greglandrum/gist:8351725

## gistfile1.json
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Inspired by this RDKit-discuss thread started by Liz Wylie:\n",
      "http://www.mail-archive.com/rdkit-discuss@lists.sourceforge.net/msg03676.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rdkit import Chem\n",
      "from rdkit.Chem import MCS\n",
      "from rdkit.Chem.Draw import IPythonConsole"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Start with two sets of simple molecules"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smis = ['CC(C)=C','CC(C)C']\n",
      "ms = [Chem.MolFromSmiles(x) for x in smis]\n",
      "smis2 = ['CC(C)=C','CC(C)=N']\n",
      "ms2 = [Chem.MolFromSmiles(x) for x in smis2]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "default MCS behavior:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcs=MCS.FindMCS(ms)\n",
      "mcs.smarts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "'[#6]-[#6]-[#6]'"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcs=MCS.FindMCS(ms2)\n",
      "mcs.smarts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "'[#6]-[#6]-[#6]'"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "define a simple hash that combines atomic num and hybridization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def label(a): \n",
      "    return 100*int(a.GetHybridization())+a.GetAtomicNum()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and now use that to label the atoms of the first set of molecules:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nms = [Chem.Mol(x) for x in ms]\n",
      "for nm in nms:\n",
      "    for at in nm.GetAtoms():\n",
      "        at.SetIsotope(label(at))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Chem.MolToSmiles(nms[0],True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "'[406CH3][306C]([406CH3])=[306CH2]'"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we now use \"isotopes\" as the MCS atom comparator, we get nothing for the first set of molecules:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcs=MCS.FindMCS(nms,atomCompare='isotopes')\n",
      "mcs.smarts\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The second set returns a result:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nms2 = [Chem.Mol(x) for x in ms2]\n",
      "for nm in nms2:\n",
      "    for at in nm.GetAtoms():\n",
      "        at.SetIsotope(label(at))\n",
      "mcs=MCS.FindMCS(nms2,atomCompare='isotopes')\n",
      "mcs.smarts\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "'[406*]-[306*]-[406*]'"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But that's kind of ugly... let's get the SMILES for it."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We start by finding the matching atoms in the first of the modified molecules:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcsp = Chem.MolFromSmarts(mcs.smarts)\n",
      "match = nms2[0].GetSubstructMatch(mcsp)\n",
      "match"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(0, 1, 2)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now generate smiles for the first unmodified molecule, but only including those atoms.\n",
      "We can do this because we know that the atom numbers didn't change."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print Chem.MolFragmentToSmiles(ms2[0],atomsToUse=match,isomericSmiles=True,canonical=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CCC\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ok, let's try it with Liz's test case:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smis=[\"COc1ccc(C(Nc2nc3c(ncn3COCC=O)c(=O)[nH]2)(c2ccccc2)c2ccccc2)cc1\",\n",
      "      \"COc1ccc(C(Nc2nc3c(ncn3COC(CO)(CO)CO)c(=O)[nH]2)(c2ccccc2)c2ccccc2)cc1\"]\n",
      "ms = [Chem.MolFromSmiles(x) for x in smis]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ms[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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PpKQAmzZxm5caGnL7IRkbA0ZGgKUlHpmaIs3AAGZmZjAyMoKZmRlMTExgaGgI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\nAAAAAElFTkSuQmCC\n",
       "prompt_number": 14,
       "text": [
        "<rdkit.Chem.rdchem.Mol at 0x1078ed9f0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ms[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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/siAmLxo2TDvzJzpaW6BuhZ0tihQpwtdff039+vUZNmwYYWFhzJw586Eu1qSk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\ngg==\n",
       "prompt_number": 15,
       "text": [
        "<rdkit.Chem.rdchem.Mol at 0x1078eda60>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nms = [Chem.Mol(x) for x in ms]\n",
      "for nm in nms:\n",
      "    for at in nm.GetAtoms():\n",
      "        at.SetIsotope(label(at))\n",
      "mcs=MCS.FindMCS(nms,atomCompare='isotopes')\n",
      "mcs.smarts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "'[406*]-[308*]-[306*]:1:[306*]:[306*]:[306*](:[306*]:[306*]:1)-[406*](-[306*]:1:[306*]:[306*]:[306*]:[306*]:[306*]:1)(-[306*]:1:[306*]:[306*]:[306*]:[306*]:[306*]:1)-[307*]-[306*]:1:[307*]:[306*]:2:[306*](:[306*](:[307*]:1)=[308*]):[307*]:[306*]:[307*]:2-[406*]-[408*]-[406*]'"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcsp = Chem.MolFromSmarts(mcs.smarts)\n",
      "match = nms[0].GetSubstructMatch(mcsp)\n",
      "smi=Chem.MolFragmentToSmiles(ms[0],atomsToUse=match,isomericSmiles=True,canonical=False)\n",
      "print smi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "COc1ccc(C(Nc2nc3c(ncn3COC)c(=O)[nH]2)(c2ccccc2)c2ccccc2)cc1\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "core = Chem.MolFromSmiles(smi)\n",
      "core"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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Rrh1R+/ZEb7+tpbS0tIdeV5utFIYWGxtLcrmc5s6dS3FxceTj4/PQSlvGGhu+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\nRK5CYII=\n",
       "prompt_number": 18,
       "text": [
        "<rdkit.Chem.rdchem.Mol at 0x1078e5bb0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}