greglandrum/MCS.ipynb

## MCS.ipynb
{
 "metadata": {
  "name": "MCS"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rdkit import Chem\n",
      "from rdkit.Chem import Draw\n",
      "from rdkit.Chem.Draw import IPythonConsole\n",
      "from rdkit.Chem import MCS"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smis = (\"O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C\", \"CC(C)CCCCCC(=O)NCC1=CC(=C(C=C1)O)OC\", \"c1(C(=O)O)cc(OC)c(O)cc1\")\n",
      "mols = [Chem.MolFromSmiles(x) for x in smis]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Draw.MolsToGridImage(mols)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAADICAIAAAC7/QjhAAAwAUlEQVR4nO3dezxU+f8H8DfDuCT3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\nEUIISTQMQoQQQhLt/wH12U4OqSuoSgAAAABJRU5ErkJggg==\n",
       "prompt_number": 15,
       "text": [
        "<Image.Image image mode=RGB size=600x200 at 0x33D5200>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcs=MCS.FindMCS(mols)\n",
      "mcs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "MCSResult(numAtoms=10, numBonds=10, smarts='[#6]-[#6]:1:[#6]:[#6](:[#6](:[#6]:[#6]:1)-[#8])-[#8]-[#6]', completed=1)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Chem.MolFromSmarts(mcs.smarts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAYAAABNcIgQAAAZCklEQVR4nO3de1CU9f4H8DeC3EEx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\nEREp2v8DACNAGnEAuIMAAAAASUVORK5CYII=\n",
       "prompt_number": 17,
       "text": [
        "<rdkit.Chem.rdchem.Mol at 0x33db2f0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mcs=MCS.FindMCS(mols,atomCompare=\"any\")\n",
      "mcs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "MCSResult(numAtoms=11, numBonds=11, smarts='[*]:1(:[*]:[*](:[*](:[*]:[*]:1)-[*])-[*]-[*])-[*]-[*]', completed=1)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Chem.MolFromSmarts(mcs.smarts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAYAAABNcIgQAAAWuklEQVR4nO3d61OT6fkH8C/hEAJG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\nrkJggg==\n",
       "prompt_number": 20,
       "text": [
        "<rdkit.Chem.rdchem.Mol at 0x32c2d70>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols = (Chem.MolFromSmiles('NCC'),Chem.MolFromSmiles('OC=C'))\n",
      "print MCS.FindMCS(mols)\n",
      "print MCS.FindMCS(mols, atomCompare=\"any\")\n",
      "print MCS.FindMCS(mols, bondCompare=\"any\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "MCS None has -1 atoms and -1 bonds\n",
        "MCS '[*]-[*]' has 2 atoms and 1 bonds\n",
        "MCS '[#6]~[#6]' has 2 atoms and 1 bonds\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "MCS.FindMCS(mols)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "MCSResult(numAtoms=-1, numBonds=-1, smarts=None, completed=1)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols = (Chem.MolFromSmiles('NC1OC1'),Chem.MolFromSmiles('C1OC1[N+](=O)[O-]'))\n",
      "print repr(MCS.FindMCS(mols))\n",
      "print repr(MCS.FindMCS(mols,matchValences=True))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "MCSResult(numAtoms=4, numBonds=4, smarts='[#7]-[#6]-1-[#8]-[#6]-1', completed=1)\n",
        "MCSResult(numAtoms=3, numBonds=3, smarts='[#6v4]-1-[#8v2]-[#6v4]-1', completed=1)\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Chem.MolFromSmiles(\"CC(C)CCCCCC(=O)(O)NCC1=CC(=C(C=C1)O)OC\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols = [Chem.MolFromSmiles(\"Nc1ccccc1\"*100), Chem.MolFromSmiles(\"Nc1ccccccccc1\"*100)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}