02_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "861ad489",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors, Lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ed21e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>class</th>\n",
       "      <th>standard_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL133897</td>\n",
       "      <td>CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1</td>\n",
       "      <td>active</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "      <td>active</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL131588</td>\n",
       "      <td>CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL130628</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F</td>\n",
       "      <td>active</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL130478</td>\n",
       "      <td>CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C</td>\n",
       "      <td>active</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>CHEMBL4554172</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>CHEMBL4533844</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>7570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>CHEMBL4570655</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>CHEMBL4571704</td>\n",
       "      <td>COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>15500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>CHEMBL4556664</td>\n",
       "      <td>COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>68300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     molecule_chembl_id                                   canonical_smiles  \\\n",
       "0          CHEMBL133897              CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1   \n",
       "1          CHEMBL336398         O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1   \n",
       "2          CHEMBL131588  CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1   \n",
       "3          CHEMBL130628      O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F   \n",
       "4          CHEMBL130478          CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C   \n",
       "...                 ...                                                ...   \n",
       "5038      CHEMBL4554172  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...   \n",
       "5039      CHEMBL4533844  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...   \n",
       "5040      CHEMBL4570655  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...   \n",
       "5041      CHEMBL4571704  COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...   \n",
       "5042      CHEMBL4556664  COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...   \n",
       "\n",
       "             class  standard_value  \n",
       "0           active           750.0  \n",
       "1           active           100.0  \n",
       "2         inactive         50000.0  \n",
       "3           active           300.0  \n",
       "4           active           800.0  \n",
       "...            ...             ...  \n",
       "5038      inactive         10000.0  \n",
       "5039  intermediate          7570.0  \n",
       "5040      inactive         10000.0  \n",
       "5041      inactive         15500.0  \n",
       "5042      inactive         68300.0  \n",
       "\n",
       "[5043 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('acetylcholinesterase_03_bioactivity_data_curated.csv')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "603fa2b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lipinski(smiles, verbose=False):\n",
    "    #create a fuction that converts smiles into molecule and add the molecules to the moldata list\n",
    "    moldata = []\n",
    "    for elem in smiles:\n",
    "        mol = Chem.MolFromSmiles(elem)\n",
    "        moldata.append(mol)\n",
    "    \n",
    "    baseData= np.array([])\n",
    "    i=0\n",
    "    for mol in moldata:\n",
    "        desc_MolWt = Descriptors.MolWt(mol)\n",
    "        desc_MolLogP = Descriptors.MolLogP(mol)\n",
    "        desc_NumHDonors = Lipinski.NumHDonors(mol)\n",
    "        desc_NumHAcceptors = Lipinski.NumHAcceptors(mol)\n",
    "        \n",
    "        row = np.array([desc_MolWt, desc_MolLogP, desc_NumHDonors, desc_NumHAcceptors])\n",
    "        if i==0:\n",
    "            baseData = row\n",
    "        else:\n",
    "            baseData = np.vstack([baseData, row])\n",
    "        i+=1\n",
    "    columnNames = ['MW', 'LogP', 'NumHDonors', 'NumHAcceptors']\n",
    "    descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n",
    "    \n",
    "    return descriptors\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fec262f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>312.325</td>\n",
       "      <td>2.80320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>376.913</td>\n",
       "      <td>4.55460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>426.851</td>\n",
       "      <td>5.35740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>404.845</td>\n",
       "      <td>4.70690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>346.334</td>\n",
       "      <td>3.09530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>5038</th>\n",
       "      <td>499.655</td>\n",
       "      <td>7.08374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>549.662</td>\n",
       "      <td>7.96344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>495.692</td>\n",
       "      <td>7.25306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>576.510</td>\n",
       "      <td>4.06432</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>558.539</td>\n",
       "      <td>6.27724</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           MW     LogP  NumHDonors  NumHAcceptors\n",
       "0     312.325  2.80320         0.0            6.0\n",
       "1     376.913  4.55460         0.0            5.0\n",
       "2     426.851  5.35740         0.0            5.0\n",
       "3     404.845  4.70690         0.0            5.0\n",
       "4     346.334  3.09530         0.0            6.0\n",
       "...       ...      ...         ...            ...\n",
       "5038  499.655  7.08374         1.0            4.0\n",
       "5039  549.662  7.96344         1.0            4.0\n",
       "5040  495.692  7.25306         1.0            4.0\n",
       "5041  576.510  4.06432         6.0           11.0\n",
       "5042  558.539  6.27724         5.0            9.0\n",
       "\n",
       "[5043 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lipinski = lipinski(df.canonical_smiles)\n",
    "df_lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0a79424",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>class</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL133897</td>\n",
       "      <td>CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1</td>\n",
       "      <td>active</td>\n",
       "      <td>750.0</td>\n",
       "      <td>312.325</td>\n",
       "      <td>2.80320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "      <td>active</td>\n",
       "      <td>100.0</td>\n",
       "      <td>376.913</td>\n",
       "      <td>4.55460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL131588</td>\n",
       "      <td>CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>426.851</td>\n",
       "      <td>5.35740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL130628</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F</td>\n",
       "      <td>active</td>\n",
       "      <td>300.0</td>\n",
       "      <td>404.845</td>\n",
       "      <td>4.70690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL130478</td>\n",
       "      <td>CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C</td>\n",
       "      <td>active</td>\n",
       "      <td>800.0</td>\n",
       "      <td>346.334</td>\n",
       "      <td>3.09530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>CHEMBL4554172</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>499.655</td>\n",
       "      <td>7.08374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>CHEMBL4533844</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>7570.0</td>\n",
       "      <td>549.662</td>\n",
       "      <td>7.96344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>CHEMBL4570655</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>495.692</td>\n",
       "      <td>7.25306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>CHEMBL4571704</td>\n",
       "      <td>COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>15500.0</td>\n",
       "      <td>576.510</td>\n",
       "      <td>4.06432</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>CHEMBL4556664</td>\n",
       "      <td>COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>68300.0</td>\n",
       "      <td>558.539</td>\n",
       "      <td>6.27724</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
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       "<p>5043 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "     molecule_chembl_id                                   canonical_smiles  \\\n",
       "0          CHEMBL133897              CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1   \n",
       "1          CHEMBL336398         O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1   \n",
       "2          CHEMBL131588  CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1   \n",
       "3          CHEMBL130628      O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F   \n",
       "4          CHEMBL130478          CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C   \n",
       "...                 ...                                                ...   \n",
       "5038      CHEMBL4554172  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...   \n",
       "5039      CHEMBL4533844  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...   \n",
       "5040      CHEMBL4570655  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...   \n",
       "5041      CHEMBL4571704  COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...   \n",
       "5042      CHEMBL4556664  COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...   \n",
       "\n",
       "             class  standard_value       MW     LogP  NumHDonors  \\\n",
       "0           active           750.0  312.325  2.80320         0.0   \n",
       "1           active           100.0  376.913  4.55460         0.0   \n",
       "2         inactive         50000.0  426.851  5.35740         0.0   \n",
       "3           active           300.0  404.845  4.70690         0.0   \n",
       "4           active           800.0  346.334  3.09530         0.0   \n",
       "...            ...             ...      ...      ...         ...   \n",
       "5038      inactive         10000.0  499.655  7.08374         1.0   \n",
       "5039  intermediate          7570.0  549.662  7.96344         1.0   \n",
       "5040      inactive         10000.0  495.692  7.25306         1.0   \n",
       "5041      inactive         15500.0  576.510  4.06432         6.0   \n",
       "5042      inactive         68300.0  558.539  6.27724         5.0   \n",
       "\n",
       "      NumHAcceptors  \n",
       "0               6.0  \n",
       "1               5.0  \n",
       "2               5.0  \n",
       "3               5.0  \n",
       "4               6.0  \n",
       "...             ...  \n",
       "5038            4.0  \n",
       "5039            4.0  \n",
       "5040            4.0  \n",
       "5041           11.0  \n",
       "5042            9.0  \n",
       "\n",
       "[5043 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combine 2 dataframes\n",
    "\n",
    "df_combined = pd.concat([df, df_lipinski], axis = 1)\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ce93cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pIC50(input):\n",
    "    pIC50 = []\n",
    "    \n",
    "    for i in input['standard_value_norm']:\n",
    "        molar = i*(10**-9) #convert nM to M\n",
    "        log = -np.log10(molar)\n",
    "        pIC50.append(log)\n",
    "        \n",
    "    input['pIC50'] = pIC50\n",
    "    x = input.drop('standard_value_norm', axis = 1)\n",
    "    return x\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "021f3f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_i</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>bioactivity_class</th>\n",
       "      <th>standard_value_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL133897</td>\n",
       "      <td>CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1</td>\n",
       "      <td>active</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "      <td>active</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL131588</td>\n",
       "      <td>CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL130628</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F</td>\n",
       "      <td>active</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL130478</td>\n",
       "      <td>CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C</td>\n",
       "      <td>active</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>CHEMBL4554172</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>CHEMBL4533844</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>7570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>CHEMBL4570655</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>CHEMBL4571704</td>\n",
       "      <td>COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>15500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>CHEMBL4556664</td>\n",
       "      <td>COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>68300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     molecule_chembl_i                                   canonical_smiles  \\\n",
       "0         CHEMBL133897              CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1   \n",
       "1         CHEMBL336398         O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1   \n",
       "2         CHEMBL131588  CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1   \n",
       "3         CHEMBL130628      O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F   \n",
       "4         CHEMBL130478          CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C   \n",
       "...                ...                                                ...   \n",
       "5038     CHEMBL4554172  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...   \n",
       "5039     CHEMBL4533844  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...   \n",
       "5040     CHEMBL4570655  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...   \n",
       "5041     CHEMBL4571704  COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...   \n",
       "5042     CHEMBL4556664  COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...   \n",
       "\n",
       "     bioactivity_class  standard_value_norm  \n",
       "0               active                750.0  \n",
       "1               active                100.0  \n",
       "2             inactive              50000.0  \n",
       "3               active                300.0  \n",
       "4               active                800.0  \n",
       "...                ...                  ...  \n",
       "5038          inactive              10000.0  \n",
       "5039      intermediate               7570.0  \n",
       "5040          inactive              10000.0  \n",
       "5041          inactive              15500.0  \n",
       "5042          inactive              68300.0  \n",
       "\n",
       "[5043 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns =['molecule_chembl_i','canonical_smiles','bioactivity_class','standard_value_norm']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9a36d17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45221cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.043000e+03\n",
       "mean     2.989356e+12\n",
       "std      1.147822e+14\n",
       "min      5.000000e-06\n",
       "25%      1.218000e+02\n",
       "50%      2.090000e+03\n",
       "75%      1.540000e+04\n",
       "max      5.888437e+15\n",
       "Name: standard_value, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a function to cap the value \n",
    "# Values greater than 100,000,000 will be fixed at 100,000,000 otherwise the negative logarithmic value will become negative\n",
    "df_combined.standard_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46916fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-np.log10( (10**-9)* 100000000 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8af141bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-np.log10( (10**-9)* 10000000000  ) #testing number >100000000: negative\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f26f405",
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_value(input):\n",
    "    norm = []\n",
    "    \n",
    "    for i in input['standard_value']:\n",
    "        if i>100000000:\n",
    "            i = 100000000\n",
    "        norm.append(i)\n",
    "        \n",
    "    input['standard_value_norm'] = norm\n",
    "    x = input.drop('standard_value', 1)\n",
    "    \n",
    "    return x\n",
    "    \n",
    "        \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed89aa45",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-d20ce80de4f0>:10: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  x = input.drop('standard_value', 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>class</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>standard_value_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL133897</td>\n",
       "      <td>CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1</td>\n",
       "      <td>active</td>\n",
       "      <td>312.325</td>\n",
       "      <td>2.80320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "      <td>active</td>\n",
       "      <td>376.913</td>\n",
       "      <td>4.55460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL131588</td>\n",
       "      <td>CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>426.851</td>\n",
       "      <td>5.35740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL130628</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F</td>\n",
       "      <td>active</td>\n",
       "      <td>404.845</td>\n",
       "      <td>4.70690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL130478</td>\n",
       "      <td>CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C</td>\n",
       "      <td>active</td>\n",
       "      <td>346.334</td>\n",
       "      <td>3.09530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>CHEMBL4554172</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>499.655</td>\n",
       "      <td>7.08374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>CHEMBL4533844</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>549.662</td>\n",
       "      <td>7.96344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>CHEMBL4570655</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>495.692</td>\n",
       "      <td>7.25306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>CHEMBL4571704</td>\n",
       "      <td>COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>576.510</td>\n",
       "      <td>4.06432</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>CHEMBL4556664</td>\n",
       "      <td>COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>558.539</td>\n",
       "      <td>6.27724</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     molecule_chembl_id                                   canonical_smiles  \\\n",
       "0          CHEMBL133897              CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1   \n",
       "1          CHEMBL336398         O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1   \n",
       "2          CHEMBL131588  CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1   \n",
       "3          CHEMBL130628      O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F   \n",
       "4          CHEMBL130478          CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C   \n",
       "...                 ...                                                ...   \n",
       "5038      CHEMBL4554172  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...   \n",
       "5039      CHEMBL4533844  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...   \n",
       "5040      CHEMBL4570655  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...   \n",
       "5041      CHEMBL4571704  COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...   \n",
       "5042      CHEMBL4556664  COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...   \n",
       "\n",
       "             class       MW     LogP  NumHDonors  NumHAcceptors  \\\n",
       "0           active  312.325  2.80320         0.0            6.0   \n",
       "1           active  376.913  4.55460         0.0            5.0   \n",
       "2         inactive  426.851  5.35740         0.0            5.0   \n",
       "3           active  404.845  4.70690         0.0            5.0   \n",
       "4           active  346.334  3.09530         0.0            6.0   \n",
       "...            ...      ...      ...         ...            ...   \n",
       "5038      inactive  499.655  7.08374         1.0            4.0   \n",
       "5039  intermediate  549.662  7.96344         1.0            4.0   \n",
       "5040      inactive  495.692  7.25306         1.0            4.0   \n",
       "5041      inactive  576.510  4.06432         6.0           11.0   \n",
       "5042      inactive  558.539  6.27724         5.0            9.0   \n",
       "\n",
       "      standard_value_norm  \n",
       "0                   750.0  \n",
       "1                   100.0  \n",
       "2                 50000.0  \n",
       "3                   300.0  \n",
       "4                   800.0  \n",
       "...                   ...  \n",
       "5038              10000.0  \n",
       "5039               7570.0  \n",
       "5040              10000.0  \n",
       "5041              15500.0  \n",
       "5042              68300.0  \n",
       "\n",
       "[5043 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#apply the norm_value() function so that the values in the standard_value column is normalized\n",
    "df_norm = norm_value(df_combined)\n",
    "df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fdaee341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.043000e+03\n",
       "mean     3.063436e+05\n",
       "std      4.553341e+06\n",
       "min      5.000000e-06\n",
       "25%      1.218000e+02\n",
       "50%      2.090000e+03\n",
       "75%      1.540000e+04\n",
       "max      1.000000e+08\n",
       "Name: standard_value_norm, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.standard_value_norm.describe() #making sure that the max is 100000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "97ebd8e0",
   "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>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>class</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL133897</td>\n",
       "      <td>CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1</td>\n",
       "      <td>active</td>\n",
       "      <td>312.325</td>\n",
       "      <td>2.80320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.124939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "      <td>active</td>\n",
       "      <td>376.913</td>\n",
       "      <td>4.55460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL131588</td>\n",
       "      <td>CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>426.851</td>\n",
       "      <td>5.35740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.301030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL130628</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F</td>\n",
       "      <td>active</td>\n",
       "      <td>404.845</td>\n",
       "      <td>4.70690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.522879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL130478</td>\n",
       "      <td>CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C</td>\n",
       "      <td>active</td>\n",
       "      <td>346.334</td>\n",
       "      <td>3.09530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.096910</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>5038</th>\n",
       "      <td>CHEMBL4554172</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>499.655</td>\n",
       "      <td>7.08374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>CHEMBL4533844</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>549.662</td>\n",
       "      <td>7.96344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.120904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>CHEMBL4570655</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>495.692</td>\n",
       "      <td>7.25306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>CHEMBL4571704</td>\n",
       "      <td>COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>576.510</td>\n",
       "      <td>4.06432</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.809668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>CHEMBL4556664</td>\n",
       "      <td>COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>558.539</td>\n",
       "      <td>6.27724</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.165579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     molecule_chembl_id                                   canonical_smiles  \\\n",
       "0          CHEMBL133897              CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1   \n",
       "1          CHEMBL336398         O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1   \n",
       "2          CHEMBL131588  CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1   \n",
       "3          CHEMBL130628      O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F   \n",
       "4          CHEMBL130478          CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C   \n",
       "...                 ...                                                ...   \n",
       "5038      CHEMBL4554172  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...   \n",
       "5039      CHEMBL4533844  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C(F)(F)F)CC3...   \n",
       "5040      CHEMBL4570655  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...   \n",
       "5041      CHEMBL4571704  COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...   \n",
       "5042      CHEMBL4556664  COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...   \n",
       "\n",
       "             class       MW     LogP  NumHDonors  NumHAcceptors     pIC50  \n",
       "0           active  312.325  2.80320         0.0            6.0  6.124939  \n",
       "1           active  376.913  4.55460         0.0            5.0  7.000000  \n",
       "2         inactive  426.851  5.35740         0.0            5.0  4.301030  \n",
       "3           active  404.845  4.70690         0.0            5.0  6.522879  \n",
       "4           active  346.334  3.09530         0.0            6.0  6.096910  \n",
       "...            ...      ...      ...         ...            ...       ...  \n",
       "5038      inactive  499.655  7.08374         1.0            4.0  5.000000  \n",
       "5039  intermediate  549.662  7.96344         1.0            4.0  5.120904  \n",
       "5040      inactive  495.692  7.25306         1.0            4.0  5.000000  \n",
       "5041      inactive  576.510  4.06432         6.0           11.0  4.809668  \n",
       "5042      inactive  558.539  6.27724         5.0            9.0  4.165579  \n",
       "\n",
       "[5043 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pIC50(df_norm)\n",
    "df_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45d817ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('acetylcholinesterase_04_bioactivity_data_3class_pIC50.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a6726c45",
   "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>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>class</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>NumHDonors</th>\n",
       "      <th>NumHAcceptors</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL133897</td>\n",
       "      <td>CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1</td>\n",
       "      <td>active</td>\n",
       "      <td>312.325</td>\n",
       "      <td>2.80320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.124939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "      <td>active</td>\n",
       "      <td>376.913</td>\n",
       "      <td>4.55460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL131588</td>\n",
       "      <td>CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>426.851</td>\n",
       "      <td>5.35740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.301030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL130628</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F</td>\n",
       "      <td>active</td>\n",
       "      <td>404.845</td>\n",
       "      <td>4.70690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.522879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL130478</td>\n",
       "      <td>CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C</td>\n",
       "      <td>active</td>\n",
       "      <td>346.334</td>\n",
       "      <td>3.09530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.096910</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>5036</th>\n",
       "      <td>CHEMBL4578266</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccc(F)cc4Cl)CC3)sc...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>534.100</td>\n",
       "      <td>7.73714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>CHEMBL4554172</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>499.655</td>\n",
       "      <td>7.08374</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>CHEMBL4570655</td>\n",
       "      <td>Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>495.692</td>\n",
       "      <td>7.25306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>CHEMBL4571704</td>\n",
       "      <td>COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>576.510</td>\n",
       "      <td>4.06432</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.809668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>CHEMBL4556664</td>\n",
       "      <td>COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>558.539</td>\n",
       "      <td>6.27724</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.165579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3809 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     molecule_chembl_id                                   canonical_smiles  \\\n",
       "0          CHEMBL133897              CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1   \n",
       "1          CHEMBL336398         O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1   \n",
       "2          CHEMBL131588  CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1   \n",
       "3          CHEMBL130628      O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F   \n",
       "4          CHEMBL130478          CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C   \n",
       "...                 ...                                                ...   \n",
       "5036      CHEMBL4578266  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccc(F)cc4Cl)CC3)sc...   \n",
       "5038      CHEMBL4554172  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4cccc(F)c4)CC3)sc2-...   \n",
       "5040      CHEMBL4570655  Cc1ccc(-c2nc(NC(=O)C3CCN(Cc4ccccc4C)CC3)sc2-c2...   \n",
       "5041      CHEMBL4571704  COc1cc(O)c2c(c1)C1=C(c3c(O)c(O)cc(C)c3-c3cc(OC...   \n",
       "5042      CHEMBL4556664  COc1cc(O)c(C(C)=O)c(-c2c(C)cc(O)c3oc4c(O)cc(C)...   \n",
       "\n",
       "         class       MW     LogP  NumHDonors  NumHAcceptors     pIC50  \n",
       "0       active  312.325  2.80320         0.0            6.0  6.124939  \n",
       "1       active  376.913  4.55460         0.0            5.0  7.000000  \n",
       "2     inactive  426.851  5.35740         0.0            5.0  4.301030  \n",
       "3       active  404.845  4.70690         0.0            5.0  6.522879  \n",
       "4       active  346.334  3.09530         0.0            6.0  6.096910  \n",
       "...        ...      ...      ...         ...            ...       ...  \n",
       "5036  inactive  534.100  7.73714         1.0            4.0  5.000000  \n",
       "5038  inactive  499.655  7.08374         1.0            4.0  5.000000  \n",
       "5040  inactive  495.692  7.25306         1.0            4.0  5.000000  \n",
       "5041  inactive  576.510  4.06432         6.0           11.0  4.809668  \n",
       "5042  inactive  558.539  6.27724         5.0            9.0  4.165579  \n",
       "\n",
       "[3809 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2class = df_final[df_final['class'] != 'intermediate'] #create new df that only contains active and inactive class\n",
    "df_2class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ef76af1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (0.11.1)\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from seaborn) (3.4.2)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from seaborn) (1.3.0)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from seaborn) (1.21.0)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from seaborn) (1.7.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from matplotlib>=2.2->seaborn) (8.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: six in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\ngkha\\anaconda3\\envs\\my-rdkit-env\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn\n",
    "import seaborn as sns\n",
    "sns.set_style(\"ticks\")\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ee2e9aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a graph to compare 2 classes\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "sns.countplot(x='class', data=df_2class, edgecolor='black')\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_bioactivity_class.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5550ca13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x468 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot of the MW (molecular weight) vs the logP (the solubility)\n",
    "\n",
    "plt.figure(figsize=(6.5, 6.5))\n",
    "\n",
    "sns.scatterplot(x='MW', y='LogP', data=df_2class, hue='class', size='pIC50', edgecolor='black', alpha=0.7)\n",
    "plt.xlabel('MW', fontsize = 14, fontweight='bold')\n",
    "plt.ylabel('LogP', fontsize = 14, fontweight='bold')\n",
    "\n",
    "\n",
    "plt.savefig('plot_MW_vs_LogP.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "66b55830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create box plot bioactivity class vs pIC50 value\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.boxplot(x='class', y='pIC50', data=df_2class)\n",
    "plt.xlabel('bioactivity_class', fontsize = 14, fontweight='bold')\n",
    "plt.ylabel('pIC50', fontsize = 14, fontweight='bold')\n",
    "plt.savefig('plot_ic50.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2fb80178",
   "metadata": {},
   "outputs": [],
   "source": [
    "#performing Mann-Whitney U Test in order to look at the difference between 2 class\n",
    "#this test is to determine wheter inactive and active classes are different\n",
    "\n",
    "def mannwhitney(descriptor, verbose=False):\n",
    "  # https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/\n",
    "    from numpy.random import seed\n",
    "    from numpy.random import randn\n",
    "    from scipy.stats import mannwhitneyu\n",
    "\n",
    "# seed the random number generator\n",
    "    seed(1)\n",
    "\n",
    "# actives and inactives\n",
    "    selection = [descriptor, 'class']\n",
    "    df = df_2class[selection]\n",
    "    active = df[df['class'] == 'active']\n",
    "    active = active[descriptor]\n",
    "    \n",
    "    selection = [descriptor, 'class']\n",
    "    df = df_2class[selection]\n",
    "    inactive = df[df['class'] == 'inactive']\n",
    "    inactive = inactive[descriptor]\n",
    "    \n",
    "#compare two samples\n",
    "    stat, p = mannwhitneyu(active, inactive)\n",
    "    \n",
    "#print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "\n",
    "# interpret\n",
    "    alpha = 0.05\n",
    "    if p > alpha:\n",
    "        interpretation = 'Fail to reject H0: same distribution'\n",
    "    else:\n",
    "        interpretation = 'Reject H0: different distribution'\n",
    "  \n",
    "    results = pd.DataFrame({'Descriptor':descriptor,\n",
    "                          'Statistics':stat,\n",
    "                          'p':p,\n",
    "                          'alpha':alpha,\n",
    "                          'Interpretation':interpretation}, index=[0])\n",
    "    filename = 'mannwhitneyu_' + descriptor + '.csv'\n",
    "    results.to_csv(filename)\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d59e1844",
   "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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pIC50</td>\n",
       "      <td>3530710.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Reject H0: different distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics    p  alpha                     Interpretation\n",
       "0      pIC50   3530710.0  0.0   0.05  Reject H0: different distribution"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('pIC50')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ca9362c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#apply mannwhitney for the remaining descriptors\n",
    "#MW\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "sns.boxplot(x='class', y='MW', data=df_2class)\n",
    "plt.xlabel('bioactivity_class', fontsize = 14, fontweight='bold')\n",
    "plt.ylabel('MW', fontsize = 14, fontweight='bold')\n",
    "plt.savefig('plot_MW.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f1d5d8f",
   "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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MW</td>\n",
       "      <td>2300261.5</td>\n",
       "      <td>1.870916e-57</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Reject H0: different distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics             p  alpha  \\\n",
       "0         MW   2300261.5  1.870916e-57   0.05   \n",
       "\n",
       "                      Interpretation  \n",
       "0  Reject H0: different distribution  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('MW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "83db9cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LogP\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'LogP', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_LogP.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4a2c750b",
   "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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogP</td>\n",
       "      <td>2252267.5</td>\n",
       "      <td>6.489680e-48</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Reject H0: different distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics             p  alpha  \\\n",
       "0       LogP   2252267.5  6.489680e-48   0.05   \n",
       "\n",
       "                      Interpretation  \n",
       "0  Reject H0: different distribution  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('LogP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ecb04c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8RUVFeT0YAMD7qiyB5cuXV5geP368MjIytGzZMq+GAgD4hsf3EwAA1D6UAAAYrMrdQV9++WWF6bNnz17xcUnq0KFDNcYCAPhClSXwwAMPVDo7yLIsJSQkVHjM4XBo37591Z8OAOBVVZYA5/4DQO1WZQm8/vrrvsoBALDBNd1o/vvvv9fBgwdVXFxc4XGHw6HBgwdXazAAgPd5XAKvvvqqFixYUOE+w5ejBADgxuNxCSxZskRlZWXq1auXbr75ZjmdnF0KADc6j0vAsix169ZNK1eu9GYeAIAPefxxfuzYsTpw4IA+/fTTq+4SAgDcWDweCfTt21evvPKK+8qiAQEB7uccDoe++OILb+QDAHiRxyUwZcoUnTt3TtKlXUNFRUVeCwUA8A2PS+DIkSO6/fbblZqaqiZNmnDbSQCoBTw+JjBgwABJUuPGjSkAAKglPB4JtG7dWlu2bNGQIUPUuXNn1a1b1/2cw+HQrFmzvBIQAOA9HpfAokWLJEmHDx/W4cOHKzx3vSWQm5uruLg4rVixQq1bt/7ZywEAXBuPS2Du3Lle2Q1UUlKimTNnql69etW+bABA1Twugbi4OK8EmD9/vkaMGKGlS5d6ZfkAgKvzuARGjx591eccDodSU1OveeXp6elq1KiR+vTpc9USSEtLU1pamiQpPz//mtcBALg6j0tg165dV33u5+4mWr16tRwOhz7++GPt379fU6dO1eLFi9W4cWP3PAkJCe6b2HhrNAIApvK4BMo/jUuXvixWXFys7OxsLV++XElJST9r5atWrXL/96hRo5SYmFihAAAA3uVxCXTs2LHSY927d9c333yjRYsWKSoqqlqDAQC8z+MSyM3NrTBdVlamkydPKjs7WydOnLjuINzFDAB8z+MSiIqKuuK+//JLTAMAbjwel8Ctt95a6bF69erprrvu0pQpU6o1FADANzwugYyMDG/mAADYgHtEAoDBqhwJ3HXXXR4txOFwaN++fdUSCADgO1WWQNOmTa/63OnTp1VcXFztgQAAvlNlCVzpOMDx48f1xz/+Ue+++64kqUWLFnrmmWe8kw4A4FUeHxi+ePGiXnnlFa1cuVKFhYWqX7++HnvsMY0ZM6bC/YYBADcOj0pgzZo1+vOf/6yTJ0/K6XQqPj5ekyZNUqNGjbydDwDgRVWWwO7duzVv3jzt379fktSzZ09Nnz5dd955p0/CAQC8q8oSGDVqlPtbwk6nU//5z380bty4SvM5HA5t2bLFOwkBAF7zk7uDLMuSJJWWluro0aNeDwQA8J0qS+DAgQO+ygEAsAHfGAYAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBglAAAGIwSAACDUQIAYDBKAAAMRgkAgMEoAQAwGCUAAAajBADAYJQAABiMEgAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBg/nauvKSkRNOnT9fRo0dVXFyscePGacCAAXZGAgCj2FoC69evV0hIiF566SXl5+dr+PDhlAAA+JCtJRATE6Po6Gj3tJ+fn1fXl5ycrJycHK+u40ZR/nOYOHGizUlqhoiICD355JO2ZmD7/BHbZ0Xe3D5tLYH69etLkgoKCjRhwgRNmjSp0jxpaWlKS0uTJOXn51/X+nJycvTFnv0qDWp0XcupDRyll371Wd+dsDmJ/fwu5NkdQdKl7fPg3s/VIrjU7ii2a2g5JElF339qcxL7/VDg3Q/HtpaAJP3f//2fxo8fr5EjR2rYsGGVnk9ISFBCQoIkKS4u7rrXVxrUSBfbxF73clB7BB7YaHcEtxbBpZre5azdMVCDzP2soVeXb2sJnD59WmPHjtXMmTPVq1cvO6MAgJFsPUU0JSVFZ8+e1d/+9jeNGjVKo0aNUmFhoZ2RAMAoto4EZsyYoRkzZtgZAQCMxpfFAMBglAAAGIwSAACDUQIAYDBKAAAMRgkAgMEoAQAwGCUAAAajBADAYJQAABiMEgAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADCYv90BfCkvL09+F3IVeGCj3VFQg/hdyFVeXoDdMZSXl6fT5/w097OGdkdBDfL9OT/dlJfnteUzEgAAgxk1EmjUqJEOnSnRxTaxdkdBDRJ4YKMaNWpkdww1atRI9c99p+ldztodBTXI3M8aqq4Xt09GAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBglAAAGIwSAACDUQIAYDBKAAAMRgkAgMEoAQAwGCUAAAajBADAYJQAABiMEgAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMH87V15WVqbExER9/fXXqlOnjubMmaPbb7/dzkgAYBRbRwJbtmxRcXGx0tLS9PTTT+vFF1+0Mw4AGMfWkUBWVpb69OkjSerUqZP27Nnj9XX6XchT4IGNXl9PVRwlF+UsuWBrhpqkLCBIVkCgbev3u5AnqYlt67/cDwV+mvtZQ9vW//+KHTpTxF7iciF1y/SLOpatGX4o8NMdXly+rSVQUFCg4OBg97Sfn59cLpf8/X+MlZaWprS0NElSfn7+da0vIiLiul5fXfLy8pSX57I7Ro3RqFFDNWrUyMYETWrEtlETMvjn5cmZl2d3jBrDv1Ej1bV125TukHe3DVtLIDg4WOfPn3dPl5WVVSgASUpISFBCQoIkKS4u7rrW9+STT17X6wFvYvuEHWwd93Xp0kXbtm2TJH3xxRe688477YwDAMaxdSQwaNAgffTRRxoxYoQsy9LcuXPtjAMAxrG1BJxOp/73f//XzggAYDROAwAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMFsvG3Gtjh49et1XEsWP8vPzFRoaancM4IrYPqvP0aNHr/qcw7Ise++YANvExcUpPT3d7hjAFbF9+ga7gwDAYJQAABiMEjBY+R3bgJqI7dM3OCYAAAZjJAAABqMEDPP1119r9+7dkqTJkyeruLjY5kSojbZt26a0tLTrXs7u3bt14MABSdITTzxx3ctDZewOMkxycrJuuukmPfjgg3ZHAX7StGnTFBsbq759+9odpda6ob4shqsrKCjQs88+q3Pnzik/P1/x8fFq166dXnjhBVmWpSZNmui5557TmjVrFBAQoHbt2mnSpElav369hg8frnXr1ikoKEjLli2Tv7+/oqOj9dxzz6moqEh169bV888/r6ZNm9r9NnGDSE9P1/bt23Xs2DHdcsst+s9//qP27dtr9uzZOn78uBITE1VUVKQzZ85o/PjxGjhwoN5//3395S9/kSS1bdtWCQkJ2r59u/bu3auIiAjFx8drw4YN+v3vf6+NGzfK4XBo9uzZuvfee9WiRQvNmTNHkhQSEqK5c+eqQYMGdv4IbhwWaoU9e/ZYmzdvtizLso4fP24NGjTIGjZsmJWTk2NZlmW98cYb1p49e6xFixZZb775pmVZltWvXz+rsLDQWrBggbVmzRrLsixr+PDhVl5enjVx4kTrgw8+sCzLsnbs2GE99dRTvn9TuGGtXr3amjRpktW9e3fr3Llzlsvlsn71q19ZJ0+etD766CPrk08+sSzLsrKysqyHHnrIKikpsfr162edPn3asizLSk5Oto4ePWpNnTrVyszMtCzLsu69917Lsixr4sSJ1q5du6yioiIrNjbWKikpseLj462DBw9almVZ//znP60//elPNrzrGxMjgVripptuUmpqqv79738rODhYLpdLubm5at26tSTp97//vSQpIyOj0mvj4+OVmJioVq1aKTw8XKGhofrmm2+0ZMkSLVu2TJZlKSAgwKfvB7VDixYtFBwcLElq3LixioqK1LhxYy1evFhvv/22HA6HXC6X8vPz1bBhQ4WFhUmqev//Aw88oDVr1ujUqVPq37+//P399e2332r27NmSpJKSErVs2dL7b66WoARqiRUrVqhTp04aOXKkPvnkE2VmZurmm2/W4cOHFR4erqVLl6ply5ZyOBwqKyur8Nrw8HBZlqVly5a5jxW0atVKY8eOVZcuXfTtt9+6DyYD18LhcFR6bOHChYqPj9cvf/lLrV69WmvWrFFYWJjOnj2rM2fOKCQkRHPmzNF9990nh8Mh678OW/bq1UsvvfSSTpw4oZkzZ0qSWrZsqfnz5+vWW29VVlaWTp065ZP3VxtQArVEv379lJiYqA0bNigkJER+fn5KTEzU9OnT5XQ61bhxYz300EMKCAjQggUL3COEcr/73e+0cOFC9ezZU5I0depU937bwsJCPfvss3a8LdRCMTExeuGFF7RkyRI1bdpU+fn5cjqdmjVrlh599FE5nU61bdtW7du31759+/THP/5Rt912m/v1DodD0dHR2rFjh26//XZJUmJioqZOnarS0lJJ0gsvvGDLe7sRcXYQABiM7wkAgMEoAQAwGCUAAAajBADAYJQAABiMEoDPRUZGVvinbdu2ioqK0ty5c+Vyudzz9e/fX5GRkT495/u9997Td999J0k6cuSIIiMjFRMT49Frk5OTFRkZqaVLl7ofO3DggDIzM72SNT09XZGRke5z5YGfgxKALZxOpwYMGKABAwaoT58+crlcSk1N1csvv+yeJyoqSgMGDFDdunV9kmn27Nl64okndPbsWUlSYGCgBgwYoHvvvdej17dq1UoDBgxQeHi4JOmdd97Rb3/7W3399dfeigxcN74sBlv4+/vrb3/7m3v6yy+/VHx8vDIyMjRlyhRJ0vPPP+/TTDk5ORWmw8LCKmT8KUOGDNGQIUPc04cOHaowsgFqIkYCqBHK72vQrFkz92P/vTuosLBQ8+bNU+/evdW+fXs98MAD2rlzp3t+l8ulF154Qb1799bdd9+t/v37a9myZe7nLcvS0qVL1a9fP3Xo0EHDhg3Tli1bJF26ZPGuXbskXbqtYXJycoXdQRcvXlTnzp3VsWNHXbhwwb3MhIQERUZG6uuvv66wOyg9Pd19RcykpCSNGjVKM2bMUGRkpFatWuV+/fLlyxUZGVlhBHS5b7/9Vg8//LA6duyonj17atq0aTp37twV5/2p95+dna2RI0eqS5cu6tatmx555BF9//337ufXrl2r2NhYdejQQVFRUXruuedUWFhYxW8NtQElAFu4XC49/vjjevzxx/Xwww/r0UcfVVhYmJ5++umrvubJJ5/Uq6++Kj8/P3Xs2FFfffWVxo4dq6ysLEnSsmXL9Nprr8nf31/du3fXqVOn9NJLL2nv3r2SpDfffFNJSUk6f/687rnnHh0+fFgTJ07U3r171bZtW4WEhEiSunXrplatWlVYd2BgoKKjo1VYWKgPPvhAknTs2DFlZ2e7j21crmnTpu6LmEVERKhLly4aOnSoJLmLR/rxgn6xsbGV3m9hYaEeeeQRffjhh4qIiFBoaKjWrFmj6dOnX/HnU9X7Lysr07hx45Sdna327dvr9ttv1/bt290Xajtw4ICmTZumU6dOqUePHgoMDNQ///lPvfTSS1f9faB2oARgi7KyMm3dulVbt27Vhx9+qIKCAjkcDv3www9XnD8rK0vbtm1TeHi4/vWvf+mNN95QYmKiXC6XFi1aJEnq2LGjHnvsMW3YsEErVqxw36i8/NPukiVL5HQ69fe//10rV67U888/r6ioKJ0+fVqjR4/WnXfeKUmaMmVKhd065X7zm99IkjZt2uT+t2VZuu+++yrN26tXL/cyfvOb32jy5Mnq3r27mjRpot27d+vs2bPKz8/X559/rtatW7vXfbkNGzbo2LFjGjp0qFavXq1169apd+/eCgkJUUlJSaX5q3r/58+fV25urlq1aqWkpCS9/fbbmjlzpiZMmCCXy6WjR4/KsiwNGDBAycnJSk9P19SpUzV48OAqfouoDTgmAFvUqVNHX331lSTp4sWL2r59uyZPnqynnnpKW7duVZMmTSrMn52dLUkaNGiQgoKCJF364zpz5kz3c7169VJhYaGSkpKUlZWlb775RpJUVFSkgoICnThxQk2bNnVfPO/+++/X/fff73HmHj166JZbblFmZqYuXLigf/3rX3I6nRo2bJhHr3c6nYqNjdXKlSv1wQcfqLS0VKWlpVccBUg/HqPo1auXpEs/s+XLl191+VW9/wYNGuj+++/X2rVr1adPH7Vp00Z9+/Z1X4q5R48euvPOO7VmzRpt3LhRnTt3Vv/+/dWuXTuPfz64MTESgO0CAwP161//WnfccYdKSkr02WefVZrH6bz6plp+ueKkpCQ99thjys3N1WOPPea+h8Ll10i8/ECty+W6pgO35X/wCwsL9dZbb+nLL790f7r31OW7hKraFXR57vIrY0qqch/9T73/F198UStWrNCIESNUXFyslJQUDR06VMePH1dwcLDeeustLViwQIMGDdJ3332nuXPnupeB2osSQI1w8eJF9wHghg0bVnr+rrvuknTpPP7yA7Pr1q2TJHXq1EmS9MYbb8jhcGjevHkaMmSIjhw54n59cHCwbr75Zp0+fdp94/I1a9aoU6dO7gO45WXy3/dbuFz5yCE5OVmSrrgrqNyVlnf33XerZcuW2r59uz788EO1adOm0vGHchEREZKkjz76SNKlm6UMGTJEvXv31pkzZyrNX9X7P3TokGbMmKGPP/5Ys2bN0rvvvqvBgweroKBA2dnZyszMdF92PCkpSZmZmWrcuLH27t2r/Pz8q75H3PjYHQRblB8YLv/vb775RqdPn1azZs3UrVu3SvN369ZN3bt3165duzR48GA1b95cWVlZCggI0IQJEyRJt956q3JychQfH686deq4/9iXl8bDDz+sefPmadSoUbr77ruVlZUly7LcNzEPDQ2VJM2aNUvDhw/Xr3/960o5IiIi1K5dO+3du1d169ZVdHT0Vd9j+fJWrVqlQ4cOaf78+ZIujQbKS+RqowBJGjZsmJKTk7V582bFxcWpsLBQR44cUb9+/dwHsS9X1ftv3LixMjMzderUKX3yyScKCgpSVlaW6tWrp7vvvlunT5/W5s2b9e9//1urV6/W2bNnderUKfcBadRejARgi8sPDG/btk1nzpxR9+7dtXTpUtWpU6fS/E6nUykpKRo9erTKysqUnZ2tDh06aOXKlercubMkad68eYqMjNSxY8ckyV0yn3/+uSRp9OjRmjhxogIDA5WVlaXw8HC9/PLL6tChgyRpzJgxat68uX744QcVFBRcNXv5AeL+/fu7b514JTExMerSpYvOnDmjEydOuB+//KBzVQdeAwMDtXz5cvXs2VM5OTnKz8/X8OHD3WXy36p6/8HBwUpNTVW/fv30ww8/aM+ePWrfvr1SUlLUrFkzdezYUX/961/Vpk0bZWdn69ixYxo4cKAWL1581XyoHbipDOBjW7Zs0fjx49WuXTulp6fbHQeGY3cQ4CP79u3T4sWL9fHHH0uSRowYYXMigN1BgM/UqVNHH374ofz8/DR69Gj97ne/szsSwO4gADAZIwEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgsP8PsuTU3EqSGg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NumHDonors\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'NumHDonors', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('NumHDonors', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_NumHDonors.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "71c9b0ce",
   "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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NumHDonors</td>\n",
       "      <td>2016978.0</td>\n",
       "      <td>3.259256e-15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Reject H0: different distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Descriptor  Statistics             p  alpha  \\\n",
       "0  NumHDonors   2016978.0  3.259256e-15   0.05   \n",
       "\n",
       "                      Interpretation  \n",
       "0  Reject H0: different distribution  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHDonors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5a9a456b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NumHAcceptors\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'NumHAcceptors', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('NumHAcceptors', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_NumHAcceptors.pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad52a159",
   "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>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NumHAcceptors</td>\n",
       "      <td>1905933.0</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Reject H0: different distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Descriptor  Statistics        p  alpha  \\\n",
       "0  NumHAcceptors   1905933.0  0.00002   0.05   \n",
       "\n",
       "                      Interpretation  \n",
       "0  Reject H0: different distribution  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHAcceptors')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}