align3Dsmarts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbabel import pybel\n",
    "import os\n",
    "import re\n",
    "import argparse\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem.rdMolAlign import AlignMol\n",
    "import copy\n",
    "from openbabel import openbabel as ob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = \"/home/jpam/google_drive/UFMG/TCC/bianca/moleculas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = [f for f in os.listdir(directory) if f.endswith(\"mol2\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mols = [pybel.readfile('mol2',os.path.join(directory,f)) \n",
    "\t    for f in files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdmols = []\n",
    "for mol in mols:\n",
    "    for c in mol:\n",
    "        rdmol = Chem.MolFromMolBlock(c.write('mol'),removeHs=False)\n",
    "    rdmols.append(copy.copy(rdmol))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<rdkit.Chem.rdchem.Mol at 0x7ff26604dc10>,\n",
       " <rdkit.Chem.rdchem.Mol at 0x7ff26604dbc0>,\n",
       " <rdkit.Chem.rdchem.Mol at 0x7ff2660564e0>,\n",
       " <rdkit.Chem.rdchem.Mol at 0x7ff2660568a0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdmols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "smarts = \"[#6]1-[#6](-[#6](-[#6](-[#6](-[#6]-1-[#1])-[#1])-[#6](-[#6](-[#7](-[#1])-[#1])-[#8])-[#7](-[#6](-[#6](-[#6](-[#6](-[#6](-[#6](-[#7](-[#1])-[#1])(-[#1])-[#1])(-[#1])-[#1])(-[#1])-[#1])(-[#1])-[#1])(-[#7](-[#6](-[#6](-[#6](-[#6]-[#6]-[#1])(-[#1])-[#1])(-[#7](-[#6](-[#6])-[#8])-[#1])-[#1])-[#8])-[#1])-[#1])-[#8])-[#1])-[#1])-[#1]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "patt = Chem.MolFromSmarts(smarts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c18-t2.mol2\n",
      "c75-t2.mol2\n",
      "c68-t2.mol2\n"
     ]
    }
   ],
   "source": [
    "refMol = rdmols[0]\n",
    "refAtoms = list(refMol.GetSubstructMatch(patt))\n",
    "mols_aligned = [pybel.readstring(\"mol\",Chem.MolToMolBlock(refMol))]\n",
    "for i,prbMol in enumerate(rdmols[1:]):\n",
    "    try:\n",
    "        prbAtoms = list(prbMol.GetSubstructMatch(patt))\n",
    "        alinhamento = AlignMol(prbMol,refMol,atomMap=list(zip(prbAtoms,refAtoms)))\n",
    "        mols_aligned.append(pybel.readstring(\"mol\",Chem.MolToMolBlock(prbMol)))\n",
    "    except Exception as e:\n",
    "        print(prbMol.GetProp(\"_Name\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,(m1,m2) in enumerate(zip(mols_aligned,mols)):\n",
    "    for (a1,a2) in zip(m1,m2):\n",
    "        print(a1,a2)\n",
    "        a1.partialcharge = a2.partialcharge\n",
    "    m1.write(\"mol2\",files[i][:-4]+\"_aligned.mol2\",True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obConversion = ob.OBConversion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obConversion.SetInAndOutFormats(\"g09\",\"mol2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obmols = []\n",
    "for i in range(len(files)):\n",
    "    obmol = ob.OBMol()\n",
    "    obConversion.ReadFile(obmol, os.path.join(directory,df.iloc[i,0]+\".log\"))\n",
    "    obmols.append(obmol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.path.basename(obmols[0].GetTitle())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for at in ob.OBMolAtomIter(obmols[0]):\n",
    "    print(at.GetPartialCharge())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m1,m2 in zip(mols_aligned,obmols):\n",
    "    for (a1,a2) in zip(ob.OBMolAtomIter(m1.OBMol),ob.OBMolAtomIter(m2)):\n",
    "#         a1.partialcharge = a2.partialcharge\n",
    "        a1.SetPartialCharge(a2.GetPartialCharge())\n",
    "    m1.write(\"mol2\",m1.title[:-4]+\"_aligned.mol2\",True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ArgumentError",
     "evalue": "Python argument types in\n    Mol.GetSubstructMatch(Mol, str)\ndid not match C++ signature:\n    GetSubstructMatch(RDKit::ROMol self, RDKit::MolBundle query, RDKit::SubstructMatchParameters params)\n    GetSubstructMatch(RDKit::ROMol self, RDKit::ROMol query, RDKit::SubstructMatchParameters params)\n    GetSubstructMatch(RDKit::ROMol self, RDKit::MolBundle query, bool useChirality=False, bool useQueryQueryMatches=False)\n    GetSubstructMatch(RDKit::ROMol self, RDKit::ROMol query, bool useChirality=False, bool useQueryQueryMatches=False)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-e42a0e255b7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrefMol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetSubstructMatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/lqtaqsar/lib/python3.7/site-packages/rdkit/Chem/Draw/IPythonConsole.py\u001b[0m in \u001b[0;36m_GetSubstructMatch\u001b[0;34m(mol, query, *args, **kwargs)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_GetSubstructMatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m   \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__GetSubstructMatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mhighlightSubstructs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0mmol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__sssAtoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mArgumentError\u001b[0m: Python argument types in\n    Mol.GetSubstructMatch(Mol, str)\ndid not match C++ signature:\n    GetSubstructMatch(RDKit::ROMol self, RDKit::MolBundle query, RDKit::SubstructMatchParameters params)\n    GetSubstructMatch(RDKit::ROMol self, RDKit::ROMol query, RDKit::SubstructMatchParameters params)\n    GetSubstructMatch(RDKit::ROMol self, RDKit::MolBundle query, bool useChirality=False, bool useQueryQueryMatches=False)\n    GetSubstructMatch(RDKit::ROMol self, RDKit::ROMol query, bool useChirality=False, bool useQueryQueryMatches=False)"
     ]
    }
   ],
   "source": [
    "list(refMol.GetSubstructMatch(patt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "img=Chem.Draw.MolsToGridImage(rdmols,subImgSize=(200,200))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x400 at 0x7FF294408650>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lqtaqsar",
   "language": "python",
   "name": "lqtaqsar"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}