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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Scaffold analysis of ChEMBL data with pandas and RDKit\n",
      "### Dr. Samo Turk\n",
      "#### BioMed X Innovation Center, Heidelberg  \n",
      "<img src=\"files/biomedx.png\">  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<p style=\"font-size:20px;\"><strong>Python</strong></p>\n",
      "<p style=\"font-size:20px;\">[Python](http://www.python.org/) very popular programming language especially in science.</p>\n",
      "\n",
      "<p style=\"font-size:20px;\"><strong>pandas</strong></p>\n",
      "<p style=\"font-size:20px;\">[Pandas](http://pandas.pydata.org/) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.  \n",
      "No need for R!</p>\n",
      "\n",
      "<p style=\"font-size:20px;\"><strong>RDKit</strong></p>\n",
      "<p style=\"font-size:20px;\">[RDKit](http://www.rdkit.org/) is an open source chemistry toolkit.</p>\n",
      "\n",
      "<p style=\"font-size:20px;\"><strong>IPython</strong></p>\n",
      "<p style=\"font-size:20px;\">[IPython](http://ipython.org/) interactive Python shell. Has web-based interactive computational environment IPython Notebook.</p>\n",
      "\n",
      "<p style=\"font-size:20px;\"><strong>chembl_webresource_client</strong></p>\n",
      "<p style=\"font-size:20px;\">[chembl_webresource_client](https://github.com/chembl/chembl_webresource_client) Python client for accessing ChEMBL webservices.</p>\n",
      "\n",
      "### More notebooks: https://github.com/Team-SKI/snippets\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd # Import pandas\n",
      "from chembl_webresource_client import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## chembl_webresource_client"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Targets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Target object handler for API access and check connection to the db"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "targets = TargetResource()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print targets.status()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### ABL1 kinase \n",
      "uniprotID: **P00519**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "targets.get(uniprot=['P00519'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[{u'bioactivityCount': 9984,\n",
        "  u'chemblId': u'CHEMBL1862',\n",
        "  u'compoundCount': 2997,\n",
        "  u'description': u'Tyrosine-protein kinase ABL',\n",
        "  u'geneNames': u'Unspecified',\n",
        "  u'organism': u'Homo sapiens',\n",
        "  u'preferredName': u'Tyrosine-protein kinase ABL',\n",
        "  u'proteinAccession': u'P00519',\n",
        "  u'synonyms': u'Proto-oncogene c-Abl,JTK7,ABL1,2.7.10.2,Abelson murine leukemia viral oncogene homolog 1,Abelson tyrosine-protein kinase 1,ABL ,Tyrosine-protein kinase ABL1,p150',\n",
        "  u'targetType': u'SINGLE PROTEIN'}]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load targets in data frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "targetsDF = pd.DataFrame.from_dict(targets.get(uniprot=['P00519']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "targetsDF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bioactivityCount</th>\n",
        "      <th>chemblId</th>\n",
        "      <th>compoundCount</th>\n",
        "      <th>description</th>\n",
        "      <th>geneNames</th>\n",
        "      <th>organism</th>\n",
        "      <th>preferredName</th>\n",
        "      <th>proteinAccession</th>\n",
        "      <th>synonyms</th>\n",
        "      <th>targetType</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 9984</td>\n",
        "      <td> CHEMBL1862</td>\n",
        "      <td> 2997</td>\n",
        "      <td> Tyrosine-protein kinase ABL</td>\n",
        "      <td> Unspecified</td>\n",
        "      <td> Homo sapiens</td>\n",
        "      <td> Tyrosine-protein kinase ABL</td>\n",
        "      <td> P00519</td>\n",
        "      <td> Proto-oncogene c-Abl,JTK7,ABL1,2.7.10.2,Abelso...</td>\n",
        "      <td> SINGLE PROTEIN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "   bioactivityCount    chemblId  compoundCount                  description  \\\n",
        "0              9984  CHEMBL1862           2997  Tyrosine-protein kinase ABL   \n",
        "\n",
        "     geneNames      organism                preferredName proteinAccession  \\\n",
        "0  Unspecified  Homo sapiens  Tyrosine-protein kinase ABL           P00519   \n",
        "\n",
        "                                            synonyms      targetType  \n",
        "0  Proto-oncogene c-Abl,JTK7,ABL1,2.7.10.2,Abelso...  SINGLE PROTEIN  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Bioactivities\n",
      "Download all bioactivities for targets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bioactsDF = pd.DataFrame.from_dict(targets.bioactivities('CHEMBL1862'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bioactsDF.head(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>activity_comment</th>\n",
        "      <th>assay_chemblid</th>\n",
        "      <th>assay_description</th>\n",
        "      <th>assay_type</th>\n",
        "      <th>bioactivity_type</th>\n",
        "      <th>ingredient_cmpd_chemblid</th>\n",
        "      <th>name_in_reference</th>\n",
        "      <th>operator</th>\n",
        "      <th>organism</th>\n",
        "      <th>parent_cmpd_chemblid</th>\n",
        "      <th>reference</th>\n",
        "      <th>target_chemblid</th>\n",
        "      <th>target_confidence</th>\n",
        "      <th>target_name</th>\n",
        "      <th>units</th>\n",
        "      <th>value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Unspecified</td>\n",
        "      <td> CHEMBL1063789</td>\n",
        "      <td> Binding constant for ABL1(E255K) kinase domain</td>\n",
        "      <td> B</td>\n",
        "      <td> Kd</td>\n",
        "      <td> CHEMBL440084</td>\n",
        "      <td> SB-431542</td>\n",
        "      <td> &gt;</td>\n",
        "      <td> Homo sapiens</td>\n",
        "      <td> CHEMBL440084</td>\n",
        "      <td> Nat. Biotechnol., (2008) 26:1:127</td>\n",
        "      <td> CHEMBL1862</td>\n",
        "      <td> 9</td>\n",
        "      <td> Tyrosine-protein kinase ABL</td>\n",
        "      <td> nM</td>\n",
        "      <td> 10000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "  activity_comment assay_chemblid  \\\n",
        "0      Unspecified  CHEMBL1063789   \n",
        "\n",
        "                                assay_description assay_type bioactivity_type  \\\n",
        "0  Binding constant for ABL1(E255K) kinase domain          B               Kd   \n",
        "\n",
        "  ingredient_cmpd_chemblid name_in_reference operator      organism  \\\n",
        "0             CHEMBL440084         SB-431542        >  Homo sapiens   \n",
        "\n",
        "  parent_cmpd_chemblid                          reference target_chemblid  \\\n",
        "0         CHEMBL440084  Nat. Biotechnol., (2008) 26:1:127      CHEMBL1862   \n",
        "\n",
        "   target_confidence                  target_name units  value  \n",
        "0                  9  Tyrosine-protein kinase ABL    nM  10000  "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Filter bioactivities (safe defaults)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bioactsDF = bioactsDF[(bioactsDF['bioactivity_type'] == 'IC50') &  # keep ony IC50\n",
      "                      (bioactsDF['operator'] == '=') & # only exact measurements\n",
      "                      (bioactsDF['assay_type'] == 'B') & # only binding data\n",
      "                      (bioactsDF['target_confidence'] == 9)] # only high target confidence"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(bioactsDF), len(bioactsDF['ingredient_cmpd_chemblid'].unique())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(282, 251)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Compouds"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compound object handler for API access and check connection to the db"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "compounds = CompoundResource()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "compounds.status()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the compounds in the dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF = pd.DataFrame.from_dict(compounds.get(list(bioactsDF['ingredient_cmpd_chemblid'].unique())))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>acdAcidicPka</th>\n",
        "      <th>acdBasicPka</th>\n",
        "      <th>acdLogd</th>\n",
        "      <th>acdLogp</th>\n",
        "      <th>alogp</th>\n",
        "      <th>chemblId</th>\n",
        "      <th>knownDrug</th>\n",
        "      <th>medChemFriendly</th>\n",
        "      <th>molecularFormula</th>\n",
        "      <th>molecularWeight</th>\n",
        "      <th>numRo5Violations</th>\n",
        "      <th>passesRuleOfThree</th>\n",
        "      <th>preferredCompoundName</th>\n",
        "      <th>rotatableBonds</th>\n",
        "      <th>smiles</th>\n",
        "      <th>species</th>\n",
        "      <th>stdInChiKey</th>\n",
        "      <th>synonyms</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   NaN</td>\n",
        "      <td> 8.97</td>\n",
        "      <td> 3.71</td>\n",
        "      <td> 5.27</td>\n",
        "      <td> 3.82</td>\n",
        "      <td>  CHEMBL388978</td>\n",
        "      <td> No</td>\n",
        "      <td> Yes</td>\n",
        "      <td>    C28H26N4O3</td>\n",
        "      <td> 466.53</td>\n",
        "      <td> 0</td>\n",
        "      <td> No</td>\n",
        "      <td> STAUROSPORINE</td>\n",
        "      <td> 2</td>\n",
        "      <td> CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n3c4ccccc4...</td>\n",
        "      <td>    BASE</td>\n",
        "      <td> HKSZLNNOFSGOKW-FYTWVXJKSA-N</td>\n",
        "      <td> SID26755744,SID26755745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   NaN</td>\n",
        "      <td> 6.88</td>\n",
        "      <td> 4.02</td>\n",
        "      <td> 4.13</td>\n",
        "      <td> 3.76</td>\n",
        "      <td> CHEMBL1290072</td>\n",
        "      <td> No</td>\n",
        "      <td> Yes</td>\n",
        "      <td>    C19H16N2O2</td>\n",
        "      <td> 304.34</td>\n",
        "      <td> 0</td>\n",
        "      <td> No</td>\n",
        "      <td>           NaN</td>\n",
        "      <td> 4</td>\n",
        "      <td>                 COc1ccc(CNc2ccnc3oc4ccccc4c23)cc1</td>\n",
        "      <td> NEUTRAL</td>\n",
        "      <td> MNMQBQOLMHVBJT-UHFFFAOYSA-N</td>\n",
        "      <td>                     NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12.27</td>\n",
        "      <td> 3.32</td>\n",
        "      <td> 4.18</td>\n",
        "      <td> 4.18</td>\n",
        "      <td> 5.10</td>\n",
        "      <td>  CHEMBL565609</td>\n",
        "      <td> No</td>\n",
        "      <td> Yes</td>\n",
        "      <td> C28H28Cl2N6O4</td>\n",
        "      <td> 583.47</td>\n",
        "      <td> 2</td>\n",
        "      <td> No</td>\n",
        "      <td>           NaN</td>\n",
        "      <td> 9</td>\n",
        "      <td> CN1C(=O)C(=Cc2cnc(Nc3ccc(NC(=O)CCNC(=O)OC(C)(C...</td>\n",
        "      <td> NEUTRAL</td>\n",
        "      <td> APGJWCRRERWKDQ-UHFFFAOYSA-N</td>\n",
        "      <td>                     NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 12.80</td>\n",
        "      <td> 7.86</td>\n",
        "      <td> 5.67</td>\n",
        "      <td> 6.62</td>\n",
        "      <td> 5.26</td>\n",
        "      <td> CHEMBL1171085</td>\n",
        "      <td> No</td>\n",
        "      <td> Yes</td>\n",
        "      <td>   C30H25F3N6O</td>\n",
        "      <td> 542.55</td>\n",
        "      <td> 2</td>\n",
        "      <td> No</td>\n",
        "      <td>           NaN</td>\n",
        "      <td> 8</td>\n",
        "      <td> CN(C)Cc1nccn1c2cc(NC(=O)c3ccc(C)c(c3)C#Cc4cnc5...</td>\n",
        "      <td> NEUTRAL</td>\n",
        "      <td> QZYXEXGJVTZDIZ-UHFFFAOYSA-N</td>\n",
        "      <td>                     NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  8.13</td>\n",
        "      <td> 7.54</td>\n",
        "      <td> 4.88</td>\n",
        "      <td> 5.35</td>\n",
        "      <td> 4.75</td>\n",
        "      <td> CHEMBL1171657</td>\n",
        "      <td> No</td>\n",
        "      <td> Yes</td>\n",
        "      <td>   C29H27F3N6O</td>\n",
        "      <td> 532.56</td>\n",
        "      <td> 1</td>\n",
        "      <td> No</td>\n",
        "      <td>           NaN</td>\n",
        "      <td> 7</td>\n",
        "      <td> CN1CCN(Cc2ccc(NC(=O)c3ccc(C)c(c3)C#Cc4cnc5[nH]...</td>\n",
        "      <td> NEUTRAL</td>\n",
        "      <td> XGDBPKHGUYPWPJ-UHFFFAOYSA-N</td>\n",
        "      <td>                     NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "   acdAcidicPka  acdBasicPka  acdLogd  acdLogp  alogp       chemblId  \\\n",
        "0           NaN         8.97     3.71     5.27   3.82   CHEMBL388978   \n",
        "1           NaN         6.88     4.02     4.13   3.76  CHEMBL1290072   \n",
        "2         12.27         3.32     4.18     4.18   5.10   CHEMBL565609   \n",
        "3         12.80         7.86     5.67     6.62   5.26  CHEMBL1171085   \n",
        "4          8.13         7.54     4.88     5.35   4.75  CHEMBL1171657   \n",
        "\n",
        "  knownDrug medChemFriendly molecularFormula  molecularWeight  \\\n",
        "0        No             Yes       C28H26N4O3           466.53   \n",
        "1        No             Yes       C19H16N2O2           304.34   \n",
        "2        No             Yes    C28H28Cl2N6O4           583.47   \n",
        "3        No             Yes      C30H25F3N6O           542.55   \n",
        "4        No             Yes      C29H27F3N6O           532.56   \n",
        "\n",
        "   numRo5Violations passesRuleOfThree preferredCompoundName  rotatableBonds  \\\n",
        "0                 0                No         STAUROSPORINE               2   \n",
        "1                 0                No                   NaN               4   \n",
        "2                 2                No                   NaN               9   \n",
        "3                 2                No                   NaN               8   \n",
        "4                 1                No                   NaN               7   \n",
        "\n",
        "                                              smiles  species  \\\n",
        "0  CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n3c4ccccc4...     BASE   \n",
        "1                  COc1ccc(CNc2ccnc3oc4ccccc4c23)cc1  NEUTRAL   \n",
        "2  CN1C(=O)C(=Cc2cnc(Nc3ccc(NC(=O)CCNC(=O)OC(C)(C...  NEUTRAL   \n",
        "3  CN(C)Cc1nccn1c2cc(NC(=O)c3ccc(C)c(c3)C#Cc4cnc5...  NEUTRAL   \n",
        "4  CN1CCN(Cc2ccc(NC(=O)c3ccc(C)c(c3)C#Cc4cnc5[nH]...  NEUTRAL   \n",
        "\n",
        "                   stdInChiKey                 synonyms  \n",
        "0  HKSZLNNOFSGOKW-FYTWVXJKSA-N  SID26755744,SID26755745  \n",
        "1  MNMQBQOLMHVBJT-UHFFFAOYSA-N                      NaN  \n",
        "2  APGJWCRRERWKDQ-UHFFFAOYSA-N                      NaN  \n",
        "3  QZYXEXGJVTZDIZ-UHFFFAOYSA-N                      NaN  \n",
        "4  XGDBPKHGUYPWPJ-UHFFFAOYSA-N                      NaN  "
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bioactsDF['value'] = bioactsDF['value'].astype(float) # making sure everything is float"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Chemoinformatics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import rdkit.Chem as Chem\n",
      "from rdkit.Chem import Draw\n",
      "from rdkit.Chem import PandasTools\n",
      "from rdkit.Chem.Draw import IPythonConsole # Enables RDKit IPython integration"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "molnames = 'chemblId'\n",
      "smiles = 'smiles'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Add molecule column"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.AddMoleculeColumnToFrame(cpdsDF, smilesCol='smiles')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF = cpdsDF[[molnames, smiles, 'ROMol', 'knownDrug', 'preferredCompoundName']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Add bioactivities and ligand efficiency\n",
      "Ligand efficiency: *LE = 1.4 x pIC50/HAC*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "def getBioacts(cpd, target):\n",
      "    value = bioactsDF[(bioactsDF['ingredient_cmpd_chemblid'] == cpd)& # All rows of compound AND\n",
      "                     (bioactsDF['target_chemblid'] == target)]['value'].mean() # target. Get mean of values\n",
      "    return np.log10(value*10**-9)*-1 # returns pIC50\n",
      "\n",
      "def getLE(mol, pIC50): \n",
      "    return 1.4*pIC50/mol.GetNumHeavyAtoms()        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF['pIC50'] = cpdsDF.apply(lambda x: getBioacts(x[molnames], 'CHEMBL1862'), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF['LE'] = cpdsDF.apply(lambda x: getLE(x['ROMol'], x['pIC50']), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF.head(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>chemblId</th>\n",
        "      <th>smiles</th>\n",
        "      <th>ROMol</th>\n",
        "      <th>knownDrug</th>\n",
        "      <th>preferredCompoundName</th>\n",
        "      <th>pIC50</th>\n",
        "      <th>LE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> CHEMBL388978</td>\n",
        "      <td> CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n3c4ccccc4c5c6CNC(=O)c6c7c8ccccc8n2c7c35</td>\n",
        "      <td> <img 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\" alt=\"Mol\"/></td>\n",
        "      <td> No</td>\n",
        "      <td> STAUROSPORINE</td>\n",
        "      <td> 6.677781</td>\n",
        "      <td> 0.267111</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "       chemblId                                                                        smiles                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          ROMol knownDrug preferredCompoundName     pIC50        LE\n",
        "0  CHEMBL388978  CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n3c4ccccc4c5c6CNC(=O)c6c7c8ccccc8n2c7c35  <img 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\" alt=\"Mol\"/>        No         STAUROSPORINE  6.677781  0.267111"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(cpdsDF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "251"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "## Murcko scaffold decomposition\n",
      "  \n",
      "Bemis, G. W.; Murcko, M. A. \u201cThe Properties of Known Drugs. 1. Molecular Frameworks.\u201d J. Med. Chem. 39:2887-93 (1996).  \n",
      "\n",
      "Decomposition of molecules to scaffolds or generic frameworks  \n",
      "\n",
      "Functionality present in RDKit. Added it to PandasTools \n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rdkit.Chem.Scaffolds import MurckoScaffold"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mol  = Chem.MolFromSmiles(cpdsDF.ix[2]['smiles'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scaffold = MurckoScaffold.GetScaffoldForMol(mol)\n",
      "generic = MurckoScaffold.MakeScaffoldGeneric(MurckoScaffold.GetScaffoldForMol(mol))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Draw.MolsToGridImage([mol, scaffold, generic])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 28,
       "text": [
        "<PIL.Image.Image image mode=RGBA size=600x200 at 0x7F0965C9AB00>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "## AddMurckoToFrame(pandasFrame, MurcoCol=, Generic=False)\n",
      "Add column with SMILES of scaffolds (or generic frameworks)\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.AddMurckoToFrame(cpdsDF)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "##### AlignToScaffold(dataframe, molCol=, scaffoldCol=)\n",
      "Aligns compounds to scaffolds\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.AlignToScaffold(cpdsDF, molCol='ROMol', scaffoldCol='Murcko_SMILES')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF.head(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>chemblId</th>\n",
        "      <th>smiles</th>\n",
        "      <th>ROMol</th>\n",
        "      <th>knownDrug</th>\n",
        "      <th>preferredCompoundName</th>\n",
        "      <th>pIC50</th>\n",
        "      <th>LE</th>\n",
        "      <th>Murcko_SMILES</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> CHEMBL388978</td>\n",
        "      <td> CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n3c4ccccc4c5c6CNC(=O)c6c7c8ccccc8n2c7c35</td>\n",
        "      <td> <img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
        "      <td> No</td>\n",
        "      <td> STAUROSPORINE</td>\n",
        "      <td> 6.677781</td>\n",
        "      <td> 0.267111</td>\n",
        "      <td> O=C1NCc2c1c1c3ccccc3n3c1c1c2c2ccccc2n1C1CCCC3O1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "       chemblId                                                                        smiles                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            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preferredCompoundName     pIC50        LE                                    Murcko_SMILES\n",
        "0  CHEMBL388978  CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n3c4ccccc4c5c6CNC(=O)c6c7c8ccccc8n2c7c35  <img 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\" alt=\"Mol\"/>        No         STAUROSPORINE  6.677781  0.267111  O=C1NCc2c1c1c3ccccc3n3c1c1c2c2ccccc2n1C1CCCC3O1"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can use pandas **groupby()** functionality and group by scaffolds and create a new frame with scaffolds sorted by number of members"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sortedScaffolds = cpdsDF.groupby(['Murcko_SMILES']).count().sort(smiles, ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sortedScaffolds = sortedScaffolds[[smiles]] # Keep only smiles column\n",
      "sortedScaffolds = sortedScaffolds.rename(columns={smiles:'count'}) # rename smiles column to count\n",
      "sortedScaffolds['Murcko_SMILES'] = sortedScaffolds.index # actual SMILES are only in index column, move it\n",
      "sortedScaffolds.index = range(len(sortedScaffolds)+1)[1:]\n",
      "sortedScaffolds.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>Murcko_SMILES</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 17</td>\n",
        "      <td>             O=c1[nH]c2nc(Nc3ccccc3)ncc2cc1-c1ccccc1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 11</td>\n",
        "      <td>        O=C(Nc1cccc(Nc2nccc(-c3cccnc3)n2)c1)c1ccccc1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 10</td>\n",
        "      <td>    O=C(Nc1ccc(CN2CCNCC2)cc1)c1cccc(C#Cc2cnc[nH]2)c1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  9</td>\n",
        "      <td>       O=C(COc1ccc2c(=O)cc(-c3ccccc3)oc2c1)Nc1ccccn1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>  8</td>\n",
        "      <td> O=C(Nc1ccc(CN2CCNCC2)cc1)c1cccc(C#Cc2cnc3cccnn23)c1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "   count                                        Murcko_SMILES\n",
        "1     17              O=c1[nH]c2nc(Nc3ccccc3)ncc2cc1-c1ccccc1\n",
        "2     11         O=C(Nc1cccc(Nc2nccc(-c3cccnc3)n2)c1)c1ccccc1\n",
        "3     10     O=C(Nc1ccc(CN2CCNCC2)cc1)c1cccc(C#Cc2cnc[nH]2)c1\n",
        "4      9        O=C(COc1ccc2c(=O)cc(-c3ccccc3)oc2c1)Nc1ccccn1\n",
        "5      8  O=C(Nc1ccc(CN2CCNCC2)cc1)c1cccc(C#Cc2cnc3cccnn23)c1"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(sortedScaffolds)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "140"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Add RDKit's ROMol column to scaffolds dataframe so we can visualize it"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.AddMoleculeColumnToFrame(sortedScaffolds, smilesCol='Murcko_SMILES')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "#### FrameToGridImage(pandasFrame, legendsCol=, molsPerRow=)\n",
      "Visualize molecules in data frame as a grid image\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.FrameToGridImage(sortedScaffolds.dropna().head(8), legendsCol='count', \n",
      "                             molsPerRow=4) # dropna() drops compounds without scaffold"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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9PXr06CHrdL7Lxka8OEiHDl/u8/AAgoK+LA4CAEeOiNeQqFr1HdTVH+Pdu3fY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\nRK5CYII=\n",
       "prompt_number": 36,
       "text": [
        "<PIL.Image.Image image mode=RGBA size=800x400 at 0x7F096593B7E8>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get compounds that belong to certain scaffold"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.FrameToGridImage(cpdsDF[cpdsDF['Murcko_SMILES'] == 'O=c1[nH]c2nc(Nc3ccccc3)ncc2cc1-c1ccccc1'].head(4),\n",
      "                            legendsCol=molnames, molsPerRow=4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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oYOlSoRTcrVvAxo3pzx87FliyRPSw0tm0aRO0tLQwYMAAbNu2DT169MjUee/fv8eQIUNw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\njDHGGGOMMZXhBIQxxhhjjDGmMv8D5a9SAuj+makAAAAASUVORK5CYII=\n",
       "prompt_number": 37,
       "text": [
        "<PIL.Image.Image image mode=RGBA size=800x200 at 0x7F096580D200>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cpdsDF[cpdsDF['Murcko_SMILES'] == 'O=c1[nH]c2nc(Nc3ccccc3)ncc2cc1-c1ccccc1'\n",
      "       ]['pIC50'].max() # min(), mean(), sum(), anything you can do with pandas Series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "8.6382721639824069"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exploratory plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####  pIC50s of compounds with certain scaffolds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = [cpdsDF[cpdsDF['Murcko_SMILES'] == x]['pIC50'] for x in sortedScaffolds['Murcko_SMILES'].head(8)]\n",
      "data.append(cpdsDF[cpdsDF['knownDrug'] == 'Yes']['pIC50'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "pylab.boxplot(data)\n",
      "pylab.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zqur2Aes4Hfg08Myquj/JB4GPV9VlA9b0LOAK4AXAA8AngD+oqttW8XyjHmFX1eeAe4au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\njLIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f094f3bf990>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####  Ligand efficiencies of compounds with certain scaffolds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = [cpdsDF[cpdsDF['Murcko_SMILES'] == x]['LE'] for x in sortedScaffolds['Murcko_SMILES'].head(8)]\n",
      "data.append(cpdsDF[cpdsDF['knownDrug'] == 'Yes']['LE'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.boxplot(data)\n",
      "pylab.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f094f3b4e50>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "\n",
      "### Check our GitHub https://github.com/Team-SKI/snippets\n",
      "### Check our web page http://www.bio.mx.\n",
      "<img src=\"files/biomedx.png\">  \n",
      "<br>\n",
      "<img src=\"files/merck.jpeg\">  \n",
      "<br>\n",
      "<img src=\"files/rdkit.png\">  \n",
      "<br>\n",
      "\n",
      "\n",
      "# Thank you!\n",
      "\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Copyright (C) 2013, 2014 by Samo Turk, [BioMed X GmbH](http://www.bio.mx)\n",
      "\n",
      "This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}