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     "source": [
      "# Another method for finding related documents in ChEMBL.\n",
      "\n",
      "Back in September there were a couple of posts on the ChEMBL blog about identifying related documents. [George and Mark](http://chembl.blogspot.ch/2013/09/document-similarity-in-chembl.html) started with an overview of the method and great graphic of the results and then [John followed up](http://chembl.blogspot.ch/2013/09/document-similarity-in-chembl-2.html) with slide for those of us who are more graphically oriented. The general idea was to look at overlap (Tanimoto score) in compounds, targets, and text between the documents to something of a holistic view.\n",
      "\n",
      "Here I'm going to explore a different approach: just looking at similarity between compounds in the papers.\n",
      "\n",
      "I'll use the RDKit Morgan2 fingerprint for this example\n",
      "\n",
      "\n",
      "## Technical bits\n",
      "This is going to use a mix of python and SQL and will take advantage of Catherine Devlin's [excellent sql magic for ipython](https://github.com/catherinedevlin/ipython-sql). It's available [from PyPi](https://pypi.python.org/pypi/ipython-sql). This is my first time playing with ipython-sql and I'm super impressed.\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rdkit import Chem\n",
      "import psycopg2\n",
      "from rdkit.Chem import Draw,PandasTools\n",
      "from rdkit.Chem.Draw import IPythonConsole\n",
      "from rdkit import rdBase\n",
      "from __future__ import print_function\n",
      "import requests\n",
      "from xml.etree import ElementTree\n",
      "import pandas as pd\n",
      "%load_ext sql\n",
      "print(rdBase.rdkitVersion)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014.03.1pre\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Preparation\n",
      "\n",
      "I started out by generating a table in my chembl_16 instance containing all pairs of molecules from papers in 2012 that have at least 0.8 MFP2 tanimoto similiarity to each other:\n",
      "\n",
      "    create temporary table acts_2012 as select * from (select doc_id from docs where year=2012) t1 join (select doc_id,activity_id,molregno from activities) t2 using (doc_id);\n",
      "    create temporary table fps_2012 as select molregno,mfp2 from rdk.fps join (select distinct molregno from acts_2012) tbl using (molregno);\n",
      "    create index ffp2_idx on fps_2012 using gist (mfp2);\n",
      "    set rdkit.tanimoto_threshold=0.8;\n",
      "    create schema papers_pairs;\n",
      "    create table papers_pairs.pairs_2012 as select t1.molregno molregno_1, t2.molregno molregno_2,tanimoto_sml(t1.mfp2,t2.mfp2) sim from (select * from fps_2012) t1 cross join fps_2012 t2 where t1.mfp2%t2.mfp2;\n",
      "\n",
      "This last bit, which in principle needs to do around 5.1 billion similarity comparisons, takes a while. For me it was about an hour and 20 minutes. This would be a great opportunity to use a special-purpose similarity calculator like Andrew Dalke's [chemfp](http://chemfp.com/). That's for another blog post. \n",
      "\n",
      "\n",
      "Now that we have the pairs, add the document info back in:\n",
      "\n",
      "    create table papers_pairs.pairs_and_docs_2012 as select distinct * from (select p1.doc_id doc_id_1,p2.doc_id doc_id_2,p1.molregno molregno_1,p2.molregno molregno_2 from acts_2012 p1 cross join acts_2012 p2 where p1.doc_id>p2.doc_id and p1.molregno!=p2.molregno) ttbl join papers_pairs.pairs_2012 using (molregno_1,molregno_2);\n",
      "\n",
      "And now let's see what we got:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%sql postgresql://localhost/chembl_16 \\\n",
      "    select count(*) from papers_pairs.pairs_2012;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 rows affected.\n"
       ]
      },
      {
       "html": [
        "<table>\n",
        "    <tr>\n",
        "        <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td>225703</td>\n",
        "    </tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[(225703L,)]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%sql postgresql://localhost/chembl_16 \\\n",
      "    select count(*) from papers_pairs.pairs_and_docs_2012;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 rows affected.\n"
       ]
      },
      {
       "html": [
        "<table>\n",
        "    <tr>\n",
        "        <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "        <td>13401</td>\n",
        "    </tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[(13401L,)]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = %sql  \\\n",
      "    select (doc_id_1,doc_id_2) doc_ids,count(molregno_1) pair_count, avg(sim) avg_sim \\\n",
      "    from papers_pairs.pairs_and_docs_2012 \\\n",
      "    group by (doc_id_1, doc_id_2) \\\n",
      "    order by pair_count desc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7073 rows affected.\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The result object from the sql query can be trivally converted into a Pandas data frame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = data.DataFrame()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>doc_ids</th>\n",
        "      <th>pair_count</th>\n",
        "      <th>avg_sim</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
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        "      <td> 161</td>\n",
        "      <td> 0.852017644705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
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        "      <td> 102</td>\n",
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        "      <td> 102</td>\n",
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        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
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       "prompt_number": 6,
       "text": [
        "         doc_ids pair_count         avg_sim\n",
        "0  (67106,61208)        248  0.854043190537\n",
        "1  (62996,60628)        190  0.830029102851\n",
        "2  (61096,60864)        161  0.852017644705\n",
        "3  (65439,65426)        102  0.863242558624\n",
        "4  (67047,62125)        102  0.841522325209"
       ]
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     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 7073 entries, 0 to 7072\n",
        "Data columns (total 3 columns):\n",
        "doc_ids       7073  non-null values\n",
        "pair_count    7073  non-null values\n",
        "avg_sim       7073  non-null values\n",
        "dtypes: object(3)\n",
        "</pre>"
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       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 7073 entries, 0 to 7072\n",
        "Data columns (total 3 columns):\n",
        "doc_ids       7073  non-null values\n",
        "pair_count    7073  non-null values\n",
        "avg_sim       7073  non-null values\n",
        "dtypes: object(3)"
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      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the number of unique molecules in each document:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "doc_counts = %sql  \\\n",
      "  select doc_id,count(distinct molregno) num_cmpds from (select doc_id,molregno from docs join activities using (doc_id) where year=2012) tbl group by (doc_id);\n",
      "doc_counts_d=dict(list(doc_counts))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3661 rows affected.\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the number of molecules in common in the document pairs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# turn off row count printing\n",
      "%config SqlMagic.feedback = False\n",
      "\n",
      "nInCommon=[]\n",
      "for k in df.doc_ids:\n",
      "    docid1,docid2 = eval(k)\n",
      "    d = %sql  select count(*) from (select distinct molregno from activities where doc_id=:docid1 intersect\\\n",
      "                                    select distinct molregno from activities where doc_id=:docid2)t\n",
      "    nInCommon.append(d[0][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['nInCommon']=nInCommon"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And add columns for the compound tanimoto (based on number of compounds in common) and the fraction of possible compound pairs that are highly similar."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nDoc1=[]\n",
      "nDoc2=[]\n",
      "tanis=[]\n",
      "for k in df.doc_ids:\n",
      "    docid1,docid2 = eval(k)\n",
      "    nDoc1.append(doc_counts_d[docid1])\n",
      "    nDoc2.append(doc_counts_d[docid2])\n",
      "df['nDoc1']=nDoc1\n",
      "df['nDoc2']=nDoc2\n",
      "\n",
      "df['compond_id_tani']=df.apply(lambda row: float(row['nInCommon'])/(row['nDoc1']+row['nDoc2']-row['nInCommon']),axis=1)\n",
      "df['frac_high_sim']=df.apply(lambda row: float(row['pair_count'])/((row['nDoc1']-row['nInCommon'])*(row['nDoc2']-row['nInCommon'])|10000),axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the possible presence of duplicates in the sets and the fact that duplicates were dropped when calculating the high-similarity pairs means that we need to subtract out the number in common from each term in the denominator of frac_high_sim."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <th>compond_id_tani</th>\n",
        "      <th>frac_high_sim</th>\n",
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        "      <td> 102</td>\n",
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        "      <td>  1</td>\n",
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        "      <td> 102</td>\n",
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       "prompt_number": 12,
       "text": [
        "         doc_ids pair_count         avg_sim  nInCommon  nDoc1  nDoc2  compond_id_tani  frac_high_sim\n",
        "0  (67106,61208)        248  0.854043190537         32     47     40         0.581818       0.024545\n",
        "1  (62996,60628)        190  0.830029102851         40     49     40         0.816327       0.019000\n",
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        "4  (67047,62125)        102  0.841522325209          2     19     32         0.040816       0.009963"
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     "prompt_number": 12
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort('frac_high_sim',ascending=False).head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "      <td> 32</td>\n",
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        "      <td> (62996,60628)</td>\n",
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        "      <td> 0.830029102851</td>\n",
        "      <td> 40</td>\n",
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        "      <td> 0.816327</td>\n",
        "      <td> 0.019000</td>\n",
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        "    <tr>\n",
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        "      <td> 16</td>\n",
        "      <td>  26</td>\n",
        "      <td> 71</td>\n",
        "      <td> 0.197531</td>\n",
        "      <td> 0.016039</td>\n",
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        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> (65439,65426)</td>\n",
        "      <td> 102</td>\n",
        "      <td> 0.863242558624</td>\n",
        "      <td>  1</td>\n",
        "      <td>  22</td>\n",
        "      <td> 15</td>\n",
        "      <td> 0.027778</td>\n",
        "      <td> 0.010161</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> (61777,61771)</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.875883535125</td>\n",
        "      <td>  5</td>\n",
        "      <td>  25</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0.098039</td>\n",
        "      <td> 0.009992</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> (67047,62125)</td>\n",
        "      <td> 102</td>\n",
        "      <td> 0.841522325209</td>\n",
        "      <td>  2</td>\n",
        "      <td>  19</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0.040816</td>\n",
        "      <td> 0.009963</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> (61733,60448)</td>\n",
        "      <td>  76</td>\n",
        "      <td> 0.868513977117</td>\n",
        "      <td>  0</td>\n",
        "      <td>  21</td>\n",
        "      <td> 21</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.007474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> (66889,65537)</td>\n",
        "      <td>  92</td>\n",
        "      <td> 0.833532600736</td>\n",
        "      <td>  7</td>\n",
        "      <td> 138</td>\n",
        "      <td> 39</td>\n",
        "      <td> 0.041176</td>\n",
        "      <td> 0.006483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> (66968,61801)</td>\n",
        "      <td>  66</td>\n",
        "      <td> 0.815926490544</td>\n",
        "      <td>  1</td>\n",
        "      <td>  27</td>\n",
        "      <td>  9</td>\n",
        "      <td> 0.028571</td>\n",
        "      <td> 0.006476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> (65787,65440)</td>\n",
        "      <td>  64</td>\n",
        "      <td> 0.872092468112</td>\n",
        "      <td>  3</td>\n",
        "      <td>  36</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0.046154</td>\n",
        "      <td> 0.006291</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "         doc_ids pair_count         avg_sim  nInCommon  nDoc1  nDoc2  compond_id_tani  frac_high_sim\n",
        "0  (67106,61208)        248  0.854043190537         32     47     40         0.581818       0.024545\n",
        "1  (62996,60628)        190  0.830029102851         40     49     40         0.816327       0.019000\n",
        "2  (61096,60864)        161  0.852017644705         16     26     71         0.197531       0.016039\n",
        "3  (65439,65426)        102  0.863242558624          1     22     15         0.027778       0.010161\n",
        "5  (61777,61771)        100  0.875883535125          5     25     31         0.098039       0.009992\n",
        "4  (67047,62125)        102  0.841522325209          2     19     32         0.040816       0.009963\n",
        "7  (61733,60448)         76  0.868513977117          0     21     21         0.000000       0.007474\n",
        "6  (66889,65537)         92  0.833532600736          7    138     39         0.041176       0.006483\n",
        "8  (66968,61801)         66  0.815926490544          1     27      9         0.028571       0.006476\n",
        "9  (65787,65440)         64  0.872092468112          3     36     32         0.046154       0.006291"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[(df.nDoc1>10)&(df.nDoc2>10)].sort('frac_high_sim',ascending=False).head(10)"
     ],
     "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>doc_ids</th>\n",
        "      <th>pair_count</th>\n",
        "      <th>avg_sim</th>\n",
        "      <th>nInCommon</th>\n",
        "      <th>nDoc1</th>\n",
        "      <th>nDoc2</th>\n",
        "      <th>compond_id_tani</th>\n",
        "      <th>frac_high_sim</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> (67106,61208)</td>\n",
        "      <td> 248</td>\n",
        "      <td> 0.854043190537</td>\n",
        "      <td> 32</td>\n",
        "      <td>  47</td>\n",
        "      <td> 40</td>\n",
        "      <td> 0.581818</td>\n",
        "      <td> 0.024545</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> (62996,60628)</td>\n",
        "      <td> 190</td>\n",
        "      <td> 0.830029102851</td>\n",
        "      <td> 40</td>\n",
        "      <td>  49</td>\n",
        "      <td> 40</td>\n",
        "      <td> 0.816327</td>\n",
        "      <td> 0.019000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> (61096,60864)</td>\n",
        "      <td> 161</td>\n",
        "      <td> 0.852017644705</td>\n",
        "      <td> 16</td>\n",
        "      <td>  26</td>\n",
        "      <td> 71</td>\n",
        "      <td> 0.197531</td>\n",
        "      <td> 0.016039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> (65439,65426)</td>\n",
        "      <td> 102</td>\n",
        "      <td> 0.863242558624</td>\n",
        "      <td>  1</td>\n",
        "      <td>  22</td>\n",
        "      <td> 15</td>\n",
        "      <td> 0.027778</td>\n",
        "      <td> 0.010161</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> (61777,61771)</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.875883535125</td>\n",
        "      <td>  5</td>\n",
        "      <td>  25</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0.098039</td>\n",
        "      <td> 0.009992</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> (67047,62125)</td>\n",
        "      <td> 102</td>\n",
        "      <td> 0.841522325209</td>\n",
        "      <td>  2</td>\n",
        "      <td>  19</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0.040816</td>\n",
        "      <td> 0.009963</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> (61733,60448)</td>\n",
        "      <td>  76</td>\n",
        "      <td> 0.868513977117</td>\n",
        "      <td>  0</td>\n",
        "      <td>  21</td>\n",
        "      <td> 21</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.007474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> (66889,65537)</td>\n",
        "      <td>  92</td>\n",
        "      <td> 0.833532600736</td>\n",
        "      <td>  7</td>\n",
        "      <td> 138</td>\n",
        "      <td> 39</td>\n",
        "      <td> 0.041176</td>\n",
        "      <td> 0.006483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> (65787,65440)</td>\n",
        "      <td>  64</td>\n",
        "      <td> 0.872092468112</td>\n",
        "      <td>  3</td>\n",
        "      <td>  36</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0.046154</td>\n",
        "      <td> 0.006291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> (66833,66017)</td>\n",
        "      <td>  61</td>\n",
        "      <td> 0.862428480606</td>\n",
        "      <td> 21</td>\n",
        "      <td>  42</td>\n",
        "      <td> 75</td>\n",
        "      <td> 0.218750</td>\n",
        "      <td> 0.006034</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "          doc_ids pair_count         avg_sim  nInCommon  nDoc1  nDoc2  compond_id_tani  frac_high_sim\n",
        "0   (67106,61208)        248  0.854043190537         32     47     40         0.581818       0.024545\n",
        "1   (62996,60628)        190  0.830029102851         40     49     40         0.816327       0.019000\n",
        "2   (61096,60864)        161  0.852017644705         16     26     71         0.197531       0.016039\n",
        "3   (65439,65426)        102  0.863242558624          1     22     15         0.027778       0.010161\n",
        "5   (61777,61771)        100  0.875883535125          5     25     31         0.098039       0.009992\n",
        "4   (67047,62125)        102  0.841522325209          2     19     32         0.040816       0.009963\n",
        "7   (61733,60448)         76  0.868513977117          0     21     21         0.000000       0.007474\n",
        "6   (66889,65537)         92  0.833532600736          7    138     39         0.041176       0.006483\n",
        "9   (65787,65440)         64  0.872092468112          3     36     32         0.046154       0.006291\n",
        "10  (66833,66017)         61  0.862428480606         21     42     75         0.218750       0.006034"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Are the articles actually related?\n",
      "\n",
      "Quick check: use the pubmed API to pull back titles"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titlePairs=[]\n",
      "for row in df[(df.nDoc1>10)&(df.nDoc2>10)].sort('frac_high_sim',ascending=False).head(10).doc_ids:\n",
      "    tpl = eval(row)\n",
      "    did1,did2=tpl\n",
      "    pmids=%sql  select doc_id,pubmed_id,title from docs where doc_id in (:did1,:did2)\n",
      "    tpl = pmids[0][1],pmids[1][1]\n",
      "    if tpl[0] is None:\n",
      "        print('no pmid found for item: %s'%(str(pmids[0])))\n",
      "        continue\n",
      "    if tpl[1] is None:\n",
      "        print('no pmid found for item: %s'%(str(pmids[1])))\n",
      "        continue\n",
      "        \n",
      "    txt=requests.get('http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=%d,%d'%tpl).text\n",
      "    et = ElementTree.fromstring(txt.encode('utf-8'))\n",
      "    ts=[x.text for x in et.findall(\".//*[@Name='Title']\")]\n",
      "    titlePairs.append(ts)\n",
      "    print(str(tpl))\n",
      "    print('   '+ts[0])\n",
      "    print('   '+ts[1])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(22365562L, 23124213L)\n",
        "   Fluorinated dual antithrombotic compounds based on 1,4-benzoxazine scaffold.\n",
        "   Novel 1,4-benzoxazine and 1,4-benzodioxine inhibitors of angiogenesis.\n",
        "(22119125L, 22850214L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   From COX-2 inhibitor nimesulide to potent anti-cancer agent: synthesis, in vitro, in vivo and pharmacokinetic evaluation.\n",
        "   Identification of selective tubulin inhibitors as potential anti-trypanosomal agents.\n",
        "(22206869L, 22168134L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   Semisynthetic neoboutomellerone derivatives as ubiquitin-proteasome pathway inhibitors.\n",
        "   Proteasome inhibitors from Neoboutonia melleri.\n",
        "(22975302L, 22995619L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   Serum stability of selected decapeptide agonists of KISS1R using pseudopeptides.\n",
        "   Trypsin resistance of a decapeptide KISS1R agonist containing an N\u03c9-methylarginine substitution.\n",
        "(22503248L, 22503453L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   Identification of a series of 1,3,4-oxadiazol-2-amines as potent alpha-7 agonists with efficacy in the novel object recognition model of cognition.\n",
        "   The discovery of 2-fluoro-N-(3-fluoro-4-(5-((4-morpholinobutyl)amino)-1,3,4-oxadiazol-2-yl)phenyl)benzamide, a full agonist of the alpha-7 nicotinic acetylcholine receptor showing efficacy in the novel object recognition model of cognition enhancement.\n",
        "(22471376L, 23142614L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   Discovery of novel urea-based hepatitis C protease inhibitors with high potency against protease-inhibitor-resistant mutants.\n",
        "   Synthesis and antiviral activity of novel HCV NS3 protease inhibitors with P4 capping groups.\n",
        "(22063755L, 22465634L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   Anti-AIDS agents 85. Design, synthesis, and evaluation of 1R,2R-dicamphanoyl-3,3-dimethyldihydropyrano-[2,3-c]xanthen-7(1H)-one (DCX) derivatives as novel anti-HIV agents.\n",
        "   Anti-AIDS agents 89. Identification of DCX derivatives as anti-HIV and chemosensitizing dual function agents to overcome P-gp-mediated drug resistance for AIDS therapy.\n",
        "no pmid found for item: (65537, None, None)\n",
        "(22995620L, 22650305L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   Identification of a potent and metabolically stable series of fluorinated diphenylpyridylethanamine-based cholesteryl ester transfer protein inhibitors.\n",
        "   Diphenylpyridylethanamine (DPPE) derivatives as cholesteryl ester transfer protein (CETP) inhibitors.\n",
        "(22989363L, 22320402L)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   Flavonoid dimers as novel, potent antileishmanial agents.\n",
        "   Amine linked flavonoid dimers as modulators for P-glycoprotein-based multidrug resistance: structure-activity relationship and mechanism of modulation.\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Those look pretty good.\n",
      "\n",
      "Let's check ChEMBL to see what it says:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for row in df[(df.nDoc1>10)&(df.nDoc2>10)].sort('frac_high_sim',ascending=False).head(10).doc_ids:\n",
      "    tpl = eval(row)\n",
      "    did1,did2=tpl\n",
      "    pmids=%sql  select doc_id,pubmed_id,chembl_id from docs where doc_id in (:did1,:did2)\n",
      "    print('https://www.ebi.ac.uk/chembl/doc/inspect/%s - https://www.ebi.ac.uk/chembl/doc/inspect/%s'%(pmids[0][2],pmids[1][2]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL1949509 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2216753\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL1932938 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2069209\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL1938254 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL1949544\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2150961 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2151012\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2021886 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2021786\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2034968 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2202978\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL1926665 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2021899\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2150910 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2203235\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2150934 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2163208\n",
        "https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2169835 - https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL2203168\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Following those links (I did it manually... didn't feel like writing a web scraper), it looks like none of these documents are flagged as being related. Many of them don't have related docs flagged at all.\n",
      "So maybe this is another interesting way to find related docs.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Let's look at some molecules."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d1,d2=67106,61208\n",
      "data = %sql\\\n",
      "  select molregno_1,t1.m m1,molregno_2,t2.m m2,sim from papers_pairs.pairs_and_docs_2012 \\\n",
      "    join rdk.mols t1 on (molregno_1=t1.molregno) \\\n",
      "    join rdk.mols t2 on (molregno_2=t2.molregno) \\\n",
      "    where doc_id_1=:d1 and doc_id_2=:d2\n",
      "data = data.DataFrame()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m1',molCol='mol1')\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m2',molCol='mol2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rows=[]\n",
      "for m1,m2 in zip(data['mol1'],data['mol2']):\n",
      "    rows.append(m1)\n",
      "    rows.append(m2)\n",
      "Draw.MolsToGridImage(rows[:6],molsPerRow=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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7JssRg9SQYQNk586dzJUrF9977z16eXlx27ZtSfqUdukSWaYMac4qB9HR0dyy\nZQuLFSvGr7/+mhs3bnzp4asXGTBgALt06WK4fbylS5eyYMGCJE0vwPh1qfLly0dfX1+eO3fumccf\nPHiQoS1akFWrkqGhSd5OREQEZ8+ezapVq7Jdu3Y8cOAACxYsyBo1apj1SalKlSqcMWMGa9SowcmT\nJye5XXLo3z9VN2fIjRs36OHhwcqVK/PKlSsJP3/48CEjli4l27Qh7e3JQYOSdUmf5NS6dWt+/PHH\n7NGjB3v37m1W2ydPnrBHjx48evQohwwZwiZNmphfwOrVZLZsjPH1TZi6/PPPPzNHjhy8du2a+f31\n60f+/aLGNm2S3HT+/PnMmjUre/fuzY0bNzJ//vzPTCt++PBhwoc9a2vrhA97GSU8yAwcIOvXr2e2\nbNnYrVs3tm7dmlmyZKGjoyN79erFdevWJfrpODj4FKtWjWKbNn9dT2CO3r17s3v37vzkk0/YsmVL\nw7XPnz/f4nnepOkYL4CERdcqVqzIqVOn8sGDB89vdO+e6erkPHmeHRiJOXeOe8aMYd68eenq6kof\nHx8WKVKEr7/+Ok+cOMFmzZoxR44c9Pf3T1K9LVq04O7du+nk5MQ1a9Yk/YlayJJjvKklODiYxYoV\nY7169RJWYk3Ugwemv5sFb2wp6auvvmLdunU5ZcoUuru7J7ndlStXWKlSJVarVo3BwcF0d3ens7Mz\nXV1d+emnnzL44MGkdfTmm6bjMn8TFxfHWrVqsVu3buY8FRMLV744ePAgXV1dWadOHR46dIiRkZHc\nuXMne/fuzRw5ciQsQpoWFwUnh4wZIEuWsKmX1zNLsUdHR3Pnzp308fFhgQIFmCVLloRF2u7du8eg\noCDmz5+fgwZ9z6QcTk7M7NmzWbJkSa5YsYKOjo6Gyw8JCSEAi5dr+PHHH5krVy6WKVOGO3bsSHrD\nuDhyyxbT14kdCjlyxLTWgbU1H7ZuzYCAgIRPReHh4Wzfvn3CCrt+fn60tramj49PkiYG7Nu3j1ZW\nVmZdsW8JS4/xpoadO3cyT548fPvtt5N+WDAZl/RJTuvWraODgwP37NmT5BPpJ0+epKurK5s1a8ZD\nhw6xWLFirFOnDs+dO8eff/6ZjRo14vw6dcjixU1L9B49+vxDeM/59H7s2DHa2NhwS/zrPqmSYeWL\nq1ev0tvbm0WKFKGXlxczZcrERo0a0d/f3+LJNGkt4wXITz+RdnaM/fXX5z4kKiqK69atY69evejo\n6MgsWbKwYMGCHDJkiOErcknTp0QrKyuePHmSABhiwVLyTk5OXBk/ZchccXEcP3Ik7ezs+OWXX9La\n2tr4SfnEDoWcOmU6afCPQ2B/bT6OEydOpK2tLX19fbl161YWKFCAderUSfSTVGRkJOfPn89atWrR\n1taWY8aMyVC76SlpyZIltLe354gRI8x7babTJX3i13M6dOgQR48ezfCXzGTatm0bc+fOzV69enH7\n9u3MkycPO3bs+O/gCQsjp08nGzQgP/rI0CG8jz76iOXKlUuTN+2oqCguXLiQw4YNs+h9I71JkwCJ\njIykj48PPTw82L9/f27atClpf9ToaPKNN0yrhCVRTEwMt2/fzlOnTllQsUlcXBzz5MnDNWvW0NXV\nlfPnzzfcV8uWLfmpkdljMTFkr1687+WV8GmqR48erFy5srGBYcGhkB07dtDZ2Zn16tVjUFAQPT09\nOXTo0ITfHzt2LGFXvVy5cpw+fXqiU0tfVT///DPt7Ow4adKktC4lWZUqVYqdOnV66YeaBQsW0M7O\njiNGjEgI0gEDBiRtxpmB1+2DBw/o4uLCb7/99uX9S5KkeoA8ePCADRs2ZIkSJThx4kR26NCBDg4O\nvNiggWk33N+ffPgwWW8clZyaNm3KYcOG8a233mJ/M8/M3rt3j/Xr1+fWrVs5fPhwuru7v/h4d2J6\n9SKdncm/HRO+c+cOnZyc+N1335nXF2nxoZBbt26xYcOGdHFx4bZt2/jw4UMuXbqUtWrVyrAnBlPL\n9OnTX7rgYEYUHBzMRo0a0dramsWLF+fQoUN58B/nMI4ePUobGxtOmDCBU6ZMSXTl3hcy+LpdtmwZ\ns2bNyvPnzyd9W/JcqRogV65cYfny5VmzZk3e/dsKgpGRkYwODDS9ORYoQA4blm5nmYwaNYoNGjTg\n5MmTzTpJePHiRZYpU4aNGjXi8ePHWaxYMZYoUYI2NjZs2LAh18ya9fK5xSQZFJToDKpFixYxa9as\n/5p59VLJcCgkKiqKPj4+tLe3Z5EiRZg7d24OHjyYp1P7/pqSrjx+/JhLly5l8+bNaWdnRxcXF/r4\n+HDnzp2Mi4vjwYMHE143y5YtM69zC163PXv25F6jd6qSZ6RagJw+fZqurq5s3Ljxi5c7jo0lHz9O\nt7NMNm7cSAcHB+7fv5/W1tZ8+PDhS9scOXKEzs7O7NixI/ft20dHR8eE47xBQUEcPnw4V3bsSFpZ\nkR4eZPycdTP3wlq2bMl69epZdJ7HEgcOHOCKFSsS7iwnEu/WrVucNm0a69evz8yZM7NUqVJs2rQp\nHRwcLLr6XdJWqgTIgQMH6OTkxI4dO2b4WSbh4eHMlCkTg4KC2K9fv5cutxC/1Hvfvn25fv16Zs+e\nnX369En8OO+5c+S4cX9NQzRzL+zixYt0cHDg3LlzjTw1kVRx8+ZNTp06lePGjTN8tbqkDykeIBs3\nbnzxm+bzpNNZJiRZtmxZduvWjZcuXXrh49asWcNs2bLRz8+Py5Yto52dnXn3wDCwFzZx4kTmzZvX\n4nWEREReJkUDZOnSpea/aWYAR44cYeXKlWllZUVPT0+OGTPmX7O8rl+/zmzZsnHs2LH8+eefaWNj\nY/5yCgb2wqKjo+nu7s6vv/7avG2JiJjJijR3IfOkGzJkCHLnzo1hw4al1CbSVFhYGNatWwd/f39s\n2LABBQsWRKtWrdCiRQvUrVsXly5dwvLlyzF8+HDMnj0bXbp0MW8DERHAoEGAvT1w9y4wYoTpBiYv\ncfPmTeTPnx+ZMmXM1fpFJGNI0QB5lVy7dg2rVq3CypUrsW3bNri4uKBKlSrYsGEDFi9ejJYtW6Z1\niSIiyUoBkgLu3r2LgIAA3L59G97e3qhZs2ZalyQikuwUICIiYogOkouIiCEKEBERMUQBIiIihihA\nRETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogAR\nERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRE\nRAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBER\nMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETE\nEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFD\nFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxR\ngIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQB\nIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWI\niIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAi\nImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiI\niCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIi\nhihARETEEAWIiIgYogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgY\nogARERFDFCAiImKIAkRERAxRgIiIiCEKEBERMUQBIiIihihARETEEAWIiIgYogARERFDFCAiImKI\ndVoXkN5tvHsXAXfuwNnODg1z5YJXjhxpXZKISLpgRZJpXUR6tvHuXdhmyoS6uXKldSkiIumKDmEl\nwfKwMHxz6RKuREWldSkiIumG9kBeQnsgIiKJ0zkQEUlXdN4x49AeiIikK9rrzzh0DkRE0h2dd8wY\ntAciIumK9kAyDu2BmOHIo0e4Gx2d1mWI/OdciozED1evQp9mMxadRDfD+CtX0CxPHnR2dEzrUkTS\nHZIYOXIkTp48CVdXV7Rv3x5eXl6wsrJ6Ybvjjx9j0LlzcHdwQCyJRnnypFLFYintgZihsr09Lty4\nkdZliKQ70dHReOedd/DDDz+gTJkyOHnyJF577TUULVoUgwcPxu7duxEXF/evdgFr1uCr06fRKE8e\n+Lm5wfolYSPpiwLEDM579uCnFi3SugyRdOX+/ft44403sG/fPgQFBaFRo0aoX78+jh49ih9//BF3\n7txB06ZNkT9/fnTv3h0BAQF4+vQpfv75Z7Rr2xa1g4LwaeHCejPKgPQ3M0PZsmVx//59TJw4ETe0\nJyKCy5cvo3bt2oiOjsbevXvh5uaG8PBwLFy4EGXLlsXo0aNRuXJl7N27Fz/88AMePXqETp06wcXF\nBT4+PliwYAE+7N07rZ+GGKRZWEl07do1NGnSBHZ2dnj48CHOnj2L6tWro3Xr1mjdujVKlSqV1iWK\npKqjR4+iadOmcHd3x+LFi5EtW7Znfh8SEoKlS5fC398fhw8fhpeXFzp06IAWLVpg1KhRuHHjBrZs\n2ZJG1Uty0B5IEhw9ehQeHh4oW7YsFi5ciODgYISGhqJHjx7YuXMnKlSoAFdXV/Tp0wcBAQGI1kwt\n+Y/buXMn6tati5YtW2LVqlX/Cg8AKF68OIYOHYqDBw/izJkzaNWqFRYsWICgoCC89dZbOHr0aBpU\nLsmK8kK7du1i7ty52bdvX27fvp25cuXikiVLnnnM1atX+dNPP7FRo0a0tbVloUKF6O/vn0YVi6Ss\nJUuW0N7enn5+fob7uHHjBgEwNDQ0GSuT1KYAeYG1a9cya9as9PPz49KlS2lnZ/fSQXP//n1OnTqV\ndnZ2vHr1aipVKpI64uLi2KRJE44aNcrivgoXLsylS5cmQ1WSVnQI6zlmzZqFNm3aYOzYsciTJw+6\ndu2K8ePHw9fX94XtJk2aBC8vL+TLlw979uxJpWpFUoeVlRUuX74MFxcXtGjRAj/88IPhvjw9PfHn\nn38mY3WS2hQgifjuu+/wwQcfYM6cOXj06BEGDBiAOXPmoH///i9tu2PHDqxduxbVq1fH3r17U6Fa\nkdTl6emJoKAgFCtWDPv377e4H8m4FCD/sG/fPowcORJLlizBvn37MHr0aKxbtw5dunRJUvsaNWpg\n37598Pb2VoDIf5KHhweCgoLg4eFh0R6Ep6cnDhw4kOgFhpIxKED+Ydu2bahSpQq8vb2xadMmrF27\nFvXr109y+xo1amDv3r3w8vLCwYMHEaXVROU/xsPDA0eOHEHlypVx9uxZ3L9/33A/jx49wpkzZ5K3\nQEk1CpB/8Pb2xpEjR5AnTx4cP34cderUMbt9eHg4cuTIAZI4dOhQClUqkjYqV64MAIiKioKDgwMO\nHDhgdh83b95Ezpw5UaJECZ0HycAUIP/g6emJqKgoHDt2DJkymf/fkzdvXpQoUQKHDx9G5cqVsW/f\nvhSoUiTt2Nvbo0KFCjh48CCqVq1qdgDMmjULJUqUwOXLl1G0aFGcP38+hSqVlKYA+YesWbOiYsWK\nFp2/8Pb21nkQ+U+LPw9izkyquLg4DBw4EIMGDcKKFSswd+5c7Nq1Cy1btkzhaiWlKEASYekbf/x5\nEAWI/FfFn0B3d3dP0iGsiIgIdOzYEYsXL8aGDRswb948TJ48Gdu3b0eVKlVSvuD/oMWLAS8v09dr\n1pi+X7wYWLXqr5/Nn5+yNeh+IImoUaMGRo0aZbh9w4YNER4ejmrVquHq1au4desWHHUPEfkP8fT0\nxIkTJ9CgQYOXTsW9ffs2WrZsiTt37mDjxo34+OOPcenSJezduxfFixdPpYr/m0qUAFauBGxs0mb7\n2gNJhLe3N0JCQnDz5k1D7UuWLAkfHx8MGjQINWrUUHgYlB4+YUnirl69isKFC8PLywtfffUVdu3a\nlejjLl68iNdffx1WVlZYtmwZunXrhnv37mHHjh1mhUf//v0xceLEZKr+v6NzZ8DfH/j7krjTpgF9\n+wJTp6b89hUgiShevDjy58+P/fv3Izo6GiEhIWa1v3XrFl577TXcu3cPAQEBKVTlqyH+E5akH1On\nTkX79u3h4+ODDz/8EH/++Sdee+01VKpUCV9++SVOnjyZ8NizZ8+icOHC+P7779GyZUsUKlQI27dv\nR4ECBczaZtasWbFz587kfioZXqZMQPv2pg9W8fr2NYXIBx+kwvZTfhMZj5WVVcKV5H/++SdKliyJ\nSpUq4YsvvsDBgwdf2DYkJATe3t7Ily8fNm3ahDy6PadF0voTlvyFJAYOHIjPPvsMq1evxuDBg/Hx\nxx9jz549uH37NoYMGYI///wTVatWRdGiRTFw4EDY29tj6NChaNKkCWrVqoXffvsN2bNnN3vbNWv2\nRHj4ZynwrDK+Nm2As2fTaONpvRhXetWzZ09Wr16dgYGBvHbtGufOncvmzZvT1taW+fLlY7du3bh6\n9WpGRkYmtDl8+DCdnZ3ZqVMnRkVFJXlbd+7cYalSpbT44j8sWkQGBJDLl5Ndupi+X7SIXLnS9PuA\nAHLevDQt8ZXx9OlTduvWjU5OTvzjjz9e+Nhr165xypQprFu3LjNnzszixYtzwIABjI2NNbz9kBAS\nIK9dM9yFpADtgSRi/vz5mDdvHhwcHNClSxeULl0aa9euRZcuXXDs2DF89dVXuHHjBtq2bYu33noL\nALBr1y7UrVs34Z4Htra2Sd5enjx58Pjx4+ceR37VpeknLEF4eDiaNm2KP/74A3v37oWnp+cLH+/s\n7Iz+/fvj999/x7Vr17Bt2zb88MMPhq6rilesGPDzz0DWrIa7kBSgOxL+w8iRIzFmzBjMmjULXbt2\nRVxcHA4dOoSAgAAsWbIEZ86cQdWqVdG8eXM0a9YMOXLkQEhICDp27Igvvvjipav1Ps+AAfuRO3dJ\nfPWVDnlJ+nHlypWE1/nq1at1SFaeoQD5n7i4OAwePBizZs3CypUr0bBhw389hiSCgoKwatUqrFq1\nCsHBwahYsSJOnz6Nr7/+GkOGDDG8/QkTgGXLAK0AL+nF+fPn0ahRI7i5uWHZsmVwcHBI65Lkb9at\nA6ysgIoVgcyZATPnJSQLBQiAmJgY9OzZE+vWrcPatWvhFT939CXOnj2LTZs2oWbNmhZfDHXpEnDi\nBNC0qUXdiCSbn376CatWrcKaNWtgk1YXGvxHPXnyBPb29hYd1uvUCShSBAgLA7JlA378MRkLTKJX\n/hxIREQE2rZti23btmH79u1JDg/AdL3HBx98kCxX0hYpovD4p8mTgd9/B3bvBkJD07qaDCIy0jR/\nc+BA4J13LPqPI4nbt2+ni/BI7JqgjCY6OhorV65E06ZNUaJECVStWtWilYitrC6gfPl7uHAhDiVK\nJGOhZnilA+TBgwdo2rQpzpw5g507d6JcuXJpXZL8zbRppj0zHx9g48a0riaDmDkTaNECmDTJdFx0\nxAjDXXl6NkKOHB8gvdyRIKNeE3T06FH06dMHjo6O6NOnDypXroy1a9eiTJkyqFatGvz9/c3ukyTW\nrKmAokUP4+RJZ5QuvTkFKn+5VzpAhg4ditu3b+P3339H0aJF07qc/4Tg4GB8+OGHaN68OYYMGYKY\nmBhD/ZBEwYKHUbz4A8TERMLNLTaZK/2PCg4GqlY1fZ03L/D4seGuKlUqgT173kN6uSNBYtcE3br1\n7PfpxZMnTzBjxgx4eHigSpUqCA0NxYwZM7B161bExcVh9erVWLJkCcaPH4+3334bAwcONGus3Llz\nB9mzZ8fhw4cRFnYLpUq5ptyTeYFXOkC2b9+O999/H87OzmldSobeRY+Ojoa/vz/q16+PSpUq4erV\nq3jzzTexfPly1KtXD9euXTO7z+vXr2PLlqrInfsyjh7NAjc3LfmdJGXKIOEd/84di+a92tkBQ4aY\njq+nB4lddd2yJeDmBnz6KXDwoPGwTE7ffPMN3Nzc8Omnn6J69erYt28f3nnnHfz444+oVKkS9uzZ\ng7JlywIAevfujU2bNmHp0qV44403cOvWrRf2fe3aNYwePRpeXl548OABFi5ciEKFCuHBgwep8dT+\nJeMFSDIe461evTrOnbucjMVZJqPtooeGhmLgwIFwdHTEp59+isaNGyMkJATNmzeHm5sbjh49Cmdn\nZ1SqVAnr1q0zq+9bt26hVKlSWLZsGTJnzowiRYqk0LP4j+nVy7RY2MCBwKBBwMiRFnU3erRplk9a\niYsDoqP/+v6f1wQtXw588okpMz/6aDDc3Nzw2Wef4ejRo6lfLIDffvsN/v7+GDNmDBYsWICwsDC8\n/vrrGD58OJo2bYpLly5h165d6NixY0Kb1157DUFBQYiMjExYJv/v4j+gvfHGGyhcuDC2bduGb7/9\nFnfv3sWePXvQpk0bNGzYHP7+T1L76WbAK9EnTyYDA01f375Nvv224a5+/vkpy5VLprostGgR+dtv\n5FtvkatXm74nyVOnyLi4tK3t7+Li4rh582a2a9eO1tbWfP3117lkyRKeOHGCgwcPZu7cuenm5sYF\nCxYktJk+fTrt7Ozo6+vLmJiYF/Z/5swZfvTRRwn9eHl50cXFhXv27EnppyYp6M8//+TXX3/Nb775\nhufOnUtSmydPyHbtyP79k7aN27dv8+eff2ajRo1YvXp1njp1yoKKjRkxYgSbNGnC27dv087Ojg0b\nNuTixYufWbHieSIjI+nj40N7e3vOnj2b586do6+vLwsUKEBHR0f6+vo+9zktWnSV9vbkkCHkS4ZY\nssp4AdKvH3n9+l/ft2ljuKtjx0hrazI8PBnqslBiy3Y8eUJmz04WKmR62lu3hpi1REpK2L59O0uV\nKsX+/fvzwIEDnD59OqtVq0YbGxt269aNQUFBibbbuXMnCxYsyLp16/L63/9+JCMiIp7bT0xMDD//\n/HMWKuTOiROj01WYStIEBgYye/bsbNWqFatXr04rKyu6u7vTz8+PISEhibYJC7vDOnXiWLo0eeGC\n+dv08vLi5MmTLazcfF26dKGPjw9JMiwszFAfU6ZMoYODA62trVm7dm3OmTOHjx8/fmm7/ftJFxdy\n5kxDmzUk4wXIpEnP7oF07Wq4q7i4tA+P+A8m8QESF0d6ev61BxIebvq6UyeyUqXqzJkzJzt37syd\nO3emSb2DBg1i165defbsWRYqVIhOTk4cOnQoLyRhlIeFhbFRo0YsVKgQd+3axfPnz9PX15dOTk4s\nUqQI/fz8/hUu8davf8TcuclWrch795L3OcnzLVpkej2Sptdn/OsyqaZMmUIbGxtOnDiR9+/f5/Ll\ny3nlypWEteWsra1Zrlw5jhgxgidOnCBJXrhwgWXKlGG3bstp8D2Y/fv3Z/fu3Y01toCnpyd/+OEH\nw+3HjRvHU6dOsWLFihw9erTZ7cPCTHsgif3dUmIduYwXIE+ekL17kz4+psNXZ8+mdUUkyVOnTnHt\n2rUJgyApbt4k3d2T/oeMiori+vXr2bNnT7q7u6fJ4outWrXi//3f/zE2NpbLly83e4/o6dOnHDx4\nMHPmzElbW1t6e3tz3rx5SdrFP3eOrFxZCygmSXQ0+egRuWQJefCg4W4WLTIdVl2xwrwAiY2NTTgc\ns3z5cpLknj17mC9fPmbPnp2dO3fm8uXLefLkSX777bf08PAgAFarVo0FChRg69atk/Sp+3lmz57N\nsmXLGm5vVM6cOblhwwZDbZ8+fUobGxvu27eP2bJl47Zt2wzXkdjfTQGSTu3atYu5c+dmtWrVmClT\nJpYsWZKffPIJd+/e/dwVSC9efMDSpck6dci7d83fpouLC5csWWJh5earWLEiZ82aZbj9zJkzef36\ndbq5udHPz8/s9n/fY0uNT1gZVv36pJ8f2bw5+fnnhrt53rm5F4mIiGD79u2ZN2/ef527io2N5c6d\nO+nj48NChQrRzs6OzZs359y5c3nkyBH27NmThQoVeum5spc5fvw4M2XKxPBUPMRw48YNAmBoaKih\n9mfPniUAnjx5kgAs+oCY2N9t0SKycWOyTx+yadPkGR8ZbxZWMkqOqbPLli1DgwYN4OPjg40bNyI4\nOBijR4/GjRs30LRpU+TLlw8dO3bEr7/+mjDVLjg4GLVrl0eTJkewcSOQO7f5242/73pqIonQ0FAU\nK1bMUPvIyEj07t0b165dw+XLl1G3bl2z+7Cz++vrjDZrLVVVrgwEBeFB7do4ffu2RV39c/rshx8C\ntWubrlX85wzt8PBwNGvWDH/88Qd27NgBb2/vf/SVCbVr18akSZNw4cIFBAQEwNnZGR9//DEmT56M\nL774IuE20JYoW7YssmXLhkOpeBHLmTNnYG9vb/iasjNnziB//vy4ceMGHBwcULBgQYvqSY2bTb3S\nAQJY9iY0c+ZMdOnSBePGjcPIkSPh7++PsmXLYvLkyahUqRJ27dqFGTNmwNbWFgMHDoSLiwtatGiB\n2rVro3Hjxhg/vgLs7Y1t29vbO9UDJCwsDI8fP4abm5uh9hcvXgRJ2NnZITo62nAQxdPNpp7vd29v\neJ44gZ0VKsDb3x+08Gq7v0+f7d0beO014IcfgLfemo8mTZrgl19+wbFjx1C7dm3cv38f+/bte+nK\nDtbW1njjjTcwY8YMXL9+Hd9++y2KFi0KR0dHHDhwwKJ6M2XKhCpVquDPP/984eN27tyJ7t2748sv\nv8SNGzcs2ubZs2dRvHhxw+tbnTlzBqVKlcKZM2dQIpnWJknpWyFkzAAhgb17TRdKvfsuYMF6Mom9\nCSXF2LFjMWDAAMyZMwcDBgwAAPTp0wehoaFo3749NmzYAHd3d3z66afImzcvFi1ahKVLlyI6Ohol\nSpTAjBkzkDlzZsN1e3t749ChQ4iMjDTch7lCQ0NhZ2cHFxcXw+3z5s2LsLAwODg4WHyv+LS+nWd6\nVsTdHUHBwbh48SLy5ctn1sWcv/wCdO1q+rpzZ6B5c9Oqr3/8Yfq+YkXgm2+Ac+eAiRM9ULlyZYwe\nPRqNGzeGra0tNm/ebPbFudbW1sibNy8AoFq1ai99408KT0/Pf11TAZiu4v7+++9Rrlw5NGjQAI8e\nPUoYr5bck+fs2bMoVaqURe1LliyZ8K8lEvu7de4MtG5t+n3z5sDbb1u0CQAZNUCsrICOHYHNm4Gg\nINOKewYl9iY0fz6wYcOzFzDF4/9u6/n1118jMDAQXbp0eeb38bfy3LRpE0JDQ/HRRx/hxIkTaNGi\nBfbv34/PP/8cx44dM7zERzx3d3dYWVm99Ba7gGlZhbi4OIu2B5gCoEiRIoY/YYWGhsLNzc2iw2D/\npJtNJS579uyoWLEipk2bhvDwcJQqVQrt27fH4sWL8fDhw+e2GzOG6N8fePPNpG2natUy+PbbbxEa\nGgpPT0+89tprWLRoEYYNG2a4dk9Pz2QJEA8Pj4R+YmJiEi7Gc3Jywty5czFo0CCEhYVhxYoV2LFj\nB9577z1c/uIL066VAcePH0eJEiXw559/onbt2rh06ZJZ7eODI35PJDWsW2e6WPSTTwx2YPlplLQR\n2749748YwcPDhnHJp58a6uN5U2fff5/MkoXMk4ccOnQXAwICGBERwejoaHbv3p358uV76W09/+nO\nnTu8efMmnzx5Qhsbm+deL2GOGjVqcNy4cc/9fXBwMH18fJgjRw4WL16cc+bMsWh7X331FRs3bmy4\n/ccff8xOnTpx+PDhbNWqlUW1yAucOcNW9eqxcePGfPDgAUnTSeURI0awZMmSzJw5M2vVqsWJEycm\nTJuOjo5m3759+cYbc2h08s+3337L2rVr86effmKlSpUMl7969Wrmy5fPcPt4Z8+epZWVFb/88kuW\nLl2atra27NixIzdt2sS4511QtHo1mSMH2aMHGRHx0m1ERERwzpw5rF69OvPly8eyZcvyxIkTfOed\nd5gjRw6uWLEiyfUWLVqU/v7+LF26NOfOnZvkdskhqRdr/lOGDZCxY8eyZs2anDVrFsuUKWNW27Aw\n8vLlFz/m0SPTRX1Dhkxknjx5mC1bNtaqVYtOTk48dOiQ8cJJVqtWzaK54vEGDx7Mdu3aPfOzJ0+e\nJFyUlzlzZnbo0IGbNm3i7NmzmSVLFvr262eaCm1Az5492a9fP969e5dffPGF2VN427Zty6FDh7JL\nly4cPHiwoRrMFRhIfv216QrdV8Kff5L58/P2gAGMjo7+169jY2O5Y8cO+vj40MXFJWEWVO3atVmo\nUCEeO3bM8Ka3bNnCrFmzct++fcycOTMfPXpkqJ/r168TQJKuLXqRrVu30tHRkUWKFOH06dN5//79\npDUMDjZdIvCiqeVHjpA+Plxbrx6LFCnCkSNH8syZM+zZsycdHBzo7+/P6dOn09bWlr6+vi+9H3xc\nXBzfffddnjp1ira2tqm68sLYseSBA8baZtgA2blzJ3PlysX33nuPXl5e3LZtW5Km/l26RJYpQ/bq\nlfRtRUdHc8uWLSxWrBi//vprbty4kZs3bzZc+4ABA9ilSxfD7eMtXbqUBQsWJEmGhobSx8eHuXLl\nYr58+ejr6/uvJSMOHjzI0BYtyKpVSTOmGkZERHD27NmsWrUq27VrxwMHDrBgwYKsUaMGL168mOR+\nqlSpwhkzZrBGjRqpfpWw0U9YaSEqKoq9e/dmxYoVE6a4Jmk6amys6UKZoUOTtP5NXFwc9+zZw48+\n+oijRo0y62+ZmPv37zNTpkw8cOAA7ezsuHv3bsN9FSpUiP7+/obbz58/n3Z2dmzRogXz5s1r+Kpw\nRkSYloHw8SG7dydDQsg5c8hMmcjmzRkXGPivcPj70j3btm1jgQIF2LRpU959yXz9iIgITpo0idmy\nZeOtW7eM1WumCRNM12KPH2+sfYYNkPXr1zNbtmzs1q0bW7duzSxZstDR0ZG9evXiunXrEv10HBx8\nilWrRrFNmxd/uHie3r17s3v37vzkk0/YsmVLw7XPnz+fxYoVM9w+3uXLlwmAzZo1o42NDStWrMip\nU6cmHLZI1L17ZOvWpuNzz7nqO8G5c9wzZgzz5s1LV1dX+vj4sEiRInz99dd54sQJNmvWjDly5Ejy\nQG/RogV3795NJycnrlmzJulP1EKWfMJKbeHh4WzQoAGLFCnCiRMnsmvXrsyVKxdPNGxoGum//WZ6\n8Sb2xmbqIE3rL126NGfNmkVPT09OnDjR7PbxF5S2bt2anxo8NM2xY3msbl3+/PPPjI2Npbe3N3v3\n7m2sr8TW3rt3jzx//oXNdu3albB0z+HDh+nt7c0iRYrwzz///Ndjjx8/zoEDBzJPnjzMly8fFy5c\naKzWNJAxA2TJEjb18uL333+f8KPo6OiEC5QKFCjALFmyJHx6u3fvHoOCgpg/f34OGvS94cXGZs+e\nzZIlS3LFihV0dHQ0XH5ISAgBPHfZjqT68ccfmStXLpYpU4Y7duxIesO4OHLLFtPXib0RHTliutLI\n2poPW7dmQEBAwqes8PDwhIvEAgMD6efnR2tra/r4+PDp06cv3fS+fftoZWVl1hX7lrD0E1Zqunjx\nIsuVK8eaNWvy9u3bCT9/+vQpn27YQL77Lpk7Nzl4cLIuKpqc3n77bX7wwQfs168f3zazprNnz7Jk\nyZLctGkTfX19Wa1aNfMvpvvoI9MCcmvXJvzo6NGjtLW1NbZHZMHae7du3WL9+vVZuHBh7tixg+++\n+y5X/u9K1zt37tDPz4/lypWjtbV1wqHmlx3qSm8yXoD89BNpZ8fYX3997kOioqK4bt069urVi46O\njsySJQsLFizIIUOGPP/kWRIEBwfTysoq4UrR5y0ElxROTk4JLyazxcVx/MiRtLOz45dffklra2vj\nJ+UTeyM6dcp00uA5q6bGxcVx4sSJCcd3t27dygIFCrBOnTqJDvjIyEjOnz+ftWrVoq2tLceMGZPh\nBkpKO3ToEJ2dndm8efMXnzuIijItXZCMi4omp4kTJ9LT05O//PKLWecmt2/fzty5c/Ptt9/moUOH\nWKhQIZYpU4ZWVlasXr061/z44197WS8ydWqiu5uDBg1ixYoVk/Qh5xkWrr0XHR1NX19f2tvbc+bM\nmdy0aRM7dOhAe3t7Fi1alH5+fmmyJFFySZMAiV+22MPDg/379+emTZuS9oeNjibfeMO0G59EMTEx\n3L59e7Is7RwXF8c8efJwzZo1dHV15fz58w331bJlS2O76DExZK9evO/lxS3/24vo0aMHK1eubP7g\nIC16I9qxYwednZ1Zr149BgUF0dPTk0OHDk34/bFjx9i7d2/myJGD5cqVM+9E5itk27ZtzJUrF/v2\n7Zv0JTyScVHR5LR7927a2toyKCiIVlZWvJeElS8XL15MOzs7jhgxgmvXrmX27Nk5YsQIxsXF8fDh\nw/z888+5pUULEiA9PMhff33+IbznePDgAV1cXF44azFRybT23s8//0xnZ2daWVmxQYMGXLJkSZqv\nrJ0cUj1AHjx4wIYNG7JEiRKcOHEiO3ToQAcHB15s0MA0CPz9yYcPzX6BpJamTZty2LBhfOutt9jf\nzDOz9+7dY/369bl161YOHz6c7u7uSRpgz+jVi3R2fmaBvDt37tDJyYnfffedeX2RFr8R3bp1iw0b\nNqSLiwu3bdvGhw8fcunSpaxVq1aG3jVPLfFvnmavC5ZOFxV9/Pgxra2tuW/fPq5YsYIRL5kK+913\n39HW1pa//PILZ8yYQVtbW06aNCnxB584YZpSN2uWoUN4/v7+zJo1K8+/5PxFSomMjDS8TlZ6laoB\ncuXKFZYvX541a9Z8ZkZCZGQkowMDTW+OBQqQw4al22O8o0aNYoMGDTh58mS6u7snud3FixdZpkwZ\nNmrUiMePH2exYsVYokQJ2tjYsGHDhlwza9bL5xaTZFBQojOoFi1axKxZsyb5Zj0JkuGNKCoqKmHl\n1SJFijB37twcPHgwT58+bXZfr5KZM2fSzs6OU6dOTetSklW7du3o4eHBadOmPXc2UfxqvQ4ODtyw\nYQNHjBjBrFmzJv26CYN7zs2bN2fbtm2Ttg15qVQLkNOnT9PV1ZWNGzd+8THe2Fjy8eN0e4x348aN\ndHBw4P79+2ltbc2HDx++tM2RI0fo7OzMjh07ct++fXR0dGTHjh0ZGRnJoKAgDh8+nCs7diStrEy7\n6FOmmBqauRfWsmVL1qtXz6LzPJY4cOAAV6xYYdEy3K+S6dOnc9WqVWldRrK7dOkS+/fvT2dnZ2bO\nnJkNGzbkjBkznplKe+rUKRYqVIjbt29nz549mS9fPu7duzfpGzG453zmzBna29tz/fr15jwleY5U\nCZADBw7QycmJHTt2TPpxv3R6jDc8PJyZMmViUFAQ+/Xrx5s3b77w8fFLvfft25fr169n9uzZ2adP\nn8SPdZ87R44bZ5o6RJq9F3bx4kU6ODik+lWsIs8TfwV86dKlmSlTpoQr4K9evcq7d++yRYsWLFq0\nKE+ePGlexxbsOQcEBDwzy02MS/EA2bhx44vfNJ8nnR7jJcmyZcuyW7duvHTp0gsft2bNGmbLlo1+\nfn5ctmyZ+ce6DeyFTZw4kXnz5n1psImkpr9ftFi0aFHa2NjQ09OT5cuXt/gCRkk7KRogS5cuNXaC\nMJ07cuQIK1euTCsrK3p6enLMmDH/muV1/fp1ZsuWjWPHjuXPP/9MGxsbTok/NJVUBvbCoqOj6e7u\nzq+//tq8bYmkkri4OP7xxx/cvHnzS6/OlvTNirTwRgEvMGTIEOTOnduilTnTs7CwMKxbtw7+/v7Y\nsGEDChYsiFatWqFFixaoW7cuLl26hOXLl2P48OGYPXv2v1bufamICGDQIMDeHrh7FxgxwnQDk5e4\nefMm8ufPb3jVXBGRpEjRAHmVXLt2DatWrcLKlSuxbds2uLi4oEqVKtiwYQMWL16Mli1bpnWJIiLJ\nSgGSAu7evYuAgADcvn0b3t7eqFmzZlqXJCKS7BQgIiJiiA6Si4iIIQoQERExRAEiIiKGKEBERMQQ\nBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMU\nICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGA\niIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEi\nIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiI\niBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIi\nYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiI\nIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKG\nKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBii\nABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogC\nREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQ\nERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBE\nRMQQBYiIiBiiABEREUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABER\nEUMUICIiYogCREREDFGAiIiIIQoQERExRAEiIiKGKEBERMQQBYiIiBiiABEREUMUICIiYoh1WheQ\n3m28excBd+7A2c4ODXPlgleOHGldkki6ofHxarMiybQuIj3bePcubDNlQt1cudK6FJF0R+Pj1aZD\nWEmwPCwM31y6hCtRUWldiki6o/Hx6tIeyEvoE5bI82l8vNq0ByIiIoZoD0RERAzRHoiIiBiiabwi\nkm5oWnDGokNYZjjy6BEK29khj41NWpcikq7EkTj2+DFKZc2KLJmMH9jQSfmMRYewzDD+yhVsvHcv\nrcsQSXesrKwwOCQEBx4+tLgvTQvOOBQgZqhsb48LN26kdRki6Y4VAHd7e5y+etXivtrlz4/PixSB\ni52d5YVJilKAmMF5zx781KJFWpchki7l8vfH/P79zW73JC4OU69exdO4uBSoSlKSAsQMZcuWxf37\n9zFx4kTc0J6IyDOKFy+O06dPY968ebiXxEO992Ni0O/MGewMD8fjuDg0ypNH5z8yEJ1ET6Jr166h\nSZMmsLOzw8OHD3H27FlUr14drVu3RuvWrVGqVKm0LlEkzZw8eRKNGzdGiRIlEBoaiuvXr+P1119H\nq1at0KpVKxQuXPhfba5cuYKxFy7gYb58mFSyJHJkzpwGlYsltAeSBEePHoWHhwfKli2LhQsXIjg4\nGKGhoejRowd27tyJChUqwNXVFX369EFAQACio6PTumSRVLNjxw7UrFkTHTp0wC+//IKLFy/i1KlT\naN68ORYvXoyiRYuiePHiGDhwIHbt2gWSCAkJQZ06dXD+++/xY6lSCo+MivJCu3btYu7cudm3b19u\n376duXLl4pIlS555zNWrV/nTTz+xUaNGtLW1ZaFChejv759GFYuknmXLltHOzo5+fn5cuXIl7e3t\nuWfPnmceExISwvHjx7NOnTrMnDkz3dzc6OzszI4dOzIyMjKNKpfkoAB5gbVr1zJr1qz08/Pj0qVL\nEwbKi9y/f59Tp06lnZ0dr169mkqViqS+mTNn0sbGhlOmTOH3339Pa2trTp069YVtbt26xeHDh9PG\nxoZPnjxJpUolpegQ1nPMmjULbdq0wdixY5EnTx507doV48ePh6+v7wvbTZo0CV5eXsiXLx/27NmT\nStWKpK5vv/0W/fv3x5w5c3D58mUMHToU/v7+6Nev33PbxMbG4ptvvkG3bt2QOXNmHDx4MBUrlpSg\nAEnEd999hw8++ABz5szBo0ePMGDAAMyZMwf9kzBFcceOHVi7di2qV6+OvXv3pkK1IqknLi4OAwcO\nxKhRo7Bs2TLs3bsXP/30EwIDA9G6desXts2cOTPWrVuHnTt3omrVqhof/wFaC+sf9u3bh5EjR2LJ\nkiXYunUrZs+ejXXr1qF+/fpJal+jRg3s27cP9evXx4oVK1K4WpHUtWbNGvzyyy9Yvnw5FixYgE2b\nNmHr1q2oVq1aktrXrFkTe/fuhbe3twLkP0B7IP+wbds2VKlSBd7e3ti0aRPWrl2b5PAATAGyd+9e\neHl54eDBg4jScgzyH7J27Vo0btwYxYsXx8GDB7Fly5YkhweAhODw9vbG7t27U7BSSQ0KkH/w9vbG\nkSNHkCdPHhw/fhx16tQxu314eDhy5MgBkjh06FAKVSqS+mrVqoUjR46gVKlSOH78OCpUqGBW+5o1\nayI4OBjlypXDzZs3ceHChZQpVFKFAuQfPD09ERUVhWPHjiGTgVVF8+bNixIlSuDw4cOoXLky9u3b\nlwJViqQNb29vhISE4MaNG4bGR7ly5ZArVy5cuHABRYsW1USTDE4B8g9Zs2ZFxYoVLTo+6+3tjX37\n9uk4r/znlChRAvnz58f+/fsNtbeyskqYYKLxkfEpQBJh6Qs7/jyIBoj811hZWVn8uv77eRCNj4xN\ns7ASUaNGDYwaNcpw+4YNGyI8PBzVqlXD1atXcevWLTg6OiZjhSJpx9vbG4GBgYbbt23bFm5ubnB2\ndsbx48cRGRkJe3v7ZKxQUosWU0zEuXPnULJkSdy4cQNOTk6G+njy5Ak6dOiA+/fva7aJQYsXAxMm\nAH/8AaxZAzx6ZPq5vT3QurXpZ/fvA2+/nZZVvnq2b9+Opk2b4v79+7C1tTXUx61bt9C4cWMULlwY\nq1evTuYKXw3pYXzoEFYiihcvnnCcNzo6GiEhIWa1v3XrFl577TXcu3cPAQEBKVTlq6FECWDlyrSu\nQv7O09MTMTExOHLkCKKiopK8dHu8+JWsHR0dsXDhwhSq8tWQ1uNDAZKIv5/o+/PPP1GyZElUqlQJ\nX3zxxUuXXwgJCYG3tzfy5cuHTZs2IU+ePKlU9X9T586Avz/w9/3kadOAvn2BqVPTrq5XWdasWVGp\nUiXs2bMHgYGBcHR0RP369TFx4kSEhoa+sO3hw4dRp04deHp6IiAgANmzZ0+lqv+b0np8KECew9HR\nEb///jvCw8Nx9epVDBkyBIcOHYK3tzfy58+P7t27IyAg4JkLBY8cOZIwOFavXo1s2bIlaVt3795F\n6dKlce3atZR6OhlWpkxA+/am3fV4ffuaBskHH6RdXa+6okWLYtmyZciRIwdOnDiBVq1aYdmyZShR\nogSKFSuW6K0Ndu3ahXr16qFVq1ZYtGhRkg9/nT9/Hvnz50d4eHhKPZ0MK63HhwIkEfPnz8e8efPg\n4OCALl26oHTp0li7di26dOmCY8eO4auvvsKNGzfQtm1bvPXWWwBMg6Nu3bpo1aoVFixYYNax4Tx5\n8uDx48fYtWtXSj2lDK1NG+Ds2bSuQuLNmzcPa9euhY2NDVq3bo0aNWogKCgIgwYNwuHDh/HBBx/g\n+PHjaN26NT7437tYYGAgGjdujIEDB2LatGnIbMb9P4oWLYqYmBhdM/IcaTo+0ng14HRnxIgRtLW1\n5fz580mSsbGxDAoK4ogRI1imTBlmypSJ1apV44gRI7h//34GBwdzzZo1Ccu+G9W//z4OH34nuZ6G\nSIoYMWIEs2TJwtWrV5MkY2JiuHPnTvr6+rJkyZLMnDkza9WqRT8/P+7evZtnz57l7NmzaWNjw8mT\nJxvebvfux/jll1r+Pb1RgPxPbGwsfXx8mD17dm7atCnRx8TFxfGPP/7g559/znLlytHKyoqVKlWi\nnZ0dv/vuO4u2P3486e1tURciKSYuLo4+Pj7MkSMHt23bluhjYmNjuWvXLn7yyScsWbIkM2XKxCpV\nqtDW1pYzZsywaPsjRpBvvGFRF5ICNI0XQExMDHr27Il169Zh7dq18PLySlK7s2fPYtOmTahZsyaq\nVKliUQ2XLgEnTgBNm1rUjUiyix8f69evx7p165K8eOKJEyewZ88eeHp6Wjw+zp0Dbt0Cata0qBtJ\nZq98gERERKBTp044fPgw1q9fj3LlyqV1SfI/kycDFSsCtraAszPg5pbWFb16IiIi0LlzZxw+fBgb\nNmxAmTJl0rok+Z+OHYF27YCrVwEvL6B27dSv4ZU+if7gwQM0bdoUZ86cwc6dOxUe6cy0aaY9Mx8f\nYOPGtK7m1fPgwQM0a9YM58+fx969exUeyejx48eIi4uzqI+dO4EsWYApU0whkhZe6QAZOnQobt++\njd9//x1FixZN63L+E4KDg/Hhhx+iefPmGDJkCGJiYgz1QxIFCx5G8eIPEBMTCTe32GSuVF5m8ODB\nuH37NjZu3IiCBQumdTkZXmRkJObOnYvq1avD29sbzZo1Q1hYmKG+IiIi4Oo6FkWLXoer6zEUK/Yw\nmatNmlc6QLZv3473338fzs7OaV0KFi827YYCpiUI/j6vO72Ljo6Gv78/6tevj0qVKuHq1at48803\nsXz5ctSrV8/Q9S3Xr1/Hli1VkTv3ZRw9mgVubudToHJ5kTVr1uCTTz5BgQIF0rqUDD0+jh49ij59\n+sDR0RHffvst3nvvPaxevRpWVlYoX748NhrYvb5w4QL27fOFjc09/P57JRQrFpkClb9cxguQyEjT\nFTIDBwLvvAO85MrXF6levTrOnbucjMVZJq2XJTBXaGgoBg4cCEdHR3z66ado3LgxQkJC0Lx5c7i5\nueHo0aNwdnZGpUqVsG7dOrP6vnXrFkqVKoVly5Yhc+bMKFKkSAo9i/+YZBwftWvXxqlT6ecCnIw0\nPqKjo/Hrr7/Cw8MD7u7uuHfvHlasWIGFCxfi0KFD+P333xEYGIiPP/4Yb775Jj777DOzDmkdP34c\nFStWxLBhw1ClShXkz58/BZ/NC6TpHDAjJk8mAwNNX9++Tb79tuGufv75KcuVS6a6LLRoEfnbb+Rb\nb5GrV5u+J8lTp8i4uLSt7e/i4uK4efNmtmvXjtbW1nz99de5ZMkSnjhxgoMHD2bu3Lnp5ubGBQsW\nJLSZPn067ezs6Ovry5iYmBf2f+bMGX700UcJ/Xh5edHFxYV79uxJ6af235CM42PChEjWqpVMdVno\neeNj3z7y6dO0re3vYmJiOGbMGLq6ujJ79uzs168fDx48yAULFrBWrVq0trZmu3btuHfv3oQ2W7du\nZYECBVivXj1ev379uX0/ePCAU6dOZYUKFZg1a1a+//777NOnD21sbDh27FjGpcEbRcYLkH79yL//\nJ7dpY7irY8dIa2syPDwZ6rLQokVkQAC5fDnZpYvp+ydPyOzZyUKFTE9769YQRkVFpWmd27dvZ6lS\npdi/f38eOHCA06dPZ7Vq1WhjY8Nu3boxKCgo0XY7d+5kwYIFWbdu3X8NkoiIiOf2ExMTw88//5yF\nCrlz4sTodBWm6VIyjo9du0h7ezIyMhnqslBi4+P+fTJHDjJ3brJrV3L16sN88OBBmtY5f/58VqtW\njZMmTeLevXvZu3dv5siRg0WLFqWfnx9v3ryZaLsrV66wVq1aLFSoEHfu3PnM73bt2sUOHTrQzs6O\nFStW5PTp0595nqtWraKn5wds2zaW9++n6NP7l4wXIJMmPfsJq2tXw13FxaV9eMQPzvgBEhdHenr+\n9QkrPNz0dadOZKVK1ZkzZ0527tz5Xy+y1DJo0CB27dqVZ8+eZaFChejk5MShQ4fywoULL20bFhbG\nRo0asVChQty1axfPnz9PX19fOjk5sUiRIvTz83vuJ7D16x8xd26yVSvy3r3kfU7/Kck4Pp4+Ja9e\nTaa6DHrZ+IiMJNeuJXv3JitX9qS9vT2bNWvGrVu3pkm977//Pt9//30ePXqUmTJlYvXq1Tlv3jxG\nJiGFo6Oj6evrS2tra37++eecNm0aq1WrRmtra3bo0OGFY/7MGbJKFbJkSfLo0eR8Ri+W8QLkyRPT\nq8XHx7R7fvZsWldEkjx16hTXrl3LEydOJLnNzZukuzs5b17SHh8VFcX169ezZ8+edHd359U0GN2t\nWrXi//3f/zE2NpbLly83e4/o6dOnHDx4MHPmzElbW1t6e3sneYCdO0dWrpz0/69XUjodHydPnuSi\nRYu4d+9exsbGJqnN1atkhQqkv3/SthEbG8s9e/bQx8eHhQsX5uXLly2o2JgGDRpw9OjRJMnDhw8b\n6mPevHksUKAAHRwcOHjwYJ45cyZJ7SIiTDugu3aZAtbT0/TzgADT94sWkStX/vWz5BhHGS9A0qFd\nu3Yxd+7crFatGjNlysSSJUvyk08+4e7du587WC5efMDSpck6dci7d83fpouLC5csWWJh5earWLEi\nZ82aZbj9zJkzef36dbq5uRlaO+zvn0hTY4CI5bZu3UoHBwdWrlyZmTJlorOzM/v06cN169Y994ND\nSMgDurmRb75pykRzubi4cPHixRZWbj43NzcuXLjQcPv33nuPp06dorOzs0Xje9Ei0/miFStSdnxk\nvFlYySg5pgYuW7YMDRo0gI+PDzZu3Ijg4GCMHj0aN27cQNOmTZEvXz507NgRv/76Kx48eADAdK1E\n7drl0aTJEWzcCOTObf524++7nppIIjQ0FMWKFTPUPjIyEr1798a1a9dw+fJl1K1b1+w+7Oz++joj\nzcrJiJJjfCxevBhNmjTBsGHDsGXLFgQHB2PSpEl48uQJOnbsiFy5cuGNN97ApEmTcOPGDQDAgQMH\nUKtWabRtexIrV5ouljNXWtxvPTY2FpcvXzY8Pu7fv49ffvkFT58+xY0bN1C6dGmL6kmNe4W80gEC\nWPYmNHPmTHTp0gXjxo3DyJEj4e/vj7Jly2Ly5MmoVKkSdu3ahRkzZsDW1hYDBw6Ei4sLWrRogdq1\na6Nx48YYP74CjN4KOi0GSFhYGB4/fgw3g2uKXLx4ESRhZ2eH6OhowwMtXlrfTOdVYMn4mDhxIrp1\n64ZJkybB19cXCxYsQLly5TBlyhRUrVoVu3fvxpw5c5AvXz6MGDECRYsWRZ06dVC3bl20a9cW335b\nBjY2xradFuPjypUrFr2uL1y4gEyZTG/JJC0eH6lxr5CMGSAksHcvcOcO8O67wJkzhrtK7E0oKcaO\nHYsBAwZgzpw5GDBgAACgT58+CA0NRfv27bFhwwa4u7vj008/Rd68ebFo0SIsXboU0dHRKFGiBGbM\nmGHWPRH+ydvbG4cOHUJkZOpdQBQaGgo7Ozu4uLgYbp83b16EhYXBwcEBjo6OFtWT1jfTSddu3AAe\nPjRdD2LBOhdGx8dnn32GoUOHwt/fH3379gUA+Pj44Ny5c2jbti0CAwPh7u6OoUOHwtHREStXrsTq\n1auRNWtWlCpVClOmTEl4MzUifnw8efLEcB/mOn/+PLJmzWr4dX3+/HkULFgQV65cQZ48eZAjRw6L\na0rpe4VkzACxsjKtJLZ5MxAUBPz+u+GuEnsTmj8f2LAB+NvN1BKQxMCBA/H1118jMDAQXbp0eeb3\nRYsWxcCBA7Fp0yaEhobio48+wokTJ9CiRQvs378fn3/+OY4dO2Z4iY947u7usLKyeuktdgHgyZMn\nFq+7A5gCoEiRIoYHdmhoKNzc3Cw6DPZPutlUImJjgVKlTB+yVq0Cduww3FVi42PZMmDPHiCxl1Rc\nXBw+/PBD/PTTTwgMDETr1q2f+b2rq2vC+AgJCcGgQYNw/PhxNGrUCAcOHMCnn36K06dPJ8v4yJw5\nc5LGx6VLl5LlbocXLlyAq6srrKysDLU/f/48ihUrlvCvJTp3Bpo3N71V/vGH6fvOnYH4P0fz5sDb\nb1u0CQAZNUAAxNWogfDgYBxp1QpLLbjaFvj3m9D27aafFSgAfP75bqxZswaRkZGIiYlBjx49sHDh\nQmzZsgUNGjR4Yb+FCxfGgAEDsHnzZty8eRP9+vWDp6cnYmJicPToUYt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xJMMu5y4iImlL\nASIiIoYoQERExBAFiIiIGKIAERERQxQgIiJiiAJEREQMUYCIiIghChARETFEASIiIoYoQERExBAF\niIiIGKIAERERQxQgIiJiiAJEREQMUYCIiIghChARETFEASIiIoYoQERExBAFiIiIGKIAERERQxQg\nIiJiiAJEREQMUYCIiIghChARETFEASIiIoYoQERExBAFiIiIGKIAERERQxQgIiJiiAJEREQMUYCI\niIghChARETFEASIiIoYoQERExBAFiIiIGKIAERERQxQgIiJiiAJEREQMUYCIiIghChARETFEASIi\nIoYoQERExBAFiIiIGKIAERERQxQgIiJiiAJEREQMUYCIiIghChARETFEASIiIoYoQERExBAFiIiI\nGKIAERERQxQgIiJiiAJEREQMUYCIiIghChARETFEASIiIoYoQERExBAFiIiIGKIAERERQxQgIiJi\niAJEREQMUYCIiIghChARETFEASIiIoYoQERExBAFiIiIGPL/mubi+CEJkXwAAAAASUVORK5CYII=\n",
       "prompt_number": 19,
       "text": [
        "<Image.Image image mode=RGBA size=400x600 at 0x5C66518>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Yeah, those are definitely related to each other.\n",
      "\n",
      "Those two datasets share a lot of molecules in common, what about another one where there's less identity overlap?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d1,d2=61733,60448\n",
      "data = %sql\\\n",
      "  select molregno_1,t1.m m1,molregno_2,t2.m m2,sim from papers_pairs.pairs_and_docs_2012 \\\n",
      "    join rdk.mols t1 on (molregno_1=t1.molregno) \\\n",
      "    join rdk.mols t2 on (molregno_2=t2.molregno) \\\n",
      "    where doc_id_1=:d1 and doc_id_2=:d2\n",
      "data = data.DataFrame()\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m1',molCol='mol1')\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m2',molCol='mol2')\n",
      "rows=[]\n",
      "for m1,m2 in zip(data['mol1'],data['mol2']):\n",
      "    rows.append(m1)\n",
      "    rows.append(m2)\n",
      "Draw.MolsToGridImage(rows[:6],molsPerRow=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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IyZMnMXz4cDRr1gw5OTnYs2cPfvrpJ7PZJ9qwYcPqb4EUFgIvvghMmgSMGQOk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       "prompt_number": 20,
       "text": [
        "<Image.Image image mode=RGBA size=400x600 at 0x56B3290>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That one may be a curation problem."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d1,d2=66968,61801\n",
      "data = %sql\\\n",
      "  select molregno_1,t1.m m1,molregno_2,t2.m m2,sim from papers_pairs.pairs_and_docs_2012 \\\n",
      "    join rdk.mols t1 on (molregno_1=t1.molregno) \\\n",
      "    join rdk.mols t2 on (molregno_2=t2.molregno) \\\n",
      "    where doc_id_1=:d1 and doc_id_2=:d2\n",
      "data = data.DataFrame()\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m1',molCol='mol1')\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m2',molCol='mol2')\n",
      "rows=[]\n",
      "for m1,m2 in zip(data['mol1'],data['mol2']):\n",
      "    rows.append(m1)\n",
      "    rows.append(m2)\n",
      "Draw.MolsToGridImage(rows[:6],molsPerRow=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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GhnSV1ZUr5TewSxeiHTuIRo8mat9eOm7TrBmn51pTVKX3hoODA+chLP9/9/o8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\ngnAAYYwxJggHEMYYY4L8P9Q+Q5xQUF5mAAAAAElFTkSuQmCC\n",
       "prompt_number": 21,
       "text": [
        "<Image.Image image mode=RGBA size=400x600 at 0x4BAE200>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "hmmm, maybe that one too"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d1,d2=65787,65440\n",
      "data = %sql\\\n",
      "  select molregno_1,t1.m m1,molregno_2,t2.m m2,sim from papers_pairs.pairs_and_docs_2012 \\\n",
      "    join rdk.mols t1 on (molregno_1=t1.molregno) \\\n",
      "    join rdk.mols t2 on (molregno_2=t2.molregno) \\\n",
      "    where doc_id_1=:d1 and doc_id_2=:d2\n",
      "data = data.DataFrame()\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m1',molCol='mol1')\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m2',molCol='mol2')\n",
      "rows=[]\n",
      "for m1,m2 in zip(data['mol1'],data['mol2']):\n",
      "    rows.append(m1)\n",
      "    rows.append(m2)\n",
      "Draw.MolsToGridImage(rows[:6],molsPerRow=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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eWq0WOp0Oev2DKCwMQllZ7Vk3dTqgtBTo3h2YPt0yf4Y6HBAmNs4Hk5LNN5CQ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\n",
       "prompt_number": 22,
       "text": [
        "<Image.Image image mode=RGBA size=400x600 at 0x56C3B90>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d1,d2=66889,65537\n",
      "data = %sql\\\n",
      "  select molregno_1,t1.m m1,molregno_2,t2.m m2,sim from papers_pairs.pairs_and_docs_2012 \\\n",
      "    join rdk.mols t1 on (molregno_1=t1.molregno) \\\n",
      "    join rdk.mols t2 on (molregno_2=t2.molregno) \\\n",
      "    where doc_id_1=:d1 and doc_id_2=:d2\n",
      "data = data.DataFrame()\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m1',molCol='mol1')\n",
      "PandasTools.AddMoleculeColumnToFrame(data,smilesCol='m2',molCol='mol2')\n",
      "rows=[]\n",
      "for m1,m2 in zip(data['mol1'],data['mol2']):\n",
      "    rows.append(m1)\n",
      "    rows.append(m2)\n",
      "Draw.MolsToGridImage(rows[:6],molsPerRow=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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KZQusggYJFPrTBvX892rdurV+lu14RVy/fh2enp6KJuMtWbJEb02TiYmJaNSo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\n37Nnz8LBwQEffvghAPUQ4Xnz5qFy5coQEbi5uak3fM1HCxo8QNatWwd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       "prompt_number": 23,
       "text": [
        "<Image.Image image mode=RGBA size=400x600 at 0x56C4878>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}