{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Dataset Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/cephyr/users/ribes/Alvis/PROTAC-Degradation-Predictor/notebooks\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Change directory to current file path (define __file__ first)\n",
    "os.chdir('/cephyr/users/ribes/Alvis/PROTAC-Degradation-Predictor/notebooks/')\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#83B8FE', '#FFA54C', '#94ED67', '#FF7FFF']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import colorsys\n",
    "\n",
    "def increase_saturation(hex_color, increase_by=0.3):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "    # Convert RGB to HSV\n",
    "    hsv = colorsys.rgb_to_hsv(rgb[0]/255, rgb[1]/255, rgb[2]/255)\n",
    "    # Increase saturation\n",
    "    new_saturation = min(hsv[1] + increase_by, 1)  # Ensure saturation doesn't exceed 1\n",
    "    # Convert back to RGB and then to hex\n",
    "    new_rgb = colorsys.hsv_to_rgb(hsv[0], new_saturation, hsv[2])\n",
    "    new_hex = '#' + ''.join(f'{int(c*255):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "def darken_color(hex_color, darkening_factor=1.0):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "\n",
    "    # Darken color\n",
    "    new_rgb = [(color * darkening_factor) for color in rgb]\n",
    "\n",
    "    # Convert RGB back to hex\n",
    "    new_hex = '#' + ''.join(f'{int(c):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "palette = [\n",
    "    '#D0E4FE', # blue\n",
    "    '#FFCC99', # orange\n",
    "    '#C4EDAF', # green\n",
    "    '#FFCCFF', # pink\n",
    "]\n",
    "\n",
    "\n",
    "# Adjusted palette\n",
    "palette = adjusted_palette = [increase_saturation(color) for color in palette]\n",
    "print(adjusted_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "palette = [\n",
    "    '#D0E4FE', # blue\n",
    "    '#FFCC99', # orange\n",
    "    '#C4EDAF', # green\n",
    "    '#FFCCFF', # pink\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Heavy Atom Count</th>\n",
       "      <th>Ring Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>...</th>\n",
       "      <th>Name</th>\n",
       "      <th>Assay (DC50/Dmax)</th>\n",
       "      <th>Exact Mass</th>\n",
       "      <th>XLogP3</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "      <th>POI Sequence</th>\n",
       "      <th>E3 Ligase Uniprot</th>\n",
       "      <th>E3 Ligase Sequence</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "      <th>Active - OR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
       "      <td>1486.282</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
       "      <td>1500.309</td>\n",
       "      <td>102</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
       "      <td>1514.336</td>\n",
       "      <td>103</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
       "      <td>1528.363</td>\n",
       "      <td>104</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
       "      <td>1542.390</td>\n",
       "      <td>105</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>2342</td>\n",
       "      <td>O60885</td>\n",
       "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...</td>\n",
       "      <td>VRVWHAZIBGEPEK-DPSJZEHMSA-N</td>\n",
       "      <td>1001.673</td>\n",
       "      <td>69</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD4 long in HEK293 cells after...</td>\n",
       "      <td>1000.374231</td>\n",
       "      <td>6.76</td>\n",
       "      <td>BRD4 long</td>\n",
       "      <td>MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>HEK293</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2137</th>\n",
       "      <td>2887</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...</td>\n",
       "      <td>FOOHAGZPIHCYKX-ZSFXBAAMSA-N</td>\n",
       "      <td>1161.359</td>\n",
       "      <td>82</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>1160.534044</td>\n",
       "      <td>6.81</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>2889</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...</td>\n",
       "      <td>RDCVMTUYWQXPEC-FSHOLZCKSA-N</td>\n",
       "      <td>1073.253</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>1072.481615</td>\n",
       "      <td>7.11</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>2890</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...</td>\n",
       "      <td>SLSLLSIRBMAERC-MGVZSLQJSA-N</td>\n",
       "      <td>1029.200</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>1028.455400</td>\n",
       "      <td>7.26</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2140</th>\n",
       "      <td>2891</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...</td>\n",
       "      <td>ASRIXACKPXMNKY-FCFVTTBASA-N</td>\n",
       "      <td>999.174</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of FAK in A549 cells after 24 h tr...</td>\n",
       "      <td>998.444835</td>\n",
       "      <td>7.31</td>\n",
       "      <td>FAK</td>\n",
       "      <td>MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>A549 Cas9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Compound ID Uniprot                                             Smiles  \\\n",
       "0               1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "1               2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "2               3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "3               4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "4               5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "...           ...     ...                                                ...   \n",
       "2136         2342  O60885  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...   \n",
       "2137         2887  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCCOCCO...   \n",
       "2138         2889  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCCOCC(=O)...   \n",
       "2139         2890  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCOCC(=O)N[C...   \n",
       "2140         2891  Q05397  CNC(=O)c1ccccc1Nc1cc(Nc2ccc(N3CCN(CCC(=O)N[C@H...   \n",
       "\n",
       "     E3 Ligase                                              InChI  \\\n",
       "0          VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
       "1          VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
       "2          VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
       "3          VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
       "4          VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
       "...        ...                                                ...   \n",
       "2136       VHL  InChI=1S/C50H61ClN8O8S2/c1-29-31(3)69-49-42(29...   \n",
       "2137       VHL  InChI=1S/C58H75F3N10O10S/c1-37(39-12-14-40(15-...   \n",
       "2138       VHL  InChI=1S/C54H67F3N10O8S/c1-33(35-12-14-36(15-1...   \n",
       "2139       VHL  InChI=1S/C52H63F3N10O7S/c1-31(33-12-14-34(15-1...   \n",
       "2140       VHL  InChI=1S/C51H61F3N10O6S/c1-30(32-12-14-33(15-1...   \n",
       "\n",
       "                        InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "0     SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
       "1     HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
       "2     ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
       "3     FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
       "4     PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
       "...                           ...               ...               ...   \n",
       "2136  VRVWHAZIBGEPEK-DPSJZEHMSA-N          1001.673                69   \n",
       "2137  FOOHAGZPIHCYKX-ZSFXBAAMSA-N          1161.359                82   \n",
       "2138  RDCVMTUYWQXPEC-FSHOLZCKSA-N          1073.253                76   \n",
       "2139  SLSLLSIRBMAERC-MGVZSLQJSA-N          1029.200                73   \n",
       "2140  ASRIXACKPXMNKY-FCFVTTBASA-N           999.174                71   \n",
       "\n",
       "      Ring Count  Rotatable Bond Count  ...  Name  \\\n",
       "0             10                    24  ...   NaN   \n",
       "1             10                    25  ...   NaN   \n",
       "2             10                    26  ...   NaN   \n",
       "3             10                    27  ...   NaN   \n",
       "4             10                    28  ...   NaN   \n",
       "...          ...                   ...  ...   ...   \n",
       "2136           7                    20  ...   NaN   \n",
       "2137           7                    27  ...   NaN   \n",
       "2138           7                    21  ...   NaN   \n",
       "2139           7                    18  ...   NaN   \n",
       "2140           7                    16  ...   NaN   \n",
       "\n",
       "                                      Assay (DC50/Dmax)   Exact Mass XLogP3  \\\n",
       "0                                                   NaN          NaN    NaN   \n",
       "1                                                   NaN          NaN    NaN   \n",
       "2                                                   NaN          NaN    NaN   \n",
       "3                                                   NaN          NaN    NaN   \n",
       "4                                                   NaN          NaN    NaN   \n",
       "...                                                 ...          ...    ...   \n",
       "2136  Degradation of BRD4 long in HEK293 cells after...  1000.374231   6.76   \n",
       "2137  Degradation of FAK in A549 cells after 24 h tr...  1160.534044   6.81   \n",
       "2138  Degradation of FAK in A549 cells after 24 h tr...  1072.481615   7.11   \n",
       "2139  Degradation of FAK in A549 cells after 24 h tr...  1028.455400   7.26   \n",
       "2140  Degradation of FAK in A549 cells after 24 h tr...   998.444835   7.31   \n",
       "\n",
       "      Target (Parsed)                                       POI Sequence  \\\n",
       "0                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "1                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "2                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "3                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "4                 NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "...               ...                                                ...   \n",
       "2136        BRD4 long  MSAESGPGTRLRNLPVMGDGLETSQMSTTQAQAQPQPANAASTNPP...   \n",
       "2137              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
       "2138              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
       "2139              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
       "2140              FAK  MAAAYLDPNLNHTPNSSTKTHLGTGMERSPGAMERVLKVFHYFESN...   \n",
       "\n",
       "      E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
       "0                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "1                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "3                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "4                P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "...                 ...                                                ...   \n",
       "2136             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2137             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2138             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2139             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2140             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "\n",
       "     Cell Line Identifier Active - OR  \n",
       "0                  MOLT-4         NaN  \n",
       "1                  MOLT-4         NaN  \n",
       "2                  MOLT-4         NaN  \n",
       "3                  MOLT-4         NaN  \n",
       "4                  MOLT-4        True  \n",
       "...                   ...         ...  \n",
       "2136               HEK293        True  \n",
       "2137            A549 Cas9       False  \n",
       "2138            A549 Cas9       False  \n",
       "2139            A549 Cas9        True  \n",
       "2140            A549 Cas9        True  \n",
       "\n",
       "[2141 rows x 35 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_df = pd.read_csv('../data/PROTAC-Degradation-DB.csv')\n",
    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')\n",
    "protac_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of non-nan Dmax/DC50 values: Dmax (%)      812\n",
      "DC50 (nM)    1350\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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OHW3Lli1e/UaNGmWSPM/nM9WuXdvq16+f5WMcPHjQ83zr27evZ9/Ytm2bmXnv22+88YbNmTPHXnrpJXO5XNa/f/9M66NEiRLWqFEj++yzz2z27Nm2adMmmzdvngUGBtqNN95okydPtlmzZlnnzp0znaXp2rWrFSxY0IYOHWrz58+3GTNm2KuvvmojRozw9MluPceOHbPq1atbaGiovfHGGzZ79mx78cUXzd/f31q1auW1Ds4+Y7Jr1y6LioqyEiVK2NixYz3PgdKlS2c6G+D+NPt8n9SdPHnSgoKC7I477rA333zTSpcubQUKFLCyZcva66+/7vVJpi852V9yatu2bVagQAG7++67zcysb9++JskWLFjg1W/16tVWqFAhK1OmjI0aNcrmzp1rn3zyibVr184OHTpku3fvtkGDBnnqdD+Xdu/ebWaZX4Peeustk2S//vqr1+PMnDnTJNmXX35pZqc/lXV/mty/f39LTk62jz76yEqUKGFVqlTxOmPtfi77Ovvny4cffmhhYWG2Y8cOM7NznjE5deqUnTx50g4ePGjffPONxcTEWPv27c85/19//dUk2cMPP5ytenzxdcbE/dpQsWJFe+mllyw5OdmGDh1qQUFB1qVLF6/7P/nkk/bee+/ZrFmzbN68eTZs2DCLjIzM1O+jjz4yl8tlDRs2tAkTJticOXNs5MiR1qNHDzMz+/333+2uu+4ySZ5tu2TJEs9Z0bP3ozvuuMNKlSrl9am62ekzTYGBgbZnzx4zs2y/d5qdfg5Jsk2bNmVr3XXo0MHq1atnGRkZOTrzdODAAQsLC7M77rjD53T32eD169fbbbfdZlFRUbZ169Zs1XS2M8+MP/nkk+bv728bN270TPd1xqRz5842evRoS05OtuTkZHv55ZctJCTE6zXQ7PSn7lWrVrVTp05ZfHy8XX311XbixAkzy/o9LC4uLlef9p/rjIkv7u3x1ltvebXfdtttFhsba/PmzbPU1FRbv369NW3a1EqXLm379u3zOa/8OmOSneOttWvX2g033GAxMTFe+4lbTrbd2cdcOXn+Z3c9XoxjtuzI02BiZhYdHW2VK1f2/O9eWWd+DeXkyZN21VVXmSRbuXKlp33v3r3m5+dnTz31VJbzT09Pt5MnT1qTJk0yvTDs2bPHSpYsaXXq1LGVK1dawYIF7f7778/Wsp3vq1zuF2C3IUOGmCSvU6txcXHm5+fn9cJhdvqgqlChQpkOGt944w2vg8aePXta0aJFz1lndutZv369z34//vijSbIXXnjB03b2QcE333zj80Vh4MCBPndwPz+/bAWAihUrWnBwsBUuXNgGDRpk8+fPtyFDhlhISIjdcMMNngOwH374wSTZxIkTM83DfXCzc+dOMzt9oHj99debJJNkrVq1sqNHj9p3331nISEhmQ5sLkRcXJy5XC5bvXq1V3uzZs2sSJEiduTIETPLXjC55557TJLnNHZwcLDVq1cv27V8/PHHNnDgQJs5c6bNmzfPXn31VQsPD7fo6Gjbvn27p597m539FQAzs+bNm1uFChXO+Tjn+yqXJPu///s/r/ZWrVpZxYoVPf+718eZb3xulSpVslq1ankCmlvr1q2tePHingOXatWq2e23337OWrNbjzusnd3vtddeM0k2e/ZsT9vZB1TPPvtsls+Bs4PJggULzM/PL9ObytlSUlJMkhUpUsRKlixp48ePt7lz51q3bt0y7au+5GR/yakBAwaYJJs1a5aZnf7Kocvlso4dO3r1a9y4sRUtWtQTNHw511e5zn4N2rNnjwUGBmZa9nbt2ll0dLTn+eL+MOfM9xez/z1vR44c6WkbP368+fn52fjx48+73Nu3b7ewsDB7//33PW3nCibuOty3Ll26ZHpOn+3ZZ5/1HMjn1rmCyZAhQ7z69ujRw4KDg7MMuhkZGXby5En7+OOPzc/Pz3OAkpqaakWKFLF//etf5wzJ5/oq19n70ZdffplpX0tPT7fY2Fi78847PW3Zfe80M3vwwQfNz8/P88HkucyYMcMCAgLsl19+MbOcfSWuQ4cO5u/v7/UB6pmCgoI8z4MKFSrYunXrzjvPrJwZTPbs2WNhYWFe6+d8X+Vyb9MBAwZYRESE1/ZzBxMzszlz5pgkzwc9Wb2HXX311Xb11VfneDlyEkwOHTpklStXtlKlSllqaqrXtBMnTtgjjzzita9Vr179nAfj+RFMcnK8ld2vcp1v250dTHLy/M/uerwYx2zZkefDBZuPU04ul0utWrXy/O/v769rrrlGxYsXV61atTzt4eHhioqKynTxzahRo3TdddcpODhY/v7+CggI0Ny5c7V+/XqvfhEREZo8ebJWrlypBg0aqHTp0ho1alSeLNett97q9X/16tUlKVOt1atXV4UKFbzaZsyYoUaNGik2Nlbp6emeW8uWLSVJCxculCTVqVNHBw4cUPv27fXFF19oz549ua7H/TWZs0fCqFOnjipXrnzOEarc9+3QoYNX+3333eezf3p6erZGvDp16pSOHz+uF154Qc8//7waNmyof//73xo8eLB++OGHTPM416lY97To6Gj9+OOP2rRpk3bs2KGvv/5afn5+6tq1q/r27avy5ctr6tSpqlq1qsLDw9W6detsnYLPStWqVVWjRg2vtvvuu0+HDh3SypUrsz0fX/tJTnTs2FEvvPCCWrZsqUaNGunZZ5/VN998k+VFklmtyws93e1yudSmTRuvturVq2faL6TTz1n3aWTp9FeuNmzY4HmenblvtGrVSikpKZ6LRuvUqaNvvvlGzz33nBYsWKBjx47lup558+YpNDRUd911l1c/975yvn0jq+fA2RISEpSenq6XXnopy/lJp/cL6fTXJ6dMmaIHHnhAjRs31nvvvafbb79dQ4cOzdYIatnZX3LC/v8oYaVKlVKzZs0kSWXLllXDhg01depUHTp0SNLpr9ktXLhQ7dq183x15UJFRESoTZs2Gj9+vGf97N+/X1988YUeeOABz1djZsyYoaJFi6pNmzZez5+aNWsqJibGa5S0Bx54QOnp6XrggQfO+/jdunVTjRo19Mgjj2Sr3hYtWmjZsmWaN2+eBg4cqKlTp+rOO+/01H629PR0jR8/XlWrVlW9evWy9Rg55es94vjx416jZ65atUq33nqrIiIi5Ofnp4CAAD3wwAPKyMjwfHV38eLFOnTokHr06JFnX49p2bKlYmJiNHbsWE/bt99+q507d+rBBx/0tGX3vVM6/bW49PR0xcXFnfOxDx48qK5du+rZZ59VtWrVclT3iy++qE8//VTDhg1TfHy8zz6LFy/WkiVL9Mknn6hw4cJq1KjRBY3M5RYREaFnn31WU6dO1Y8//phlv3nz5qlp06YKCwvzbNOXXnpJe/fuzXLk1CZNmqh58+YaMGDAOb+W+vvvv2c5EEReOH78uNq2bastW7ZoypQpmUZZ7N69u6ZOnaphw4Zp4cKFmjx5sgIDA9W4cWOf7zn55UKOt86Um23nlt3nv5T99XgxjtmyI0+DyZEjR7R3717FxsZ6tRcsWDDTqBeBgYEKDw/PNI/AwEDPNQLS6e/ed+/eXXXr1tXUqVO1dOlSLVu2TDfffLPPg5O6deuqatWqOn78uLp37+51/cGFiIiI8Po/KChIkjLVULx48Uz3/euvv/TVV18pICDA61a1alVJ8gSQjh07asyYMdqyZYvuvPNORUVFqW7dukpOTs5xPXv37s2yntjYWM90X/bu3St/f/9Mj3Gho8a459eiRQuvdvebjPvA3t3PV4379u2Ty+XyfB9SOv2EL1OmjOd5577e6N///rfnwPfNN9/U9u3bFRkZqfvvvz/Xy+BrHbjbzrVOz7ZlyxYFBQV59oHSpUtr06ZNua5LOv0iWKFCBS1dutTTdr516WsfzAlf+3ZQUJDXPux29nPRPeLKM888k2nf6NGjh6T/7Rtvv/22nn32WX3++edq1KiRwsPDdfvtt+u3337LcT179+5VTExMphfKqKgo+fv7n3ffONdzIDeKFSsml8ulIkWKZDpIbdmypY4fP65169Zlef+c7i/ZNW/ePG3atEl33323Dh06pAMHDujAgQNq166djh496rm+Z//+/crIyFDJkiVz/Bjn8uCDD2rHjh2e17+JEycqLS3N683/r7/+0oEDBxQYGJjpObRr165zfriTlc8++0yzZs3SkCFDdPDgQc9yS6evZzlw4ECmazCKFSum2rVrq1GjRnrhhRf0wQcf6Msvv8xyqOeZM2dq165d2RrFJ7fO9x6xdetW3XjjjdqxY4feeustLVq0SMuWLfNcK+Lu575WJy+3r7+/vzp27Kjp06d71u24ceNUvHhxr/eH7L535kSfPn0UEBCgnj17eratO/gfPXpUBw4c8PnBUf/+/fXKK69o4MCBmYaNPtN1112nevXqqUOHDpo/f77MzOd1ormRmJio2NhY9e7d2+f0//73v2revLkk6cMPP9QPP/ygZcuWqU+fPpIyH6+c6bXXXtOePXsuaIjgC5GWlqY77rhD33//vb788kvVrVvXa/qsWbM0evRovf/++0pMTNRNN92kdu3aKTk5Wfv27bvgnzG4EBdyvOV2IdsuJ3KyHi/WMdv5XNgVWWf5+uuvlZGRcUHjK5/tk08+UcOGDfXee+95tWeV6vv166dffvlF8fHxeumll9S6dWuVK1cuz+o5H1+pMDIyUtWrV9fAgQN93ufMINelSxd16dJFR44c0Xfffad+/fqpdevW+vXXX7OVhN3cT5SUlJRMbyg7d+4858XgERERSk9P1969e73e3HIzzv6Zqlev7nXQ7OZ+E3BfLHf11VcrJCTE54AFv/zyi6655posh3fcuHGjXn31Vc2ZM0cBAQGaM2eOqlat6hmr/amnnlKNGjV0+PDhXP3mha914G47+0AgKzt27NCKFSuUkJDg+eS3RYsWGjFihJYuXXpBn6CamddFh9dee62k0+utSpUqnvb09HTPxcr55ex9w/0cfP7557O8sL9ixYqSpNDQUPXv31/9+/fXX3/95Tl70qZNG23YsCFHdUREROjHH3+UmXnVtHv3bqWnp5933zjXcyA33OPC+5rH2fuGLxeyv5zL6NGjJZ3+cGjo0KE+p3ft2lXh4eHy8/PL1qAaOdGiRQvFxsZq7NixatGihcaOHau6det6PY/dg3/MmjXL5zwKFy6c48dds2aN0tPTfe6HH374oT788ENNnz79nMOQ1qlTR5KyHDDEPaBAx44dc1xfXvn888915MgRTZs2zeu95eyhwd1nwfJ6+3bp0kWvv/66Jk2apHvuuUdffvmlEhMT5efn5+mTk/fO7FqzZo02b97s88OETp06STodts88kOrfv7+SkpKUlJSUo5DhHsDmQgeOcQsJCVFSUpIeffRRff3115mmT5o0SQEBAZoxY4bXPp+dn0KoWbOm2rdvr6FDh3p9wyU/uIf1nT9/vr744gufQ/u7n5fuAVncihYtqmuuuUZr1qzJj1J9upDjLbcL2XY5kZP1mB/HbL7k2RmTrVu36plnnlFYWJi6du2aV7OVy+XyfNLj9vPPP2vJkiWZ+iYnJ2vw4MHq27evkpOTFRYWpnvuuUcnTpw47+MEBQXlWSI9W+vWrbVmzRpdffXVql27dqabrxfX0NBQtWzZUn369NGJEydyfCq4cePGkuT5/QG3ZcuWaf369ef8TY9GjRpJUqbfA5kwYUKOajjbnXfeKUn65ptvvNpnzpwpSZ4DAX9/f7Vp00bTpk3zCqBbt27V/Pnzzzk6VdeuXdW5c2c1aNBA0ukd6MwfOnN/Mpbbr1KtXbtWP/30k1fbhAkTVLhwYV133XXnvf+xY8f08MMPKz093etTryeffFKhoaHq0aNHppFz3PWeb2SLpUuX6rfffvM6oKpbt66KFy/uGf3I7bPPPtPhw4fPO9JXVmcG80LFihVVvnx5/fTTTz73i9q1a/s8sIyOjlbnzp3Vvn17bdy4MVu/83GmJk2a6PDhw5le8D/++GPP9Ky4v5bh6zlwIe68804dOnRIixcv9mqfOXOmChUq5PmE2JcL2V+ysn//fk2fPl033HCD5s+fn+nWoUMHLVu2TGvWrFFISIgSEhI0ZcqUc36CndPnkp+fnzp27KjPP/9cixYt0vLly72+5iOdfm3du3evMjIyfD5/3ME2Jzp37uxzmSV5Dp7+9a9/nXMe7v7XXHNNpmm7du3SzJkzdfvtt2f7w4yLwR3Kz3x/NTN9+OGHXv0aNGigsLAwjRo16pyvmzndvpUrV1bdunU1duxYTZgwQWlpaZlGD8zNe+f5DB8+PNO2HTZsmKTTI5rNnz/f60Orl19+WUlJSerbt6/69euXo8fas2eP58Asrzz44IOqXLmynnvuuUxfFXS5XPL39/cKd8eOHdN//vOfbM37lVde0YkTJ7L8PZqLwX2mZN68eZo6dWqmT+fd3Nv67APlvXv36tdff83zM7Y5kZPjrayONS9022VXTtZjfhyz+ZTtq1HMe1STJUuW2KJFi2zq1KmWmJhoYWFhFh4ebvPmzfO6T1a/LXDmhVdnOvtiLveoOi+99JLNnTvXRo4caTExMXb11Vd7XUC0c+dOi4qKskaNGnkumF2yZIkFBATYE088cd5lS0hIsKioKPvyyy9t2bJltmHDBq9lPvuCf1+/W5DVhWg7d+60uLg4q1Spko0cOdLmzp1rX3/9tb377rt2yy23eEY5evjhh61Xr142adIkW7hwoU2ePNlq1qxpYWFhnotKc1LPo48+ai6XyxITE+3bb7+1999/36KioqxUqVKeUU/MMl94mpGRYTfddJMFBQXZoEGDzjsqV3Yvfjcza9OmjQUFBdnLL79sycnJNnjwYAsODrbWrVt79Vu/fr0VKlTIbrrpJps5c6ZNmzbNqlWrZrGxsVleYDt69GgrXry41++ArF271vz8/OzFF1+02bNnW/369e2GG27wup+yOarY2aNyffPNN55RudwjgZn978LBXr162ZIlS+yHH36wmTNn2sCBA+3qq682f39/e/PNNzPN/6uvvrKCBQtamTJl7I033rC5c+fa3LlzbcSIEVarVi2rWbOmp2/16tVtyJAh9tVXX1lycrINHDjQihYtarGxsZkuMvvPf/5jkuzRRx+1+fPn2wcffGBFixa1Zs2anXeZjxw54rnQbf78+bZs2TLPKEVZ7dvuCzbPXh++BgOYN2+eBQUFWfPmzW3ChAm2cOFCmz59ug0aNMjuuusuT786derYgAED7PPPP7eFCxfaqFGjLCIiwmtUsezW4x6Vq3DhwjZ06FBLTk62fv36WUBAwHlH5UpJSbGrrroq06hcpUqVyvXF72anB/4oXbq0xcbGekYpdF+c+MYbb3j19XUBak72l4SEhCwvUnYbMWKESVn/rsrPP/9skiwxMdHM/jcqV7ly5eyDDz6wefPm2cSJE619+/Z26NAhMzt94bwku/32223RokW2bNkyz+tQVr+ltHHjRpNkJUuWtJCQkEy/8ZOenm4tW7a08PBw69+/v33zzTc2Z84cGzdunHXq1MmmTZvm6ZuTi999kY+L30eNGmUdOnSw8ePH27x58+yrr76y3r17W0hIiDVo0MDnBfCvvvpqpgu/z+Z+zp7vN1/OdfH72b9x5H7vcF/gun79egsMDLSGDRt6njPNmjWz8uXLZ3rsjz76yCRZ48aNbeLEiTZv3jz74IMPvNaHe/79+vWzpUuX2rJlyywtLc3Msv49oPfff9+zfRs0aJBpenbfO81ydvFvdtaj2f8usr/55pu9RlE6ezSlAwcO2PXXX2/Dhg2zGTNm2Ny5c+29996zSpUqWcGCBTO9X2dnHzTLelu6f/dIktcxx9y5c02S3XXXXTZ79mybOHGixcfHe7bpmRc3Z3UM9sQTT3jmfSEXvy9YsMCmTJliU6ZMseDgYGvYsKHn/zNfk9yjsfbp0yfT+j1zcIPU1FSLi4uzYsWK2RtvvGHz5s2zTz/91GrWrGl+fn6Z9pWZM2falClTPKOz3n333Z7Hdw9UY5bz0frOvM/Zo3Jl53jLvU1HjhxpP/74o2ceOd12ub34Pafr8WIes2UlV8HEfQsMDLSoqChLSEiwQYMG+XzwCw0maWlp9swzz1iJEiUsODjYrrvuOvv888+93sjS09MtISHBoqOjM40+9Prrr5skmz59+jmXbfXq1XbDDTdYwYIFvQ5U8yKYmJ0eGeLxxx+3smXLWkBAgIWHh1t8fLz16dPHDh8+bGan3zgbNWpk0dHRFhgYaLGxsdauXTv7+eefPfPJST0ZGRn22muvWYUKFSwgIMAiIyPt/vvv93oxN/N9UHDgwAF78MEHrWjRolawYEFr1qyZZ0i43A4XbHb6BwafffZZK1WqlPn7+1vp0qXt+eef9/lje8uXL7cmTZpYwYIFrUiRInb77bfb77//7nO+u3fvtvDwcJ+jqnz66adWvnx5K1SokDVr1sz+/PNPz7TU1FSTZPfee+95a3dv388++8yqVq1qgYGBVqZMGa8fJDL734G4++bn52fFihWz+Ph4S0xM9Dl0r9sff/xhPXr0sGuuucaCgoIsJCTEqlSpYk899ZTXC9O9995r11xzjYWGhlpAQIDFxcVZt27dshz5YsKECVa9enULDAy0mJgYe/zxxzONeJKViRMnWqVKlSwgIMBr++dFMDEz++mnn6xdu3YWFRVlAQEBFhMTY40bN7ZRo0Z5+jz33HNWu3ZtK1asmAUFBVm5cuXsySefzBSws1OP2ekg0K1bNytevLj5+/tbXFycz+ehrwOqdevWWbNmzSw4ONjCw8PtoYcesi+++CLT/pfd4YLdtm7davfee68VK1bMAgMDrXr16jZmzJhM/bIasjO7+0t8fLzFxMScs5aaNWtaVFSU58DSl3r16llkZKSnz7p16+zuu++2iIgICwwMtNKlS1vnzp291unw4cOtbNmy5ufn53UwcK4feW3QoIFJsg4dOvicfvLkSXvjjTesRo0aFhwcbIUKFbJKlSpZ165d7bfffvP0y80ByJl8BZMffvjBWrdubbGxsRYYGGgFCxa0GjVq2Msvv+x18HOmChUqWJkyZc45wtXTTz9tLpfL1q9ff86aLiSYmJ3+MMS93kqUKGH//ve/PaMy+jrQS0hIsNDQUCtYsKBVqVLF6wOZtLQ0e/jhh+2qq64yl8vl9VhZBZODBw9aSEiISbIPP/zQ5zJm573TLOfDBZ8pq2DiDhBZ3dyOHz9uDz/8sFWuXNkKFSpk/v7+VrJkSbv//vt9vt5nZx80O/cP6br3i7OPOcaMGWMVK1b0vE4OHjzYRo8ene1g8vfff1uRIkV8vmbnZLjgc627M59b51q/Zx9XpKSkWM+ePe2aa66x4OBgi42NtVtuucXnyHZxcXFZzvfM9eD+EMY98mB2+DoOy+7x1r59++yuu+6yokWLevYTt5xsuwsZLjgn6/FiHbOdi8ssD3+5BbjMzJw5U61bt9ZPP/3kuR4jK2XKlFG1atU0Y8aMfKoOyFupqakKDw/X8OHD9dhjjzldDrJQp04dxcXFacqUKU6XgjzGPnhpadeunTZt2qRly5Y5XQr+vzy9+B243MyfP1/33nvveUMJcCX47rvvVKJEiWwPg4v8d+jQIf30008aP36806XgImAfvHSYmRYsWJDp2hA4izMmQDZxxgQAAODiIZgAAAAAcFye//I7AAAAAOQUwQQAAACA4wgmAAAAABzHqFz/QKdOndLOnTtVuHBhz6//AgAAXArMTKmpqYqNjVWBAnyG/k9CMPkH2rlzp0qVKuV0GQAAAFnatm2bSpYs6XQZyEcEk3+gwoULSzq9wxcpUsThagAAAP7n0KFDKlWqlOd4Bf8cBJN/IPfXt4oUKUIwAQAAlyS+bv7Pwxf3AAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOH+nCwAAAPgn2rp1q/bs2eN0GTkWGRmp0qVLO10GrkAEEwAAgHy2detWVapUWceOHXW6lBwLCSmoDRvWE06Q5wgmAAAA+WzPnj06duyo6j7YT0WKl3G6nGw7lLJZP47prz179hBMkOcIJgAAAA4pUryMwktXdLoM4JLAxe8AAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOP4gUVcNFu3btWePXucLiPHIiMj+TVbAACAfEYwwUWxdetWVapUWceOHXW6lBwLCSmoDRvWE04AAADyEcEEF8WePXt07NhR1X2wn4oUL+N0Odl2KGWzfhzTX3v27CGYAAAA5COCCS6qIsXLKLx0RafLAAAAwCWOi98BAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5g4ZPDgwXK5XEpMTPS0mZmSkpIUGxurkJAQNWzYUGvXrvW6X1pamnr16qXIyEiFhobq1ltv1fbt2/O5egAAACBvEUwcsGzZMn3wwQeqXr26V/uQIUM0dOhQvfPOO1q2bJliYmLUrFkzpaamevokJiZq+vTpmjRpkr7//nsdPnxYrVu3VkZGRn4vBgAAAJBnCCb57PDhw+rQoYM+/PBDFStWzNNuZho+fLj69Omjtm3bqlq1aho/fryOHj2qCRMmSJIOHjyo0aNH680331TTpk1Vq1YtffLJJ/rll180Z84cpxYJAAAAuGAEk3z22GOP6ZZbblHTpk292jdt2qRdu3apefPmnragoCAlJCRo8eLFkqQVK1bo5MmTXn1iY2NVrVo1Tx8AAADgcuTvdAH/JJMmTdLKlSu1bNmyTNN27dolSYqOjvZqj46O1pYtWzx9AgMDvc60uPu47+9LWlqa0tLSPP8fOnQo18sAAAAAXAycMckn27Zt0xNPPKFPPvlEwcHBWfZzuVxe/5tZprazna/P4MGDFRYW5rmVKlUqZ8UDAAAAFxnBJJ+sWLFCu3fvVnx8vPz9/eXv76+FCxfq7bfflr+/v+dMydlnPnbv3u2ZFhMToxMnTmj//v1Z9vHl+eef18GDBz23bdu25fHSAQAAABeGYJJPmjRpol9++UWrV6/23GrXrq0OHTpo9erVKleunGJiYpScnOy5z4kTJ7Rw4UI1aNBAkhQfH6+AgACvPikpKVqzZo2njy9BQUEqUqSI1w0AAAC4lHCNST4pXLiwqlWr5tUWGhqqiIgIT3tiYqIGDRqk8uXLq3z58ho0aJAKFiyo++67T5IUFhamhx56SE8//bQiIiIUHh6uZ555Rtdee22mi+kBAACAywnB5BLSu3dvHTt2TD169ND+/ftVt25dzZ49W4ULF/b0GTZsmPz9/dWuXTsdO3ZMTZo00bhx4+Tn5+dg5QAAAMCFIZg4aMGCBV7/u1wuJSUlKSkpKcv7BAcHa8SIERoxYsTFLQ4AAADIR1xjAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOY5KP33ntP1atXV5EiRVSkSBHVr19f33zzjWe6mSkpKUmxsbEKCQlRw4YNtXbtWq95pKWlqVevXoqMjFRoaKhuvfVWbd++Pb8XBQAAAMhTBJN8VLJkSb366qtavny5li9frsaNG+u2227zhI8hQ4Zo6NCheuedd7Rs2TLFxMSoWbNmSk1N9cwjMTFR06dP16RJk/T999/r8OHDat26tTIyMpxaLAAAAOCCEUzyUZs2bdSqVStVqFBBFSpU0MCBA1WoUCEtXbpUZqbhw4erT58+atu2rapVq6bx48fr6NGjmjBhgiTp4MGDGj16tN588001bdpUtWrV0ieffKJffvlFc+bMcXjpAAAAgNwjmDgkIyNDkyZN0pEjR1S/fn1t2rRJu3btUvPmzT19goKClJCQoMWLF0uSVqxYoZMnT3r1iY2NVbVq1Tx9fElLS9OhQ4e8bgAAAMClhGCSz3755RcVKlRIQUFB6tatm6ZPn64qVapo165dkqTo6Giv/tHR0Z5pu3btUmBgoIoVK5ZlH18GDx6ssLAwz61UqVJ5vFQAAADAhSGY5LOKFStq9erVWrp0qbp3765OnTpp3bp1nukul8urv5llajvb+fo8//zzOnjwoOe2bdu2C1sIAAAAII8RTPJZYGCgrrnmGtWuXVuDBw9WjRo19NZbbykmJkaSMp352L17t+csSkxMjE6cOKH9+/dn2ceXoKAgz0hg7hsAAABwKSGYOMzMlJaWprJlyyomJkbJycmeaSdOnNDChQvVoEEDSVJ8fLwCAgK8+qSkpGjNmjWePgAAAMDlyN/pAv5JXnjhBbVs2VKlSpVSamqqJk2apAULFmjWrFlyuVxKTEzUoEGDVL58eZUvX16DBg1SwYIFdd9990mSwsLC9NBDD+npp59WRESEwsPD9cwzz+jaa69V06ZNHV46AAAAIPcIJvnor7/+UseOHZWSkqKwsDBVr15ds2bNUrNmzSRJvXv31rFjx9SjRw/t379fdevW1ezZs1W4cGHPPIYNGyZ/f3+1a9dOx44dU5MmTTRu3Dj5+fk5tVgAAADABSOY5KPRo0efc7rL5VJSUpKSkpKy7BMcHKwRI0ZoxIgReVwdAAAA4ByuMQEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMsqFcuXLau3dvpvYDBw6oXLlyDlQEAAAAXFkIJtmwefNmZWRkZGpPS0vTjh07HKgIAAAAuLL4O13ApezLL7/0/P3tt98qLCzM839GRobmzp2rMmXKOFAZAAAAcGUhmJzD7bffLklyuVzq1KmT17SAgACVKVNGb775pgOVAQAAAFcWgsk5nDp1SpJUtmxZLVu2TJGRkQ5XBAAAAFyZCCbZsGnTJqdLAAAAAK5oBJNsmjt3rubOnavdu3d7zqS4jRkzxqGqAAAAgCsDwSQb+vfvrwEDBqh27doqXry4XC6X0yUBAAAAVxSCSTaMGjVK48aNU8eOHZ0uBQAAALgi8Tsm2XDixAk1aNDA6TIAAACAKxbBJBsefvhhTZgwwekyAAAAgCsWX+XKhuPHj+uDDz7QnDlzVL16dQUEBHhNHzp0qEOVAQAAAFcGgkk2/Pzzz6pZs6Ykac2aNV7TuBAeAAAAuHAEk2yYP3++0yUAAAAAVzSuMQEAAADgOM6YZEOjRo3O+ZWtefPm5WM1AAAAwJWHYJIN7utL3E6ePKnVq1drzZo16tSpkzNFAQAAAFcQgkk2DBs2zGd7UlKSDh8+nM/VAAAAAFcerjG5APfff7/GjBnjdBkAAADAZY9gcgGWLFmi4OBgp8sAAAAALnt8lSsb2rZt6/W/mSklJUXLly/Xiy++6FBVAAAAwJWDYJINYWFhXv8XKFBAFStW1IABA9S8eXOHqgIAAACuHASTbBg7dqzTJQAAAABXNIJJDqxYsULr16+Xy+VSlSpVVKtWLadLAgAAAK4IBJNs2L17t+69914tWLBARYsWlZnp4MGDatSokSZNmqSrrrrK6RIBAACAyxqjcmVDr169dOjQIa1du1b79u3T/v37tWbNGh06dEiPP/640+UBAAAAlz3OmGTDrFmzNGfOHFWuXNnTVqVKFb377rtc/A4AAADkAc6YZMOpU6cUEBCQqT0gIECnTp1yoCIAAADgykIwyYbGjRvriSee0M6dOz1tO3bs0JNPPqkmTZo4WBkAAABwZSCYZMM777yj1NRUlSlTRldffbWuueYalS1bVqmpqRoxYoTT5QEAAACXPa4xyYZSpUpp5cqVSk5O1oYNG2RmqlKlipo2bep0aQAAAMAVgTMm5zBv3jxVqVJFhw4dkiQ1a9ZMvXr10uOPP67rr79eVatW1aJFixyuEgAAALj8EUzOYfjw4XrkkUdUpEiRTNPCwsLUtWtXDR061IHKAAAAgCsLweQcfvrpJ918881ZTm/evLlWrFiRjxUBAAAAVyaCyTn89ddfPocJdvP399fff/+djxUBAAAAVyaCyTmUKFFCv/zyS5bTf/75ZxUvXjwfKwIAAACuTASTc2jVqpVeeuklHT9+PNO0Y8eOqV+/fmrdurUDlQEAAABXFoYLPoe+fftq2rRpqlChgnr27KmKFSvK5XJp/fr1evfdd5WRkaE+ffo4XSYAAABw2SOYnEN0dLQWL16s7t276/nnn5eZSZJcLpdatGihkSNHKjo62uEqAQAAgMsfX+U6j7i4OM2cOVN79uzRjz/+qKVLl2rPnj2aOXOmypQpk6N5DR48WNdff70KFy6sqKgo3X777dq4caNXHzNTUlKSYmNjFRISooYNG2rt2rVefdLS0tSrVy9FRkYqNDRUt956q7Zv336hiwoAAAA4hmCSTcWKFdP111+vOnXqqFixYrmax8KFC/XYY49p6dKlSk5OVnp6upo3b64jR454+gwZMkRDhw7VO++8o2XLlikmJkbNmjVTamqqp09iYqKmT5+uSZMm6fvvv9fhw4fVunVrZWRkXPByAgAAAE7gq1z5aNasWV7/jx07VlFRUVqxYoVuuukmmZmGDx+uPn36qG3btpKk8ePHKzo6WhMmTFDXrl118OBBjR49Wv/5z3/UtGlTSdInn3yiUqVKac6cOWrRokW+LxcAAABwoThj4qCDBw9KksLDwyVJmzZt0q5du9S8eXNPn6CgICUkJGjx4sWSpBUrVujkyZNefWJjY1WtWjVPn7OlpaXp0KFDXjcAAADgUkIwcYiZ6amnntK//vUvVatWTZK0a9cuScp0QX10dLRn2q5duxQYGJjp62Rn9jnb4MGDFRYW5rmVKlUqrxcHAAAAuCAEE4f07NlTP//8syZOnJhpmsvl8vrfzDK1ne1cfZ5//nkdPHjQc9u2bVvuCwcAAAAuAoKJA3r16qUvv/xS8+fPV8mSJT3tMTExkpTpzMfu3bs9Z1FiYmJ04sQJ7d+/P8s+ZwsKClKRIkW8bgAAAMClhGCSj8xMPXv21LRp0zRv3jyVLVvWa3rZsmUVExOj5ORkT9uJEye0cOFCNWjQQJIUHx+vgIAArz4pKSlas2aNpw8AAABwuWFUrnz02GOPacKECfriiy9UuHBhz5mRsLAwhYSEyOVyKTExUYMGDVL58uVVvnx5DRo0SAULFtR9993n6fvQQw/p6aefVkREhMLDw/XMM8/o2muv9YzSBQAAAFxuCCb56L333pMkNWzY0Kt97Nix6ty5sySpd+/eOnbsmHr06KH9+/erbt26mj17tgoXLuzpP2zYMPn7+6tdu3Y6duyYmjRponHjxsnPzy+/FgUAAADIUwSTfGRm5+3jcrmUlJSkpKSkLPsEBwdrxIgRGjFiRB5WBwAAADiHa0wAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gko++++47tWnTRrGxsXK5XPr888+9ppuZkpKSFBsbq5CQEDVs2FBr16716pOWlqZevXopMjJSoaGhuvXWW7V9+/Z8XAoAAAAg7xFM8tGRI0dUo0YNvfPOOz6nDxkyREOHDtU777yjZcuWKSYmRs2aNVNqaqqnT2JioqZPn65Jkybp+++/1+HDh9W6dWtlZGTk12IAAAAAec7f6QL+SVq2bKmWLVv6nGZmGj58uPr06aO2bdtKksaPH6/o6GhNmDBBXbt21cGDBzV69Gj95z//UdOmTSVJn3zyiUqVKqU5c+aoRYsW+bYsAAAAQF7ijMklYtOmTdq1a5eaN2/uaQsKClJCQoIWL14sSVqxYoVOnjzp1Sc2NlbVqlXz9AEAAAAuR5wxuUTs2rVLkhQdHe3VHh0drS1btnj6BAYGqlixYpn6uO/vS1pamtLS0jz/Hzp0KK/KBgAAAPIEZ0wuMS6Xy+t/M8vUdrbz9Rk8eLDCwsI8t1KlSuVJrQAAAEBeIZhcImJiYiQp05mP3bt3e86ixMTE6MSJE9q/f3+WfXx5/vnndfDgQc9t27ZteVw9AAAAcGEIJpeIsmXLKiYmRsnJyZ62EydOaOHChWrQoIEkKT4+XgEBAV59UlJStGbNGk8fX4KCglSkSBGvGwAAAHAp4RqTfHT48GH9/vvvnv83bdqk1atXKzw8XKVLl1ZiYqIGDRqk8uXLq3z58ho0aJAKFiyo++67T5IUFhamhx56SE8//bQiIiIUHh6uZ555Rtdee61nlC4AAADgckQwyUfLly9Xo0aNPP8/9dRTkqROnTpp3Lhx6t27t44dO6YePXpo//79qlu3rmbPnq3ChQt77jNs2DD5+/urXbt2OnbsmJo0aaJx48bJz88v35cHAAAAyCsEk3zUsGFDmVmW010ul5KSkpSUlJRln+DgYI0YMUIjRoy4CBUCAAAAzuAaEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5gAAAAAcBzBBAAAAIDjCCYAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzABAAAA4DiCCQAAAADHEUwAAAAAOI5gAgAAAMBxBBMAAAAAjiOYAAAAAHAcwQQAAACA4wgmAAAAABxHMAEAAADgOIIJAAAAAMcRTAAAAAA4jmACAAAAwHEEEwAAAACOI5hcpkaOHKmyZcsqODhY8fHxWrRokdMlAQAAALlGMLkMTZ48WYmJierTp49WrVqlG2+8US1bttTWrVudLg0AAADIFYLJZWjo0KF66KGH9PDDD6ty5coaPny4SpUqpffee8/p0gAAAIBcIZhcZk6cOKEVK1aoefPmXu3NmzfX4sWLHaoKAAAAuDD+TheAnNmzZ48yMjIUHR3t1R4dHa1du3b5vE9aWprS0tI8/x88eFCSdOjQoYtW5+HDhyVJ+7ZsVHrasYv2OHnt0K7TX4dbsWKFZxkuFwUKFNCpU6ecLiPHLse6L8eaJerOb9Sdfy7Hmjdu3Cjp8n2fPHz48EU7jnDP18wuyvxx6SKYXKZcLpfX/2aWqc1t8ODB6t+/f6b2UqVKXZTazrTik1cv+mNcDI8++qjTJQAA/gEu1/fJhISEi/4YqampCgsLu+iPg0sHweQyExkZKT8/v0xnR3bv3p3pLIrb888/r6eeesrz/6lTp7Rv3z5FRERkGWYu1KFDh1SqVClt27ZNRYoUuSiPgZxhm1x62CaXFrbHpYdtcunJj21iZkpNTVVsbOxFmT8uXQSTy0xgYKDi4+OVnJysO+64w9OenJys2267zed9goKCFBQU5NVWtGjRi1mmR5EiRXgzucSwTS49bJNLC9vj0sM2ufRc7G3CmZJ/JoLJZeipp55Sx44dVbt2bdWvX18ffPCBtm7dqm7dujldGgAAAJArBJPL0D333KO9e/dqwIABSklJUbVq1TRz5kzFxcU5XRoAAACQKwSTy1SPHj3Uo0cPp8vIUlBQkPr165fpK2RwDtvk0sM2ubSwPS49bJNLD9sEF5PLGIsNAAAAgMP4gUUAAAAAjiOYAAAAAHAcwQQAAACA4wgmyLWRI0eqbNmyCg4OVnx8vBYtWnTO/gsXLlR8fLyCg4NVrlw5jRo1Kp8q/efIyTaZNm2amjVrpquuukpFihRR/fr19e233+Zjtf8MOd1P3H744Qf5+/urZs2aF7fAf5icbo+0tDT16dNHcXFxCgoK0tVXX60xY8bkU7X/DDndJp9++qlq1KihggULqnjx4urSpYv27t2bT9Ve2b777ju1adNGsbGxcrlc+vzzz897H97bkZcIJsiVyZMnKzExUX369NGqVat04403qmXLltq6davP/ps2bVKrVq104403atWqVXrhhRf0+OOPa+rUqflc+ZUrp9vku+++U7NmzTRz5kytWLFCjRo1Ups2bbRq1ap8rvzKldNt4nbw4EE98MADatKkST5V+s+Qm+3Rrl07zZ07V6NHj9bGjRs1ceJEVapUKR+rvrLldJt8//33euCBB/TQQw9p7dq1mjJlipYtW6aHH344nyu/Mh05ckQ1atTQO++8k63+vLcjzxmQC3Xq1LFu3bp5tVWqVMmee+45n/179+5tlSpV8mrr2rWr1atX76LV+E+T023iS5UqVax///55Xdo/Vm63yT333GN9+/a1fv36WY0aNS5ihf8sOd0e33zzjYWFhdnevXvzo7x/pJxuk9dff93KlSvn1fb2229byZIlL1qN/1SSbPr06efsw3s78hpnTJBjJ06c0IoVK9S8eXOv9ubNm2vx4sU+77NkyZJM/Vu0aKHly5fr5MmTF63Wf4rcbJOznTp1SqmpqQoPD78YJf7j5HabjB07Vn/88Yf69et3sUv8R8nN9vjyyy9Vu3ZtDRkyRCVKlFCFChX0zDPP6NixY/lR8hUvN9ukQYMG2r59u2bOnCkz019//aXPPvtMt9xyS36UjLPw3o68xg8sIsf27NmjjIwMRUdHe7VHR0dr165dPu+za9cun/3T09O1Z88eFS9e/KLV+0+Qm21ytjfffFNHjhxRu3btLkaJ/zi52Sa//fabnnvuOS1atEj+/rw856XcbI8///xT33//vYKDgzV9+nTt2bNHPXr00L59+7jOJA/kZps0aNBAn376qe655x4dP35c6enpuvXWWzVixIj8KBln4b0deY0zJsg1l8vl9b+ZZWo7X39f7ci9nG4Tt4kTJyopKUmTJ09WVFTUxSrvHym72yQjI0P33Xef+vfvrwoVKuRXef84OdlHTp06JZfLpU8//VR16tRRq1atNHToUI0bN46zJnkoJ9tk3bp1evzxx/XSSy9pxYoVmjVrljZt2qRu3brlR6nwgfd25CU+kkOORUZGys/PL9MnWrt37870yYlbTEyMz/7+/v6KiIi4aLX+U+Rmm7hNnjxZDz30kKZMmaKmTZtezDL/UXK6TVJTU7V8+XKtWrVKPXv2lHT6wNjM5O/vr9mzZ6tx48b5UvuVKDf7SPHixVWiRAmFhYV52ipXriwz0/bt21W+fPmLWvOVLjfbZPDgwbrhhhv073//W5JUvXp1hYaG6sYbb9Qrr7zCJ/T5jPd25DXOmCDHAgMDFR8fr+TkZK/25ORkNWjQwOd96tevn6n/7NmzVbt2bQUEBFy0Wv8pcrNNpNNnSjp37qwJEybwHe08ltNtUqRIEf3yyy9avXq159atWzdVrFhRq1evVt26dfOr9CtSbvaRG264QTt37tThw4c9bb/++qsKFCigkiVLXtR6/wlys02OHj2qAgW8D138/Pwk/e+TeuQf3tuR5xy66B6XuUmTJllAQICNHj3a1q1bZ4mJiRYaGmqbN282M7PnnnvOOnbs6On/559/WsGCBe3JJ5+0devW2ejRoy0gIMA+++wzpxbhipPTbTJhwgTz9/e3d99911JSUjy3AwcOOLUIV5ycbpOzMSpX3srp9khNTbWSJUvaXXfdZWvXrrWFCxda+fLl7eGHH3ZqEa44Od0mY8eONX9/fxs5cqT98ccf9v3331vt2rWtTp06Ti3CFSU1NdVWrVplq1atMkk2dOhQW7VqlW3ZssXMeG/HxUcwQa69++67FhcXZ4GBgXbdddfZwoULPdM6depkCQkJXv0XLFhgtWrVssDAQCtTpoy99957+VzxlS8n2yQhIcEkZbp16tQp/wu/guV0PzkTwSTv5XR7rF+/3po2bWohISFWsmRJe+qpp+zo0aP5XPWVLafb5O2337YqVapYSEiIFS9e3Dp06GDbt2/P56qvTPPnzz/n+wLv7bjYXGac+wQAAADgLK4xAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAcRzAB8I83btw4FS1aNF8eq2PHjho0aFC+PBb+ua6//npNmzbN6TIAIEcIJgAuO4sXL5afn59uvvnmHN+3TJkyGj58uFfbPffco19//TWPqsvazz//rK+//lq9evXytDVs2FAul0sul0tBQUEqUaKE2rRpc8kfVP7yyy9KSEhQSEiISpQooQEDBsjMznu/r7/+WnXr1lVISIgiIyPVtm3bPK8tKSnJs079/f0VGRmpm266ScOHD1daWlqm/r///ru6dOmikiVLKigoSGXLllX79u21fPlyT58yZcp45um+Pffcc17z2bp1q9q0aaPQ0FBFRkbq8ccf14kTJ85b75IlS9S4cWOFhoaqaNGiatiwoY4dO3bO+4wcOVJly5ZVcHCw4uPjtWjRIq/pL774op577jmdOnXqvI8PAJcKggmAy86YMWPUq1cvff/999q6desFzy8kJERRUVF5UNm5vfPOO7r77rtVuHBhr/ZHHnlEKSkp+v333zV16lRVqVJF9957rx599NGLXlNuHDp0SM2aNVNsbKyWLVumESNG6I033tDQoUPPeb+pU6eqY8eO6tKli3766Sf98MMPuu+++y5KjVWrVlVKSoq2bt2q+fPn6+6779bgwYPVoEEDpaamevotX75c8fHx+vXXX/X+++9r3bp1mj59uipVqqSnn37aa54DBgxQSkqK59a3b1/PtIyMDN1yyy06cuSIvv/+e02aNElTp07NNI+zLVmyRDfffLOaN2+u//73v1q2bJl69uypAgWyfnuePHmyEhMT1adPH61atUo33nijWrZs6bUv3HLLLTp48KC+/fbbnK46AHCOAcBl5PDhw1a4cGHbsGGD3XPPPda/f/9Mfb744guLj4+3oKAgi4iIsDvuuMPMzBISEkyS183MbOzYsRYWFmZmZhs2bDBJtn79eq95vvnmmxYXF2enTp0yM7O1a9day5YtLTQ01KKiouz++++3v//+O8u6MzIyrGjRojZjxgyv9oSEBHviiScy9R8zZoxJsuTkZDMz27Rpk0myyZMn27/+9S8LDg622rVr28aNG+2///2vxcfHW2hoqLVo0cJ2797tmc9///tfa9q0qUVERFiRIkXspptushUrVnimz58/3wICAuy7777ztL3xxhsWERFhO3fu9LksI0eOtLCwMDt+/LinbfDgwRYbG+tZP2c7efKklShRwj766KMs11F29OvXz2rUqGGjRo2ykiVLWkhIiN111122f//+TH3Otn79egsMDLQ+ffqYmdmpU6esatWqFh8fbxkZGZn6nznPuLg4GzZsWJZ1zZw50woUKGA7duzwtE2cONGCgoLs4MGDWd6vbt261rdv36wX2Ic6depYt27dvNoqVapkzz33nFdb586drWPHjjmaNwA4iTMmAC4rkydPVsWKFVWxYkXdf//9Gjt2rNdXiL7++mu1bdtWt9xyi1atWqW5c+eqdu3akqRp06apZMmSXp98n61ixYqKj4/Xp59+6tU+YcIE3XfffXK5XEpJSVFCQoJq1qyp5cuXa9asWfrrr7/Url27LOv++eefdeDAAU8t59OpUycVK1Ys01e6+vXrp759+2rlypXy9/dX+/bt1bt3b7311ltatGiR/vjjD7300kue/qmpqerUqZMWLVqkpUuXqnz58mrVqpXnrEHDhg2VmJiojh076uDBg/rpp5/Up08fffjhhypevLjP2pYsWaKEhAQFBQV52lq0aKGdO3dq8+bNPu+zcuVK7dixQwUKFFCtWrVUvHhxtWzZUmvXrs3W+jjT77//rv/7v//TV199pVmzZmn16tV67LHHznu/SpUqqWXLlp51unr1aq1du1ZPP/20zzMUZ1939NprrykiIkI1a9bUwIEDvb6mtWTJElWrVk2xsbGethYtWigtLU0rVqzwWc/u3bv1448/KioqSg0aNFB0dLQSEhL0/fffZ7kMJ06c0IoVK9S8eXOv9ubNm2vx4sVebXXq1Mn0FS8AuKQ5nYwAICcaNGhgw4cPN7PTn8JHRkZ6ziqYmdWvX986dOiQ5f19ffJ95hkTM7OhQ4dauXLlPP9v3LjRJNnatWvNzOzFF1+05s2be81j27ZtJsk2btzo83GnT59ufn5+mc4oZHXGxOz0p+ktW7Y0s/+dMTnzjMPEiRNNks2dO9fTNnjwYKtYsaLvhTez9PR0K1y4sH311VeetrS0NKtVq5a1a9fOqlatag8//HCW9zcza9asmT3yyCNebTt27DBJtnjxYp/3cddaunRp++yzz2z58uXWvn17i4iIsL17957z8c7Ur18/8/Pzs23btnnavvnmGytQoIClpKR4+vg6Y2Jm9uyzz1pISIiZmU2ePNkk2cqVK8/7uEOHDrUFCxbYTz/9ZB9++KFFRkbaQw895Jn+yCOPWLNmzTLdLzAw0CZMmOBznkuWLDFJFh4ebmPGjLGVK1daYmKiBQYG2q+//urzPu71/MMPP3i1Dxw40CpUqODV9sUXX1iBAgV8ng0CgEsRZ0wAXDY2btyo//73v7r33nslSf7+/rrnnns0ZswYT5/Vq1erSZMmF/Q49957r7Zs2aKlS5dKkj799FPVrFlTVapUkSStWLFC8+fPV6FChTy3SpUqSZL++OMPn/M8duyYgoKC5HK5sl2HmWXqX716dc/f0dHRkqRrr73Wq2337t2e/3fv3q1u3bqpQoUKCgsLU1hYmA4fPux1PUJgYKA++eQTTZ06VceOHcs0OIAvZ9dl//+sVVbL574Iu0+fPrrzzjsVHx+vsWPHyuVyacqUKed9vDOVLl1aJUuW9Pxfv359nTp1Shs3bjzvfc9cp+er+UxPPvmkEhISVL16dT388MMaNWqURo8erb1793r6+JqPr23o5l4nXbt2VZcuXVSrVi0NGzZMFStW9HpO++Jr/Z/dFhISolOnTvm84B8ALkX+ThcAANk1evRopaenq0SJEp42M1NAQID279+vYsWKKSQk5IIfp3jx4mrUqJEmTJigevXqaeLEieratatn+qlTp9SmTRu99tprPu/rS2RkpI4ePaoTJ04oMDDwvDVkZGTot99+0/XXX+/VHhAQ4PnbfSB6dtuZIzF17txZf//9t4YPH664uDgFBQWpfv36mUaLcn8NaN++fdq3b59CQ0OzrC0mJka7du3yanOHIXdYOpt7vbjDnSQFBQWpXLlyFzyAgXs9ZCdgrF+/XmXLlpUkVahQwdNWs2bNHD1mvXr1JJ3+WllERIRiYmL0448/evXZv3+/Tp48maN1IkmVK1fOcp1ERkbKz8/P5/o/+3H27dunggUL5sk+AQD5gTMmAC4L6enp+vjjj/Xmm29q9erVnttPP/2kuLg4zzUh1atX19y5c7OcT2BgoDIyMs77eB06dNDkyZO1ZMkS/fHHH56zNJJ03XXXae3atSpTpoyuueYar1tWB/TuA99169Zla3nHjx+v/fv3684778xW/6wsWrRIjz/+uFq1aqWqVasqKChIe/bs8erzxx9/6Mknn9SHH36oevXq6YEHHjjnMLP169fXd9995xVuZs+erdjYWJUpU8bnfeLj4xUUFOR1VuPkyZPavHmz4uLicrRMW7du1c6dOz3/L1myRAUKFPAEjaxs2LBBs2bN8qxT91mwN9980+fyHjhwIMt5rVq1StL/wkX9+vW1Zs0ar+uWZs+eraCgIMXHx/ucR5kyZRQbG5vpTM+vv/6a5ToJDAxUfHy8kpOTvdqTk5PVoEEDr7Y1a9bouuuuy3IZAOCS4+DXyAAg26ZPn26BgYF24MCBTNNeeOEFq1mzppmdHmWqQIEC9tJLL9m6devs559/ttdee83Tt1mzZnbrrbfa9u3bPaNonX2NiZnZwYMHLTg42GrUqGFNmjTxmrZjxw676qqr7K677rIff/zR/vjjD/v222+tS5culp6enuUyXHfddTZixAivtoSEBHvkkUcsJSXFtm3bZkuXLrXevXtbQECAde/e3dPPfY3JqlWrPG3z5883SV6jR529LDVr1rRmzZrZunXrbOnSpXbjjTdaSEiI5zqb9PR0q1+/vrVt29bMzFJSUiwyMtKGDBmS5XIcOHDAoqOjrX379vbLL7/YtGnTrEiRIvbGG294+vz4449WsWJF2759u6ftiSeesBIlSti3335rGzZssIceesiioqJs3759WT7W2fr162ehoaHWtGlTW716tX333XdWoUIFu/fee736VK1a1VJSUmzHjh32888/29tvv21RUVF2/fXXW2pqqledhQsXthtuuMG+/vpr++OPP+ynn36yV155xW666SYzM1u8eLENHTrUVq1aZX/++adNnjzZYmNj7dZbb/XMJz093apVq2ZNmjSxlStX2pw5c6xkyZLWs2fPcy7PsGHDrEiRIjZlyhT77bffrG/fvhYcHGy///67p0/jxo29njeTJk2ygIAAGz16tK1bt84SExMtNDTUNm/e7DXvhIQEGzBgQLbXLQA4jWAC4LLQunVra9Wqlc9pK1asMEmeYXCnTp1qNWvWtMDAQIuMjPQcdJudvuC4evXqFhQU5HO44DPdfffdJsnGjBmTadqvv/5qd9xxhxUtWtRCQkKsUqVKlpiYmOVwuWZmo0aNsnr16nm1nTmEcWBgoBUvXtxat25t06ZN8+qX22CycuVKq127tgUFBVn58uVtypQpXgMA9O/f34oXL2579uzx3Ofzzz+3wMBAr8c6288//2w33nijBQUFWUxMjCUlJXktu7u2TZs2edpOnDhhTz/9tEVFRVnhwoWtadOmtmbNGq/5xsXFWb9+/bJ8XPeF7SNHjrTY2FgLDg62tm3beoWbfv36edapn5+fhYeH27/+9S8bNmyY1xDHbhs3brQHHnjAYmNjLTAw0OLi4qx9+/aei+JXrFhhdevWtbCwMAsODraKFStav3797MiRI17z2bJli91yyy0WEhJi4eHh1rNnT5+Pd7bBgwdbyZIlrWDBgla/fn1btGjRedfJu+++a3FxcRYYGGjXXXedLVy40Gv69u3bLSAgwGuQAAC41LnMsvFTvQCAC3b8+HFVrFhRkyZNUv369Z0u55Jz7NgxhYeHa+bMmWrUqJHPPklJSfr888+1evXq/C3uMvPvf/9bBw8e1AcffOB0KQCQbVz8DgD5JDg4WB9//HGmazxw2sKFC9W4ceMsQwmyLyoqSs8884zTZQBAjnDGBABw2eCMCQBcuQgmAAAAABzHcMEAAAAAHEcwAQAAAOA4ggkAAAAAxxFMAAAAADiOYAIAAADAcQQTAAAAAI4jmAAAAABwHMEEAAAAgOMIJgAAAAAc9/8A4qp5elP98RIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:\n",
    "    \"\"\" Check if a PROTAC is active based on DC50 and Dmax.\t\n",
    "    Args:\n",
    "        DC50(float): DC50 in nM\n",
    "        Dmax(float): Dmax in %\n",
    "    Returns:\n",
    "        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN\n",
    "    \"\"\"\n",
    "    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan\n",
    "    Dmax = Dmax / 100\n",
    "    if pd.notnull(pDC50):\n",
    "        if pDC50 < pDC50_threshold:\n",
    "            return False\n",
    "    if pd.notnull(Dmax):\n",
    "        if Dmax < Dmax_threshold:\n",
    "            return False\n",
    "    if oring:\n",
    "        if pd.notnull(pDC50):\n",
    "            return True if pDC50 >= pDC50_threshold else False\n",
    "        elif pd.notnull(Dmax):\n",
    "            return True if Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "    else:\n",
    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
    "            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "print(f'Number of non-nan Dmax/DC50 values: {protac_df[[\"Dmax (%)\", \"DC50 (nM)\"]].count()}')\n",
    "\n",
    "# Add a column for a definition of activivity in which \n",
    "for Dmax_threshold in range(10):\n",
    "    for pDC50_threshold in [5 + 0.5 * i for i in range(10)]:\n",
    "        protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'] = protac_df.apply(\n",
    "            lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold * 0.1), axis=1\n",
    "        )\n",
    "        num_active = protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].value_counts()\n",
    "        num_nans = protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].isnull().sum()\n",
    "        total = len(protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'])\n",
    "        # If the number of active, i.e. num_active[True], is close to the number of inactive, i.e. num_active[False],\n",
    "        # then plot the histogram of the active values with different Dmax and pDC50 definitions\n",
    "        if abs(num_active[True] - num_active[False]) < 50:\n",
    "            sns.histplot(protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].dropna())\n",
    "            plt.title(f'Dmax threshold: {Dmax_threshold * 0.1:.0%}, pDC50 threshold: {round(pDC50_threshold, 1)}, Active: {num_active[True]}, Inactive: {num_active[False]}, NaN: {num_nans}, Total: {num_active[True] + num_active[False]}')\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "\n",
    "    # protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)})'] = protac_df.apply(\n",
    "    #     lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], Dmax_threshold=Dmax_threshold * 0.1, pDC50_threshold=6.5), axis=1\n",
    "    # )\n",
    "    # num_active = protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'].value_counts()\n",
    "    # num_inactive = protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'].isnull().sum()\n",
    "    # total = len(protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'])\n",
    "    # # Plot the KDE of the active values with different Dmax definitions\n",
    "    # plt.figure(figsize=(8, 6))\n",
    "    # sns.kdeplot(protac_df[f'Active (Dmax {Dmax_threshold * 0.1})'].dropna(), shade=True)\n",
    "    # plt.title(f'Dmax threshold: {Dmax_threshold * 0.1:.0%}, Active: {num_active[True]}, Inactive: {num_active[False]}, NaN: {num_inactive}, Total: {total}')\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Active',\n",
       " 'Active - OR',\n",
       " 'Active (Dmax 0.0, pDC50 5.0)',\n",
       " 'Active (Dmax 0.0, pDC50 5.5)',\n",
       " 'Active (Dmax 0.0, pDC50 6.0)',\n",
       " 'Active (Dmax 0.0, pDC50 6.5)',\n",
       " 'Active (Dmax 0.0, pDC50 7.0)',\n",
       " 'Active (Dmax 0.0, pDC50 7.5)',\n",
       " 'Active (Dmax 0.0, pDC50 8.0)',\n",
       " 'Active (Dmax 0.0, pDC50 8.5)',\n",
       " 'Active (Dmax 0.0, pDC50 9.0)',\n",
       " 'Active (Dmax 0.0, pDC50 9.5)',\n",
       " 'Active (Dmax 0.1, pDC50 5.0)',\n",
       " 'Active (Dmax 0.1, pDC50 5.5)',\n",
       " 'Active (Dmax 0.1, pDC50 6.0)',\n",
       " 'Active (Dmax 0.1, pDC50 6.5)',\n",
       " 'Active (Dmax 0.1, pDC50 7.0)',\n",
       " 'Active (Dmax 0.1, pDC50 7.5)',\n",
       " 'Active (Dmax 0.1, pDC50 8.0)',\n",
       " 'Active (Dmax 0.1, pDC50 8.5)',\n",
       " 'Active (Dmax 0.1, pDC50 9.0)',\n",
       " 'Active (Dmax 0.1, pDC50 9.5)',\n",
       " 'Active (Dmax 0.2, pDC50 5.0)',\n",
       " 'Active (Dmax 0.2, pDC50 5.5)',\n",
       " 'Active (Dmax 0.2, pDC50 6.0)',\n",
       " 'Active (Dmax 0.2, pDC50 6.5)',\n",
       " 'Active (Dmax 0.2, pDC50 7.0)',\n",
       " 'Active (Dmax 0.2, pDC50 7.5)',\n",
       " 'Active (Dmax 0.2, pDC50 8.0)',\n",
       " 'Active (Dmax 0.2, pDC50 8.5)',\n",
       " 'Active (Dmax 0.2, pDC50 9.0)',\n",
       " 'Active (Dmax 0.2, pDC50 9.5)',\n",
       " 'Active (Dmax 0.3, pDC50 5.0)',\n",
       " 'Active (Dmax 0.3, pDC50 5.5)',\n",
       " 'Active (Dmax 0.3, pDC50 6.0)',\n",
       " 'Active (Dmax 0.3, pDC50 6.5)',\n",
       " 'Active (Dmax 0.3, pDC50 7.0)',\n",
       " 'Active (Dmax 0.3, pDC50 7.5)',\n",
       " 'Active (Dmax 0.3, pDC50 8.0)',\n",
       " 'Active (Dmax 0.3, pDC50 8.5)',\n",
       " 'Active (Dmax 0.3, pDC50 9.0)',\n",
       " 'Active (Dmax 0.3, pDC50 9.5)',\n",
       " 'Active (Dmax 0.4, pDC50 5.0)',\n",
       " 'Active (Dmax 0.4, pDC50 5.5)',\n",
       " 'Active (Dmax 0.4, pDC50 6.0)',\n",
       " 'Active (Dmax 0.4, pDC50 6.5)',\n",
       " 'Active (Dmax 0.4, pDC50 7.0)',\n",
       " 'Active (Dmax 0.4, pDC50 7.5)',\n",
       " 'Active (Dmax 0.4, pDC50 8.0)',\n",
       " 'Active (Dmax 0.4, pDC50 8.5)',\n",
       " 'Active (Dmax 0.4, pDC50 9.0)',\n",
       " 'Active (Dmax 0.4, pDC50 9.5)',\n",
       " 'Active (Dmax 0.5, pDC50 5.0)',\n",
       " 'Active (Dmax 0.5, pDC50 5.5)',\n",
       " 'Active (Dmax 0.5, pDC50 6.0)',\n",
       " 'Active (Dmax 0.5, pDC50 6.5)',\n",
       " 'Active (Dmax 0.5, pDC50 7.0)',\n",
       " 'Active (Dmax 0.5, pDC50 7.5)',\n",
       " 'Active (Dmax 0.5, pDC50 8.0)',\n",
       " 'Active (Dmax 0.5, pDC50 8.5)',\n",
       " 'Active (Dmax 0.5, pDC50 9.0)',\n",
       " 'Active (Dmax 0.5, pDC50 9.5)',\n",
       " 'Active (Dmax 0.6, pDC50 5.0)',\n",
       " 'Active (Dmax 0.6, pDC50 5.5)',\n",
       " 'Active (Dmax 0.6, pDC50 6.0)',\n",
       " 'Active (Dmax 0.6, pDC50 6.5)',\n",
       " 'Active (Dmax 0.6, pDC50 7.0)',\n",
       " 'Active (Dmax 0.6, pDC50 7.5)',\n",
       " 'Active (Dmax 0.6, pDC50 8.0)',\n",
       " 'Active (Dmax 0.6, pDC50 8.5)',\n",
       " 'Active (Dmax 0.6, pDC50 9.0)',\n",
       " 'Active (Dmax 0.6, pDC50 9.5)',\n",
       " 'Active (Dmax 0.7, pDC50 5.0)',\n",
       " 'Active (Dmax 0.7, pDC50 5.5)',\n",
       " 'Active (Dmax 0.7, pDC50 6.0)',\n",
       " 'Active (Dmax 0.7, pDC50 6.5)',\n",
       " 'Active (Dmax 0.7, pDC50 7.0)',\n",
       " 'Active (Dmax 0.7, pDC50 7.5)',\n",
       " 'Active (Dmax 0.7, pDC50 8.0)',\n",
       " 'Active (Dmax 0.7, pDC50 8.5)',\n",
       " 'Active (Dmax 0.7, pDC50 9.0)',\n",
       " 'Active (Dmax 0.7, pDC50 9.5)',\n",
       " 'Active (Dmax 0.8, pDC50 5.0)',\n",
       " 'Active (Dmax 0.8, pDC50 5.5)',\n",
       " 'Active (Dmax 0.8, pDC50 6.0)',\n",
       " 'Active (Dmax 0.8, pDC50 6.5)',\n",
       " 'Active (Dmax 0.8, pDC50 7.0)',\n",
       " 'Active (Dmax 0.8, pDC50 7.5)',\n",
       " 'Active (Dmax 0.8, pDC50 8.0)',\n",
       " 'Active (Dmax 0.8, pDC50 8.5)',\n",
       " 'Active (Dmax 0.8, pDC50 9.0)',\n",
       " 'Active (Dmax 0.8, pDC50 9.5)',\n",
       " 'Active (Dmax 0.9, pDC50 5.0)',\n",
       " 'Active (Dmax 0.9, pDC50 5.5)',\n",
       " 'Active (Dmax 0.9, pDC50 6.0)',\n",
       " 'Active (Dmax 0.9, pDC50 6.5)',\n",
       " 'Active (Dmax 0.9, pDC50 7.0)',\n",
       " 'Active (Dmax 0.9, pDC50 7.5)',\n",
       " 'Active (Dmax 0.9, pDC50 8.0)',\n",
       " 'Active (Dmax 0.9, pDC50 8.5)',\n",
       " 'Active (Dmax 0.9, pDC50 9.0)',\n",
       " 'Active (Dmax 0.9, pDC50 9.5)']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[c for c in protac_df.columns if 'Active' in c]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a PROTAC as active when Dmax >= 0.6 and pDC50 >= 6.0 seems to be a good compromise, resulting in a balanced amount of active and inactive entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique samples: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
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       "      <th></th>\n",
       "      <th>Active</th>\n",
       "      <th>Active - OR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
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       "      <th>793</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "</table>\n",
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      "text/plain": [
       "     Active Active - OR\n",
       "173   False       False\n",
       "793   False       False\n",
       "924    True        True\n",
       "1606  False       False\n",
       "1956  False       False\n",
       "1980  False       False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of active/inactive PROTACs in test set:\n",
      "False    0.666667\n",
      "True     0.333333\n",
      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Heavy Atom Count</th>\n",
       "      <th>Ring Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>...</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 5.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 5.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 6.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 6.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 7.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 7.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 8.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 8.5)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 9.0)</th>\n",
       "      <th>Active (Dmax 0.9, pDC50 9.5)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>192</td>\n",
       "      <td>Q8IXJ6</td>\n",
       "      <td>Cc1cc(C)nc(SCC(=O)Nc2ncc(Cc3cccc(OCc4cn(CCCCNC...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C40H40N10O8S2/c1-23-15-24(2)44-40(43-...</td>\n",
       "      <td>GRYRXFYWVDWPQY-UHFFFAOYSA-N</td>\n",
       "      <td>852.956</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>1016</td>\n",
       "      <td>P10276</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](O)[C@H](N)Cc1ccccc1)C...</td>\n",
       "      <td>cIAP1</td>\n",
       "      <td>InChI=1S/C51H72N4O11/c1-34(2)27-42(55-48(60)46...</td>\n",
       "      <td>ZAOSGDCLGNWLSI-ACALULJJSA-N</td>\n",
       "      <td>917.154</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>1215</td>\n",
       "      <td>P62937</td>\n",
       "      <td>CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C/C=C/CCC...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C89H147N15O16S/c1-29-63-83(115)97(22)...</td>\n",
       "      <td>RSBPUBFGFMTCCP-NIDZIXQMSA-N</td>\n",
       "      <td>1715.311</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>1782</td>\n",
       "      <td>O14744</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C55H76N10O12S/c1-36(38-10-12-40(13-11...</td>\n",
       "      <td>XUJMNOQMXWVXQE-STHBVIMFSA-N</td>\n",
       "      <td>1101.338</td>\n",
       "      <td>78</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>2672</td>\n",
       "      <td>P14679</td>\n",
       "      <td>N[C@@H](Cc1ccc(O)c(O)c1)C(=O)NCCCCCNc1cccc2c1C...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C27H31N5O7/c28-17(13-15-7-9-20(33)21(...</td>\n",
       "      <td>GTUJRUVNUQLCDT-KKFHFHRHSA-N</td>\n",
       "      <td>537.573</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>2720</td>\n",
       "      <td>P07900</td>\n",
       "      <td>COc1c(C)cnc(Cn2cc(C#CCCNc3cccc4c3C(=O)N(C3CCC(...</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>InChI=1S/C32H29ClN8O5/c1-16-13-36-21(17(2)26(1...</td>\n",
       "      <td>ZSERQSKFLSKTGE-UHFFFAOYSA-N</td>\n",
       "      <td>641.088</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Compound ID Uniprot                                             Smiles  \\\n",
       "173           192  Q8IXJ6  Cc1cc(C)nc(SCC(=O)Nc2ncc(Cc3cccc(OCc4cn(CCCCNC...   \n",
       "793          1016  P10276  CC(C)C[C@H](NC(=O)[C@@H](O)[C@H](N)Cc1ccccc1)C...   \n",
       "924          1215  P62937  CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C/C=C/CCC...   \n",
       "1606         1782  O14744  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "1956         2672  P14679  N[C@@H](Cc1ccc(O)c(O)c1)C(=O)NCCCCCNc1cccc2c1C...   \n",
       "1980         2720  P07900  COc1c(C)cnc(Cn2cc(C#CCCNc3cccc4c3C(=O)N(C3CCC(...   \n",
       "\n",
       "     E3 Ligase                                              InChI  \\\n",
       "173       CRBN  InChI=1S/C40H40N10O8S2/c1-23-15-24(2)44-40(43-...   \n",
       "793      cIAP1  InChI=1S/C51H72N4O11/c1-34(2)27-42(55-48(60)46...   \n",
       "924        VHL  InChI=1S/C89H147N15O16S/c1-29-63-83(115)97(22)...   \n",
       "1606       VHL  InChI=1S/C55H76N10O12S/c1-36(38-10-12-40(13-11...   \n",
       "1956      CRBN  InChI=1S/C27H31N5O7/c28-17(13-15-7-9-20(33)21(...   \n",
       "1980      CRBN  InChI=1S/C32H29ClN8O5/c1-16-13-36-21(17(2)26(1...   \n",
       "\n",
       "                        InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "173   GRYRXFYWVDWPQY-UHFFFAOYSA-N           852.956                60   \n",
       "793   ZAOSGDCLGNWLSI-ACALULJJSA-N           917.154                66   \n",
       "924   RSBPUBFGFMTCCP-NIDZIXQMSA-N          1715.311               121   \n",
       "1606  XUJMNOQMXWVXQE-STHBVIMFSA-N          1101.338                78   \n",
       "1956  GTUJRUVNUQLCDT-KKFHFHRHSA-N           537.573                39   \n",
       "1980  ZSERQSKFLSKTGE-UHFFFAOYSA-N           641.088                46   \n",
       "\n",
       "      Ring Count  Rotatable Bond Count  ...  Active (Dmax 0.9, pDC50 5.0)  \\\n",
       "173            7                    18  ...                         False   \n",
       "793            3                    28  ...                          True   \n",
       "924            4                    26  ...                          True   \n",
       "1606           7                    29  ...                         False   \n",
       "1956           4                    11  ...                         False   \n",
       "1980           6                     7  ...                         False   \n",
       "\n",
       "      Active (Dmax 0.9, pDC50 5.5)  Active (Dmax 0.9, pDC50 6.0)  \\\n",
       "173                          False                         False   \n",
       "793                          False                         False   \n",
       "924                           True                          True   \n",
       "1606                         False                         False   \n",
       "1956                         False                         False   \n",
       "1980                         False                         False   \n",
       "\n",
       "     Active (Dmax 0.9, pDC50 6.5)  Active (Dmax 0.9, pDC50 7.0)  \\\n",
       "173                         False                         False   \n",
       "793                         False                         False   \n",
       "924                          True                          True   \n",
       "1606                        False                         False   \n",
       "1956                        False                         False   \n",
       "1980                        False                         False   \n",
       "\n",
       "      Active (Dmax 0.9, pDC50 7.5)  Active (Dmax 0.9, pDC50 8.0)  \\\n",
       "173                          False                         False   \n",
       "793                          False                         False   \n",
       "924                           True                         False   \n",
       "1606                         False                         False   \n",
       "1956                         False                         False   \n",
       "1980                         False                         False   \n",
       "\n",
       "     Active (Dmax 0.9, pDC50 8.5) Active (Dmax 0.9, pDC50 9.0)  \\\n",
       "173                         False                        False   \n",
       "793                         False                        False   \n",
       "924                         False                        False   \n",
       "1606                        False                        False   \n",
       "1956                        False                        False   \n",
       "1980                        False                        False   \n",
       "\n",
       "     Active (Dmax 0.9, pDC50 9.5)  \n",
       "173                         False  \n",
       "793                         False  \n",
       "924                         False  \n",
       "1606                        False  \n",
       "1956                        False  \n",
       "1980                        False  \n",
       "\n",
       "[6 rows x 135 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "\n",
    "\n",
    "active_col = f'Active (Dmax 0.6, pDC50 6.0)'\n",
    "active_df = protac_df[protac_df[active_col].notna()]\n",
    "\n",
    "# Find the samples that:\n",
    "# * have their SMILES appearing only once in the dataframe\n",
    "# * have their Uniprot appearing only once in the dataframe\n",
    "# * have their (Smiles, Uniprot) pair appearing only once in the dataframe\n",
    "unique_smiles = active_df['Smiles'].value_counts() == 1\n",
    "unique_uniprot = active_df['Uniprot'].value_counts() == 1\n",
    "unique_smiles_uniprot = active_df.groupby(['Smiles', 'Uniprot']).size() == 1\n",
    "\n",
    "# Get the indices of the unique samples\n",
    "unique_smiles_idx = active_df['Smiles'].map(unique_smiles)\n",
    "unique_uniprot_idx = active_df['Uniprot'].map(unique_uniprot)\n",
    "unique_smiles_uniprot_idx = active_df.set_index(['Smiles', 'Uniprot']).index.map(unique_smiles_uniprot)\n",
    "\n",
    "# Cross the indices to get the unique samples\n",
    "unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx & unique_smiles_uniprot_idx].index\n",
    "# unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx].index\n",
    "test_df = active_df.loc[unique_samples]\n",
    "\n",
    "# Reporting\n",
    "print(f'Number of unique samples: {len(unique_samples)}')\n",
    "img = Draw.MolsToGridImage(\n",
    "    [Chem.MolFromSmiles(s) for s in test_df['Smiles']],\n",
    "    molsPerRow=5,\n",
    "    subImgSize=(400, 200),\n",
    "    legends=[f'{u}\\n({s})' for u, s in zip(test_df['Article DOI'], test_df['Database'])],\n",
    ")\n",
    "display(img)\n",
    "display(test_df[['Active', 'Active - OR']])\n",
    "print(f'Percentage of active/inactive PROTACs in test set:\\n{test_df[active_col].value_counts(normalize=True)}')\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               O60885\n",
       "Number of entries                                         55\n",
       "Number of entries per E3 ligase                            5\n",
       "Number of entries with same SMILES                        69\n",
       "Number of entries with same SMILES and not Uniprot        14\n",
       "Number of active entries                                  41\n",
       "Number of inactive entries                                14\n",
       "Name: 2, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               P00533\n",
       "Number of entries                                         45\n",
       "Number of entries per E3 ligase                            4\n",
       "Number of entries with same SMILES                        45\n",
       "Number of entries with same SMILES and not Uniprot         0\n",
       "Number of active entries                                  22\n",
       "Number of inactive entries                                23\n",
       "Name: 3, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               P10275\n",
       "Number of entries                                        107\n",
       "Number of entries per E3 ligase                            3\n",
       "Number of entries with same SMILES                       107\n",
       "Number of entries with same SMILES and not Uniprot         0\n",
       "Number of active entries                                  66\n",
       "Number of inactive entries                                41\n",
       "Name: 0, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               O43353\n",
       "Number of entries                                          5\n",
       "Number of entries per E3 ligase                            3\n",
       "Number of entries with same SMILES                         5\n",
       "Number of entries with same SMILES and not Uniprot         0\n",
       "Number of active entries                                   5\n",
       "Number of inactive entries                                 0\n",
       "Name: 36, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_16240\\2323036171.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Uniprot                                               P00520\n",
       "Number of entries                                         16\n",
       "Number of entries per E3 ligase                            3\n",
       "Number of entries with same SMILES                        17\n",
       "Number of entries with same SMILES and not Uniprot         1\n",
       "Number of active entries                                   1\n",
       "Number of inactive entries                                15\n",
       "Name: 16, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In active_df, for each uniprot ID, count the corresponding number of entries,\n",
    "# then count its corresponding number of entries per E3 ligase. The final dataframe\n",
    "# should have the following columns:\n",
    "# * Uniprot\n",
    "# * Number of entries\n",
    "# * Number of entries per E3 ligase type (e.g. CRL, VHL, etc.)\n",
    "test_candidate_df = active_df['Uniprot'].value_counts().reset_index()\n",
    "test_candidate_df.columns = ['Uniprot', 'Number of entries']\n",
    "test_candidate_df['Number of entries per E3 ligase'] = test_candidate_df['Uniprot'].map(active_df.groupby('Uniprot')['E3 Ligase'].nunique())\n",
    "# Sort by the number of entries per E3 ligase\n",
    "test_candidate_df = test_candidate_df.sort_values('Number of entries per E3 ligase', ascending=False)\n",
    "# Take the first row, then get all the SMILES associated to that Uniprot ID\n",
    "for row_idx in range(5):\n",
    "    uniprot_id = test_candidate_df['Uniprot'].iloc[row_idx]\n",
    "    smiles = active_df[active_df['Uniprot'] == uniprot_id]['Smiles']\n",
    "    # Get the entries in active_df that have the same SMILES but NOT the same Uniprot ID\n",
    "    uniprot_count = test_candidate_df.iloc[row_idx]\n",
    "    # uniprot_count['SMILES'] = smiles\n",
    "    uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
    "    uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
    "    # Get the number of active and inactive entries with the same UniProt ID\n",
    "    uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
    "    uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
    "    display(uniprot_count)\n",
    "    # Plot the distribution of E3 in active_df[active_df['Uniprot'] == uniprot_id]\n",
    "    sns.countplot(data=active_df[active_df['Uniprot'] == uniprot_id], x='E3 Ligase', hue=active_col)\n",
    "    plt.title(f'Distribution of E3 ligase for UniProt ID {uniprot_id}')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isolating _all_ entries with Uniprot ID (target) corresponding to P00533 seems to be a good addition to the test set. In fact, it has a balanced distribution of active and inactive entries, plus, the E3 ligase distribution is also quite balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution (in percentage) of E3 ligases\n",
    "warnings.filterwarnings('ignore')\n",
    "ax = sns.countplot(y='E3 Ligase', data=protac_df, order=protac_df['E3 Ligase'].value_counts().index)\n",
    "total = len(protac_df['E3 Ligase'])\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.2f}%'.format(100 * p.get_width() / total)\n",
    "    x = p.get_x() + p.get_width() + 0.02\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    ax.annotate(percentage, (x, y))\n",
    "# Set the x-axis to log scale\n",
    "plt.xscale('log')\n",
    "plt.title('Distribution of E3 Ligases')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('E3 Ligase')\n",
    "plt.grid(axis='x', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of entries: 20,594\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4b0cf1cf31f486eba7c1792295bbae0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading protein embeddings: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tid: Q07817, \tembeddings shape: (1024,), \tembeddings mean: -0.0005679130554199219\n",
      "\tid: P00533, \tembeddings shape: (1024,), \tembeddings mean: 0.001171112060546875\n",
      "\tid: Q9NWZ3, \tembeddings shape: (1024,), \tembeddings mean: 0.00041985511779785156\n",
      "\tid: P00519, \tembeddings shape: (1024,), \tembeddings mean: 0.0009603500366210938\n",
      "\tid: P11474, \tembeddings shape: (1024,), \tembeddings mean: -0.0018215179443359375\n",
      "\tid: Q16288, \tembeddings shape: (1024,), \tembeddings mean: 0.0010194778442382812\n",
      "\tid: O60674, \tembeddings shape: (1024,), \tembeddings mean: 0.0015687942504882812\n",
      "\tid: Q06187, \tembeddings shape: (1024,), \tembeddings mean: 0.0006914138793945312\n",
      "\tid: Q9UHD2, \tembeddings shape: (1024,), \tembeddings mean: 0.0012235641479492188\n",
      "\tid: Q8IXJ6, \tembeddings shape: (1024,), \tembeddings mean: -0.00042366981506347656\n",
      "KeyError for P31750\n",
      "KeyError for P00520\n",
      "KeyError for A8DG50\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "protein_embeddings = {}\n",
    "with h5py.File(\"../data/uniprot2embedding.h5\", \"r\") as file:\n",
    "    print(f\"number of entries: {len(file.items()):,}\")\n",
    "    uniprots = protac_df['Uniprot'].unique().tolist()\n",
    "    uniprots += protac_df['E3 Ligase Uniprot'].unique().tolist()\n",
    "    for i, sequence_id in tqdm(enumerate(uniprots), desc='Loading protein embeddings'):\n",
    "        try:\n",
    "            embedding = file[sequence_id][:]\n",
    "            protein_embeddings[sequence_id] = np.array(embedding)\n",
    "            if i < 10:\n",
    "                print(\n",
    "                    f\"\\tid: {sequence_id}, \"\n",
    "                    f\"\\tembeddings shape: {embedding.shape}, \"\n",
    "                    f\"\\tembeddings mean: {np.array(embedding).mean()}\"\n",
    "                )\n",
    "        except KeyError:\n",
    "            print(f'KeyError for {sequence_id}')\n",
    "            protein_embeddings[sequence_id] = np.zeros((1024,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the cosine similarity between E3 ligases embeddings\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "# Create a name mapping for the E3 ligases names and their uniprots\n",
    "e3_ligase_uniprot_mapping = protac_df[['E3 Ligase', 'E3 Ligase Uniprot']].drop_duplicates()\n",
    "e3_ligase_uniprot_mapping = e3_ligase_uniprot_mapping.set_index('E3 Ligase Uniprot').to_dict()['E3 Ligase']\n",
    "# Calculate the cosine similarity between E3 ligases embeddings\n",
    "e3_ligase_embeddings = {}\n",
    "for e3_ligase_uniprot in e3_ligase_uniprot_mapping.keys():\n",
    "    e3_ligase_embeddings[e3_ligase_uniprot] = protein_embeddings[e3_ligase_uniprot]\n",
    "e3_ligase_similarity = pd.DataFrame(cosine_similarity(list(e3_ligase_embeddings.values())), columns=e3_ligase_uniprot_mapping.values())\n",
    "# Set the index and columns of the cosine similarity dataframe as the E3 ligases names\n",
    "e3_ligase_similarity.index = e3_ligase_uniprot_mapping.values()\n",
    "# e3_ligase_similarity = e3_ligase_similarity.rename(columns=e3_ligase_uniprot_mapping)\n",
    "# Plot the cosine similarity between E3 ligases embeddings as it was a correlation matrix,\n",
    "# only the lower triangle is shown\n",
    "sns.heatmap(e3_ligase_similarity, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.yticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.title('Cosine Similarity between E3 Ligases Embeddings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use a sequence similarity tool to double check which E3s are similar to each others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add pDC50 column to the PROTAC dataframe\n",
    "protac_df['pDC50'] = protac_df['DC50 (nM)'].apply(lambda x: -1 * np.log10(x * 1e-9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'Uniprot', 'Smiles', 'E3 Ligase', 'InChI', 'InChI Key',\n",
       "       'Molecular Weight', 'Heavy Atom Count', 'Ring Count',\n",
       "       'Rotatable Bond Count',\n",
       "       ...\n",
       "       'Active (Dmax 0.9, pDC50 5.5)', 'Active (Dmax 0.9, pDC50 6.0)',\n",
       "       'Active (Dmax 0.9, pDC50 6.5)', 'Active (Dmax 0.9, pDC50 7.0)',\n",
       "       'Active (Dmax 0.9, pDC50 7.5)', 'Active (Dmax 0.9, pDC50 8.0)',\n",
       "       'Active (Dmax 0.9, pDC50 8.5)', 'Active (Dmax 0.9, pDC50 9.0)',\n",
       "       'Active (Dmax 0.9, pDC50 9.5)', 'pDC50'],\n",
       "      dtype='object', length=136)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "860"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:\n",
    "    \"\"\" Check if a PROTAC is active based on DC50 and Dmax.\t\n",
    "    Args:\n",
    "        DC50(float): DC50 in nM\n",
    "        Dmax(float): Dmax in %\n",
    "    Returns:\n",
    "        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN\n",
    "    \"\"\"\n",
    "    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan\n",
    "    Dmax = Dmax / 100\n",
    "    if pd.notnull(pDC50):\n",
    "        if pDC50 < pDC50_threshold:\n",
    "            return False\n",
    "    if pd.notnull(Dmax):\n",
    "        if Dmax < Dmax_threshold:\n",
    "            return False\n",
    "    if oring:\n",
    "        if pd.notnull(pDC50):\n",
    "            return True if pDC50 >= pDC50_threshold else False\n",
    "        elif pd.notnull(Dmax):\n",
    "            return True if Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "    else:\n",
    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
    "            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "\n",
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)',\n",
    "protac_df[active_col] = protac_df.apply(\n",
    "    lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1\n",
    ")\n",
    "tot_len = len(protac_df.dropna(subset=active_col))\n",
    "tot_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "E3 ligase &  E3 ligase (\\%) &  Unique PROTACs (\\% per E3) &  Unique targets (\\% per E3) &  Unique cell lines (\\% per E3) \\\\\n",
      "\\midrule\n",
      "      VHL &           43.8 &                       41.2 &                       58.0 &                          60.2 \\\\\n",
      "     CRBN &           50.7 &                       52.2 &                       77.0 &                          79.7 \\\\\n",
      "    Other &            5.5 &                        6.6 &                       15.0 &                          10.5 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E3 ligase</th>\n",
       "      <th>E3 ligase (%)</th>\n",
       "      <th>Unique PROTACs (% per E3)</th>\n",
       "      <th>Unique targets (% per E3)</th>\n",
       "      <th>Unique cell lines (% per E3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VHL</td>\n",
       "      <td>43.792464</td>\n",
       "      <td>41.164659</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.150376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CRBN</td>\n",
       "      <td>50.710315</td>\n",
       "      <td>52.208835</td>\n",
       "      <td>77.0</td>\n",
       "      <td>79.699248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other</td>\n",
       "      <td>5.497221</td>\n",
       "      <td>6.626506</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.526316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  E3 ligase  E3 ligase (%)  Unique PROTACs (% per E3)  \\\n",
       "0       VHL      43.792464                  41.164659   \n",
       "1      CRBN      50.710315                  52.208835   \n",
       "2     Other       5.497221                   6.626506   \n",
       "\n",
       "   Unique targets (% per E3)  Unique cell lines (% per E3)  \n",
       "0                       58.0                     60.150376  \n",
       "1                       77.0                     79.699248  \n",
       "2                       15.0                     10.526316  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "E3 ligase                       VHLCRBNOther\n",
       "E3 ligase (%)                          100.0\n",
       "Unique PROTACs (% per E3)              100.0\n",
       "Unique targets (% per E3)              150.0\n",
       "Unique cell lines (% per E3)       150.37594\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.\n",
      "NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.\n"
     ]
    }
   ],
   "source": [
    "# Plot the Dmax (%) and DC50 (nM) distributions side by side\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "# ax_idx = 0\n",
    "# sns.histplot(protac_df['pDC50'], bins=20, ax=axes[ax_idx], kde=False, color=palette[0])\n",
    "# # axes[ax_idx].set_title('pDC50 Distribution')\n",
    "# axes[ax_idx].set_xlabel('$pDC_{50}$ [$-log_{10}(M)$]')\n",
    "# axes[ax_idx].set_ylabel('Count')\n",
    "# axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# ax_idx = 1\n",
    "# sns.histplot(protac_df['Dmax (%)'], bins=20, ax=axes[ax_idx], kde=False, color=palette[1])\n",
    "# # axes[ax_idx].set_title('Dmax (%) Distribution')\n",
    "# axes[ax_idx].set_xlabel('$D_{max}$ [%]')\n",
    "# # Remove y-axis\n",
    "# axes[ax_idx].set_ylabel('')\n",
    "# axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Plot the Dmax (%) and DC50 (nM) distributions in one plot, in axes[ax_idx == 1]\n",
    "ax_idx = 0\n",
    "# sns.kdeplot(protac_df['pDC50'] * 10, ax=axes[ax_idx], color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5)\n",
    "# sns.kdeplot(protac_df['Dmax (%)'], ax=axes[ax_idx], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5)\n",
    "sns.histplot(protac_df['pDC50'] * 10, ax=axes[ax_idx], color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5, kde=True)\n",
    "sns.histplot(protac_df['Dmax (%)'], ax=axes[ax_idx], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5, kde=True)\n",
    "axes[ax_idx].set_xlabel('')\n",
    "axes[ax_idx].set_ylabel('')\n",
    "axes[ax_idx].legend(loc='upper left')\n",
    "axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "\n",
    "\n",
    "# # Plot the E3 ligase distribution\n",
    "# ax_idx = 3\n",
    "# sns.countplot(y='E3 Ligase', data=protac_df, ax=axes[ax_idx], order=protac_df['E3 Ligase'].value_counts().index, color=palette[2])\n",
    "# axes[ax_idx].set_xscale('log')\n",
    "# axes[ax_idx].set_xlabel('Count')\n",
    "# axes[ax_idx].set_ylabel('E3 Ligase')\n",
    "# axes[ax_idx].grid(axis='x', alpha=0.5)\n",
    "\n",
    "# Create a new dataframe for which, for each E3 ligase name, we have:\n",
    "# - the percentage of unique PROTACs associated to it\n",
    "# - The percentage of unique POI associated to it\n",
    "# - The percentage of unique cell lines associated to it\n",
    "tmp = protac_df[protac_df[active_col].notna()].copy()\n",
    "tmp['E3 ligase'] = tmp['E3 Ligase'].apply(lambda x: x if x == 'VHL' or x == 'CRBN' else 'Other')\n",
    "e3_ligase_stats = pd.DataFrame()\n",
    "e3_ligase_stats['E3 ligase'] = tmp['E3 ligase'].unique()\n",
    "e3_ligase_stats['E3 ligase (%)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * len(tmp[tmp['E3 ligase'] == x]) / len(tmp['E3 ligase'])\n",
    ")\n",
    "e3_ligase_stats['Unique PROTACs (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Smiles'].nunique() / tmp['Smiles'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique targets (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Uniprot'].nunique() / tmp['Uniprot'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique cell lines (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Cell Line Identifier'].nunique() / tmp['Cell Line Identifier'].nunique()\n",
    ")\n",
    "\n",
    "print(e3_ligase_stats.round(1).to_latex(index=False))\n",
    "display(e3_ligase_stats)\n",
    "display(e3_ligase_stats.sum(axis=0))\n",
    "\n",
    "# stacked Plot the distribution of PROTACs, POI and cell lines associated to each E3 ligase\n",
    "ax_idx = 1\n",
    "e3_ligase_stats.plot.barh(x='E3 ligase', y=['E3 ligase (%)', 'Unique PROTACs (% per E3)', 'Unique targets (% per E3)', 'Unique cell lines (% per E3)'],\n",
    "    stacked=True,\n",
    "    ax=axes[ax_idx],\n",
    "    color=adjusted_palette,\n",
    "    grid=False,\n",
    ")\n",
    "axes[ax_idx].set_xlabel('Percentage (stacked)')\n",
    "axes[ax_idx].set_ylabel('')\n",
    "axes[ax_idx].legend()\n",
    "# Set the x-axis to log scale\n",
    "axes[ax_idx].grid(axis='x', alpha=0.3)\n",
    "# For 'VHL' and 'CRBN' E3 ligases, show the percentage of PROTACs, POI and cell lines associated to them\n",
    "# for i, e3_ligase in enumerate(['VHL', 'CRBN']):\n",
    "#     axes[ax_idx].text(\n",
    "#         0.5, i, f'{e3_ligase}\\n'\n",
    "#         f'{e3_ligase_stats.loc[e3_ligase_stats[\"E3 Ligase\"] == e3_ligase, \"PROTACs (% per E3)\"].values[0]:.1f}%',\n",
    "#         ha='center', va='center', color='black'\n",
    "#     )\n",
    "# Put the percentages on top of the bars if the bar corresponding to the E3 ligases 'VHL' and 'CRBN'\n",
    "for i, p in enumerate(axes[ax_idx].patches):\n",
    "    if p.get_width() < 20:\n",
    "        continue\n",
    "    percentage = '{:.1f}%'.format(p.get_width())\n",
    "    x = p.get_x() + p.get_width() / 2\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    axes[ax_idx].annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "# # Plot the number of active and inactive PROTACs\n",
    "# ax_idx = 2\n",
    "# sns.countplot(x=active_col, data=protac_df, ax=axes[ax_idx], palette=palette[2:])\n",
    "# # Change the x-axis labels to 'Inactive' and 'Active'\n",
    "# axes[ax_idx].set_xticklabels(['Inactive', 'Active'])\n",
    "# axes[ax_idx].set_xlabel('')\n",
    "# axes[ax_idx].set_ylabel('')\n",
    "# axes[ax_idx].grid(axis='y', alpha=0.3)\n",
    "# # Put the percentages on top of the bars\n",
    "# total = len(protac_df[protac_df[active_col].notna()])\n",
    "# for p in axes[ax_idx].patches:\n",
    "#     percentage = '{:.1f}%'.format(100 * p.get_height() / total)\n",
    "#     x = p.get_x() + p.get_width() / 2\n",
    "#     y = p.get_height() + 0.02\n",
    "#     axes[ax_idx].annotate(percentage, (x, y), ha='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('dataset_distributions.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "print('NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.')\n",
    "print('NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRBN      53.39\n",
       "VHL       40.12\n",
       "IAP        2.80\n",
       "MDM2       1.26\n",
       "cIAP1      0.98\n",
       "XIAP       0.93\n",
       "FEM1B      0.37\n",
       "Ubr1       0.09\n",
       "RNF114     0.05\n",
       "Name: E3 Ligase, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename E3 ligase Mdm2 to MDM2\n",
    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Mdm2', 'MDM2')\n",
    "# Percentage of each E3 ligase in the dataset\n",
    "e3_ligase_percentage = protac_df['E3 Ligase'].value_counts(normalize=True) * 100\n",
    "e3_ligase_percentage.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrr}\n",
      "\\toprule\n",
      "E3 Ligase &  E3 Ligase (\\%) &  Active (\\%) &  Inactive (\\%) \\\\\n",
      "\\midrule\n",
      "     CRBN &          53.39 &       49.49 &         50.51 \\\\\n",
      "    FEM1B &           0.37 &       50.00 &         50.00 \\\\\n",
      "      IAP &           2.80 &       10.00 &         90.00 \\\\\n",
      "     MDM2 &           1.26 &       16.67 &         83.33 \\\\\n",
      "     Ubr1 &           0.09 &       50.00 &         50.00 \\\\\n",
      "      VHL &          40.12 &       56.09 &         43.91 \\\\\n",
      "    cIAP1 &           0.98 &        9.09 &         90.91 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "\n",
    "# Percentage of True/False in active_col per E3 ligase\n",
    "tmp = protac_df.groupby('E3 Ligase')[active_col].value_counts(normalize=True).unstack().fillna(0).round(4) * 100\n",
    "# Rename the columns\n",
    "tmp.columns = ['Inactive (%)', 'Active (%)']\n",
    "tmp.reset_index(inplace=True)\n",
    "# Add a column with the percentage of each E3 ligase in the dataset\n",
    "tmp['E3 Ligase (%)'] = tmp['E3 Ligase'].map(e3_ligase_percentage)\n",
    "tmp = tmp.round(2)\n",
    "print(tmp[['E3 Ligase', 'E3 Ligase (%)', 'Active (%)', 'Inactive (%)']].to_latex(index=False, bold_rows=True))\n",
    "# Plot tmp as a countplot of E3 ligases percentages, with each bar showing the percentage of active and inactive PROTACs\n",
    "# Percentage of True/False in active_col per E3 ligase\n",
    "tmp = protac_df.groupby('E3 Ligase')[active_col].value_counts(normalize=True).unstack().fillna(0).round(4) * 100\n",
    "# Rename the columns\n",
    "tmp.columns = ['Inactive (%)', 'Active (%)']\n",
    "tmp.reset_index(inplace=True)\n",
    "# Add a column with the percentage of each E3 ligase in the dataset\n",
    "tmp['E3 Ligase (%)'] = tmp['E3 Ligase'].map(e3_ligase_percentage)\n",
    "tmp = tmp.round(2)\n",
    "\n",
    "# Sort the rows according to the E3 ligase percentage\n",
    "tmp = tmp.sort_values('E3 Ligase (%)', ascending=False)\n",
    "\n",
    "# Create the bottom bar which is the 'Inactive (%)', we will stack 'Active (%)' on top of it\n",
    "ax_inactive = plt.bar(tmp['E3 Ligase'], tmp['E3 Ligase (%)'] * tmp['Inactive (%)'] / 100, color=adjusted_palette[0], label='Inactive (%)')\n",
    "# The bottom parameter is set to 'Inactive (%)' so that 'Active (%)' starts where 'Inactive (%)' ends\n",
    "ax_active = plt.bar(tmp['E3 Ligase'], tmp['E3 Ligase (%)'] * tmp['Active (%)'] / 100, bottom=tmp['E3 Ligase (%)'] * tmp['Inactive (%)'] / 100, color=adjusted_palette[1], label='Active (%)')\n",
    "\n",
    "# Add the value of column E3 ligase (%) on top of the bars Active (%)\n",
    "for i, p in enumerate(ax_active):\n",
    "    percentage = tmp['E3 Ligase (%)'].iloc[i]\n",
    "    if percentage < 3:\n",
    "        continue\n",
    "    percentage = tmp['Active (%)'].iloc[i]\n",
    "    percentage = f'{percentage:.1f}%'\n",
    "    x = p.get_x() + p.get_width() / 2\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    plt.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "for i, p in enumerate(ax_inactive):\n",
    "    percentage = tmp['E3 Ligase (%)'].iloc[i]\n",
    "    if percentage < 3:\n",
    "        continue\n",
    "    percentage = tmp['Inactive (%)'].iloc[i]\n",
    "    percentage = f'{percentage:.1f}%'\n",
    "    x = p.get_x() + p.get_width() / 2\n",
    "    y = p.get_y() + p.get_height() / 2\n",
    "    plt.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "for i, (active_p, ax_inactive_p) in enumerate(zip(ax_active, ax_inactive)):\n",
    "    percentage = tmp['E3 Ligase (%)'].iloc[i]\n",
    "    percentage = f'{percentage:.1f}%'\n",
    "    x = active_p.get_x() + active_p.get_width() / 2\n",
    "    y = active_p.get_y() + active_p.get_height() + 1\n",
    "    plt.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "# Set y-axis labels as percentages\n",
    "for ax in [ax_active, ax_inactive]:\n",
    "    plt.gca().set_yticklabels([f'{int(y)}%' for y in plt.gca().get_yticks()])\n",
    "\n",
    "plt.ylabel('')\n",
    "plt.xlabel('E3 Ligase')\n",
    "# plt.title('Percentage of Active/Inactive PROTACs per E3 Ligase')\n",
    "# Set legend below the plot\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2)\n",
    "\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "plt.tight_layout()\n",
    "plt.savefig('active_inactive_per_e3_ligase.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "E3 ligase &  E3 ligase (\\%) &  Unique PROTACs (\\% per E3) &  Unique targets (\\% per E3) &  Unique cell lines (\\% per E3) \\\\\n",
      "\\midrule\n",
      "      VHL &            0.5 &                        0.4 &                        0.3 &                           0.3 \\\\\n",
      "    Other &            0.1 &                        0.0 &                        0.0 &                           0.0 \\\\\n",
      "     CRBN &            0.5 &                        0.5 &                        0.3 &                           0.3 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E3 ligase</th>\n",
       "      <th>E3 ligase (%)</th>\n",
       "      <th>Unique PROTACs (% per E3)</th>\n",
       "      <th>Unique targets (% per E3)</th>\n",
       "      <th>Unique cell lines (% per E3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VHL</td>\n",
       "      <td>0.487209</td>\n",
       "      <td>0.441536</td>\n",
       "      <td>0.338235</td>\n",
       "      <td>0.263736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>0.059302</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CRBN</td>\n",
       "      <td>0.453488</td>\n",
       "      <td>0.481675</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  E3 ligase  E3 ligase (%)  Unique PROTACs (% per E3)  \\\n",
       "0       VHL       0.487209                   0.441536   \n",
       "1     Other       0.059302                   0.000000   \n",
       "2      CRBN       0.453488                   0.481675   \n",
       "\n",
       "   Unique targets (% per E3)  Unique cell lines (% per E3)  \n",
       "0                   0.338235                      0.263736  \n",
       "1                   0.000000                      0.000000  \n",
       "2                   0.264706                      0.285714  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "E3 ligase                       VHLOtherCRBN\n",
       "E3 ligase (%)                            1.0\n",
       "Unique PROTACs (% per E3)           0.923211\n",
       "Unique targets (% per E3)           0.602941\n",
       "Unique cell lines (% per E3)        0.549451\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.\n",
      "NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.\n"
     ]
    }
   ],
   "source": [
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# sns.kdeplot(protac_df['pDC50'] * 10, ax=axes[ax_idx], color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5)\n",
    "# sns.kdeplot(protac_df['Dmax (%)'], ax=axes[ax_idx], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5)\n",
    "sns.histplot(protac_df['pDC50'] * 10, color=palette[0], label='$pDC_{50}$ [$-10 \\cdot log_{10}(M)$]', fill=True, alpha=0.5, kde=True, ax=ax)\n",
    "sns.histplot(protac_df['Dmax (%)'], color=palette[1], label='$D_{max}$ [%]', fill=True, alpha=0.5, kde=True, ax=ax)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "# plt.legend(loc='upper left')\n",
    "# Set legend below the plot\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.savefig('dc50_dmax_distributions.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# Create a new dataframe for which, for each E3 ligase name, we have:\n",
    "# - the percentage of unique PROTACs associated to it\n",
    "# - The percentage of unique POI associated to it\n",
    "# - The percentage of unique cell lines associated to it\n",
    "tmp = protac_df[protac_df[active_col].notna()].copy()\n",
    "tmp['E3 ligase'] = tmp['E3 Ligase'].apply(lambda x: x if x == 'VHL' or x == 'CRBN' else 'Other')\n",
    "e3_ligase_stats = pd.DataFrame()\n",
    "e3_ligase_stats['E3 ligase'] = tmp['E3 ligase'].unique()\n",
    "\n",
    "e3_ligase_stats['E3 ligase (%)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    lambda x: len(tmp[tmp['E3 ligase'] == x]) / len(tmp['E3 ligase'])\n",
    ")\n",
    "\n",
    "def get_unique_per_e3(df: pd.DataFrame, e3: str, column: str) -> pd.DataFrame:\n",
    "    \"\"\" Get the unique number of entries per E3 ligase which are NOT in the other E3.\n",
    "\n",
    "    Args:\n",
    "        df(pd.DataFrame): The dataframe containing the data\n",
    "        e3(str): The E3 ligase name\n",
    "        column(str): The column name to count the unique entries\n",
    "    Returns:\n",
    "        pd.DataFrame: A dataframe containing the unique number of entries per E3 ligase\n",
    "    \"\"\"\n",
    "    e3_df = df[df['E3 Ligase'] == e3]\n",
    "    other_e3_df = df[df['E3 Ligase'] != e3]\n",
    "    e3_unique = e3_df[~e3_df[column].isin(other_e3_df[column])][column].nunique()\n",
    "    return e3_unique\n",
    "\n",
    "e3_ligase_stats['Unique PROTACs (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    # lambda x: 100 * tmp[tmp['E3 ligase'] == x]['Smiles'].nunique() / tmp['Smiles'].nunique()\n",
    "    lambda x: get_unique_per_e3(tmp, x, 'Smiles') / tmp['Smiles'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique targets (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    # lambda x: tmp[tmp['E3 ligase'] == x]['Uniprot'].nunique() / tmp['Uniprot'].nunique()\n",
    "    lambda x: get_unique_per_e3(tmp, x, 'Uniprot') / tmp['Uniprot'].nunique()\n",
    ")\n",
    "e3_ligase_stats['Unique cell lines (% per E3)'] = e3_ligase_stats['E3 ligase'].apply(\n",
    "    # lambda x: tmp[tmp['E3 ligase'] == x]['Cell Line Identifier'].nunique() / tmp['Cell Line Identifier'].nunique()\n",
    "    lambda x: get_unique_per_e3(tmp, x, 'Cell Line Identifier') / tmp['Cell Line Identifier'].nunique()\n",
    ")\n",
    "\n",
    "\n",
    "print(e3_ligase_stats.round(1).to_latex(index=False))\n",
    "display(e3_ligase_stats)\n",
    "display(e3_ligase_stats.sum(axis=0))\n",
    "\n",
    "# Sort the e3_ligase_stats as: CRBN, VHL, Other\n",
    "e3_ligase_stats = e3_ligase_stats.sort_values('E3 ligase', key=lambda x: x.map({'CRBN': 0, 'VHL': 1, 'Other': 2}))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# stacked Plot the distribution of PROTACs, POI and cell lines associated to each E3 ligase\n",
    "e3_ligase_stats.plot.bar(\n",
    "    x='E3 ligase',\n",
    "    # y=['E3 ligase (%)', 'Unique PROTACs (% per E3)', 'Unique targets (% per E3)', 'Unique cell lines (% per E3)'],\n",
    "    y=['Unique PROTACs (% per E3)', 'Unique targets (% per E3)', 'Unique cell lines (% per E3)'],\n",
    "    stacked=True,\n",
    "    ax=ax,\n",
    "    color=adjusted_palette,\n",
    "    grid=False,\n",
    ")\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "# Set the y-axis to log scale\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "# Put the percentages on top of the bars if the bar corresponding to the E3 ligases 'VHL' and 'CRBN'\n",
    "for i, p in enumerate(ax.patches):\n",
    "    if p.get_height() < 0.20 and p.get_height() > 0:\n",
    "        percentage = f'{p.get_height() * 100:.1f}%'\n",
    "        if percentage == '0.0%':\n",
    "            continue\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = p.get_y() + p.get_height() / 2 + 0.08\n",
    "        ax.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "    else:\n",
    "        percentage = f'{p.get_height() * 100:.1f}%'\n",
    "        if percentage == '0.0%':\n",
    "            continue\n",
    "        x = p.get_x() + p.get_width() / 2\n",
    "        y = p.get_y() + p.get_height() / 2\n",
    "        ax.annotate(percentage, (x, y), ha='center', va='center', color='black')\n",
    "\n",
    "# Set the x-axis to percentage\n",
    "ax.yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
    "\n",
    "# Set x-axis labels to orientation 90 degrees\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n",
    "\n",
    "# Set the legend below the plot and outside the plot in 4 columns\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('e3_distributions.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print('NOTE: The $$pDC_{50}$$ values are scaled by 10 to better show it next to the $$D_{max}$$ distribution.')\n",
    "print('NOTE: Some percentages do not \"add up\" because the same target might be \"associated to\"/\"attacked by\" multiple E3 ligases or tested in multiple cell lines.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting CV Scores and Ablation Study Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#83B8FE', '#FFA54C', '#94ED67', '#FF7FFF']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import colorsys\n",
    "\n",
    "def increase_saturation(hex_color, increase_by=0.3):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "    # Convert RGB to HSV\n",
    "    hsv = colorsys.rgb_to_hsv(rgb[0]/255, rgb[1]/255, rgb[2]/255)\n",
    "    # Increase saturation\n",
    "    new_saturation = min(hsv[1] + increase_by, 1)  # Ensure saturation doesn't exceed 1\n",
    "    # Convert back to RGB and then to hex\n",
    "    new_rgb = colorsys.hsv_to_rgb(hsv[0], new_saturation, hsv[2])\n",
    "    new_hex = '#' + ''.join(f'{int(c*255):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "def darken_color(hex_color, darkening_factor=1.0):\n",
    "    # Convert hex to RGB\n",
    "    hex_color = hex_color.lstrip('#')\n",
    "    rgb = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
    "\n",
    "    # Darken color\n",
    "    new_rgb = [(color * darkening_factor) for color in rgb]\n",
    "\n",
    "    # Convert RGB back to hex\n",
    "    new_hex = '#' + ''.join(f'{int(c):02X}' for c in new_rgb)\n",
    "    return new_hex\n",
    "\n",
    "palette = [\n",
    "    '#D0E4FE', # blue\n",
    "    '#FFCC99', # orange\n",
    "    '#C4EDAF', # green\n",
    "    '#FFCCFF', # pink\n",
    "]\n",
    "\n",
    "\n",
    "# Adjusted palette\n",
    "palette = adjusted_palette = [increase_saturation(color) for color in palette]\n",
    "print(adjusted_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>group_type</th>\n",
       "      <th>train_len</th>\n",
       "      <th>val_len</th>\n",
       "      <th>train_perc</th>\n",
       "      <th>val_perc</th>\n",
       "      <th>train_active_perc</th>\n",
       "      <th>train_inactive_perc</th>\n",
       "      <th>val_active_perc</th>\n",
       "      <th>val_inactive_perc</th>\n",
       "      <th>...</th>\n",
       "      <th>hparam_batch_size</th>\n",
       "      <th>hparam_learning_rate</th>\n",
       "      <th>hparam_join_embeddings</th>\n",
       "      <th>hparam_smote_k_neighbors</th>\n",
       "      <th>hparam_use_smote</th>\n",
       "      <th>hparam_apply_scaling</th>\n",
       "      <th>hparam_dropout</th>\n",
       "      <th>disabled_embeddings</th>\n",
       "      <th>train_unique_groups</th>\n",
       "      <th>val_unique_groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
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       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled poi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>616</td>\n",
       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>616</td>\n",
       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled smiles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>random</td>\n",
       "      <td>616</td>\n",
       "      <td>155</td>\n",
       "      <td>0.798962</td>\n",
       "      <td>0.201038</td>\n",
       "      <td>0.514610</td>\n",
       "      <td>0.485390</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>concat</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0.119461</td>\n",
       "      <td>disabled e3 cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled poi</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled cell</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled smiles</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled e3 cell</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>tanimoto</td>\n",
       "      <td>661</td>\n",
       "      <td>111</td>\n",
       "      <td>0.856218</td>\n",
       "      <td>0.143782</td>\n",
       "      <td>0.521936</td>\n",
       "      <td>0.478064</td>\n",
       "      <td>0.468468</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>beginning</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.217163</td>\n",
       "      <td>disabled poi e3 cell</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    fold group_type  train_len  val_len  train_perc  val_perc  \\\n",
       "0      0     random        616      155    0.798962  0.201038   \n",
       "1      0     random        616      155    0.798962  0.201038   \n",
       "2      0     random        616      155    0.798962  0.201038   \n",
       "3      0     random        616      155    0.798962  0.201038   \n",
       "4      0     random        616      155    0.798962  0.201038   \n",
       "..   ...        ...        ...      ...         ...       ...   \n",
       "30     4   tanimoto        661      111    0.856218  0.143782   \n",
       "31     4   tanimoto        661      111    0.856218  0.143782   \n",
       "32     4   tanimoto        661      111    0.856218  0.143782   \n",
       "33     4   tanimoto        661      111    0.856218  0.143782   \n",
       "34     4   tanimoto        661      111    0.856218  0.143782   \n",
       "\n",
       "    train_active_perc  train_inactive_perc  val_active_perc  \\\n",
       "0            0.514610             0.485390         0.516129   \n",
       "1            0.514610             0.485390         0.516129   \n",
       "2            0.514610             0.485390         0.516129   \n",
       "3            0.514610             0.485390         0.516129   \n",
       "4            0.514610             0.485390         0.516129   \n",
       "..                ...                  ...              ...   \n",
       "30           0.521936             0.478064         0.468468   \n",
       "31           0.521936             0.478064         0.468468   \n",
       "32           0.521936             0.478064         0.468468   \n",
       "33           0.521936             0.478064         0.468468   \n",
       "34           0.521936             0.478064         0.468468   \n",
       "\n",
       "    val_inactive_perc  ...  hparam_batch_size  hparam_learning_rate  \\\n",
       "0            0.483871  ...                 32              0.000056   \n",
       "1            0.483871  ...                 32              0.000056   \n",
       "2            0.483871  ...                 32              0.000056   \n",
       "3            0.483871  ...                 32              0.000056   \n",
       "4            0.483871  ...                 32              0.000056   \n",
       "..                ...  ...                ...                   ...   \n",
       "30           0.531532  ...                 16              0.000019   \n",
       "31           0.531532  ...                 16              0.000019   \n",
       "32           0.531532  ...                 16              0.000019   \n",
       "33           0.531532  ...                 16              0.000019   \n",
       "34           0.531532  ...                 16              0.000019   \n",
       "\n",
       "    hparam_join_embeddings  hparam_smote_k_neighbors  hparam_use_smote  \\\n",
       "0                   concat                         6              True   \n",
       "1                   concat                         6              True   \n",
       "2                   concat                         6              True   \n",
       "3                   concat                         6              True   \n",
       "4                   concat                         6              True   \n",
       "..                     ...                       ...               ...   \n",
       "30               beginning                         5             False   \n",
       "31               beginning                         5             False   \n",
       "32               beginning                         5             False   \n",
       "33               beginning                         5             False   \n",
       "34               beginning                         5             False   \n",
       "\n",
       "    hparam_apply_scaling  hparam_dropout   disabled_embeddings  \\\n",
       "0                   True        0.119461                   NaN   \n",
       "1                   True        0.119461          disabled poi   \n",
       "2                   True        0.119461         disabled cell   \n",
       "3                   True        0.119461       disabled smiles   \n",
       "4                   True        0.119461      disabled e3 cell   \n",
       "..                   ...             ...                   ...   \n",
       "30                  True        0.217163          disabled poi   \n",
       "31                  True        0.217163         disabled cell   \n",
       "32                  True        0.217163       disabled smiles   \n",
       "33                  True        0.217163      disabled e3 cell   \n",
       "34                  True        0.217163  disabled poi e3 cell   \n",
       "\n",
       "    train_unique_groups  val_unique_groups  \n",
       "0                   NaN                NaN  \n",
       "1                   NaN                NaN  \n",
       "2                   NaN                NaN  \n",
       "3                   NaN                NaN  \n",
       "4                   NaN                NaN  \n",
       "..                  ...                ...  \n",
       "30                 47.0                8.0  \n",
       "31                 47.0                8.0  \n",
       "32                 47.0                8.0  \n",
       "33                 47.0                8.0  \n",
       "34                 47.0                8.0  \n",
       "\n",
       "[140 rows x 43 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dummy_val_acc</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>val_roc_auc</th>\n",
       "      <th>dummy_test_acc</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>test_roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <td>0.515</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tanimoto</th>\n",
       "      <td>0.555</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniprot</th>\n",
       "      <td>0.580</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dummy_val_acc  val_acc  val_roc_auc  dummy_test_acc  test_acc  \\\n",
       "group_type                                                                  \n",
       "random              0.515    0.846        0.905           0.535     0.714   \n",
       "tanimoto            0.555    0.775        0.852           0.529     0.715   \n",
       "uniprot             0.580    0.628        0.654           0.541     0.584   \n",
       "\n",
       "            test_roc_auc  \n",
       "group_type                \n",
       "random             0.779  \n",
       "tanimoto           0.840  \n",
       "uniprot            0.592  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_show = [\n",
    "    # 'val_active_perc',\n",
    "    # 'val_inactive_perc',\n",
    "    'dummy_val_acc',\n",
    "    'val_acc',\n",
    "    'val_roc_auc',\n",
    "    # 'test_active_perc',\n",
    "    # 'test_inactive_perc',\n",
    "    'dummy_test_acc',\n",
    "    'test_acc',\n",
    "    'test_roc_auc',\n",
    "]\n",
    "\n",
    "report = pd.read_csv('../reports/cv_report_hparam_search_5-splits_Active_Dmax_0.6_pDC50_6.0_test_split_0.1.csv')\n",
    "report_tanimoto = pd.read_csv('../reports/cv_report_hparam_search_5-splits_Active_Dmax_0.6_pDC50_6.0_test_split_0.1_tanimoto.csv')\n",
    "report = pd.concat([report, report_tanimoto])\n",
    "\n",
    "# report = pd.read_csv('../reports/cv_report_hparam_search_5-splits_Active_Dmax_0.6_pDC50_6.0_test_split_0.2.csv')\n",
    "report.columns = [c.replace('split_type', 'group_type') for c in report.columns]\n",
    "\n",
    "display(report)\n",
    "\n",
    "# Remove group_type e3_ligase\n",
    "report = report[report['group_type'] != 'e3_ligase']\n",
    "\n",
    "report['dummy_val_acc'] = report[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
    "report['dummy_test_acc'] = report[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
    "\n",
    "tmp = report[report['disabled_embeddings'].isna()]\n",
    "# Suppress future warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# tmp.groupby(['group_type', 'active']).mean().round(3)[cols_to_show]\n",
    "tmp.groupby(['group_type',]).mean().round(3)[cols_to_show]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "857"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:\n",
    "    \"\"\" Check if a PROTAC is active based on DC50 and Dmax.\t\n",
    "    Args:\n",
    "        DC50(float): DC50 in nM\n",
    "        Dmax(float): Dmax in %\n",
    "    Returns:\n",
    "        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN\n",
    "    \"\"\"\n",
    "    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan\n",
    "    Dmax = Dmax / 100\n",
    "    if pd.notnull(pDC50):\n",
    "        if pDC50 < pDC50_threshold:\n",
    "            return False\n",
    "    if pd.notnull(Dmax):\n",
    "        if Dmax < Dmax_threshold:\n",
    "            return False\n",
    "    if oring:\n",
    "        if pd.notnull(pDC50):\n",
    "            return True if pDC50 >= pDC50_threshold else False\n",
    "        elif pd.notnull(Dmax):\n",
    "            return True if Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "    else:\n",
    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
    "            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "\n",
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "pDC50_threshold = 6.0\n",
    "Dmax_threshold = 0.6\n",
    "protac_df[active_col] = protac_df.apply(\n",
    "    lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1\n",
    ")\n",
    "tot_len = len(protac_df.dropna(subset=active_col))\n",
    "tot_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of active PROTACs: 437 (50.99%)\n",
      "Number of inactive PROTACs: 420 (49.01%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True     437\n",
       "False    420\n",
       "Name: Active (Dmax 0.6, pDC50 6.0), dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = protac_df.dropna(subset=active_col)\n",
    "print(f'Number of active PROTACs: {len(tmp[tmp[active_col] == True])} ({100 * len(tmp[tmp[active_col] == True]) / len(tmp):.2f}%)')\n",
    "print(f'Number of inactive PROTACs: {len(tmp[tmp[active_col] == False])} ({100 * len(tmp[tmp[active_col] == False]) / len(tmp):.2f}%)')\n",
    "tmp[active_col].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2141\n",
      "812 0.3792620270901448\n",
      "1350 0.6305464736104625\n"
     ]
    }
   ],
   "source": [
    "print(len(protac_df))\n",
    "print(len(protac_df.dropna(subset='Dmax (%)')), len(protac_df.dropna(subset='Dmax (%)')) / len(protac_df))\n",
    "print(len(protac_df.dropna(subset='DC50 (nM)')), len(protac_df.dropna(subset='DC50 (nM)')) / len(protac_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "\n",
    "# Display train and test set informations\n",
    "cols_to_show = [\n",
    "    'fold',\n",
    "    'group_type',\n",
    "    'train_len',\n",
    "    'train_perc',\n",
    "    'val_len',\n",
    "    'val_perc',\n",
    "    'test_len',\n",
    "    'test_perc',\n",
    "    'train_active_perc',\n",
    "    'val_active_perc',\n",
    "    'test_active_perc',\n",
    "    'train_leaking_uniprot_perc',\n",
    "    'train_leaking_smiles_perc',\n",
    "    # 'train_unique_groups',\n",
    "    # 'val_unique_groups',\n",
    "]\n",
    "report['test_len'] = tot_len - report['train_len'] - report['val_len']\n",
    "report['train_perc'] = report['train_len'] / tot_len\n",
    "report['val_perc'] = report['val_len'] / tot_len\n",
    "report['test_perc'] = report['test_len'] / tot_len\n",
    "\n",
    "tmp = report[report['group_type'] != 'e3_ligase'].groupby('group_type').mean().round(3).copy()\n",
    "# tmp = report[cols_to_show].groupby('group_type').mean().round(3).copy()\n",
    "\n",
    "# \"Collapse\" group_type into another column along side the others\n",
    "tmp = tmp.reset_index()\n",
    "# Display the columns with '_perc' in their name as percentages\n",
    "for c in tmp.columns:\n",
    "    if '_perc' in c:\n",
    "        tmp[c] *= 100\n",
    "# Plot a stacked barplot of the train/val/test percentages\n",
    "# fig, ax = plt.subplots() #figsize=(8, 6))\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "tmp.plot.bar(x='group_type', y=['train_perc', 'val_perc', 'test_perc'], stacked=True, ax=ax, color=palette, grid=False)\n",
    "ax.set_xlabel('')\n",
    "# ax.set_xlabel('Split Type')\n",
    "# ax.set_ylabel('Percentage')\n",
    "# ax.set_title('Train/Validation/Test Split')\n",
    "\n",
    "# Write the train/val/test len inside the stacked bars\n",
    "for i, p in enumerate(ax.patches):\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy()\n",
    "    ax.text(x + width / 2, y + height / 2, f'{round(height / 100 * tot_len)} ({height/100:.1%})', ha='center', va='center')\n",
    "\n",
    "# Rename the legend labels\n",
    "ax.legend(['Train set', 'Validation set', 'Test set'])\n",
    "# Rename x-axis labels\n",
    "ax.set_xticklabels(['Standard split', 'Similarity split', 'Target split'])\n",
    "# Set x ticks to 90 degree orientation\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "\n",
    "# Set the y-axis labels to percentage\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "plt.tight_layout()\n",
    "plt.savefig('train_val_test_split.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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