QSAR Model_Beta Secratese 1_pIC50.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from chembl_webresource_client.new_client import new_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>[{'xref_id': 'Q9Y5Z0', 'xref_name': None, 'xre...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta secretase 2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL2525</td>\n",
       "      <td>[{'accession': 'Q9Y5Z0', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[{'xref_id': 'Beta-secretase_1', 'xref_name': ...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL4822</td>\n",
       "      <td>[{'accession': 'P56817', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[{'xref_id': 'P05067', 'xref_name': None, 'xre...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta amyloid A4 protein</td>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL2487</td>\n",
       "      <td>[{'accession': 'P05067', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Gamma-secretase-activating protein</td>\n",
       "      <td>14.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL3638343</td>\n",
       "      <td>[{'accession': 'A4D1B5', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[{'xref_id': 'Q9NZ42', 'xref_name': None, 'xre...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Gamma-secretase subunit PEN-2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL2374</td>\n",
       "      <td>[{'accession': 'Q9NZ42', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</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>269</th>\n",
       "      <td>[]</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Growth/differentiation factor 15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL3120039</td>\n",
       "      <td>[{'accession': 'Q99988', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>[]</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Stromal cell-derived factor 1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL3286074</td>\n",
       "      <td>[{'accession': 'P48061', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>[]</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Prosaposin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL3580523</td>\n",
       "      <td>[{'accession': 'P07602', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>[]</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Melanocyte protein PMEL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL3712988</td>\n",
       "      <td>[{'accession': 'P40967', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>[]</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Filamin-B</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>CHEMBL4295677</td>\n",
       "      <td>[{'accession': 'O75369', 'component_descriptio...</td>\n",
       "      <td>SINGLE PROTEIN</td>\n",
       "      <td>9606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>274 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      cross_references      organism  \\\n",
       "0    [{'xref_id': 'Q9Y5Z0', 'xref_name': None, 'xre...  Homo sapiens   \n",
       "1    [{'xref_id': 'Beta-secretase_1', 'xref_name': ...  Homo sapiens   \n",
       "2    [{'xref_id': 'P05067', 'xref_name': None, 'xre...  Homo sapiens   \n",
       "3                                                   []  Homo sapiens   \n",
       "4    [{'xref_id': 'Q9NZ42', 'xref_name': None, 'xre...  Homo sapiens   \n",
       "..                                                 ...           ...   \n",
       "269                                                 []  Homo sapiens   \n",
       "270                                                 []  Homo sapiens   \n",
       "271                                                 []  Homo sapiens   \n",
       "272                                                 []  Homo sapiens   \n",
       "273                                                 []  Homo sapiens   \n",
       "\n",
       "                              pref_name  score  species_group_flag  \\\n",
       "0                      Beta secretase 2   18.0               False   \n",
       "1                      Beta-secretase 1   18.0               False   \n",
       "2               Beta amyloid A4 protein   15.0               False   \n",
       "3    Gamma-secretase-activating protein   14.0               False   \n",
       "4         Gamma-secretase subunit PEN-2   12.0               False   \n",
       "..                                  ...    ...                 ...   \n",
       "269    Growth/differentiation factor 15    2.0               False   \n",
       "270       Stromal cell-derived factor 1    2.0               False   \n",
       "271                          Prosaposin    2.0               False   \n",
       "272             Melanocyte protein PMEL    2.0               False   \n",
       "273                           Filamin-B    2.0               False   \n",
       "\n",
       "    target_chembl_id                                  target_components  \\\n",
       "0         CHEMBL2525  [{'accession': 'Q9Y5Z0', 'component_descriptio...   \n",
       "1         CHEMBL4822  [{'accession': 'P56817', 'component_descriptio...   \n",
       "2         CHEMBL2487  [{'accession': 'P05067', 'component_descriptio...   \n",
       "3      CHEMBL3638343  [{'accession': 'A4D1B5', 'component_descriptio...   \n",
       "4         CHEMBL2374  [{'accession': 'Q9NZ42', 'component_descriptio...   \n",
       "..               ...                                                ...   \n",
       "269    CHEMBL3120039  [{'accession': 'Q99988', 'component_descriptio...   \n",
       "270    CHEMBL3286074  [{'accession': 'P48061', 'component_descriptio...   \n",
       "271    CHEMBL3580523  [{'accession': 'P07602', 'component_descriptio...   \n",
       "272    CHEMBL3712988  [{'accession': 'P40967', 'component_descriptio...   \n",
       "273    CHEMBL4295677  [{'accession': 'O75369', 'component_descriptio...   \n",
       "\n",
       "        target_type  tax_id  \n",
       "0    SINGLE PROTEIN    9606  \n",
       "1    SINGLE PROTEIN    9606  \n",
       "2    SINGLE PROTEIN    9606  \n",
       "3    SINGLE PROTEIN    9606  \n",
       "4    SINGLE PROTEIN    9606  \n",
       "..              ...     ...  \n",
       "269  SINGLE PROTEIN    9606  \n",
       "270  SINGLE PROTEIN    9606  \n",
       "271  SINGLE PROTEIN    9606  \n",
       "272  SINGLE PROTEIN    9606  \n",
       "273  SINGLE PROTEIN    9606  \n",
       "\n",
       "[274 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Protein of interest is Beta-Secretase, a protein associated with ALzeihemers's Disease\n",
    "target_protein = new_client.target\n",
    "target_query = target_protein.search('Beta-Secretase').filter(target_type='SINGLE PROTEIN').filter(organism='Homo sapiens')\n",
    "data = pd.DataFrame.from_dict(target_query)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we want only the first protein: Beta secratese 1 \n",
    "chosen_one = data.iloc[1]['target_chembl_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_activity = new_client.activity\n",
    "target_query = target_activity.filter(target_chembl_id=chosen_one).filter(standard_type='IC50')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(target_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>bao_endpoint</th>\n",
       "      <th>bao_format</th>\n",
       "      <th>bao_label</th>\n",
       "      <th>canonical_smiles</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>78857</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL653511</td>\n",
       "      <td>Inhibitory activity against Beta-secretase 1 w...</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>IC50</td>\n",
       "      <td>nM</td>\n",
       "      <td>UO_0000065</td>\n",
       "      <td>None</td>\n",
       "      <td>413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>391560</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL653332</td>\n",
       "      <td>Compound was tested for its inhibitory activit...</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>391983</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL653512</td>\n",
       "      <td>Inhibition of human Beta-secretase 1</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>395858</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL653512</td>\n",
       "      <td>Inhibition of human Beta-secretase 1</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>395859</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL653512</td>\n",
       "      <td>Inhibition of human Beta-secretase 1</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>5.6</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>9811</th>\n",
       "      <td>None</td>\n",
       "      <td>18771806</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL4268509</td>\n",
       "      <td>Inhibition of human BACE1 using rhodamine-EVNL...</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9812</th>\n",
       "      <td>None</td>\n",
       "      <td>18771807</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL4268509</td>\n",
       "      <td>Inhibition of human BACE1 using rhodamine-EVNL...</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9813</th>\n",
       "      <td>None</td>\n",
       "      <td>18771808</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL4268509</td>\n",
       "      <td>Inhibition of human BACE1 using rhodamine-EVNL...</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>None</td>\n",
       "      <td>18771809</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL4268509</td>\n",
       "      <td>Inhibition of human BACE1 using rhodamine-EVNL...</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9815</th>\n",
       "      <td>None</td>\n",
       "      <td>18796525</td>\n",
       "      <td>[]</td>\n",
       "      <td>CHEMBL4273887</td>\n",
       "      <td>Inhibition of human BACE-1 after 30 mins by FR...</td>\n",
       "      <td>B</td>\n",
       "      <td>BAO_0000190</td>\n",
       "      <td>BAO_0000357</td>\n",
       "      <td>single protein format</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>...</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>Beta-secretase 1</td>\n",
       "      <td>9606</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>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9816 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     activity_comment  activity_id activity_properties assay_chembl_id  \\\n",
       "0                None        78857                  []    CHEMBL653511   \n",
       "1                None       391560                  []    CHEMBL653332   \n",
       "2                None       391983                  []    CHEMBL653512   \n",
       "3                None       395858                  []    CHEMBL653512   \n",
       "4                None       395859                  []    CHEMBL653512   \n",
       "...               ...          ...                 ...             ...   \n",
       "9811             None     18771806                  []   CHEMBL4268509   \n",
       "9812             None     18771807                  []   CHEMBL4268509   \n",
       "9813             None     18771808                  []   CHEMBL4268509   \n",
       "9814             None     18771809                  []   CHEMBL4268509   \n",
       "9815             None     18796525                  []   CHEMBL4273887   \n",
       "\n",
       "                                      assay_description assay_type  \\\n",
       "0     Inhibitory activity against Beta-secretase 1 w...          B   \n",
       "1     Compound was tested for its inhibitory activit...          B   \n",
       "2                  Inhibition of human Beta-secretase 1          B   \n",
       "3                  Inhibition of human Beta-secretase 1          B   \n",
       "4                  Inhibition of human Beta-secretase 1          B   \n",
       "...                                                 ...        ...   \n",
       "9811  Inhibition of human BACE1 using rhodamine-EVNL...          B   \n",
       "9812  Inhibition of human BACE1 using rhodamine-EVNL...          B   \n",
       "9813  Inhibition of human BACE1 using rhodamine-EVNL...          B   \n",
       "9814  Inhibition of human BACE1 using rhodamine-EVNL...          B   \n",
       "9815  Inhibition of human BACE-1 after 30 mins by FR...          B   \n",
       "\n",
       "     bao_endpoint   bao_format              bao_label  \\\n",
       "0     BAO_0000190  BAO_0000357  single protein format   \n",
       "1     BAO_0000190  BAO_0000357  single protein format   \n",
       "2     BAO_0000190  BAO_0000357  single protein format   \n",
       "3     BAO_0000190  BAO_0000357  single protein format   \n",
       "4     BAO_0000190  BAO_0000357  single protein format   \n",
       "...           ...          ...                    ...   \n",
       "9811  BAO_0000190  BAO_0000357  single protein format   \n",
       "9812  BAO_0000190  BAO_0000357  single protein format   \n",
       "9813  BAO_0000190  BAO_0000357  single protein format   \n",
       "9814  BAO_0000190  BAO_0000357  single protein format   \n",
       "9815  BAO_0000190  BAO_0000357  single protein format   \n",
       "\n",
       "                                       canonical_smiles  ... target_organism  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...  ...    Homo sapiens   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...  ...    Homo sapiens   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...  ...    Homo sapiens   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...  ...    Homo sapiens   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...  ...    Homo sapiens   \n",
       "...                                                 ...  ...             ...   \n",
       "9811  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...  ...    Homo sapiens   \n",
       "9812  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...  ...    Homo sapiens   \n",
       "9813  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...  ...    Homo sapiens   \n",
       "9814      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1  ...    Homo sapiens   \n",
       "9815  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...  ...    Homo sapiens   \n",
       "\n",
       "      target_pref_name target_tax_id text_value  toid  type units    uo_units  \\\n",
       "0     Beta-secretase 1          9606       None  None  IC50    nM  UO_0000065   \n",
       "1     Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "2     Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "3     Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "4     Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "...                ...           ...        ...   ...   ...   ...         ...   \n",
       "9811  Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "9812  Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "9813  Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "9814  Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "9815  Beta-secretase 1          9606       None  None  IC50    uM  UO_0000065   \n",
       "\n",
       "     upper_value  value  \n",
       "0           None  413.0  \n",
       "1           None  0.002  \n",
       "2           None   0.46  \n",
       "3           None    9.0  \n",
       "4           None    5.6  \n",
       "...          ...    ...  \n",
       "9811        None   10.0  \n",
       "9812        None   10.0  \n",
       "9813        None   10.0  \n",
       "9814        None   10.0  \n",
       "9815        None    7.0  \n",
       "\n",
       "[9816 rows x 43 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df ## constains all of the compunds that have been tested in this protein. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping all the null values. (lost 108 observations)\n",
    "df.dropna(subset=['standard_value'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Saving to csv \n",
    "df.to_csv('Beta-secretase-1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Beta-secretase-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9708, 43)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Changing the standard values to class type for classification type model. \n",
    "bioactivity_class =[]\n",
    "for x in data.standard_value:\n",
    "    if float(x) >= 10000:\n",
    "        bioactivity_class.append('inactive')\n",
    "    elif float(x) < 1000:\n",
    "        bioactivity_class.append('active')\n",
    "    else:\n",
    "        bioactivity_class.append('min effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'active': 6410, 'min effect': 1922, 'inactive': 1376})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(bioactivity_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## There is imbalance in the set of active and inactive compunds. \n",
    "## Now, we make a dataframe containing only the chembl_id, standard_value, canonical smile, and bioactivity class.\n",
    "data['bioactivity_class'] = bioactivity_class\n",
    "data2 = data[['bioactivity_class', 'molecule_chembl_id', 'canonical_smiles', 'standard_value']]\n",
    "data2.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>index</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>5600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9703</th>\n",
       "      <td>9703</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>9704</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>9705</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>9706</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>9707</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index bioactivity_class molecule_chembl_id  \\\n",
       "0         0            active       CHEMBL406146   \n",
       "1         1            active        CHEMBL78946   \n",
       "2         2            active       CHEMBL324109   \n",
       "3         3        min effect       CHEMBL114147   \n",
       "4         4        min effect       CHEMBL419949   \n",
       "...     ...               ...                ...   \n",
       "9703   9703          inactive      CHEMBL4292235   \n",
       "9704   9704          inactive      CHEMBL4286556   \n",
       "9705   9705          inactive      CHEMBL4284235   \n",
       "9706   9706          inactive      CHEMBL4284580   \n",
       "9707   9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0  \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0  \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0  \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0  \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0  \n",
       "...                                                 ...             ...  \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...         10000.0  \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...         10000.0  \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...         10000.0  \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1         10000.0  \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...          7000.0  \n",
       "\n",
       "[9708 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.hist(data2['bioactivity_class'])\n",
    "plt.xlabel('Class Label', fontsize=24)\n",
    "plt.ylabel('Count', fontsize=24)\n",
    "plt.title('Inhibition Response Distribution', fontsize=24)\n",
    "plt.savefig('Inhibition Response Distribution.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA using Lipinski Descriptors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Lipinski, Descriptors\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## calculate the Lipinski descriptors of the compunds: Molecular Weight, Hydrogen bond donors, Hydrogen bond acceptors, and \n",
    "## octanol-water partition coefficent. \n",
    "def mol_descriptors(molecules, verbose=False):\n",
    "    \n",
    "    molData = []\n",
    "    for x in molecules:\n",
    "        mol = Chem.MolFromSmiles(x)\n",
    "        molData.append(mol)\n",
    "        \n",
    "    storage = np.arange(1,1)\n",
    "    i=0\n",
    "    \n",
    "    for y in molData:\n",
    "        MolWt = Descriptors.MolWt(y)\n",
    "        NumHDonors = Descriptors.NumHDonors(y)\n",
    "        NumHAccept = Descriptors.NumHAcceptors(y)\n",
    "        MolLogP = Descriptors.MolLogP(y)\n",
    "        \n",
    "        insert = np.array([MolWt, NumHDonors, NumHAccept, MolLogP])\n",
    "        \n",
    "        if (i==0):\n",
    "            LipinskiData = insert\n",
    "        else:\n",
    "            LipinskiData = np.vstack([LipinskiData, insert])\n",
    "        i=+1\n",
    "        \n",
    "    column_names = ['Molecular Weight', 'Number of Hydrogen Donors', 'Number of Hydrogen Acceptors', 'OWPC']\n",
    "    Lipinski_descriptors = pd.DataFrame(LipinskiData,columns=column_names)\n",
    "    \n",
    "    return Lipinski_descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lipinski = mol_descriptors(data2.canonical_smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</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>9703</th>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Molecular Weight  Number of Hydrogen Donors  \\\n",
       "0              999.085                       13.0   \n",
       "1              893.005                       12.0   \n",
       "2              751.988                        8.0   \n",
       "3              737.895                        8.0   \n",
       "4              828.020                        8.0   \n",
       "...                ...                        ...   \n",
       "9703           420.465                        1.0   \n",
       "9704           406.438                        1.0   \n",
       "9705           420.465                        1.0   \n",
       "9706           335.315                        2.0   \n",
       "9707           537.939                        3.0   \n",
       "\n",
       "      Number of Hydrogen Acceptors     OWPC  \n",
       "0                             13.0 -1.43550  \n",
       "1                             12.0 -1.73610  \n",
       "2                              9.0  2.35350  \n",
       "3                              8.0  1.96260  \n",
       "4                              8.0  3.57390  \n",
       "...                            ...      ...  \n",
       "9703                           6.0  3.47172  \n",
       "9704                           6.0  3.16330  \n",
       "9705                           6.0  3.47172  \n",
       "9706                           4.0  3.14310  \n",
       "9707                          10.0  4.64850  \n",
       "\n",
       "[9708 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lipinski.to_csv('df-lipinski.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = pd.concat([data2, df_lipinski], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.drop(columns=['index'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.to_csv('Lipinksi - complete dataset', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>413.0</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>460.0</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9703</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "...                                                 ...             ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...         10000.0   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...         10000.0   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...         10000.0   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1         10000.0   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...          7000.0   \n",
       "\n",
       "      Molecular Weight  Number of Hydrogen Donors  \\\n",
       "0              999.085                       13.0   \n",
       "1              893.005                       12.0   \n",
       "2              751.988                        8.0   \n",
       "3              737.895                        8.0   \n",
       "4              828.020                        8.0   \n",
       "...                ...                        ...   \n",
       "9703           420.465                        1.0   \n",
       "9704           406.438                        1.0   \n",
       "9705           420.465                        1.0   \n",
       "9706           335.315                        2.0   \n",
       "9707           537.939                        3.0   \n",
       "\n",
       "      Number of Hydrogen Acceptors     OWPC  \n",
       "0                             13.0 -1.43550  \n",
       "1                             12.0 -1.73610  \n",
       "2                              9.0  2.35350  \n",
       "3                              8.0  1.96260  \n",
       "4                              8.0  3.57390  \n",
       "...                            ...      ...  \n",
       "9703                           6.0  3.47172  \n",
       "9704                           6.0  3.16330  \n",
       "9705                           6.0  3.47172  \n",
       "9706                           4.0  3.14310  \n",
       "9707                          10.0  4.64850  \n",
       "\n",
       "[9708 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_full.boxplot(column=['standard_value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see the standard value (response variable) is highly skewed to the right, with many outliers. \n",
    "#### To improve our model's ability to predict standard values, we will convert the IC50 values to pIC50 using the formula:\n",
    "#### -log(IC50 * 10^-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_full = pd.read_csv('Lipinski- complete dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>413.0</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>460.0</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9703</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "...                                                 ...             ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...         10000.0   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...         10000.0   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...         10000.0   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1         10000.0   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...          7000.0   \n",
       "\n",
       "      Molecular Weight  Number of Hydrogen Donors  \\\n",
       "0              999.085                       13.0   \n",
       "1              893.005                       12.0   \n",
       "2              751.988                        8.0   \n",
       "3              737.895                        8.0   \n",
       "4              828.020                        8.0   \n",
       "...                ...                        ...   \n",
       "9703           420.465                        1.0   \n",
       "9704           406.438                        1.0   \n",
       "9705           420.465                        1.0   \n",
       "9706           335.315                        2.0   \n",
       "9707           537.939                        3.0   \n",
       "\n",
       "      Number of Hydrogen Acceptors     OWPC  \n",
       "0                             13.0 -1.43550  \n",
       "1                             12.0 -1.73610  \n",
       "2                              9.0  2.35350  \n",
       "3                              8.0  1.96260  \n",
       "4                              8.0  3.57390  \n",
       "...                            ...      ...  \n",
       "9703                           6.0  3.47172  \n",
       "9704                           6.0  3.16330  \n",
       "9705                           6.0  3.47172  \n",
       "9706                           4.0  3.14310  \n",
       "9707                          10.0  4.64850  \n",
       "\n",
       "[9708 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9.708000e+03\n",
       "mean     3.465020e+04\n",
       "std      9.917863e+05\n",
       "min      2.000000e-04\n",
       "25%      2.800000e+01\n",
       "50%      2.100000e+02\n",
       "75%      2.250000e+03\n",
       "max      8.600000e+07\n",
       "Name: standard_value, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full['standard_value'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Setting a cutoff to the standad value will help with interpreting test results. Large IC50 values are already poor \n",
    "## candidates for clinical trials, so capping pIC50 values at 10000000 will our data more uniform without sacrificing \n",
    "## the validity of our conclusions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize_and_conversion(dataset):\n",
    "    store = []\n",
    "    for x in dataset['standard_value']:\n",
    "        ### Setting IC50 value cutoff at 10000000\n",
    "        if x > 10000000:\n",
    "            x = 10000000\n",
    "        \n",
    "        #### Converting IC50 value to molar values \n",
    "        molar_value = x*(10**-9)\n",
    "        insert = -math.log(molar_value,10)\n",
    "        store.append(insert)\n",
    "    return store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Standard_value_norm = standardize(df_full.standard_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9.708000e+03\n",
       "mean     2.280430e+04\n",
       "std      2.498172e+05\n",
       "min      2.000000e-04\n",
       "25%      2.800000e+01\n",
       "50%      2.100000e+02\n",
       "75%      2.250000e+03\n",
       "max      1.000000e+07\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Standard_value_norm = pd.Series(Standard_value_norm)\n",
    "Standard_value_norm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1f8f87cf240>,\n",
       "  <matplotlib.lines.Line2D at 0x1f8f87cf518>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1f8f87cf7f0>,\n",
       "  <matplotlib.lines.Line2D at 0x1f8f87cfac8>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1f8f87c2f98>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1f8f87cfda0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1f8f87da0b8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.boxplot(Standard_value_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pIC50 = conversion(Standard_value_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1f8f8825860>,\n",
       "  <matplotlib.lines.Line2D at 0x1f8f8825b38>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1f8f8825e10>,\n",
       "  <matplotlib.lines.Line2D at 0x1f8f8839128>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1f8f88255f8>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1f8f8839400>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1f8f88396d8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v3ljWgAOrlZnlvuhKN2A1YwkFAAJFwAEgUAQcuKs37gGA5WINHAACxRE4AASKgANAoAg4Es3MjpvZNTMbinsWYLkIOJLuDUkvxj0EUAgCjkRz9/ckjcc9B1AIAg4AgSLgABAoAg4AgSLgABAoAo5EM7OspPclPW1mY2bWFvdMQFRcSg8AgeIIHAACRcABIFAEHAACRcABIFAEHAACRcABIFAEHAAC9f8e45O7vIdS/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(pIC50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_full = pd.read_csv('Lipinski- complete dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>413.0</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>460.0</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9703</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "...                                                 ...             ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...         10000.0   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...         10000.0   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...         10000.0   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1         10000.0   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...          7000.0   \n",
       "\n",
       "      Molecular Weight  Number of Hydrogen Donors  \\\n",
       "0              999.085                       13.0   \n",
       "1              893.005                       12.0   \n",
       "2              751.988                        8.0   \n",
       "3              737.895                        8.0   \n",
       "4              828.020                        8.0   \n",
       "...                ...                        ...   \n",
       "9703           420.465                        1.0   \n",
       "9704           406.438                        1.0   \n",
       "9705           420.465                        1.0   \n",
       "9706           335.315                        2.0   \n",
       "9707           537.939                        3.0   \n",
       "\n",
       "      Number of Hydrogen Acceptors     OWPC  \n",
       "0                             13.0 -1.43550  \n",
       "1                             12.0 -1.73610  \n",
       "2                              9.0  2.35350  \n",
       "3                              8.0  1.96260  \n",
       "4                              8.0  3.57390  \n",
       "...                            ...      ...  \n",
       "9703                           6.0  3.47172  \n",
       "9704                           6.0  3.16330  \n",
       "9705                           6.0  3.47172  \n",
       "9706                           4.0  3.14310  \n",
       "9707                          10.0  4.64850  \n",
       "\n",
       "[9708 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math \n",
    "def standardize_and_conversion_1(dataset):\n",
    "    store = []\n",
    "    for x in dataset['standard_value']:\n",
    "        ### Setting IC50 value cutoff at 10000000\n",
    "        if x > 1000000:\n",
    "            x = 1000000\n",
    "        \n",
    "        #### Converting IC50 value to molar values \n",
    "        molar_value = x*(10**-9)\n",
    "        insert = -math.log(molar_value,10)\n",
    "        store.append(insert)\n",
    "    return store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "setting = standardize_and_conversion_1(df_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x24328bfdaf0>,\n",
       "  <matplotlib.lines.Line2D at 0x24328bfd310>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x243285a28e0>,\n",
       "  <matplotlib.lines.Line2D at 0x243285a2e80>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x24328bfd670>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x243289c73a0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x243289c7b20>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot shoing the effect of the molar value conversion on sample space. \n",
    "import matplotlib.pyplot as plt \n",
    "fig = plt.figure(figsize=(10,7))\n",
    "plt.boxplot(setting)\n",
    "#plt.savefig('pIC50 values(log1000)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9708.000000\n",
       "mean        6.558409\n",
       "std         1.320022\n",
       "min         3.000000\n",
       "25%         5.647817\n",
       "50%         6.677781\n",
       "75%         7.552842\n",
       "max        12.698970\n",
       "dtype: float64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setting = pd.Series(setting)\n",
    "setting.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4300    10.508638\n",
       "8327    10.958607\n",
       "8328    11.086186\n",
       "8329    11.214670\n",
       "8330    11.244125\n",
       "8331    12.045757\n",
       "8332    12.698970\n",
       "8333    12.045757\n",
       "8334    11.744727\n",
       "8335    12.000000\n",
       "9270    10.619789\n",
       "9271    11.221849\n",
       "9466    10.481486\n",
       "9479    10.853872\n",
       "9480    10.468521\n",
       "dtype: float64"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = setting.quantile(0.75)\n",
    "q1 = setting.quantile(0.25)\n",
    "iqr = 1.5*(q3 -q1) + q3\n",
    "setting.loc[setting > iqr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Since subset of outliers is small(15), removal of these values will be the best course of action as supposed to fitting \n",
    "## them into the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "insert_2 = setting[setting <= iqr]\n",
    "df_full['pIC50'] = insert_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9708"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.pIC50.isnull().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9693"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insert_2.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.insert(8, 'pIC50', insert_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "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>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>413.0</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "      <td>6.384050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "      <td>8.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>460.0</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "      <td>6.337242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "      <td>5.045757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</td>\n",
       "      <td>5.251812</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>9703</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "      <td>5.154902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "...                                                 ...             ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...         10000.0   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...         10000.0   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...         10000.0   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1         10000.0   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...          7000.0   \n",
       "\n",
       "      Molecular Weight  Number of Hydrogen Donors  \\\n",
       "0              999.085                       13.0   \n",
       "1              893.005                       12.0   \n",
       "2              751.988                        8.0   \n",
       "3              737.895                        8.0   \n",
       "4              828.020                        8.0   \n",
       "...                ...                        ...   \n",
       "9703           420.465                        1.0   \n",
       "9704           406.438                        1.0   \n",
       "9705           420.465                        1.0   \n",
       "9706           335.315                        2.0   \n",
       "9707           537.939                        3.0   \n",
       "\n",
       "      Number of Hydrogen Acceptors     OWPC     pIC50  \n",
       "0                             13.0 -1.43550  6.384050  \n",
       "1                             12.0 -1.73610  8.698970  \n",
       "2                              9.0  2.35350  6.337242  \n",
       "3                              8.0  1.96260  5.045757  \n",
       "4                              8.0  3.57390  5.251812  \n",
       "...                            ...      ...       ...  \n",
       "9703                           6.0  3.47172  5.000000  \n",
       "9704                           6.0  3.16330  5.000000  \n",
       "9705                           6.0  3.47172  5.000000  \n",
       "9706                           4.0  3.14310  5.000000  \n",
       "9707                          10.0  4.64850  5.154902  \n",
       "\n",
       "[9708 rows x 9 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9693.000000\n",
       "mean        6.551103\n",
       "std         1.307642\n",
       "min         3.000000\n",
       "25%         5.643974\n",
       "50%         6.677781\n",
       "75%         7.545155\n",
       "max        10.000000\n",
       "Name: pIC50, dtype: float64"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.pIC50.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: pIC50, dtype: float64)"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = df_full['pIC50'].quantile(0.75)\n",
    "q1 = df_full['pIC50'].quantile(0.25)\n",
    "iqr = 1.5*(q3 -q1) + q3\n",
    "df_full['pIC50'].loc[df_full['pIC50'] > iqr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_full.boxplot(column='pIC50') ## All outliers have been removed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "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>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>413.0</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "      <td>6.384050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "      <td>8.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>460.0</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "      <td>6.337242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "      <td>5.045757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</td>\n",
       "      <td>5.251812</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>9703</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "      <td>5.154902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9693 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "...                                                 ...             ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...         10000.0   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...         10000.0   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...         10000.0   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1         10000.0   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...          7000.0   \n",
       "\n",
       "      Molecular Weight  Number of Hydrogen Donors  \\\n",
       "0              999.085                       13.0   \n",
       "1              893.005                       12.0   \n",
       "2              751.988                        8.0   \n",
       "3              737.895                        8.0   \n",
       "4              828.020                        8.0   \n",
       "...                ...                        ...   \n",
       "9703           420.465                        1.0   \n",
       "9704           406.438                        1.0   \n",
       "9705           420.465                        1.0   \n",
       "9706           335.315                        2.0   \n",
       "9707           537.939                        3.0   \n",
       "\n",
       "      Number of Hydrogen Acceptors     OWPC     pIC50  \n",
       "0                             13.0 -1.43550  6.384050  \n",
       "1                             12.0 -1.73610  8.698970  \n",
       "2                              9.0  2.35350  6.337242  \n",
       "3                              8.0  1.96260  5.045757  \n",
       "4                              8.0  3.57390  5.251812  \n",
       "...                            ...      ...       ...  \n",
       "9703                           6.0  3.47172  5.000000  \n",
       "9704                           6.0  3.16330  5.000000  \n",
       "9705                           6.0  3.47172  5.000000  \n",
       "9706                           4.0  3.14310  5.000000  \n",
       "9707                          10.0  4.64850  5.154902  \n",
       "\n",
       "[9693 rows x 9 columns]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Remove rows that are missing a pIC50 value \n",
    "df_full.dropna(inplace=True) ## Datset is now without any outliers in the response variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.drop(columns='standard_value',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "      <td>6.384050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "      <td>8.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "      <td>6.337242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "      <td>5.045757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</td>\n",
       "      <td>5.251812</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9703</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "      <td>5.154902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           999.085   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...           893.005   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           751.988   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...           737.895   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...           828.020   \n",
       "...                                                 ...               ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...           420.465   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...           406.438   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...           420.465   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1           335.315   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...           537.939   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors     OWPC  \\\n",
       "0                          13.0                          13.0 -1.43550   \n",
       "1                          12.0                          12.0 -1.73610   \n",
       "2                           8.0                           9.0  2.35350   \n",
       "3                           8.0                           8.0  1.96260   \n",
       "4                           8.0                           8.0  3.57390   \n",
       "...                         ...                           ...      ...   \n",
       "9703                        1.0                           6.0  3.47172   \n",
       "9704                        1.0                           6.0  3.16330   \n",
       "9705                        1.0                           6.0  3.47172   \n",
       "9706                        2.0                           4.0  3.14310   \n",
       "9707                        3.0                          10.0  4.64850   \n",
       "\n",
       "         pIC50  \n",
       "0     6.384050  \n",
       "1     8.698970  \n",
       "2     6.337242  \n",
       "3     5.045757  \n",
       "4     5.251812  \n",
       "...        ...  \n",
       "9703  5.000000  \n",
       "9704  5.000000  \n",
       "9705  5.000000  \n",
       "9706  5.000000  \n",
       "9707  5.154902  \n",
       "\n",
       "[9708 rows x 8 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.to_csv('data-no outliers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_full = pd.read_csv('data-no outliers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
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       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>active</td>\n",
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       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "      <td>6.384050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "      <td>8.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "      <td>6.337242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "      <td>5.045757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</td>\n",
       "      <td>5.251812</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>9703</th>\n",
       "      <td>9703</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>9704</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>9705</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>9706</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>9707</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "      <td>5.154902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 bioactivity_class molecule_chembl_id  \\\n",
       "0              0            active       CHEMBL406146   \n",
       "1              1            active        CHEMBL78946   \n",
       "2              2            active       CHEMBL324109   \n",
       "3              3        min effect       CHEMBL114147   \n",
       "4              4        min effect       CHEMBL419949   \n",
       "...          ...               ...                ...   \n",
       "9703        9703          inactive      CHEMBL4292235   \n",
       "9704        9704          inactive      CHEMBL4286556   \n",
       "9705        9705          inactive      CHEMBL4284235   \n",
       "9706        9706          inactive      CHEMBL4284580   \n",
       "9707        9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           999.085   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...           893.005   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           751.988   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...           737.895   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...           828.020   \n",
       "...                                                 ...               ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...           420.465   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...           406.438   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...           420.465   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1           335.315   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...           537.939   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors     OWPC  \\\n",
       "0                          13.0                          13.0 -1.43550   \n",
       "1                          12.0                          12.0 -1.73610   \n",
       "2                           8.0                           9.0  2.35350   \n",
       "3                           8.0                           8.0  1.96260   \n",
       "4                           8.0                           8.0  3.57390   \n",
       "...                         ...                           ...      ...   \n",
       "9703                        1.0                           6.0  3.47172   \n",
       "9704                        1.0                           6.0  3.16330   \n",
       "9705                        1.0                           6.0  3.47172   \n",
       "9706                        2.0                           4.0  3.14310   \n",
       "9707                        3.0                          10.0  4.64850   \n",
       "\n",
       "         pIC50  \n",
       "0     6.384050  \n",
       "1     8.698970  \n",
       "2     6.337242  \n",
       "3     5.045757  \n",
       "4     5.251812  \n",
       "...        ...  \n",
       "9703  5.000000  \n",
       "9704  5.000000  \n",
       "9705  5.000000  \n",
       "9706  5.000000  \n",
       "9707  5.154902  \n",
       "\n",
       "[9708 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "      <th>pIC50</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...</td>\n",
       "      <td>999.085</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.43550</td>\n",
       "      <td>6.384050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>893.005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.73610</td>\n",
       "      <td>8.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.35350</td>\n",
       "      <td>6.337242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "      <td>5.045757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.57390</td>\n",
       "      <td>5.251812</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>9703</th>\n",
       "      <td>9703</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>9704</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
       "      <td>406.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.16330</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>9705</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>9706</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>9707</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "      <td>5.154902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9693 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 bioactivity_class molecule_chembl_id  \\\n",
       "0              0            active       CHEMBL406146   \n",
       "1              1            active        CHEMBL78946   \n",
       "2              2            active       CHEMBL324109   \n",
       "3              3        min effect       CHEMBL114147   \n",
       "4              4        min effect       CHEMBL419949   \n",
       "...          ...               ...                ...   \n",
       "9703        9703          inactive      CHEMBL4292235   \n",
       "9704        9704          inactive      CHEMBL4286556   \n",
       "9705        9705          inactive      CHEMBL4284235   \n",
       "9706        9706          inactive      CHEMBL4284580   \n",
       "9707        9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           999.085   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...           893.005   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           751.988   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...           737.895   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...           828.020   \n",
       "...                                                 ...               ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...           420.465   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...           406.438   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...           420.465   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1           335.315   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...           537.939   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors     OWPC  \\\n",
       "0                          13.0                          13.0 -1.43550   \n",
       "1                          12.0                          12.0 -1.73610   \n",
       "2                           8.0                           9.0  2.35350   \n",
       "3                           8.0                           8.0  1.96260   \n",
       "4                           8.0                           8.0  3.57390   \n",
       "...                         ...                           ...      ...   \n",
       "9703                        1.0                           6.0  3.47172   \n",
       "9704                        1.0                           6.0  3.16330   \n",
       "9705                        1.0                           6.0  3.47172   \n",
       "9706                        2.0                           4.0  3.14310   \n",
       "9707                        3.0                          10.0  4.64850   \n",
       "\n",
       "         pIC50  \n",
       "0     6.384050  \n",
       "1     8.698970  \n",
       "2     6.337242  \n",
       "3     5.045757  \n",
       "4     5.251812  \n",
       "...        ...  \n",
       "9703  5.000000  \n",
       "9704  5.000000  \n",
       "9705  5.000000  \n",
       "9706  5.000000  \n",
       "9707  5.154902  \n",
       "\n",
       "[9693 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Obtain the graphs of descriptors to show differences between class (active, inactive, min Effect)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.boxplot(x='bioactivity_class', y='Molecular Weight', data=df_full)\n",
    "plt.title('Molecular Weight (by class)')\n",
    "plt.savefig('Molecular Weight Dist.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='bioactivity_class', y='Number of Hydrogen Donors', data=df_full)\n",
    "plt.title('Number of H-Donors (by class)')\n",
    "plt.savefig('Number of H-Donors Dist.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='bioactivity_class', y='Number of Hydrogen Acceptors', data=df_full)\n",
    "plt.title('Number of H-Acceptors (by class)')\n",
    "plt.savefig('Number of H-Acceptors.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='bioactivity_class', y='OWPC', data=df_full)\n",
    "plt.title('Octanol-Water Partition Coefficent (by class)')\n",
    "plt.savefig('octanol-water partition coefficent.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='bioactivity_class', y='pIC50', data=df_full)\n",
    "plt.title('pIC50 Dist. - Target')\n",
    "plt.savefig('pIC50 class pic ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Remove compounds that violates more than one of the Lipinki's Rule of 5:\n",
    "## . Molecular Weight < 500 daltons\n",
    "## . Octonal water partition coefficent (logP) < 5\n",
    "## . Number of hydrogen bond donor < 5\n",
    "## . Number of Hydrogen bond acceptors < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lipinski_checker(data):\n",
    "          \n",
    "    num_hdonors = data['Number of Hydrogen Donors']\n",
    "    num_hacceptors = data['Number of Hydrogen Acceptors']\n",
    "    mol_weight = data['Molecular Weight']\n",
    "    mol_logp = data['OWPC']\n",
    "    \n",
    "    result = []\n",
    "    \n",
    "    if num_hdonors > 5:\n",
    "        result.append(1)\n",
    "    else:\n",
    "        result.append(0)\n",
    "        \n",
    "    if num_hacceptors > 10:\n",
    "        result.append(1)\n",
    "    else:\n",
    "        result.append(0)\n",
    "        \n",
    "    if mol_weight >= 500:\n",
    "        result.append(1)\n",
    "    else:\n",
    "        result.append(0)\n",
    "        \n",
    "    if mol_logp >= 5:\n",
    "        result.append(1)\n",
    "    else:\n",
    "        result.append(0)\n",
    "    \n",
    "    return result\n",
    "    \n",
    "def lipinski_pass(compounds):\n",
    "    checked_df = {'HD':[], 'HA':[], 'logP':[], 'MW':[]}\n",
    "    numTrials = len(compounds)\n",
    "    for x in range(numTrials):\n",
    "        result = lipinski_checker(compounds.iloc[x])\n",
    "        checked_df['HD'].append(result[0])\n",
    "        checked_df['HA'].append(result[1])\n",
    "        checked_df['logP'].append(result[2])\n",
    "        checked_df['MW'].append(result[3])\n",
    "    return checked_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_df = lipinski_pass(df_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_df = pd.DataFrame(checked_df)"
   ]
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   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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       "      HD  HA  logP  MW\n",
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       "9691   0   0     0   0\n",
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       "\n",
       "[9693 rows x 4 columns]"
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     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "checked_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_df['sum'] = checked_df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "      HD  HA  logP  MW  sum\n",
       "0      1   1     1   0    3\n",
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       "2      1   0     1   0    2\n",
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       "\n",
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  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_df = checked_df.loc[checked_df['sum'] <= 1]"
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   "execution_count": 66,
   "metadata": {},
   "outputs": [
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       "      HD  HA  logP  MW  sum\n",
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   "execution_count": 79,
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       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.14310</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9707</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL1164540</td>\n",
       "      <td>O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...</td>\n",
       "      <td>537.939</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.64850</td>\n",
       "      <td>5.154902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9693 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           999.085   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...           893.005   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           751.988   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...           737.895   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...           828.020   \n",
       "...                                                 ...               ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...           420.465   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...           406.438   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...           420.465   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1           335.315   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...           537.939   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors     OWPC  \\\n",
       "0                          13.0                          13.0 -1.43550   \n",
       "1                          12.0                          12.0 -1.73610   \n",
       "2                           8.0                           9.0  2.35350   \n",
       "3                           8.0                           8.0  1.96260   \n",
       "4                           8.0                           8.0  3.57390   \n",
       "...                         ...                           ...      ...   \n",
       "9703                        1.0                           6.0  3.47172   \n",
       "9704                        1.0                           6.0  3.16330   \n",
       "9705                        1.0                           6.0  3.47172   \n",
       "9706                        2.0                           4.0  3.14310   \n",
       "9707                        3.0                          10.0  4.64850   \n",
       "\n",
       "         pIC50  \n",
       "0     6.384050  \n",
       "1     8.698970  \n",
       "2     6.337242  \n",
       "3     5.045757  \n",
       "4     5.251812  \n",
       "...        ...  \n",
       "9703  5.000000  \n",
       "9704  5.000000  \n",
       "9705  5.000000  \n",
       "9706  5.000000  \n",
       "9707  5.154902  \n",
       "\n",
       "[9693 rows x 8 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
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       "      <td>active</td>\n",
       "      <td>CHEMBL406146</td>\n",
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       "      <td>751.988</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>6.337242</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>737.895</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.96260</td>\n",
       "      <td>5.045757</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>828.020</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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>9703</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4292235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.47172</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>9704</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4286556</td>\n",
       "      <td>CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9705</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284235</td>\n",
       "      <td>Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...</td>\n",
       "      <td>420.465</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>9705</td>\n",
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       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>inactive</td>\n",
       "      <td>CHEMBL4284580</td>\n",
       "      <td>O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1</td>\n",
       "      <td>335.315</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>9707</th>\n",
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       "      <td>9707</td>\n",
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      ],
      "text/plain": [
       "     bioactivity_class molecule_chembl_id  \\\n",
       "0               active       CHEMBL406146   \n",
       "1               active        CHEMBL78946   \n",
       "2               active       CHEMBL324109   \n",
       "3           min effect       CHEMBL114147   \n",
       "4           min effect       CHEMBL419949   \n",
       "...                ...                ...   \n",
       "9703          inactive      CHEMBL4292235   \n",
       "9704          inactive      CHEMBL4286556   \n",
       "9705          inactive      CHEMBL4284235   \n",
       "9706          inactive      CHEMBL4284580   \n",
       "9707        min effect      CHEMBL1164540   \n",
       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           999.085   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...           893.005   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           751.988   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...           737.895   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...           828.020   \n",
       "...                                                 ...               ...   \n",
       "9703  Cc1ccc(NC(=O)c2cc(=O)c3cc(/C=C/C(=O)OCCN(C)C)c...           420.465   \n",
       "9704  CN(C)CCOC(=O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=...           406.438   \n",
       "9705  Cc1ccc(NC(=O)c2coc3ccc(/C=C/C(=O)OCCN(C)C)cc3c...           420.465   \n",
       "9706      O=C(O)/C=C/c1ccc2occ(C(=O)Nc3ccccc3)c(=O)c2c1           335.315   \n",
       "9707  O=C(O)c1cc(-c2ccc(/C=N/Nc3nc(Nc4ccc(F)cc4)nc(N...           537.939   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors     OWPC  \\\n",
       "0                          13.0                          13.0 -1.43550   \n",
       "1                          12.0                          12.0 -1.73610   \n",
       "2                           8.0                           9.0  2.35350   \n",
       "3                           8.0                           8.0  1.96260   \n",
       "4                           8.0                           8.0  3.57390   \n",
       "...                         ...                           ...      ...   \n",
       "9703                        1.0                           6.0  3.47172   \n",
       "9704                        1.0                           6.0  3.16330   \n",
       "9705                        1.0                           6.0  3.47172   \n",
       "9706                        2.0                           4.0  3.14310   \n",
       "9707                        3.0                          10.0  4.64850   \n",
       "\n",
       "         pIC50   lit  \n",
       "0     6.384050     0  \n",
       "1     8.698970     1  \n",
       "2     6.337242     2  \n",
       "3     5.045757     3  \n",
       "4     5.251812     4  \n",
       "...        ...   ...  \n",
       "9703  5.000000  9703  \n",
       "9704  5.000000  9704  \n",
       "9705  5.000000  9705  \n",
       "9706  5.000000  9706  \n",
       "9707  5.154902  9707  \n",
       "\n",
       "[9693 rows x 9 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>9692</td>\n",
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       "</table>\n",
       "<p>8598 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "      HD  HA  logP  MW  sum   lit\n",
       "12     0   0     0   1    1    12\n",
       "18     0   0     1   0    1    18\n",
       "21     0   0     1   0    1    21\n",
       "24     0   0     1   0    1    24\n",
       "133    0   0     1   0    1   133\n",
       "...   ..  ..   ...  ..  ...   ...\n",
       "9688   0   0     0   0    0  9688\n",
       "9689   0   0     0   0    0  9689\n",
       "9690   0   0     0   0    0  9690\n",
       "9691   0   0     0   0    0  9691\n",
       "9692   0   0     1   0    1  9692\n",
       "\n",
       "[8598 rows x 6 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checked_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_df = pd.merge(checked_df,df_full, on='lit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HD</th>\n",
       "      <th>HA</th>\n",
       "      <th>logP</th>\n",
       "      <th>MW</th>\n",
       "      <th>sum</th>\n",
       "      <th>lit</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of Hydrogen Donors</th>\n",
       "      <th>Number of Hydrogen Acceptors</th>\n",
       "      <th>OWPC</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL142715</td>\n",
       "      <td>c1ccc(-c2ccc(COc3ccc4c(c3)CCC(CCN3CCCCC3)C4)cc...</td>\n",
       "      <td>425.616</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.9135</td>\n",
       "      <td>6.455932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL143687</td>\n",
       "      <td>COC(=O)c1cc(NC(=O)[C@@H](NC(=O)C[C@H](O)[C@H](...</td>\n",
       "      <td>745.776</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4964</td>\n",
       "      <td>6.920819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL82270</td>\n",
       "      <td>CC(C)[C@H](F)C(=O)N[C@@H](C)C(=O)N[C@@H](Cc1cc...</td>\n",
       "      <td>599.748</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.4520</td>\n",
       "      <td>5.946922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>min effect</td>\n",
       "      <td>CHEMBL84835</td>\n",
       "      <td>CC(C)CC(=O)N[C@@H](C)C(=O)N[C@@H](Cc1ccccc1)[C...</td>\n",
       "      <td>581.758</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5040</td>\n",
       "      <td>5.759451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>133</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL296166</td>\n",
       "      <td>COC(=O)[C@@H]1CC(NC(=O)[C@H](NC(=O)C[C@H](O)[C...</td>\n",
       "      <td>751.824</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.5316</td>\n",
       "      <td>6.920819</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8578</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9688</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL4291981</td>\n",
       "      <td>NC1=N[C@@]2(CO1)c1cc(NC(=O)c3ccc(Cl)cn3)ccc1Oc...</td>\n",
       "      <td>442.262</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.7299</td>\n",
       "      <td>6.721246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8579</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9689</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL4284110</td>\n",
       "      <td>CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(C...</td>\n",
       "      <td>450.498</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0129</td>\n",
       "      <td>8.920819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8580</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9690</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL4276834</td>\n",
       "      <td>CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(C...</td>\n",
       "      <td>450.498</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0129</td>\n",
       "      <td>8.657577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8581</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9691</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL4287483</td>\n",
       "      <td>CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(-...</td>\n",
       "      <td>463.472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.4103</td>\n",
       "      <td>8.387216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8582</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9692</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL4282964</td>\n",
       "      <td>NC1=N[C@@]2(CO1)c1cc(-c3cncc(C#CC4CC4)c3)ccc1O...</td>\n",
       "      <td>476.536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.4030</td>\n",
       "      <td>7.060481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8583 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      HD  HA  logP  MW  sum   lit bioactivity_class molecule_chembl_id  \\\n",
       "0      0   0     0   1    1    12            active       CHEMBL142715   \n",
       "1      0   0     1   0    1    18            active       CHEMBL143687   \n",
       "2      0   0     1   0    1    21        min effect        CHEMBL82270   \n",
       "3      0   0     1   0    1    24        min effect        CHEMBL84835   \n",
       "4      0   0     1   0    1   133            active       CHEMBL296166   \n",
       "...   ..  ..   ...  ..  ...   ...               ...                ...   \n",
       "8578   0   0     0   0    0  9688            active      CHEMBL4291981   \n",
       "8579   0   0     0   0    0  9689            active      CHEMBL4284110   \n",
       "8580   0   0     0   0    0  9690            active      CHEMBL4276834   \n",
       "8581   0   0     0   0    0  9691            active      CHEMBL4287483   \n",
       "8582   0   0     1   0    1  9692            active      CHEMBL4282964   \n",
       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
       "0     c1ccc(-c2ccc(COc3ccc4c(c3)CCC(CCN3CCCCC3)C4)cc...           425.616   \n",
       "1     COC(=O)c1cc(NC(=O)[C@@H](NC(=O)C[C@H](O)[C@H](...           745.776   \n",
       "2     CC(C)[C@H](F)C(=O)N[C@@H](C)C(=O)N[C@@H](Cc1cc...           599.748   \n",
       "3     CC(C)CC(=O)N[C@@H](C)C(=O)N[C@@H](Cc1ccccc1)[C...           581.758   \n",
       "4     COC(=O)[C@@H]1CC(NC(=O)[C@H](NC(=O)C[C@H](O)[C...           751.824   \n",
       "...                                                 ...               ...   \n",
       "8578  NC1=N[C@@]2(CO1)c1cc(NC(=O)c3ccc(Cl)cn3)ccc1Oc...           442.262   \n",
       "8579  CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(C...           450.498   \n",
       "8580  CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(C...           450.498   \n",
       "8581  CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(-...           463.472   \n",
       "8582  NC1=N[C@@]2(CO1)c1cc(-c3cncc(C#CC4CC4)c3)ccc1O...           476.536   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors    OWPC  \\\n",
       "0                           0.0                           2.0  6.9135   \n",
       "1                           5.0                           9.0  4.4964   \n",
       "2                           5.0                           6.0  2.4520   \n",
       "3                           5.0                           6.0  2.5040   \n",
       "4                           5.0                           9.0  3.5316   \n",
       "...                         ...                           ...     ...   \n",
       "8578                        2.0                           7.0  3.7299   \n",
       "8579                        1.0                           7.0  4.0129   \n",
       "8580                        1.0                           7.0  4.0129   \n",
       "8581                        1.0                           7.0  4.4103   \n",
       "8582                        1.0                           7.0  4.4030   \n",
       "\n",
       "         pIC50  \n",
       "0     6.455932  \n",
       "1     6.920819  \n",
       "2     5.946922  \n",
       "3     5.759451  \n",
       "4     6.920819  \n",
       "...        ...  \n",
       "8578  6.721246  \n",
       "8579  8.920819  \n",
       "8580  8.657577  \n",
       "8581  8.387216  \n",
       "8582  7.060481  \n",
       "\n",
       "[8583 rows x 14 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checked_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pIC50 Dist. - Target')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='bioactivity_class', y='pIC50', data=checked_df)\n",
    "plt.title('pIC50 Dist. - Target (valid candidates)')\n",
    "plt.savefig('pIC50 class pic (pass Lipinski) ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_compounds_classification = checked_df[checked_df.bioactivity_class != 'min effect']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_compounds_classification.to_csv('df_ready_classification.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_df.to_csv('df-ready-regression.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Now data is prepared for feature engineering: obtaining 1D/2D descriptors for compound library. \n",
    "import pandas as pd\n",
    "data_1 = pd.read_csv('df-ready-regression.csv')\n",
    "data_2 = pd.read_csv('df_ready_classification.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "selections = ['canonical_smiles', 'molecule_chembl_id']\n",
    "df_reg = data_1[selections]\n",
    "df_class = data_2[selections]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.to_csv('Padel_regression_ready.smi', sep='\\t', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      Unnamed: 0  HD  HA  logP  MW  sum bioactivity_class molecule_chembl_id  \\\n",
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       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
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       "...                                                 ...               ...   \n",
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       "8581  CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(-...           463.472   \n",
       "8582  NC1=N[C@@]2(CO1)c1cc(-c3cncc(C#CC4CC4)c3)ccc1O...           476.536   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors    OWPC  \\\n",
       "0                           0.0                           2.0  6.9135   \n",
       "1                           5.0                           9.0  4.4964   \n",
       "2                           5.0                           6.0  2.4520   \n",
       "3                           5.0                           6.0  2.5040   \n",
       "4                           5.0                           9.0  3.5316   \n",
       "...                         ...                           ...     ...   \n",
       "8578                        2.0                           7.0  3.7299   \n",
       "8579                        1.0                           7.0  4.0129   \n",
       "8580                        1.0                           7.0  4.0129   \n",
       "8581                        1.0                           7.0  4.4103   \n",
       "8582                        1.0                           7.0  4.4030   \n",
       "\n",
       "         pIC50  \n",
       "0     6.455932  \n",
       "1     6.920819  \n",
       "2     5.946922  \n",
       "3     5.759451  \n",
       "4     6.920819  \n",
       "...        ...  \n",
       "8578  6.721246  \n",
       "8579  8.920819  \n",
       "8580  8.657577  \n",
       "8581  8.387216  \n",
       "8582  7.060481  \n",
       "\n",
       "[8583 rows x 14 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_class.to_csv('Padel_classification_ready.smi', sep='\\t', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(C...</td>\n",
       "      <td>450.498</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0129</td>\n",
       "      <td>8.657577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6877</th>\n",
       "      <td>8581</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL4287483</td>\n",
       "      <td>CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(-...</td>\n",
       "      <td>463.472</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.4103</td>\n",
       "      <td>8.387216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6878</th>\n",
       "      <td>8582</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>active</td>\n",
       "      <td>CHEMBL4282964</td>\n",
       "      <td>NC1=N[C@@]2(CO1)c1cc(-c3cncc(C#CC4CC4)c3)ccc1O...</td>\n",
       "      <td>476.536</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.4030</td>\n",
       "      <td>7.060481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6879 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  HD  HA  logP  MW  sum bioactivity_class molecule_chembl_id  \\\n",
       "0              0   0   0     0   1    1            active       CHEMBL142715   \n",
       "1              1   0   0     1   0    1            active       CHEMBL143687   \n",
       "2              4   0   0     1   0    1            active       CHEMBL296166   \n",
       "3              5   0   0     1   0    1          inactive       CHEMBL296166   \n",
       "4              6   0   0     1   0    1          inactive        CHEMBL53668   \n",
       "...          ...  ..  ..   ...  ..  ...               ...                ...   \n",
       "6874        8578   0   0     0   0    0            active      CHEMBL4291981   \n",
       "6875        8579   0   0     0   0    0            active      CHEMBL4284110   \n",
       "6876        8580   0   0     0   0    0            active      CHEMBL4276834   \n",
       "6877        8581   0   0     0   0    0            active      CHEMBL4287483   \n",
       "6878        8582   0   0     1   0    1            active      CHEMBL4282964   \n",
       "\n",
       "                                       canonical_smiles  Molecular Weight  \\\n",
       "0     c1ccc(-c2ccc(COc3ccc4c(c3)CCC(CCN3CCCCC3)C4)cc...           425.616   \n",
       "1     COC(=O)c1cc(NC(=O)[C@@H](NC(=O)C[C@H](O)[C@H](...           745.776   \n",
       "2     COC(=O)[C@@H]1CC(NC(=O)[C@H](NC(=O)C[C@H](O)[C...           751.824   \n",
       "3     COC(=O)[C@@H]1CC(NC(=O)[C@H](NC(=O)C[C@H](O)[C...           751.824   \n",
       "4     COC(=O)[C@@H]1CC(NC(=O)[C@@H](NC(=O)C[C@H](O)[...           655.789   \n",
       "...                                                 ...               ...   \n",
       "6874  NC1=N[C@@]2(CO1)c1cc(NC(=O)c3ccc(Cl)cn3)ccc1Oc...           442.262   \n",
       "6875  CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(C...           450.498   \n",
       "6876  CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(C...           450.498   \n",
       "6877  CC#Cc1cncc(-c2ccc3c(c2)[C@@]2(COC(N)=N2)c2cc(-...           463.472   \n",
       "6878  NC1=N[C@@]2(CO1)c1cc(-c3cncc(C#CC4CC4)c3)ccc1O...           476.536   \n",
       "\n",
       "      Number of Hydrogen Donors  Number of Hydrogen Acceptors    OWPC  \\\n",
       "0                             0                             2  6.9135   \n",
       "1                             5                             9  4.4964   \n",
       "2                             5                             9  3.5316   \n",
       "3                             5                             9  3.5316   \n",
       "4                             5                             9  2.5430   \n",
       "...                         ...                           ...     ...   \n",
       "6874                          2                             7  3.7299   \n",
       "6875                          1                             7  4.0129   \n",
       "6876                          1                             7  4.0129   \n",
       "6877                          1                             7  4.4103   \n",
       "6878                          1                             7  4.4030   \n",
       "\n",
       "         pIC50  \n",
       "0     6.455932  \n",
       "1     6.920819  \n",
       "2     6.920819  \n",
       "3     5.000000  \n",
       "4     3.853872  \n",
       "...        ...  \n",
       "6874  6.721246  \n",
       "6875  8.920819  \n",
       "6876  8.657577  \n",
       "6877  8.387216  \n",
       "6878  7.060481  \n",
       "\n",
       "[6879 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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