Open_Bioinformatics_Research_Project.ipynb

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      "name": "Open Bioinformatics Research Project v2.ipynb",
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    {
      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rflameiro/projects/blob/main/Open_Bioinformatics_Research_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMPNQ3uhso--"
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      "source": [
        "# Activity cliffs on the beta-lactamase database\n",
        "\n",
        "In this Notebook, we will explore the presence of activity cliffs on the beta-lactamase database\n",
        "\n",
        "Activity cliffs occur when compounds with similar molecular structures have great activity differences.\n",
        "\n",
        "This type of relationship between compounds break the QSAR assumption that \"small changes in molecular structure lead to small changes in the measured property\". It is important to identify activity cliffs as they may present useful information, such as the sensitivity of a molecular target to stereochemistry changes in the inhibitor, or the size of an enzyme pocket (for instance, if activity drops by changing a methyl group to an ethyl, it is likely that the enzyme pocket cannot physically accomodate the ethyl group).\n",
        "\n",
        "Let's start by downloading useful packages:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqZdLiocs0Pd"
      },
      "source": [
        "!pip3 install rdkit-pypi\n",
        "!pip install networkx\n",
        "!pip install bokeh"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGJM6UncB6l2"
      },
      "source": [
        "## Import dataset and generate molecular fingerprints"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqMRe11Eso_F"
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJ7LxhmTso_H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": ""
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtjfJjrmso_H"
      },
      "source": [
        "# Store all CSV files in a list\n",
        "import glob\n",
        "\n",
        "all_csv = glob.glob('/content/gdrive/My Drive/Colab Notebooks/beta_lactamase_CHEMBL29/*.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VF7rsu4xso_I"
      },
      "source": [
        "# Combine contents of all CSV files into a single DataFrame\n",
        "df = pd.concat((pd.read_csv(csv)) for csv in all_csv)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bh81YCrAso_I"
      },
      "source": [
        "# Non-missing data with pChEMBL value\n",
        "df2 = df[df.pchembl_value.notnull()]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vz7bFspbso_J"
      },
      "source": [
        "# Remove duplicates, keeping the most active instance\n",
        "df2 = df2.sort_values(\"pchembl_value\", ascending=False)\n",
        "df2 = df2.drop_duplicates(subset=\"canonical_smiles\").reset_index(drop=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SV1kNmh3so_M"
      },
      "source": [
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit import DataStructs\n",
        "from rdkit.Chem import Draw"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMujf8_hso_N"
      },
      "source": [
        "# Calculate RDKit Mol from SMILES\n",
        "df2[\"rdkit_mol\"] = df2[\"canonical_smiles\"].astype(\"str\").apply(Chem.MolFromSmiles)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_XHya9dso_N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36e6dd09-ecda-4bb2-8331-c6f239904d70"
      },
      "source": [
        "# Remove failed conversions\n",
        "df2 = df2[df2.rdkit_mol.notnull()].reset_index(drop=True)\n",
        "print(f\"Number of valid structures in the dataset: {len(df2)}\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of valid structures in the dataset: 63022\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOgkLYAEso_O"
      },
      "source": [
        "# Calculate Morgan fingerprints\n",
        "df2[\"Morgan\"] = df2[\"rdkit_mol\"].apply(lambda mol: AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=1024))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIWhSzghso_O"
      },
      "source": [
        "## Finding activity cliffs\n",
        "\n",
        "Now, for the identification of activity cliffs, let's loop over all pairs of activities and, only if the difference in activities is greater than 2.0, we will check the Tanimoto similarity.\n",
        "\n",
        "For this analysis, we will be assuming that molecules representing an activity cliff need to have at least a 100x difference in activity (pChEMBL value difference of 2.0 (remember that pChEMBL represents log units) and a Tanimoto similarity greater than 0.8.\n",
        "\n",
        "This is not the only definition of activity cliff, as you could also use, for instance, molecular pairs derived from Bemis-Murcko scaffolds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uavVeqF5so_P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e48f2702-ef97-4934-a76c-6c4be14b401b"
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      "source": [
        "%%time\n",
        "# Calculate activity cliffs\n",
        "\n",
        "# List of indexes of cliff pairs\n",
        "cliffs = []\n",
        "\n",
        "# Convert Pandas column to np.array for faster and more intuitive indexing\n",
        "fps = np.array(df2[\"Morgan\"])\n",
        "\n",
        "# np.array with activity values\n",
        "pchembl = np.array(df2[\"pchembl_value\"])  \n",
        "\n",
        "# np.array with activity values + 2.0 for faster comparison\n",
        "# This way, we don't need to add or subtract 2.0 for each comparison\n",
        "pchembl2 = pchembl + 2  # note that this syntax is possible with np.arrays, but not lists\n",
        "\n",
        "# Loop over all activity pairs\n",
        "for i in range(len(fps)):\n",
        "    for j in range(i+1, len(fps)):\n",
        "        # Calculate Tanimoto index only if activity difference is >= 2.0\n",
        "        if pchembl[i] >= pchembl2[j]:\n",
        "            sim = DataStructs.FingerprintSimilarity(fps[i], fps[j], metric=DataStructs.TanimotoSimilarity)\n",
        "            if (sim > 0.8):\n",
        "                cliffs.append((i,j))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 24min 46s, sys: 2.52 s, total: 24min 48s\n",
            "Wall time: 24min 50s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4LuDS_IHso_P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0236f87d-a1ef-4edf-f6c7-3e585c7199bc"
      },
      "source": [
        "# Number of cliffs found\n",
        "print(f\"{len(cliffs)} activity cliffs were found\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "212 activity cliffs were found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWpP-4N87It0"
      },
      "source": [
        "Our code has found **212 activity cliffs**, pairs of molecular structures with an activity difference greater than or equal to 2.0. That is too much to visualize individually, so let's create an image to represent this chemical space.\n",
        "\n",
        "Since we would like to visualize relationships between compounds (that is, if two compounds form an activity cliff), a good choice of representation would be a set of graphs. We can build graphs using the Python module `networkx`. \n",
        "\n",
        "Each node in our set of graphs will represent a molecular structure, and it will be colored according to its activity value. We will also represent an activity cliff relationship between two compounds by connecting the nodes with a line (edge).\n",
        "\n",
        "Finally, we will plot the resulting graphs using `bokeh`, a Python module that allows for interactive plots.\n",
        "\n",
        "Inspiration for this representation comes from *J. Med. Chem.* 2010, 53, 23, 8209–8223. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMTuHGUm66Zc"
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      "source": [
        "# Create list of unique indexes\n",
        "cliff_indexes = []\n",
        "  \n",
        "for pair in cliffs:\n",
        "    for i in pair:\n",
        "        cliff_indexes.append(i)\n",
        "\n",
        "cliff_indexes = list(set(cliff_indexes))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "GjVz_rOc-Odz",
        "outputId": "ae2145a7-360d-4f92-fd35-7c72b4c9e80b"
      },
      "source": [
        "# Separate compounds involved in activity cliffs in another pd.DataFrame\n",
        "cliff_df = df2.loc[cliff_indexes][[\"rdkit_mol\", \"pchembl_value\"]]\n",
        "cliff_df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>55299</th>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f24a8364ad0&gt;</td>\n",
              "      <td>4.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2063</th>\n",
              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f24b1cbaf30&gt;</td>\n",
              "      <td>6.45</td>\n",
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              "      <td>6.05</td>\n",
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              "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x7f24a830e120&gt;</td>\n",
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              "                                              rdkit_mol  pchembl_value\n",
              "55299  <rdkit.Chem.rdchem.Mol object at 0x7f24a8364ad0>           4.05\n",
              "2063   <rdkit.Chem.rdchem.Mol object at 0x7f24b1cbaf30>           6.45\n",
              "12306  <rdkit.Chem.rdchem.Mol object at 0x7f24a8688d00>           6.05\n",
              "57368  <rdkit.Chem.rdchem.Mol object at 0x7f24a830e120>           4.00\n",
              "10282  <rdkit.Chem.rdchem.Mol object at 0x7f24a86e0580>           6.15"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wHVzKZtBcmN"
      },
      "source": [
        "## Use networkx to create a graph of our activity cliff dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqBG0C4KJh0n"
      },
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyFGF4tdt8AT"
      },
      "source": [
        "# Create graph object\n",
        "G = nx.Graph()\n",
        "\n",
        "# Add nodes and define edges based on activity cliff pairs\n",
        "G.add_nodes_from(cliff_df.index.to_list())\n",
        "G.add_edges_from(cliffs)\n",
        "\n",
        "# Define node values for activity and color\n",
        "pchembl_dict = {i:cliff_df.loc[i][\"pchembl_value\"] for i in cliff_df.index}\n",
        "nx.set_node_attributes(G, name='activity', values=pchembl_dict)\n",
        "# Color values will be derived from activity and mapped to a colormap\n",
        "pchembl = np.array(cliff_df[\"pchembl_value\"])\n",
        "colors = MinMaxScaler().fit_transform(pchembl.reshape(-1, 1))\n",
        "cliff_df[\"colors\"] = colors\n",
        "colors_dict = {i:cliff_df.loc[i][\"colors\"] for i in cliff_df.index}\n",
        "nx.set_node_attributes(G, name='node_color', values=colors_dict)\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1B5ck4RtBkdK"
      },
      "source": [
        "## Use bokeh to create an interactive representation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3ay901XTFB1"
      },
      "source": [
        "from bokeh.io import output_notebook, show, save\n",
        "from bokeh.models import Range1d, Circle, MultiLine\n",
        "from bokeh.plotting import figure, from_networkx\n",
        "from bokeh.palettes import Viridis256\n",
        "from bokeh.transform import linear_cmap"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vJWC-GJZw5h"
      },
      "source": [
        "output_notebook()  # Call this to output the plot on Google Colab"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "5IOJfTxyTFpu",
        "outputId": "133eaf90-61dd-422b-eabe-7edcdb073fe5"
      },
      "source": [
        "# Make an Interactive Network Visualization with Bokeh\n",
        "# https://melaniewalsh.github.io/Intro-Cultural-Analytics/06-Network-Analysis/02-Making-Network-Viz-with-Bokeh.html\n",
        "\n",
        "# Title\n",
        "title = \"Activity cliffs on the beta-lactamase dataset\"\n",
        "\n",
        "# Establish which categories will appear when hovering over each node\n",
        "HOVER_TOOLTIPS = [(\"Index\", \"@index\"), \n",
        "                  (\"Activity\", \"@activity\")]\n",
        "\n",
        "# Create a plot — set dimensions, toolbar, and title\n",
        "plot = figure(tooltips=HOVER_TOOLTIPS,\n",
        "              tools=\"pan,wheel_zoom,save,reset\", active_scroll='wheel_zoom',\n",
        "              x_range=Range1d(-10.1, 10.1), y_range=Range1d(-10.1, 10.1), title=title)\n",
        "\n",
        "# Create a network graph object with spring layout\n",
        "# https://networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.drawing.layout.spring_layout.html\n",
        "network_graph = from_networkx(G, nx.spring_layout, scale=10, center=(0, 0))\n",
        "\n",
        "# Set node size and color\n",
        "network_graph.node_renderer.glyph = Circle(size=10, fill_color=linear_cmap(\"node_color\", \"Viridis256\", 0, 1))\n",
        "\n",
        "# Set edge opacity and width\n",
        "network_graph.edge_renderer.glyph = MultiLine(line_alpha=0.9, line_width=3)\n",
        "\n",
        "# Add network graph to the plot\n",
        "plot.renderers.append(network_graph)\n",
        "\n",
        "show(plot)"
      ],
      "execution_count": 32,
      "outputs": [
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              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      var bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      var script_attrs = bk_div.children[0].attributes;\n",
              "      for (var i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      var toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    var events = require('base/js/events');\n",
              "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  \n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    var el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error(url) {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < css_urls.length; i++) {\n",
              "      const url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\n",
              "\n",
              "    for (let i = 0; i < js_urls.length; i++) {\n",
              "      const url = js_urls[i];\n",
              "      const element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      if (url in hashes) {\n",
              "        element.crossOrigin = \"anonymous\";\n",
              "        element.integrity = \"sha384-\" + hashes[url];\n",
              "      }\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"48e77d22-8db0-4449-83dc-d10e7fd4017b\" data-root-id=\"1892\"></div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "    \n",
              "  var docs_json = 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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvMiuxgi_vKw"
      },
      "source": [
        "A nice addition to this Notebook would be the option to visualize the molecular structure when hovering over each point. I tried, but could not make it work, so the index of the compound and its pChEMBL value show up instead. \n",
        "\n",
        "If you want to visualize an activity cliff pair or cluster, take note of the indexes on the variable `mol_indexes` below and run the cell to output a list of structures with corresponding pChEMBL values.\n",
        "\n",
        "Let's take a look at the cliff formed between structures 75 and 29589:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bBmMFzAso_Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "03c19754-2d89-41e3-e468-5f2166385fb2"
      },
      "source": [
        "mol_indexes = [75, 29586]\n",
        "\n",
        "ms = []\n",
        "leg = []\n",
        "\n",
        "for ind in mol_indexes:\n",
        "    ms.append(cliff_df.loc[ind][\"rdkit_mol\"])\n",
        "    leg.append(f'Index: {str(ind)}, Activity: {str(cliff_df.loc[ind][\"pchembl_value\"])}')\n",
        "\n",
        "img = Draw.MolsToGridImage(ms, molsPerRow=2, subImgSize=(500, 300), legends=leg)\n",
        "img"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x300 at 0x7F2492BABA50>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhPqHrWaso_R"
      },
      "source": [
        "See that a simple stereochemistry change at the bond to the oxygen ester makes a huge impact in activity (one stereoisomer is 1000x more active than the other)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jev0LqLmQBIX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}