{ "metadata": { "name": "", "signature": "sha256:d2b745ea0522b7a5656517719c6b0ae94c7a8711a2a41c7b90c36415a51ed43e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "SureChEMBL iPython Notebook Tutorial" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "An introduction to patent chemoinformatics using SureChEMBL data and the RDKit toolkit" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "George Papadatos, ChEMBL group, EMBL-EBI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##In this tutorial:\n", "1. Read a file that contains all chemistry extracted from the Levitra US patent (US6566360) along with *all* the other members of the same patent family.\n", "2. Filter by different text-mining and chemoinformatics properties to remove noise and enrich the genuinely novel structures claimed in the patent documents. \n", "3. Visualise the chemical space using MDS and dimensionality reduction. \n", "i. Identify outliers in the chemistry space. \n", "ii. Fix wrongly extracted structures. \n", "4. Find the Murcko and MCS scaffolds of the claimed compounds in the patent family. \n", "i. Compare the derived core with the actual Markush structure as reported in the original patent document.\n", "6. Identify **key** compounds using structural information only. This is based on the publications by [Hattori et al.](http://pubs.acs.org/doi/abs/10.1021/ci7002686) and [Tyrchan et al.](http://pubs.acs.org/doi/abs/10.1021/ci3001293). \n", "i. Count the number of NNs per compound. \n", "ii. Perform R-Group decomposition." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "from IPython.display import display\n", "from IPython.display import display_pretty, display_html, HTML, Javascript, Image\n", "\n", "from rdkit.Chem import PandasTools\n", "from rdkit.Chem.Draw import IPythonConsole\n", "from rdkit.Chem import AllChem as Chem\n", "from rdkit.Chem import DataStructs\n", "from rdkit.Chem import MCS\n", "from rdkit.Chem import Draw\n", "Draw.DrawingOptions.elemDict[0]=(0.,0.,0.) # draw dummy atoms in black\n", "\n", "from itertools import cycle\n", "from sklearn import manifold\n", "from scipy.spatial.distance import *\n", "\n", "import mpld3\n", "mpld3.enable_notebook()\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "from rdkit import rdBase\n", "rdBase.rdkitVersion" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 81, "text": [ "'2014.03.1'" ] } ], "prompt_number": 81 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the base URL of the publicly accessible [ChEMBL Beaker](https://www.ebi.ac.uk/chembl/api/utils/docs) server." ] }, { "cell_type": "code", "collapsed": false, "input": [ "base_url = 'www.ebi.ac.uk/chembl/api'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "pd.options.display.mpl_style = 'default'\n", "pd.options.display.float_format = '{:.2f}'.format" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "rcParams['figure.figsize'] = 16,10" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Read and filter the input file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file was generated by extracting all chemistry from a list of patent documents in [SureChEMBL](https://www.surechembl.org). The Lucene query used to retrieve the list of relevant patents was: pn:\"US6566360\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_csv('document chemistry_20141011_114421_271.csv',sep=',')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 85 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check the contents:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Int64Index: 8497 entries, 0 to 8496\n", "Data columns (total 28 columns):\n", "Patent ID 8497 non-null object\n", "Annotation Reference 8497 non-null object\n", "Chemical ID 8497 non-null int64\n", "SMILES 8497 non-null object\n", "Type 8497 non-null object\n", "Chemical Document Count 8497 non-null int64\n", "Annotation Document Count 3372 non-null float64\n", "Title Count 3372 non-null float64\n", "Abstract Count 3372 non-null float64\n", "Claims Count 3372 non-null float64\n", "Description Count 3372 non-null float64\n", "Chemical Corpus Count 8497 non-null int64\n", "Annotation Corpus Count 3372 non-null float64\n", "Molecular Weight 8497 non-null float64\n", "Med Chem Alert 8497 non-null int64\n", "Log P 8497 non-null float64\n", "Donor Count 8497 non-null int64\n", "Acceptor Count 8497 non-null int64\n", "Ring Count 8497 non-null int64\n", "Rotatable Bound Count 8497 non-null int64\n", "Radical 8497 non-null int64\n", "Fragment 8497 non-null int64\n", "Connected 8497 non-null int64\n", "Singleton 8497 non-null int64\n", "Simple 8497 non-null int64\n", "Lipinski 8497 non-null int64\n", "Lead Likeness 8497 non-null int64\n", "Bio Availability 8497 non-null int64\n", "dtypes: float64(8), int64(16), object(4)" ] } ], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "df.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 87, "text": [ "(8497, 28)" ] } ], "prompt_number": 87 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add a SCHEMBL ID column and sort:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df['SCHEMBL ID'] = df['Chemical ID'].map(lambda x: 'SCHEMBL{0}'.format(x))\n", "df = df.sort('Chemical ID')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "#df.head()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 89 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**First round of filtering: Novel compounds appear in the description or claims section of the document. Alternatively, they are extracted from images or mol files**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff = df[(df['Claims Count'] > 0) | (df['Description Count'] > 0) | (df['Type'] != \"TEXT\")]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 90 }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 91, "text": [ "(8484, 29)" ] } ], "prompt_number": 91 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Second round of filtering: Simple physicochemical properties and counts**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff = dff[(dff['Rotatable Bound Count'] < 15) & (dff['Ring Count'] > 0) & (df['Radical'] == 0) & (df['Singleton'] == 0) & (df['Simple'] == 0)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 92 }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 93, "text": [ "(8218, 29)" ] } ], "prompt_number": 93 }, { "cell_type": "code", "collapsed": false, "input": [ "dff = dff[(dff['Molecular Weight'] >= 300) & (dff['Molecular Weight'] <= 800) & (dff['Log P'] > 0) & (dff['Log P'] < 7)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 94 }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 95, "text": [ "(6711, 29)" ] } ], "prompt_number": 95 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Third round of filtering: Using Global Corpus Count**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff = dff[(dff['Chemical Corpus Count'] < 400) & ((dff['Annotation Corpus Count'] < 400) | (dff['Annotation Corpus Count'].isnull()))]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 96 }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 97, "text": [ "(6459, 29)" ] } ], "prompt_number": 97 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Keep largest fragment and convert SMILES to RDKit molecules**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff['SMILES'] = dff['SMILES'].map(lambda x: max(x.split('.'), key=len))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 98 }, { "cell_type": "code", "collapsed": false, "input": [ "PandasTools.AddMoleculeColumnToFrame(dff, smilesCol = 'SMILES')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 99 }, { "cell_type": "code", "collapsed": false, "input": [ "#dff.head(10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 100 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Fourth round of filtering: Remove duplicates based on Chemical IDs and InChI keys**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff['InChI'] = dff['ROMol'].map(Chem.MolToInchi)\n", "dff['InChIKey'] = dff['InChI'].map(Chem.InchiToInchiKey)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 101 }, { "cell_type": "code", "collapsed": false, "input": [ "#dff.head()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 102 }, { "cell_type": "code", "collapsed": false, "input": [ "dff = dff.drop_duplicates('Chemical ID')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 103 }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 104, "text": [ "(401, 32)" ] } ], "prompt_number": 104 }, { "cell_type": "code", "collapsed": false, "input": [ "dff = dff.drop_duplicates('InChIKey')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 105 }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 106, "text": [ "(394, 32)" ] } ], "prompt_number": 106 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow, that was a lot of duplicates. This is because the same compounds come from different patents in the same patent family. In addition, in US patents a compound may come from 3 different sources: text, image and mol file. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Fifth round of filtering: Remove Boron-containing compounds as they are likely to be reactants.**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff = dff.ix[~(dff['ROMol'] >= Chem.MolFromSmarts('[#5]'))]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 107 }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 108, "text": [ "(394, 32)" ] } ], "prompt_number": 108 }, { "cell_type": "code", "collapsed": false, "input": [ "dff = dff.set_index('Chemical ID', drop=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 109 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That was the end of the filters - let's see a summary of the remaining compounds and their patent document source:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff_counts = dff[['Patent ID','ROMol']].groupby('Patent ID').count()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 110 }, { "cell_type": "code", "collapsed": false, "input": [ "dff_counts['Link'] = dff_counts.index.map(lambda x: '{0}'.format(x))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 111 }, { "cell_type": "code", "collapsed": false, "input": [ "dff_counts = dff_counts.rename(columns={'ROMol':'# Compounds'})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 112 }, { "cell_type": "code", "collapsed": false, "input": [ "dff_counts" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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# CompoundsLink
Patent ID
EP-1049695-A1 2 EP-1049695-A1
EP-1049695-B1 7 EP-1049695-B1
EP-1174431-A2 1 EP-1174431-A2
EP-2295436-A1 68 EP-2295436-A1
US-20040067945-A1 4 US-20040067945-A1
US-20050070541-A1 2 US-20050070541-A1
US-20060189615-A1 2 US-20060189615-A1
US-20080113972-A1 34 US-20080113972-A1
US-20100016323-A1 29 US-20100016323-A1
US-20110009367-A1 43 US-20110009367-A1
US-20130059844-A1 46 US-20130059844-A1
US-6362178-B1 6 US-6362178-B1
US-6566360-B1 4 US-6566360-B1
US-6890922-B2 2 US-6890922-B2
US-7122540-B2 7 US-7122540-B2
US-7314871-B2 43 US-7314871-B2
US-7696206-B2 32 US-7696206-B2
US-7704999-B2 59 US-7704999-B2
WO-1999024433-A1 3 WO-1999024433-A1
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 113, "text": [ " # Compounds Link\n", "Patent ID \n", "EP-1049695-A1 2 EP-1049695-A1\n", "EP-1049695-B1 7 EP-1049695-B1\n", "EP-1174431-A2 1 EP-1174431-A2\n", "EP-2295436-A1 68 EP-2295436-A1\n", "US-20040067945-A1 4 US-20040067945-A1\n", "US-20050070541-A1 2 US-20050070541-A1\n", "US-20060189615-A1 2 US-20060189615-A1\n", "US-20080113972-A1 34 US-20080113972-A1\n", "US-20100016323-A1 29 US-20100016323-A1\n", "US-20110009367-A1 43 US-20110009367-A1\n", "US-20130059844-A1 46 US-20130059844-A1\n", "US-6362178-B1 6 US-6362178-B1\n", "US-6566360-B1 4 US-6566360-B1\n", "US-6890922-B2 2 US-6890922-B2\n", "US-7122540-B2 7 US-7122540-B2\n", "US-7314871-B2 43 US-7314871-B2\n", "US-7696206-B2 32 US-7696206-B2\n", "US-7704999-B2 59 US-7704999-B2\n", "WO-1999024433-A1 3 WO-1999024433-A1" ] } ], "prompt_number": 113 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Right, is Levitra included in this set? ChEMBL tells me that its InChiKey is 'SECKRCOLJRRGGV-UHFFFAOYSA-N'." ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff.ix[dff.InChIKey == 'SECKRCOLJRRGGV-UHFFFAOYSA-N',['SCHEMBL ID','ROMol']]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SCHEMBL IDROMol
Chemical ID
5441 SCHEMBL5441 \"Mol\"/
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 114, "text": [ " SCHEMBL ID ROMol\n", "Chemical ID \n", "5441 SCHEMBL5441 \"Mol\"/" ] } ], "prompt_number": 114 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Good - it is..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Visualise the patent chemical space - MDS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Preparation of the pairwise distance matrix." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in dff['ROMol']]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 115 }, { "cell_type": "code", "collapsed": false, "input": [ "dist_mat = squareform(pdist(fps,'jaccard'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 116 }, { "cell_type": "code", "collapsed": false, "input": [ "mds = manifold.MDS(n_components=2, dissimilarity=\"precomputed\", random_state=3, n_jobs = 2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 117 }, { "cell_type": "code", "collapsed": false, "input": [ "results = mds.fit(dist_mat)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 118 }, { "cell_type": "code", "collapsed": false, "input": [ "coords = results.embedding_" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 119 }, { "cell_type": "code", "collapsed": false, "input": [ "dff['X'] = [c[0] for c in coords]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 120 }, { "cell_type": "code", "collapsed": false, "input": [ "dff['Y'] = [c[1] for c in coords]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 121 }, { "cell_type": "code", "collapsed": false, "input": [ "csss = \"\"\"\n", "table\n", "{\n", " border-collapse: collapse;\n", "}\n", "th\n", "{\n", " color: #ffffff;\n", " background-color: #848482;\n", "}\n", "td\n", "{\n", " background-color: #f2f3f4;\n", "}\n", "table, th, td\n", "{\n", " font-family:Arial, Helvetica, sans-serif;\n", " border: 1px solid black;\n", " text-align: right;\n", "}\n", "\"\"\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 122 }, { "cell_type": "code", "collapsed": false, "input": [ "import base64" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 123 }, { "cell_type": "code", "collapsed": false, "input": [ "dff['base64smi'] = dff['SMILES'].map(base64.b64encode)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 124 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's produce a scatter plot of the chemical space - points represent compounds, color-coded by the patent document they were found in. Thanks to [mpld3](http://mpld3.github.io/), the scatter plot is interactive with *live* structure rendering calls to the *local* myChEMBL Beaker server. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots()\n", "fig.set_size_inches(14.0, 12.0)\n", "#colors = cycle('bgrcmykwbgrcmykbgrcmykwbgrcmykw')\n", "colors = cycle(cm.Dark2(np.linspace(0,1,len(dff_counts.index))))\n", "for name, group in dff.groupby('Patent ID'):\n", " labels = []\n", " for i in group.index:\n", " zz = group.ix[[i],['Patent ID','SCHEMBL ID','base64smi']]\n", " zz['mol'] = zz['base64smi'].map(lambda x: ''.format(base_url,x))\n", " del zz['base64smi']\n", " label = zz.T\n", " del zz\n", " label.columns = ['Row: {}'.format(i)]\n", " labels.append(str(label.to_html()))\n", " points = ax.scatter(group['X'], group['Y'],c=colors.next(), s=80, alpha=0.8)\n", " tooltip = mpld3.plugins.PointHTMLTooltip(points, labels, voffset=10, hoffset=10, css=csss)\n", " mpld3.plugins.connect(fig,tooltip) " ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", "\n", "
\n", "" ], "metadata": {}, "output_type": "display_data", "png": 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Dg5qbm3X8+HFFo1HdddddchxHX//613X//fczbW0J45eFucjObORnLrIzG/mZjfwKy4xH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\nqvN7ihUY8mnrwHZta3xIxcFq2bZb4VC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Ooh1tCJKIv76FuoOPIUgyksWCbLHN+/yzXXup\n7HQlSkWyk4omAdD1PIahIyCQTo7g9gRIxIbJZbPYFAu6liWfTaBYJOrrDqCmhtHU+6WpJ+/X3a3v\nUFa/B2kJ1djmhCDMWntorrWJVpIjRw7Te/MDrp/5LmpmfE4EQaSi6QDNez+DwzNpuMSjQ5x776dF\njer46BAdtz5h54EnV2zsJvN79unZNKmbHxA//TJaYhgAS0ULnsNfxFa/c0nDVU2WB9PQMVkUaqQX\nLTZIrv9W0XYjl8HIjKFGe01Dx8RknuSy01f1p6LmMis0kgcLSZJp3rKfUHkdkaEeVDWLw+klWF6D\nw+kp6Gv3BrB7A5Rv2b1i47NbpnuHJElBHRs3VkRRQhBEdC1L183TbNr7HOc/eGX8Zfpung4CKDJs\n2LCbaM90g1m0yQh3pbdTiSE0NbPiho7V6Sa0YRsDV84V7yAIOIIzq3auBsPdV7jw5l+TV3UEASwW\nEUSdvrbT6FqebY/9M2Rl3ACODPUWGNP303b1I1q2Hpj2vzNZGyQvv0Hs3b8r2Jbrv8Xwj/6U0Av/\nBnvDrlUamclcWXtLJSbrit72WyXjqycwNHPVYw1i1hJY+1TVlVYkTCaTlFc1ruBoHiwEQcAXLKdp\n81427XiE2qatK/ayOdu1F/Y2I4uWgm1ZNYnTN66UZrN4EAQRQ8+j5VXaLr3F7kNPsnnnI9gdLlze\nMNv2PsqenTsQB0exWqZ/L9FlnVCBdrhCSMr8PVOLRRBFyjfvQiohNlC16yDOYPkKj2pmYqNjnD/5\nUzo60nR2ZunozNLTmyWdGq9ZNNB+jsRw10T//CxFXnPZDFrelNJeSeb67FMjvcQ//F7xRi1P4uMf\noZuLTWse09AxWRS6bEONDWGp3lyyj2B1oARMZTUTk/lSWduCrBR/CbTaHJRVLV9NFJPVw++q5tGt\nX0cUJoMuDEPHcKh4PZVYZPv4xrvem2w6yfXzr1Fb5Wf3lkp2bS7DkfqI1MBJLOU+HPkwonLXcJJF\n5KAD0TophV2z5fGVD1u7i7uimq2/8IsEGjZMiEMoDheNjz5Dzd6jiPLKB54Y2TxaLI2eKfTE5HI6\nF872cf3SFSbSnQxIjen09GRJp3XAYCw+MLGPwzWzjLcvWI51isJfLDJI152rdN+5Rjw2slRfyWQB\nqCNdGGppQybbfZV8bKBku8nawAxdM1kUjTsPER26jCVYQ67vJuhaQbtgseM9/CXkQPUqjdCkFGaO\nztrHH6ri0We/yodvfY/0vRojjCfMHz7xBXxrbLV7OdDGcuS642iJHKJTwVrtQfKsb7nhuVx7jeUH\ncNoCdA1fpD96A6fNT0P5PjaXH+f0W98nlYwhShYEUUaxWNlz8HFSna+iZWMTxwg0HsG/bzu52gw7\nm3+DK+9/CyQDQZr0sNdsfpxg9ermeXkqa3E++3kyoxF0LY/F6cbqWvlQLj2VJX+1j+w7N9GHkwhe\nO9ZHNyBvr0Ly2Ll9O83lSyksipW8WqiIp+sQj+Wx2yyIwuQacjBcjdcfJhYdKnrOTTuPYLHYyGZS\n3Lh8mtYLp8jnx4+tWGzs2P8ETZv3oTwgEttrgbk++wxdn60HGLP1MVltTEPHZFEIooh77/NE3v5r\n/Ce+TvLSG6gD4yo6srcM/5O/jnPHk2bomonJAqmoaeaZz/0mkaFespkUVruTYLga+xKqfK1VMh2j\njPzDVdS+SSNPCTkIfGkr9g3BGfZc/wiCQLmvhXLfdAn+pz/3G0QGe0inEgh6Bn30Cqm+19DVyZwu\n2eIhWP0M6dYook2mouUArsoqhrsuMzpwC5srSFnDHvzlzShW50p+tQm0VA51KAWagRy04wytnuGu\nZ1QyP71M7uSNiW1GIkP6O2eQ99bh+OJe2toyfPixxGcf20ffrQ+mHSOR0AiFJVzB2oltNoeLwye+\nwPuvf5fEFA+NIAhs2/s41fWbALh19QyXP36z4HhqLsO593+GpFho2bJ/qb+yySwo/kqQZNCKhxbK\n4Xokz9rLITMpxDR0TBbFPT364NP/B9meawjW8ergsiuAUt6EtdwMrVmrmLUE1g8Ol3dacdAHff7U\noTGG/uoCWqIwx0EdTjH41xeo/K0DWKrXZwL3YufO4fTgaBz/7oZhkBwMErFArOccgijjrz6KLb2B\nyJ91Q348X8RS7Sbwuc207PvMknyHxWBoOunWYUZ/fotcz7gRK3tteJ9pxrm7HNG+8mF02p3hAiNn\nKvlzneR316LrDnp6VVLiHiy2C+QyhWIhhgENO5/D5a8q2B4IV3PiM19nZKCLRDyCLCsEwtX4g5VI\nskx8dJhrF0rnjVz++C2q6jYuWw5ZMjPCYOw28VQ/imQj7G0i4KpFliyz77wOmev1p4Rqce18huT5\nnxZpFfAe+CySbXUWCUzmjmnomCwJsieE7HkU85I3MTFZCjK3ItOMnHsYmTzp1uF1a+gsJYIg4C7f\nhiu8mfzOOFpKI/q3t0h2FEpU53oSDP7leSr+5eobiOnrIwz+9QXQJ4Vs8rEMI9+9gp7N4z3esOJj\nUq/2Fd1u6AZGTiPz7i0OntjFjs02vvntFL/967+OEX+HoY6LGIaByxeiac+naNhxFFGUph3H4fTg\naNpW9ByJ2AhqrrRoQXosTjIWWRZDZyh2m7cu/zmJ9GRonYDA7sYX2F7/7GQ+2EOIIEp49v8CgiSR\nvPAKxt2QQtHuwXvsq9ib9q7IOLTUeG0s0e4xo2MWgGnomCyKB3lF+UHHnLv1zYM+f5mbkRnbU1eG\n8D65Pj3GyzF3giih2P3krvWR7YgV7aOn86SvDi2LoaPlcuhaHtlqm7H2jZbJE3v9doGRM5XRV9tw\nbA2jlK3sspkRny7lrmfz5KMZDFXH6IiivnKdr4Wh9UsV/PnfxGlsfI6De04giRq9QzZ2lNVhd85/\n3OIcagUJ4tK/4KayMU5e+WaBkQNgYHD+zg/xOitprji05OddbeZz/UlOP96jX8Wx+VHyo/0IkoQS\nrEX2LH+R19zgHVI3T5NqfQ8MHXvzfhybjmCt2rTs536QMA0dExMTk4cQVcvTlYqRymdxyzZqXD4k\nYe0IcQrW6aviUxFnaX+Q0DWNseEBEv1d5NJj2L0B3BW1OPzT85Qyt6MzHit5cQDPE40I8tLMdSYW\nJdJxi4Er58hnM3iqainbvAtvdQOiNH2O8sMpsu3TC6Lew8jkyfUnV9zQkeqDqOc6Jz7rOQ11OAV3\nc83F2gB6JIszmmVjIsOLJ+p5+fUEbW0C4bCdf/kvq9mzZ2F5cx5/GJvdRSadLNru9Zfh8S39i/VQ\n7DaxVH/J9qudr1Mb2vlQe3Vg3GtqCdViCdXO3nmJyPbdZPgHf4KemfxPJC++ytiVkwRf+D3s9TtX\nbCzrHdPQMVkUD3qewIOMOXfrm8XM353ECP/YcZGzw90YGMiCyKPlTbxQt51qh3f2A6wA9q1lJE/3\nACDXOsnXi2iGhpJXEK6mce6rmuUIa5f5zJ2WVxm4co47771eULNMsljZ9Ozn8dc1F+4gzbzyL0rC\nRP2cxZKORbjx6sskByfDvoZvXmX41jWajz9P+Zbd00NtZqu7Ntc+S4y8sRwsEuTGlUP1TH7CyEEW\nodyH3jmIoghYx2J8+XNWdj8axGIRaGmxEwwuPJ/F6fKx69DTnH775WltgiCw8+CTWG2OInsujrHM\nzF7TSLKLrDr2wBk6a/3ZZ+RV4qdfKjByJtuyjL7zbSxf+o9ItgdfkGYpMA0dExMTk4eInlSMP738\nJsPZ1MS2vKHzVv8tusZG+dfbjxNYJRWuqdgavNi3hUmX57jc8xH958aLEytWGxv3HMZbuzp1X1aa\nWE8Hd069Nm27lsty/ZWX2PmFX8URmFztt28IkHi3c1r/ezj3VyFIS+PNGWlrLTByJjAM7rz7Ku7y\napzBQlUqOWBHqXCh9hf3XgiKuGzeHDWbIj7cQWKkCwMDt78aT7gBi82FXOPH+bWjpL71AXpKRU/d\nraFjlbF9Zjfx1kkvlGHoyANxHv3i0tWHq2vZgSjJXDn7NvHR8VCyQKiKbfuOU1W/PKFKyixFYi2K\nA0l8OK6ztYQa6SbTcbFke36kG3WwA6lu+wqOav1iGjomi2Itr4qYzIw5d+ubhc7fxZGeAiNnKrcS\nw9yIDXF4hcOGiiG5rUjPBjnz0l+SHoqBYSCIAobF4Gb3x2Qv5jj4+GexWGd+WVuLzKeOx1Br6Rce\nLZcl3tdVYOhY633YNwRIF8lxkoMObJtC8x9wEXKpJH2XPi7ZrudVkoN90wwdyWnB90wzQ9++CEUc\nN57HG1AqXEsyxqmkkxFunP4uA+1nC7YHq7aw+egv4/SUoWyvxvWvnyHfPkz22hA6ApqsELsWQU9N\nSgy7XK4lC/27hywrNGzYSUVNM2PxKAjg9gaxWJfPmxJyNyCLFvJ6rmj7lpoTOKxrw8O7lKz1Z5+u\nZmf1as5UyNSkkLUTkG1iYmJisqzohsF7g3dm7HMpWlx9ajXo7mpFsxgo5S4sFS6UcheSz4YgS3Td\nvkJkqGe1h7isaGqO5GDpHAqA9GihQSO5rQS+vA3vEw2TeU6SgGt/FWVf341liYxYPZ8nn52ewD8V\nTS2uJGbfFib0i9uR3JPhXoJVwvtMM57H6pdFWarr2tvTjByAkd5r3LnwM/S7xa6lcg/WQ03QXMHo\nJ1ES5wYLjJx72DYElnyMADa7k2B5DcGymmU1cgB8zioOb/oqQpFYxqCrnsYys3bPaiA5vAiWGeZe\nlJBc/pUb0DpnUR6dN998k4GBAcrKynjyySeXakwm64i1HutqUhpz7tY3C5k/AwN9lkreerFl9lUg\nkx6j49YnACVXz6NDvYTKa5CV9VU1fq5zJ0gyitNJJl5aYMDimG64KEEH/hc24TpUgzaWQ7TJKGXO\nJQtZA5BtdhyBMpIDpY1Nq6u4upuoSLgOVGNrCaD2JzF0AyXkQClfek8OwFisn65rb5Vs72v7kLqt\nT+AJ1U1ss20OIYcc5Ienez/z1Vas9b5lGetKIggCGyqP4rQGudH3LgOjN1EkG5urj1MX3o3H8WAW\nw1zrzz7FX4lr93MkzkzP2QJwbDyMMuW/ajIzCzZ0bt26xSuvvMIf//Ef87u/+7ts2rSJmprxeNVk\nMsmrr77K4OAge/bs4dChB0+e0MTExGS9IQkiR8oauZ0snYS83VexgiOaAcPAKGF06VqeXC5LZLiP\nV1/6CypqW6ht2ka44sF6+EuyTMXWvST6uot3EATclaWVoJQyJ8oyVTeTLVaqdh7gxmvFDR2bx4er\nrHrmY/jtyP7lT3TPpmIlvUsAhq6RTY0Ck/8fS9hJ2dd2Ez/ZQfJsL2gGglXCc7SO4UoVyfVgFNMU\nRZma0HaqApvJqClkSXngxAfWI64dT6KN9pO68UHBdkvNNjyHvoAgmblTc2XBhs6FCxfw+cZXNLxe\nL5cvX54wdL73ve8xMDDAL//yL/N7v/d7/NEf/REtLS1LM2KTNcVaXhUxmRlz7tYnhmGQiGfZtnUv\nmbSKbZ5V5HcHq/l59zVqrU4qZBtjep7zqQgJNUud088mb/kyjXx+WO1Oahu3cuPy6YLtmpYnGY+g\n5VUkWSYWHSQWHeTmlTMcferL1DRuWaURz535XHu+2kYCTZuI3L4+ra3+8BO4QqtnmPobNlC7/xhd\nZ98ryCmwujxsePqzWF1rQxVKkq2MS82V9lZKRbyClko3wS9txXO8Hj2jIToVLGEnD2LQkCjKOKwP\nRwHe9fDsk91BfE/+c5zbT5AbbAdDRwnVYqloRrI/HPO0VCzY0InH4xNFrkRRJBqddK0fP36caDSK\n3z9+O0gmi6urmKw/dDVDfnQQDA3JE0ayjYcaGLqOlhwBAySXH0EydS5MTJaayMgYN68N0XZ9CE3T\ncXttbNtZSV1TAKt1btdcmWTlX3hqOPPxW/QOd2OzOnh28148dVvZWt1CyLb6QgQwHlZT37KT263n\nyecnk6WzmRRaXqWipoXklJAuXctz5uQP8IUqcLkfnFdRi9NN82PPEWzcSP+Vc+TGkrjCFZRt2YW3\nuhFRXr17rWyxUr3vKL66FpIDPeRzWey+IO6KamyetRPa5fRXEajaTKT3WtF2d6AWV6C4gpogiVgq\nVt5g0w2N2Fg/uXwam8WF17FGPK0mK4ZkdSLVbcdmqqstigXfIXO5yQePruvk85PJevX19dTX1/PO\nO++wdetWdu40CxutdwzDINN1hcRHPyTbdRkwkEO1GJufJlBZw9jVd0jfPA2Gga1hN66dT2Gt2bos\nSaUmS8Naj1M2KSQaSfHWz2+QGhu/995bQPrw3TvE4xl27atBnkUJKq+qXD13ktZP3sdqGFTaPeiG\nwdiNSzgiI3jLm5b9e8yHUEUdjz73S5z/4OeMjvSja3nUbIamTXvx+MN0tl0u6J/NjBEZ6l3zhs58\nrz2L003Z5l0EW7ah51UkxVK0GOdqIMkKnsoaPJVLJ7W81MiyhZa9n+HccAf5XGHOjSRb2Xjwi1jm\nIam+3PfOSKKbSx2vcHvgNLqRn8ib2VLzBG7H0hcOBUhG+4gN3iYzFsFi9+Ara8IVqHkgn+Hms+/h\nYsGGjtPpZHR0Ulfe7S5c8YhGo1y9epXf+q3foqenh9ra0nHEU/90p06dAjA/r7HP++q8DP/gT0jG\nxufc5XKRH+5GHrxJ75t/hmIZl3hNJpMkoq+Tvv0xoU//Luf6MhiGserjNz+bn9f7547bIwwOjOfW\nuFzjntR7xs61T/qoa/Bz/ebFGY/XeuUcH7//Gi6XE1EQyKfH8xYcLhfx6CDXLp1Bk7wcOXJk1b/v\n1M9PvvB1opF+Rgb7Ge5vJxYdoLPt8sT3n/p7DPb3Ute0bU2N//7P95jv/h98+OGaGP96/Owrb6Zq\n11cYG7rO2FArhqFjD27AU7GNYPWWeR3vHssxXk9QoTXyEon00JT/N1zq/Dmd/Vdp8X2a3TsPLNn5\nZFmmscLK5Xf+ilhkZPx8bheiJFO17QUSup9Dhw4v+/ys5Od7rJXxmJ/n/tnhmH/hXMEwFlaC+MyZ\nM/zkJz/hD//wD/m3//bfcuLECS5evMg3vvENFEXhT//0T9m+fTu9vb0cPXqUbdu2FT3OG2+8wd69\nexcyBJMVQs9lGPrBn5DrKXT7K+Ut5KM9ZHtaUQJV05LjZF8lZV/+AySHGU9qYrIY0imVn758ecKb\nU4yDRxvYtG3m/JprF09x4YNXSrZ7fGGe/vxvYLGszdo06VSSV1/6n6SSsZJ9Hn/+n1BVt3EFR2Wy\n3silExgYWGzuNeexuNr5Bh/c+LuS7U/t+gb14T1Ldr7YcAcf/fg/o2vT7y2CILH/+d/DX7Fhyc5n\nYrIYzp07N2+V5wVrTR48eJBQKMR3vvMdamtraWlp4caNGySTSV566SXOnz/Pt7/9bd544w3C4eVx\ntZqsDPnRvmlGDoASqCTbdQV0DSM//SaZH+1DHS5dodvExGRu6IaOlp9ZFlrTZm4HyKulDSWAfD6H\nrmnzGttKYne42LLrWMl2rz9MIDyz0td6Q1Vz9HXe5Mzb3+fVl/6Cj975IX1dt8jn1dUe2rpFlK1k\noqMMtl5k6MZlUpEhFrjmu6TohsbNvvdn7NMXKZ5ntFCiva1FjRwAw9AYaD+/pOczMVlp5MXs/I1v\nfKPg8ze/+U0AvvzlL/PlL395MYc2WUMYevEXH0PX0HQdSRRLitkY+dKSniary6lTZpzyesFmUyiv\n8tB5Z1IWOplMToRsAXi8s3thPL6ZF53KqhqXvUjhYqlr3kZkqJc7NwpfwBxOD4eOfw6bfW2IKczE\nXK+9vKpy/ZP3uPTRmxPbRga7uHX1I3YfeoYNOw4jy6bM7HxIxyK0v/9GgYqdKCvUHXqcim17kZTZ\nZaOX695pGAa6kZ+xj6bP3D5f4rMsRkb7b2IYxprzfC0G89n3cLEoQ8fk4UB2h5C8ZWixwcIGXUO0\nuyGXQij2sJVkJHdoZQZpYvIAI0kiG7aE6WqPUGzhORh2EiqbvdhiqKIWlydAMj69jo4giDRu3D2h\nprlWsTnc7D36PPUbdjLY104+lyUQriZUWYfbszzV6leLof6OAiNnKhdOv0qgrIby6sYVHtX6RVNz\ndNxn5ADoeZX2917H4nQT3lA8zH4lkESZhrJ9RJJdJftU+JY2LNPqmFkdz+4KLrmRk48Pk+27Tq73\nJoKsYK3egqWyxZRNNlkWpD/4gz/4g9UcwJ07d6isrFzNIZjcRcuMkeu5TqbrMmqkB0GUEO1uRIsd\nwWInc/tsQX89FcfZvB812jsuM33fzdC140mcWx9DENb2i9PDSl3dg1Vc8UHH6bLi9toY6EugaToW\ny/jKc3mlh8OPNuLyzO7RsVhshMrrGB7oJJsem9iuWGwcfPxFquo3rYuVW0mWcXsDVFQ3UVW3EX+o\nAusa90RNZa7XXuvF94gMFS/ICWC1O6isbSGdGCbaf5PkaB+GrmGxudbFPK40ycFe2t9/o2R7JhYl\n2LIVaRYv2XLeOy2yjdsDH6Hp00MTfY5KdjX9AhZ5/gnZpRHoa/uwZGvL/hdx+WZ/RzMMHTU1D7BY\nZQAAIABJREFUgqamEGVryed+brCdoR/9KalLb5IbaCPXd4PU9fdRI71YqzYhWpfyuxXHfPatX/r6\n+mhqmp86qOnRMQEgN9JF9I2/JNc7xZ3vDOA9+hUA9Fwa77GvkhtsJ932EWh5dDWNpWYLHpef1NWT\nBQXj7BsO49r/AoK4NiRQTUzWO6Io0LQhRLjcRWR4jHxex+myEgg5sFjmfisPllVz4oWvExnqIZWM\nIStWAuEqPD7T+7rWiEcHZ2kfoq/tDK0ffAc1O264ipJC3dYT1G9/CqvDuxLDXDekY9M9mVMZGx4g\nl0qi2FbPaA6663l61+/w4fW/Y2TCsyNQHdjGwQ1fwmULLun5vOWN1G9/ho7Lr05rq2w5PCchguTQ\ndSJ33iba+SEYBt6a/QSbT+Au21rQT89lGH3n22jRvmnHyNw5R7KsHt8jZtqDydJiGjomaOkk0de/\nSa7vxsQ2weHFVr+DoX/4A0S7C/FujQHR4SHw9G+CKKH4KvikY4i9xz+Fa9sT48IDho4crMVS1oi4\nRpWbTMYx45TXJ26PDbfHNj5/Gxc2fza786FSJsuNJYj3dTF86ypqJo2/tglfXTOu8OoUYbx37eXV\nLKORQdRcFpvdiTdQXhA66AtVMNjXXvI4DqerwMgB0DWV9kvjynobDnzuofOoJ+IRhvs6iAz1olis\nhCsbCJbVYLHaEGcpZC2IEsIcQjeX+95Z4d/Ip/b9PiOJTnJqCrvVQ9BVhyxbl/xcimKnaffzeMMN\ndLe+y1isD5srSO3mxwnVbMVim7lYamLgCm0n/xg9n5nYFrnzDtHOD2h69N/grZpUiFMH28l2Xy15\nrOSFV3FueRzFN7N65GIxn30PF6ahY4I62FZg5AA4GvcQPfkt0PIYhoZgsSMIInoqzujJb1H25T9E\n8VeSudGDqFiwVm3EWvXwvDiZmJisDzKJGLdP/pRoR9vEtnhPB91n32PTc1/EX7fyRVIFQWB4oIuL\np19lsLcDMBBFiYaNu9iy51E83nHvWnXDFm5cOk1RtRdDx26B6BQjZyqdV9+gsuUQ7kDN8n2RNcbw\nQBenXv0O6bHElK0nad6yj50Hn8YZLENUFHS1uGJdeMNW7J61UWzWqjipCmxZkXMpVgcVTfsJ1+1C\nUzOIihVZnl2UQVPT9H7y9wVGzj0MTaXn/N/iDLYgW8eNJS09Oq1fwT7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/oiWn3EgMg8zt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\nfvz/5OT1HzISPM+V0UO8deb/4di175FM5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wXFfgFufPQuVl8Ad3Nxj5Ws5NTK9DL5P4oh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guecT2Ju2kRrvxTINgu89x/ThZ+Had0fxluM/9Ns4Grcu558iENwWIkdHsCDEXtfi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\nAAAAACmNNToAAAAAIBodxIm5ruYiO7ORn7n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"text": [ "" ] } ], "prompt_number": 125 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nice, we get Beaker rendering on the fly. However, there is a bunch of compounds on the left that are distinctively different. This is because of an image extraction error. \n", "At least the error is systematic: extraction breaks the imidazole ring. Let's try to 'repair' the ring and rescue these compounds - we'll use an appropriate intramolecular reaction SMARTS." ] }, { "cell_type": "code", "collapsed": false, "input": [ "resmarts = \"([R:5][C;!R:1]=[C;H2].[C;H0,H1;!R:4]-[N;H2:2])>>[R:5][*:1]=[*:2]-[*:4]\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 126 }, { "cell_type": "code", "collapsed": false, "input": [ "Draw.ReactionToImage(Chem.ReactionFromSmarts(resmarts))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 127, "text": [ "" ] } ], "prompt_number": 127 }, { "cell_type": "code", "collapsed": false, "input": [ "def fixRing(mol,resmarts=resmarts):\n", " rxn = Chem.ReactionFromSmarts(resmarts)\n", " ps = rxn.RunReactants((mol,))\n", " if ps:\n", " m = ps[0][0]\n", " Chem.SanitizeMol(m)\n", " return m\n", " else:\n", " return mol" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 128 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So this..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mm = dff.ix[12823029]['ROMol']\n", "mm" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 129, "text": [ "" ] } ], "prompt_number": 129 }, { "cell_type": "markdown", "metadata": {}, "source": [ "...will become this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fixRing(mm)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 130, "text": [ "" ] } ], "prompt_number": 130 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's apply the repair to all affected structures:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i, p, m in zip(dff.index, dff['Patent ID'], dff['ROMol']):\n", " if p == 'EP-2295436-A1':\n", " dff.ix[i,'ROMol'] = fixRing(m)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 131 }, { "cell_type": "code", "collapsed": false, "input": [ "dff['InChI'] = dff['ROMol'].map(Chem.MolToInchi)\n", "dff['InChIKey'] = dff['InChI'].map(Chem.InchiToInchiKey)\n", "dff = dff.drop_duplicates('InChIKey')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 132 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So after the fix, we have even less structures because some of the fixed ones already existed. Hopefully, however, we have less false positives too:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 133, "text": [ "(363, 35)" ] } ], "prompt_number": 133 }, { "cell_type": "code", "collapsed": false, "input": [ "dff['RD_SMILES'] = dff['ROMol'].map(Chem.MolToSmiles)\n", "dff['base64rdsmi'] = dff['RD_SMILES'].map(base64.b64encode)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 134 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to re-calculate the distance matrix and plot the MCS just to double-check they were no side-effects from the applications of the reaction." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fps = [Chem.GetMorganFingerprintAsBitVect(m,2,nBits=2048) for m in dff['ROMol']]\n", "dist_mat = squareform(pdist(fps,'jaccard'))\n", "mds = manifold.MDS(n_components=2, dissimilarity=\"precomputed\", random_state=3, n_jobs = 2)\n", "results = mds.fit(dist_mat)\n", "coords = results.embedding_\n", "dff['X'] = [c[0] for c in coords]\n", "dff['Y'] = [c[1] for c in coords]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 135 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots()\n", "fig.set_size_inches(14.0, 12.0)\n", "#colors = cycle('bgrcmykwbgrcmykbgrcmykwbgrcmykw')\n", "colors = cycle(cm.Dark2(np.linspace(0,1,len(dff_counts.index))))\n", "for name, group in dff.groupby('Patent ID'):\n", " labels = []\n", " for i in group.index:\n", " zz = group.ix[[i],['Patent ID','SCHEMBL ID','base64rdsmi']]\n", " zz['mol'] = zz['base64rdsmi'].map(lambda x: ''.format(base_url,x))\n", " del zz['base64rdsmi']\n", " label = zz.T\n", " del zz\n", " label.columns = ['Row: {}'.format(i)]\n", " labels.append(str(label.to_html()))\n", " points = ax.scatter(group['X'], group['Y'],c=colors.next(), s=80, alpha=0.8)\n", " tooltip = mpld3.plugins.PointHTMLTooltip(points, labels, voffset=10, hoffset=10, css=csss)\n", " mpld3.plugins.connect(fig,tooltip) " ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", "\n", "
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b2/dgYvRa3vM1DZtW5T7J6AzSyQhkgxF2d+Wq7fuTHr+C9HD+le4ip1+A/8kd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uMbSfdF0R6Yi2Mpc+N/pn7eSQ6AMTKzz9PahxY230cQ1jMxR2cdE+NE80fENj/WYlzjqXFOtXyd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\nERFRXuMcHSIiIiIiIrDQKWgcJyo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"text": [ "" ] } ], "prompt_number": 136 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the \"island\" of outliers has disappeared. There's only one obvious outlier on the far left. This might be a reactant which wasn't filtered out. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Define the MCS for this set of claimed molecules" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A helper function, inspired by Greg Landrum's post [here](http://rdkit.blogspot.co.uk/2014/02/more-on-datasets-ii.html)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def MCS_Report(ms,atomCompare='any',**kwargs):\n", " mcs = MCS.FindMCS(ms,atomCompare=atomCompare,timeout=120,**kwargs)\n", " nAts = np.array([x.GetNumAtoms() for x in ms])\n", " print 'Mean nAts: {0}, mcs nAts: {1}'.format(nAts.mean(),mcs.numAtoms)\n", " print 'MCS SMARTS: {0}'.format(mcs.smarts)\n", " mcsM = Chem.MolFromSmarts(mcs.smarts)\n", " # find the common atoms\n", " if atomCompare == 'any':\n", " mcsM2 = Chem.MolFromSmiles(mcs.smarts,sanitize=False)\n", " atNums=[-1]*mcsM.GetNumAtoms()\n", " for m in ms:\n", " match = m.GetSubstructMatch(mcsM) \n", " if not match:\n", " continue\n", " for qidx,midx in enumerate(match):\n", " anum = m.GetAtomWithIdx(midx).GetAtomicNum()\n", " if atNums[qidx]==-1:\n", " atNums[qidx]=anum\n", " elif anum!=atNums[qidx]:\n", " atNums[qidx]=0\n", " for idx,atnum in enumerate(atNums):\n", " if atnum>0:\n", " mcsM.GetAtomWithIdx(idx).SetAtomicNum(atnum)\n", " \n", " mcsM.UpdatePropertyCache(False)\n", " Chem.SetHybridization(mcsM)\n", " smi = Chem.MolToSmiles(mcsM,isomericSmiles=True)\n", " print \"MCS SMILES: {0}\".format(smi)\n", " img=Draw.MolToImage(mcsM,kekulize=False)\n", " return mcs.smarts,smi,img,mcsM" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 137 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add the Murcko framework to our data frame:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "PandasTools.AddMurckoToFrame(dff)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 138 }, { "cell_type": "code", "collapsed": false, "input": [ "PandasTools.AddMoleculeColumnToFrame(dff,smilesCol='Murcko_SMILES', molCol='MurMol')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 139 }, { "cell_type": "code", "collapsed": false, "input": [ "PandasTools.AlignToScaffold(dff)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 140 }, { "cell_type": "code", "collapsed": false, "input": [ "dff[['ROMol','MurMol']].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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ROMolMurMol
Chemical ID
5441 \"Mol\"/ \"Mol\"/
5517 \"Mol\"/ \"Mol\"/
5530 \"Mol\"/ \"Mol\"/
5537 \"Mol\"/ \"Mol\"/
5606 \"Mol\"/ \"Mol\"/
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 141, "text": [ " ROMol MurMol\n", "Chemical ID \n", "5441 \"Mol\"/ \"Mol\"/\n", "5517 \"Mol\"/ \"Mol\"/\n", "5530 \"Mol\"/ \"Mol\"/\n", "5537 \"Mol\"/ \"Mol\"/\n", "5606 \"Mol\"/ \"Mol\"/" ] } ], "prompt_number": 141 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now we'll extract the MCS core:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mols = list(dff.MurMol)\n", "smarts,smi,img,mcsM = MCS_Report(mols,threshold=0.8,ringMatchesRingOnly=True)\n", "img" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Mean nAts: 24.6914600551, mcs nAts: 16\n", "MCS SMARTS: [*]=!@[*]:1:[*]:[*](:[*]:[*]:2:[*]:[*]:[*]:[*]:1:2)-!@[*]:1:[*]:[*]:[*]:[*]:[*]:1\n", "MCS SMILES: O=C1:N:C(C2:C:C:C:C:C:2):N:N2:C:[*]:C:C:1:2\n" ] }, { "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 142, "text": [ "" ] } ], "prompt_number": 142 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Compare the extracted MCS core with the actual claimed Markush structure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given the MCS core, we'll do an R-Group decomposition first to define the R-Groups and their attachement points." ] }, { "cell_type": "code", "collapsed": false, "input": [ "core = Chem.MolFromSmarts(smarts) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 143 }, { "cell_type": "code", "collapsed": false, "input": [ "pind = []\n", "rgroups = []\n", "for i, m in zip(dff.index, dff.ROMol):\n", " rgrps = Chem.ReplaceCore(m,core,labelByIndex=True)\n", " if rgrps:\n", " s = Chem.MolToSmiles(rgrps, True)\n", " for e in s.split(\".\"):\n", " pind.append(e[1:4].replace('*','').replace(']',''))\n", " rgroups.append(e)\n", " #print \"R-Groups: \", i, s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 144 }, { "cell_type": "code", "collapsed": false, "input": [ "pind = sorted(list(set(pind)),key=int)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 145 }, { "cell_type": "code", "collapsed": false, "input": [ "pind = map(int,pind)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 146 }, { "cell_type": "code", "collapsed": false, "input": [ "Draw.MolToImage(core,includeAtomNumbers=True,highlightAtoms=pind)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 147, "text": [ "" ] } ], "prompt_number": 147 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is the MCS similar to the claimed Markush structure?\n", "Let's fetch the original document in PDF and check in page 4:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "HTML('')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "pyout", "prompt_number": 148, "text": [ "" ] } ], "prompt_number": 148 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty close!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Identify key compounds - in this case, vardenafil itself" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is based on the publication by [Hattori et al.](http://pubs.acs.org/doi/abs/10.1021/ci7002686) and [Tyrchan et al.](http://pubs.acs.org/doi/abs/10.1021/ci3001293). First we'll try the Hattori et al. version of finding the compounds with the highest number of nearest neighbours (NNs) for a given similarity threshold. We will need to calculate the similarity matrix." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sim_mat = []\n", "CUTOFF = 0.8\n", "for i,fp in enumerate(fps):\n", " tt = DataStructs.BulkTanimotoSimilarity(fps[i],fps)\n", " for i,t in enumerate(tt):\n", " if tt[i] < CUTOFF:\n", " tt[i] = None\n", " sim_mat.append(tt)\n", "sim_mat=numpy.array(sim_mat) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 149 }, { "cell_type": "code", "collapsed": false, "input": [ "sim_df = pd.DataFrame(sim_mat, columns = dff['Chemical ID'], index=dff['Chemical ID'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 150 }, { "cell_type": "code", "collapsed": false, "input": [ "sim_df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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Chemical ID
5441 1.00 0.86 None0.87 0.80 None None None None None None None None 0.87 None None None None None None 0.83 None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None None None None None 0.84 None None None None None None None None None 0.81 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None 0.83 None None None None None None None 0.83 None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None
5517 0.86 1.00 None0.84 None None None None None None None None None None None None None None None None 0.92 None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None None None None None None None None None None None None None 0.90 None None 0.85 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.80 None None None None None None None None None None 0.81 None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None
5530 None None 1.000.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None 0.94 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.87 None None None None None
5537 0.87 0.84 0.841.00 0.81 None None None None None None 0.81 None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None None 0.89 None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None 0.85 None None None None None None None None None 0.82 0.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.80 None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.94 None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.86 None None None None None None None None None None 0.84 None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None
5606 0.80 None None0.81 1.00 None None None None None None None None None None None None None 0.90 None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None 0.80 None None 0.91 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.87 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.82 None None None None None None None None None None None 0.80 None None None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None 0.87 None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None
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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 151, "text": [ "Chemical ID 5441 5517 5530 5537 5606 5613 5673 12070 13527 13579 13598 13607 13631 13694 13812 13825 13848 14279 972506 972565 972730 973032 973183 973241 973332 973348 973349 973382 973386 973387 973603 973661 973671 973678 973718 973727 973746 973867 973955 974006 974073 974080 974182 974189 974215 974259 974265 974295 974365 974554 974562 974563 974600 974749 974752 974824 974886 975008 975154 975172 975193 975207 975233 975270 975295 975296 975385 975388 975390 975490 975627 1456170 1456512 1457300 3173334 3182150 4338924 4580977 5931932 5949554 6400765 6793077 7126552 7130126 7419685 7601429 8251760 8255604 8256987 8259092 12822592 12822595 12822605 12822651 12822661 12822664 12822668 12822851 12822865 12822870 12822871 12822873 12822881 12822901 12822909 12822916 12823010 12823018 12823023 12823025 12823027 12823028 12823029 12823050 12823052 12823062 12823071 12823075 12823076 12823078 12823080 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12823790 12823791 12823792 12823793 12823795 12823796 12823797 12823799 12823800 12823801 12823802 12823805 12823808 12823810 12823815 12823816 12823818 12823819 12823821 12823823 12823967 12823974 12823977 12823978 12823982 12823983 12823984 12823985 12823986 12823987 12823988 12823992 12823998 12823999 12824000 12824002 12824003 12824004 12824005 12824006 12824007 12824008 12824009 12824010 12824011 12824012 12824013 12824016 12824017 12824018 12824022 12824024 12824026 12824028 12824029 12824030 12824031 12824034 12824035 12824036 12824037 12824038 12824040 12824041 12824042 12824044 12824047 12824049 12824053 12824055 12824057 12824059 12824060 12824062 12824063 12824064 12824065 12824066 12824067 12824068 12824069 12824070 12824071 12824072 12824073 12824074 12824080 12824081 12824085 12824086 12824087 12824089 12922516 12922517 12922519 12922520 12922521 12922522 12922527 12922529 12922530 12922536 12922538 12922546 12922547 12922549 12922550 12922555 12922558 12922560 12922561 12922563 12922566 12922567 12922569 12922570 12922574 12922577 12922578 12922579 13401084 13401207 13401208 13435578 13533556 14199034 14278252 14278326 14301695 14752321 14752362\n", "Chemical ID \n", "5441 1.00 0.86 None 0.87 0.80 None None None None None None None None 0.87 None None None None None None 0.83 None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None None None None None 0.84 None None None None None None None None None 0.81 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None 0.83 None None None None None None None 0.83 None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None\n", "5517 0.86 1.00 None 0.84 None None None None None None None None None None None None None None None None 0.92 None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None None None None None None None None None None None None None 0.90 None None 0.85 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.80 None None None None None None None None None None 0.81 None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None\n", "5530 None None 1.00 0.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None 0.94 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.87 None None None None None\n", "5537 0.87 0.84 0.84 1.00 0.81 None None None None None None 0.81 None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None None 0.89 None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None 0.85 None None None None None None None None None 0.82 0.84 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.80 None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.94 None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.86 None None None None None None None None None None 0.84 None None None None None None None None None 0.82 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None\n", "5606 0.80 None None 0.81 1.00 None None None None None None None None None None None None None 0.90 None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.85 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None 0.80 None None 0.91 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.87 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0.82 None None None None None None None None None None None 0.80 None None None None None None None None None None None None None None None None None None None None None None None None None 0.84 None None None None None None None None None None None None None None None None None 0.87 None None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None None None None None None None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None 0.83 None None None None None None None None None None 0.81 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None" ] } ], "prompt_number": 151 }, { "cell_type": "code", "collapsed": false, "input": [ "d = sim_df.count().to_dict()\n", "nn_df = pd.DataFrame(d.items(),columns=['Chemical ID', '# NNs'])\n", "nn_df.set_index('Chemical ID',inplace=True)\n", "nn_df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
# NNs
Chemical ID
975193 5
974886 3
1456170 1
12822592 2
12822595 1
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 152, "text": [ " # NNs\n", "Chemical ID \n", "975193 5\n", "974886 3\n", "1456170 1\n", "12822592 2\n", "12822595 1" ] } ], "prompt_number": 152 }, { "cell_type": "code", "collapsed": false, "input": [ "pd.merge(dff[['Chemical ID','ROMol']],nn_df,left_index=True, right_index=True, sort=False).sort('# NNs',ascending=False).head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Chemical IDROMol# NNs
Chemical ID
12823654 12823654 \"Mol\"/ 23
5441 5441 \"Mol\"/ 22
12824064 12824064 \"Mol\"/ 21
5537 5537 \"Mol\"/ 21
12822873 12822873 \"Mol\"/ 18
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 153, "text": [ " Chemical ID ROMol # NNs\n", "Chemical ID \n", "12823654 12823654 \"Mol\"/ 23\n", "5441 5441 \"Mol\"/ 22\n", "12824064 12824064 \"Mol\"/ 21\n", "5537 5537 \"Mol\"/ 21\n", "12822873 12822873 \"Mol\"/ 18" ] } ], "prompt_number": 153 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, the method seems to be working in this case. Vardenafil has the second largest number of NNs with a cutoff of 0.8. Next, we'll try one of the Tyrchan et al. methods (Frequency of Groups, FOG) and count the frequency of the derived R-Groups:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import Counter\n", "rgroups = [r.replace('[15*]','[11*]').replace('[14*]','[12*]') for r in rgroups]\n", "c = Counter(rgroups)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 154 }, { "cell_type": "code", "collapsed": false, "input": [ "rgroup_counts = pd.DataFrame(c.most_common(20),columns=['RGroups','Frequency'])\n", "rgroup_counts.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RGroupsFrequency
0 [6*]CCC 349
1 [11*]OCC 306
2 [8*]C 294
3 [8*]CC 63
4 [11*]OCCC 28
5 [11*]O 16
6 [6*]C 10
7 [12*]S(=O)(=O)N1CCN(C)CC1 8
8 [13*]OC 8
9 [12*]S(=O)(=O)N(CC)CCO 7
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 155, "text": [ " RGroups Frequency\n", "0 [6*]CCC 349\n", "1 [11*]OCC 306\n", "2 [8*]C 294\n", "3 [8*]CC 63\n", "4 [11*]OCCC 28\n", "5 [11*]O 16\n", "6 [6*]C 10\n", "7 [12*]S(=O)(=O)N1CCN(C)CC1 8\n", "8 [13*]OC 8\n", "9 [12*]S(=O)(=O)N(CC)CCO 7" ] } ], "prompt_number": 155 }, { "cell_type": "code", "collapsed": false, "input": [ "PandasTools.AddMoleculeColumnToFrame(rgroup_counts, smilesCol = 'RGroups')\n", "rgroup_counts.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RGroupsFrequencyROMol
0 [6*]CCC 349 \"Mol\"/
1 [11*]OCC 306 \"Mol\"/
2 [8*]C 294 \"Mol\"/
3 [8*]CC 63 \"Mol\"/
4 [11*]OCCC 28 \"Mol\"/
5 [11*]O 16 \"Mol\"/
6 [6*]C 10 \"Mol\"/
7 [12*]S(=O)(=O)N1CCN(C)CC1 8 \"Mol\"/
8 [13*]OC 8 \"Mol\"/
9 [12*]S(=O)(=O)N(CC)CCO 7 \"Mol\"/
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 156, "text": [ " RGroups Frequency ROMol\n", "0 [6*]CCC 349 \"Mol\"/\n", "1 [11*]OCC 306 \"Mol\"/\n", "2 [8*]C 294 \"Mol\"/\n", "3 [8*]CC 63 \"Mol\"/\n", "4 [11*]OCCC 28 \"Mol\"/\n", "5 [11*]O 16 \"Mol\"/\n", "6 [6*]C 10 \"Mol\"/\n", "7 [12*]S(=O)(=O)N1CCN(C)CC1 8 \"Mol\"/\n", "8 [13*]OC 8 \"Mol\"/\n", "9 [12*]S(=O)(=O)N(CC)CCO 7 \"Mol\"/" ] } ], "prompt_number": 156 }, { "cell_type": "code", "collapsed": false, "input": [ "rgroup_counts[[r.startswith('[12*]') for r in rgroup_counts.RGroups]].head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RGroupsFrequencyROMol
7 [12*]S(=O)(=O)N1CCN(C)CC1 8 \"Mol\"/
9 [12*]S(=O)(=O)N(CC)CCO 7 \"Mol\"/
10 [12*]S(=O)(=O)Cl 6 \"Mol\"/
11 [12*]S(=O)(=O)NCCCN1CCOCC1 4 \"Mol\"/
12 [12*]S(=O)(=O)N1CCN(CCO)CC1 4 \"Mol\"/
13 [12*]S(=O)(=O)N1CCC(O)CC1 4 \"Mol\"/
14 [12*]S(=O)(=O)N(CC)CC 4 \"Mol\"/
15 [12*]S(=O)(=O)N1CCN(CC)CC1 4 \"Mol\"/
16 [12*]S(=O)(=O)N1CCC(CO)CC1 3 \"Mol\"/
17 [12*]S(=O)(=O)NCCOC 3 \"Mol\"/
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 157, "text": [ " RGroups Frequency ROMol\n", "7 [12*]S(=O)(=O)N1CCN(C)CC1 8 \"Mol\"/\n", "9 [12*]S(=O)(=O)N(CC)CCO 7 \"Mol\"/\n", "10 [12*]S(=O)(=O)Cl 6 \"Mol\"/\n", "11 [12*]S(=O)(=O)NCCCN1CCOCC1 4 \"Mol\"/\n", "12 [12*]S(=O)(=O)N1CCN(CCO)CC1 4 \"Mol\"/\n", "13 [12*]S(=O)(=O)N1CCC(O)CC1 4 \"Mol\"/\n", "14 [12*]S(=O)(=O)N(CC)CC 4 \"Mol\"/\n", "15 [12*]S(=O)(=O)N1CCN(CC)CC1 4 \"Mol\"/\n", "16 [12*]S(=O)(=O)N1CCC(CO)CC1 3 \"Mol\"/\n", "17 [12*]S(=O)(=O)NCCOC 3 \"Mol\"/" ] } ], "prompt_number": 157 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the most frequent R-groups in positions 6, 8 and 11 correspond to those in Vardenafil (see below), the exact piperazine R-group in position 12 is the 7th most frequent one in that position." ] }, { "cell_type": "code", "collapsed": false, "input": [ "dff.ix[dff.InChIKey == 'SECKRCOLJRRGGV-UHFFFAOYSA-N',['SCHEMBL ID','ROMol']]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SCHEMBL IDROMol
Chemical ID
5441 SCHEMBL5441 \"Mol\"/
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 158, "text": [ " SCHEMBL ID ROMol\n", "Chemical ID \n", "5441 SCHEMBL5441 \"Mol\"/" ] } ], "prompt_number": 158 } ], "metadata": {} } ] }