tutorial1-2.ipynb

{
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  {
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   "source": [
    "## Bioinformatics Project - Computational Drug Discovery [Part 1] Download Bioactivity Data (Concised version)\n",
    "\n",
    "* https://github.com/dataprofessor/bioinformatics_freecodecamp/blob/main/CDD_ML_Part_1_Acetylcholinesterase_Bioactivity_Data_Concised.ipynb\n",
    "* https://github.com/dataprofessor/bioinformatics_freecodecamp/blob/main/CDD_ML_Part_2_Acetylcholinesterase_Exploratory_Data_Analysis.ipynb\n",
    "* https://youtu.be/jBlTQjcKuaY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# installing ChEMBL webresource client\n",
    "#! pip install chembl_webresource_client --quiet\n",
    "#! pip install pandas --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "from chembl_webresource_client.new_client import new_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cross_references</th>\n",
       "      <th>organism</th>\n",
       "      <th>pref_name</th>\n",
       "      <th>score</th>\n",
       "      <th>species_group_flag</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_components</th>\n",
       "      <th>target_type</th>\n",
       "      <th>tax_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>17.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL613732</td>\n",
       "      <td>[]</td>\n",
       "      <td>ORGANISM</td>\n",
       "      <td>11119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[]</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>14.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL612575</td>\n",
       "      <td>[]</td>\n",
       "      <td>ORGANISM</td>\n",
       "      <td>227859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[]</td>\n",
       "      <td>Feline coronavirus</td>\n",
       "      <td>Feline coronavirus</td>\n",
       "      <td>14.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL612744</td>\n",
       "      <td>[]</td>\n",
       "      <td>ORGANISM</td>\n",
       "      <td>12663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td>Murine coronavirus</td>\n",
       "      <td>Murine coronavirus</td>\n",
       "      <td>14.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL5209664</td>\n",
       "      <td>[]</td>\n",
       "      <td>ORGANISM</td>\n",
       "      <td>694005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "      <td>Human coronavirus 229E</td>\n",
       "      <td>Human coronavirus 229E</td>\n",
       "      <td>12.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL613837</td>\n",
       "      <td>[]</td>\n",
       "      <td>ORGANISM</td>\n",
       "      <td>11137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[]</td>\n",
       "      <td>Human coronavirus OC43</td>\n",
       "      <td>Human coronavirus OC43</td>\n",
       "      <td>12.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL5209665</td>\n",
       "      <td>[]</td>\n",
       "      <td>ORGANISM</td>\n",
       "      <td>31631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[{'xref_id': 'P0C6U8', 'xref_name': None, 'xre...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL3927</td>\n",
       "      <td>[{'accession': 'P0C6U8', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>227859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[]</td>\n",
       "      <td>Middle East respiratory syndrome-related coron...</td>\n",
       "      <td>Middle East respiratory syndrome-related coron...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL4296578</td>\n",
       "      <td>[]</td>\n",
       "      <td>ORGANISM</td>\n",
       "      <td>1335626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[{'xref_id': 'P0C6X7', 'xref_name': None, 'xre...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>Replicase polyprotein 1ab</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL5118</td>\n",
       "      <td>[{'accession': 'P0C6X7', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>227859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[]</td>\n",
       "      <td>Severe acute respiratory syndrome coronavirus 2</td>\n",
       "      <td>Replicase polyprotein 1ab</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL4523582</td>\n",
       "      <td>[{'accession': 'P0DTD1', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>2697049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                    cross_references  \\\n",
       "0                                                 []   \n",
       "1                                                 []   \n",
       "2                                                 []   \n",
       "3                                                 []   \n",
       "4                                                 []   \n",
       "5                                                 []   \n",
       "6  [{'xref_id': 'P0C6U8', 'xref_name': None, 'xre...   \n",
       "7                                                 []   \n",
       "8  [{'xref_id': 'P0C6X7', 'xref_name': None, 'xre...   \n",
       "9                                                 []   \n",
       "\n",
       "                                            organism  \\\n",
       "0                                        Coronavirus   \n",
       "1                                   SARS coronavirus   \n",
       "2                                 Feline coronavirus   \n",
       "3                                 Murine coronavirus   \n",
       "4                             Human coronavirus 229E   \n",
       "5                             Human coronavirus OC43   \n",
       "6                                   SARS coronavirus   \n",
       "7  Middle East respiratory syndrome-related coron...   \n",
       "8                                   SARS coronavirus   \n",
       "9    Severe acute respiratory syndrome coronavirus 2   \n",
       "\n",
       "                                           pref_name  score  \\\n",
       "0                                        Coronavirus   17.0   \n",
       "1                                   SARS coronavirus   14.0   \n",
       "2                                 Feline coronavirus   14.0   \n",
       "3                                 Murine coronavirus   14.0   \n",
       "4                             Human coronavirus 229E   12.0   \n",
       "5                             Human coronavirus OC43   12.0   \n",
       "6                SARS coronavirus 3C-like proteinase   10.0   \n",
       "7  Middle East respiratory syndrome-related coron...    9.0   \n",
       "8                          Replicase polyprotein 1ab    4.0   \n",
       "9                          Replicase polyprotein 1ab    4.0   \n",
       "\n",
       "   species_group_flag target_chembl_id  \\\n",
       "0               False     CHEMBL613732   \n",
       "1               False     CHEMBL612575   \n",
       "2               False     CHEMBL612744   \n",
       "3               False    CHEMBL5209664   \n",
       "4               False     CHEMBL613837   \n",
       "5               False    CHEMBL5209665   \n",
       "6               False       CHEMBL3927   \n",
       "7               False    CHEMBL4296578   \n",
       "8               False       CHEMBL5118   \n",
       "9               False    CHEMBL4523582   \n",
       "\n",
       "                                   target_components     target_type   tax_id  \n",
       "0                                                 []        ORGANISM    11119  \n",
       "1                                                 []        ORGANISM   227859  \n",
       "2                                                 []        ORGANISM    12663  \n",
       "3                                                 []        ORGANISM   694005  \n",
       "4                                                 []        ORGANISM    11137  \n",
       "5                                                 []        ORGANISM    31631  \n",
       "6  [{'accession': 'P0C6U8', 'component_descriptio...  SINGLE PROTEIN   227859  \n",
       "7                                                 []        ORGANISM  1335626  \n",
       "8  [{'accession': 'P0C6X7', 'component_descriptio...  SINGLE PROTEIN   227859  \n",
       "9  [{'accession': 'P0DTD1', 'component_descriptio...  SINGLE PROTEIN  2697049  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target search for acetylcholinesterase\n",
    "target = new_client.target\n",
    "target_query = target.search('coronavirus')\n",
    "targets = pd.DataFrame.from_dict(target_query)\n",
    "targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CHEMBL3927'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select and retrieve bioactivity data for single protein target (SARS coronavirus 3C-like proteinase)\n",
    "# in tutorial video, the id was 4. but chembl has updated their database\n",
    "selected_target = targets.target_chembl_id[6]\n",
    "selected_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'action_type': None, 'activity_comment': None, 'activity_id': 1480935, 'activity_properties': [], 'assay_chembl_id': 'CHEMBL829584', 'assay_description': 'In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease)', 'assay_type': 'B', 'assay_variant_accession': None, 'assay_variant_mutation': None, 'bao_endpoint': 'BAO_0000190', 'bao_format': 'BAO_0000357', 'bao_label': 'single protein format', 'canonical_smiles': 'Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21', 'data_validity_comment': None, 'data_validity_description': None, 'document_chembl_id': 'CHEMBL1139624', 'document_journal': 'Bioorg Med Chem Lett', 'document_year': 2005, 'ligand_efficiency': {'bei': '18.28', 'le': '0.33', 'lle': '3.25', 'sei': '5.90'}, 'molecule_chembl_id': 'CHEMBL187579', 'molecule_pref_name': None, 'parent_molecule_chembl_id': 'CHEMBL187579', 'pchembl_value': '5.14', 'potential_duplicate': 0, 'qudt_units': 'http://www.openphacts.org/units/Nanomolar', 'record_id': 384103, 'relation': '=', 'src_id': 1, 'standard_flag': 1, 'standard_relation': '=', 'standard_text_value': None, 'standard_type': 'IC50', 'standard_units': 'nM', 'standard_upper_value': None, 'standard_value': '7200.0', 'target_chembl_id': 'CHEMBL3927', 'target_organism': 'SARS coronavirus', 'target_pref_name': 'SARS coronavirus 3C-like proteinase', 'target_tax_id': '227859', 'text_value': None, 'toid': None, 'type': 'IC50', 'units': 'uM', 'uo_units': 'UO_0000065', 'upper_value': None, 'value': '7.2'}, {'action_type': None, 'activity_comment': None, 'activity_id': 1480936, 'activity_properties': [], 'assay_chembl_id': 'CHEMBL829584', 'assay_description': 'In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease)', 'assay_type': 'B', 'assay_variant_accession': None, 'assay_variant_mutation': None, 'bao_endpoint': 'BAO_0000190', 'bao_format': 'BAO_0000357', 'bao_label': 'single protein format', 'canonical_smiles': 'O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21', 'data_validity_comment': None, 'data_validity_description': None, 'document_chembl_id': 'CHEMBL1139624', 'document_journal': 'Bioorg Med Chem Lett', 'document_year': 2005, 'ligand_efficiency': {'bei': '12.10', 'le': '0.33', 'lle': '1.22', 'sei': '13.45'}, 'molecule_chembl_id': 'CHEMBL188487', 'molecule_pref_name': None, 'parent_molecule_chembl_id': 'CHEMBL188487', 'pchembl_value': '5.03', 'potential_duplicate': 0, 'qudt_units': 'http://www.openphacts.org/units/Nanomolar', 'record_id': 383984, 'relation': '=', 'src_id': 1, 'standard_flag': 1, 'standard_relation': '=', 'standard_text_value': None, 'standard_type': 'IC50', 'standard_units': 'nM', 'standard_upper_value': None, 'standard_value': '9400.0', 'target_chembl_id': 'CHEMBL3927', 'target_organism': 'SARS coronavirus', 'target_pref_name': 'SARS coronavirus 3C-like proteinase', 'target_tax_id': '227859', 'text_value': None, 'toid': None, 'type': 'IC50', 'units': 'uM', 'uo_units': 'UO_0000065', 'upper_value': None, 'value': '9.4'}, {'action_type': None, 'activity_comment': None, 'activity_id': 1481061, 'activity_properties': [], 'assay_chembl_id': 'CHEMBL830868', 'assay_description': 'In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease) at 20 uM', 'assay_type': 'B', 'assay_variant_accession': None, 'assay_variant_mutation': None, 'bao_endpoint': 'BAO_0000190', 'bao_format': 'BAO_0000357', 'bao_label': 'single protein format', 'canonical_smiles': 'O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21', 'data_validity_comment': None, 'data_validity_description': None, 'document_chembl_id': 'CHEMBL1139624', 'document_journal': 'Bioorg Med Chem Lett', 'document_year': 2005, 'ligand_efficiency': {'bei': '11.56', 'le': '0.29', 'lle': '2.21', 'sei': '8.72'}, 'molecule_chembl_id': 'CHEMBL185698', 'molecule_pref_name': None, 'parent_molecule_chembl_id': 'CHEMBL185698', 'pchembl_value': '4.87', 'potential_duplicate': 0, 'qudt_units': 'http://www.openphacts.org/units/Nanomolar', 'record_id': 384106, 'relation': '=', 'src_id': 1, 'standard_flag': 1, 'standard_relation': '=', 'standard_text_value': None, 'standard_type': 'IC50', 'standard_units': 'nM', 'standard_upper_value': None, 'standard_value': '13500.0', 'target_chembl_id': 'CHEMBL3927', 'target_organism': 'SARS coronavirus', 'target_pref_name': 'SARS coronavirus 3C-like proteinase', 'target_tax_id': '227859', 'text_value': None, 'toid': None, 'type': 'IC50', 'units': 'uM', 'uo_units': 'UO_0000065', 'upper_value': None, 'value': '13.5'}, {'action_type': None, 'activity_comment': None, 'activity_id': 1481065, 'activity_properties': [], 'assay_chembl_id': 'CHEMBL829584', 'assay_description': 'In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease)', 'assay_type': 'B', 'assay_variant_accession': None, 'assay_variant_mutation': None, 'bao_endpoint': 'BAO_0000190', 'bao_format': 'BAO_0000357', 'bao_label': 'single protein format', 'canonical_smiles': 'O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21', 'data_validity_comment': None, 'data_validity_description': None, 'document_chembl_id': 'CHEMBL1139624', 'document_journal': 'Bioorg Med Chem Lett', 'document_year': 2005, 'ligand_efficiency': {'bei': '16.64', 'le': '0.32', 'lle': '1.25', 'sei': '13.06'}, 'molecule_chembl_id': 'CHEMBL426082', 'molecule_pref_name': None, 'parent_molecule_chembl_id': 'CHEMBL426082', 'pchembl_value': '4.88', 'potential_duplicate': 0, 'qudt_units': 'http://www.openphacts.org/units/Nanomolar', 'record_id': 384075, 'relation': '=', 'src_id': 1, 'standard_flag': 1, 'standard_relation': '=', 'standard_text_value': None, 'standard_type': 'IC50', 'standard_units': 'nM', 'standard_upper_value': None, 'standard_value': '13110.0', 'target_chembl_id': 'CHEMBL3927', 'target_organism': 'SARS coronavirus', 'target_pref_name': 'SARS coronavirus 3C-like proteinase', 'target_tax_id': '227859', 'text_value': None, 'toid': None, 'type': 'IC50', 'units': 'uM', 'uo_units': 'UO_0000065', 'upper_value': None, 'value': '13.11'}, '...(remaining elements truncated)...']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity = new_client.activity # activity is dictionary data type.\n",
    "res = activity.filter(target_chembl_id=selected_target).filter(standard_type=\"IC50\")\n",
    "res # also dictionary data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
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       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481065</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481066</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  action_type activity_comment  activity_id activity_properties  \\\n",
       "0        None             None      1480935                  []   \n",
       "1        None             None      1480936                  []   \n",
       "2        None             None      1481061                  []   \n",
       "3        None             None      1481065                  []   \n",
       "4        None             None      1481066                  []   \n",
       "\n",
       "  assay_chembl_id                                  assay_description  \\\n",
       "0    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "1    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "2    CHEMBL830868  In vitro inhibitory concentration against SARS...   \n",
       "3    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "4    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "\n",
       "  assay_type assay_variant_accession assay_variant_mutation bao_endpoint  ...  \\\n",
       "0          B                    None                   None  BAO_0000190  ...   \n",
       "1          B                    None                   None  BAO_0000190  ...   \n",
       "2          B                    None                   None  BAO_0000190  ...   \n",
       "3          B                    None                   None  BAO_0000190  ...   \n",
       "4          B                    None                   None  BAO_0000190  ...   \n",
       "\n",
       "    target_organism                     target_pref_name target_tax_id  \\\n",
       "0  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "1  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "2  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "3  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "4  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "\n",
       "  text_value  toid  type units    uo_units upper_value  value  \n",
       "0       None  None  IC50    uM  UO_0000065        None    7.2  \n",
       "1       None  None  IC50    uM  UO_0000065        None    9.4  \n",
       "2       None  None  IC50    uM  UO_0000065        None   13.5  \n",
       "3       None  None  IC50    uM  UO_0000065        None  13.11  \n",
       "4       None  None  IC50    uM  UO_0000065        None    2.0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(res) # convert dictionary to dataframe\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IC50'], dtype=object)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.standard_type.unique() # check unique values in standard_type column, to see if there are any other values than IC50.\n",
    "# possible values are: ['IC50' 'EC50' etc..]\n",
    "# standard value is the actual value of the bioactivity data. \n",
    "# we want the standard value low. because the lower the value, the more potent the compound is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data/bioactivity_data.csv', index=False) # save dataframe to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bioactivity_data.csv  bioactivity_preprocessed_data.csv\n"
     ]
    }
   ],
   "source": [
    "! ls ./data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "action_type,activity_comment,activity_id,activity_properties,assay_chembl_id,assay_description,assay_type,assay_variant_accession,assay_variant_mutation,bao_endpoint,bao_format,bao_label,canonical_smiles,data_validity_comment,data_validity_description,document_chembl_id,document_journal,document_year,ligand_efficiency,molecule_chembl_id,molecule_pref_name,parent_molecule_chembl_id,pchembl_value,potential_duplicate,qudt_units,record_id,relation,src_id,standard_flag,standard_relation,standard_text_value,standard_type,standard_units,standard_upper_value,standard_value,target_chembl_id,target_organism,target_pref_name,target_tax_id,text_value,toid,type,units,uo_units,upper_value,value\n",
      ",,1480935,[],CHEMBL829584,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease),B,,,BAO_0000190,BAO_0000357,single protein format,Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '18.28', 'le': '0.33', 'lle': '3.25', 'sei': '5.90'}\",CHEMBL187579,,CHEMBL187579,5.14,0,http://www.openphacts.org/units/Nanomolar,384103,=,1,1,=,,IC50,nM,,7200.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,7.2\n",
      ",,1480936,[],CHEMBL829584,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease),B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '12.10', 'le': '0.33', 'lle': '1.22', 'sei': '13.45'}\",CHEMBL188487,,CHEMBL188487,5.03,0,http://www.openphacts.org/units/Nanomolar,383984,=,1,1,=,,IC50,nM,,9400.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,9.4\n",
      ",,1481061,[],CHEMBL830868,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease) at 20 uM,B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '11.56', 'le': '0.29', 'lle': '2.21', 'sei': '8.72'}\",CHEMBL185698,,CHEMBL185698,4.87,0,http://www.openphacts.org/units/Nanomolar,384106,=,1,1,=,,IC50,nM,,13500.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,13.5\n",
      ",,1481065,[],CHEMBL829584,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease),B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '16.64', 'le': '0.32', 'lle': '1.25', 'sei': '13.06'}\",CHEMBL426082,,CHEMBL426082,4.88,0,http://www.openphacts.org/units/Nanomolar,384075,=,1,1,=,,IC50,nM,,13110.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,13.11\n",
      ",,1481066,[],CHEMBL829584,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease),B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-],,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '16.84', 'le': '0.32', 'lle': '2.16', 'sei': '7.08'}\",CHEMBL187717,,CHEMBL187717,5.70,0,http://www.openphacts.org/units/Nanomolar,384234,=,1,1,=,,IC50,nM,,2000.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,2.0\n",
      ",,1481068,[],CHEMBL828143,In vitro inhibitory concentration SARS coronavirus main protease (SARS CoV 3C-like protease) ,B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(Cc2cc3ccccc3s2)c2c(Br)cccc21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '16.14', 'le': '0.37', 'lle': '1.62', 'sei': '16.07'}\",CHEMBL365134,,CHEMBL365134,6.01,0,http://www.openphacts.org/units/Nanomolar,384081,=,1,1,=,,IC50,nM,,980.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,0.98\n",
      ",,1481088,[],CHEMBL829584,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease),B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccc(F)cc21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '17.08', 'le': '0.33', 'lle': '1.55', 'sei': '14.22'}\",CHEMBL187598,,CHEMBL187598,5.32,0,http://www.openphacts.org/units/Nanomolar,384303,=,1,1,=,,IC50,nM,,4820.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,4.82\n",
      ",,1481089,[],CHEMBL829584,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease),B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccc(I)cc21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '14.36', 'le': '0.37', 'lle': '1.78', 'sei': '16.11'}\",CHEMBL190743,,CHEMBL190743,6.02,0,http://www.openphacts.org/units/Nanomolar,384329,=,1,1,=,,IC50,nM,,950.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,0.95\n",
      ",,1481093,[],CHEMBL829584,In vitro inhibitory concentration against SARS coronavirus main protease (SARS CoV 3C-like protease),B,,,BAO_0000190,BAO_0000357,single protein format,O=C1C(=O)N(Cc2cc3ccccc3s2)c2cccc(Cl)c21,,,CHEMBL1139624,Bioorg Med Chem Lett,2005,\"{'bei': '15.10', 'le': '0.31', 'lle': '0.67', 'sei': '13.24'}\",CHEMBL365469,,CHEMBL365469,4.95,0,http://www.openphacts.org/units/Nanomolar,384283,=,1,1,=,,IC50,nM,,11200.0,CHEMBL3927,SARS coronavirus,SARS coronavirus 3C-like proteinase,227859,,,IC50,uM,UO_0000065,,11.2\n"
     ]
    }
   ],
   "source": [
    "! head ./data/bioactivity_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling missing data (just drop missing data)\n",
    "df2 = df[df.standard_value.notna()] # drop rows with missing values in standard_value column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>action_type</th>\n",
       "      <th>activity_comment</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>activity_properties</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>assay_type</th>\n",
       "      <th>assay_variant_accession</th>\n",
       "      <th>assay_variant_mutation</th>\n",
       "      <th>bao_endpoint</th>\n",
       "      <th>...</th>\n",
       "      <th>target_organism</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>target_tax_id</th>\n",
       "      <th>text_value</th>\n",
       "      <th>toid</th>\n",
       "      <th>type</th>\n",
       "      <th>units</th>\n",
       "      <th>uo_units</th>\n",
       "      <th>upper_value</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1480935</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1480936</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481061</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL830868</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481065</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481066</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</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>128</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12041507</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL2150313</td>\n",
       "      <td>Inhibition of SARS-CoV PLpro expressed in Esch...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12041508</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL2150313</td>\n",
       "      <td>Inhibition of SARS-CoV PLpro expressed in Esch...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12041509</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL2150313</td>\n",
       "      <td>Inhibition of SARS-CoV PLpro expressed in Esch...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12041510</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL2150313</td>\n",
       "      <td>Inhibition of SARS-CoV PLpro expressed in Esch...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12041511</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL2150313</td>\n",
       "      <td>Inhibition of SARS-CoV PLpro expressed in Esch...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>78.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    action_type activity_comment  activity_id activity_properties  \\\n",
       "0          None             None      1480935                  []   \n",
       "1          None             None      1480936                  []   \n",
       "2          None             None      1481061                  []   \n",
       "3          None             None      1481065                  []   \n",
       "4          None             None      1481066                  []   \n",
       "..          ...              ...          ...                 ...   \n",
       "128        None             None     12041507                  []   \n",
       "129        None             None     12041508                  []   \n",
       "130        None             None     12041509                  []   \n",
       "131        None             None     12041510                  []   \n",
       "132        None             None     12041511                  []   \n",
       "\n",
       "    assay_chembl_id                                  assay_description  \\\n",
       "0      CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "1      CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "2      CHEMBL830868  In vitro inhibitory concentration against SARS...   \n",
       "3      CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "4      CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "..              ...                                                ...   \n",
       "128   CHEMBL2150313  Inhibition of SARS-CoV PLpro expressed in Esch...   \n",
       "129   CHEMBL2150313  Inhibition of SARS-CoV PLpro expressed in Esch...   \n",
       "130   CHEMBL2150313  Inhibition of SARS-CoV PLpro expressed in Esch...   \n",
       "131   CHEMBL2150313  Inhibition of SARS-CoV PLpro expressed in Esch...   \n",
       "132   CHEMBL2150313  Inhibition of SARS-CoV PLpro expressed in Esch...   \n",
       "\n",
       "    assay_type assay_variant_accession assay_variant_mutation bao_endpoint  \\\n",
       "0            B                    None                   None  BAO_0000190   \n",
       "1            B                    None                   None  BAO_0000190   \n",
       "2            B                    None                   None  BAO_0000190   \n",
       "3            B                    None                   None  BAO_0000190   \n",
       "4            B                    None                   None  BAO_0000190   \n",
       "..         ...                     ...                    ...          ...   \n",
       "128          B                    None                   None  BAO_0000190   \n",
       "129          B                    None                   None  BAO_0000190   \n",
       "130          B                    None                   None  BAO_0000190   \n",
       "131          B                    None                   None  BAO_0000190   \n",
       "132          B                    None                   None  BAO_0000190   \n",
       "\n",
       "     ...   target_organism                     target_pref_name target_tax_id  \\\n",
       "0    ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "1    ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "2    ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "3    ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "4    ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "..   ...               ...                                  ...           ...   \n",
       "128  ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "129  ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "130  ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "131  ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "132  ...  SARS coronavirus  SARS coronavirus 3C-like proteinase        227859   \n",
       "\n",
       "    text_value  toid  type units    uo_units upper_value  value  \n",
       "0         None  None  IC50    uM  UO_0000065        None    7.2  \n",
       "1         None  None  IC50    uM  UO_0000065        None    9.4  \n",
       "2         None  None  IC50    uM  UO_0000065        None   13.5  \n",
       "3         None  None  IC50    uM  UO_0000065        None  13.11  \n",
       "4         None  None  IC50    uM  UO_0000065        None    2.0  \n",
       "..         ...   ...   ...   ...         ...         ...    ...  \n",
       "128       None  None  IC50    uM  UO_0000065        None   10.6  \n",
       "129       None  None  IC50    uM  UO_0000065        None   10.1  \n",
       "130       None  None  IC50    uM  UO_0000065        None   11.5  \n",
       "131       None  None  IC50    uM  UO_0000065        None   10.7  \n",
       "132       None  None  IC50    uM  UO_0000065        None   78.9  \n",
       "\n",
       "[133 rows x 46 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data Pre-processing of the bioactivity data\n",
    "# Labeling compounds as either being active, inactive or intermediate\n",
    "# we will use IC50 values to define compounds as either being active, inactive or intermediate.\n",
    "# <1000nM = active, >10000nM = inactive, in between = intermediate\n",
    "bioactivity_class = []\n",
    "for i in df2.standard_value:\n",
    "    if float(i) >= 10000:\n",
    "        bioactivity_class.append(\"inactive\")\n",
    "    elif float(i) <= 1000:\n",
    "        bioactivity_class.append(\"active\")\n",
    "    else:\n",
    "        bioactivity_class.append(\"intermediate\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action_type</th>\n",
       "      <th>activity_comment</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>activity_properties</th>\n",
       "      <th>assay_chembl_id</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>assay_type</th>\n",
       "      <th>assay_variant_accession</th>\n",
       "      <th>assay_variant_mutation</th>\n",
       "      <th>bao_endpoint</th>\n",
       "      <th>...</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>target_tax_id</th>\n",
       "      <th>text_value</th>\n",
       "      <th>toid</th>\n",
       "      <th>type</th>\n",
       "      <th>units</th>\n",
       "      <th>uo_units</th>\n",
       "      <th>upper_value</th>\n",
       "      <th>value</th>\n",
       "      <th>bioactivity_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1480935</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>7.2</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1480936</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>9.4</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481061</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL830868</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>13.5</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481065</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>13.11</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1481066</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL829584</td>\n",
       "      <td>In vitro inhibitory concentration against SARS...</td>\n",
       "      <td>B</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>...</td>\n",
       "      <td>SARS coronavirus 3C-like proteinase</td>\n",
       "      <td>227859</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>uM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  action_type activity_comment  activity_id activity_properties  \\\n",
       "0        None             None      1480935                  []   \n",
       "1        None             None      1480936                  []   \n",
       "2        None             None      1481061                  []   \n",
       "3        None             None      1481065                  []   \n",
       "4        None             None      1481066                  []   \n",
       "\n",
       "  assay_chembl_id                                  assay_description  \\\n",
       "0    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "1    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "2    CHEMBL830868  In vitro inhibitory concentration against SARS...   \n",
       "3    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "4    CHEMBL829584  In vitro inhibitory concentration against SARS...   \n",
       "\n",
       "  assay_type assay_variant_accession assay_variant_mutation bao_endpoint  ...  \\\n",
       "0          B                    None                   None  BAO_0000190  ...   \n",
       "1          B                    None                   None  BAO_0000190  ...   \n",
       "2          B                    None                   None  BAO_0000190  ...   \n",
       "3          B                    None                   None  BAO_0000190  ...   \n",
       "4          B                    None                   None  BAO_0000190  ...   \n",
       "\n",
       "                      target_pref_name target_tax_id text_value  toid  type  \\\n",
       "0  SARS coronavirus 3C-like proteinase        227859       None  None  IC50   \n",
       "1  SARS coronavirus 3C-like proteinase        227859       None  None  IC50   \n",
       "2  SARS coronavirus 3C-like proteinase        227859       None  None  IC50   \n",
       "3  SARS coronavirus 3C-like proteinase        227859       None  None  IC50   \n",
       "4  SARS coronavirus 3C-like proteinase        227859       None  None  IC50   \n",
       "\n",
       "  units    uo_units  upper_value  value bioactivity_class  \n",
       "0    uM  UO_0000065         None    7.2      intermediate  \n",
       "1    uM  UO_0000065         None    9.4      intermediate  \n",
       "2    uM  UO_0000065         None   13.5          inactive  \n",
       "3    uM  UO_0000065         None  13.11          inactive  \n",
       "4    uM  UO_0000065         None    2.0      intermediate  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bioactivity_class = pd.Series(bioactivity_class, name=\"bioactivity_class\")\n",
    "df2 = pd.concat([df2, bioactivity_class], axis=1) # add bioactivity_class column to df2 dataframe\n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       CHEMBL187579\n",
       "1       CHEMBL188487\n",
       "2       CHEMBL185698\n",
       "3       CHEMBL426082\n",
       "4       CHEMBL187717\n",
       "           ...      \n",
       "128    CHEMBL2146517\n",
       "129     CHEMBL187460\n",
       "130     CHEMBL363535\n",
       "131     CHEMBL227075\n",
       "132      CHEMBL45830\n",
       "Name: molecule_chembl_id, Length: 133, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.molecule_chembl_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2[['molecule_chembl_id', 'canonical_smiles', 'standard_value', 'bioactivity_class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>bioactivity_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL187579</td>\n",
       "      <td>Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL188487</td>\n",
       "      <td>O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL185698</td>\n",
       "      <td>O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL426082</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL187717</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>intermediate</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>128</th>\n",
       "      <td>CHEMBL2146517</td>\n",
       "      <td>COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>CHEMBL187460</td>\n",
       "      <td>C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>CHEMBL363535</td>\n",
       "      <td>Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>CHEMBL227075</td>\n",
       "      <td>Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>CHEMBL45830</td>\n",
       "      <td>CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    molecule_chembl_id                                   canonical_smiles  \\\n",
       "0         CHEMBL187579             Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21   \n",
       "1         CHEMBL188487             O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21   \n",
       "2         CHEMBL185698            O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21   \n",
       "3         CHEMBL426082                O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21   \n",
       "4         CHEMBL187717    O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]   \n",
       "..                 ...                                                ...   \n",
       "128      CHEMBL2146517  COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...   \n",
       "129       CHEMBL187460        C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C   \n",
       "130       CHEMBL363535             Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12   \n",
       "131       CHEMBL227075      Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1   \n",
       "132        CHEMBL45830             CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C   \n",
       "\n",
       "    standard_value bioactivity_class  \n",
       "0           7200.0      intermediate  \n",
       "1           9400.0      intermediate  \n",
       "2          13500.0          inactive  \n",
       "3          13110.0          inactive  \n",
       "4           2000.0      intermediate  \n",
       "..             ...               ...  \n",
       "128        10600.0          inactive  \n",
       "129        10100.0          inactive  \n",
       "130        11500.0          inactive  \n",
       "131        10700.0          inactive  \n",
       "132        78900.0          inactive  \n",
       "\n",
       "[133 rows x 4 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.to_csv('./data/bioactivity_preprocessed_data.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part II EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install rdkit --quiet\n",
    "# https://www.rdkit.org/docs/Install.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Unnamed: 0</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>bioactivity_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CHEMBL187579</td>\n",
       "      <td>Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL188487</td>\n",
       "      <td>O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL185698</td>\n",
       "      <td>O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CHEMBL426082</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL187717</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>intermediate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>128</td>\n",
       "      <td>CHEMBL2146517</td>\n",
       "      <td>COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>129</td>\n",
       "      <td>CHEMBL187460</td>\n",
       "      <td>C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>130</td>\n",
       "      <td>CHEMBL363535</td>\n",
       "      <td>Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>131</td>\n",
       "      <td>CHEMBL227075</td>\n",
       "      <td>Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>132</td>\n",
       "      <td>CHEMBL45830</td>\n",
       "      <td>CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 molecule_chembl_id  \\\n",
       "0             0       CHEMBL187579   \n",
       "1             1       CHEMBL188487   \n",
       "2             2       CHEMBL185698   \n",
       "3             3       CHEMBL426082   \n",
       "4             4       CHEMBL187717   \n",
       "..          ...                ...   \n",
       "128         128      CHEMBL2146517   \n",
       "129         129       CHEMBL187460   \n",
       "130         130       CHEMBL363535   \n",
       "131         131       CHEMBL227075   \n",
       "132         132        CHEMBL45830   \n",
       "\n",
       "                                      canonical_smiles  standard_value  \\\n",
       "0               Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21          7200.0   \n",
       "1               O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21          9400.0   \n",
       "2              O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21         13500.0   \n",
       "3                  O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21         13110.0   \n",
       "4      O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]          2000.0   \n",
       "..                                                 ...             ...   \n",
       "128  COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...         10600.0   \n",
       "129        C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C         10100.0   \n",
       "130             Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12         11500.0   \n",
       "131      Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1         10700.0   \n",
       "132             CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C         78900.0   \n",
       "\n",
       "    bioactivity_class  \n",
       "0        intermediate  \n",
       "1        intermediate  \n",
       "2            inactive  \n",
       "3            inactive  \n",
       "4        intermediate  \n",
       "..                ...  \n",
       "128          inactive  \n",
       "129          inactive  \n",
       "130          inactive  \n",
       "131          inactive  \n",
       "132          inactive  \n",
       "\n",
       "[133 rows x 5 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/bioactivity_preprocessed_data.csv')\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Lipinski decriptors\n",
    "* Christopher Lipinski, a scientist at Pfizer, came up with a set of rule-of-thumb for evaluating the druglikeness of compounds. Such druglikeness is based on the Absorption, Distribution, Metabolism and Excretion (ADME) that is also known as the pharmacokinetic profile. Lipinski analyzed all orally active FDA-approved drugs in the formulation of what is to be known as the Rule-of-Five or Lipinski's Rule.\n",
    "\n",
    "* The Lipinski's Rule stated the following:\n",
    "    * Molecular weight < 500 Dalton\n",
    "    * Octanol-water partition coefficient (LogP) < 5\n",
    "    * Hydrogen bond donors < 5\n",
    "    * Hydrogen bond acceptors < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors, Lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation\n",
    "\n",
    "def lipinski(smiles, verbose=False):\n",
    "\n",
    "    moldata= []\n",
    "    for elem in smiles:\n",
    "        mol=Chem.MolFromSmiles(elem) \n",
    "        moldata.append(mol)\n",
    "       \n",
    "    baseData= np.arange(1,1)\n",
    "    i=0  \n",
    "    for mol in moldata:        \n",
    "       \n",
    "        desc_MolWt = Descriptors.MolWt(mol)\n",
    "        desc_MolLogP = Descriptors.MolLogP(mol)\n",
    "        desc_NumHDonors = Lipinski.NumHDonors(mol)\n",
    "        desc_NumHAcceptors = Lipinski.NumHAcceptors(mol)\n",
    "           \n",
    "        row = np.array([desc_MolWt,\n",
    "                        desc_MolLogP,\n",
    "                        desc_NumHDonors,\n",
    "                        desc_NumHAcceptors])   \n",
    "    \n",
    "        if(i==0):\n",
    "            baseData=row\n",
    "        else:\n",
    "            baseData=np.vstack([baseData, row])\n",
    "        i=i+1      \n",
    "    \n",
    "    columnNames=[\"MW\",\"LogP\",\"NumHDonors\",\"NumHAcceptors\"]   \n",
    "    descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n",
    "    \n",
    "    return descriptors\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>281.271</td>\n",
       "      <td>1.89262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>415.589</td>\n",
       "      <td>3.81320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>421.190</td>\n",
       "      <td>2.66050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>293.347</td>\n",
       "      <td>3.63080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>338.344</td>\n",
       "      <td>3.53900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</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>128</th>\n",
       "      <td>338.359</td>\n",
       "      <td>3.40102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>296.366</td>\n",
       "      <td>3.44330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>276.291</td>\n",
       "      <td>4.09564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>278.307</td>\n",
       "      <td>3.29102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>282.383</td>\n",
       "      <td>4.10530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          MW     LogP  NumHDonors  NumHAcceptors\n",
       "0    281.271  1.89262         0.0            5.0\n",
       "1    415.589  3.81320         0.0            2.0\n",
       "2    421.190  2.66050         0.0            4.0\n",
       "3    293.347  3.63080         0.0            3.0\n",
       "4    338.344  3.53900         0.0            5.0\n",
       "..       ...      ...         ...            ...\n",
       "128  338.359  3.40102         0.0            5.0\n",
       "129  296.366  3.44330         0.0            3.0\n",
       "130  276.291  4.09564         0.0            3.0\n",
       "131  278.307  3.29102         0.0            3.0\n",
       "132  282.383  4.10530         0.0            2.0\n",
       "\n",
       "[133 rows x 4 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lipinski = lipinski(df.canonical_smiles)\n",
    "df_lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CHEMBL187579</td>\n",
       "      <td>Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>281.271</td>\n",
       "      <td>1.89262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL188487</td>\n",
       "      <td>O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>415.589</td>\n",
       "      <td>3.81320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL185698</td>\n",
       "      <td>O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>421.190</td>\n",
       "      <td>2.66050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CHEMBL426082</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>293.347</td>\n",
       "      <td>3.63080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL187717</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>338.344</td>\n",
       "      <td>3.53900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>128</td>\n",
       "      <td>CHEMBL2146517</td>\n",
       "      <td>COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>338.359</td>\n",
       "      <td>3.40102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>129</td>\n",
       "      <td>CHEMBL187460</td>\n",
       "      <td>C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>296.366</td>\n",
       "      <td>3.44330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>130</td>\n",
       "      <td>CHEMBL363535</td>\n",
       "      <td>Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>276.291</td>\n",
       "      <td>4.09564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>131</td>\n",
       "      <td>CHEMBL227075</td>\n",
       "      <td>Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>278.307</td>\n",
       "      <td>3.29102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>132</td>\n",
       "      <td>CHEMBL45830</td>\n",
       "      <td>CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>282.383</td>\n",
       "      <td>4.10530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 molecule_chembl_id  \\\n",
       "0             0       CHEMBL187579   \n",
       "1             1       CHEMBL188487   \n",
       "2             2       CHEMBL185698   \n",
       "3             3       CHEMBL426082   \n",
       "4             4       CHEMBL187717   \n",
       "..          ...                ...   \n",
       "128         128      CHEMBL2146517   \n",
       "129         129       CHEMBL187460   \n",
       "130         130       CHEMBL363535   \n",
       "131         131       CHEMBL227075   \n",
       "132         132        CHEMBL45830   \n",
       "\n",
       "                                      canonical_smiles  standard_value  \\\n",
       "0               Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21          7200.0   \n",
       "1               O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21          9400.0   \n",
       "2              O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21         13500.0   \n",
       "3                  O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21         13110.0   \n",
       "4      O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]          2000.0   \n",
       "..                                                 ...             ...   \n",
       "128  COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...         10600.0   \n",
       "129        C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C         10100.0   \n",
       "130             Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12         11500.0   \n",
       "131      Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1         10700.0   \n",
       "132             CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C         78900.0   \n",
       "\n",
       "    bioactivity_class       MW     LogP  NumHDonors  NumHAcceptors  \n",
       "0        intermediate  281.271  1.89262         0.0            5.0  \n",
       "1        intermediate  415.589  3.81320         0.0            2.0  \n",
       "2            inactive  421.190  2.66050         0.0            4.0  \n",
       "3            inactive  293.347  3.63080         0.0            3.0  \n",
       "4        intermediate  338.344  3.53900         0.0            5.0  \n",
       "..                ...      ...      ...         ...            ...  \n",
       "128          inactive  338.359  3.40102         0.0            5.0  \n",
       "129          inactive  296.366  3.44330         0.0            3.0  \n",
       "130          inactive  276.291  4.09564         0.0            3.0  \n",
       "131          inactive  278.307  3.29102         0.0            3.0  \n",
       "132          inactive  282.383  4.10530         0.0            2.0  \n",
       "\n",
       "[133 rows x 9 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined = pd.concat([df, df_lipinski], axis=1)\n",
    "df_combined"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert IC50 to pIC50\n",
    "* To allow IC50 data to be more uniformly distributed, we will convert IC50 to the negative logarithmic scale which is essentially -log10(IC50).\n",
    "\n",
    "* This custom function pIC50() will accept a DataFrame as input and will:\n",
    "\n",
    "    * Take the IC50 values from the standard_value column and converts it from nM to M by multiplying the value by 10\n",
    "    * Take the molar value and apply -log10\n",
    "    * Delete the standard_value column and create a new pIC50 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/chaninlab/estrogen-receptor-alpha-qsar/blob/master/02_ER_alpha_RO5.ipynb\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def pIC50(input):\n",
    "    pIC50 = []\n",
    "\n",
    "    for i in input['standard_value_norm']:\n",
    "        molar = i*(10**-9) # Converts nM to M\n",
    "        pIC50.append(-np.log10(molar))\n",
    "\n",
    "    input['pIC50'] = pIC50\n",
    "    input.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "        \n",
    "    return input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        133.000000\n",
       "mean       85967.130075\n",
       "std       158897.319181\n",
       "min           50.000000\n",
       "25%        10100.000000\n",
       "50%        17500.000000\n",
       "75%        70000.000000\n",
       "max      1000000.000000\n",
       "Name: standard_value, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.standard_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Although we don't need cap, just follow the original tutorial\n",
    "def norm_value(input):\n",
    "    norm = []\n",
    "\n",
    "    for i in input['standard_value']:\n",
    "        if i > 100000000:\n",
    "          i = 100000000 # 100 uM is the highest value, unless there is an negative value\n",
    "        norm.append(i)\n",
    "\n",
    "    input['standard_value_norm'] = norm\n",
    "        \n",
    "    return input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "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>Unnamed: 0</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>standard_value_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>133.00000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>85967.130075</td>\n",
       "      <td>376.392692</td>\n",
       "      <td>3.908219</td>\n",
       "      <td>0.909774</td>\n",
       "      <td>5.248120</td>\n",
       "      <td>85967.130075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.53786</td>\n",
       "      <td>158897.319181</td>\n",
       "      <td>87.221623</td>\n",
       "      <td>1.385540</td>\n",
       "      <td>0.882993</td>\n",
       "      <td>2.072204</td>\n",
       "      <td>158897.319181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>204.185000</td>\n",
       "      <td>-0.055400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.00000</td>\n",
       "      <td>10100.000000</td>\n",
       "      <td>296.366000</td>\n",
       "      <td>3.220320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>17500.000000</td>\n",
       "      <td>369.515000</td>\n",
       "      <td>3.769900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>99.00000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>437.437000</td>\n",
       "      <td>4.667120</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>132.00000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>588.635000</td>\n",
       "      <td>7.051540</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  standard_value          MW        LogP  NumHDonors  \\\n",
       "count   133.00000      133.000000  133.000000  133.000000  133.000000   \n",
       "mean     66.00000    85967.130075  376.392692    3.908219    0.909774   \n",
       "std      38.53786   158897.319181   87.221623    1.385540    0.882993   \n",
       "min       0.00000       50.000000  204.185000   -0.055400    0.000000   \n",
       "25%      33.00000    10100.000000  296.366000    3.220320    0.000000   \n",
       "50%      66.00000    17500.000000  369.515000    3.769900    1.000000   \n",
       "75%      99.00000    70000.000000  437.437000    4.667120    2.000000   \n",
       "max     132.00000  1000000.000000  588.635000    7.051540    3.000000   \n",
       "\n",
       "       NumHAcceptors  standard_value_norm  \n",
       "count     133.000000           133.000000  \n",
       "mean        5.248120         85967.130075  \n",
       "std         2.072204        158897.319181  \n",
       "min         2.000000            50.000000  \n",
       "25%         3.000000         10100.000000  \n",
       "50%         5.000000         17500.000000  \n",
       "75%         6.000000         70000.000000  \n",
       "max        12.000000       1000000.000000  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm = norm_value(df_combined)\n",
    "df_norm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>standard_value_norm</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL187579</td>\n",
       "      <td>Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>281.271</td>\n",
       "      <td>1.89262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>5.142668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL188487</td>\n",
       "      <td>O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>415.589</td>\n",
       "      <td>3.81320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>5.026872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL185698</td>\n",
       "      <td>O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>421.190</td>\n",
       "      <td>2.66050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>4.869666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL426082</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>293.347</td>\n",
       "      <td>3.63080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>4.882397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL187717</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>338.344</td>\n",
       "      <td>3.53900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>5.698970</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>CHEMBL2146517</td>\n",
       "      <td>COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>338.359</td>\n",
       "      <td>3.40102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>4.974694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>CHEMBL187460</td>\n",
       "      <td>C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>296.366</td>\n",
       "      <td>3.44330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>4.995679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>CHEMBL363535</td>\n",
       "      <td>Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>276.291</td>\n",
       "      <td>4.09564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>4.939302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>CHEMBL227075</td>\n",
       "      <td>Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>278.307</td>\n",
       "      <td>3.29102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>4.970616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>CHEMBL45830</td>\n",
       "      <td>CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>282.383</td>\n",
       "      <td>4.10530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>4.102923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    molecule_chembl_id                                   canonical_smiles  \\\n",
       "0         CHEMBL187579             Cc1noc(C)c1CN1C(=O)C(=O)c2cc(C#N)ccc21   \n",
       "1         CHEMBL188487             O=C1C(=O)N(Cc2ccc(F)cc2Cl)c2ccc(I)cc21   \n",
       "2         CHEMBL185698            O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21   \n",
       "3         CHEMBL426082                O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21   \n",
       "4         CHEMBL187717    O=C1C(=O)N(Cc2cc3ccccc3s2)c2c1cccc2[N+](=O)[O-]   \n",
       "..                 ...                                                ...   \n",
       "128      CHEMBL2146517  COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...   \n",
       "129       CHEMBL187460        C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C   \n",
       "130       CHEMBL363535             Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12   \n",
       "131       CHEMBL227075      Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1   \n",
       "132        CHEMBL45830             CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C   \n",
       "\n",
       "     standard_value bioactivity_class       MW     LogP  NumHDonors  \\\n",
       "0            7200.0      intermediate  281.271  1.89262         0.0   \n",
       "1            9400.0      intermediate  415.589  3.81320         0.0   \n",
       "2           13500.0          inactive  421.190  2.66050         0.0   \n",
       "3           13110.0          inactive  293.347  3.63080         0.0   \n",
       "4            2000.0      intermediate  338.344  3.53900         0.0   \n",
       "..              ...               ...      ...      ...         ...   \n",
       "128         10600.0          inactive  338.359  3.40102         0.0   \n",
       "129         10100.0          inactive  296.366  3.44330         0.0   \n",
       "130         11500.0          inactive  276.291  4.09564         0.0   \n",
       "131         10700.0          inactive  278.307  3.29102         0.0   \n",
       "132         78900.0          inactive  282.383  4.10530         0.0   \n",
       "\n",
       "     NumHAcceptors  standard_value_norm     pIC50  \n",
       "0              5.0               7200.0  5.142668  \n",
       "1              2.0               9400.0  5.026872  \n",
       "2              4.0              13500.0  4.869666  \n",
       "3              3.0              13110.0  4.882397  \n",
       "4              5.0               2000.0  5.698970  \n",
       "..             ...                  ...       ...  \n",
       "128            5.0              10600.0  4.974694  \n",
       "129            3.0              10100.0  4.995679  \n",
       "130            3.0              11500.0  4.939302  \n",
       "131            3.0              10700.0  4.970616  \n",
       "132            2.0              78900.0  4.102923  \n",
       "\n",
       "[133 rows x 10 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_final = pIC50(df_norm)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    133.000000\n",
       "mean       4.718269\n",
       "std        0.920910\n",
       "min        3.000000\n",
       "25%        4.154902\n",
       "50%        4.756962\n",
       "75%        4.995679\n",
       "max        7.301030\n",
       "Name: pIC50, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.pIC50.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing the 'intermediate' bioactivity class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2class = df_final[df_final.bioactivity_class != 'intermediate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>standard_value_norm</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL185698</td>\n",
       "      <td>O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>421.190</td>\n",
       "      <td>2.66050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>4.869666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL426082</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>293.347</td>\n",
       "      <td>3.63080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13110.0</td>\n",
       "      <td>4.882397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHEMBL365134</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2c(Br)cccc21</td>\n",
       "      <td>980.0</td>\n",
       "      <td>active</td>\n",
       "      <td>372.243</td>\n",
       "      <td>4.39330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>6.008774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHEMBL190743</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccc(I)cc21</td>\n",
       "      <td>950.0</td>\n",
       "      <td>active</td>\n",
       "      <td>419.243</td>\n",
       "      <td>4.23540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>6.022276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHEMBL365469</td>\n",
       "      <td>O=C1C(=O)N(Cc2cc3ccccc3s2)c2cccc(Cl)c21</td>\n",
       "      <td>11200.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>327.792</td>\n",
       "      <td>4.28420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11200.0</td>\n",
       "      <td>4.950782</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>CHEMBL2146517</td>\n",
       "      <td>COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>338.359</td>\n",
       "      <td>3.40102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>4.974694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>CHEMBL187460</td>\n",
       "      <td>C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>296.366</td>\n",
       "      <td>3.44330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>4.995679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>CHEMBL363535</td>\n",
       "      <td>Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>276.291</td>\n",
       "      <td>4.09564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>4.939302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>CHEMBL227075</td>\n",
       "      <td>Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>278.307</td>\n",
       "      <td>3.29102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>4.970616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>CHEMBL45830</td>\n",
       "      <td>CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>282.383</td>\n",
       "      <td>4.10530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78900.0</td>\n",
       "      <td>4.102923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    molecule_chembl_id                                   canonical_smiles  \\\n",
       "2         CHEMBL185698            O=C1C(=O)N(CC2COc3ccccc3O2)c2ccc(I)cc21   \n",
       "3         CHEMBL426082                O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccccc21   \n",
       "5         CHEMBL365134            O=C1C(=O)N(Cc2cc3ccccc3s2)c2c(Br)cccc21   \n",
       "7         CHEMBL190743             O=C1C(=O)N(Cc2cc3ccccc3s2)c2ccc(I)cc21   \n",
       "8         CHEMBL365469            O=C1C(=O)N(Cc2cc3ccccc3s2)c2cccc(Cl)c21   \n",
       "..                 ...                                                ...   \n",
       "128      CHEMBL2146517  COC(=O)[C@@]1(C)CCCc2c1ccc1c2C(=O)C(=O)c2c(C)c...   \n",
       "129       CHEMBL187460        C[C@H]1COC2=C1C(=O)C(=O)c1c2ccc2c1CCCC2(C)C   \n",
       "130       CHEMBL363535             Cc1coc2c1C(=O)C(=O)c1c-2ccc2c(C)cccc12   \n",
       "131       CHEMBL227075      Cc1cccc2c3c(ccc12)C1=C(C(=O)C3=O)[C@@H](C)CO1   \n",
       "132        CHEMBL45830             CC(C)C1=Cc2ccc3c(c2C(=O)C1=O)CCCC3(C)C   \n",
       "\n",
       "     standard_value bioactivity_class       MW     LogP  NumHDonors  \\\n",
       "2           13500.0          inactive  421.190  2.66050         0.0   \n",
       "3           13110.0          inactive  293.347  3.63080         0.0   \n",
       "5             980.0            active  372.243  4.39330         0.0   \n",
       "7             950.0            active  419.243  4.23540         0.0   \n",
       "8           11200.0          inactive  327.792  4.28420         0.0   \n",
       "..              ...               ...      ...      ...         ...   \n",
       "128         10600.0          inactive  338.359  3.40102         0.0   \n",
       "129         10100.0          inactive  296.366  3.44330         0.0   \n",
       "130         11500.0          inactive  276.291  4.09564         0.0   \n",
       "131         10700.0          inactive  278.307  3.29102         0.0   \n",
       "132         78900.0          inactive  282.383  4.10530         0.0   \n",
       "\n",
       "     NumHAcceptors  standard_value_norm     pIC50  \n",
       "2              4.0              13500.0  4.869666  \n",
       "3              3.0              13110.0  4.882397  \n",
       "5              3.0                980.0  6.008774  \n",
       "7              3.0                950.0  6.022276  \n",
       "8              3.0              11200.0  4.950782  \n",
       "..             ...                  ...       ...  \n",
       "128            5.0              10600.0  4.974694  \n",
       "129            3.0              10100.0  4.995679  \n",
       "130            3.0              11500.0  4.939302  \n",
       "131            3.0              10700.0  4.970616  \n",
       "132            2.0              78900.0  4.102923  \n",
       "\n",
       "[118 rows x 10 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2class.to_csv('./data/bioactivity_data_2class_pIC50.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA via Lipinski decrpitors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install seaborn --quiet\n",
    "import seaborn as sns\n",
    "sns.set(style='ticks')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.countplot(x='bioactivity_class', data=df_2class, edgecolor='black')\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('./images/plot_bioactivity_class.pdf')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter plot of MW versus LogP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.scatterplot(x='MW', y='LogP', data=df_2class, hue='bioactivity_class', size='pIC50', edgecolor='black', alpha=0.7)\n",
    "\n",
    "plt.xlabel('MW', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)\n",
    "plt.savefig('./images/plot_MW_vs_LogP.pdf')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pIC50 value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'bioactivity_class', y = 'pIC50', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('pIC50 value', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('./images/plot_ic50.pdf')\n",
    "     "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mann-Whitney U test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install scipy --quiet\n",
    "def mannwhitney(descriptor, verbose=False):\n",
    "  # https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/\n",
    "  from numpy.random import seed\n",
    "  #from numpy.random import randn\n",
    "  from scipy.stats import mannwhitneyu\n",
    "\n",
    "# seed the random number generator\n",
    "  seed(1)\n",
    "\n",
    "# actives and inactives\n",
    "  selection = [descriptor, 'bioactivity_class']\n",
    "  df = df_2class[selection]\n",
    "  active = df[df['bioactivity_class'] == 'active']\n",
    "  active = active[descriptor]\n",
    "\n",
    "  selection = [descriptor, 'bioactivity_class']\n",
    "  df = df_2class[selection]\n",
    "  inactive = df[df['bioactivity_class'] == 'inactive']\n",
    "  inactive = inactive[descriptor]\n",
    "\n",
    "# compare samples\n",
    "  stat, p = mannwhitneyu(active, inactive)\n",
    "  print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "\n",
    "# interpret\n",
    "  alpha = 0.05\n",
    "  if p > alpha:\n",
    "    interpretation = 'Same distribution (fail to reject H0)'\n",
    "  else:\n",
    "    interpretation = 'Different distribution (reject H0)'\n",
    "  \n",
    "  results = pd.DataFrame({'Descriptor':descriptor,\n",
    "                          'Statistics':stat,\n",
    "                          'p':p,\n",
    "                          'alpha':alpha,\n",
    "                          'Interpretation':interpretation}, index=[0])\n",
    "  filename = 'mannwhitneyu_' + descriptor + '.csv'\n",
    "  results.to_csv('./statistical_results/'+filename)\n",
    "\n",
    "  return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1545.000, p=0.000\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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pIC50</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>4.428384e-10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics             p  alpha  \\\n",
       "0      pIC50      1545.0  4.428384e-10   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('pIC50')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'bioactivity_class', y = 'MW', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('MW', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('./images/plot_MW.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=408.500, p=0.003\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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MW</td>\n",
       "      <td>408.5</td>\n",
       "      <td>0.003313</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics         p  alpha                      Interpretation\n",
       "0         MW       408.5  0.003313   0.05  Different distribution (reject H0)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('MW')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For LogP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'bioactivity_class', y = 'LogP', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('./images/plot_LogP.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=712.500, p=0.631\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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogP</td>\n",
       "      <td>712.5</td>\n",
       "      <td>0.630683</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Same distribution (fail to reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics         p  alpha  \\\n",
       "0       LogP       712.5  0.630683   0.05   \n",
       "\n",
       "                          Interpretation  \n",
       "0  Same distribution (fail to reject H0)  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('LogP')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For NumHDonors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'bioactivity_class', y = 'NumHDonors', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('NumHDonors', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_NumHDonors.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=298.500, p=0.000\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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NumHDonors</td>\n",
       "      <td>298.5</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Descriptor  Statistics         p  alpha                      Interpretation\n",
       "0  NumHDonors       298.5  0.000053   0.05  Different distribution (reject H0)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHDonors')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For NumHAcceptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'bioactivity_class', y = 'NumHAcceptors', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('NumHAcceptors', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('./images/plot_NumHAcceptors.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=414.000, p=0.003\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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NumHAcceptors</td>\n",
       "      <td>414.0</td>\n",
       "      <td>0.003402</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Descriptor  Statistics         p  alpha  \\\n",
       "0  NumHAcceptors       414.0  0.003402   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHAcceptors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "drug-discovery",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
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