{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Curation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.display import display_html\n",
    "\n",
    "import logging\n",
    "import warnings\n",
    "import re\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import pickle\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from typing import Literal, Union, List, Dict, Any, Callable\n",
    "from collections import defaultdict\n",
    "from tqdm.auto import tqdm\n",
    "from rdkit import RDLogger\n",
    "\n",
    "RDLogger.DisableLog('rdApp.*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out some warnings..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_global_logging_level(level=logging.ERROR, prefices=[\"\"]):\n",
    "    \"\"\"\n",
    "    Override logging levels of different modules based on their name as a prefix.\n",
    "    It needs to be invoked after the modules have been loaded so that their loggers have been initialized.\n",
    "\n",
    "    Args:\n",
    "        - level: desired level. e.g. logging.INFO. Optional. Default is logging.ERROR\n",
    "        - prefices: list of one or more str prefices to match (e.g. [\"transformers\", \"torch\"]). Optional.\n",
    "          Default is `[\"\"]` to match all active loggers.\n",
    "          The match is a case-sensitive `module_name.startswith(prefix)`\n",
    "    \"\"\"\n",
    "    prefix_re = re.compile(fr'^(?:{ \"|\".join(prefices) })')\n",
    "    for name in logging.root.manager.loggerDict:\n",
    "        if re.match(prefix_re, name):\n",
    "            logging.getLogger(name).setLevel(level)\n",
    "\n",
    "\n",
    "# Filter out annoying Pytorch Lightning printouts\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(\n",
    "    'ignore', '.*Covariance of the parameters could not be estimated.*')\n",
    "warnings.filterwarnings(\n",
    "    'ignore', '.*You seem to be using the pipelines sequentially on GPU.*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup working directories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = os.path.join(os.getcwd(), '..', 'data')\n",
    "src_dir = os.path.join(os.getcwd(), '..', 'src')\n",
    "fig_dir = os.path.join(data_dir, 'figures')\n",
    "checkpoint_dir = os.path.join(os.getcwd(), '..', 'checkpoints')\n",
    "dirs_to_make = [\n",
    "    data_dir,\n",
    "    os.path.join(data_dir, 'raw'),\n",
    "    os.path.join(data_dir, 'processed'),\n",
    "    fig_dir,\n",
    "    # os.path.join(data_dir, 'train'),\n",
    "    # os.path.join(data_dir, 'val'),\n",
    "    # os.path.join(data_dir, 'test'),\n",
    "    # src_dir,\n",
    "    # checkpoint_dir,\n",
    "]\n",
    "for d in dirs_to_make:\n",
    "    if not os.path.exists(d):\n",
    "        os.makedirs(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download or load the raw PROTAC-DB dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded protac.csv\n"
     ]
    }
   ],
   "source": [
    "protacdb_file = os.path.join(data_dir, 'PROTAC-DB.csv')\n",
    "protacdb_url = 'http://cadd.zju.edu.cn/protacdb/statics/binaryDownload/csv/protac/protac.csv'\n",
    "if os.path.exists(protacdb_file):\n",
    "    protac_df = pd.read_csv(protacdb_file).reset_index(drop=True)\n",
    "    print(f'Loaded protac.csv')\n",
    "else:\n",
    "    print(f'Downloading {protacdb_url}')\n",
    "    !wget {protacdb_url} {protacdb_file}\n",
    "    protac_df = pd.read_csv(protacdb_file).reset_index(drop=True)\n",
    "    print(f'PROTAC-DB loaded')\n",
    "\n",
    "old2new = {\n",
    "    'E3 ligase': 'E3 Ligase',\n",
    "}\n",
    "protac_df = protac_df.rename(columns=old2new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Target</th>\n",
       "      <th>Percent degradation (%)</th>\n",
       "      <th>Article DOI</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "      <th>Dmax (%)</th>\n",
       "      <th>Assay (DC50/Dmax)</th>\n",
       "      <th>Assay (Percent degradation)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BRD7</td>\n",
       "      <td>20/12 (WB)</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD7 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>19/30 (WB)</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BRD7</td>\n",
       "      <td>19/27 (WB)</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD7 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>5/21 (WB)</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>94/93 (WB)</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound ID Target Percent degradation (%)                   Article DOI  \\\n",
       "0            1   BRD7              20/12 (WB)  10.1021/acs.jmedchem.8b01413   \n",
       "1            1   BRD9              19/30 (WB)  10.1021/acs.jmedchem.8b01413   \n",
       "2            2   BRD7              19/27 (WB)  10.1021/acs.jmedchem.8b01413   \n",
       "3            2   BRD9               5/21 (WB)  10.1021/acs.jmedchem.8b01413   \n",
       "4            3   BRD9              94/93 (WB)  10.1021/acs.jmedchem.8b01413   \n",
       "\n",
       "  DC50 (nM) Dmax (%) Assay (DC50/Dmax)  \\\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",
       "                         Assay (Percent degradation)  \n",
       "0  % BRD7 degradation in HeLa cells after 4/16 h ...  \n",
       "1  % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "2  % BRD7 degradation in HeLa cells after 4/16 h ...  \n",
       "3  % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "4  % BRD9 degradation in HeLa cells after 4/16 h ...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROTAC-DB scraped len: 6000\n"
     ]
    }
   ],
   "source": [
    "scraped_protac_df = pd.read_csv(os.path.join(\n",
    "    data_dir, 'PROTAC-DB-Scraped.csv'))\n",
    "# Rename columns\n",
    "old2new = {\n",
    "    \"Assay (Percent degradation)\": \"Percent degradation (%)\",\n",
    "    \"PROTAC to Target\": \"Assay (Percent degradation)\",\n",
    "    \"DOI\": \"Article DOI\",\n",
    "    \"DC50\": \"DC50 (nM)\",\n",
    "    \"Dmax\": \"Dmax (%)\",\n",
    "}\n",
    "scraped_protac_df = scraped_protac_df.rename(columns=old2new)\n",
    "\n",
    "display(scraped_protac_df.head())\n",
    "print(f'PROTAC-DB scraped len: {len(scraped_protac_df)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merged df len: 5343\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>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Target</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>PDB</th>\n",
       "      <th>Name</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>IC50 (nM, Protac to Target)</th>\n",
       "      <th>Assay (Protac to Target, IC50)</th>\n",
       "      <th>EC50 (nM, Protac to Target)</th>\n",
       "      <th>...</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>Topological Polar Surface Area</th>\n",
       "      <th>Molecular Formula</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Percent degradation (%)</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "      <th>Dmax (%)</th>\n",
       "      <th>Assay (DC50/Dmax)</th>\n",
       "      <th>Assay (Percent degradation)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Q9NPI1</td>\n",
       "      <td>BRD7</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>189.92</td>\n",
       "      <td>C50H64N8O9S</td>\n",
       "      <td>InChI=1S/C50H64N8O9S/c1-32-45(68-31-53-32)34-1...</td>\n",
       "      <td>RPMQBLMPGMFXLD-PDUNVWSESA-N</td>\n",
       "      <td>20/12 (WB)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD7 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>189.92</td>\n",
       "      <td>C50H64N8O9S</td>\n",
       "      <td>InChI=1S/C50H64N8O9S/c1-32-45(68-31-53-32)34-1...</td>\n",
       "      <td>RPMQBLMPGMFXLD-PDUNVWSESA-N</td>\n",
       "      <td>19/30 (WB)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Q9NPI1</td>\n",
       "      <td>BRD7</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>208.38</td>\n",
       "      <td>C54H72N8O11S</td>\n",
       "      <td>InChI=1S/C54H72N8O11S/c1-36-49(74-35-57-36)38-...</td>\n",
       "      <td>NGWWVKZONFCNQP-SHPBXJAASA-N</td>\n",
       "      <td>19/27 (WB)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD7 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>208.38</td>\n",
       "      <td>C54H72N8O11S</td>\n",
       "      <td>InChI=1S/C54H72N8O11S/c1-36-49(74-35-57-36)38-...</td>\n",
       "      <td>NGWWVKZONFCNQP-SHPBXJAASA-N</td>\n",
       "      <td>5/21 (WB)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>CRBN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>202.97</td>\n",
       "      <td>C43H50N8O10</td>\n",
       "      <td>InChI=1S/C43H50N8O10/c1-48-24-31(28-9-10-44-23...</td>\n",
       "      <td>RMBNUDOJPQLHMV-UHFFFAOYSA-N</td>\n",
       "      <td>94/93 (WB)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound ID Uniprot Target E3 Ligase  PDB Name  \\\n",
       "0            1  Q9NPI1   BRD7       VHL  NaN  NaN   \n",
       "1            1  Q9H8M2   BRD9       VHL  NaN  NaN   \n",
       "2            2  Q9NPI1   BRD7       VHL  NaN  NaN   \n",
       "3            2  Q9H8M2   BRD9       VHL  NaN  NaN   \n",
       "4            3  Q9H8M2   BRD9      CRBN  NaN  NaN   \n",
       "\n",
       "                                              Smiles  \\\n",
       "0  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...   \n",
       "1  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...   \n",
       "2  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...   \n",
       "3  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...   \n",
       "4  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...   \n",
       "\n",
       "  IC50 (nM, Protac to Target) Assay (Protac to Target, IC50)  \\\n",
       "0                         NaN                            NaN   \n",
       "1                         NaN                            NaN   \n",
       "2                         NaN                            NaN   \n",
       "3                         NaN                            NaN   \n",
       "4                         NaN                            NaN   \n",
       "\n",
       "  EC50 (nM, Protac to Target)  ... Rotatable Bond Count  \\\n",
       "0                         NaN  ...                   19   \n",
       "1                         NaN  ...                   19   \n",
       "2                         NaN  ...                   25   \n",
       "3                         NaN  ...                   25   \n",
       "4                         NaN  ...                   18   \n",
       "\n",
       "  Topological Polar Surface Area Molecular Formula  \\\n",
       "0                         189.92       C50H64N8O9S   \n",
       "1                         189.92       C50H64N8O9S   \n",
       "2                         208.38      C54H72N8O11S   \n",
       "3                         208.38      C54H72N8O11S   \n",
       "4                         202.97       C43H50N8O10   \n",
       "\n",
       "                                               InChI  \\\n",
       "0  InChI=1S/C50H64N8O9S/c1-32-45(68-31-53-32)34-1...   \n",
       "1  InChI=1S/C50H64N8O9S/c1-32-45(68-31-53-32)34-1...   \n",
       "2  InChI=1S/C54H72N8O11S/c1-36-49(74-35-57-36)38-...   \n",
       "3  InChI=1S/C54H72N8O11S/c1-36-49(74-35-57-36)38-...   \n",
       "4  InChI=1S/C43H50N8O10/c1-48-24-31(28-9-10-44-23...   \n",
       "\n",
       "                     InChI Key Percent degradation (%) DC50 (nM) Dmax (%)  \\\n",
       "0  RPMQBLMPGMFXLD-PDUNVWSESA-N              20/12 (WB)       NaN      NaN   \n",
       "1  RPMQBLMPGMFXLD-PDUNVWSESA-N              19/30 (WB)       NaN      NaN   \n",
       "2  NGWWVKZONFCNQP-SHPBXJAASA-N              19/27 (WB)       NaN      NaN   \n",
       "3  NGWWVKZONFCNQP-SHPBXJAASA-N               5/21 (WB)       NaN      NaN   \n",
       "4  RMBNUDOJPQLHMV-UHFFFAOYSA-N              94/93 (WB)       NaN      NaN   \n",
       "\n",
       "  Assay (DC50/Dmax)                        Assay (Percent degradation)  \n",
       "0               NaN  % BRD7 degradation in HeLa cells after 4/16 h ...  \n",
       "1               NaN  % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "2               NaN  % BRD7 degradation in HeLa cells after 4/16 h ...  \n",
       "3               NaN  % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "4               NaN  % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "\n",
       "[5 rows x 89 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge scraped data with PROTAC-DB on Compound ID and get non-assay columns\n",
    "param_cols = [\n",
    "    \"Percent degradation (%)\",\n",
    "    \"Dmax (%)\",\n",
    "    \"DC50 (nM)\",\n",
    "    \"Assay (Percent degradation)\",\n",
    "    \"Assay (DC50/Dmax)\",\n",
    "]\n",
    "cols = [c for c in protac_df.columns if c not in param_cols]\n",
    "on_cols = [c for c in scraped_protac_df.columns if c not in param_cols]\n",
    "scraped_protac_df = protac_df[cols].merge(\n",
    "    scraped_protac_df,\n",
    "    on=on_cols,\n",
    ").drop_duplicates()\n",
    "print(f'Merged df len: {len(scraped_protac_df)}')\n",
    "scraped_protac_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "362\n",
      "737\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>Percent degradation (%)</th>\n",
       "      <th>Assay (Percent degradation)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20/12 (WB)</td>\n",
       "      <td>% BRD7 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19/30 (WB)</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19/27 (WB)</td>\n",
       "      <td>% BRD7 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5/21 (WB)</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94/93 (WB)</td>\n",
       "      <td>% BRD9 degradation in HeLa cells after 4/16 h ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>54 (WB)</td>\n",
       "      <td>% JAK3 degradation in MHH-CALL-4 cells at 100 nM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4863</th>\n",
       "      <td>42 (WB)</td>\n",
       "      <td>% JAK3 degradation in MHH-CALL-4 cells at 100 nM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4864</th>\n",
       "      <td>1/32/69/19 (WB)</td>\n",
       "      <td>% JAK2 degradation in MHH-CALL-4 cells at 1/10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4881</th>\n",
       "      <td>0/30/30/53/29 (WB)</td>\n",
       "      <td>% EGFR del19 degradation in HCC827 cells at 5/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4882</th>\n",
       "      <td>10/7/7/27/34 (WB)</td>\n",
       "      <td>% EGFR EGFR L858R/T790M degradation in H1975 c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>334 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Percent degradation (%)  \\\n",
       "0                 20/12 (WB)   \n",
       "1                 19/30 (WB)   \n",
       "2                 19/27 (WB)   \n",
       "3                  5/21 (WB)   \n",
       "4                 94/93 (WB)   \n",
       "...                      ...   \n",
       "4861                 54 (WB)   \n",
       "4863                 42 (WB)   \n",
       "4864         1/32/69/19 (WB)   \n",
       "4881      0/30/30/53/29 (WB)   \n",
       "4882       10/7/7/27/34 (WB)   \n",
       "\n",
       "                            Assay (Percent degradation)  \n",
       "0     % BRD7 degradation in HeLa cells after 4/16 h ...  \n",
       "1     % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "2     % BRD7 degradation in HeLa cells after 4/16 h ...  \n",
       "3     % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "4     % BRD9 degradation in HeLa cells after 4/16 h ...  \n",
       "...                                                 ...  \n",
       "4861   % JAK3 degradation in MHH-CALL-4 cells at 100 nM  \n",
       "4863   % JAK3 degradation in MHH-CALL-4 cells at 100 nM  \n",
       "4864  % JAK2 degradation in MHH-CALL-4 cells at 1/10...  \n",
       "4881  % EGFR del19 degradation in HCC827 cells at 5/...  \n",
       "4882  % EGFR EGFR L858R/T790M degradation in H1975 c...  \n",
       "\n",
       "[334 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(scraped_protac_df.dropna(\n",
    "    subset=['DC50 (nM)', 'Dmax (%)']).dropna(how='all')))\n",
    "print(len(scraped_protac_df.dropna(\n",
    "    subset=['Percent degradation (%)']).dropna(how='all')))\n",
    "\n",
    "tmp = scraped_protac_df.dropna(subset=['Percent degradation (%)'])\n",
    "\n",
    "tmp[tmp['Percent degradation (%)'].str.contains(\n",
    "    'WB')][['Percent degradation (%)', 'Assay (Percent degradation)']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "protac_pedia_df len: 1203\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>PROTACDB ID</th>\n",
       "      <th>PROTAC SMILES</th>\n",
       "      <th>Active/Inactive</th>\n",
       "      <th>Best PROTAC</th>\n",
       "      <th>Cells</th>\n",
       "      <th>cLogP</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Curator</th>\n",
       "      <th>Dc50</th>\n",
       "      <th>Dmax</th>\n",
       "      <th>...</th>\n",
       "      <th>Proteomics Data Available</th>\n",
       "      <th>Secondary Pubmed</th>\n",
       "      <th>Status</th>\n",
       "      <th>Target</th>\n",
       "      <th>Tested A Non Binding E3 Control</th>\n",
       "      <th>Tested Competition With Ligand</th>\n",
       "      <th>Tested Engagement In Cells</th>\n",
       "      <th>Tested Proteaseome Inhibitor</th>\n",
       "      <th>Time</th>\n",
       "      <th>TPSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>No</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>10.83732</td>\n",
       "      <td>IC50's are for cell viability assays</td>\n",
       "      <td>Ronen Gabizon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reviewed</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>48</td>\n",
       "      <td>251.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>No</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>11.22742</td>\n",
       "      <td>IC50's are for cell viability assays</td>\n",
       "      <td>Ronen Gabizon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reviewed</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>48</td>\n",
       "      <td>251.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>Inactive</td>\n",
       "      <td>No</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>11.61752</td>\n",
       "      <td>IC50's are for cell viability assays</td>\n",
       "      <td>Ronen Gabizon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reviewed</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>48</td>\n",
       "      <td>251.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>Active</td>\n",
       "      <td>No</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>12.00762</td>\n",
       "      <td>IC50's are for cell viability assays</td>\n",
       "      <td>Ronen Gabizon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reviewed</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>48</td>\n",
       "      <td>251.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>Active</td>\n",
       "      <td>No</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>12.39772</td>\n",
       "      <td>IC50's are for cell viability assays</td>\n",
       "      <td>Ronen Gabizon</td>\n",
       "      <td>53 nM</td>\n",
       "      <td>~ 100 %</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reviewed</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>48</td>\n",
       "      <td>251.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PROTACDB ID                                      PROTAC SMILES  \\\n",
       "0            1  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "1            2  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "2            3  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "3            4  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "4            5  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "\n",
       "  Active/Inactive Best PROTAC   Cells     cLogP  \\\n",
       "0        Inactive          No  MOLT-4  10.83732   \n",
       "1        Inactive          No  MOLT-4  11.22742   \n",
       "2        Inactive          No  MOLT-4  11.61752   \n",
       "3          Active          No  MOLT-4  12.00762   \n",
       "4          Active          No  MOLT-4  12.39772   \n",
       "\n",
       "                               Comments        Curator   Dc50     Dmax  ...  \\\n",
       "0  IC50's are for cell viability assays  Ronen Gabizon    NaN      NaN  ...   \n",
       "1  IC50's are for cell viability assays  Ronen Gabizon    NaN      NaN  ...   \n",
       "2  IC50's are for cell viability assays  Ronen Gabizon    NaN      NaN  ...   \n",
       "3  IC50's are for cell viability assays  Ronen Gabizon    NaN      NaN  ...   \n",
       "4  IC50's are for cell viability assays  Ronen Gabizon  53 nM  ~ 100 %  ...   \n",
       "\n",
       "  Proteomics Data Available Secondary Pubmed    Status  Target  \\\n",
       "0                        No              NaN  Reviewed  Q07817   \n",
       "1                        No              NaN  Reviewed  Q07817   \n",
       "2                        No              NaN  Reviewed  Q07817   \n",
       "3                        No              NaN  Reviewed  Q07817   \n",
       "4                        No              NaN  Reviewed  Q07817   \n",
       "\n",
       "  Tested A Non Binding E3 Control  Tested Competition With Ligand  \\\n",
       "0                              No                              No   \n",
       "1                              No                              No   \n",
       "2                              No                              No   \n",
       "3                              No                              No   \n",
       "4                              No                              No   \n",
       "\n",
       "   Tested Engagement In Cells Tested Proteaseome Inhibitor Time    TPSA  \n",
       "0                          No                           No   48  251.07  \n",
       "1                          No                           No   48  251.07  \n",
       "2                          No                           No   48  251.07  \n",
       "3                          No                           No   48  251.07  \n",
       "4                         Yes                           No   48  251.07  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_file = os.path.join(data_dir, 'PROTAC-Pedia.csv')\n",
    "protac_pedia_df = pd.read_csv(df_file)\n",
    "print(f'protac_pedia_df len: {len(protac_pedia_df)}')\n",
    "protac_pedia_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-100.0, -5.0, nan, 90.317, 1000.0, nan]\n",
      "[0.0]\n",
      "[96.0, 73.0]\n",
      "[1.0, 3.14]\n"
     ]
    }
   ],
   "source": [
    "def clean_string(s: str) -> str:\n",
    "    \"\"\" Clean a string by removing <, >, =, NaN, and ranges like 100-200.\n",
    "    Args:\n",
    "        s(str): string to clean\n",
    "    Returns:\n",
    "        str: cleaned string\n",
    "    \"\"\"\n",
    "    if pd.isnull(s) or s in {'nan', 'n/a', 'NaN', ''}:\n",
    "        return np.nan\n",
    "    if 'N.D.' in s:\n",
    "        return '0'\n",
    "    s = s.strip('(WB)').strip()\n",
    "    # # Combine regex operations for efficiency\n",
    "    # s = re.sub(r'[<=>]|NaN|[\\d]+[-~]', '', s)  # Remove <, >, =, NaN, and ranges like 100-200\n",
    "    # Remove <, >, =, NaN\n",
    "    s = re.sub(r'[<=>]|NaN', '', s)\n",
    "    # Replace ranges like 100-200 or 1~3 with the left-most value in the range\n",
    "    s = re.sub(r'\\b(\\d+)[-~]\\d+\\b', r'\\1', s)\n",
    "    # Replace (n/a) with nan\n",
    "    s = s.replace('(n/a)', 'nan')\n",
    "    s = re.sub(r'[~<=>% ]', '', s)  # Remove ~, <, >, =, % and spaces\n",
    "    return s\n",
    "\n",
    "\n",
    "def split_clean_str(s: str, return_floats: bool = False) -> Union[List[str], List[float]]:\n",
    "    \"\"\" Split a string by '/' and clean each part.\n",
    "    Args:\n",
    "        s(str): string to split\n",
    "        return_floats(bool): whether to return floats or strings\n",
    "    Returns:\n",
    "        list: list of cleaned strings or floats\n",
    "    \"\"\"\n",
    "    if pd.isnull(s) or s in {'nan', 'n/a', 'NaN', ''}:\n",
    "        return np.nan\n",
    "    cleaned_values = [clean_string(part.strip())\n",
    "                      for part in s.replace('(n/a)', 'nan').split('/')]\n",
    "    return [float(value) if return_floats else value for value in cleaned_values]\n",
    "\n",
    "\n",
    "print(split_clean_str('-100-200/-5/(n/a)/<=90.317/>1000/NaN', return_floats=True))\n",
    "print(split_clean_str('N.D.', return_floats=True))\n",
    "print(split_clean_str('96/73 (WB)', return_floats=True))\n",
    "print(split_clean_str('1.0~3/3.14', return_floats=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "def is_active(DC50: float, Dmax: float, oring=False) -> 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 < 7.0:\n",
    "            return False\n",
    "    if pd.notnull(Dmax):\n",
    "        if Dmax < 0.8:\n",
    "            return False\n",
    "    if oring:\n",
    "        if pd.notnull(pDC50):\n",
    "            return True if pDC50 >= 7.0 else False\n",
    "        elif pd.notnull(Dmax):\n",
    "            return True if Dmax >= 0.8 else False\n",
    "        else:\n",
    "            return np.nan\n",
    "    else:\n",
    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
    "            return True if pDC50 >= 7.0 and Dmax >= 0.8 else False\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "\n",
    "print(is_active(20, 80))\n",
    "print(is_active(100, 70))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean PROTAC-Pedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns in PROTAC-Pedia dataframe\n",
    "old2new = {\n",
    "    \"Cells\": \"Cell Type\",\n",
    "    \"MW\": \"Molecular Weight\",\n",
    "    \"TPSA\": \"Topological Polar Surface Area\",\n",
    "    \"Time\": \"Treatment Time (h)\",\n",
    "    \"Dc50\": \"DC50 (nM)\",\n",
    "    \"Dmax\": \"Dmax (%)\",\n",
    "    \"Hbond acceptors\": \"Hydrogen Bond Acceptor Count\",\n",
    "    \"Hbond donors\": \"Hydrogen Bond Donor Count\",\n",
    "    \"PROTACDB ID\": \"Compound ID\",\n",
    "    \"PROTAC SMILES\": \"Smiles\",\n",
    "    'Target': 'Uniprot',\n",
    "}\n",
    "protac_pedia_df = protac_pedia_df.rename(columns=old2new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename Cereblon to CRBN in E3 Ligase column\n",
    "protac_pedia_df['E3 Ligase'] = protac_pedia_df['E3 Ligase'].replace(\n",
    "    'Cereblon', 'CRBN')\n",
    "protac_pedia_df['E3 Ligase'] = protac_pedia_df['E3 Ligase'].str.replace('Iap', 'IAP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with single cell types: 925\n"
     ]
    }
   ],
   "source": [
    "# Get all rows that do not contain \", \" nor \"; \" in \"Cell Type\" column\n",
    "multiple_cells_idx = protac_pedia_df['Cell Type'].str.contains(\n",
    "    ', |; |\\sand\\s|/', regex=True, na=False,\n",
    ")\n",
    "nan_comments_idx = protac_pedia_df['Comments'].isna()\n",
    "protac_pedia_df[multiple_cells_idx & ~\n",
    "                nan_comments_idx][['Compound ID', 'Cell Type', 'Comments', 'DC50 (nM)', 'Dmax (%)']].to_csv(\n",
    "                    os.path.join(data_dir, 'processed', 'multiple_cell_types.csv'), index=False)\n",
    "\n",
    "multiple_cells_df = protac_pedia_df[multiple_cells_idx].copy()\n",
    "\n",
    "protac_pedia_df = protac_pedia_df[~multiple_cells_idx]\n",
    "print(f'Number of rows with single cell types: {len(protac_pedia_df)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'Smiles', 'Active/Inactive', 'Best PROTAC', 'Cell Type',\n",
       "       'cLogP', 'Comments', 'Curator', 'DC50 (nM)', 'Dmax (%)',\n",
       "       'E3 Binder SMILES', 'E3 Ligase', 'Ec50 of Ligand Cells',\n",
       "       'Ec50 of PROTAC Cells', 'exit_vector', 'Hydrogen Bond Acceptor Count',\n",
       "       'Hydrogen Bond Donor Count', 'Ic50 of Ligand', 'Ic50 of PROTAC',\n",
       "       'Ligand Name', 'Ligand SMILES', 'Linker', 'Linker Type', 'linker_ha',\n",
       "       'linker_no', 'linker_rb', 'Molecular Weight', 'Off Targets Reported',\n",
       "       'PATENT', 'Ligand PDB', 'Ligand ID', 'Pubmed', 'PROTAC Name',\n",
       "       'Proteomics Data Available', 'Secondary Pubmed', 'Status', 'Uniprot',\n",
       "       'Tested A Non Binding E3 Control', 'Tested Competition With Ligand',\n",
       "       'Tested Engagement In Cells', 'Tested Proteaseome Inhibitor',\n",
       "       'Treatment Time (h)', 'Topological Polar Surface Area'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiple_cells_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RS4;11, MOLM-13\n",
      "MV4-11, MOLM-13, KG1\n",
      "MOLT-4, MOLM14\n",
      "MOLM14, MV4-11\n",
      "Panc02.13, H23, A549, H1792, H2110, HCC827\n",
      "NAMALWA, XLA\n",
      "MV4;11, SUM149, SUM159, MOLM13, MM1.SCRBN−/−, MM1.SW\n",
      "MV4;11, 293FT, 293FTCRBN−/−\n",
      "MV-4-11 SK-MEL-5, NCI-H1568\n",
      "HeLa, DLBCL\n",
      "Jurkat and Panc Tu‐I\n",
      "HeLa, Mino, Jeko-1, HUVEC, MDA-MB-231, MM.1S\n",
      "Hela, MM.1S\n",
      "HeLa, MV4;11, A549\n",
      "AML cells, CRBN-/-\n",
      "SU-DHL-1 and NCI-H2228\n",
      "MCF-7, MM.1S\n",
      "DU145/Cy\n",
      "PC3, MDA-MB-231\n",
      "Namalwa, CA-46, Ramos\n",
      "HBL1, Ramos, Mino, IgE MM\n",
      "Ramos, THP-1\n",
      "MV4-11, MOLM-14\n",
      "MDA-MB-231, HeLa\n",
      "HEK293, CAMA-1, ZR-75-1\n",
      "MiaPaCa2, HPNE\n",
      "MCF-7, MDA-MB-231, HepG2, LO2, B16\n",
      "Karpas 422,ULA, SUDHL4, OCI-Ly1, Ramos\n",
      "SR, H2228, NCI–H69, NCI–H1688, NCI–H446\n",
      "OVCAR8 (WT EGFR), HeLa (EGFR Exon 20 Ins), SKBr3 (HER2)\n",
      "HCC827 (Exon 19 del), H3255 (L858R)\n",
      "MDA-MB-231, GTL16\n",
      "RS4;11, MV4;11, MOLM-13,\n",
      "MCF-7, U2OS, MDA-MB-231, MDA-MB-435, Flag-Cdc20, MDA-MB-231, 22Rv1, LNCaP\n",
      "Hella, RI-1\n",
      "Hella, RI-1, EOL-1, A-204\n",
      "MM1S, HEK293T, CRBNY384A/W386A\n",
      "Molm-16, SU-DHL-1\n",
      "RS4;11, MV4;11\n",
      "LNCaP, VCaP, and 22Rv\n",
      "MV4-11, Molm-13\n",
      "Mino, Ramos\n",
      "MCF7, A549\n",
      "HOP62/INC-H23\n",
      "E14 mouse embryonic stem cells; Human colon cancer cell line HCT116\n",
      "Pancreatic cancer cell lines BxPC-3; MIA PaCa-2\n",
      "KYSE520 esophageal cancer cell line; MV4;11 cell line\n",
      "HeLa, HL60, MV4;11\n",
      "HeLa, HEK293, U2OS\n",
      "MDA-MB-231, A549, A549/DDP, HUVEC\n",
      "HeLa, HEK293\n",
      "231MFP breast cancer cells, HeLa\n",
      "K562, A549, HCT116, MCF-7, HEK293T\n",
      "MM1S; Mouse 4935 cells\n",
      "PC9; HCC827; H1975\n",
      "T47D, BT474, MDAMB231, MCF7, HS578T, LS180, HCT116, HCT15, DLD1, SW480, LOVO, SW620, HEL, KCL22, THP1, K562, ML2, NOMO1, MONOMAC6, MOLM13, LAMA84, SHI1, MV411, KU812, KG1, KBM7, HL60, BV173, HUH7, NCIH460, A549, NCIH2228, PC9, NCIH446, HELA, T98G, MCF10A, HAP1, DU145, PC3, LNCaP, A375, LOXIMVI\n",
      "5W1573; NCI-H2030; NCI-H358; HCT116; MIA-PaCa-2\n",
      "Fibroblasts from patients; HeLa\n",
      "SU-DHL-1; H3122\n",
      "IgEMM; hTERT-RPE1; HeLa; Jurkat; RPMI8226; MM.1S\n",
      "Jurkat; Molt4; Granta-519; Mino; Jeko; Rec1; Maver\n",
      "Mice primary Sertoli cells and primary Germ cells\n",
      "Ba/F3; H1975\n",
      "Ba/F3\n",
      "HT1080, IMR-32, MCF-7\n",
      "HT1080, IMR-32\n",
      "BxPC3, DLD-1, HCT-116, MDA-MB-231, A549, PC-9\n",
      "MDA-MB-231; K562\n",
      "MOLT4; BT549; HCC1806; COV362; Kuramochi; OVCAR8\n",
      "SRD15; Huh7\n",
      "SW480, HCT116\n",
      "K562, BaF3\n",
      "BBL358, T47D\n",
      "A549; H1299; B16-F10; MDAMB231; Jurkat\n"
     ]
    }
   ],
   "source": [
    "for c in multiple_cells_df['Cell Type'].unique():\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Direct degration studies in this paper only conducted for first and final compounds; during the med chem campaign cell viability was used as the read out. Complex structure with the ligand was modeled based on 4Z93. IC50 of the ligand is between 2nM and 7nM, depending on which BRD. DC50 is between 3nM-10nM.\n",
      "RS4;11, MOLM-13\n",
      "100 %\n",
      "< 10 nM\n",
      "--------------------\n",
      "General kinase PROTAC, DCmax is for the most degraded kinase. IC50 of the ligand is for 193 kinases in the panel. IC50 of the PROTAC is by FLT3 kinase activity.\n",
      "MOLT-4, MOLM14\n",
      "> 85 %\n",
      "< 100 nM\n",
      "--------------------\n",
      "DC50 is 9.1 nM (WT BTK, NAMALWA cells), 14.6 nM (WT BTK, XLA cells), 14.9 nM (C481S, XLA cells). IC50 of PROTAC is 46.9 (WT BTK), 20.9 (C481S). IC50 of ligand is 51.0 nM (WT BTK), 30.7 (C481S)\n",
      "NAMALWA, XLA\n",
      "> 99 %\n",
      "< 14.9 nM\n",
      "--------------------\n",
      "EC50 of PROTAC is 28nM in MV-4-11, 68nM in NCI-H1568. DC50 is (SMARCA2 6nM, SMARCA4 11nM, PBRM1 32nM in MV-4-11; SMARCA2 3.3nM, PBRM1 15.6nM in NCI-H1568)\n",
      "MV-4-11 SK-MEL-5, NCI-H1568\n",
      "nan\n",
      "< 32 nM\n",
      "--------------------\n",
      "DC50 value is for HeLa cells. DC50 is 0.61 uM for DLBCL cells. DMAX value is for HeLa cells. DMAX is 96 % for DLBCL cells.\n",
      "HeLa, DLBCL\n",
      "92 %\n",
      "0.79 uM\n",
      "--------------------\n",
      "proteomics of PDEdelta degradation show upregulation of enzymes involved in lipid metabolism - deltasonamide 1 also causes this. Dmax was measured in Panc-Tu-1 cell line. DC50 is 83.4% (24 h, Panc-Tu-1), 85% (24 h, 1 uM, Jurkat). IC50 Deltasonamide 1 is 203 pM, and of the Bn derivative is 8 nM.\n",
      "Jurkat and Panc Tu‐I\n",
      "> 83.4 %\n",
      "48 nM\n",
      "--------------------\n",
      "DC50 is 1-3nM\n",
      "HeLa, MV4;11, A549\n",
      "100 %\n",
      "< 3 nM\n",
      "--------------------\n",
      "DC50 is 10-30nM\n",
      "HeLa, MV4;11, A549\n",
      "99 %\n",
      "< 30 nM\n",
      "--------------------\n",
      "competition with ligand only slightly rescued protein degradation. EC50 of ligand and PROTAC is measured on SUDHL-1 cell line. DC50 is SU-DHL-1: 3 ± 1 nM, NCI-H2228: 34 ± 9 nM.\n",
      "SU-DHL-1 and NCI-H2228\n",
      "> 90 %\n",
      "< 34 nM\n",
      "--------------------\n",
      "competition with ligand only slightly rescued protein degradation. EC50 of ligand and PROTAC is measured on SUDHL-1 cell line. DC50 is SU-DHL-1: 11 ± 2 nM, NCI-H2228: 59 ± 16 nM.\n",
      "SU-DHL-1 and NCI-H2228\n",
      "> 90 %\n",
      "< 59 nM\n",
      "--------------------\n",
      "\n",
      "\n",
      "DC50 and Dmax are for MCF-7 cells\n",
      "MCF-7, MM.1S\n",
      "70.5 %\n",
      "34 nM\n",
      "--------------------\n",
      "also degrades ibrutinib-resistant C481S BTK. DC50 is 6.3 nM (HBL1), 8.5 nM (Ramos), 9.2 nM (Mino), 11.4 nM (IgE MM)\n",
      "HBL1, Ramos, Mino, IgE MM\n",
      "94 %\n",
      "< 11.4 nM\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 18-33%\n",
      "Ramos, THP-1\n",
      "< 33 %\n",
      "nan\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 15-34%\n",
      "Ramos, THP-1\n",
      "< 34 %\n",
      "nan\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 36-64%. DC50 is 1487 - 1994.5 nM.\n",
      "Ramos, THP-1\n",
      "< 64 %\n",
      "< 1994.5 nM\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 68-85%. DC50 is 36.9 - 398.5 nM.\n",
      "Ramos, THP-1\n",
      "> 68 %\n",
      "< 398.5 nM\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 63-84%. DC50 is 21.8 - 469.9 nM.\n",
      "Ramos, THP-1\n",
      "> 63 %\n",
      "< 469.9 nM\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 75-87%. DC50 is 4.5 - 90.5 nM.\n",
      "Ramos, THP-1\n",
      "> 75 %\n",
      "< 90.5 nM\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 71-85%. DC50 is 5.9 - 217.7 nM.\n",
      "Ramos, THP-1\n",
      "> 71 %\n",
      "< 217.7 nM\n",
      "--------------------\n",
      "Ligand name is from PMID 24068666. DCmax is 80-87%. DC50 is 1.1 - 37.4 nM.\n",
      "Ramos, THP-1\n",
      "> 80 %\n",
      "< 37.4 nM\n",
      "--------------------\n",
      "\n",
      "\n",
      "Ligand name is from PMID 24068666. DCmax is 70-85%. DC50 is 9.7 - 184.1 nM.\n",
      "Ramos, THP-1\n",
      "> 70 %\n",
      "< 184.1 nM\n",
      "--------------------\n",
      "DC50 error ± 1.0 nM. DMAX error ± 1.1 %.\n",
      "MDA-MB-231, HeLa\n",
      "99.6 %\n",
      "9.5 nM\n",
      "--------------------\n",
      "DC50 error ± 81.3 nM. DMAX error ± 10.1 %.\n",
      "MDA-MB-231, HeLa\n",
      "34.5 %\n",
      "45.9 nM\n",
      "--------------------\n",
      "DC50 is for WT EGFR. DC50 for EGFR Exon 20 Ins is 736.2 nM. DMAX is for WT EGFR. DMAX for EGFR Exon 20 Ins is 68.8 %.\n",
      "OVCAR8 (WT EGFR), HeLa (EGFR Exon 20 Ins), SKBr3 (HER2)\n",
      "97.6 %\n",
      "39.2 nM\n",
      "--------------------\n",
      "DC50 is for EGFR (Exon 19 del). DC50 for EGFR (L858R) 22.3 nM. DMAX is for EGFR (Exon 19 del). DMAX for EGFR (L858R) 96.6 %.\n",
      "HCC827 (Exon 19 del), H3255 (L858R)\n",
      "98.9 %\n",
      "11.7 nM\n",
      "--------------------\n",
      "DC50 is 1.76 nM and 4.5 nM\n",
      "Hella, RI-1, EOL-1, A-204\n",
      "90 %\n",
      "< 4.5 nM\n",
      "--------------------\n",
      "DC50 is 0.86nM in LNCaP, 0.76 in VCaP and 10.4 nM at 1uM in 22Rv1. EC50 in cells is 0.25nM for LNCaP, 0.34 nM for VCaP, 183nM for 22Rv1.\n",
      "LNCaP, VCaP, and 22Rv\n",
      "> 95 %\n",
      "< 10.4 nM\n",
      "--------------------\n",
      "XD2-149 was initially designed to degrade STAT3. However, experiments showed that XD2-149 down-regulate STAT3 level in a proteasome-independent manner. Proteomics data revealed that an E3 ligase, ZFP91, was the true substrate for this PROTAC. This paper reported a total of 22 PROTACs, which differed from each other in linker design and E3 binder choices (pomalidomide/thalidomide/lenalidomide). However, the authors didn't mention whether the remaining 21 molecules could degrade ZFP91 or not. It is also interesting that pomalidomide itself can induce the degradation of CRBN neo-substrates like ZFP91 (DC50: 0.42uM, 5-fold less potent than XD2-149), since pomalidomide can remodel CRBN surface for binding proteins like ZFP91 (Nat Med., 2019, doi: 10.1038/s41591-019-0668-z).\n",
      "Pancreatic cancer cell lines BxPC-3; MIA PaCa-2\n",
      "> 90 %\n",
      "~ 70 nM\n",
      "--------------------\n",
      "DC50 for KYSE520 cell: 6.0nM;DC50 for MV4;11 cell: 2.6nM; EC50 for KYSE520 cell: 0.66uM; EC50 for MV4;11 cell: 9.9nM;\n",
      "KYSE520 esophageal cancer cell line; MV4;11 cell line\n",
      "100 %\n",
      "6.0 nM\n",
      "--------------------\n",
      "pEC50 for MV4;11 and HL60 cells: 6.75±0.03 and 5.84±0.06, respectively.\n",
      "pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 7.0/7.0/6.5/6.2, respectively (24h, HeLa cells).\n",
      "Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 96%/97%/97%/93%, respectively (HeLa cells).\n",
      "HeLa, HL60, MV4;11\n",
      "> 93 %\n",
      "< 0.1 uM\n",
      "--------------------\n",
      "\n",
      "\n",
      "pEC50 for MV4;11 and HL60 cells: 7.57±0.03 and 6.66±0.05, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.1/8.6/7.0/7.4, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 98%/100%/100%/98%, respectively (HeLa cells).\n",
      "HeLa, HL60, MV4;11\n",
      "> 98 %\n",
      "< 2.5 nM\n",
      "--------------------\n",
      "pEC50 for MV4;11 and HL60 cells: 6.91±0.04 and 5.90±0.05, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.4/8.0/6.5/6.7, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 99%/100%/99%/97%, respectively (HeLa cells).\n",
      "HeLa, HL60, MV4;11\n",
      "> 97 %\n",
      "< 4 nM\n",
      "--------------------\n",
      "pEC50 for MV4;11 and HL60 cells: 7.77±0.06 and 7.46±0.03, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 9.2/9.0/9.1/8.2, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 97%/100%/98%/83%, respectively (HeLa cells).\n",
      "HeLa, HL60, MV4;11\n",
      "> 83 %\n",
      "< 1 nM\n",
      "--------------------\n",
      "pEC50 for MV4;11 and HL60 cells: 6.24±0.05 and 6.17±0.03, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 6.9/6.7/6.8/NA, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 94%/78%/74%/37%, respectively (HeLa cells).\n",
      "HeLa, HL60, MV4;11\n",
      "nan\n",
      "~ 0.1 uM\n",
      "--------------------\n",
      "pEC50 for MV4;11 and HL60 cells: 7.31±0.03 and 6.57±0.02, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.1/7.6/7.3/NA, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 98%/95%/91%/43%, respectively (HeLa cells).\n",
      "HeLa, HL60, MV4;11\n",
      "> 90 %\n",
      "~ 7.9 nM\n",
      "--------------------\n",
      "pEC50 for MV4;11 and HL60 cells: 7.08±0.05 and 6.37±0.03, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.1/7.5/7.7/NA, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 95%/93%/92%/26%, respectively (HeLa cells).\n",
      "HeLa, HL60, MV4;11\n",
      "> 90 %\n",
      "~ 7.9 nM\n",
      "--------------------\n",
      "Reported DC50 and Dmax above are in HeLa cells. DC50 for HEK293 cells: 230nM; Dmax for HEK293 cells: 98%. 14a can degrade VHL at higher concentration. See Target UniprotID P40337 for details.\n",
      "HeLa, HEK293\n",
      "88 %\n",
      "200 nM\n",
      "--------------------\n",
      "EC50/DC50/Dmax reported above were obtained using NCI-H2030 cells. DC50: 0.25~0.76uM; Dmax: ~75%-90%, specific value depends on the cell line.\n",
      "5W1573; NCI-H2030; NCI-H358; HCT116; MIA-PaCa-2\n",
      "~ 80 %\n",
      "0.59 uM\n",
      "--------------------\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "for i, row in multiple_cells_df.dropna(subset=['Comments', 'Cell Type', 'Dmax (%)', 'DC50 (nM)'], how='all').iterrows():\n",
    "    if pd.isnull(row['Comments']):\n",
    "        continue\n",
    "    if 'DC' in row['Comments'].upper() or 'max' in row['Comments'].upper():\n",
    "        cnt += 1\n",
    "        print(row['Comments'])\n",
    "        print(row['Cell Type'])\n",
    "        print(row['Dmax (%)'])\n",
    "        print(row['DC50 (nM)'])\n",
    "        print('-' * 20)\n",
    "        if cnt % 10 == 0:\n",
    "            print('\\n')\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell Type</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Dmax (%)</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>RS4;11, MOLM-13</td>\n",
       "      <td>Direct degration studies in this paper only co...</td>\n",
       "      <td>100 %</td>\n",
       "      <td>&lt; 10 nM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>RS4;11, MOLM-13</td>\n",
       "      <td>Complex structure with the ligand was modeled ...</td>\n",
       "      <td>100 %</td>\n",
       "      <td>&lt; 1 nM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>RS4;11, MOLM-13</td>\n",
       "      <td>Complex structure with the ligand was modeled ...</td>\n",
       "      <td>100 %</td>\n",
       "      <td>&lt; 1 nM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>RS4;11, MOLM-13</td>\n",
       "      <td>Complex structure with the ligand was modeled ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~ 3 nM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>RS4;11, MOLM-13</td>\n",
       "      <td>Complex structure with the ligand was modeled ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>~ 10 nM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>A549; H1299; B16-F10; MDAMB231; Jurkat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>A549; H1299; B16-F10; MDAMB231; Jurkat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>A549; H1299; B16-F10; MDAMB231; Jurkat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>A549; H1299; B16-F10; MDAMB231; Jurkat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>A549; H1299; B16-F10; MDAMB231; Jurkat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>278 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Cell Type  \\\n",
       "105                          RS4;11, MOLM-13   \n",
       "106                          RS4;11, MOLM-13   \n",
       "107                          RS4;11, MOLM-13   \n",
       "108                          RS4;11, MOLM-13   \n",
       "109                          RS4;11, MOLM-13   \n",
       "...                                      ...   \n",
       "1182  A549; H1299; B16-F10; MDAMB231; Jurkat   \n",
       "1185  A549; H1299; B16-F10; MDAMB231; Jurkat   \n",
       "1194  A549; H1299; B16-F10; MDAMB231; Jurkat   \n",
       "1195  A549; H1299; B16-F10; MDAMB231; Jurkat   \n",
       "1198  A549; H1299; B16-F10; MDAMB231; Jurkat   \n",
       "\n",
       "                                               Comments Dmax (%) DC50 (nM)  \n",
       "105   Direct degration studies in this paper only co...    100 %   < 10 nM  \n",
       "106   Complex structure with the ligand was modeled ...    100 %    < 1 nM  \n",
       "107   Complex structure with the ligand was modeled ...    100 %    < 1 nM  \n",
       "108   Complex structure with the ligand was modeled ...      NaN    ~ 3 nM  \n",
       "109   Complex structure with the ligand was modeled ...      NaN   ~ 10 nM  \n",
       "...                                                 ...      ...       ...  \n",
       "1182                                                NaN      NaN       NaN  \n",
       "1185                                                NaN      NaN       NaN  \n",
       "1194                                                NaN      NaN       NaN  \n",
       "1195                                                NaN      NaN       NaN  \n",
       "1198                                                NaN      NaN       NaN  \n",
       "\n",
       "[278 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiple_cells_df[['Cell Type', 'Comments', 'Dmax (%)', 'DC50 (nM)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_mole_to_nM(s: str) -> float:\n",
    "    \"\"\" Clean a string by removing <, >, =, NaN, and ranges like 100-200. Then convert to nM.\n",
    "    Args:\n",
    "        s(str): string to clean\n",
    "    Returns:\n",
    "        float: cleaned string\n",
    "    \"\"\"\n",
    "    s = clean_string(str(s))\n",
    "    if pd.isnull(s) or s in {'nan', 'n/a', 'NaN', ''}:\n",
    "        return np.nan\n",
    "    if 'nM' in s:\n",
    "        return float(s.replace('nM', '').strip())\n",
    "    elif 'uM' in s:\n",
    "        tmp = float(s.replace('uM', '').strip())\n",
    "        # Convert from uM to nM\n",
    "        return float(tmp) * 1e3\n",
    "    else:\n",
    "        return float(s) * 1e9\n",
    "\n",
    "\n",
    "protac_pedia_df['DC50 (nM)'] = protac_pedia_df['DC50 (nM)'].apply(\n",
    "    convert_mole_to_nM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "protac_pedia_df['Dmax (%)'] = protac_pedia_df['Dmax (%)'].apply(\n",
    "    lambda s: float(clean_string(s)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-Nan Active: 297\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "protac_pedia_df['Active'] = protac_pedia_df.apply(\n",
    "    lambda row: is_active(row['DC50 (nM)'], row['Dmax (%)']),\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "protac_pedia_df['Active'] = protac_pedia_df.apply(\n",
    "    lambda row: row['Active/Inactive'] if pd.isnull(\n",
    "        row['Active']) and row['Active/Inactive'] == False else row['Active'],\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "print(\n",
    "    f'Non-Nan Active: {len(protac_pedia_df[protac_pedia_df[\"Active\"].notnull()])}')\n",
    "\n",
    "# Histogram of Active\n",
    "sns.histplot(data=protac_pedia_df, x='Active', bins=16)\n",
    "plt.title('Histogram of Active')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "270288154fa2473a8a0151a8c4e93ecb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/97 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Bio import Entrez\n",
    "\n",
    "\n",
    "def get_doi_from_pubmed(pmid):\n",
    "    Entrez.email = \"steribes92@gmail.com\"  # Always provide your email\n",
    "    handle = Entrez.efetch(db=\"pubmed\", id=pmid, retmode=\"xml\")\n",
    "    records = Entrez.read(handle)\n",
    "    handle.close()\n",
    "\n",
    "    # Extracting DOI\n",
    "    try:\n",
    "        article = records['PubmedArticle'][0]\n",
    "        for el in article['MedlineCitation']['Article']['ELocationID']:\n",
    "            if el.attributes['EIdType'] == 'doi':\n",
    "                return el\n",
    "    except IndexError:\n",
    "        return f'https://pubmed.ncbi.nlm.nih.gov/{pmid}/'\n",
    "\n",
    "\n",
    "# Load DOI pickle if it exists\n",
    "doi_pickle_file = os.path.join(data_dir, 'processed', 'pubmed2doi.pkl')\n",
    "if os.path.exists(doi_pickle_file):\n",
    "    with open(doi_pickle_file, 'rb') as f:\n",
    "        pubmed2doi = pickle.load(f)\n",
    "else:\n",
    "    pubmed2doi = {}\n",
    "# Get a DOI for each PubMed ID\n",
    "pubmed_ids = protac_pedia_df['Pubmed'].unique().tolist()\n",
    "pubmed2doi.update(\n",
    "    {k: get_doi_from_pubmed(int(k)) for k in tqdm(\n",
    "        pubmed_ids) if pd.notnull(k) and int(k) not in pubmed2doi}\n",
    ")\n",
    "# Save DOI pickle\n",
    "with open(doi_pickle_file, 'wb') as f:\n",
    "    pickle.dump(pubmed2doi, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       10.1016/j.ejmech.2020.112186\n",
       "1       10.1016/j.ejmech.2020.112186\n",
       "2       10.1016/j.ejmech.2020.112186\n",
       "3       10.1016/j.ejmech.2020.112186\n",
       "4       10.1016/j.ejmech.2020.112186\n",
       "                    ...             \n",
       "1197    10.1016/j.ejmech.2020.112377\n",
       "1199    10.1016/j.ejmech.2020.112377\n",
       "1200    10.1016/j.ejmech.2020.112377\n",
       "1201          10.1002/anie.202101864\n",
       "1202             10.7554/eLife.52237\n",
       "Name: Article DOI, Length: 925, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a DOI column to the PROTAC-Pedia dataframe\n",
    "protac_pedia_df['Article DOI'] = protac_pedia_df['Pubmed'].apply(\n",
    "    lambda pmid: pubmed2doi.get(pmid, np.nan)\n",
    ")\n",
    "protac_pedia_df['Article DOI']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the treatment time, the maximum value is reported. Let's replace the `Treatment Time (h)` column with the maximum value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[48.0,\n",
       " 16.0,\n",
       " 24.0,\n",
       " 4.0,\n",
       " 6.0,\n",
       " 8.0,\n",
       " nan,\n",
       " 12.0,\n",
       " 7.0,\n",
       " 20.0,\n",
       " 18.0,\n",
       " 2.0,\n",
       " 10.0,\n",
       " 96.0,\n",
       " 72.0,\n",
       " 5.0,\n",
       " 32.0]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_time(s):\n",
    "    if pd.isnull(s):\n",
    "        return np.nan\n",
    "    return max([float(t) for t in s.split(',')])\n",
    "\n",
    "\n",
    "protac_pedia_df['Treatment Time (h)'] = protac_pedia_df['Treatment Time (h)'].apply(\n",
    "    get_time\n",
    ")\n",
    "protac_pedia_df['Treatment Time (h)'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b01d418bb7f433db823484196bd1ac3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "InChI:   0%|          | 0/925 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b19f9aaf2864a8ab7ce0203ff8b6852",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "InChI Key:   0%|          | 0/925 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_inchi(s):\n",
    "    if pd.isnull(s):\n",
    "        return np.nan\n",
    "    return Chem.MolToInchi(Chem.MolFromSmiles(s))\n",
    "\n",
    "\n",
    "def get_inchi_key(s):\n",
    "    if pd.isnull(s):\n",
    "        return np.nan\n",
    "    return Chem.MolToInchiKey(Chem.MolFromSmiles(s))\n",
    "\n",
    "\n",
    "tqdm.pandas(desc='InChI')\n",
    "protac_pedia_df['InChI'] = protac_pedia_df['Smiles'].progress_apply(get_inchi)\n",
    "tqdm.pandas(desc='InChI Key')\n",
    "protac_pedia_df['InChI Key'] = protac_pedia_df['Smiles'].progress_apply(\n",
    "    get_inchi_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import Descriptors, rdMolDescriptors\n",
    "\n",
    "\n",
    "def get_heavy_atoms(smi):\n",
    "    return Descriptors.HeavyAtomCount(Chem.MolFromSmiles(smi))\n",
    "\n",
    "\n",
    "def get_ring_count(smi):\n",
    "    return rdMolDescriptors.CalcNumRings(Chem.MolFromSmiles(smi))\n",
    "\n",
    "\n",
    "def get_rotatable_bonds(smi):\n",
    "    return rdMolDescriptors.CalcNumRotatableBonds(Chem.MolFromSmiles(smi))\n",
    "\n",
    "\n",
    "def get_molecular_formula(smi):\n",
    "    return rdMolDescriptors.CalcMolFormula(Chem.MolFromSmiles(smi))\n",
    "\n",
    "\n",
    "protac_pedia_df['Heavy Atom Count'] = protac_pedia_df['Smiles'].apply(\n",
    "    get_heavy_atoms\n",
    ")\n",
    "protac_pedia_df['Ring Count'] = protac_pedia_df['Smiles'].apply(\n",
    "    get_ring_count\n",
    ")\n",
    "protac_pedia_df['Rotatable Bond Count'] = protac_pedia_df['Smiles'].apply(\n",
    "    get_rotatable_bonds\n",
    ")\n",
    "protac_pedia_df['Molecular Formula'] = protac_pedia_df['Smiles'].apply(\n",
    "    get_molecular_formula\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "protac_pedia_df['Database'] = 'PROTAC-Pedia'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppedia_cols = [\n",
    "    'Compound ID',\n",
    "    # 'Target',\n",
    "    'Uniprot',\n",
    "    'Smiles',\n",
    "    'E3 Ligase',\n",
    "    'InChI',\n",
    "    'InChI Key',\n",
    "    'Molecular Weight',\n",
    "    'Heavy Atom Count',\n",
    "    'Ring Count',\n",
    "    'Rotatable Bond Count',\n",
    "    'Topological Polar Surface Area',\n",
    "    'Hydrogen Bond Acceptor Count',\n",
    "    'Hydrogen Bond Donor Count',\n",
    "    'Cell Type',\n",
    "    'Treatment Time (h)',\n",
    "    'DC50 (nM)',\n",
    "    'Dmax (%)',\n",
    "    'Active',\n",
    "    'Article DOI',\n",
    "    'Comments',\n",
    "    'Database',\n",
    "    'Molecular Formula',\n",
    "    'cLogP',\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse Functions per Assay Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "813\n",
      "848\n"
     ]
    }
   ],
   "source": [
    "def get_assay_texts(df: pd.DataFrame, assay_column: str) -> List[str]:\n",
    "    tmp = df[assay_column].dropna()\n",
    "    if tmp.empty:\n",
    "        return []\n",
    "    return tmp.unique().tolist()\n",
    "\n",
    "\n",
    "def clean_assay_text(assay):\n",
    "    tmp = assay.replace('/', ' and ')\n",
    "    tmp = tmp.replace('BRD4 BD1 and 2', 'BRD4 BD1 and BRD4 BD2')\n",
    "    tmp = tmp.replace('(Ba and F3 WT)', '(Ba/F3 WT)')\n",
    "    tmp = tmp.replace('(EGFR L858R and T790M)', '(EGFR L858R/T790M)')\n",
    "    return tmp\n",
    "\n",
    "\n",
    "assays = {}\n",
    "for c in protac_df.columns:\n",
    "    if 'Assay' in c:\n",
    "        assays[c] = get_assay_texts(protac_df, c)\n",
    "texts = list(set([x for y in assays.values() for x in y]))\n",
    "print(len(texts))\n",
    "print(sum([len(x) for x in assays.values()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Assay (Ternary complex, IC50)</th>\n",
       "      <th>IC50 (nM, Ternary complex)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>Inhibition of CRBN in the presence of BRD4 BD1...</td>\n",
       "      <td>1800/4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>TR-FRET</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>TR-FRET/AlphaLISA</td>\n",
       "      <td>205/120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>TR-FRET/AlphaLISA</td>\n",
       "      <td>230/330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>TR-FRET</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>TR-FRET/AlphaLISA</td>\n",
       "      <td>45/120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>TR-FRET/AlphaLISA</td>\n",
       "      <td>76/240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>TR-FRET/AlphaLISA</td>\n",
       "      <td>26/730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>TR-FRET/AlphaLISA</td>\n",
       "      <td>51/720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>TR-FRET</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>FP</td>\n",
       "      <td>3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>FP</td>\n",
       "      <td>2570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>FP</td>\n",
       "      <td>2170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>Titrations of PROTAC over an immobilised VCB s...</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>FP</td>\n",
       "      <td>3040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2859</th>\n",
       "      <td>FP</td>\n",
       "      <td>2980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2860</th>\n",
       "      <td>FP</td>\n",
       "      <td>2460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4883</th>\n",
       "      <td>IC50 between VHL and the complex of protac and...</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4884</th>\n",
       "      <td>IC50 between VHL and the complex of protac and...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4885</th>\n",
       "      <td>IC50 between VHL and the complex of protac and...</td>\n",
       "      <td>7.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4886</th>\n",
       "      <td>IC50 between VHL and the complex of protac and...</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4887</th>\n",
       "      <td>IC50 between VHL and the complex of protac and...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4888</th>\n",
       "      <td>IC50 between VHL and the complex of protac and...</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Assay (Ternary complex, IC50)  \\\n",
       "539   Inhibition of CRBN in the presence of BRD4 BD1...   \n",
       "1185                                            TR-FRET   \n",
       "1186                                  TR-FRET/AlphaLISA   \n",
       "1187                                  TR-FRET/AlphaLISA   \n",
       "1188                                            TR-FRET   \n",
       "1189                                  TR-FRET/AlphaLISA   \n",
       "1190                                  TR-FRET/AlphaLISA   \n",
       "1191                                  TR-FRET/AlphaLISA   \n",
       "1192                                  TR-FRET/AlphaLISA   \n",
       "1193                                            TR-FRET   \n",
       "1508                                                 FP   \n",
       "1526                                                 FP   \n",
       "1528                                                 FP   \n",
       "2300  Titrations of PROTAC over an immobilised VCB s...   \n",
       "2858                                                 FP   \n",
       "2859                                                 FP   \n",
       "2860                                                 FP   \n",
       "4883  IC50 between VHL and the complex of protac and...   \n",
       "4884  IC50 between VHL and the complex of protac and...   \n",
       "4885  IC50 between VHL and the complex of protac and...   \n",
       "4886  IC50 between VHL and the complex of protac and...   \n",
       "4887  IC50 between VHL and the complex of protac and...   \n",
       "4888  IC50 between VHL and the complex of protac and...   \n",
       "\n",
       "     IC50 (nM, Ternary complex)  \n",
       "539                   1800/4100  \n",
       "1185                        220  \n",
       "1186                    205/120  \n",
       "1187                    230/330  \n",
       "1188                         28  \n",
       "1189                     45/120  \n",
       "1190                     76/240  \n",
       "1191                     26/730  \n",
       "1192                     51/720  \n",
       "1193                         42  \n",
       "1508                       3750  \n",
       "1526                       2570  \n",
       "1528                       2170  \n",
       "2300                        290  \n",
       "2858                       3040  \n",
       "2859                       2980  \n",
       "2860                       2460  \n",
       "4883                       5.01  \n",
       "4884                          2  \n",
       "4885                       7.94  \n",
       "4886                       6.31  \n",
       "4887                         10  \n",
       "4888                       1.26  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_df[[\"Assay (Cellular activities, EC50)\",\n",
    "           \"EC50 (nM, Cellular activities)\"]].dropna(how='all').drop_duplicates()\n",
    "\n",
    "protac_df[[\"Assay (Ternary complex, IC50)\", \"IC50 (nM, Ternary complex)\"]].dropna(\n",
    "    how='all').drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-assay columns: ['E3 Ligase', 'Heavy Atom Count', 'Hydrogen Bond Acceptor Count', 'Compound ID', 'Molecular Formula', 'Rotatable Bond Count', 'Molecular Weight', 'InChI Key', 'Target', 'XLogP3', 'Smiles', 'Article DOI', 'Ring Count', 'Name', 'Hydrogen Bond Donor Count', 'Uniprot', 'PDB', 'InChI', 'Topological Polar Surface Area', 'Exact Mass']\n",
      "Assay columns: ['DC50 (nM)', 'Dmax (%)', 'Assay (DC50/Dmax)', 'Percent degradation (%)', 'Assay (Percent degradation)', 'IC50 (nM, Protac to Target)', 'Assay (Protac to Target, IC50)', 'EC50 (nM, Protac to Target)', 'Assay (Protac to Target, EC50)', 'Kd (nM, Protac to Target)', 'Assay (Protac to Target, Kd)', 'Ki (nM, Protac to Target)', 'Assay (Protac to Target, Ki)', 'delta G (kcal/mol, Protac to Target)', 'delta H (kcal/mol, Protac to Target)', '-T*delta S (kcal/mol, Protac to Target)', 'Assay (Protac to Target, G/H/-TS)', 'kon (1/Ms, Protac to Target)', 'koff (1/s, Protac to Target)', 't1/2 (s, Protac to Target)', 'Assay (Protac to Target, kon/koff/t1/2)', 'IC50 (nM, Protac to E3)', 'Assay (Protac to E3, IC50)', 'EC50 (nM, Protac to E3)', 'Assay (Protac to E3, EC50)', 'Kd (nM, Protac to E3)', 'Assay (Protac to E3, Kd)', 'Ki (nM, Protac to E3)', 'Assay (Protac to E3, Ki)', 'delta G (kcal/mol, Protac to E3)', 'delta H (kcal/mol, Protac to E3)', '-T*delta S (kcal/mol, Protac to E3)', 'Assay (Protac to E3, G/H/-TS)', 'kon (1/Ms, Protac to E3)', 'koff (1/s, Protac to E3)', 't1/2 (s, Protac to E3)', 'Assay (Protac to E3, kon/koff/t1/2)', 'IC50 (nM, Ternary complex)', 'Assay (Ternary complex, IC50)', 'EC50 (nM, Ternary complex)', 'Assay (Ternary complex, EC50)', 'Kd (nM, Ternary complex)', 'Assay (Ternary complex, Kd)', 'Ki (nM, Ternary complex)', 'Assay (Ternary complex, Ki)', 'delta G (kcal/mol, Ternary complex)', 'delta H (kcal/mol, Ternary complex)', '-T*delta S (kcal/mol, Ternary complex)', 'Assay (Ternary complex, G/H/-TS)', 'kon (1/Ms, Ternary complex)', 'koff (1/s, Ternary complex)', 't1/2 (s, Ternary complex)', 'Assay (Ternary complex, kon/koff/t1/2)', 'IC50 (nM, Cellular activities)', 'Assay (Cellular activities, IC50)', 'EC50 (nM, Cellular activities)', 'Assay (Cellular activities, EC50)', 'GI50 (nM, Cellular activities)', 'Assay (Cellular activities, GI50)', 'ED50 (nM, Cellular activities)', 'Assay (Cellular activities, ED50)', 'GR50 (nM, Cellular activities)', 'Assay (Cellular activities, GR50)', 'PAMPA Papp (nm/s, Permeability)', 'Assay (Permeability, PAMPA Papp)', 'Caco-2 A2B Papp (nm/s, Permeability)', 'Assay (Permeability, Caco-2 A2B Papp)', 'Caco-2 B2A Papp (nm/s, Permeability)', 'Assay (Permeability, Caco-2 B2A Papp)']\n"
     ]
    }
   ],
   "source": [
    "assay_to_parameters = {\n",
    "    \"Assay (DC50/Dmax)\": [\"DC50 (nM)\", \"Dmax (%)\"],\n",
    "    \"Assay (Percent degradation)\": [\"Percent degradation (%)\"],\n",
    "    \"Assay (Protac to Target, IC50)\": [\"IC50 (nM, Protac to Target)\"],\n",
    "    \"Assay (Protac to Target, EC50)\": [\"EC50 (nM, Protac to Target)\"],\n",
    "    \"Assay (Protac to Target, Kd)\": [\"Kd (nM, Protac to Target)\"],\n",
    "    \"Assay (Protac to Target, Ki)\": [\"Ki (nM, Protac to Target)\"],\n",
    "    \"Assay (Protac to Target, G/H/-TS)\": [\"delta G (kcal/mol, Protac to Target)\", \"delta H (kcal/mol, Protac to Target)\", \"-T*delta S (kcal/mol, Protac to Target)\"],\n",
    "    \"Assay (Protac to Target, kon/koff/t1/2)\": [\"kon (1/Ms, Protac to Target)\", \"koff (1/s, Protac to Target)\", \"t1/2 (s, Protac to Target)\"],\n",
    "    \"Assay (Protac to E3, IC50)\": [\"IC50 (nM, Protac to E3)\"],\n",
    "    \"Assay (Protac to E3, EC50)\": [\"EC50 (nM, Protac to E3)\"],\n",
    "    \"Assay (Protac to E3, Kd)\": [\"Kd (nM, Protac to E3)\"],\n",
    "    \"Assay (Protac to E3, Ki)\": [\"Ki (nM, Protac to E3)\"],\n",
    "    \"Assay (Protac to E3, G/H/-TS)\": [\"delta G (kcal/mol, Protac to E3)\", \"delta H (kcal/mol, Protac to E3)\", \"-T*delta S (kcal/mol, Protac to E3)\"],\n",
    "    \"Assay (Protac to E3, kon/koff/t1/2)\": [\"kon (1/Ms, Protac to E3)\", \"koff (1/s, Protac to E3)\", \"t1/2 (s, Protac to E3)\"],\n",
    "    \"Assay (Ternary complex, IC50)\": [\"IC50 (nM, Ternary complex)\"],\n",
    "    \"Assay (Ternary complex, EC50)\": [\"EC50 (nM, Ternary complex)\"],\n",
    "    \"Assay (Ternary complex, Kd)\": [\"Kd (nM, Ternary complex)\"],\n",
    "    \"Assay (Ternary complex, Ki)\": [\"Ki (nM, Ternary complex)\"],\n",
    "    \"Assay (Ternary complex, G/H/-TS)\": [\"delta G (kcal/mol, Ternary complex)\", \"delta H (kcal/mol, Ternary complex)\", \"-T*delta S (kcal/mol, Ternary complex)\"],\n",
    "    \"Assay (Ternary complex, kon/koff/t1/2)\": [\"kon (1/Ms, Ternary complex)\", \"koff (1/s, Ternary complex)\", \"t1/2 (s, Ternary complex)\"],\n",
    "    \"Assay (Cellular activities, IC50)\": [\"IC50 (nM, Cellular activities)\"],\n",
    "    \"Assay (Cellular activities, EC50)\": [\"EC50 (nM, Cellular activities)\"],\n",
    "    \"Assay (Cellular activities, GI50)\": [\"GI50 (nM, Cellular activities)\"],\n",
    "    \"Assay (Cellular activities, ED50)\": [\"ED50 (nM, Cellular activities)\"],\n",
    "    \"Assay (Cellular activities, GR50)\": [\"GR50 (nM, Cellular activities)\"],\n",
    "    \"Assay (Permeability, PAMPA Papp)\": [\"PAMPA Papp (nm/s, Permeability)\"],\n",
    "    \"Assay (Permeability, Caco-2 A2B Papp)\": [\"Caco-2 A2B Papp (nm/s, Permeability)\"],\n",
    "    \"Assay (Permeability, Caco-2 B2A Papp)\": [\"Caco-2 B2A Papp (nm/s, Permeability)\"]\n",
    "}\n",
    "assay_cols = []\n",
    "for assay_col, param_cols in assay_to_parameters.items():\n",
    "    assay_cols += param_cols + [assay_col]\n",
    "non_assay_cols = list(set(protac_df) - set(assay_cols))\n",
    "print(f'Non-assay columns: {non_assay_cols}')\n",
    "print(f'Assay columns: {assay_cols}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Assay (DC50/Dmax)</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "      <th>Dmax (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Degradation of BRD9 in HeLa cells after 4 h tr...</td>\n",
       "      <td>560</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Degradation of BRD9 in RI-1 cells after 8 h tr...</td>\n",
       "      <td>1.76</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Degradation of HiBiT-BRD9 in HEK293 cells afte...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Degradation of BRD9 in EOL-1/A-204 cells after...</td>\n",
       "      <td>2/8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Degradation of BRD7 in RI-1 cells after 8 h tr...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Assay (DC50/Dmax) DC50 (nM) Dmax (%)\n",
       "19  Degradation of BRD9 in HeLa cells after 4 h tr...       560       80\n",
       "40  Degradation of BRD9 in RI-1 cells after 8 h tr...      1.76       95\n",
       "41  Degradation of HiBiT-BRD9 in HEK293 cells afte...         4      NaN\n",
       "42  Degradation of BRD9 in EOL-1/A-204 cells after...       2/8      NaN\n",
       "43  Degradation of BRD7 in RI-1 cells after 8 h tr...       4.5       95"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of (unique) rows: 854\n",
      "--------------------------------------------------------------------------------\n"
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       "      <th></th>\n",
       "      <th>Assay (Percent degradation)</th>\n",
       "      <th>Percent degradation (%)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>26/35/28</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>15/23/23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>16/20/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>11/25/29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>54/84/64</td>\n",
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       "                           Assay (Percent degradation) Percent degradation (%)\n",
       "129  % AR degradation in LNCaP cells after 6 h trea...                26/35/28\n",
       "130  % AR degradation in LNCaP cells after 6 h trea...                15/23/23\n",
       "131  % AR degradation in LNCaP cells after 6 h trea...                16/20/25\n",
       "132  % AR degradation in LNCaP cells after 6 h trea...                11/25/29\n",
       "133  % AR degradation in LNCaP cells after 6 h trea...                54/84/64"
      ]
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     "text": [
      "Number of (unique) rows: 298\n",
      "--------------------------------------------------------------------------------\n"
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       "      <th>Assay (Protac to Target, IC50)</th>\n",
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       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Inhibition of SIRT2 in a fluorescence-based de...</td>\n",
       "      <td>250</td>\n",
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       "    <tr>\n",
       "      <th>307</th>\n",
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       "      <th>311</th>\n",
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       "      <td>7.33</td>\n",
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       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Inhibition of ERRalpha by TR-FRET Coactivator ...</td>\n",
       "      <td>6.33</td>\n",
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       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Inhibition of ERRalpha by TR-FRET Coactivator ...</td>\n",
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       "                        Assay (Protac to Target, IC50)  \\\n",
       "234  Inhibition of SIRT2 in a fluorescence-based de...   \n",
       "307  Inhibition of CDK6/Cyciln D3 by cell-free kina...   \n",
       "311  Inhibition of ERRalpha by TR-FRET Coactivator ...   \n",
       "312  Inhibition of ERRalpha by TR-FRET Coactivator ...   \n",
       "313  Inhibition of ERRalpha by TR-FRET Coactivator ...   \n",
       "\n",
       "    IC50 (nM, Protac to Target)  \n",
       "234                         250  \n",
       "307                        6.61  \n",
       "311                        7.33  \n",
       "312                        6.33  \n",
       "313                       12.67  "
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     "text": [
      "Number of (unique) rows: 728\n",
      "--------------------------------------------------------------------------------\n"
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       "      <th>545</th>\n",
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       "      <th>5282</th>\n",
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       "                     Assay (Protac to Target, EC50)  \\\n",
       "545        EC50 of BRD4 BD1/2 was tested by TR-FRET   \n",
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       "     EC50 (nM, Protac to Target)  \n",
       "545                       95/298  \n",
       "5282                          31  "
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      "Number of (unique) rows: 2\n",
      "--------------------------------------------------------------------------------\n"
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       "      <th>Assay (Protac to Target, Kd)</th>\n",
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       "      <td>15</td>\n",
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       "      <th>19</th>\n",
       "      <td>ITC</td>\n",
       "      <td>8.8</td>\n",
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       "      <th>40</th>\n",
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       "      <th>71</th>\n",
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       "      <td>30</td>\n",
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       "      <th>286</th>\n",
       "      <td>Kd was assessed by competition binding assays</td>\n",
       "      <td>310</td>\n",
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       "                      Assay (Protac to Target, Kd) Kd (nM, Protac to Target)\n",
       "1                                              ITC                        15\n",
       "19                                             ITC                       8.8\n",
       "40                                             ITC                       5.1\n",
       "71                                             SPR                        30\n",
       "286  Kd was assessed by competition binding assays                       310"
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      "Number of (unique) rows: 172\n",
      "--------------------------------------------------------------------------------\n"
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       "      <th>1059</th>\n",
       "      <td>Ki was determined by AlphaScreen competitive b...</td>\n",
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       "      <th>1946</th>\n",
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       "      <th>2190</th>\n",
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       "      <th>2197</th>\n",
       "      <td>Ki was assessed using a radio-ligand displacem...</td>\n",
       "      <td>5</td>\n",
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       "      <th>4864</th>\n",
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       "                           Assay (Protac to Target, Ki)  \\\n",
       "1059  Ki was determined by AlphaScreen competitive b...   \n",
       "1946  Ki was tested by the AlphaScreen competitive b...   \n",
       "2190  Ki was assessed using a radio-ligand displacem...   \n",
       "2197  Ki was assessed using a radio-ligand displacem...   \n",
       "4864                                                 FP   \n",
       "\n",
       "     Ki (nM, Protac to Target)  \n",
       "1059                     0.009  \n",
       "1946                       1.9  \n",
       "2190                        11  \n",
       "2197                         5  \n",
       "4864                       5.5  "
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      "Number of (unique) rows: 35\n",
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       "      <td>ITC</td>\n",
       "      <td>-10.7</td>\n",
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       "      <th>19</th>\n",
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       "      <td>0.44</td>\n",
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       "      <th>40</th>\n",
       "      <td>ITC</td>\n",
       "      <td>-11.35</td>\n",
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       "      <td>1.485</td>\n",
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       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>ITC in BRD2 BD1</td>\n",
       "      <td>-9.57</td>\n",
       "      <td>-13.7</td>\n",
       "      <td>4.2</td>\n",
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       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>ITC in BRD4 BD2</td>\n",
       "      <td>-10.3</td>\n",
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       "      <td>0.1</td>\n",
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       "    Assay (Protac to Target, G/H/-TS) delta G (kcal/mol, Protac to Target)  \\\n",
       "1                                 ITC                                -10.7   \n",
       "19                                ITC                                  -11   \n",
       "40                                ITC                               -11.35   \n",
       "347                   ITC in BRD2 BD1                                -9.57   \n",
       "349                   ITC in BRD4 BD2                                -10.3   \n",
       "\n",
       "    delta H (kcal/mol, Protac to Target)  \\\n",
       "1                                  -12.5   \n",
       "19                                 -11.4   \n",
       "40                                 -12.8   \n",
       "347                                -13.7   \n",
       "349                                -10.4   \n",
       "\n",
       "    -T*delta S (kcal/mol, Protac to Target)  \n",
       "1                                       1.8  \n",
       "19                                     0.44  \n",
       "40                                    1.485  \n",
       "347                                     4.2  \n",
       "349                                     0.1  "
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      "Number of (unique) rows: 24\n",
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       "      <th>Assay (Protac to Target, kon/koff/t1/2)</th>\n",
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       "      <th>71</th>\n",
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       "      <th>1526</th>\n",
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       "      <td>0.400</td>\n",
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       "     Assay (Protac to Target, kon/koff/t1/2)  kon (1/Ms, Protac to Target)  \\\n",
       "71                                       SPR                      490000.0   \n",
       "1526                                     NaN                           1.4   \n",
       "2869                                     NaN                           1.8   \n",
       "\n",
       "      koff (1/s, Protac to Target)  t1/2 (s, Protac to Target)  \n",
       "71                           0.015                         NaN  \n",
       "1526                         0.770                         NaN  \n",
       "2869                         0.400                         NaN  "
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      "Number of (unique) rows: 3\n",
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       "      <th>Assay (Protac to E3, IC50)</th>\n",
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       "      <th>361</th>\n",
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       "      <td>2200</td>\n",
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       "      <th>511</th>\n",
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       "      <td>656</td>\n",
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       "      <th>539</th>\n",
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       "      <th>800</th>\n",
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       "      <th>805</th>\n",
       "      <td>FP</td>\n",
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       "                          Assay (Protac to E3, IC50) IC50 (nM, Protac to E3)\n",
       "361                               Inhibition of CRBN                    2200\n",
       "511   Inhibition of VHL by competition binding assay                     656\n",
       "539  Inhibition of CRBN by competition binding assay                     800\n",
       "800                                               FP                    1200\n",
       "805                                               FP                    2400"
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      "Number of (unique) rows: 141\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>EC50 was tested using a BRD4 BRD2-eGFP-mCherry...</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>EC50 was tested using a BRD4 BRD2-eGFP-mCherry...</td>\n",
       "      <td>641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>EC50 was tested using a BRD4 BRD2-eGFP-mCherry...</td>\n",
       "      <td>703.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Assay (Protac to E3, EC50)  \\\n",
       "1943  EC50 was tested using a BRD4 BRD2-eGFP-mCherry...   \n",
       "1944  EC50 was tested using a BRD4 BRD2-eGFP-mCherry...   \n",
       "1945  EC50 was tested using a BRD4 BRD2-eGFP-mCherry...   \n",
       "\n",
       "      EC50 (nM, Protac to E3)  \n",
       "1943                    576.0  \n",
       "1944                    641.0  \n",
       "1945                    703.0  "
      ]
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     "text": [
      "Number of (unique) rows: 3\n",
      "--------------------------------------------------------------------------------\n"
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       "      <th>Assay (Protac to E3, Kd)</th>\n",
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       "      <th>1</th>\n",
       "      <td>ITC/FP</td>\n",
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       "      <th>19</th>\n",
       "      <td>ITC/FP</td>\n",
       "      <td>87/70</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ITC/FP</td>\n",
       "      <td>26/35</td>\n",
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       "      <th>293</th>\n",
       "      <td>SPR</td>\n",
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       "      <th>295</th>\n",
       "      <td>SPR</td>\n",
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       "    Assay (Protac to E3, Kd) Kd (nM, Protac to E3)\n",
       "1                     ITC/FP                 33/24\n",
       "19                    ITC/FP                 87/70\n",
       "40                    ITC/FP                 26/35\n",
       "293                      SPR                  5000\n",
       "295                      SPR                  3500"
      ]
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      "Number of (unique) rows: 61\n",
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       "      <th>1188</th>\n",
       "      <td>FP</td>\n",
       "      <td>490.0</td>\n",
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       "      <th>1191</th>\n",
       "      <td>FP</td>\n",
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       "     Assay (Protac to E3, Ki)  Ki (nM, Protac to E3)\n",
       "522                        FP                   77.0\n",
       "525                        FP                   72.0\n",
       "1185                       FP                  210.0\n",
       "1188                       FP                  490.0\n",
       "1191                       FP                  450.0"
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      "Number of (unique) rows: 6\n",
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       "      <th>Assay (Protac to E3, G/H/-TS)</th>\n",
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       "      <td>4.9</td>\n",
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       "      <th>19</th>\n",
       "      <td>ITC</td>\n",
       "      <td>-9.64</td>\n",
       "      <td>-6.56</td>\n",
       "      <td>-3.1</td>\n",
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       "      <th>40</th>\n",
       "      <td>ITC</td>\n",
       "      <td>-10.40</td>\n",
       "      <td>-7.04</td>\n",
       "      <td>-3.4</td>\n",
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       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>ITC</td>\n",
       "      <td>-9.01</td>\n",
       "      <td>-7.90</td>\n",
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       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>ITC</td>\n",
       "      <td>-8.49</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-3.7</td>\n",
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       "    Assay (Protac to E3, G/H/-TS)  delta G (kcal/mol, Protac to E3)  \\\n",
       "1                             ITC                            -10.20   \n",
       "19                            ITC                             -9.64   \n",
       "40                            ITC                            -10.40   \n",
       "347                           ITC                             -9.01   \n",
       "350                           ITC                             -8.49   \n",
       "\n",
       "     delta H (kcal/mol, Protac to E3)  -T*delta S (kcal/mol, Protac to E3)  \n",
       "1                              -15.20                                  4.9  \n",
       "19                              -6.56                                 -3.1  \n",
       "40                              -7.04                                 -3.4  \n",
       "347                             -7.90                                 -1.1  \n",
       "350                             -5.00                                 -3.7  "
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      "Number of (unique) rows: 17\n",
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       "      <th>Assay (Protac to E3, kon/koff/t1/2)</th>\n",
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       "      <th>457</th>\n",
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       "      <td>600000.0</td>\n",
       "      <td>0.060</td>\n",
       "      <td>17.0</td>\n",
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       "      <td>0.019</td>\n",
       "      <td>43.0</td>\n",
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       "      <th>2312</th>\n",
       "      <td>SPR</td>\n",
       "      <td>270000.0</td>\n",
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       "     Assay (Protac to E3, kon/koff/t1/2)  kon (1/Ms, Protac to E3)  \\\n",
       "457                                  SPR                  600000.0   \n",
       "525                                  SPR                  700000.0   \n",
       "2312                                 SPR                  270000.0   \n",
       "\n",
       "      koff (1/s, Protac to E3)  t1/2 (s, Protac to E3)  \n",
       "457                      0.060                    17.0  \n",
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       "2312                     0.015                    48.0  "
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      "Number of (unique) rows: 3\n",
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       "      <th>539</th>\n",
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       "      <th>1188</th>\n",
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       "                          Assay (Ternary complex, IC50)  \\\n",
       "539   Inhibition of CRBN in the presence of BRD4 BD1...   \n",
       "1185                                            TR-FRET   \n",
       "1186                                  TR-FRET/AlphaLISA   \n",
       "1187                                  TR-FRET/AlphaLISA   \n",
       "1188                                            TR-FRET   \n",
       "\n",
       "     IC50 (nM, Ternary complex)  \n",
       "539                   1800/4100  \n",
       "1185                        220  \n",
       "1186                    205/120  \n",
       "1187                    230/330  \n",
       "1188                         28  "
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      "Number of (unique) rows: 23\n",
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       "    Assay (Ternary complex, Kd) Kd (nM, Ternary complex)\n",
       "1                        ITC/FP                    73/98\n",
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       "40                       ITC/FP                    27/35\n",
       "293                         SPR                     1200\n",
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      "Number of (unique) rows: 53\n",
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       "                            Assay (Ternary complex, Ki)  \\\n",
       "525   Ki between VHL and MZ1-BRD2 BD1/BD2 was tested...   \n",
       "526   Ki between VHL and MZ1-BRD3 BD1/BD2 was tested...   \n",
       "527   Ki between VHL and MZ1-BRD4 BD1/BD2 was tested...   \n",
       "1185                                                 FP   \n",
       "1186                                                 FP   \n",
       "\n",
       "     Ki (nM, Ternary complex)  \n",
       "525                    33/2.5  \n",
       "526                      18/8  \n",
       "527                    13/1.3  \n",
       "1185                       79  \n",
       "1186                       48  "
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      "Number of (unique) rows: 9\n",
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       "      <th></th>\n",
       "      <th>Assay (Ternary complex, G/H/-TS)</th>\n",
       "      <th>delta G (kcal/mol, Ternary complex)</th>\n",
       "      <th>delta H (kcal/mol, Ternary complex)</th>\n",
       "      <th>-T*delta S (kcal/mol, Ternary complex)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ITC between VHL and BRD9-BD:PROTAC</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>-3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ITC between VHL and BRD9-BD:PROTAC</td>\n",
       "      <td>-10.33</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>ITC between VHL and BRD2 BD1:PROTAC</td>\n",
       "      <td>-9.85</td>\n",
       "      <td>-9.17</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>ITC between VHL and BRD4 BD2:PROTAC</td>\n",
       "      <td>-10.13</td>\n",
       "      <td>-10.9</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>ITC between VHL and BRD2 BD1:PROTAC</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Assay (Ternary complex, G/H/-TS) delta G (kcal/mol, Ternary complex)  \\\n",
       "1     ITC between VHL and BRD9-BD:PROTAC                                -9.7   \n",
       "40    ITC between VHL and BRD9-BD:PROTAC                              -10.33   \n",
       "347  ITC between VHL and BRD2 BD1:PROTAC                               -9.85   \n",
       "349  ITC between VHL and BRD4 BD2:PROTAC                              -10.13   \n",
       "350  ITC between VHL and BRD2 BD1:PROTAC                                -8.6   \n",
       "\n",
       "    delta H (kcal/mol, Ternary complex) -T*delta S (kcal/mol, Ternary complex)  \n",
       "1                                  -6.4                                   -3.3  \n",
       "40                                 -5.1                                   -5.3  \n",
       "347                               -9.17                                   -0.6  \n",
       "349                               -10.9                                   -0.6  \n",
       "350                                -5.1                                   -3.6  "
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of (unique) rows: 24\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Assay (Ternary complex, kon/koff/t1/2)</th>\n",
       "      <th>kon (1/Ms, Ternary complex)</th>\n",
       "      <th>koff (1/s, Ternary complex)</th>\n",
       "      <th>t1/2 (s, Ternary complex)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>SPR</td>\n",
       "      <td>70100</td>\n",
       "      <td>0.072</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>SPR</td>\n",
       "      <td>39600</td>\n",
       "      <td>0.018</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>SPR between VHL and AT1-BRD4 BD1/2</td>\n",
       "      <td>1.6E+6/1.4E+6</td>\n",
       "      <td>0.9/0.03</td>\n",
       "      <td>0.8/26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>SPR between VHL and MZ1-BRD2 BD1/2</td>\n",
       "      <td>3.9E+8/1.2E+7</td>\n",
       "      <td>&gt;1/0.01</td>\n",
       "      <td>&lt;0.7/67.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>SPR between VHL and MZ1-BRD3 BD1/2</td>\n",
       "      <td>1.4E+8/1.6E+7</td>\n",
       "      <td>&gt;1/0.12</td>\n",
       "      <td>&lt;0.7/6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    Assay (Ternary complex, kon/koff/t1/2) kon (1/Ms, Ternary complex)  \\\n",
       "293                                    SPR                       70100   \n",
       "295                                    SPR                       39600   \n",
       "457     SPR between VHL and AT1-BRD4 BD1/2               1.6E+6/1.4E+6   \n",
       "525     SPR between VHL and MZ1-BRD2 BD1/2               3.9E+8/1.2E+7   \n",
       "526     SPR between VHL and MZ1-BRD3 BD1/2               1.4E+8/1.6E+7   \n",
       "\n",
       "    koff (1/s, Ternary complex) t1/2 (s, Ternary complex)  \n",
       "293                       0.072                         8  \n",
       "295                       0.018                        38  \n",
       "457                    0.9/0.03                    0.8/26  \n",
       "525                     >1/0.01                 <0.7/67.4  \n",
       "526                     >1/0.12                    <0.7/6  "
      ]
     },
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     "name": "stdout",
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     "text": [
      "Number of (unique) rows: 8\n",
      "--------------------------------------------------------------------------------\n"
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    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Assay (Cellular activities, IC50)</th>\n",
       "      <th>IC50 (nM, Cellular activities)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Inhibit growth of RS4;11 cells</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Inhibit growth of RS4;11 cells</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Inhibit growth of RS4;11 cells</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Inhibit growth of RS4;11 cells</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Inhibit growth of RS4;11 cells</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Assay (Cellular activities, IC50) IC50 (nM, Cellular activities)\n",
       "72    Inhibit growth of RS4;11 cells                             10\n",
       "73    Inhibit growth of RS4;11 cells                              7\n",
       "74    Inhibit growth of RS4;11 cells                           68.4\n",
       "75    Inhibit growth of RS4;11 cells                             15\n",
       "76    Inhibit growth of RS4;11 cells                              9"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of (unique) rows: 919\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Assay (Cellular activities, EC50)</th>\n",
       "      <th>EC50 (nM, Cellular activities)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inhibit proliferation of EOL-1/A-402 cells</td>\n",
       "      <td>35/305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Inhibit proliferation of EOL-1/A-402 cells</td>\n",
       "      <td>3/40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Inhibit viability of MDA-MB-468/MDA-MB-231 cells</td>\n",
       "      <td>0.5146/0.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Inhibit proliferation of MV4;11 cells</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Inhibit viability of K562 cells</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Assay (Cellular activities, EC50)  \\\n",
       "4          Inhibit proliferation of EOL-1/A-402 cells   \n",
       "40         Inhibit proliferation of EOL-1/A-402 cells   \n",
       "212  Inhibit viability of MDA-MB-468/MDA-MB-231 cells   \n",
       "347             Inhibit proliferation of MV4;11 cells   \n",
       "473                   Inhibit viability of K562 cells   \n",
       "\n",
       "    EC50 (nM, Cellular activities)  \n",
       "4                           35/305  \n",
       "40                            3/40  \n",
       "212                   0.5146/0.881  \n",
       "347                            316  \n",
       "473                            4.4  "
      ]
     },
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    },
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     "text": [
      "Number of (unique) rows: 95\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Assay (Cellular activities, GI50)</th>\n",
       "      <th>GI50 (nM, Cellular activities)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>Inhibit viability of HBL-1/RAMOS cells</td>\n",
       "      <td>&gt;200/&gt;1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>Inhibit viability of HBL-1 cells expressing BT...</td>\n",
       "      <td>28.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Inhibit viability of HBL-1/RAMOS cells</td>\n",
       "      <td>1.5/&gt;1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Inhibit viability of HBL-1/RAMOS cells</td>\n",
       "      <td>71.1/&gt;1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Inhibit viability of HBL-1 cells</td>\n",
       "      <td>109.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Assay (Cellular activities, GI50)  \\\n",
       "379             Inhibit viability of HBL-1/RAMOS cells   \n",
       "380  Inhibit viability of HBL-1 cells expressing BT...   \n",
       "381             Inhibit viability of HBL-1/RAMOS cells   \n",
       "382             Inhibit viability of HBL-1/RAMOS cells   \n",
       "384                   Inhibit viability of HBL-1 cells   \n",
       "\n",
       "    GI50 (nM, Cellular activities)  \n",
       "379                     >200/>1000  \n",
       "380                           28.8  \n",
       "381                      1.5/>1000  \n",
       "382                     71.1/>1000  \n",
       "384                          109.9  "
      ]
     },
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    },
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     "text": [
      "Number of (unique) rows: 78\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Assay (Cellular activities, ED50)</th>\n",
       "      <th>ED50 (nM, Cellular activities)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>Inhibit proliferation of TMD8/Maver-1/Granta-5...</td>\n",
       "      <td>44.3/193/203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>Inhibit proliferation of TMD8 cells with C481S...</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>Inhibit proliferation of Mino/TMD8/Maver-1 cells</td>\n",
       "      <td>12/29.2/47.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>Inhibit proliferation of TMD8 cells with C481S...</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>Inhibit proliferation of Mino cells</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Assay (Cellular activities, ED50)  \\\n",
       "1754  Inhibit proliferation of TMD8/Maver-1/Granta-5...   \n",
       "1755  Inhibit proliferation of TMD8 cells with C481S...   \n",
       "1756   Inhibit proliferation of Mino/TMD8/Maver-1 cells   \n",
       "1757  Inhibit proliferation of TMD8 cells with C481S...   \n",
       "1758                Inhibit proliferation of Mino cells   \n",
       "\n",
       "     ED50 (nM, Cellular activities)  \n",
       "1754                   44.3/193/203  \n",
       "1755                            149  \n",
       "1756                   12/29.2/47.7  \n",
       "1757                            128  \n",
       "1758                             69  "
      ]
     },
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    },
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     "text": [
      "Number of (unique) rows: 5\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Assay (Cellular activities, GR50)</th>\n",
       "      <th>GR50 (nM, Cellular activities)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>Inhibit proliferation of ZR-75-1/T47D/LNCaP cells</td>\n",
       "      <td>16/178/130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>Inhibit growth of OVCAR8/OVSAHO/FUOV1 cells</td>\n",
       "      <td>96.6/33.3/53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>Inhibit growth of OVCAR8 cells</td>\n",
       "      <td>33.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4611</th>\n",
       "      <td>11a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4612</th>\n",
       "      <td>11b</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Assay (Cellular activities, GR50)  \\\n",
       "1360  Inhibit proliferation of ZR-75-1/T47D/LNCaP cells   \n",
       "1651        Inhibit growth of OVCAR8/OVSAHO/FUOV1 cells   \n",
       "1694                     Inhibit growth of OVCAR8 cells   \n",
       "4611                                                11a   \n",
       "4612                                                11b   \n",
       "\n",
       "     GR50 (nM, Cellular activities)  \n",
       "1360                     16/178/130  \n",
       "1651                   96.6/33.3/53  \n",
       "1694                           33.1  \n",
       "4611                            NaN  \n",
       "4612                            NaN  "
      ]
     },
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    },
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     "text": [
      "Number of (unique) rows: 20\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Assay (Permeability, PAMPA Papp)</th>\n",
       "      <th>PAMPA Papp (nm/s, Permeability)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>PAMPA assay</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>PAMPA assay</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Assay (Permeability, PAMPA Papp)  \\\n",
       "18   Compounds were administered at 10000 nM and in...   \n",
       "30   Compounds were administered at 10000 nM and in...   \n",
       "395  Compounds were administered at 10000 nM and in...   \n",
       "482                                        PAMPA assay   \n",
       "513                                        PAMPA assay   \n",
       "\n",
       "    PAMPA Papp (nm/s, Permeability)  \n",
       "18                             0.01  \n",
       "30                             0.02  \n",
       "395                              <1  \n",
       "482                             0.2  \n",
       "513                            0.01  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of (unique) rows: 10\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Assay (Permeability, Caco-2 A2B Papp)</th>\n",
       "      <th>Caco-2 A2B Papp (nm/s, Permeability)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Compounds were administered at 5000 nM and inc...</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Assay (Permeability, Caco-2 A2B Papp)  \\\n",
       "395   Compounds were administered at 10000 nM and in...   \n",
       "397   Compounds were administered at 5000 nM and inc...   \n",
       "506   Compounds were administered at 10000 nM and in...   \n",
       "711   Compounds were administered at 10000 nM and in...   \n",
       "2188  Compounds were administered at 10000 nM and in...   \n",
       "\n",
       "     Caco-2 A2B Papp (nm/s, Permeability)  \n",
       "395                                  0.35  \n",
       "397                                 0.081  \n",
       "506                                  11.6  \n",
       "711                                  0.02  \n",
       "2188                                  1.7  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of (unique) rows: 25\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Assay (Permeability, Caco-2 B2A Papp)</th>\n",
       "      <th>Caco-2 B2A Papp (nm/s, Permeability)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>Compounds were administered at 10000 nM and in...</td>\n",
       "      <td>&lt;0.79</td>\n",
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      ],
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       "                  Assay (Permeability, Caco-2 B2A Papp)  \\\n",
       "395   Compounds were administered at 10000 nM and in...   \n",
       "711   Compounds were administered at 10000 nM and in...   \n",
       "2188  Compounds were administered at 10000 nM and in...   \n",
       "2190  Compounds were administered at 10000 nM and in...   \n",
       "2191  Compounds were administered at 10000 nM and in...   \n",
       "\n",
       "     Caco-2 B2A Papp (nm/s, Permeability)  \n",
       "395                                  0.24  \n",
       "711                                  0.07  \n",
       "2188                                 14.1  \n",
       "2190                                  1.4  \n",
       "2191                                <0.79  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of (unique) rows: 12\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for assay, cols in assay_to_parameters.items():\n",
    "    tmp = protac_df[[assay] + cols].dropna(how='all').drop_duplicates()\n",
    "    display(tmp.head())\n",
    "    print(f'Number of (unique) rows: {len(tmp)}')\n",
    "    print('-' * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DC50 and Dmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degradation of ENL in MV4;11 cells after 24 h treatment\n",
      "['ENL'] | ['MV4;11'] | [24.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of BRD4 in HEK293T cells after 8 h treatment\n",
      "['BRD4'] | ['HEK293T'] | [8.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of BRD4 in 231MFP/HAP1 cells after 8 h treatment\n",
      "['BRD4'] | ['231MFP', 'HAP1'] | [8.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of ER in ER-positive breast cancer cell lines\n",
      "['ER'] | ['ER-positive breast cancer cell lines'] | nan\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of AR in LNCaP cells\n",
      "['AR'] | ['LNCaP'] | nan\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of AR in LNCaP/VCaP AR+ cells after 6 h treatment\n",
      "['AR'] | ['LNCaP', 'VCaP AR+'] | [6.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of BRD4 BD1/2 assessed by EGFP/mCherry reporter assay\n",
      "['BRD4 BD1', 'BRD4 BD2'] | nan | nan\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of BRD4 BD1 assessed by EGFP/mCherry reporter assay\n",
      "['BRD4 BD1'] | nan | nan\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of PARP1 in Primary Cardiomyocytes after 24 h treatment\n",
      "['PARP1'] | ['Primary Cardiomyocytes'] | [24.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of HDAC6 in MM1S after 6 h treatment by in-cell ELISA analysis\n",
      "['HDAC6'] | ['MM.1S'] | [6.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of total tau/P-tau in A152T neurons after 24 h treatment\n",
      "['tau/P-tau'] | ['A152T neurons'] | [24.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of Rpn13 in MM.1S after 16 h treatment\n",
      "['Rpn13'] | ['MM.1S'] | [16.0]\n",
      "--------------------------------------------------------------------------------\n",
      "Degradation of HDAC6 in MM1S after 6 h treatment by in-cell ELISA analysis\n",
      "['HDAC6'] | ['MM.1S'] | [6.0]\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def extract_dc50_info(sentence):\n",
    "    # Regex patterns for proteins/genes, cell types, and treatment hours\n",
    "    protein_regex = r\"Degradation of total\\s(.+?)\\s(in|after|using|proteins)\"\n",
    "    cell_regex = r\"in\\s([A-Za-z0-9-/.;\\(\\)\\s\\+]+)\\scells\"\n",
    "    treatment_regex = r\"after\\s(\\d+/?\\d*?/?\\d*?\\s?h)\"\n",
    "\n",
    "    # Extracting protein information\n",
    "    if 'total' in sentence.lower():\n",
    "        protein_match = re.search(protein_regex, sentence)\n",
    "        proteins = protein_match.group(1).split(' and ') if protein_match else [\n",
    "            re.search(r\"Degradation of\\s([A-Za-z0-9-]+)\", sentence).group(1)]\n",
    "    else:\n",
    "        if ' in ' in sentence.lower():\n",
    "            proteins = sentence.split(' in ')[0].split('Degradation of ')[-1]\n",
    "            proteins = proteins.split('/') if '/' in proteins else [proteins]\n",
    "        else:\n",
    "            protein_match = re.search(protein_regex, sentence)\n",
    "            proteins = protein_match.group(1).split(\n",
    "                '/') if protein_match else [re.search(r\"Degradation of\\s([A-Za-z0-9-\\/]+)\", sentence).group(1)]\n",
    "    # Handle special cases...\n",
    "    if 'BRD4 short/long' in sentence:\n",
    "        proteins = ['BRD4 short', 'BRD4 long']\n",
    "    if 'BRD4 BD1/2' in sentence:\n",
    "        proteins = ['BRD4 BD1', 'BRD4 BD2']\n",
    "    elif 'BRD4 BD1' in sentence:\n",
    "        proteins = ['BRD4 BD1']\n",
    "    if 'EGFR L858R/T790M' in sentence:\n",
    "        proteins = ['EGFR L858R/T790M']\n",
    "    if 'EGFR del19/T790M/C797S' in sentence:\n",
    "        proteins = ['EGFR del19/T790M/C797S']\n",
    "\n",
    "    # Extracting cell types\n",
    "    cell_match = re.search(cell_regex, sentence)\n",
    "    cells = cell_match.group(1).split('/') if cell_match else np.nan\n",
    "    # Handle special cases...\n",
    "    if 'Ba/F3' in sentence:\n",
    "        # Replace any occurences that contain 'Ba' or 'F3' with 'Ba/F3' and remove duplicates while preserving the order in the other cells\n",
    "        cells = ['Ba/F3' if 'Ba' in c or 'F3' in c else c for c in cells]\n",
    "        cells.pop(cells.index('Ba/F3'))\n",
    "    if 'ER-positive breast cancer cell lines' in sentence:\n",
    "        cells = ['ER-positive breast cancer cell lines']\n",
    "    if 'LNCaP (AR T878A)' in sentence:\n",
    "        cells = ['LNCaP']\n",
    "    if 'in A152T neurons' in sentence:\n",
    "        cells = ['A152T neurons']\n",
    "    if 'of Rpn13 in MM.1S after' in sentence:\n",
    "        cells = ['MM.1S']\n",
    "    if 'Primary Cardiomyocytes' in sentence:\n",
    "        cells = ['Primary Cardiomyocytes']\n",
    "    if ' HDAC6 in MM1S after' in sentence:\n",
    "        cells = ['MM.1S']\n",
    "\n",
    "    # Extracting treatment hours\n",
    "    treatment_hours_match = re.search(treatment_regex, sentence)\n",
    "    if treatment_hours_match:\n",
    "        treatment_hours = treatment_hours_match.group(1).strip('h')\n",
    "        treatment_hours = split_clean_str(treatment_hours, return_floats=True)\n",
    "    else:\n",
    "        treatment_hours = np.nan\n",
    "\n",
    "    return {\n",
    "        'Target (Parsed)': proteins,\n",
    "        'Cell Type': cells,\n",
    "        'Treatment Time (h)': treatment_hours,\n",
    "    }\n",
    "\n",
    "\n",
    "corner_cases = [\n",
    "    # 'Degradation of BRD4',\n",
    "    # 'Degradation of BRD4 short/long in HeLa cells after 24 h treatment',\n",
    "    # 'Degradation of BRD4 BD1 assessed by EGFP/mCherry reporter assay',\n",
    "    # 'Degradation of BRD4 BD1/2 assessed by EGFP/mCherry reporter assay',\n",
    "    # 'Degradation of WT/Exon 20 Ins EGFR in OVCAR8/HeLa cells after 24 h treatment',\n",
    "    # 'Degradation of TPM3-TRKA/TRKA in KM12/HEL cells after 6 h treatment',\n",
    "    # 'Degradation of Exon 19 del/L858R EGFR in HCC827/H3255 cells after 24 h treatment',\n",
    "    # 'Degradation of NPM-ALK/EML4-ALK in SU-DHL-1/NCI-H2228 cells after 16 h treatment',\n",
    "    # 'Degradation of BCR-ABL T315I in Ba/F3 cells after 24 h treatment',\n",
    "    # 'Degradation of BCR-ABL T315I in MOL/(Ba/F3)/R4;11 cells after 24 h treatment',\n",
    "    # 'Degradation of ALK in H3122/Karpas 299/Kelly cells 16 h treatment',\n",
    "    'Degradation of AR in LNCaP/VCaP AR+ cells after 6 h treatment',\n",
    "    'Degradation of BRD4 BD1/2 assessed by EGFP/mCherry reporter assay',\n",
    "    'Degradation of BRD4 BD1 assessed by EGFP/mCherry reporter assay',\n",
    "    'Degradation of PARP1 in Primary Cardiomyocytes after 24 h treatment',\n",
    "    'Degradation of HDAC6 in MM1S after 6 h treatment by in-cell ELISA analysis',\n",
    "    'Degradation of total tau/P-tau in A152T neurons after 24 h treatment',\n",
    "    'Degradation of Rpn13 in MM.1S after 16 h treatment',\n",
    "    'Degradation of HDAC6 in MM1S after 6 h treatment by in-cell ELISA analysis',\n",
    "]\n",
    "\n",
    "for assay in assays[\"Assay (DC50/Dmax)\"][-5:] + corner_cases:\n",
    "    if len(assay) < 5:\n",
    "        continue\n",
    "    print(assay)\n",
    "    extracted_info = extract_dc50_info(assay)\n",
    "    proteins, cells, treatment_hours = extracted_info[\n",
    "        'Target (Parsed)'], extracted_info['Cell Type'], extracted_info['Treatment Time (h)']\n",
    "    print(proteins, \"|\", cells, \"|\", treatment_hours)\n",
    "    print('-' * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first remove all entries with _all_ missing values in the `DC50 (nM)`, `Dmax (%)`, and `Assay (DC50/Dmax)` columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1019"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_cols = assay_to_parameters[\"Assay (DC50/Dmax)\"]\n",
    "dc50_dmax_df = pd.concat([protac_df, scraped_protac_df]).dropna(\n",
    "    subset=param_cols + [\"Assay (DC50/Dmax)\"], how='all')\n",
    "dc50_dmax_df = dc50_dmax_df[dc50_dmax_df[\"Assay (DC50/Dmax)\"].notnull()]\n",
    "# Drop all \"assay columns\" in assay_to_parameters except for \"Assay (DC50/Dmax)\" and its parameters\n",
    "dc50_dmax_df = dc50_dmax_df.drop(\n",
    "    columns=[col for col in assay_cols if col not in param_cols + [\"Assay (DC50/Dmax)\"]])\n",
    "dc50_dmax_df = dc50_dmax_df.drop_duplicates()\n",
    "len(dc50_dmax_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8dc21c335d764482b9c202ad0ddce4c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting DC50/Dmax info:   0%|          | 0/1019 [00:00<?, ?it/s]"
      ]
     },
     "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>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Target</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>PDB</th>\n",
       "      <th>Name</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>DC50 (nM)</th>\n",
       "      <th>Dmax (%)</th>\n",
       "      <th>Assay (DC50/Dmax)</th>\n",
       "      <th>...</th>\n",
       "      <th>Hydrogen Bond Acceptor Count</th>\n",
       "      <th>Hydrogen Bond Donor Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>Topological Polar Surface Area</th>\n",
       "      <th>Molecular Formula</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "      <th>Cell Type</th>\n",
       "      <th>Treatment Time (h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>560.00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Degradation of BRD9 in HeLa cells after 4 h tr...</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>199.15</td>\n",
       "      <td>C54H69FN8O10S</td>\n",
       "      <td>InChI=1S/C54H69FN8O10S/c1-34-47(74-33-58-34)35...</td>\n",
       "      <td>MXAKQOVZPDLCDK-UDVNCTHFSA-N</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>HeLa</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VZ185</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>1.76</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Degradation of BRD9 in RI-1 cells after 8 h tr...</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>180.69</td>\n",
       "      <td>C53H67FN8O8S</td>\n",
       "      <td>InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...</td>\n",
       "      <td>ZAGCLFXBHOXXEN-JPTLTNPLSA-N</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>RI-1</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VZ185</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of HiBiT-BRD9 in HEK293 cells afte...</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>180.69</td>\n",
       "      <td>C53H67FN8O8S</td>\n",
       "      <td>InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...</td>\n",
       "      <td>ZAGCLFXBHOXXEN-JPTLTNPLSA-N</td>\n",
       "      <td>HiBiT-BRD9</td>\n",
       "      <td>HEK293</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VZ185</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD9 in EOL-1/A-204 cells after...</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>180.69</td>\n",
       "      <td>C53H67FN8O8S</td>\n",
       "      <td>InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...</td>\n",
       "      <td>ZAGCLFXBHOXXEN-JPTLTNPLSA-N</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>EOL-1</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Q9H8M2</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VZ185</td>\n",
       "      <td>COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...</td>\n",
       "      <td>8.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD9 in EOL-1/A-204 cells after...</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>180.69</td>\n",
       "      <td>C53H67FN8O8S</td>\n",
       "      <td>InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...</td>\n",
       "      <td>ZAGCLFXBHOXXEN-JPTLTNPLSA-N</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>A-204</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound ID Uniprot Target E3 Ligase  PDB   Name  \\\n",
       "0           11  Q9H8M2   BRD9       VHL  NaN    NaN   \n",
       "1           22  Q9H8M2   BRD9       VHL  NaN  VZ185   \n",
       "2           22  Q9H8M2   BRD9       VHL  NaN  VZ185   \n",
       "3           22  Q9H8M2   BRD9       VHL  NaN  VZ185   \n",
       "4           22  Q9H8M2   BRD9       VHL  NaN  VZ185   \n",
       "\n",
       "                                              Smiles  DC50 (nM)  Dmax (%)  \\\n",
       "0  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...     560.00      80.0   \n",
       "1  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       1.76      95.0   \n",
       "2  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       4.00       NaN   \n",
       "3  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       2.00       NaN   \n",
       "4  COC1=CC(C2=CN(C)C(=O)C3=CN=CC=C23)=CC(OC)=C1CN...       8.00       NaN   \n",
       "\n",
       "                                   Assay (DC50/Dmax)  ...  \\\n",
       "0  Degradation of BRD9 in HeLa cells after 4 h tr...  ...   \n",
       "1  Degradation of BRD9 in RI-1 cells after 8 h tr...  ...   \n",
       "2  Degradation of HiBiT-BRD9 in HEK293 cells afte...  ...   \n",
       "3  Degradation of BRD9 in EOL-1/A-204 cells after...  ...   \n",
       "4  Degradation of BRD9 in EOL-1/A-204 cells after...  ...   \n",
       "\n",
       "  Hydrogen Bond Acceptor Count  Hydrogen Bond Donor Count  \\\n",
       "0                           16                          3   \n",
       "1                           14                          3   \n",
       "2                           14                          3   \n",
       "3                           14                          3   \n",
       "4                           14                          3   \n",
       "\n",
       "   Rotatable Bond Count  Topological Polar Surface Area  Molecular Formula  \\\n",
       "0                    22                          199.15      C54H69FN8O10S   \n",
       "1                    19                          180.69       C53H67FN8O8S   \n",
       "2                    19                          180.69       C53H67FN8O8S   \n",
       "3                    19                          180.69       C53H67FN8O8S   \n",
       "4                    19                          180.69       C53H67FN8O8S   \n",
       "\n",
       "                                               InChI  \\\n",
       "0  InChI=1S/C54H69FN8O10S/c1-34-47(74-33-58-34)35...   \n",
       "1  InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...   \n",
       "2  InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...   \n",
       "3  InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...   \n",
       "4  InChI=1S/C53H67FN8O8S/c1-33-46(71-32-57-33)34-...   \n",
       "\n",
       "                     InChI Key  Target (Parsed)  Cell Type  Treatment Time (h)  \n",
       "0  MXAKQOVZPDLCDK-UDVNCTHFSA-N             BRD9       HeLa                 4.0  \n",
       "1  ZAGCLFXBHOXXEN-JPTLTNPLSA-N             BRD9       RI-1                 8.0  \n",
       "2  ZAGCLFXBHOXXEN-JPTLTNPLSA-N       HiBiT-BRD9     HEK293                24.0  \n",
       "3  ZAGCLFXBHOXXEN-JPTLTNPLSA-N             BRD9      EOL-1                18.0  \n",
       "4  ZAGCLFXBHOXXEN-JPTLTNPLSA-N             BRD9      A-204                18.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsed table len: 1216\n"
     ]
    }
   ],
   "source": [
    "parsed_table = []\n",
    "\n",
    "for i, row in tqdm(dc50_dmax_df.iterrows(), total=len(dc50_dmax_df), desc='Extracting DC50/Dmax info'):\n",
    "    assay = row[\"Assay (DC50/Dmax)\"]\n",
    "    if len(assay) < 5:\n",
    "        continue\n",
    "    extracted_info = extract_dc50_info(assay)\n",
    "    extracted_info['DC50 (nM)'] = split_clean_str(\n",
    "        row['DC50 (nM)'], return_floats=True)\n",
    "    extracted_info['Dmax (%)'] = split_clean_str(\n",
    "        row['Dmax (%)'], return_floats=True)\n",
    "\n",
    "    # Get the max len of each list in the extracted info\n",
    "    max_len = max([len(v)\n",
    "                  for v in extracted_info.values() if isinstance(v, list)])\n",
    "    for i in range(max_len):\n",
    "        row_tmp = row.copy().to_dict()\n",
    "        row_tmp.update({k: v[i % len(v)] if isinstance(v, list)\n",
    "                       else v for k, v in extracted_info.items()})\n",
    "        parsed_table.append(row_tmp)\n",
    "\n",
    "parsed_table = pd.DataFrame(parsed_table)\n",
    "display(parsed_table.head())\n",
    "print(f'Parsed table len: {len(parsed_table)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Assay (DC50/Dmax)</th>\n",
       "      <th>Target</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Degradation of HiBiT-BRD9 in HEK293 cells afte...</td>\n",
       "      <td>BRD9</td>\n",
       "      <td>HiBiT-BRD9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Degradation of HiBiT-BRD7 in HEK293 cells afte...</td>\n",
       "      <td>BRD7</td>\n",
       "      <td>HiBiT-BRD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Degradation of NPM-ALK/EML4-ALK in SU-DHL-1/NC...</td>\n",
       "      <td>ALK</td>\n",
       "      <td>NPM-ALK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Degradation of NPM-ALK/EML4-ALK in SU-DHL-1/NC...</td>\n",
       "      <td>ALK</td>\n",
       "      <td>EML4-ALK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Degradation of Fak in PC3 cells after 24 h tre...</td>\n",
       "      <td>FAK</td>\n",
       "      <td>Fak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Degradation of WT/Exon 20 Ins EGFR in OVCAR8/H...</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Degradation of WT/Exon 20 Ins EGFR in OVCAR8/H...</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>Exon 20 Ins EGFR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Degradation of Exon 19 del/L858R EGFR in HCC82...</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>Exon 19 del</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Degradation of Exon 19 del/L858R EGFR in HCC82...</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>L858R EGFR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Degradation of L858R, T790M EGFR in H1975 cell...</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>L858R, T790M EGFR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Degradation of C481S BTK in XLA cells after 24...</td>\n",
       "      <td>BTK C481S</td>\n",
       "      <td>C481S BTK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Degradation of BRD4 short/long in HeLa cells a...</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>BRD4 short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Degradation of BRD4 short/long in HeLa cells a...</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>BRD4 long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Degradation of BRD4 BD1/2 assessed by EGFP/mCh...</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>BRD4 BD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Degradation of BRD4 BD1/2 assessed by EGFP/mCh...</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>BRD4 BD2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Degradation of total STAT3 and p-STAT3Y705 pro...</td>\n",
       "      <td>STAT3</td>\n",
       "      <td>p-STAT3Y705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Degradation of total STAT3 and p-STAT3Y705 pro...</td>\n",
       "      <td>STAT3</td>\n",
       "      <td>p-STAT3Y705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Degradation of G1202R ALK in 293T cells</td>\n",
       "      <td>ALK G1202R</td>\n",
       "      <td>G1202R ALK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Degradation of SMARCA2 in MV-4-11 cells after ...</td>\n",
       "      <td>SMARCA4</td>\n",
       "      <td>SMARCA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>Degradation of ERalpha in MCF-7/T47D cells aft...</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Degradation of ERalpha in MCF-7 cells after 6 ...</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Degradation of ERalpha in MCF-7 cells after 6h...</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>Degradation of pVHL30 in HeLa cells after 24 h...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>pVHL30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Degradation of TrkC in Hs578t cells after 24 h...</td>\n",
       "      <td>TRKC</td>\n",
       "      <td>TrkC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Degradation of HADC6 in MM.1S cells after 24 h...</td>\n",
       "      <td>HDAC6</td>\n",
       "      <td>HADC6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Degradation of ERalpha in MCF-7 cells after 4/...</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Degradation of ERalpha in MCF-7 cells after 4h...</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Degradation of ERalpha in MCF-7 cells after 4/...</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Degradation of total tau/P-tau in A152T neuron...</td>\n",
       "      <td>Tau</td>\n",
       "      <td>tau/P-tau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Degradation of ERalpha in MCF7 cells</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Degradation of Fak in primary Sertoli/germ cel...</td>\n",
       "      <td>FAK</td>\n",
       "      <td>Fak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>Degradation of Fak in HLE/HuH-7/SNU-423 cells ...</td>\n",
       "      <td>FAK</td>\n",
       "      <td>Fak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>Degradation of Fak in HUH-1/HepG2/SK-Hep-1 cel...</td>\n",
       "      <td>FAK</td>\n",
       "      <td>Fak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>Degradation of Fak in A549/Hep3B2.1-7/SNU-387 ...</td>\n",
       "      <td>FAK</td>\n",
       "      <td>Fak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>Degradation of Fak in HLF/SNU-398/HUCCT1 cells...</td>\n",
       "      <td>FAK</td>\n",
       "      <td>Fak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>Degradation of TPM3-TRKA/TRKA in KM12/HEL cell...</td>\n",
       "      <td>TRKA</td>\n",
       "      <td>TPM3-TRKA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>Degradation of EGFR del19 in HCC827 cells afte...</td>\n",
       "      <td>EGFR e19d</td>\n",
       "      <td>EGFR del19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>Degradation of ERalpha in MCF-7 cells</td>\n",
       "      <td>ER</td>\n",
       "      <td>ERalpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>Degradation of WDR5-HiBiT in MV4-11 (WDR5-HiBi...</td>\n",
       "      <td>WDR5</td>\n",
       "      <td>WDR5-HiBiT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>Degradation of BRD4 long in HEK293 cells after...</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>BRD4 long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Degradation of PYK2 in SR cells after 24 h tre...</td>\n",
       "      <td>PTK2B</td>\n",
       "      <td>PYK2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Degradation of RSK1 in NCI-H2228/A549/Calu-1 c...</td>\n",
       "      <td>RPS6KA1</td>\n",
       "      <td>RSK1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>Degradation of EGFR del19 in HCC-827 cells aft...</td>\n",
       "      <td>EGFR e19d</td>\n",
       "      <td>EGFR del19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>Degradation of total AKT in BT474 cells after ...</td>\n",
       "      <td>AKT2</td>\n",
       "      <td>AKT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>Degradation of total AKT in BT474 cells after ...</td>\n",
       "      <td>AKT1</td>\n",
       "      <td>AKT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>Degradation of total AKT in BT474 cells after ...</td>\n",
       "      <td>AKT3</td>\n",
       "      <td>AKT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>Degradation of GSK3B in SH-SY5Y cells after 24...</td>\n",
       "      <td>GSK-3beta</td>\n",
       "      <td>GSK3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>Degradation of SMARCA2 HiBiT in HT1080 cells a...</td>\n",
       "      <td>SMARCA2</td>\n",
       "      <td>SMARCA2 HiBiT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>Degradation of Tau5 in HEK293-hTau cells after...</td>\n",
       "      <td>Tau</td>\n",
       "      <td>Tau5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>Degradation of ENL in MV4;11 cells after 24 h ...</td>\n",
       "      <td>MLLT1</td>\n",
       "      <td>ENL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Assay (DC50/Dmax)      Target  \\\n",
       "2     Degradation of HiBiT-BRD9 in HEK293 cells afte...        BRD9   \n",
       "6     Degradation of HiBiT-BRD7 in HEK293 cells afte...        BRD7   \n",
       "77    Degradation of NPM-ALK/EML4-ALK in SU-DHL-1/NC...         ALK   \n",
       "78    Degradation of NPM-ALK/EML4-ALK in SU-DHL-1/NC...         ALK   \n",
       "102   Degradation of Fak in PC3 cells after 24 h tre...         FAK   \n",
       "111   Degradation of WT/Exon 20 Ins EGFR in OVCAR8/H...        EGFR   \n",
       "112   Degradation of WT/Exon 20 Ins EGFR in OVCAR8/H...        EGFR   \n",
       "113   Degradation of Exon 19 del/L858R EGFR in HCC82...        EGFR   \n",
       "114   Degradation of Exon 19 del/L858R EGFR in HCC82...        EGFR   \n",
       "115   Degradation of L858R, T790M EGFR in H1975 cell...        EGFR   \n",
       "122   Degradation of C481S BTK in XLA cells after 24...   BTK C481S   \n",
       "141   Degradation of BRD4 short/long in HeLa cells a...        BRD4   \n",
       "142   Degradation of BRD4 short/long in HeLa cells a...        BRD4   \n",
       "144   Degradation of BRD4 BD1/2 assessed by EGFP/mCh...        BRD4   \n",
       "145   Degradation of BRD4 BD1/2 assessed by EGFP/mCh...        BRD4   \n",
       "208   Degradation of total STAT3 and p-STAT3Y705 pro...       STAT3   \n",
       "212   Degradation of total STAT3 and p-STAT3Y705 pro...       STAT3   \n",
       "262             Degradation of G1202R ALK in 293T cells  ALK G1202R   \n",
       "356   Degradation of SMARCA2 in MV-4-11 cells after ...     SMARCA4   \n",
       "365   Degradation of ERalpha in MCF-7/T47D cells aft...          ER   \n",
       "367   Degradation of ERalpha in MCF-7 cells after 6 ...          ER   \n",
       "368   Degradation of ERalpha in MCF-7 cells after 6h...          ER   \n",
       "387   Degradation of pVHL30 in HeLa cells after 24 h...         VHL   \n",
       "388   Degradation of TrkC in Hs578t cells after 24 h...        TRKC   \n",
       "389   Degradation of HADC6 in MM.1S cells after 24 h...       HDAC6   \n",
       "408   Degradation of ERalpha in MCF-7 cells after 4/...          ER   \n",
       "411   Degradation of ERalpha in MCF-7 cells after 4h...          ER   \n",
       "412   Degradation of ERalpha in MCF-7 cells after 4/...          ER   \n",
       "495   Degradation of total tau/P-tau in A152T neuron...         Tau   \n",
       "523                Degradation of ERalpha in MCF7 cells          ER   \n",
       "536   Degradation of Fak in primary Sertoli/germ cel...         FAK   \n",
       "620   Degradation of Fak in HLE/HuH-7/SNU-423 cells ...         FAK   \n",
       "623   Degradation of Fak in HUH-1/HepG2/SK-Hep-1 cel...         FAK   \n",
       "626   Degradation of Fak in A549/Hep3B2.1-7/SNU-387 ...         FAK   \n",
       "629   Degradation of Fak in HLF/SNU-398/HUCCT1 cells...         FAK   \n",
       "663   Degradation of TPM3-TRKA/TRKA in KM12/HEL cell...        TRKA   \n",
       "718   Degradation of EGFR del19 in HCC827 cells afte...   EGFR e19d   \n",
       "769               Degradation of ERalpha in MCF-7 cells          ER   \n",
       "806   Degradation of WDR5-HiBiT in MV4-11 (WDR5-HiBi...        WDR5   \n",
       "824   Degradation of BRD4 long in HEK293 cells after...        BRD4   \n",
       "892   Degradation of PYK2 in SR cells after 24 h tre...       PTK2B   \n",
       "896   Degradation of RSK1 in NCI-H2228/A549/Calu-1 c...     RPS6KA1   \n",
       "899   Degradation of EGFR del19 in HCC-827 cells aft...   EGFR e19d   \n",
       "1014  Degradation of total AKT in BT474 cells after ...        AKT2   \n",
       "1015  Degradation of total AKT in BT474 cells after ...        AKT1   \n",
       "1016  Degradation of total AKT in BT474 cells after ...        AKT3   \n",
       "1058  Degradation of GSK3B in SH-SY5Y cells after 24...   GSK-3beta   \n",
       "1145  Degradation of SMARCA2 HiBiT in HT1080 cells a...     SMARCA2   \n",
       "1191  Degradation of Tau5 in HEK293-hTau cells after...         Tau   \n",
       "1192  Degradation of ENL in MV4;11 cells after 24 h ...       MLLT1   \n",
       "\n",
       "        Target (Parsed)  \n",
       "2            HiBiT-BRD9  \n",
       "6            HiBiT-BRD7  \n",
       "77              NPM-ALK  \n",
       "78             EML4-ALK  \n",
       "102                 Fak  \n",
       "111                  WT  \n",
       "112    Exon 20 Ins EGFR  \n",
       "113         Exon 19 del  \n",
       "114          L858R EGFR  \n",
       "115   L858R, T790M EGFR  \n",
       "122           C481S BTK  \n",
       "141          BRD4 short  \n",
       "142           BRD4 long  \n",
       "144            BRD4 BD1  \n",
       "145            BRD4 BD2  \n",
       "208         p-STAT3Y705  \n",
       "212         p-STAT3Y705  \n",
       "262          G1202R ALK  \n",
       "356             SMARCA2  \n",
       "365             ERalpha  \n",
       "367             ERalpha  \n",
       "368             ERalpha  \n",
       "387              pVHL30  \n",
       "388                TrkC  \n",
       "389               HADC6  \n",
       "408             ERalpha  \n",
       "411             ERalpha  \n",
       "412             ERalpha  \n",
       "495           tau/P-tau  \n",
       "523             ERalpha  \n",
       "536                 Fak  \n",
       "620                 Fak  \n",
       "623                 Fak  \n",
       "626                 Fak  \n",
       "629                 Fak  \n",
       "663           TPM3-TRKA  \n",
       "718          EGFR del19  \n",
       "769             ERalpha  \n",
       "806          WDR5-HiBiT  \n",
       "824           BRD4 long  \n",
       "892                PYK2  \n",
       "896                RSK1  \n",
       "899          EGFR del19  \n",
       "1014                AKT  \n",
       "1015                AKT  \n",
       "1016                AKT  \n",
       "1058              GSK3B  \n",
       "1145      SMARCA2 HiBiT  \n",
       "1191               Tau5  \n",
       "1192                ENL  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_table[parsed_table['Target'] !=\n",
    "             parsed_table['Target (Parsed)']][['Assay (DC50/Dmax)', 'Target', 'Target (Parsed)']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-Nan Active: 790\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parsed_table['Active'] = parsed_table.apply(\n",
    "    lambda row: is_active(row['DC50 (nM)'], row['Dmax (%)']),\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "print(f'Non-Nan Active: {len(parsed_table[parsed_table[\"Active\"].notnull()])}')\n",
    "\n",
    "# Histogram of Active\n",
    "sns.histplot(data=parsed_table, x='Active', bins=4)\n",
    "plt.title('Histogram of Active')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "parsed_table['Database'] = 'PROTAC-DB'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Parsed DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Database</th>\n",
       "      <th>Molecular Formula</th>\n",
       "      <th>cLogP</th>\n",
       "      <th>Target</th>\n",
       "      <th>PDB</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",
       "    </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>PROTAC-Pedia</td>\n",
       "      <td>C73H88ClF3N10O10S4</td>\n",
       "      <td>10.83732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>PROTAC-Pedia</td>\n",
       "      <td>C74H90ClF3N10O10S4</td>\n",
       "      <td>11.22742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>PROTAC-Pedia</td>\n",
       "      <td>C75H92ClF3N10O10S4</td>\n",
       "      <td>11.61752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>PROTAC-Pedia</td>\n",
       "      <td>C76H94ClF3N10O10S4</td>\n",
       "      <td>12.00762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>PROTAC-Pedia</td>\n",
       "      <td>C77H96ClF3N10O10S4</td>\n",
       "      <td>12.39772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2136</th>\n",
       "      <td>2342</td>\n",
       "      <td>O60885</td>\n",
       "      <td>CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=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>PROTAC-DB</td>\n",
       "      <td>C50H61ClN8O8S2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2137</th>\n",
       "      <td>2887</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCOCCO...</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>PROTAC-DB</td>\n",
       "      <td>C58H75F3N10O10S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FAK</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>2889</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCOCCO...</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>PROTAC-DB</td>\n",
       "      <td>C54H67F3N10O8S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FAK</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>2890</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCOCC(...</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>PROTAC-DB</td>\n",
       "      <td>C52H63F3N10O7S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FAK</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2140</th>\n",
       "      <td>2891</td>\n",
       "      <td>Q05397</td>\n",
       "      <td>CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCC(=O...</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>PROTAC-DB</td>\n",
       "      <td>C51H61F3N10O6S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FAK</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 30 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  CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...   \n",
       "2137         2887  Q05397  CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCOCCO...   \n",
       "2138         2889  Q05397  CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCOCCO...   \n",
       "2139         2890  Q05397  CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCOCC(...   \n",
       "2140         2891  Q05397  CNC(=O)C1=CC=CC=C1NC1=CC(NC2=CC=C(N3CCN(CCC(=O...   \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  ...      Database   Molecular Formula  \\\n",
       "0             10                    24  ...  PROTAC-Pedia  C73H88ClF3N10O10S4   \n",
       "1             10                    25  ...  PROTAC-Pedia  C74H90ClF3N10O10S4   \n",
       "2             10                    26  ...  PROTAC-Pedia  C75H92ClF3N10O10S4   \n",
       "3             10                    27  ...  PROTAC-Pedia  C76H94ClF3N10O10S4   \n",
       "4             10                    28  ...  PROTAC-Pedia  C77H96ClF3N10O10S4   \n",
       "...          ...                   ...  ...           ...                 ...   \n",
       "2136           7                    20  ...     PROTAC-DB      C50H61ClN8O8S2   \n",
       "2137           7                    27  ...     PROTAC-DB     C58H75F3N10O10S   \n",
       "2138           7                    21  ...     PROTAC-DB      C54H67F3N10O8S   \n",
       "2139           7                    18  ...     PROTAC-DB      C52H63F3N10O7S   \n",
       "2140           7                    16  ...     PROTAC-DB      C51H61F3N10O6S   \n",
       "\n",
       "         cLogP Target  PDB  Name  \\\n",
       "0     10.83732    NaN  NaN   NaN   \n",
       "1     11.22742    NaN  NaN   NaN   \n",
       "2     11.61752    NaN  NaN   NaN   \n",
       "3     12.00762    NaN  NaN   NaN   \n",
       "4     12.39772    NaN  NaN   NaN   \n",
       "...        ...    ...  ...   ...   \n",
       "2136       NaN   BRD4  NaN   NaN   \n",
       "2137       NaN    FAK  NaN   NaN   \n",
       "2138       NaN    FAK  NaN   NaN   \n",
       "2139       NaN    FAK  NaN   NaN   \n",
       "2140       NaN    FAK  NaN   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)  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "...              ...  \n",
       "2136       BRD4 long  \n",
       "2137             FAK  \n",
       "2138             FAK  \n",
       "2139             FAK  \n",
       "2140             FAK  \n",
       "\n",
       "[2141 rows x 30 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.concat([protac_pedia_df[ppedia_cols],\n",
    "                       parsed_table], ignore_index=True)\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Uniprot</th>\n",
       "      <th>Target</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "      <th>Article DOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>10.1021/acs.jmedchem.9b01264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>10.1158/0008-5472.CAN-19-1236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>10.1111/cas.13284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>10.1021/acs.jmedchem.9b02058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>10.1021/acs.jmedchem.0c00967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>10.1039/d0cc02201b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>BCR-ABL</td>\n",
       "      <td>10.1016/j.ejmech.2021.113645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BCR-ABL T315I</td>\n",
       "      <td>BCR-ABL T315I</td>\n",
       "      <td>10.1016/j.apsb.2020.11.009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Uniprot         Target Target (Parsed)                    Article DOI\n",
       "1125     NaN        BCR-ABL         BCR-ABL   10.1021/acs.jmedchem.9b01264\n",
       "1300     NaN        BCR-ABL         BCR-ABL  10.1158/0008-5472.CAN-19-1236\n",
       "1321     NaN        BCR-ABL         BCR-ABL              10.1111/cas.13284\n",
       "1463     NaN        BCR-ABL         BCR-ABL   10.1021/acs.jmedchem.9b02058\n",
       "1498     NaN        BCR-ABL         BCR-ABL   10.1021/acs.jmedchem.0c00967\n",
       "1526     NaN        BCR-ABL         BCR-ABL             10.1039/d0cc02201b\n",
       "1960     NaN        BCR-ABL         BCR-ABL   10.1016/j.ejmech.2021.113645\n",
       "2083     NaN  BCR-ABL T315I   BCR-ABL T315I     10.1016/j.apsb.2020.11.009"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[merged_df['Target'].str.contains(\n",
    "    'BCR') | merged_df['Target'].str.contains('ABL')][['Uniprot', 'Target', 'Target (Parsed)', 'Article DOI']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "target2uniprot = {\n",
    "    'BCR-ABL': 'P00520',\n",
    "    'BCR-ABL T315I': 'P00520',\n",
    "    'NS3': 'A8DG50',\n",
    "    'HSP90': 'P07900',\n",
    "}\n",
    "# Fill in Uniprot column based on Target column if Uniprot is NaN\n",
    "merged_df['Uniprot'] = merged_df.apply(\n",
    "    lambda row: target2uniprot.get(row['Target'], row['Uniprot']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print NaN percentage per column\n",
    "tmp = merged_df.isna().sum() / len(merged_df) * 100\n",
    "# Plot tmp as a horizontal bar plot\n",
    "tmp.plot(kind='barh')\n",
    "plt.title('Percentage of NaN values per column')\n",
    "plt.grid(axis='x', alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "# plt.figure(figsize=(10, 5))\n",
    "# sns.barplot(x=tmp.index, y=tmp.values)\n",
    "# plt.xticks(rotation=90)\n",
    "# plt.title('NaN percentage per column')\n",
    "# plt.grid(axis='y', alpha=0.5)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Target</th>\n",
       "      <th>Cell Type</th>\n",
       "      <th>Assay (DC50/Dmax)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>255</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>340</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD4 BD1/2 assessed by EGFP/mCh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>342</td>\n",
       "      <td>BRD4 BD1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD4 BD1 assessed by EGFP/mCher...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>343</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD4 BD1/2 assessed by EGFP/mCh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>343</td>\n",
       "      <td>BRD4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD4 BD1/2 assessed by EGFP/mCh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>350</td>\n",
       "      <td>BRD4 BD1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Degradation of BRD4 BD1 assessed by EGFP/mCher...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Compound ID    Target Cell Type  \\\n",
       "227           250       NaN       NaN   \n",
       "229           252       NaN       NaN   \n",
       "230           253       NaN       NaN   \n",
       "231           254       NaN       NaN   \n",
       "232           255       NaN       NaN   \n",
       "...           ...       ...       ...   \n",
       "1089          340      BRD4       NaN   \n",
       "1090          342  BRD4 BD1       NaN   \n",
       "1091          343      BRD4       NaN   \n",
       "1092          343      BRD4       NaN   \n",
       "1093          350  BRD4 BD1       NaN   \n",
       "\n",
       "                                      Assay (DC50/Dmax)  \n",
       "227                                                 NaN  \n",
       "229                                                 NaN  \n",
       "230                                                 NaN  \n",
       "231                                                 NaN  \n",
       "232                                                 NaN  \n",
       "...                                                 ...  \n",
       "1089  Degradation of BRD4 BD1/2 assessed by EGFP/mCh...  \n",
       "1090  Degradation of BRD4 BD1 assessed by EGFP/mCher...  \n",
       "1091  Degradation of BRD4 BD1/2 assessed by EGFP/mCh...  \n",
       "1092  Degradation of BRD4 BD1/2 assessed by EGFP/mCh...  \n",
       "1093  Degradation of BRD4 BD1 assessed by EGFP/mCher...  \n",
       "\n",
       "[88 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the Nan cell types in merged_df\n",
    "merged_df[merged_df['Cell Type'].isna()][['Compound ID', 'Target', 'Cell Type', 'Assay (DC50/Dmax)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty \"Cell Type\" values: 88\n",
      "PROTAC-Pedia    75\n",
      "PROTAC-DB       13\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Treatment Time (h)\" values: 229\n",
      "PROTAC-Pedia    115\n",
      "PROTAC-DB       114\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"DC50 (nM)\" values: 791\n",
      "PROTAC-Pedia    703\n",
      "PROTAC-DB        88\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Dmax (%)\" values: 1329\n",
      "PROTAC-DB       702\n",
      "PROTAC-Pedia    627\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Active\" values: 1054\n",
      "PROTAC-Pedia    628\n",
      "PROTAC-DB       426\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Article DOI\" values: 3\n",
      "PROTAC-Pedia    3\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Comments\" values: 1774\n",
      "PROTAC-DB       1216\n",
      "PROTAC-Pedia     558\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"cLogP\" values: 1216\n",
      "PROTAC-DB    1216\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Target\" values: 925\n",
      "PROTAC-Pedia    925\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"PDB\" values: 2119\n",
      "PROTAC-DB       1194\n",
      "PROTAC-Pedia     925\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Name\" values: 1576\n",
      "PROTAC-Pedia    925\n",
      "PROTAC-DB       651\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Assay (DC50/Dmax)\" values: 925\n",
      "PROTAC-Pedia    925\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Exact Mass\" values: 925\n",
      "PROTAC-Pedia    925\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"XLogP3\" values: 925\n",
      "PROTAC-Pedia    925\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n",
      "Empty \"Target (Parsed)\" values: 925\n",
      "PROTAC-Pedia    925\n",
      "Name: Database, dtype: int64\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for c in merged_df.columns:\n",
    "    num_nulls = merged_df[c].isnull().sum()\n",
    "    if num_nulls:\n",
    "        print(f'Empty \"{c}\" values: {num_nulls}')\n",
    "        print(merged_df[merged_df[c].isnull()]['Database'].value_counts())\n",
    "        print('-' * 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot active vs inactive in merged_df\n",
    "sns.countplot(data=merged_df, x='Active')\n",
    "# Put the number of active and inactive on top of the bars\n",
    "for i, count in enumerate(merged_df['Active'].value_counts()):\n",
    "    plt.text(i, count, count, ha='center', va='bottom')\n",
    "plt.title('Active vs Inactive')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[merged_df['Uniprot'].isnull()]['Target'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[merged_df['Uniprot'].isnull() & merged_df['Target'].str.contains(\n",
    "    'FGFR3_HUMAN-TACC3_HUMAN')]['Article DOI'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Database</th>\n",
       "      <th>Molecular Formula</th>\n",
       "      <th>cLogP</th>\n",
       "      <th>Target</th>\n",
       "      <th>PDB</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Compound ID, Uniprot, Smiles, E3 Ligase, InChI, InChI Key, Molecular Weight, Heavy Atom Count, Ring Count, Rotatable Bond Count, Topological Polar Surface Area, Hydrogen Bond Acceptor Count, Hydrogen Bond Donor Count, Cell Type, Treatment Time (h), DC50 (nM), Dmax (%), Active, Article DOI, Comments, Database, Molecular Formula, cLogP, Target, PDB, Name, Assay (DC50/Dmax), Exact Mass, XLogP3, Target (Parsed)]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 30 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[merged_df['Uniprot'] == 'FGFR3_HUMAN-TACC3_HUMAN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def canonize_smiles(smi):\n",
    "    return Chem.MolToSmiles(Chem.MolFromSmiles(smi))\n",
    "\n",
    "\n",
    "merged_df['Smiles'] = merged_df['Smiles'].apply(canonize_smiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove duplicates in UniProt ID column, _i.e._, keep the first entry:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_uniprot(uniprot):\n",
    "    return uniprot.split(',')[0].strip() if ',' in uniprot else uniprot.strip()\n",
    "\n",
    "\n",
    "merged_df['Uniprot'] = merged_df['Uniprot'].apply(clean_uniprot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Amino Acid Sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scrape FASTA sequence from UniProt database.\n",
    "\n",
    "NOTE: The Uniprot ID can be referring to both E3 and POI target, but we assume it's referring to the POI target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ac0afc72e254a3fb0dd7efeca7cb3ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Web-scraping sequences:   0%|          | 0/106 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "poi_sequences_filepath = os.path.join(\n",
    "    data_dir, 'processed', 'poi_uniprot2sequence.pkl')\n",
    "failed = False\n",
    "if os.path.exists(poi_sequences_filepath):\n",
    "    with open(poi_sequences_filepath, 'rb') as f:\n",
    "        uniprot2sequence = pickle.load(f)\n",
    "else:\n",
    "    uniprot2sequence = {}\n",
    "\n",
    "for uniprot in tqdm(merged_df['Uniprot'].unique().tolist(), desc='Web-scraping sequences'):\n",
    "    if uniprot in uniprot2sequence:\n",
    "        continue\n",
    "    try:\n",
    "        baseUrl = 'http://www.uniprot.org/uniprot/'\n",
    "        currentUrl = baseUrl + uniprot + '.fasta'\n",
    "        response = requests.post(currentUrl)\n",
    "        cData = ''.join(response.text)\n",
    "        i = cData.index('\\n') + 1\n",
    "        seq = cData[i:].rstrip().replace('\\n', '')\n",
    "        uniprot2sequence[uniprot] = seq\n",
    "    except:\n",
    "        print(f'Error getting {uniprot} from URL: {currentUrl}')\n",
    "        uniprot2sequence[uniprot] = float('nan')\n",
    "        failed = True\n",
    "\n",
    "with open(poi_sequences_filepath, 'wb') as f:\n",
    "    pickle.dump(uniprot2sequence, f)\n",
    "# Add 'Unknown' sequences corresponding to 'Unknown' genes/POIs\n",
    "uniprot2sequence['Unknown'] = float('nan')\n",
    "sequence2uniprot = {v: k for k, v in uniprot2sequence.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df['POI Sequence'] = merged_df['Uniprot'].map(uniprot2sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying Gene Mutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AR-V7, for instance, has different \"splicing variance\", meaning that the aminoacid sequence might be \"very\" dfferent, _i.e._ instead of differ by just one or a few letters, they might instead have entire different \"chucks\" of letters.\n",
    "\n",
    "NOTE: Indexing starts from one! Not zero!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_mutation(uniprot: str, gene: str, seq: str, on_error: Literal['raise', 'ignore'] = 'raise') -> str:\n",
    "    \"\"\"Apply the mutation to the sequence, if possible.\"\"\"\n",
    "    # Check if both gene and sequence are not nan\n",
    "    if pd.isna(gene) or pd.isna(seq) or gene.startswith('BRD') or len(gene.split(' ')) == 1:\n",
    "        return seq\n",
    "    # TODO: Just use a dictionary and replace these sequences straightaway...\n",
    "    uniprot_exceptions = {\n",
    "        ('O60885', 'BRD4 BD1'): uniprot2sequence['O60885'],\n",
    "        ('P25440', 'BRD2 BD2'): uniprot2sequence['P25440'],\n",
    "        ('P10275', 'AR-V7'): uniprot2sequence['P10275'],\n",
    "        # TODO: Not working... why???\n",
    "        ('P00533', 'EGFR e19d'): uniprot2sequence['P10275'],\n",
    "    }\n",
    "    # Handle exceptions\n",
    "    if (uniprot, gene) in uniprot_exceptions:\n",
    "        return uniprot_exceptions[(uniprot, gene)]\n",
    "    original_seq = seq\n",
    "    mutation = gene.split(' ')[1]\n",
    "    operations = mutation.split('/') if '/' in mutation else [mutation]\n",
    "    del_ops = 0\n",
    "    # print(\n",
    "    #     f'Applying mutation {mutation} from gene {gene} to {uniprot}... sequence: {seq}')\n",
    "    for op in operations:\n",
    "        if 'del' in op.lower():\n",
    "            idx = int(op.split('del')[1]) - 1\n",
    "            # print(\n",
    "            #     f'Operation: {op} on ...{seq[idx-8:idx]}[{seq[idx]}]{seq[idx+1:idx+8]}...')\n",
    "            seq = seq[:idx] + seq[idx + 1:]\n",
    "            del_ops += 1\n",
    "        else:\n",
    "            # Replace aminoacid at a specific index\n",
    "            # NOTE: The indexing starts from one, not zero.\n",
    "            # NOTE: If a deletion has happened before, the index is still\n",
    "            # relative to the whole sequence lenght (weird...)\n",
    "            curr, idx, new = op[0].upper(), int(op[1:-1])-1, op[-1].upper()\n",
    "            idx -= del_ops\n",
    "            print(\n",
    "                f'Operation: {op} on ...{seq[idx-8:idx]}[{seq[idx]} -> {new}]{seq[idx+1:idx+8]}...')\n",
    "            if curr != seq[idx]:\n",
    "                msg = f'Replacement at position {idx} failed. Expected \"{curr}\", found: \"{seq[idx]}\".'\n",
    "                if on_error == 'raise':\n",
    "                    raise ValueError('ERROR. ' + msg)\n",
    "                else:\n",
    "                    print('WARNING. ' + msg + ' No mutation is applied.')\n",
    "                    return original_seq\n",
    "            seq = seq[:idx] + new + seq[idx + 1:]\n",
    "    return seq\n",
    "\n",
    "\n",
    "# example = merged_df.iloc[protac_df['Target'].str.contains('EGFR'), :].iloc[0]\n",
    "# uniprot, gene, seq = example['Uniprot'], example['Target'], example['POI Sequence']\n",
    "# print(seq)\n",
    "# print(apply_mutation(uniprot, gene, seq, on_error='ignore'))\n",
    "# print(apply_mutation(uniprot, 'EGFR del19/T790M/C797S', seq, on_error='ignore'))\n",
    "# print(apply_mutation(uniprot, 'EGFR', seq, on_error='ignore'))\n",
    "\n",
    "# merged_df['POI Sequence'] = merged_df.apply(lambda row: apply_mutation(\n",
    "#     row['Uniprot'], row['Target'], row['POI Sequence'], on_error='ignore'), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply mutations to POI sequences:\n",
    "\n",
    "TODO: Some mutations are still failing... ignoring them for now..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: G1202R on ...ILLELMAG[G -> R]DLKSFLR...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: G466V on ...TVGQRIGS[G -> V]SFGTVYK...\n",
      "Operation: G466V on ...TVGQRIGS[G -> V]SFGTVYK...\n",
      "Operation: G469A on ...QRIGSGSF[G -> A]TVYKGKW...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: G1202R on ...ILLELMAG[G -> R]DLKSFLR...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: G1202R on ...ILLELMAG[G -> R]DLKSFLR...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: T878A on ...IARELHQF[T -> A]FDLLIKS...\n",
      "Operation: T878A on ...IARELHQF[T -> A]FDLLIKS...\n",
      "Operation: T878A on ...IARELHQF[T -> A]FDLLIKS...\n",
      "Operation: T878A on ...IARELHQF[T -> A]FDLLIKS...\n",
      "Operation: T878A on ...IARELHQF[T -> A]FDLLIKS...\n",
      "Operation: T878A on ...IARELHQF[T -> A]FDLLIKS...\n",
      "Operation: L858R on ...HVKITDFG[L -> R]AKLLGAE...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: R683G on ...CAKNILLI[R -> G]EEDRKTG...\n",
      "Operation: T790M on ...LTSTVQLI[T -> M]QLMPFGC...\n",
      "Operation: C797S on ...IMQLMPFG[C -> S]LLDYVRE...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: G12C on ...YKLVVVGA[G -> C]GVGKSAL...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: V600E on ...IGDFGLAT[V -> E]KSRWSGS...\n",
      "Operation: T315I on ...REPPFYII[T -> I]EFMTYGN...\n",
      "Operation: C481S on ...ITEYMANG[C -> S]LLNYLRE...\n",
      "Operation: C481T on ...ITEYMANG[C -> T]LLNYLRE...\n",
      "Operation: C481G on ...ITEYMANG[C -> G]LLNYLRE...\n",
      "Operation: C481W on ...ITEYMANG[C -> W]LLNYLRE...\n",
      "Operation: C481A on ...ITEYMANG[C -> A]LLNYLRE...\n"
     ]
    }
   ],
   "source": [
    "merged_df['POI Sequence'] = merged_df.apply(lambda row: apply_mutation(\n",
    "    row['Uniprot'], row['Target'], row['POI Sequence'], on_error='ignore'), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### E3 Ligase Sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['VHL',\n",
       " 'CRBN',\n",
       " 'Iap',\n",
       " 'Mdm2',\n",
       " 'Ubr1',\n",
       " 'RNF114',\n",
       " 'cIAP1',\n",
       " 'MDM2',\n",
       " 'IAP',\n",
       " 'XIAP',\n",
       " 'FEM1B']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['E3 Ligase'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       P40337\n",
       "1       P40337\n",
       "2       P40337\n",
       "3       P40337\n",
       "4       P40337\n",
       "         ...  \n",
       "2136    P40337\n",
       "2137    P40337\n",
       "2138    P40337\n",
       "2139    P40337\n",
       "2140    P40337\n",
       "Name: E3 Ligase Uniprot, Length: 2141, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e3ligase2genes = {\n",
    "    'VHL': 'P40337',\n",
    "    'CRBN': 'Q96SW2',\n",
    "    'DCAF11': 'Q8TEB1',\n",
    "    'DCAF15': 'Q66K64',\n",
    "    'DCAF16': 'Q9NXF7',\n",
    "    'MDM2': 'Q00987',\n",
    "    'Mdm2': 'Q00987',\n",
    "    'XIAP': 'P98170',\n",
    "    'cIAP1': 'Q7Z460',\n",
    "    'IAP': 'P98170',  # I couldn't find the Uniprot ID for IAP, so it's XIAP instead\n",
    "    'Iap': 'P98170',  # I couldn't find the Uniprot ID for IAP, so it's XIAP instead\n",
    "    'AhR': 'P35869',\n",
    "    'RNF4': 'P78317',\n",
    "    'RNF114': 'Q9Y508',\n",
    "    'FEM1B': 'Q9UK73',\n",
    "    'Ubr1': 'Q8IWV7',\n",
    "}\n",
    "merged_df['E3 Ligase Uniprot'] = merged_df['E3 Ligase'].map(e3ligase2genes)\n",
    "merged_df['E3 Ligase Uniprot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79f14bb2b87d45b2ad1902d11cd6c341",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Getting E3 ligase sequences:   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e3_ligase_sequences_filepath = os.path.join(\n",
    "    data_dir, 'processed', 'e3_ligase_uniprot2sequence.pkl')\n",
    "if os.path.exists(e3_ligase_sequences_filepath):\n",
    "    with open(e3_ligase_sequences_filepath, 'rb') as f:\n",
    "        e3_uniprot2sequence = pickle.load(f)\n",
    "else:\n",
    "    e3_uniprot2sequence = {}\n",
    "\n",
    "uniprots = merged_df['E3 Ligase Uniprot'].dropna().unique().tolist()\n",
    "for uniprot in tqdm(uniprots, desc='Getting E3 ligase sequences', total=len(uniprots)):\n",
    "    if uniprot in e3_uniprot2sequence:\n",
    "        continue\n",
    "    baseUrl = 'http://www.uniprot.org/uniprot/'\n",
    "    currentUrl = baseUrl + uniprot + '.fasta'\n",
    "    try:\n",
    "        response = requests.post(currentUrl)\n",
    "        cData = ''.join(response.text)\n",
    "        i = cData.index('\\n') + 1\n",
    "        seq = cData[i:].rstrip().replace('\\n', '')\n",
    "        e3_uniprot2sequence[uniprot] = seq\n",
    "        # print(f'Getting {cData} from URL: {currentUrl}')\n",
    "    except:\n",
    "        print(f'Error getting {uniprot} from URL: {currentUrl}')\n",
    "        e3_uniprot2sequence[uniprot] = float('nan')\n",
    "\n",
    "with open(e3_ligase_sequences_filepath, 'wb') as f:\n",
    "    pickle.dump(e3_uniprot2sequence, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NaN E3 Ligase Sequences: 0\n"
     ]
    }
   ],
   "source": [
    "merged_df['E3 Ligase Sequence'] = merged_df['E3 Ligase Uniprot'].map(\n",
    "    e3_uniprot2sequence\n",
    ")\n",
    "print(\n",
    "    f'Number of NaN E3 Ligase Sequences: {merged_df[\"E3 Ligase Sequence\"].isna().sum()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardize Cell Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Identifier for 'RS4; 11': RS4;11\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "\n",
    "def get_cell_line_identifier(cell_name):\n",
    "    \"\"\"\n",
    "    Given a cell name, return its corresponding cell line identifier using the Cellosaurus API.\n",
    "    If multiple entries are found, returns the identifier of the first match.\n",
    "    \"\"\"\n",
    "    cellosaurus_api = 'https://web.expasy.org/cellosaurus/api/search/cell-line'\n",
    "    query = cell_name.strip()\n",
    "    url = f'{cellosaurus_api}?q={query}&fields=id,sy,idsy'\n",
    "\n",
    "    try:\n",
    "        response = requests.get(url)\n",
    "        # Raises an HTTPError if the HTTP request returned an unsuccessful status code\n",
    "        response.raise_for_status()\n",
    "\n",
    "        search_results = response.json()\n",
    "\n",
    "        # Iterate through cell line entries\n",
    "        for entry in search_results.get('Cellosaurus', {}).get('cell-line-list', []):\n",
    "            for name in entry.get('name-list', []):\n",
    "                if name.get('type') == 'identifier':\n",
    "                    return name.get('value')\n",
    "    except requests.RequestException as e:\n",
    "        print(f\"Request failed: {e}\")\n",
    "\n",
    "    print(f\"No identifier found for cell line '{cell_name}'.\")\n",
    "    return np.nan\n",
    "    \n",
    "\n",
    "\n",
    "# Example usage\n",
    "cell_name = 'RS4; 11'\n",
    "identifier = get_cell_line_identifier(cell_name)\n",
    "if identifier:\n",
    "    print(f\"Identifier for '{cell_name}': {identifier}\")\n",
    "else:\n",
    "    print(\"No valid identifier found.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27356213a53b463996e9bd4ef93e35bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Getting cell line identifiers:   0%|          | 0/180 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No identifier found for cell line 'Panc02.13'.\n",
      "No identifier found for cell line 'GFP-KRASG12C reporter cell line in Flp-In 293'.\n",
      "No identifier found for cell line 'Big sellection of cacer cell lines'.\n",
      "No identifier found for cell line 'WI38 platelets'.\n",
      "No identifier found for cell line 'THPT1'.\n",
      "No identifier found for cell line '231MFP'.\n",
      "No identifier found for cell line 'SRD15'.\n",
      "No identifier found for cell line 'IgE MM'.\n",
      "No identifier found for cell line 'hPBMC'.\n",
      "No identifier found for cell line 'Mouse 4935'.\n",
      "No identifier found for cell line 'Primary Cardiomyocytes'.\n",
      "No identifier found for cell line 'A152T neurons'.\n",
      "No identifier found for cell line 'primary Sertoli'.\n",
      "No identifier found for cell line 'Hep3B2.1-7'.\n",
      "No identifier found for cell line 'PC3-S1'.\n",
      "No identifier found for cell line 'MV4-11 (WDR5-HiBiT)'.\n",
      "No identifier found for cell line 'PDX SJBALL020589'.\n",
      "No identifier found for cell line 'HEK293-hTau'.\n"
     ]
    }
   ],
   "source": [
    "# Load cell2identifier dictionary if it exists\n",
    "cell2identifier_filepath = os.path.join(\n",
    "    data_dir, 'processed', 'cell2identifier.pkl'\n",
    ")\n",
    "if os.path.exists(cell2identifier_filepath):\n",
    "    with open(cell2identifier_filepath, 'rb') as f:\n",
    "        cell2identifier = pickle.load(f)\n",
    "else:\n",
    "    cell2identifier = {}\n",
    "\n",
    "# Web-scrape cell line identifiers\n",
    "unique_cells = merged_df['Cell Type'].dropna().unique().tolist()\n",
    "for cell in tqdm(unique_cells, desc='Getting cell line identifiers'):\n",
    "    if cell not in cell2identifier:\n",
    "        cell_id = get_cell_line_identifier(cell)\n",
    "        if pd.notnull(cell_id):\n",
    "            cell2identifier[cell] = cell_id\n",
    "\n",
    "# Store cell2identifier dictionary in a pickle file\n",
    "with open(cell2identifier_filepath, 'wb') as f:\n",
    "    pickle.dump(cell2identifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Cell Type</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>MOLT-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>H1975</td>\n",
       "      <td>H1975/WR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>PBMCs</td>\n",
       "      <td>PH1-PBMCs-hiPSC4F1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>THP-1</td>\n",
       "      <td>THP-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>K562</td>\n",
       "      <td>EGFP-K562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>BaF3 FLT3-ITD</td>\n",
       "      <td>Ba/F3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2064</th>\n",
       "      <td>HEK293A</td>\n",
       "      <td>HEK293A mRFP-ATG9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>HEK293-hTau</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>HAP1</td>\n",
       "      <td>HAP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>ER-positive breast cancer cell lines</td>\n",
       "      <td>CGBC 01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Cell Type Cell Line Identifier\n",
       "0                                   MOLT-4               MOLT-4\n",
       "55                                   H1975             H1975/WR\n",
       "71                                   PBMCs   PH1-PBMCs-hiPSC4F1\n",
       "77                                   THP-1                THP-1\n",
       "81                                    K562            EGFP-K562\n",
       "...                                    ...                  ...\n",
       "2035                         BaF3 FLT3-ITD                Ba/F3\n",
       "2064                               HEK293A    HEK293A mRFP-ATG9\n",
       "2116                           HEK293-hTau                  NaN\n",
       "2124                                  HAP1                 HAP1\n",
       "2128  ER-positive breast cancer cell lines              CGBC 01\n",
       "\n",
       "[181 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign cell line identifiers to each cell line\n",
    "merged_df['Cell Line Identifier'] = merged_df['Cell Type'].map(\n",
    "    cell2identifier\n",
    ")\n",
    "merged_df[['Cell Type', 'Cell Line Identifier']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply Active - ORed Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df['Active - OR'] = merged_df.apply(\n",
    "    lambda row: is_active(row['DC50 (nM)'], row['Dmax (%)'], oring=True), axis=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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 number of \n",
    "\n",
    "# Plot the number of NaN values in Dmax and DC50 columns\n",
    "tmp = merged_df[['DC50 (nM)', 'Dmax (%)']].isna().sum()\n",
    "tmp.plot(kind='bar')\n",
    "plt.title('Number of NaN values in DC50 and Dmax columns')\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the columns Active and 'Active - OR' side by side\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "axes[0].set_title('Active')\n",
    "axes[1].set_title('Active - OR')\n",
    "sns.countplot(data=merged_df, x='Active', ax=axes[0])\n",
    "sns.countplot(data=merged_df, x='Active - OR', ax=axes[1])\n",
    "# Add the number of active and inactive on top of the bars\n",
    "for i, count in enumerate(merged_df['Active'].value_counts()):\n",
    "    axes[0].text(i, count, count, ha='center', va='bottom')\n",
    "for i, count in enumerate(merged_df['Active - OR'].value_counts()):\n",
    "    axes[1].text(i, count, count, ha='center', va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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 percentage of rows for which the columns 'Active' and 'Active - OR' are different\n",
    "\n",
    "# Isolate the ones having Active not nan\n",
    "merged_df_active = merged_df[merged_df['Active'].notnull()]\n",
    "\n",
    "tmp = (merged_df_active['Active'] != merged_df_active['Active - OR']).sum() / len(merged_df_active) * 100\n",
    "plt.bar(['Different', 'Equal'], [tmp, 100-tmp])\n",
    "plt.title('Percentage of rows for which \"Active\" and \"Active - OR\" are different')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save to HF Dataset (TODO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to csv\n",
    "merged_df.to_csv(\n",
    "    os.path.join(data_dir, 'PROTAC-Degradation-DB.csv'),\n",
    "    index=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "155614202614452bab052cc0ac717aba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36bf206aec324059ae75df7eeb83c942",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/3 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "004eaa8d39c7417ab35eeac3dbc1f253",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/1.82k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['Compound ID', 'Uniprot', 'Smiles', 'E3 Ligase', 'InChI', 'InChI Key', 'Molecular Weight', 'Heavy Atom Count', 'Ring Count', 'Rotatable Bond Count', 'Topological Polar Surface Area', 'Hydrogen Bond Acceptor Count', 'Hydrogen Bond Donor Count', 'Cell Type', 'Treatment Time (h)', 'DC50 (nM)', 'Dmax (%)', 'Active', 'Article DOI', 'Comments', 'Database', 'Molecular Formula', 'cLogP', 'Target', 'PDB', 'Name', 'Assay (DC50/Dmax)', 'Exact Mass', 'XLogP3', 'Target (Parsed)', 'POI Sequence', 'E3 Ligase Uniprot', 'E3 Ligase Sequence', 'Cell Line Identifier'],\n",
       "    num_rows: 2141\n",
       "})"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save merged_df as hugging face dataset to the hub\n",
    "from datasets import Dataset\n",
    "\n",
    "dataset = Dataset.from_pandas(merged_df)\n",
    "dataset.push_to_hub(\n",
    "    'ailab-bio/PROTAC-Degradation-DB',\n",
    "    config_name='default',\n",
    "    split='train',  # NOTE: Hack to avoid an error on loading the dataset\n",
    "    token='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',\n",
    "    private=True,\n",
    ")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Data for Each Assay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percent Degradation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Target (Parsed)': 'KRAS',\n",
       "  'Cell Type': 'H2023',\n",
       "  'Treatment Time (h)': None,\n",
       "  'Concentration (nM)': ['300', '1000', '3000']},\n",
       " {'Target (Parsed)': 'ALK',\n",
       "  'Cell Type': 'H3122',\n",
       "  'Treatment Time (h)': '16',\n",
       "  'Concentration (nM)': ['100', '1000']},\n",
       " {'Target (Parsed)': 'TPM3-TRKA',\n",
       "  'Cell Type': 'KM12',\n",
       "  'Treatment Time (h)': '6',\n",
       "  'Concentration (nM)': ['0.1', '1', '10']}]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_degradation_info(sentence):\n",
    "    # Regex patterns for gene, cell type, treatment time, and concentrations\n",
    "    gene_regex = r\"%\\s([A-Za-z0-9-/]+)\\sdegradation\"\n",
    "    cell_regex = r\"in\\s([A-Za-z0-9-/]+)\\scells\"\n",
    "    treatment_time_regex = r\"after\\s(\\d+\\s?h)\\s\"\n",
    "    concentration_regex = r\"at\\s([0-9/.]+)\\s?nM\"\n",
    "\n",
    "    gene = re.search(gene_regex, sentence)\n",
    "    cell = re.search(cell_regex, sentence)\n",
    "    treatment_time = re.search(treatment_time_regex, sentence)\n",
    "    concentration = re.search(concentration_regex, sentence)\n",
    "\n",
    "    extracted_info = {\n",
    "        \"Target (Parsed)\": gene.group(1) if gene else np.nan,\n",
    "        \"Cell Type\": cell.group(1) if cell else np.nan,\n",
    "        \"Treatment Time (h)\": treatment_time.group(1).strip(\" h\") if treatment_time else None,\n",
    "        \"Concentration (nM)\": concentration.group(1).split('/') if concentration else None\n",
    "    }\n",
    "    return extracted_info\n",
    "\n",
    "\n",
    "# Test sentences\n",
    "test_sentences = [\n",
    "    \"% KRAS degradation in H2023 cells at 300/1000/3000 nM\",\n",
    "    \"% ALK degradation in H3122 cells after 16 h treatment at 100/1000 nM\",\n",
    "    # ... (other sentences)\n",
    "    \"% TPM3-TRKA degradation in KM12 cells after 6 h treatment at 0.1/1/10 nM\",\n",
    "]\n",
    "\n",
    "# Extracting information\n",
    "extracted_info_from_sentences = [\n",
    "    extract_degradation_info(t) for t in test_sentences]\n",
    "extracted_info_from_sentences[:5]  # Displaying first 5 for brevity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows before parsing: 362\n",
      "Parameter columns: ['Percent degradation (%)']\n"
     ]
    }
   ],
   "source": [
    "param_cols = assay_to_parameters[\"Assay (Percent degradation)\"]\n",
    "dc50_dmax_df = pd.concat([protac_df, scraped_protac_df]).dropna(\n",
    "    subset=param_cols + [\"Assay (Percent degradation)\"], how='all')\n",
    "dc50_dmax_df = dc50_dmax_df.dropna(subset=\"Assay (Percent degradation)\")\n",
    "# Drop all \"assay columns\" in assay_to_parameters except for \"Assay (Percent degradation)\" and its parameters\n",
    "dc50_dmax_df = dc50_dmax_df.drop(\n",
    "    columns=[col for col in assay_cols if col not in param_cols + [\"Assay (Percent degradation)\"]])\n",
    "dc50_dmax_df = dc50_dmax_df.drop_duplicates()\n",
    "\n",
    "print(f'Number of rows before parsing: {len(dc50_dmax_df)}')\n",
    "print(f'Parameter columns: {param_cols}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a651a5f1d344ad59b7445f79d58d964",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting Percent degradation info:   0%|          | 0/362 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows after parsing: 362\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>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Target</th>\n",
       "      <th>E3 ligase</th>\n",
       "      <th>PDB</th>\n",
       "      <th>Name</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>Percent degradation (%)</th>\n",
       "      <th>Assay (Percent degradation)</th>\n",
       "      <th>Article DOI</th>\n",
       "      <th>...</th>\n",
       "      <th>Hydrogen Bond Donor Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>Topological Polar Surface Area</th>\n",
       "      <th>Molecular Formula</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "      <th>Cell Type</th>\n",
       "      <th>Treatment Time (h)</th>\n",
       "      <th>Concentration (nM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>94</td>\n",
       "      <td>P10275</td>\n",
       "      <td>AR</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...</td>\n",
       "      <td>26.0/35.0/28.0</td>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01631</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>188.07</td>\n",
       "      <td>C45H46F4N8O6S2</td>\n",
       "      <td>InChI=1S/C45H46F4N8O6S2/c1-24-36(65-23-53-24)2...</td>\n",
       "      <td>GHDHSFXBWJJYAN-XTYUYLDHSA-N</td>\n",
       "      <td>AR</td>\n",
       "      <td>LNCaP</td>\n",
       "      <td>6</td>\n",
       "      <td>100/1000/10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>P10275</td>\n",
       "      <td>AR</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...</td>\n",
       "      <td>15.0/23.0/23.0</td>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01631</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>188.07</td>\n",
       "      <td>C46H48F4N8O6S2</td>\n",
       "      <td>InChI=1S/C46H48F4N8O6S2/c1-25-37(66-24-54-25)2...</td>\n",
       "      <td>IWXCCOWEXFTLEC-HTSYFKMQSA-N</td>\n",
       "      <td>AR</td>\n",
       "      <td>LNCaP</td>\n",
       "      <td>6</td>\n",
       "      <td>100/1000/10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96</td>\n",
       "      <td>P10275</td>\n",
       "      <td>AR</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...</td>\n",
       "      <td>16.0/20.0/25.0</td>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01631</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>188.07</td>\n",
       "      <td>C47H50F4N8O6S2</td>\n",
       "      <td>InChI=1S/C47H50F4N8O6S2/c1-26-38(67-25-55-26)2...</td>\n",
       "      <td>ZFWHPGQSSVNCFB-KZBBYPRMSA-N</td>\n",
       "      <td>AR</td>\n",
       "      <td>LNCaP</td>\n",
       "      <td>6</td>\n",
       "      <td>100/1000/10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97</td>\n",
       "      <td>P10275</td>\n",
       "      <td>AR</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...</td>\n",
       "      <td>11.0/25.0/29.0</td>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01631</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>188.07</td>\n",
       "      <td>C48H52F4N8O6S2</td>\n",
       "      <td>InChI=1S/C48H52F4N8O6S2/c1-27-39(68-26-56-27)2...</td>\n",
       "      <td>LBGRZWNYLZVZQM-HOLFLANQSA-N</td>\n",
       "      <td>AR</td>\n",
       "      <td>LNCaP</td>\n",
       "      <td>6</td>\n",
       "      <td>100/1000/10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98</td>\n",
       "      <td>P10275</td>\n",
       "      <td>AR</td>\n",
       "      <td>VHL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...</td>\n",
       "      <td>54.0/84.0/64.0</td>\n",
       "      <td>% AR degradation in LNCaP cells after 6 h trea...</td>\n",
       "      <td>10.1021/acs.jmedchem.8b01631</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>188.07</td>\n",
       "      <td>C49H54F4N8O6S2</td>\n",
       "      <td>InChI=1S/C49H54F4N8O6S2/c1-28-40(69-27-57-28)3...</td>\n",
       "      <td>QPHYYAFAFYEWMM-RHMQSKSJSA-N</td>\n",
       "      <td>AR</td>\n",
       "      <td>LNCaP</td>\n",
       "      <td>6</td>\n",
       "      <td>100/1000/10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound ID Uniprot Target E3 ligase  PDB Name  \\\n",
       "0           94  P10275     AR       VHL  NaN  NaN   \n",
       "1           95  P10275     AR       VHL  NaN  NaN   \n",
       "2           96  P10275     AR       VHL  NaN  NaN   \n",
       "3           97  P10275     AR       VHL  NaN  NaN   \n",
       "4           98  P10275     AR       VHL  NaN  NaN   \n",
       "\n",
       "                                              Smiles Percent degradation (%)  \\\n",
       "0  CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...          26.0/35.0/28.0   \n",
       "1  CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...          15.0/23.0/23.0   \n",
       "2  CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...          16.0/20.0/25.0   \n",
       "3  CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...          11.0/25.0/29.0   \n",
       "4  CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)...          54.0/84.0/64.0   \n",
       "\n",
       "                         Assay (Percent degradation)  \\\n",
       "0  % AR degradation in LNCaP cells after 6 h trea...   \n",
       "1  % AR degradation in LNCaP cells after 6 h trea...   \n",
       "2  % AR degradation in LNCaP cells after 6 h trea...   \n",
       "3  % AR degradation in LNCaP cells after 6 h trea...   \n",
       "4  % AR degradation in LNCaP cells after 6 h trea...   \n",
       "\n",
       "                    Article DOI  ...  Hydrogen Bond Donor Count  \\\n",
       "0  10.1021/acs.jmedchem.8b01631  ...                          4   \n",
       "1  10.1021/acs.jmedchem.8b01631  ...                          4   \n",
       "2  10.1021/acs.jmedchem.8b01631  ...                          4   \n",
       "3  10.1021/acs.jmedchem.8b01631  ...                          4   \n",
       "4  10.1021/acs.jmedchem.8b01631  ...                          4   \n",
       "\n",
       "   Rotatable Bond Count  Topological Polar Surface Area  Molecular Formula  \\\n",
       "0                    12                          188.07     C45H46F4N8O6S2   \n",
       "1                    13                          188.07     C46H48F4N8O6S2   \n",
       "2                    14                          188.07     C47H50F4N8O6S2   \n",
       "3                    15                          188.07     C48H52F4N8O6S2   \n",
       "4                    16                          188.07     C49H54F4N8O6S2   \n",
       "\n",
       "                                               InChI  \\\n",
       "0  InChI=1S/C45H46F4N8O6S2/c1-24-36(65-23-53-24)2...   \n",
       "1  InChI=1S/C46H48F4N8O6S2/c1-25-37(66-24-54-25)2...   \n",
       "2  InChI=1S/C47H50F4N8O6S2/c1-26-38(67-25-55-26)2...   \n",
       "3  InChI=1S/C48H52F4N8O6S2/c1-27-39(68-26-56-27)2...   \n",
       "4  InChI=1S/C49H54F4N8O6S2/c1-28-40(69-27-57-28)3...   \n",
       "\n",
       "                     InChI Key  Target (Parsed)  Cell Type  \\\n",
       "0  GHDHSFXBWJJYAN-XTYUYLDHSA-N               AR      LNCaP   \n",
       "1  IWXCCOWEXFTLEC-HTSYFKMQSA-N               AR      LNCaP   \n",
       "2  ZFWHPGQSSVNCFB-KZBBYPRMSA-N               AR      LNCaP   \n",
       "3  LBGRZWNYLZVZQM-HOLFLANQSA-N               AR      LNCaP   \n",
       "4  QPHYYAFAFYEWMM-RHMQSKSJSA-N               AR      LNCaP   \n",
       "\n",
       "   Treatment Time (h) Concentration (nM)  \n",
       "0                   6     100/1000/10000  \n",
       "1                   6     100/1000/10000  \n",
       "2                   6     100/1000/10000  \n",
       "3                   6     100/1000/10000  \n",
       "4                   6     100/1000/10000  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parsed_table = []\n",
    "\n",
    "for i, row in tqdm(dc50_dmax_df.iterrows(), total=len(dc50_dmax_df), desc='Extracting Percent degradation info'):\n",
    "    assay = row[\"Assay (Percent degradation)\"]\n",
    "    if len(assay) < 5:\n",
    "        continue\n",
    "    extracted_info = extract_degradation_info(assay)\n",
    "\n",
    "    extracted_info['Percent degradation (%)'] = split_clean_str(\n",
    "        row['Percent degradation (%)'], return_floats=True)\n",
    "\n",
    "    # For now, store them as strings\n",
    "    extracted_info['Percent degradation (%)'] = '/'.join(\n",
    "        [str(f) for f in extracted_info['Percent degradation (%)']])\n",
    "    extracted_info['Concentration (nM)'] = '/'.join(\n",
    "        [str(f) for f in extracted_info['Concentration (nM)']])\n",
    "\n",
    "    # Update the row with the extracted info\n",
    "    parsed_table.append({**row.to_dict(), **extracted_info})\n",
    "\n",
    "parsed_table = pd.DataFrame(parsed_table)\n",
    "print(f'Number of rows after parsing: {len(parsed_table)}')\n",
    "display(parsed_table.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Target</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>BRAF</td>\n",
       "      <td>B-Raf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>TRKA</td>\n",
       "      <td>TPM3-TRKA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>EGFR L858R/T790M</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>EGFR e19d</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>GSK-3beta</td>\n",
       "      <td>GSK3B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Target Target (Parsed)\n",
       "46               BRAF           B-Raf\n",
       "167              TRKA       TPM3-TRKA\n",
       "256  EGFR L858R/T790M             NaN\n",
       "258         EGFR e19d             NaN\n",
       "305         GSK-3beta           GSK3B"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_table[parsed_table['Target'] !=\n",
    "             parsed_table['Target (Parsed)']][['Target', 'Target (Parsed)']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'Uniprot', 'Target', 'E3 ligase', 'PDB', 'Name',\n",
       "       'Smiles', 'Percent degradation (%)', 'Assay (Percent degradation)',\n",
       "       'Article DOI', 'Molecular Weight', 'Exact Mass', 'XLogP3',\n",
       "       'Heavy Atom Count', 'Ring Count', 'Hydrogen Bond Acceptor Count',\n",
       "       'Hydrogen Bond Donor Count', 'Rotatable Bond Count',\n",
       "       'Topological Polar Surface Area', 'Molecular Formula', 'InChI',\n",
       "       'InChI Key', 'Target (Parsed)', 'Cell Type', 'Treatment Time (h)',\n",
       "       'Concentration (nM)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_table.columns"
   ]
  }
 ],
 "metadata": {
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