LOGGER = logging.getLogger(__name__)\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# Chemistry tools\n",
    "from rdkit import Chem\n",
    "\n",
    "from polymerist.rdutils import rdkdraw # configure molecule drawing\n",
    "rdkdraw.set_rdkdraw_size(400, 3/2)\n",
    "rdkdraw.disable_substruct_highlights()\n",
    "\n",
    "# define output directory\n",
    "from pathlib import Path\n",
    "\n",
    "outdir = Path('polymer_demo_files')\n",
    "outdir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0) Predefining some monomer examples for you to play with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass, field\n",
    "from enum import Enum, auto\n",
    "\n",
    "class PortMethod(Enum):\n",
    "    '''For specifying how ports should be added to a complete RDMol'''\n",
    "    MAP_NUMBERS   = auto()\n",
    "    BOND_CLEAVAGE = auto()\n",
    "    REACTION      = auto() # not included here : provide an MDL rxn template\n",
    "\n",
    "@dataclass\n",
    "class MonomerExample:\n",
    "    '''For encapsulating info about a base monomer unit'''\n",
    "    molname : str\n",
    "    smiles  : str\n",
    "    bond_map_nums  : tuple[int, ...]\n",
    "    isotope_map    : dict[int, int] = field(default_factory=dict)\n",
    "    assign_port_by : PortMethod = field(default=PortMethod.BOND_CLEAVAGE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining some concrete examples\n",
    "monomer_examples = {\n",
    "    'PVC' : MonomerExample(\n",
    "        molname='polyvinylchloride',\n",
    "        smiles='C=C-Cl',\n",
    "        bond_map_nums=(1, 2),\n",
    "        isotope_map={5 : 0, 6 : 0},\n",
    "        assign_port_by=PortMethod.BOND_CLEAVAGE,\n",
    "    ),\n",
    "    'PE' : MonomerExample(\n",
    "        molname = 'polyethylene',\n",
    "        smiles = 'C=C',\n",
    "        bond_map_nums=(1, 2),\n",
    "        isotope_map = {3 : 0, 4 : 0},\n",
    "        assign_port_by = PortMethod.BOND_CLEAVAGE\n",
    "    ),\n",
    "    'PAAm' : MonomerExample(\n",
    "        molname = 'polyacrylamide',\n",
    "        smiles = 'C=CC(=O)N',\n",
    "        bond_map_nums=(1, 2),\n",
    "        isotope_map = {7 : 0, 8 : 0},\n",
    "        assign_port_by = PortMethod.BOND_CLEAVAGE\n",
    "    ),\n",
    "    'PS' : MonomerExample(\n",
    "        molname = 'polystyrene',\n",
    "        smiles = 'c1ccccc1C=C',\n",
    "        bond_map_nums = (7, 8),\n",
    "        isotope_map = {14 : 0, 16 : 0},\n",
    "        assign_port_by = PortMethod.BOND_CLEAVAGE\n",
    "    ),\n",
    "    'Chitosan' : MonomerExample(\n",
    "        molname = 'polyglucosamine',\n",
    "        smiles = 'N[C@H]1C(O)OC(CO)[C@@H](O)[C@@H]1O',\n",
    "        bond_map_nums=(7, 8),\n",
    "        isotope_map = {17 : 0, 23 : 0},\n",
    "        assign_port_by = PortMethod.MAP_NUMBERS\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Building monomer templates from basic SMILES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose monomer HERE to define SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fOuU6K1yDIsQDaGpq0meFCCG8vb2XLVtWUVEhO5TrNDU1aZrWs2dPIYSPj49qV0NdY9++fU888YReh3Pnzj1+/LjsRPB8FCHu1+7du9vGRYaFheXn58tO5FK7d+8eP35829svKCiQnchj6c9i6kP49Gcxy8vLZYeCJ6MIcW9FRUVtCwRGjx69bds22YlcqrCw8IUXXtDf/tixY3fs2CE7kRJu3boVFRWlLycJCgpKSEhQfDoPOg5FiF9z56iwgIAA1UaF3bx502QyeXt7CyF69eqlb9qTHUotZ8+ebVtXOWbMGLvdLjsRPBBFiJ+n+Kiw5ubmTZs29evXr+1q6LVr12SHUtfu3bsff/zxtvPSp06dkp0IHoUixM/Yv3//lClT9J87M2fOzM7Olp3Ipfbs2TNp0iT97c+fP//EiROyE8HZ2NiYkpISGBioz21YuXJlZXWl7FDwEBQh/sqdo8KCg4NVu4X93LlzbVdDR44cqdrVUPd3/fp1/dmVkFkhg04M0sq1FgfPruBRsaEeP6qrq0tKSkpKSqqvr/f393/77bdXr16tTw5TQW1tbXJycmJiYkNDQ/fu3SMiIqKjo/VHvOFujh07pjVom7tuFkJM9Z+qBWtzAubIDoVOjCKEcDqd27dvj4yMvHTpksFgePnll5OTk4cOHSo7l4s4nc4tW7ZERUWVlZUZDIbFixcnJSUNGDBAdi7cg/223XzFfKHpghDCGGhMD0kf7jtcdih0ShSh6nJzcy0Wy6FDh4QQ06ZN0zStbXKYCnJycsxmc1ZWlhBi+vTpNpvtqaeekh0K96veUZ9ekR5fFl/dWu3n5WfqZ4oZENOjSw/ZudDZSD0xC5muXr3atgpn4MCBmzZtUmpUWElJCYuTPENpU2n4hXBDrkHkikEnB22q2NTqZKkTHgBHhCpqbm7euHHj+++/X1VV5ePjs3z58nXr1umTw1RQX1+fnp4eHx9fXV3t5+dnMpliYmL0NVLovHLqcsxXzFm1WUKI6f7TtRDt6e5Pyw6FzoEiVI7dbrdarcXFxUIIo9GoadrIkSNlh3Idu91uMpkuXrwohDAajenp6cOHc2HJQziFc8vNLVGlUWXNZQZheLnXy+sHrw/xDZGdC+6OIlRIYWHhihUrvv76ayHEuHHjUlNTFy5cKDuU6xw7dsxisRw4cEAIMXXqVE3T5szhVkMPVOuoTS5PTixPbHA0dPfqHtE/Irp/dDcvbgDGL5N8ahYuoS9OahsVpmlac3Oz7FCu0/bwmRCiT58+LE5SwaXGS+EXwkWuELkiJD8k40aG7ERwXxShh1N8VJi+OEkfR6IvTqqsZByJQvZW7Z1cMFmvw3lF807UMSQIP4NTo55sz549Vqs1Pz9fCDF//nxN09omh6kgMzPTbDYXFBQIIcLCwjRNa5tXCXU4hGPrza0RJREVLRVewuu13q+lBKc85v2Y7FxwJ7KbGB3izlFho0aNUm1U2F0rC7788kvZiSDZzZabUSVRvnm+Ilf0Ot4roSyh0cEiEfyII0JPo48K0xcG6aPCVq1ape9RUsGtW7cSExPT0tKampqCgoKio6OtVqu+0w4423B2RemKr25/JYQY221s6uDUFwJfkB0K8lGEnsOp9qgwh8OxdevWyMjIa9eueXl5vfbaaykpKfqWc+BOmdWZ5ivmgoYCIURYjzBbiG1CtwmyQ0EmitBDHD161Gw2Z2dnCyFmzJhhs9lmzZolO5Tr7Nu3z2KxnDx5Uggxd+5cTdOeeOIJ2aHgvpqdzRsrNsb+EHu79baPwWd5v+VrB64N7BIoOxckkXtmFo9O8VFhly9fDg8P1/8xh4SEZGRwlzzu1/Xm66Yrpi55XUSu6HOiD0udlMURYSemjwqLi4urqanRR4UpuzjJ398/MjKSxUl4CHl1eZYSy8Gag0KIUP9QLVh7JuAZ2aHgUhRhZ3XXqLANGzYMGzZMciZXcTqd27dvj4iIuHz5sr43KiUlZciQIbJzoROz37abrpguNl0UQhgDjRtCNgzzHSY5E1yFIux88vLyLBbLwYMHhRChoaGapj3zjEK/wH733Xdms/nw4cNCiCeffNJmsz39NLOV0Q7qHHUbKjbE/RBX46jRlzqtHrg6wEuVUyxKk3tmFg9E8VFhpaWlbXujBg0atGnTptZWtu2gnZU0lbQtdRp8cnDGjQyHU6GL7mqiCDsHxUeFNTY2apqmb0ry9fU1mUxVVVWyQ8GTHak5Mqtwlj6bbUbhjKyaLNmJ0IE4NdoJ3DUqzGazTZig0GNPdrvdbDZfuHBBCGE0Gm0224gRI2SHgufTZ7O1LXVa3Htx0uCkAT6qPJirFIrQrZ09e3bFihVfffWVEGLs2LGpqakvvKDQIIwzZ85YrdZdu3YJIcaPH5+Wlvb888/LDgW16EudEsoSGp2N+lKnVQNWdTWoMqpJFbIPSfHzbt68GRUVpc8G69Wrlz4yTXYo19H3RulXQ3v37q3a1VC4m3MN5xZ9v0g/Uzrq1KhtN9Ua3uvxOCJ0O/qosIiIiIqKCgVHhTU3N3/22WcxMTHXr1/38fH54x//GB8f37dvX9m5ALGneo+1xJpfny+EmN9jvhasTfJTaJ2LB6MI3cudo8LmzZunadrkyZNlh3KdzMxMi8Vy+vRpweIkuKUWZ8unNz6NuRpzveW6t8H79T6vxw2K6+fdT3YuPBrZh6T4keKjwoqKioxGo/72R48erdreKHQuN5pvmK6YvPO8Ra7ofaK3Vq41O5plh8LD44hQvjtHhemLk5QaFVZZWZmQkKBpWmNjo744yWKxqLM3Cp1XYUOhtcS6s2qnEGJct3GpwakLey6UHQoPgyKUyel0bt++feXKlVeuXNFHha1fvz4kJER2LhfRr4a+88475eXl+tXQ5OTk/v37y84FPAD7bbu1xFrcWCyEMAYatWBtZNeRskPhwVCE0uTk5JjN5qysLCHE9OnTNU1TalTYN998Y7FYjh8/LoR49tlnNU2bMmWK7FDAw9CXOr3/w/tVrVX6Uqd1A9f17NJTdi7cN7lnZtVUWlratjhJwVFhV65caXv7wcHBqu2Ngqe62nR12aVlXrleIlcMPDlwU8WmVqdC/7/u1DgidCl9cVJ8fHx1dbWvr++bb74ZFxenTw5TQV1dXVJSUlJSUn19vb44KSoqys/PT3YuoN3k1uWaS8zf1nwrhJjmP00L1mYHzJYdCvdAEbrOXaPC0tPThw8fLjuUizidzu3bt0dGRl66dEm/GpqcnDx06FDZuYD25xTO7be2R5ZGXmq6ZBCGl3u9nDw4eagv/9rdF0XoCseOHbNYLAcOHBBCTJkyxWazzZkzR3Yo18nNzTWbzd9++60QYtq0aZqmzZ7N78jwcHWOuqTypKTypHpHvb+Xf2T/yKj+UX5enP9wS3LPzHq8OxcnKTgq7OrVq22LkwYOHKja1VDgStOVtqVOwSeDWerknijCjvK3i5Nu3bolO5Tr6IuTevbs2fb2b9++LTsUIMf+6v1TCqboo0qfPfvssbpjshPhr3BqtEMoPirMbrdbLJbvv/9eCGE0GjVNGzmSJ6ugNH2p0zul75Q3l3sJr9d6v5Y8OLm/D0/NugWKsJ0VFRWtXLnyyy+/FEKMGTNm/fr1bZPDVFBYWGi1Wnfu3CmEGDduXFpa2m9/+1vZoQB3UdlamVCWoF3TGp2NAV4BK/uvZKmTW5B9SOo5bt261bY4KSgoKCEhoaGhQXYo19EXJ3l7e4v/fzW0uZnpi8DPKGooalvqNPrUaJY6SccRYTvQR4VFRkZeu3ZNwcVJLS0tn376qb44ydvb+/XXX4+Li+vXj3n8wK/JrM60XLGcbjgthAjrEZYWnDbRb6LsUIqiCB/V/v37LRbLiRMnhBBz587VNO2JJ56QHcp19uzZY7FYTp06JYRYsGBBWlrapElsaAPuS7Oz+bMbn+lLnXwMPn/s88f4QfF9vdm+6XKyD0k7sTsXJyk4KqyoqGjRokX62x81ahSLk4CHoy916pLXpW2pU4tDoYes3AFHhA9DHxWmL07SR4UptThJ3xuVkJDQ2Nio741atWoVi5OAR3Gm4Yy1xLqrapcQYny38WnBac/3fF52KFVQhA/G6XRu3749IiLi8uXL+qiwlJSUIUOGyM7lIncuTjIYDIsXL05KShowYIDsXICHsN+2m6+YLzRdEEIYA422YNuIriNkh/J8FOED+O677ywWS9uoMJvN9pvf/EZ2KNc5evSo2WzOzs4WQsyYMcNms82aNUt2KMDTNDmbPqr46L0f3qturfY1+L7Z7824gXE9uqgyml8OuWdmO4vS0lKVR4WVlJS0LU4aPHiwaldDAdcrbSptW+o06OQgljp1KI4I76Gpqemjjz56//33q6qq9MVJ69at0yeHqaCurm7Dhg1xcXE1NTX+/v5vv/326tWrAwICZOcClPBd3XfmK+bDtYeFEE/6P2kLsT3dXaH13S5DEf6au0aF2Wy2ESMUOl9vt9tNJtPFixeFEEajccOGDcOGDZOcCVCMvtQpojTictNlfalTyuCUIb6q3JfgGhThzztz5ozVat21a5dQclRYXl6exWI5ePCgECI0NFTTtGeeeUZ2KEBdtY7a5PLkxPLEBkeDvtQpun90Ny9V7lTvcJJPzboffVTYnYuTlBoVVlFR0fb2+/Tpo9reKMCdXW68HH4hXJ/NFpIfknEjQ3YiD0ER/qS5uXnTpk19+/YVQnh7ey9btqyiokJ2KNfR90bduTipsrJSdigAd9tbtXdywWS9DucWzT1ed1x2ok6PU6M/umtUmKZpEycqNPcvMzPTZDKdOXNGCBEWFmaz2SZMmCA7FICfpy91iiyJvNZyTV/qlBKc8pi3KvON25/sJpbvzlFho0ePVm1UWGFh4QsvvKC//bFjx+7YsUN2IgD35VbLraiSKN88X5Ergo4HJZQlNDoaZYfqlJQ+IqypqUlJSdFHhQUEBKxcuVKpUWG3bt1KTExMS0tramrq1atXVFSU1WrV10gB6CyKGotWlKzYcXuHEGJM1zGpwakvBr4oO1Qno2gR3jkqTF+clJyc3L+/Ktui9cVJq1evrqio0N/++vXrWZwEdF6Z1ZnmK+aChgIhRFiPMC1Ee7zb47JDdRoqFuGRI0fMZvORI0eEEDNnztQ0TalRYXv37rVarSdPnhRCzJs3T9O0yZMnyw4F4FE1O5s3VmyM/SH2duttH4PP8n7L1w5cG9glUHauzkDyqVnXunLlStuoMAUXJ507d67tauiQIUMyMrj3GvA015uvty116nOiD0ud7ocqR4SKjwrTFyfpe6P0xUlK7Y0CVHOs7pilxHKg5oAQYqr/VC1YmxMwR3Yo9+X5Reh0Ordv3x4ZGXnp0iUhhNFo/Ld/+7ehQ4fKzuUiTqdzy5YtUVFRZWVlLE4ClGK/bTddMV1suiiEMAYa00PSh/sOlx3KHXl4Eebm5loslkOHDgkhQkNDbTbb7NmzZYdynZycHLPZnJWVJYSYPn26zWZ76qmnZIcC4Dr1jvr0ivT4svjq1mo/Lz9TP1PMgBiWOt1N6onZDnT16tVly5bpo8L69u2r2qgwFicBaFPSVBJ+IdyQaxC5YvDJwRk3MhxOfiD8xAOPCJubmzdu3KgvTvLx8Vm+fLlSi5Pq6+vT09Pj4+Orq6v9/PxMJlNMTEyPHvwCCKgupy7HfMWcVZslhJjuP90WYnuqO6eIhPC8U6N2u91qtRYXFwshjEajpmkjR46UHcp17Ha72Wy+cOGCEMJoNKanpw8fziUBAD9yCueWm1uiSqPKmssMwrC49+KkwUkDfFx300B0abR+C4+XwevQmEMu+773IPuQtN2cOXNm4cKF+psaO3bsV199JTuRS+Xl5c2Z8+NdYVOnTv3mm29kJwLgpmpaa2KvxnY71k3kiu7Husdeja1vrX/0L1vXWnfP1/zd+b/Tx4Ubcg2P/h3bi5e8Cm43t27dMpvNkyZN+vrrr3v16qVp2qlTp9pK0ePduHHDbLzyMQEAAAyCSURBVDZPnz79wIED+uKknJyctlIEgLt09+q+ZuCa/PH5LwW+VOuo/eCHDyadmbSratcDfZEmZ9POqp0rS1ZOK5w2KH9Q12Nd/Y/7dz/efeTpkcZi4yfXP6loqeig/O1PdhM/En1xkj4bTF+cdO3aNdmhXEdfnBQYGChYnATgoeyp2jOpYJLIFZtvbL7PT2lxtHx6/dOh+UP1Y7tf+q9rXtcVV1bUttbe+bnueUTYia8R7t2712Kx5OfnCyHmz5+vadqkSZNkh3KdzMxMs9lcUFAghAgLC9M07fHHGS0I4IG1OFu23dr2D73/wSAM93xxZWvl77///f7q/ff5xSf6Tdwzek/biqi/L/77/739v0IIgzA4Qh0PG7mdecsO8DDOnz//7rvvfvHFF0KIUaNGffjhh22Tw1RQVFS0YsWKHTt2CCHGjBmTmpr64osMmwfwkLwN3v/Y+x/v55XXWq4tOLfgVP2ptr/x8/J7rsdzz/V8LsQ3pKdXz5utN881nPuf2/9ztPaoUziFEKfqTz137rl9o/f19u7dUW/gkXWyItRHhemLkxQcFVZZWZmQkKAvTgoKCoqOjmZxEgDXcArnP138pztb8NVer6YEpwz2GXzXK6MHRO+v3r/s8rJzjeeEEL4G33pnvUuzPqBOU4TOvx4VFh4ertSoMH1vVGRk5LVr17y8vMLDw1NSUh57jIXUAFzk4+sff131ddsfYwbExA2K+6UXz+0xN2ts1rxz8yZ2m/jvQ//dz8vPJRkfUucowqNHj5rN5uzsbKHkqLB9+/ZZrdYTJ04IIebOnatp2hNPPCE7FACFtDpb/7XsX9v++IegP/xKC+r6ePc5NOZQjy497ufSo1zu/vhEaWnpkiVLZs2alZ2drY8KO3LkiDoteOXKlSVLlsyfP//EiRMhISEZGRn79u2jBQG42I6qHZeaLukfd/fqnh6Sfj+f1bNLT/dvQeHOR4T6qDB9cZKCo8LuXJzk7+8fGRmp1NVQAG7l85uft338h6A/DPIZJDFMu3PTIrTb7SaT6eLFi0IIo9G4YcOGYcOGSc7kKk6nc/v27REREZcvXzYYDIsWLUpJSRkyZIjsXADUdaTuSNvH93mLaSfidkWYl5dnsVgOHjwohJg6daqmaUoNSfnuu+/MZvPhw4eFEE8++aTNZnv66adlhwKgtIqWiu8bv9c/NgjDrO6z5OZpd250jVAfFTZjxoyDBw8qOCrs6tWrb7zxxsyZMw8fPjxo0KBNmzYdOXKEFgQgnf4UhG6Y77DALoESw3QEdzkiPHTo0IsvvlhVVeXr62u1WlevXq1PDlNBU1PTRx999N5771VXV/v6+r755ptxcXHqXA0F4OYqWyrbPh7oM1Bikg7iLkUYGhoaFBQ0Y8YMm802YcIE2XFcx263WyyW77//XghhNBptNtuIESNkhwKAn1S2/lSEHrnd3l2K0N/fPycnR6knxM+cOWO1Wnft2iWEGD9+fFpa2vPPPy87FADc7c65MN0MHnjvuhtdI1SnBW/evKnvjdq1a1fv3r01TcvPz6cFAbinnl492z6uddRKTNJB3OWIUBHNzc2fffZZTEzM9evXfXx8/vmf/zk+Pr5v376ycwHAL7rz7pgqR5XEJB2EInSdzMxMi8Vy+vRpIURYWFhaWtrEiRNlhwKAe+jl3avt47b5Mp7EjU6NerBz58698sorzz333OnTp0ePHr1t27bdu3fTggA6hce7Pe5t+PGoqby5/IfmH+TmaXcUYceqrKyMjo6eNGnSF198ERAQEBsbm5+fr9T2RACdnb+X/4RuP93Mf7DmoMQwHYEi7CgOh2Pz5s3jxo1LTExsbm4ODw8/f/78mjVrunbtKjsaADyYp7v/NNxj682tEpN0BIqwQ3zzzTfTpk1bunRpeXm5Pixm8+bN/fv3l50LAB7G0j5L2z7eWbXzTMMZiWHaHUXYzkpKSpYsWTJv3rzjx48HBwdnZGRkZWXNnDlTdi4AeHizus8K9Q/VP252Nr9x+Q2HcMiN1I4ownZTV1e3Zs2aMWPGbNmyxc/PLzY2tqioaMmSJQZDJ1jHBQC/Lm5QXNtywYM1B5dcXNLibLnnZzU5mzo4VzugCNuB0+n84osvJkyY8MEHHzQ0NCxatKigoGDNmjV+fn6yowFA+1jYc+Gyvsva/vgfN//j2aJnc+pyfun1J+pPvHH5jfEF41udrS4J+PB4jvBR5ebmms3mb7/9Vggxbdo0TdNmz54tOxQAtL/U4NSLTRd3Ve3S/3i49vDMwplT/acu7LlwdNfR/bz7OYSjrLksrz5vb/Xesw1n9Zf9X/X/Ley5UF7qe6MIH94PP/ywZs2aTz75xOFwDBw4cM2aNX/605+8vDjIBuCZ/L387SPtb1x+47Mbn+l/4xTOvLq8vLq8X/ms/7z5n25ehPzUfhjNzc02m23cuHEff/xxly5dTCZTYWHhsmXLaEEAns3H4PPp0E/3j9l/P+t5H/N+bNWAVbZgmwuCPQqOCB+Y3W63Wq3FxcVCCKPRqGnayJEjZYcCANd5NuDZrLFZBQ0Fu6p2fVP9TVlLWVlzWVVrVWCXwMAugaO7jn7C/4lnAp6Z3X12F0OXOz9xYeDCQb6DhBBe7nQYZnA6nbIzdBqFhYVWq3Xnzp1CiHHjxqWmpi5c6NbH+wCAe3KjTnZnbYuTdu7c2bY4iRYEAA/AEeE9tLS0fPrpp/riJG9v79dffz0uLq5fv36ycwEA2gfXCH/Nnj17rFZrfn6+EGLBggVpaWmTJk2SHQoA0J44Nfrzzp8//8orr4SFheXn548aNWrbtm2ZmZm0IAB4Ho4I71ZbW5ucnJyQkNDY2Ni9e/eIiIhVq1axMgIAPBXXCH/icDi2bt0aFRVVVlZmMBgWL16clJQ0YMAA2bkAAB2II8IfHT161Gw2Z2dnCyFmzJhhs9lmzbr346IAgM6Oa4SitLR0yZIls2bNys7OHjx4cEZGRnZ2Ni0IAIpQ+oiwrq5uw4YNcXFxNTU1fn5+JpNp9erVAQEBsnMBAFxH3SK02+0mk+nixYtCCKPRuGHDhmHDhknOBABwORVPjebl5c2ZM+ell166ePFiaGjogQMH7HY7LQgAalKrCG/cuGE2m2fMmHHw4ME+ffpomnb06NFnnnlGdi4AgDSqPD7R3Ny8cePG2NjY27dv+/j4LF++fO3atYGBgbJzAQAkU+IaYWZmpslkOnPmjBAiLCzMZrNNmDBBdigAgFvw8FOjZ8+effHFF5977rkzZ86MHTt2x44du3fvpgUBAG089ojw1q1biYmJaWlpTU1NvXr1ioqKslqtvr6+snMBANyLB14j1CelRUREVFRUeHl5vfbaa+vXr2dxEgDgZ3naEeHevXutVuvJkyeFEPPmzdM0bfLkybJDAQDcl+dcIywuLn7llVcWLFhw8uTJkJCQjIyMvXv30oIAgF/nIadGb9y4MXTo0Nra2h49esTExFgsFhYnAQDuh4cUoRDCZDKVlZWtX78+JCREdhYAQKfhOUXodDoNBoPsFACATsZzihAAgIfgOTfLAADwEChCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNIoQgCA0ihCAIDSKEIAgNL+H8bINsOgVXq9AAAAZnpUWHRyZGtpdFBLTCByZGtpdCAyMDIzLjA5LjYAAHice79v7T0GIOBnQABmIGYC4gZGNoYMkAATE5TBzCjIoAFiMHIzMHIwMTIxiIB44nogIbj2h27L9gN170My0h5EAMXtYeJiANIyDMukcYmHAAAAqnpUWHRNT0wgcmRraXQgMjAyMy4wOS42AAB4nI1QQQrDIBC8+4r5QGSzEqjHqKGUEoXW9g+FHvt/uhKsyaEhuy6Mw8wyrEKpW7i+PvgVB6UA2nnWWjwNEakZBcBN50uEz6OrjE+PmO8wYHFIb5VjTnNlenh0vWZryZzQkeZBNotFEy2gKlmUjdUD/dEZ+DcObZxi2GRZ0rkUQ0vHZVqEQpi1f60u/3oFweoLgIVDwbSZ1bIAAABBelRYdFNNSUxFUyByZGtpdCAyMDIzLjA5LjYAAHicc7Z1ds5RqNHQNdQzsrQ0MNHRNdAzMtWxNtAx0ANSqKKaNQD22An8TTXY6AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f7fdfad7f40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chosen_example = monomer_examples['PVC'] # feel at leisure to swap this out for another example\n",
    "orig_smiles_mol = Chem.MolFromSmiles(chosen_example.smiles, sanitize=False)\n",
    "display(orig_smiles_mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "moldir = outdir / chosen_example.molname\n",
    "moldir.mkdir(exist_ok=True, parents=True)\n",
    "\n",
    "monodir = moldir / 'monomer_info'\n",
    "monodir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expand SMILES to include full chemical info \n",
    "Namely, this includes explicit Hs, Kekulized aromatic rings, and atom map numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[C:1](=[C:2](-[Cl:3])-[H:6])(-[H:4])-[H:5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f7fdf891a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from polymerist.polymers.monomers import specification\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "new_smiles = specification.expanded_SMILES(chosen_example.smiles)\n",
    "FULL_SMILES_MOL = Chem.MolFromSmiles(new_smiles, sanitize=False)\n",
    "print(new_smiles)\n",
    "display(FULL_SMILES_MOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assigning linkers for inter-monomer bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.RWMol at 0x7f7fdf892340>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from polymerist.rdutils.labeling import molwise\n",
    "from polymerist.rdutils.bonding import portlib, dissolution\n",
    "\n",
    "\n",
    "if portlib.get_num_ports(FULL_SMILES_MOL) == 0: # check for existence of ports to ensure idempotency\n",
    "    if chosen_example.assign_port_by == PortMethod.MAP_NUMBERS:\n",
    "        map_nums = chosen_example.isotope_map.keys()\n",
    "        for (atom_id, map_num) in zip(molwise.atom_ids_by_map_nums(FULL_SMILES_MOL, *map_nums), map_nums):\n",
    "            linker_atom = FULL_SMILES_MOL.GetAtomWithIdx(atom_id)\n",
    "            linker_atom.SetIsotope(chosen_example.isotope_map[map_num])\n",
    "            linker_atom.SetAtomicNum(0)\n",
    "\n",
    "    elif chosen_example.assign_port_by == PortMethod.BOND_CLEAVAGE:\n",
    "        rwmol = Chem.RWMol(FULL_SMILES_MOL)\n",
    "        bond_atom_ids = molwise.atom_ids_by_map_nums(rwmol, *chosen_example.bond_map_nums)\n",
    "        FULL_SMILES_MOL = dissolution.decrease_bond_order(rwmol, *bond_atom_ids)\n",
    "        molwise.assign_ordered_atom_map_nums(FULL_SMILES_MOL, in_place=True)\n",
    "        Chem.SanitizeMol(FULL_SMILES_MOL, sanitizeOps=specification.SANITIZE_AS_KEKULE) \n",
    "\n",
    "    elif chosen_example.assign_port_by == PortMethod.REACTION:\n",
    "        raise NotImplemented\n",
    "    else:\n",
    "        raise TypeError(f'Must provide a valid port assignment method (cannot be of type {type(chosen_example.assign_port_by)})')\n",
    "\n",
    "display(FULL_SMILES_MOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enumerating \"cap\" groups from linkers and generating spec-compliant SMARTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-07 20:53:08.224 [WARNING :   specification:line 129 ] - Cleaned 8 SMARTS atom query aberrations introduced by RDKit\n",
      "2025-01-07 20:53:08.227 [WARNING :   specification:line 129 ] - Cleaned 8 SMARTS atom query aberrations introduced by RDKit\n",
      "2025-01-07 20:53:08.229 [WARNING :   specification:line 129 ] - Cleaned 8 SMARTS atom query aberrations introduced by RDKit\n"
     ]
    }
   ],
   "source": [
    "from copy import deepcopy\n",
    "from polymerist.genutils.iteration import subsets\n",
    "\n",
    "\n",
    "smarts = {}\n",
    "sat_ids = subsets(portlib.get_linker_ids(FULL_SMILES_MOL), exclude_full=True)\n",
    "for i, linkers_to_saturate in enumerate(sat_ids):\n",
    "    new_mono = deepcopy(FULL_SMILES_MOL)\n",
    "    for linker_id in linkers_to_saturate:\n",
    "        linker_atom = new_mono.GetAtomWithIdx(linker_id)\n",
    "        linker_atom.SetAtomicNum(1)\n",
    "    molwise.assign_ordered_atom_map_nums(new_mono, in_place=True) # renumber with added atoms to preserve order correspondence\n",
    "    Chem.SanitizeMol(new_mono, sanitizeOps=specification.SANITIZE_AS_KEKULE) # sanitize for good measure, making sure NOT to re-add aromaticity\n",
    "\n",
    "    key = chosen_example.molname if not i else f'{chosen_example.molname}_TERM{i}'\n",
    "    smarts[key] = [specification.compliant_mol_SMARTS(Chem.MolToSmiles(new_mono))] # for some reason, MolToSmarts doesn't reflect hydrogen addition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify orientation of terminal monomers and create monomer representation object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-07 20:53:08.361 [INFO    :            repr:line 56  ] - Creating new residue category \"polyvinylchloride\", containing singular SMARTS [\"[#6D4+0:1](-[#6D4+0:2](-[#17D1+0:3])(-[#1D1+0:4])-[*:8])(-[#1D1+0:5])(-[#1D1+0:6])-[*:7]\"])\n",
      "2025-01-07 20:53:08.363 [INFO    :            repr:line 56  ] - Creating new residue category \"polyvinylchloride_TERM1\", containing singular SMARTS [\"[#6D4+0:1](-[#6D4+0:2](-[#17D1+0:3])(-[#1D1+0:4])-[*:8])(-[#1D1+0:5])(-[#1D1+0:6])-[#1D1+0:7]\"])\n",
      "2025-01-07 20:53:08.364 [INFO    :            repr:line 56  ] - Creating new residue category \"polyvinylchloride_TERM2\", containing singular SMARTS [\"[#6D4+0:1](-[#6D4+0:2](-[#17D1+0:3])(-[#1D1+0:4])-[#1D1+0:8])(-[#1D1+0:5])(-[#1D1+0:6])-[*:7]\"])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "polyvinylchloride\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f7fdf891af0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "polyvinylchloride_TERM1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f7fdf892c00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "polyvinylchloride_TERM2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f7fdf8928f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from polymerist.polymers.monomers import MonomerGroup\n",
    "from polymerist.genutils.fileutils.pathutils import assemble_path\n",
    "\n",
    "monogrp = MonomerGroup(\n",
    "    monomers=smarts,\n",
    "    term_orient={\n",
    "        f'{chosen_example.molname}_TERM1' : 'head',\n",
    "        f'{chosen_example.molname}_TERM2' : 'tail',\n",
    "    }    \n",
    ")\n",
    "\n",
    "for (resname, rdmol) in monogrp.iter_rdmols():\n",
    "    print(resname)\n",
    "    display(rdmol)\n",
    "\n",
    "mono_path = assemble_path(monodir, chosen_example.molname, extension='json')\n",
    "monogrp.to_file(mono_path) # cache monomer SMARTS for future use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Generate coordinates and structure files with chemical info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdbdir = moldir / 'pdb_structures'\n",
    "pdbdir.mkdir(exist_ok=True)\n",
    "\n",
    "sdfdir = moldir / 'sdf_structures'\n",
    "sdfdir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grow chain of arbitrary DOP and generate PDB (only linear polymers currently supported)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[20:53:09] WARNING: not removing hydrogen atom with dummy atom neighbors\n",
      "2025-01-07 20:53:09.954 [WARNING :            repr:line 173 ] - No valid terminal monomer orientations defined, auto-assigned orientations \"{'head': 'polyvinylchloride_TERM1', 'tail': 'polyvinylchloride_TERM2'}\"; USER SHOULD VERIFY THIS YIELDS A CHEMICALLY-VALID POLYMER!\n",
      "2025-01-07 20:53:09.955 [INFO    :      sequencing:line 72  ] - Sequence kernel \"A\" is already fully reduced; no changes made\n",
      "2025-01-07 20:53:09.956 [INFO    :          linear:line 46  ] - Target chain length achievable with 28 whole 1-sequence repeat(s), namely with the sequence [polyvinylchloride_TERM1] + 28*[A] + [polyvinylchloride_TERM2]\n",
      "2025-01-07 20:53:09.957 [INFO    :          linear:line 58  ] - Registering middle monomer polyvinylchloride (block identifier \"A\")\n",
      "2025-01-07 20:53:09.991 [INFO    :          linear:line 65  ] - Registering terminal monomer polyvinylchloride_TERM1 (orientation \"head\")\n",
      "2025-01-07 20:53:09.997 [INFO    :          linear:line 65  ] - Registering terminal monomer polyvinylchloride_TERM2 (orientation \"tail\")\n",
      "2025-01-07 20:53:10.004 [INFO    :          linear:line 75  ] - Assembling linear 30-mer chain (estimated 182 atoms)\n",
      "2025-01-07 20:53:10.198 [INFO    :          linear:line 80  ] - Successfully assembled linear 30-mer chain (exactly 182 atoms)\n",
      "2025-01-07 20:53:10.199 [INFO    :          linear:line 84  ] - Energy-minimizing chain to find more stable conformer\n",
      "2025-01-07 20:53:14.102 [INFO    :          linear:line 86  ] - Energy minimization completed\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b81465a1a0ae46f8a7e63b57fcce0020",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49bc700563464c9bbd74ea6c807a4c8f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from polymerist.polymers.building import build_linear_polymer, mbmol_to_openmm_pdb\n",
    "from polymerist.polymers import estimation\n",
    "\n",
    "n_monomers : int = 30\n",
    "\n",
    "chain = build_linear_polymer(monogrp, n_monomers=n_monomers, sequence='A', energy_minimize=True)\n",
    "pdb_path = assemble_path(pdbdir, chosen_example.molname, extension='pdb')\n",
    "mbmol_to_openmm_pdb(pdb_path, chain)\n",
    "chain.visualize(backend='nglview')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill in missing PDB chemical info with our OpenFF substructure match tools\n",
    "Subsequently, partition into distinct residues and save to info-rich SDF format to avoid duplication of effort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93ac5b75241743da80928d81af8581d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from openff.toolkit import Topology, Molecule\n",
    "from polymerist.mdtools.openfftools import topology\n",
    "from polymerist.mdtools.openfftools.partition import partition\n",
    "\n",
    "\n",
    "# load PDB with substructure cover; if successful, refine into partition\n",
    "offtop = Topology.from_pdb(pdb_path, _custom_substructures=monogrp.monomers)\n",
    "was_partitioned = partition(offtop)\n",
    "assert(was_partitioned)\n",
    "\n",
    "# extract Molecule for Topology, set name and replace\n",
    "offmol = topology.get_largest_offmol(offtop)\n",
    "offmol.name = chosen_example.molname\n",
    "offtop = offmol.to_topology()\n",
    "display(offmol)\n",
    "\n",
    "# serialize Topology to SDF to preserve fully-specified system\n",
    "top_path = assemble_path(sdfdir, chosen_example.molname, extension='sdf')\n",
    "topology.topology_to_sdf(top_path, offtop) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Assignment of atomic partial charges\n",
    "Here we will use a GNN charge model to acceleralte the assignment of charges for this demo \\\n",
    "However, if you prefer to use custom-generated library charges as per our paper, check out the accompanying [RCT notebook](rct_demo.ipynb) for more details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-07 20:53:22.412 [INFO    :     molchargers:line 45  ] - Assigning partial charges via the \"Espaloma-AM1-BCC\" method\n",
      "/home/timber/miniconda3/envs/polymerist-env/lib/python3.11/site-packages/espaloma_charge/openff_wrapper.py:112: IncorrectNumConformersWarning: Molecule 'Molecule with name 'polyvinylchloride' and SMILES '[H][C@]([C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])[C@@]([H])(C([H])([H])C([H])([H])C([H])([H])Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)Cl)(C([H])([H])[H])Cl'' has 1 conformers, but charge method 'espaloma-am1bcc' expects exactly 0.\n",
      "  self._check_n_conformers(\n",
      "2025-01-07 20:53:22.696 [INFO    :     molchargers:line 48  ] - Successfully assigned \"Espaloma-AM1-BCC\" charges\n"
     ]
    }
   ],
   "source": [
    "from polymerist.mdtools.openfftools.partialcharge.molchargers import EspalomaCharger\n",
    "\n",
    "\n",
    "charger = EspalomaCharger()\n",
    "espmol = charger.charge_molecule(offmol) # inherits name from base molecule\n",
    "\n",
    "espmol_path = assemble_path(sdfdir, chosen_example.molname, postfix='charged', extension='sdf')\n",
    "topology.topology_to_sdf(espmol_path, espmol.to_topology())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "polymerist-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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