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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Multi-dimensional scaling with myChEMBL, RDKit and Pandas"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "myChEMBL team, ChEMBL group, EMBL-EBI."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a slightly modified version of [this](http://rdkit.blogspot.co.uk/search?updated-max=2014-02-02T20:05:00-08:00&max-results=7&start=7&by-date=false) article by Greg Landrum , which in turn is based on [this](http://nbviewer.ipython.org/gist/madgpap/8538507) notebook by George Papadatos.\n",
      "\n",
      "Scikit-learn makes it quite easy to apply multi-dimensional scaling (MDS) to either reduce the dimensionality of a dataset or to embed distance data into cartesian space. This enables one of the favorite activities of the cheminformatician: producing plots of where compounds land in an 2D space."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from chembl_webresource_client import *\n",
      "import pandas as pd\n",
      "from rdkit.Chem import PandasTools\n",
      "from rdkit.Chem import AllChem as Chem\n",
      "from rdkit.Chem import DataStructs\n",
      "from sklearn import manifold\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "height has been deprecated.\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rcParams['figure.figsize'] = 8,8"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's configure our client to use local version of web services. Just delete the cell below, if you want to use the [official version](https://www.ebi.ac.uk/chembl/ws). In that case, you will need internet access."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from chembl_webresource_client.settings import Settings\n",
      "Settings.Instance().WEBSERVICE_DOMAIN = 'localhost'\n",
      "Settings.Instance().WEBSERVICE_PROTOCOL = 'http'\n",
      "\n",
      "#If your VM is slow, you can consider increasing timeout from default 3 seconds, just uncomment line below:\n",
      "Settings.Instance().TIMEOUT = 120"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Grab the data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I'm going to start with a larger dataset so that I can explore the impact of dataset size on the results. The analysis of George's results are below.\n",
      "\n",
      "I'll use the Dopamine D3 receptor as the target for this exercise. It's one of the targets we used in both the benchmarking and model fusion papers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "targets = TargetResource()\n",
      "target='CHEMBL234'\n",
      "bio = targets.bioactivities(target)\n",
      "data = pd.DataFrame(bio)\n",
      "data.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>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> CHEMBL666973</td>\n",
        "      <td> Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)</td>\n",
        "      <td> B</td>\n",
        "      <td> Ki</td>\n",
        "      <td> CHEMBL278751</td>\n",
        "      <td> 2 (+)-UH232</td>\n",
        "      <td> =</td>\n",
        "      <td> Homo sapiens</td>\n",
        "      <td> CHEMBL278751</td>\n",
        "      <td> Bioorg. Med. Chem. Lett., (1994) 4:5:689</td>\n",
        "      <td> CHEMBL234</td>\n",
        "      <td> 8</td>\n",
        "      <td> Dopamine D3 receptor</td>\n",
        "      <td> nM</td>\n",
        "      <td> 4.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Unspecified</td>\n",
        "      <td> CHEMBL666973</td>\n",
        "      <td> Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)</td>\n",
        "      <td> B</td>\n",
        "      <td> Ki</td>\n",
        "      <td> CHEMBL161811</td>\n",
        "      <td>           9</td>\n",
        "      <td> =</td>\n",
        "      <td> Homo sapiens</td>\n",
        "      <td> CHEMBL161811</td>\n",
        "      <td> Bioorg. Med. Chem. Lett., (1994) 4:5:689</td>\n",
        "      <td> CHEMBL234</td>\n",
        "      <td> 8</td>\n",
        "      <td> Dopamine D3 receptor</td>\n",
        "      <td> nM</td>\n",
        "      <td>  40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Unspecified</td>\n",
        "      <td> CHEMBL666973</td>\n",
        "      <td> Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)</td>\n",
        "      <td> B</td>\n",
        "      <td> Ki</td>\n",
        "      <td> CHEMBL349843</td>\n",
        "      <td>           5</td>\n",
        "      <td> =</td>\n",
        "      <td> Homo sapiens</td>\n",
        "      <td> CHEMBL349843</td>\n",
        "      <td> Bioorg. Med. Chem. Lett., (1994) 4:5:689</td>\n",
        "      <td> CHEMBL234</td>\n",
        "      <td> 8</td>\n",
        "      <td> Dopamine D3 receptor</td>\n",
        "      <td> nM</td>\n",
        "      <td> 171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Unspecified</td>\n",
        "      <td> CHEMBL666973</td>\n",
        "      <td> Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)</td>\n",
        "      <td> B</td>\n",
        "      <td> Ki</td>\n",
        "      <td>  CHEMBL27441</td>\n",
        "      <td>  1 (+)-AJ76</td>\n",
        "      <td> =</td>\n",
        "      <td> Homo sapiens</td>\n",
        "      <td>  CHEMBL27441</td>\n",
        "      <td> Bioorg. Med. Chem. Lett., (1994) 4:5:689</td>\n",
        "      <td> CHEMBL234</td>\n",
        "      <td> 8</td>\n",
        "      <td> Dopamine D3 receptor</td>\n",
        "      <td> nM</td>\n",
        "      <td>  26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Unspecified</td>\n",
        "      <td> CHEMBL666973</td>\n",
        "      <td> Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)</td>\n",
        "      <td> B</td>\n",
        "      <td> Ki</td>\n",
        "      <td> CHEMBL161507</td>\n",
        "      <td>           6</td>\n",
        "      <td> =</td>\n",
        "      <td> Homo sapiens</td>\n",
        "      <td> CHEMBL161507</td>\n",
        "      <td> Bioorg. Med. Chem. Lett., (1994) 4:5:689</td>\n",
        "      <td> CHEMBL234</td>\n",
        "      <td> 8</td>\n",
        "      <td> Dopamine D3 receptor</td>\n",
        "      <td> nM</td>\n",
        "      <td> 253</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "  activity_comment assay_chemblid                                                                                                         assay_description assay_type bioactivity_type ingredient_cmpd_chemblid name_in_reference operator      organism parent_cmpd_chemblid                                 reference target_chemblid  target_confidence           target_name units value\n",
        "0      Unspecified   CHEMBL666973  Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)          B               Ki             CHEMBL278751       2 (+)-UH232        =  Homo sapiens         CHEMBL278751  Bioorg. Med. Chem. Lett., (1994) 4:5:689       CHEMBL234                  8  Dopamine D3 receptor    nM   4.2\n",
        "1      Unspecified   CHEMBL666973  Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)          B               Ki             CHEMBL161811                 9        =  Homo sapiens         CHEMBL161811  Bioorg. Med. Chem. Lett., (1994) 4:5:689       CHEMBL234                  8  Dopamine D3 receptor    nM    40\n",
        "2      Unspecified   CHEMBL666973  Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)          B               Ki             CHEMBL349843                 5        =  Homo sapiens         CHEMBL349843  Bioorg. Med. Chem. Lett., (1994) 4:5:689       CHEMBL234                  8  Dopamine D3 receptor    nM   171\n",
        "3      Unspecified   CHEMBL666973  Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)          B               Ki              CHEMBL27441        1 (+)-AJ76        =  Homo sapiens          CHEMBL27441  Bioorg. Med. Chem. Lett., (1994) 4:5:689       CHEMBL234                  8  Dopamine D3 receptor    nM    26\n",
        "4      Unspecified   CHEMBL666973  Binding affinity was measured at cloned mammalian dopamine D3 receptor expressed in CHO-K1 cells (using [3H]- spiperone)          B               Ki             CHEMBL161507                 6        =  Homo sapiens         CHEMBL161507  Bioorg. Med. Chem. Lett., (1994) 4:5:689       CHEMBL234                  8  Dopamine D3 receptor    nM   253"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(6812, 16)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Remove duplicates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = data.drop_duplicates(['parent_cmpd_chemblid'])\n",
      "data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(4447, 16)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Remove assays with too many or too few compounds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assays = data[['assay_chemblid','target_chemblid']].groupby('assay_chemblid').count()\n",
      "print assays.shape\n",
      "assays.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(488, 1)\n"
       ]
      },
      {
       "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>target_chemblid</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>assay_chemblid</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHEMBL1030601</th>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CHEMBL1030747</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CHEMBL1031083</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CHEMBL1031097</th>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CHEMBL1031401</th>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                target_chemblid\n",
        "assay_chemblid                 \n",
        "CHEMBL1030601                12\n",
        "CHEMBL1030747                 1\n",
        "CHEMBL1031083                 1\n",
        "CHEMBL1031097                 3\n",
        "CHEMBL1031401                 1"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assays.target_chemblid.hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f9820192e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lSQrmsC6QsEdJaAA7apLQABkdCQ2Q01HCYS1JUuXcWUuS1AF31pIkBXNYF0jYoyQ0gB01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\nkiRJkiRJkiRJkiRJkiRJkiRJkrTg/h8U9wxR3yi+1AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f983cd36a10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "goodassays = assays.ix[(assays.target_chemblid >= 10)&(assays.target_chemblid <= 30)]\n",
      "print goodassays.shape\n",
      "goodassays.target_chemblid.hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(114, 1)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f982016bf90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ABaIT7hchDACN6IS3zDa329k64a7phKEdnTAAFCGEARaITrhfhDAANKIT3jLb3G5n64S7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\n3ja63WMPAAAAAAAAAAAAAAAAAAD2uf8H+SQuuT36m80AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f981da62e50>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data2 = data.ix[data.assay_chemblid.isin(list(goodassays.index))]\n",
      "data2.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(1918, 16)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Now get the SMILES"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "compounds = CompoundResource()\n",
      "cs = compounds.get(list(data2['parent_cmpd_chemblid']))\n",
      "smiles = pd.Series(map(lambda x: x['smiles'], cs), index=data2.index)\n",
      "data2['SMILES'] = smiles"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PandasTools.AddMoleculeColumnToFrame(data2, smilesCol = 'SMILES',includeFingerprints=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's start by looking at assays that have between 10 and 12 compounds:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assays = data2[['assay_chemblid','target_chemblid']].groupby('assay_chemblid').count()\n",
      "print assays.shape\n",
      "goodassays = assays.ix[(assays.target_chemblid >= 10)&(assays.target_chemblid <= 12)]\n",
      "subset = data2.ix[data.assay_chemblid.isin(list(goodassays.index))]\n",
      "print subset.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(114, 1)\n",
        "(262, 18)\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols = subset[['parent_cmpd_chemblid','name_in_reference','SMILES', 'ROMol', 'assay_chemblid']]\n",
      "mols.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(262, 5)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols.head(5)"
     ],
     "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>parent_cmpd_chemblid</th>\n",
        "      <th>name_in_reference</th>\n",
        "      <th>SMILES</th>\n",
        "      <th>ROMol</th>\n",
        "      <th>assay_chemblid</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>  CHEMBL87260</td>\n",
        "      <td>  5</td>\n",
        "      <td> Clc1ccc(cc1)N2CCN(CCCOc3ccc(cc3)c4nc5ccccc5[nH]4)CC2</td>\n",
        "      <td>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 <img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
        "      <td> CHEMBL666891</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>  CHEMBL87478</td>\n",
        "      <td>  9</td>\n",
        "      <td> CCCSc1ccccc1N2CCN(CCCOc3ccc(cc3)c4nc5ccccc5[nH]4)CC2</td>\n",
        "      <td> <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAYAAAB5fY51AAAe+0lEQVR4nO3de1iUdf7/8RcKIigKCiOKpmge19QUT6vh5QHStIO5nioj3cSyPFS25rZKW3lgPVGGmmllmtHBLPe7ZppLppfkqr/MtDyUZirKgKKJyHCY1+8PYiLD4jDD8Glej+vq2kD5zHs2fXLfwz2f24skISJigGruHkBEpLQULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBje7h7gjy4vLw+nTp1CamoqMjMzkZmZibNnzzo+Lv7vmZmZqF69Oh599FHMmzfP3aOLVDkKlgt88cUXmDp1Ko4fP4709HTYbDYAQGBgICwWC4KDgx3/REREICQkxPGxzWZDTEwMWrdujbFjx7r5mYhULV4k6e4h/kgKCgrQunVrDBgwACNHjvxFnHx8fEq1xooVK/Dkk0/iwIEDaNq0qYsnFjHHHzJYly9fxrlz55Ceno5mzZqhUaNGlfbYa9euxeOPP47vv/8e/v7+5VqDJAYNGgTACx999BG8vJw7o4ipjAhWTk4O0tPTkZaWBqvVivT0dGRkZODs2bOOfy/+azk5OQAAHx8ftGnTBvHx8T8FwLXsdjvat2+PUaNGYdasWRVaKzU1FaNGHcPIkX3wyCNOGlDEcFX+Nazz589jwoQJWL9+PerUqYPGjRsjKCgIQUFBaNSoEcLDw9G5c2fHxw0bNkRQUBDq1auHmjVr4rnnnkNMTAwOHDiA0NBQl866bVs2AgP/gocffrjCazVq1AjTpjXCiBHALbcAHTo4YUARw1X5I6yZM2diy5Yt+PDDDxESEoLq1auX+mvtdjvsdjsiIyMREhKCDz/80IWTAj16AD17AosXO2/N8eOBlBRg716gZk3nrStioip9Hdbly5eRmJiI6dOnl/no6L333kPfvn0BFL6ulJycjFWrVrliTADA9u3AF18ATzzh3HUXLACuXAE2b3buuiImqtLBevnll9GwYUMMHToUO3fuRPv27ZGbm1uqr42KisLJkyfxz3/+E82bN8fChQsxZcoUHDt2zCWzzpsH3Hsv0Lixc9etWxfYvx+46y7nritioip7SpiTk4Pw8HDMmTMHY8eOxaBBg9CoUaMyHSXt2LED/fv3xyeffILIyEjceeedyMjIwGeffVamU8vSWLoU6NsXaNvWqcv+Qk4OcPUqEBQEFBQAWVmFQRPxGKyiVqxYwRtuuIG5ubnct28fq1evzsOHD5d5nccff5zh4eH88ccfabVaGRoayrlz57pgYtfbsIFs2pTMziZPnCCfeKJsX5+dnc3c3FwXTCZSOarkEVbRxZePPPIIHnvsMYwePRq5ublYv359mdey2Wzo1q0bunbtipUrV2Lz5s244447sGvXLkRERLhgetf54IPC17JatACGDwdeegmYMwf49lvAagVSUwv/9+xZwMtrE776KtFxyUdGRgauXLmCQYMG4ZVXXkFYWJi7n45UgszMTMTHxyMrKwvjx49Hx44d3T1ShVTJyxref/99XLp0CbGxsTh27BjeffddpKSklGstX19frF69Gt27d8fAgQPxl7/8BQ888ABiYmKwd+9e+Pn5lXlNd56a9e4NbN0KREcXfnzmDPCnPwG1agGNGgENGhT+0769Bd26dUNwcDAsFovjLUH/+Mc/MHr0aCQnJzv9tFiqlk8++QQPPPAAgoOD0axZM0RERCA6OhrTp09HZGSku8crH3cf4l3LbrezY8eOjIuLI0k+9NBD7NevX4XXnTt3LoODg5mamsqsrCy2atWKjz/+eLnWquipWXlt2ECuWUN++SU5Zkzh49rtZFZW6ddIT09naGgo58+f77I55bddvUpeuFD47/n55MWLhZ+7cuXnz2Vmln/9y5cvc8yYMaxRowbnzZvH/Px8kuTZs2cZFxfHunXrsmPHjly9ejXz8vIq9mQqWZUL1scff8zatWvz/PnzTE1Npa+vLz/++OMKr1tQUMA+ffowOjqadrude/bsYY0aNbht27Yyr7VhAzlhAvmvf7knWCR5//3lf9zNmzezZs2a3L9/v9Nmk9Ir6Rvehg3ka68V/vqpU+Qjj5Rv7R07djA8PJw33XQTDxw4QJI8fvw4v/vuO8fvuXTpEufNm8f69eszPDycCQkJzM7OrsAzqjxVLlh9+/bl1KlTSZJPPfUUu3Tp4rS1jx8/zjp16nDp0qUkyZkzZ7Jx48Y8cuQIDx06xO3bt/O9995jYmIiX3hhBx99lBw6lOzVi2zRgvT3L/xDVRSO++8n9++vvGA5U2xsLNu1a8erV6+6exSPU9I3vA0byFWryLw88vvvyx4sm83G6dOn08fHh9OnT3f8d121ahUDAgI4Y8aMX31NVlYWExISeMMNN9BisTAuLo6ZFTm0qwRVKli7du1ijRo1eOrUKWZmZrJOnTp8++23nfoYy5cvZ0hICLOzs2mz2XjTTTcRAAEwKCiIbdu2ZZ8+fTh16mpOnkw++yy5bBm5fj352WdkWlrJp2amKTotfsLE4Q1X0je8DRvIqChyyhTyr38tW7AOHz7Mrl27MiwsjFu2bCFJnj59mlFRUaxXrx7feeed3/z63Nxcrl69mu3atWNAQAAnT57MM2fOlPv5uVKVCtbQoUM5duxYkoWvObVs2dJx/u0sdrvdcXhcdLlEcnIyc3JySr2Gs07N3G3v3r309fUt12mxlF9J3/DKc0pot9uZkJBAPz8/3nPPPY6jozVr1jAwMJB9+/blyZMnSz1XXl4e161bx44dOzIgIIDLly8v61NzuSoTrFOnTtHb25tfffUVSbJTp05MTEx06WOOGjWKd999t0sfwxny8siEhLK9uF5as2bNYuPGjXmh6FXgPwir1cqvv/6a27dv57vvvsuXXnqJcXFxnDhxIu+++27ecsstbN26NadNm0abzVaps5X0Da+kYO3eTcbGFv7ea7tz5swZRkdHMygoyHEEdf78eQ4fPpz+/v58+eWXabfbyzWf3W5nUlISAwICyhS8ylBlrsO6ePEimjdvjvfeew/9+vVDdnY2qlevDl9fX5c83rFjx9C2bVukpKSga9euTlnTZgNcMe4bbxS+R/HkSaCcW2xdV35+Pnr37o0bb7wRa9eude7ilSAzMxPLly/Htm3bfrH1UH5+PgDAz88PISEhCA0NRUhICEJCQmCxWNCgQQP4+/vj2WefRUxMDObOnevmZ/Jr//tf4XtJd+wAGja8gPPnb0ZkZCTCw8OxcuVKNGnSBGvWrEGrVq2QnJzsuIRhzZo1aNeuXbke86uvvkKTJk1Qt25d+Pr6YufOnejWrZuTn1kFuLuYxc2ePZsNGzZkRkaGyx9rwoQJTrlcosiZM2RoKHnwoNOWJEkWFJBt2pDPPOPcdYv79ttvWbt2bb711luuexAXeeyxx3jzzTdz9uzZXLVqFTdu3MiUlBR+9913zCrFIemOHTvo7e1d5U+Ljx2zcvXq1Zw4cSLbtGnD++67j3l5ebxy5QpjY2Pp7e3NuLi4Cr+ToV27dnzjjTdIkqGhofz3v//tjPGdpkoFq+jSg7vuusulj3PmzBn6+vo6XqB0locfJtu1K/xxtbNs2EAGBJDnzztvzZIsXbqUgYGB5T4FyMjIoNVqdfJUv81qtdLf359bt251fM5ms/H06dPcv38/P/roI65Zs4aLFi3ijBkzOG7cON5+++3s2bMnO3To4PiaJ5980qjT4oEDB/K5554jSS5cuJCNGzd22p/lPn36cMGCBSTJDh068NVXX3XKus5SZU4Ji5w4cQKdOnXCkiVLcP/997vkMZ566ils27YNe/bsceq62dlA587A4MHAwoXOWXPUqHlo23YM4uJc+1YakrjjjjuQk5ODTZs2IT09/Vd39Lne3X6Kdnjt0qULxo0bh4kTJ7p01iKzZs3CRx995Pjv2Lx5c5w4ccLx60FBQbBYLI6bfBQ/LQwODsaoUaPg5eUFm82G7t27o0OHDnjjjTdcMmtBATBjBvDYY0DDhhVbKyYmBoGBgXjhhRdgs9mQnZ2NoKAgp8w5YsQING/eHPPmzUNUVBQGDBiA6dOnO2VtZ6hyb80JDw/H4sWLMWnSJERGRqJZs2ZOXf/ixYtYtmyZS/bG8vcHVq0CRoywYtiws/jznyv2vq3k5GR88MEzWLhwjJMmvD4vLy8sX74cUVFRqFOnjiNC3t7eCA4O/sVrPy1atECPHj1+EYCQkBDs2bMHY8eORa9evVz+nrXLly/jpZdewooVKxyfW7t2LWrXru2Yt7Q3/fD19cW6desQERGBIUOGYMSIEU6f98UXgXXrgJkzK76WxWLB6dOnARTO7szXeS0WC6xWKwAgJCQE6enpTlvbKdx8hHddw4cPZ69evZx+WcPs2bPZqlUrFhQUOHXd4mbO/CfDw8N56dKlCq0TFRXFBx980ElTlU52dja3b9/OQ4cOlesUb+zYsZVyQer8+fOd/t9x4cKFDAoK4qlTp5y2JkmePl14Wr9unXPWi4+PZ//+/Z2z2DWeeeYZDh48mCQ5ZcoUjhkzxiWPU15VNljp6els2LAh4+PjnbZmdnY2LRYLX3nlFaetWZK8vDz26NGD99xzT5m+7tKlSzxy5Ah37tzJxYsXs1q1avz6669dNKVrXLx4kRFt2/LIT6+DuEJOTg7DwsK4YsUKp65bUFDAfv36MSoqqtyXBJQkNjaTkZGF7/t0hldffZU33XSTcxa7RmJiIrt160aSfP7553nrrbe65HHKq8qdEhYJDg7G66+/jjvuuAPR0dHo1KlTqb82MzPTcZuvnJwcRP+0tcFrr72GmjVrIiYmxkVTF/L29sbatWtx8803Y+nSpYiIiEB6ejrS09NhtVqRlpbm+PF70ZzFb7hao0YNBAcHY+XKlWjryh0BXaBu3brYvXw5qkVFAX/+c+Em9062Zs0akHT6a5zVqlXDG2+8gQ4dOmDZsmVOeS3us88+wzvv3IVdu07Ay8s5W3oUP21ztuJrWyyWKndKWGWDBQDR0dEYN24c7r33XsdWMAUFBXj77bdhtVphtVodt/pKS0vDuXPnYLVaHdso16pVC+3bt0d0dDTy8/OxYMECTJ48udSvbVREixYtsGjRIixfvhx///vfERYW9ou7/TRv3hxdunT5xZ1+goKCUL9+fZdde1ZZqkVGAtOmAffcA3z5JVCnjtPWLigowLx58zBlyhSX/P8UFhaGJUuWYPz48ejfvz9at25d7rVsNhsefPBBPPTQBLRt67z9hywWCzIyMmC321GtmnN3Ob82WK4KY3lVuZ8SXis7OxudO3fGbbfdhkWLFoEkevXqhaCgoF/99Kfoxdaij4vvdfXmm29iypQpOHnyJGrVqlVp81+5cgV+fn5O/4NV5dlsQNeuwKBBQHy805Z99913ERsbix9++AEBAQFOW/dao0ePxrFjx5CSklLub3CzZ8/GK6+8gkOHDmHSpEkYMWIEBg4cWOHZTp48iWbNmsFqtSIkJKTC6xX3zTffoF27drh8+TIOHDiAfv36OX4AUxVU6SMsAPD398ebb76JXr16YfDgwejfvz927dp13d+flZWFtLQ07N+//xc3XE1KSkJMTEylxgpApT9eleHrC2zYANSv77QlSWLOnDmYOHGiS2MFAMuWLUPHjh0xZ84cxMXFOT5vs9mue2pvtVqRkZEBq9WKc+fOwc/PD/feey9q1aqFDh064O6778Z///tf9OjRo0KzFUUqPT3d6cGyWCwAAKvVCovFApvNhkuXLqFuFbl5QJUPFlB4fc+0adMwduxYPP3007hy5covXvtJT093fHz16lUAha8jFX8rho+PD77//nv3PhFP06KFU5fbunUrjhw5go8//tip6xaXk5ODgoICBAYGYvny5Rg1ahQ2b96MCxcu4Ny5c/jxxx8dv7foiL74JR/h4eGOo/7PPvsMiYmJGD16NKZOnYpvvvkGw4cPx+eff16hLar9/f1Ru3ZtWK3Wcr8F53rq1asHHx8fpKenO14/tVqtClZZFX2XS0hIcFwMGBoaiqZNmyIkJAQNGjRwfL7o9LC4ogtSV61ahb/+9a/ueAqezW4Hjh8HgoOBwMByLREfH48HHnjAcRTgCsuWLUNSUhJ2796NOnXqIDs7G4MHD0Z4ePiv/ox5e//2X59hw4ahoKAA0dHR+Pzzz5GYmIgJE1Zi5MgG+OgjoCIHiSEhIS55fcnLywvDhg2Dr68v6tSpg5o1a8JqtaJly5ZOf6xycevPKCvZypUrWatWLR45csTdo3iWy5fJYcPIF18sfP9S0VYFpXTlyhVu2LCB3t7ePH78OEny6NGjTh/z6tWrDA0NdbwdZdCgQY7tjsorNzeXAwYMYJcuXZiVlc3sbLJ7d7JPH7Iim0R0796dS5YsIVm49XF57ij1ey5evMimTZvyP//5j9PXLi+PCpbdbufAgQM5dOhgkq67cFSusXgx+eGHP38cHf3zRUlbtxYGbPFicsYMcuxYvv3oo+zRowfDw8NZq1YtxwaLN998M/Py8nj58mXWq1ePr7/+ulPHdNat5a518eJFjhz5X95xR+F+7adOkWFh5MSJ5V/z9ttv56xZsxxze3t7c9iwYdyzZ0+F5yXJTz/9lE2bNmW3bt1cepF1WXlUsEjy7Nkz3L69Nc+d+5e7R/EckyaRxf/i33vvz+/mbtuWbN2a7N2bvOsu8qGH+P8WLuSLL77IpKQkJicn8+DBgzx+/DhvvPFGx1a/a9euderRcn5+Plu0aMFFixaRLNwrbejQoU5ZmyzczeOGGwp3EyXJL74gy7sRQkFBAW+77TbeddddjpgcP36ckydPpp+fH3v16sWNGzeW6+LXrKwsxsbGOm5gUdVuUuFxwSLJzMwPuW+fD7Oy/ufuUTzDsmVkUtLPH0dFlWuZnTt30sfHh59++inJwqj07NnTKW/feueddxgcHMysrCwePXqU1atX5//+59w/HwcPkoGBZHnv43vhwgUuWLCAzZs3p5+fH+vVq8eWLVtyxYoVjh1z09LSGBcXx8DAQLZv375Md8bZu3cv27Rpw1atWnH37t3lG9LFPDJYJPn997E8eLAtCwrMuFuI0XJyyJgY8qmnyPvuI//v/8q91IwZMxzv08zMzGSTJk0cW62Ul91uZ6dOnZx+a7mSbNxI+vqS335b+q85ePAMY2Nj6e/vzzZt2vCFF17gxYsXabPZuHr1arZu3ZrBwcGMi4vj+Z+OXH/88UcmJCSwUaNGbNq0KRMSEnil6D5i18jPz2dcXBx9fHw4efLkKn0HHY8NVn7+JX71VTjPnp3j7lE8x5UrFX5DXW5uLrt27ep4MXzr1q2sUaNGhY6GtmzZ4ri1nKv2Siuu6I5bJd2fsIjNVvhm6VtuIS2WDI4cOYrbtm0r8TSvoKCAGzduZNeuXVm7dm1OnjyZP/zwA8nCexQuWLCAYWFhbNy4MdevX/+Lrz169Ch79OjBhg0bcvPmzS55vs7kscEiyatXv2FBwWV3jyFl9PXXX9PPz8+xl/mkSZPYtm3bch8Z9O/fn1OmTCFJTp8+nREREc4a9TeVdH/CU6cKf/bQoAFpsZBPP134udLasWMHhwwZwho1anDMmDGON8/bbDauXLmSu3btcvzel19+mQEBAbzzzjsrffPF8vLoYBWXl5fO7OyDtNtLf/cccZ+EhATHnbyvXr3K9u3bO6JTFikpKb+6tdzv3RbLWUq6P+G2beSAAYWnjhX54dy+ffs4ZswYent7c8iQIUxJSXH8WmpqKgcNGsTatWtz9erVFX4elUnBInnp0laeODGW6ekrmJaW4O5xpBTsdjsHDRrkuJP3vn37WKNGDW7atKlM6wwbNowxMTEkyTlz5rh8r7TiKuOGvPv27ePw4cMd4VqyZAmbNGnCzp0789ChQ85/QBdTsEiePTuP58+vdfcYUkZnzpxh/fr1HXfyfvbZZxkWFuZ44fn3ZGdn809/+hO//PLLStsrrbjKvCHv0aNHOX78eA4ZMoQzZsyo9FubOYuCRTI//0eeOfM0jx0bxLNnnbdhoLje+vXr6e/vz8OHDzM3N5c9e/bkh8UvUi2lxMREhoWFlemGuhX1R7khb2Wq8tvLVLYjRyLRqlUyvLyqu3sUKaX77rsPhw8fRkpKCqpXr16mrXyK9lIbMmQIHn30UUybNs2Fk0pFKVgALlx4C9nZe+Hl5Y38/Ato2vQVd48kZXDp0iV07NgRMTExePLJJ6+7k0dRnEq64ero0aOxbNmyKrMrgZRMwfqJ3Z4DIB/VqtV29yhSDlu2bMFTTz2FL774AgDg4+Pj2P7l2l0WSroLtMfuW2YYBUv+MIqOmiwWC+o7ceNAqTo8OlikDV5ePgA8bPtiEUN59N/UtLRF+O67u909hoiUkscGy26/irS0xQgMHOruUUSklDw2WOfPv4Zq1fxRr9497h5FRErJI4NVUFCAs2fXwWKZ/NNrWCJiAo8M1vvvv4/o6KMIDIx19ygiUgYeFyySeP755/Hww4/A11fXXImYxOMua9i0aRNGjhyJkydPol69eu4eR0TKwOOOsD7//HPceuutipWIgTwuWI0aNUJycjJSU1PdPYqIlJHHnRICwJ133onMzEwkJyejenXtyiBiCo87wgKAlStX4tixY0hISHD3KCJSBh55hAUAmzdvxtChQ7F792506NDB3eOISCl4bLAAIDY2Frt27cLevXtRs2ZNd48jIr/DI08JiyxevBh5eXmYOXOmu0cRkVLw6CMsANi7dy969+6NTZs2oV+/fu4eR0R+g0cfYQFAREQE/va3vyEmJgaZmZnuHkdEfoPHH2EBQH5+Pnr37o2WLVtizZo17h5HRK7D44+wAMDb2xtvvvkmPvjgAyQlJbl7HBG5DgXrJy1atEB8fDwefvhhnDp1yt3jiEgJdEpYDEn07dsX7du3x0svveTucUTkGjrCKsbLyws33HADrFaru0cRkRJ4u3uAquSHH35AUlISPvnkE3ePIiIl0BFWMQkJCejWrRsiIyPdPYqIlEBHWD/JyMjAihUr8NZbb7l7FBG5Dh1h/WTp0qUIDw/HkCFD3D2KiFyHjrAAZGVl4cUXX8TixYvh5eXl7nFE5Dp0hAXgtddeQ0BAAEaPHu3uUUTkN3h8sPLy8jB//nw88cQT8PbWAadIVebxwUpKSoLNZsO4cePcPYqI/A6PDpbdbsfcuXMxadIk+Pv7u3scEfkdHh2sTZs2ITU1FZMmTXL3KCJSCh4drPj4eMTGxqJu3bruHkVESsFj3/y8c+dOREVF4cSJEwgNDXX3OCJSCh57hFW7dm3Mnz9fsRIxiMceYYmIeTz2CEtEzKNgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixlCwRMQYCpaIGEPBEhFjKFgiYgwFS0SMoWCJiDEULBExhoIlIsZQsETEGAqWiBhDwRIRYyhYImIMBUtEjKFgiYgxFCwRMYaCJSLGULBExBgKlogYQ8ESEWMoWCJiDAVLRIyhYImIMRQsETGGgiUixvj/hiRGl/wP2PgAAAAASUVORK5CYII=\" alt=\"Mol\"/></td>\n",
        "      <td> CHEMBL666891</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>  CHEMBL85441</td>\n",
        "      <td>  3</td>\n",
        "      <td>   C(COc1ccc(cc1)c2nc3ccccc3[nH]2)CN4CCN(CC4)c5ccccc5</td>\n",
        "      <td>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             <img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
        "      <td> CHEMBL666891</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>  CHEMBL85700</td>\n",
        "      <td>  4</td>\n",
        "      <td>            CN1CCN(CCCOc2ccc(cc2)c3nc4ccccc4[nH]3)CC1</td>\n",
        "      <td>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             <img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
        "      <td> CHEMBL666891</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> CHEMBL314983</td>\n",
        "      <td> 11</td>\n",
        "      <td>   C(COc1ccc(cc1)c2nc3ccccc3[nH]2)CN4CCN(CC4)c5ccccn5</td>\n",
        "      <td>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             <img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
        "      <td> CHEMBL666891</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "   parent_cmpd_chemblid name_in_reference                                                SMILES                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      ROMol assay_chemblid\n",
        "23          CHEMBL87260                 5  Clc1ccc(cc1)N2CCN(CCCOc3ccc(cc3)c4nc5ccccc5[nH]4)CC2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  <img src=\"data:image/png;base64,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\" alt=\"Mol\"/>   CHEMBL666891\n",
        "24          CHEMBL87478                 9  CCCSc1ccccc1N2CCN(CCCOc3ccc(cc3)c4nc5ccccc5[nH]4)CC2  <img src=\"data:image/png;base64,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\" alt=\"Mol\"/>   CHEMBL666891\n",
        "25          CHEMBL85441                 3    C(COc1ccc(cc1)c2nc3ccccc3[nH]2)CN4CCN(CC4)c5ccccc5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAYAAAB5fY51AAAcdklEQVR4nO3deVxU9f7H8Y9siqaooIISkqkMmF5yCXPJNXErUUJF07RSM2/eLK9oedXrFhp5c1fM9V6XRHNBcS9xwQy8WVdgIEF/GiDuoIjAMO/fH3MdNyyWMzLf7vv5ePRIifnMd4pezsz5zjkVAECIiBRgU94LICIqLgaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhWIjs7W+7evVveyyCyanblvYD/JYWFhaLX6yUhIUFSU1MlPj5eEhISJCUlRW7evCktW7aU8PBwefHFF8t7qfQH8O2338rOnTvF3t5ehg8fLj4+PuW9pDKrAADlvYg/GoPBIOfOnZPExETR6/XmSOn1esnKyhJHR0fx8vISnU4n3t7eotPpRKfTydq1a2XDhg0SFxcn9erVK++HQQpbsGCBjB8/XoYPHy6XLl2SqKgoadGihQwbNkwGDhwo1atXL+8llgqDpaHCwkKZPHmyzJs3T/Lz88XZ2fmhIPn4+IhOp5P69euLjc3jr8aNRqP07t1bLl++LEePHhVHR8dyeBSksjt37sjbb78t+/fvl40bN4q/v7+IiNy8eVM2b94s4eHhcvr0aenUqZOMHDlSAgICxN7evpxXXQIgzezcuRNOTk7Yv38/Ll++XKoZmZmZ8PDwwEcf/U3j1dEf3fnz5+Hr6wsfHx/88ssvAIC9e/ciOzv7oe87c+YMQkJC4OLiAldXV4wdOxY//vhjOay45BgsDQUEBGDMmDFlnhMX9x+4uV3FkiUaLIqsSm4ucP266dcGA3DzpulrOTn3v3bjRsnnfvfdd3BxcUFAQACys7NhNBoxY8YM2NvbIzIyssjb3L17F5s3b0bv3r1ha2uLFi1a4Msvv8TVq1dL9+CeAgZLI+np6bCzs8OpU6c0mffNN4CdHRAVpck4shLbtgH16wN37gDnzgEff2z62urVpn9+8SJQ0j/zVqxYgYoVK2Lq1KkwGo24ceMGevToAVdXVxw/frxYM5KSkhASEgI3NzdUrVoVK1euLNkinpI/7LaGmzdvSn5+/lO7v127MqRNm0Bp3ry5JvP69hWZOFFk8GCRs2c1GUlWont3kUWLHv6a0ShiMIgUFhZ/TkFBgYwaNUr+8pe/yLp162TatGmSlJQkfn5+cvXqVYmLi5M2bdoUa1bjxo0lNDRULly4IPPnz5eRI0dKRkZGCR7V06F0sAoLCyU+Pl4iIiJk2rRp0r9/f2nZsqVUr15datSoIR07dpSkpCSLrwMQ+eKL5hIYuEnTudOmifj5iWzbpulYKmft2omcOSOSlXX/a5s2iYwfLzJjRvFmZGZmSufOnWX//v0SExMj/fv3l8jISPHz85PWrVvLkSNHSnyk+e7du5KbmyvDhg0TR0dHSUhIKNHtnwYl9mEVFBRIamqqJCQkSFJSkiQmJpq3DNy6dUuqVKli3ibQt29f8fLyEm9vb/n888+le/fuEhsbKy4uLhZb34kTIufPm54NacnWVmT7dpGKFUXu3hXJzRWpUcP0p/Dt2yJOTtreHz09H38sEhYmUru26feDBokMGyby668ioaGmn6eDB0UaNRLx8hJxdb1/21OnTklAQIA0atTI/LM9Z84cmTJlikyfPl0mTJggFSpUKPGa/P39pVevXjJhwgTx8vISvV4vXbp00eTxasWqg5WbmyuffPKJLF68WAoKCqRWrVrmrQGDBg0ybxnw8PAo8j/QsmXLpEOHDhIcHCx79uwROzvLPNxVq0R69xZxdtZ+dsWKpr/v3Svy4YciiYkimZmmlxRhYUXf5tq125KYeFqSkpLMf+n1emndurVMmzZNnnvuOe0XSiXSrJnIbzXl7FmRf/xDJCVFxN09T65fd5MmTZpI3bp1Ze/evRIQECDLly8XADJo0CDZs2ePbN++XXr06FHqNfn4+JifVXl7e0tiYmKpZ1lMeb+J9lvWrVsHNzc3HDlypERHLmJjY/HGG28gPz8f6enpqFu3LsaPH2+xdXbrBmzfbrHxAExvzI4aBcyde//NWgD49ltg8WLg/feBTp2A2rWBFi2uwt7eHjqdDgEBAQgJCcFXX32Fzp07o1mzZrh9+7ZlF0uaMRiA1NRbiIyMRGhoKFq1aoUOHToAMG2BeeWVV+Dp6YnTp0+X+b7mz58PPz8/AMDMmTPRpUuXMs/UmlUHq2PHjpg0aVKJb3f16lU899xzeOuttwAAMTExqFixIlatWqXxCp+ebduAf/4TGDoUOH36frDq1wd8fYHgYGDWLGDrViApyYCCgoLHZty6dQtNmzZF9+7dYTAYnur6y1tBQQGSk5Oxfft2hIaGYtiwYZgxYwbS0tLKe2klsmzZMvzpT38CAGRkZCA4OLjUe/4etX//flSrVg1GoxFbt25F3bp1NZmrJasN1i+//AIbGxskJycjOzsbXl5eOHv2bLFv/+OPP6JKlSpY8t/NTGvWrEGlSpXwww8/WGrJFnUvWD/9BAwZcj9YJXXu3Dm4uLhg8uTJmq7PWuTk5ODUqVPYsGEDPv30U7zxxhto0qQJHBwcICJwdnZG+/btMWLECLRt2xZ+fn7Izc0t72UXW3R0NBwdHVFYWKj57IsXL0JEkJaWhvj4eIgIbt68qfn9lIXVBmvy5Mlo3749ANOfKvXr1y/xf6QtW7bAwcEB3333HQBgzJgx8PDwQGZmZpnWVtTmP0u7FyzA9CyrtMECgIMHD8Le3h6bNm3SZG3l6fz58xg3bhz8/f3h6emJChUqoEKFCvD09ET37t0xbtw4LF++HNHR0bhy5cpDt733jDMwMBBGo9Gi6zx6FDh0qOxzMjMzISJITU0t+7BHGI1GVK1aFQcPHkReXh7s7Ozw/fffa34/ZWGVwTIYDKhXr575JZyfnx+mTJlSqlmTJk2Cs7MzUlNTUVBQgE6dOqFt27bIy8sr9fqK2vynmnnz5sHR0RFxcXHlvZRSy83NhaenJ7p164bJkydjw4YNOHXqFHLubRt/wI0bNxATE4MVK1bgo48+wq5duwDcf8YZGhpqsXUWFgIvvAB8+qk285ydnbF7925thj2iZcuWWLRoEQDAy8sLa9asscj9lJZVHiU8cOCAZGdnS//+/SUhIUFiY2Nl06bS7XGaOXOmnDlzRvr16yfHjx+XiIgIeemll2TcuHGyePHiUq/x3ua/oKBSjyhX48aNk/j4eAkMDJTY2FipVatWeS+pxLZs2SKFhYUSFRUltra2IiJy7do1OXXqlCQkJDz0V3p6ujg4OEjjxo1Fp9OZZ3h6esrGjRulV69e0qRJE+ndu7fm64yIEElLM+2z0oJOpxO9Xi89e/bUZuADvL29Ra/XP/Zra2GVG0dXr14tQUFBUqVKFVm9erV06tRJPD09SzXLxsZG1q9fLwUFBTJixAhxdnaWb775RtauXSvh4eHFmpGdLfLDD6Y9UfcUtflPNYsWLRJXV1fp16/fU/1UgFZWrVolb775ptja2kp0dLS4urqKi4uLvPbaa7J69Wq5deuWvPrqq7J06VJJTk6WnJwc+c9//iMRERHSq1cv85yuXbvKrFmzZNCgQZpvljQYRKZMERk3TkSrM7pYcsvBg7N1Op31bW0o76d4j7py5QocHBxw9OhR5Ofno3bt2li/fn2Z5yYnJ6NGjRr4/PPPAQBbt26Fg4MDjhw5Yv6ezMxMHD58GOHh4ZgyJR3+/oCHByBi+lzfiy+avk+rN8CL6/Jl4OefLTM7IyMD9erVwwcffGCZO7CQs2fPmg/KAMC1a9dw6NAhpKenl3rm8OHD4eXlhRul+fTxE2zcuAldu44s1Qean+SLL75Au3bttBv4gG3btpmPDq5duxaNGze2yP2UltUFa/78+dDpdABM//Jq1Kih2VGc/fv3w8HBwfz6f/z48XB3d4efnx+qV68OEYGDgwOaNGmCDz6IweTJwMaNpm0Ed+/en6PlG+DFERoKNG1qufknTpxAxYoVER4eXuzb5OfnIzExEd988w1mz56NIUOGYO7cuU/tk/5/+9vf8Morr2g6Mzc3F61atYK/v78m2z4KCgrQsGFDfPbZZxqs7r6oqCg4OztrOvMevV5vPjp48uRJ2NnZ4e6DP/zlzOqC1bRpU8yePRsA8Prrr+P999/XdP6sWbMwYcIEAPf3Z82aNQu7d+/G2bNnrW5/ktEINGwILFhg2ftZt24dHBwcEB0d/dDXs7OzERsbi3Xr1mHSpEno168fdDod7O3tISKoVasWOnbsiFGjRqFZs2bo2rWrxf8dGgwGuLu7Y/W9Uxxo6MKFC6hdu3ap9v89as2aNahduzZu376NsLAwnDt3ruwLBJCamgoR0Wz/1YPy8/PRq1cvnD9/HllZWahQoQLi4+M1v5/SsqpgxcXFwdbWFmlpaebTtVjyKNY777yDAQMGaDIrLQ2wxBHgo0eBSpWAa9e0n/2oUaNGwdXVFaNHj8arr74KDw8PiAhsbGzQoEED9OzZE+PHj8eKFStw7NgxXHtkUZcuXYKHhwf+/Oc/W3Sde/fuRbVq1Sy2Y//YsWOoWLEiNmzYUOLbpqen49ChQ1iyZAmef/55fPrppzAYDHj99dfh6emJ8+fPl3l9hYWFqFy58mN/uGgtJSUF3t7emp0ySQtWFawxY8agZ8+eAIC5c+fC19fXYveVnZ2NZ555Bnv37tVk3owZQK1apm0OWpo06SKCg7XfJFiUvLw8LFmyBP3798fUqVOxadMmnD59ukQvyU+ePAlHR0dsLcX/7MXVv39/vPvuuxabDwCLFy+Go6MjYmNjH/tnBoMBycnJ2LFjB+bMmYPhw4c/9LaCo6MjfH190aJFCzRt2hRXr16FwWBAcHAw3N3dzWcDLQtfX18sX768zHOe5ODBg3B2dkZgYCDy8/Mtdj8lZTXBunPnDpycnBAREQGj0QgvLy/Mnz/fYve3atUqeHh4aLZj2GgEgoIAnU67jaS3b99GtWrVsGfPHm0GPiVHvv4aha6ugAU+VXDvoExMTIzmsx81ZMgQeHp6Ijw8HJMnT0ZQUBBeeOGFh3bNt23bFu+++y7CwsIQFRWFlJQU889UTk4OOnXqBJ1Oh4yMDOTl5SEwcCj8/S/j//6vbGsLDg7Ghx9+CMD0jLB9+/ZYtWrVY6dDLimj0YipU6fCwcHBokEsLasJVmpqKnQ6Ha5cuQKj0YjIyEiLvoHbrl07zT+ecuuWaYNgnz6mzYJltXLlylLt8LcKEycCdeoAFy5oOnb+/Pnw8fHRdOaDcnNzsXnzZuTn5yM3Nxc6nQ5ubm7w9/fHhx9+iOXLl+Pw4cPFfv8oJycHHTt2NEfLYAAGDwaefRYowSfNHjN9+nR0794dgOml+OzZs9GoUSNUqVIFb731Fg4fPlzi3fs5OTkYMGAAatSoodkrD61ZTbDu3LmDFi1aoG/fvhb/mERiYiJsbGws8vGGxESgc+cIhIZ+XuZZbdu2xdSpU8u+qPJQWAj06mX6ZLaG7zU1bdoUc+fO1Wzeo9avX4+6deuioKAAx44dQ6VKlR57r66kbt68ifbtuyMwMAPXrwN5eUC/fsDzz5eu59nZ2RgyZAh8fHwQHh7+0Of94uLiMHbsWDg7O6Nu3boYO3Ysfvrpp9+dWdQFLKyR1QQLMO0Jcnd31+QIzW+ZOHEiOnbsaLH5Bw4cgL29PSIiIop9m7y8PJw5cwZbtmzBzJkzERQUBDc3N6SkpFhsnRZ39SrQsyfw66+ajDt16hTs7e1x6dIlTeYVpWvXrggJCQGg7UGZGzeAl14CWrQwHUDJzzd9VOeRjzf+ppycHMydOxcuLi7Q6XT46KOP4O3tjcqVK+PNN9/EoUOHzM/G711gomvXrqhQoYL5AhNFxffRC1hYM6sKFmD6oaxcuXKpjtAUR0FBAVxdXbF27VqLzL9n3rx5qFq1Ks6cOfPQ1+/tb1m9ejVCQkLQp08fNG7cGHZ2dhARuLq6onPnzhg9enSRb/j+LxszZgz69OljsfkpKSmwsbFBUlKS+aDMvn37NJufk2M6Z5lOB5Rkf2tuLrB4cThcXV3RoEEDrFmz5qGtI/Hx8QgJCUGtWrXg5ub22LOqhIQEjB8/HnXq1HnsLYYFCxbA3t7efAELa2d1wQJMe4KedISmrCIjIy16SPweo9GI4OBgNGzYEO+99x66dOmCevXqQURga2uLhg0b4rXXXsOECROwcuVKnDhxQtMd1lapsBD45ZcSXccqLS0NBw8exPz581GzZk3zGSYWLlyo+e78qVOnom3btgBMB2Us8f5hVhbw8sumZ1u/N/ruXWDhQqBePaBPnwVYtmzZbx6xe/CyXXZ2do9dtis/P98csvz8fIwcORJVqlTB119/rdnjszSrDBYAjBs3TpNTwTyqb9++GDFihKYznyQnJwfz5s3DwIEDMX36dGzevBk///yzVe0cfmpu3QICA007YEePvv9RAcB0jp6zZ4HISGDOHODtt9HxlVfg5OQEEUHlypXRvHlzNG7cGMHBwQAeP99ZWRUWFqJ+/fr46quvAADt27cv9RlCfk9WFnDwoOnXRZ2qKCsLmDoVcHEBGjUCNm8u+UGcX3/9FaGhoXj++edRqVIlBAUFYefOnTAYDLh06RLatWun2ZlKnyarDZbBYECPHj3KdCqYy5cvIzo6Gr/+9z2UjIwM2NnZPZVD4vSIf/wD2LHj/u+7dTPtBdm1y7QzVgSoWRNo0wZ4912sWbgQUVFRSE1NNb9USUpKQvXq1TFr1iwAj5/vrCwOHDiAZ555Brdu3TIflNFqZ/pvKepURSNGAO7uwKJFD38krDQMBgMiIyPRt29f2Nvbw8vLC56envDz81PubKuAFQcLAK5fv46GDRti9OjRT/yewsJCpKamIioqCmFhYRgxYgTatWsHZ2dn82cD//WvfwEAwsLC4O3t/bSWTw/64ANAr7//+8GDTe8+X75sOjF9Md9I37dv30NXM544caL5fGdlMWjQIAwbNsw8s3PnzmWaV1xFnav/8uWyh6oomZmZ+OKLL7Bp0yalzrL6IKsOFmDaguDk5IRly5aZv3bs2DEMHDgQvr6+cHR0hIjAyckJfn5+GD58OObMmYMdO3YgOTn5oXOb63Q6zJkzpzweBi1dCjx4htNXXy31qM8++wxVq1ZFfHw8CgsL0bt3b/j6+hZ54r7iuH79OipVqoTo6GjzQZl/PviS1YKedK5+KloFACifE9sU344dO2TAgAGyb98+6dChg5w4cULWr19vvuSXt7e3uLm5PXa7ixcvil6vF71eL3FxcRIVFSU//fST1K1btxwexf+4vDyRUaNE3NxMF98bOFDkgXNSlQQAGTx4sPz73/+W77//XmxtbeXll18WnU4nERERJb4m39KlS2XBggWSmJgou3btkqFDh0paWpo4OjqWan0lsX276RqTzZrdv07hky7fRiJKBEtE5O9//7ssXLhQYmNjH7qu3oMXWdXr9eYLrN67yGqdOnWkSZMmotPp5L333pOmTZuW46MguXNHxNHxty/KVwy5ubnSoUMHqVmzpuzevVtSUlKkdevWMmnSJPnrX/9aolkvvfSSBAQEyCeffCKBgYFSp04dWbJkSZnWV1z3gvXmmyJvvSVSqxaD9VuUCZbRaJQ+ffrIhQsXpFevXuYrQJ89e1YKCgrk2WefFW9vb/Hx8REfHx/zr2vWrFneSycLuXDhgrRq1UpGjBghM2fOlAMHDkjv3r1l27ZtxT598I0bN6R58+YSHR0tlSpVEnd3dzl+/Li0atXKwqun0lAmWCIiWVlZsnjxYomJiTFH6d6zp2rVqpX38qgcxMTESOfOnWXt2rUyYMAACQsLk9mzZ8vJkyelUaNGJZo1b948WbdunZw+fdoyi6Uys8qLUDyJk5OTfPLJJ+W9DLIibdq0kS+//FLeeecd8fb2lo8//lgSEhIkOTm52MHKysoSvV4vS5culffff9/CK6ayUOoZFtGTjB49Wvbs2SNxcXHi4uJS5PekpaWZ399MSEgwv62Qnp4udnZ2MnToUJk7d644Ozs/5dVTcTFY9IdQUFAg3bp1ExsbG1m0aJEkJSWZ43TvQEx2drZUrlxZvLy8RKfTmY8y63Q6ady4sTg4OJT3w6DfwWDRH0ZGRoYEBQXJiRMnxNPTU3x8fKRJkybmvzdo0EBq1KhR3sukMmCw6A/FaDTKjRs3+LLuD4rBIiJlWOWVn4mIisJgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkDAaLiJTBYBGRMhgsIlIGg0VEymCwiEgZDBYRKYPBIiJlMFhEpAwGi4iUwWARkTIYLCJSBoNFRMpgsIhIGQwWESmDwSIiZTBYRKQMBouIlMFgEZEyGCwiUgaDRUTKYLCISBkMFhEpg8EiImUwWESkjP8HIJRFuRhiJPsAAAAASUVORK5CYII=\" alt=\"Mol\"/>   CHEMBL666891\n",
        "26          CHEMBL85700                 4             CN1CCN(CCCOc2ccc(cc2)c3nc4ccccc4[nH]3)CC1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              <img src=\"data:image/png;base64,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\" alt=\"Mol\"/>   CHEMBL666891\n",
        "27         CHEMBL314983                11    C(COc1ccc(cc1)c2nc3ccccc3[nH]2)CN4CCN(CC4)c5ccccn5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              <img src=\"data:image/png;base64,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\" alt=\"Mol\"/>   CHEMBL666891"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Construct fingerprints:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now the distance matrix:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dist_mat = []\n",
      "for i,fp in enumerate(fps):\n",
      "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
      "dist_mat=numpy.array(dist_mat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now do the MDS into 2 dimensions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mds = manifold.MDS(n_components=2, dissimilarity=\"precomputed\", random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
      "results = mds.fit(dist_mat)\n",
      "coords = results.embedding_\n",
      "print 'Final stress:',mds.stress_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress: 3052.83827897\n",
        "breaking at iteration 276 with stress 3227.03049273\n",
        "breaking at iteration 248 with stress 3166.89856976\n",
        "breaking at iteration 258 with stress 3114.92766418\n",
        "breaking at iteration 297 with stress 3052.83827897\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And plot the points:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter([x for x,y in coords], [y for x,y in coords],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f981fb0d6d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9y5WzAqyMDKcjCp8NGwLHwXuyY0Hr1taOdtkyWLjQDs2R2HD00YFX16VLw4kn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\nXJS4JW5Vrhw4rlTJmThERMJ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+zw6/sb/PgOFAJWAgStzikEsvtcI2r65dbR1P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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f981ceb6210>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nice picture. How accurate is it?\n",
      "\n",
      "The strain values above lead me to believe that there are probably some real problems here.\n",
      "\n",
      "The overall strain value isn't particularly easy to interpret, so we'll check up on things by comparing the embedded distances with the Tanimoto distances we're trying to reproduce."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "def distCompare(dmat,coords,nPicks=5000,seed=0xf00d):\n",
      "    \"\"\" picks a random set of pairs of points to compare distances \"\"\"\n",
      "    nPts=len(coords)\n",
      "    random.seed(seed)\n",
      "    res=[]\n",
      "    keep=set()\n",
      "    if nPicks>0:\n",
      "        while len(res)<nPicks:\n",
      "            idx1 = random.randint(0,nPts-1)\n",
      "            idx2 = random.randint(0,nPts-1)\n",
      "            if idx1==idx2: \n",
      "                continue\n",
      "            if idx1>idx2: \n",
      "                idx1,idx2=idx2,idx1\n",
      "            if (idx1,idx2) in keep: \n",
      "                continue\n",
      "            keep.add((idx1,idx2))\n",
      "            p1 = coords[idx1]\n",
      "            p2 = coords[idx2]\n",
      "            v = p1-p2\n",
      "            d = sqrt(v.dot(v))\n",
      "            res.append((dmat[idx1][idx2],d))\n",
      "    else:\n",
      "        for idx1 in range(nPts):\n",
      "            for idx2 in range(idx1+1,nPts):\n",
      "                p1 = coords[idx1]\n",
      "                p2 = coords[idx2]\n",
      "                v = p1-p2\n",
      "                d = sqrt(v.dot(v))\n",
      "                res.append((dmat[idx1][idx2],d))\n",
      "    return res"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = distCompare(dist_mat,coords)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f981f9bded0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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x2MqRIwziW7ZwbHnyZLbshwzhAi3m9MVQ2LePNyz33MMx7p49We3N0YEDXPlt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\nEEIIIQ56/g9cbNyKX55xsQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f981fbcca50>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Yikes... that's no good. This really isn't so surprising given how much stress was left.\n",
      "\n",
      "Let's try increasing the dimensionality to see how much improvement we can get:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mds2 = manifold.MDS(n_components=20, dissimilarity=\"precomputed\", random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
      "results = mds2.fit(dist_mat)\n",
      "coords = results.embedding_\n",
      "print 'Final stress:',mds2.stress_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress: 57.0050716266\n",
        "breaking at iteration 203 with stress 57.0050716266\n",
        "breaking at iteration 199 with stress 59.5816001681\n",
        "breaking at iteration 206 with stress 58.1255498141\n",
        "breaking at iteration 207 with stress 57.0929415605\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = distCompare(dist_mat,coords)\n",
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f402994d150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8+bhmbYiKYucqo0KYISSECVolS6b+Xtm1pzlPHqBDB+DFF5ml/v77wPPPm4VR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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4024ab4e90>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Not so bad... of course, we can't visualize 20 dimensions.\n",
      "\n",
      "What about if we try doing dimensionality reduction on the 20D results instead of using the distance matrix?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mds3 = manifold.MDS(n_components=2, random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
      "results3 = mds3.fit(coords)\n",
      "coords3 = results3.embedding_\n",
      "print 'Final stress:',mds3.stress_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress: 2943.91225488\n",
        "breaking at iteration 239 with stress 3011.93802972\n",
        "breaking at iteration 263 with stress 3033.56503189\n",
        "breaking at iteration 265 with stress 3176.13501461\n",
        "breaking at iteration 406 with stress 2943.91225488\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The strain is still really high. This isn't that surprising given that we already tried the same thing from the distance matrix.\n",
      "\n",
      "Ok, so MDS doesn't work. Now that we have actual coordinates, we can use some of the other scikit-learn manifold learning approaches for embedding the points.\n",
      "\n",
      "Instead of jumping all the way to a 2D system, where we're really unlikely to get decent results, start by dropping down to 10D.\n",
      "\n",
      "First MDS in 10D:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mds3 = manifold.MDS(n_components=10, random_state=3, n_jobs = 4, verbose=1,max_iter=1000)\n",
      "results3 = mds3.fit(coords)\n",
      "coords3 = results3.embedding_\n",
      "print 'Final stress:',results3.stress_\n",
      "ds = distCompare(dist_mat,coords3)\n",
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress: 164.997967472\n",
        "breaking at iteration 187 with stress 167.970824753\n",
        "breaking at iteration 195 with stress 167.426219544\n",
        "breaking at iteration 206 with stress 168.6442109\n",
        "breaking at iteration 208 with stress 164.997967472\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f402afc6e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8+bhmbYiKYucqo0KYISSECVolS6b+Xtm1pzlPHqBDB+DFF5ml/v77wPPPm4VR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\nhXfeCdsPPwyPPppvPZKk1tZoaI8H7iYE99nA1cBZFcdcDpw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       "text": [
        "<matplotlib.figure.Figure at 0x7f4028eb5690>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now try locally linear embedding:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lle = manifold.LocallyLinearEmbedding(n_components=10,random_state=3,max_iter=1000)\n",
      "results3=lle.fit(coords)\n",
      "coords3=results3.embedding_\n",
      "print results3.reconstruction_error_\n",
      "d = distCompare(dist_mat,coords3)\n",
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7.87063669948e-15\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f402a453190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ULOl8eRKBUYxbCCGywQMP0GgDLP3yli7t1s3+bgxwzz38Pn8+MHYsV9wnT9re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3AAAI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\nRERERERERERERERERERERERERMQV/g8H8l5Nt3NIYwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f402461f590>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ick! What about Isomap?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "embed = manifold.Isomap(n_components=10,max_iter=1000)\n",
      "results3=embed.fit(coords)\n",
      "coords3=results3.embedding_\n",
      "d = distCompare(dist_mat,coords3)\n",
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f4028a24690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ecAMwb57v6xw7xt7Gq1bxuFs3tlG0MqQPHADuvdc+f+lSjsY/+sj7WsWKhb6W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\nIAiCIAiCIAiCIAiCEAT+P1GOCSWktjXbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f402a9497d0>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Also pretty grim. Does spectral embedding work?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "embed = manifold.SpectralEmbedding(n_components=10)\n",
      "results3=embed.fit(coords)\n",
      "coords3=results3.embedding_\n",
      "d = distCompare(dist_mat,coords3)\n",
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f4024241790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BAqZ5Pf44A89atgQWLeI5byevdev42bYtNe2332akc3Iy8Oqr9rhvvwW+/959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\nCCGEEEIIIYQQQgghhBBCSLHn/wCj13vzHCABewAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4029bce550>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hmm, there's at least some signal there. How does spectral embedding do with 2D?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "embed = manifold.SpectralEmbedding(n_components=2)\n",
      "results3=embed.fit(coords)\n",
      "coords3=results3.embedding_\n",
      "d = distCompare(dist_mat,coords3)\n",
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f4024146f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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W+vUDzj3Xuy74wYO+r798OfcnAyzQcu65Vi1ygAE9K4sB/sAB1ipfvBhISwN6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\ntRe+lsjFC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hGNyvy7NEGoO0sLHYjEkD1HtLz23tOIIefPHxDvvfyamKUjwQv+EBENz9z3\ndGbT+Cjz+HaCujAv4e75ZxAccLTv+++cHmA2Yue718hrQpvI3vO/TPAg30l5I8TTiLvnL0lEiwj/\n73cSHPPyCnMJtvoUgkblccpf7z13z5+X33sMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAM\nwzAMwzAMwzAMwzAMwzB+5/8AnQgevIZRsgIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f402a453bd0>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Not so hot..."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Look at stress vs dimension"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We're able to do a reasonable job of embedding into 20 dimensions with MDS. 10 even looks somewhat ok. Let's look at behavior of the residual stress as a function of dimension:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vs={}\n",
      "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
      "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
      "    results3 = mds3.fit(dist_mat)\n",
      "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
      "    vs[nComps]=results3.stress_/len(dist_mat)\n",
      "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
      "yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=140: 10.2150083984 stress per element: 0.0389885816734\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=120: 10.6831774998 stress per element: 0.0407754866405\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=100: 11.0373652537 stress per element: 0.0421273482967\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=80: 11.9702291564 stress per element: 0.0456878975435\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=60: 14.0144255401 stress per element: 0.0534901738172\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=40: 19.3925818175 stress per element: 0.074017487853\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=20: 57.0050716266 stress per element: 0.217576609262\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=10: 216.325632623 stress per element: 0.825670353524\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=5: 703.559542424 stress per element: 2.68534176498\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=2: 3052.83827897 stress per element: 11.6520544999\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f4029e672d0>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Yeah, the stress is going up exponentially with dimension. These data really don't want to be in a 2D space.\n",
      "\n",
      "Try non-metric embedding"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vs={}\n",
      "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
      "    mds3 = manifold.MDS(metric=False,n_components=nComps,random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
      "    results3 = mds3.fit(dist_mat)\n",
      "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
      "    vs[nComps]=results3.stress_/len(dist_mat)\n",
      "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
      "yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=140: 15.2927309221 stress per element: 0.0583692019926\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=120: 17.9694200702 stress per element: 0.0685855727871\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=100: 21.235102664 stress per element: 0.0810500101679\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=80: 26.4784693091 stress per element: 0.101062859958\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=60: 34.8885053235 stress per element: 0.133162234059\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=40: 52.3293595454 stress per element: 0.199730379944\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=20: 111.248026961 stress per element: 0.424610789927\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=10: 243.54472514 stress per element: 0.92956001962\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=5: 550.187026998 stress per element: 2.09995048472\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=2: 1548.48953014 stress per element: 5.91026538222\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f4007dcc490>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "helps somewhat at lower dimension, but not dramatically. Plus it's a lot slower"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "A larger data set"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Maybe it's possible to do better with a larger data set? This seems unlikely, but we can at least try:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assays = data2[['assay_chemblid','target_chemblid']].groupby('assay_chemblid').count()\n",
      "print assays.shape\n",
      "goodassays = assays.ix[(assays.target_chemblid >= 10)&(assays.target_chemblid <= 15)]\n",
      "subset = data2.ix[data.assay_chemblid.isin(list(goodassays.index))]\n",
      "print subset.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(114, 1)\n",
        "(722, 18)\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols = subset[['parent_cmpd_chemblid','name_in_reference','SMILES', 'ROMol', 'assay_chemblid']]\n",
      "print mols.shape\n",
      "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]\n",
      "dist_mat = []\n",
      "for i,fp in enumerate(fps):\n",
      "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
      "dist_mat=numpy.array(dist_mat)    \n",
      "print dist_mat.shape "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(722, 5)\n",
        "(722, 722)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vs={}\n",
      "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
      "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
      "    results3 = mds3.fit(dist_mat)\n",
      "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
      "    vs[nComps]=results3.stress_/len(dist_mat)\n",
      "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
      "yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=140: 70.1736432284 stress per element: 0.0971934116736\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=120: 74.5872943448 stress per element: 0.103306501863\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=100: 82.5548881411 stress per element: 0.114341950334\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=80: 97.553472 stress per element: 0.135115612188\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=60: 128.027969207 stress per element: 0.177324057074\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=40: 222.715384813 stress per element: 0.308470062068\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=20: 730.926915054 stress per element: 1.01236414827\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=10: 2299.36788686 stress per element: 3.18472006491\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=5: 6707.58303311 stress per element: 9.29028120929\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=2: 25389.4023638 stress per element: 35.1653772352\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f4007e8e8d0>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nope, that definitely made things worse."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "and larger still"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assays = data2[['assay_chemblid','target_chemblid']].groupby('assay_chemblid').count()\n",
      "print assays.shape\n",
      "goodassays = assays.ix[(assays.target_chemblid >= 10)&(assays.target_chemblid <= 18)]\n",
      "subset = data2.ix[data.assay_chemblid.isin(list(goodassays.index))]\n",
      "print subset.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(114, 1)\n",
        "(1039, 18)\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols = subset[['parent_cmpd_chemblid','name_in_reference','SMILES', 'ROMol', 'assay_chemblid']]\n",
      "print mols.shape\n",
      "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]\n",
      "dist_mat = []\n",
      "for i,fp in enumerate(fps):\n",
      "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
      "dist_mat=numpy.array(dist_mat)    \n",
      "print dist_mat.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1039, 5)\n",
        "(1039, 1039)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vs={}\n",
      "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
      "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
      "    results3 = mds3.fit(dist_mat)\n",
      "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
      "    vs[nComps]=results3.stress_/len(dist_mat)\n",
      "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
      "yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=140: 141.09512988 stress per element: 0.135798970048\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=120: 151.919790513 stress per element: 0.146217315219\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=100: 168.906821392 stress per element: 0.162566719337\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=80: 200.40090571 stress per element: 0.192878638797\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=60: 272.095915211 stress per element: 0.261882497796\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=40: 490.563286058 stress per element: 0.472149457226\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=20: 1628.60787554 stress per element: 1.56747629984\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=10: 5037.04251247 stress per element: 4.84797161932\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=5: 14191.8670891 stress per element: 13.6591598548\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=2: 53210.9813686 stress per element: 51.2136490555\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAHiCAYAAAAu1S8tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nihumyE0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       "text": [
        "<matplotlib.figure.Figure at 0x7f40302ad410>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Continuing to get worse."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Try less data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assays = data2[['assay_chemblid','target_chemblid']].groupby('assay_chemblid').count()\n",
      "print assays.shape\n",
      "goodassays = assays.ix[assays.target_chemblid == 15]\n",
      "subset = data2.ix[data.assay_chemblid.isin(list(goodassays.index))]\n",
      "print subset.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(114, 1)\n",
        "(180, 18)\n"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mols = subset[['parent_cmpd_chemblid','name_in_reference','SMILES', 'ROMol', 'assay_chemblid']]\n",
      "print mols.shape\n",
      "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in mols['ROMol']]\n",
      "dist_mat = []\n",
      "for i,fp in enumerate(fps):\n",
      "    dist_mat.append(DataStructs.BulkTanimotoSimilarity(fps[i],fps,returnDistance=1))\n",
      "dist_mat=numpy.array(dist_mat)    \n",
      "print dist_mat.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(180, 5)\n",
        "(180, 180)\n"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vs={}\n",
      "for nComps in (140,120,100,80,60,40,20,10,5,2):\n",
      "    mds3 = manifold.MDS(n_components=nComps, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
      "    results3 = mds3.fit(dist_mat)\n",
      "    print 'Final stress at dim=%d:'%nComps,results3.stress_,'stress per element:',results3.stress_/len(dist_mat)\n",
      "    vs[nComps]=results3.stress_/len(dist_mat)\n",
      "scatter(vs.keys(),vs.values(),edgecolors='none')\n",
      "yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=140: 4.93318539149 stress per element: 0.0274065855083\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=120: 4.92380830335 stress per element: 0.0273544905742\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=100: 5.15458013048 stress per element: 0.0286365562804\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=80: 5.31751942123 stress per element: 0.0295417745624\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=60: 5.74061848474 stress per element: 0.0318923249152\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=40: 6.83546630575 stress per element: 0.0379748128097\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=20: 12.9022649033 stress per element: 0.0716792494629\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=10: 52.2825877625 stress per element: 0.290458820903\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=5: 217.475840469 stress per element: 1.20819911372\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final stress at dim=2: 1145.28539545 stress per element: 6.36269664139\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f40296391d0>"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That's a lot less stress per point than before, but it's still pretty high. Look at the distances to be sure:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mds3 = manifold.MDS(n_components=2, random_state=3, dissimilarity=\"precomputed\",n_jobs = 4, verbose=0,max_iter=1000)\n",
      "results3 = mds3.fit(dist_mat)\n",
      "coords3=results3.embedding_\n",
      "d = distCompare(dist_mat,coords3)\n",
      "scatter([x for x,y in d],[y for x,y in d],edgecolors='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f4007e32510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fAZANwJzE+AtANwDbXM7TiFtEJI6NGwfcfbd1XK0a23W6tg9dvx648UYmc23b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\nZ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       "text": [
        "<matplotlib.figure.Figure at 0x7f40298dec50>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    }
   ],
   "metadata": {}
  }
 ]
}