Part2_QSAR_Drug_Discovery.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Descriptor Calculations and Exploratory Data Analysis\n",
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n",
      "Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\n",
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.8.3\n",
      "  latest version: 4.9.1\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - rdkit\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    boost-1.70.0               |   py38hbf1eeb5_1         347 KB  conda-forge\n",
      "    boost-cpp-1.70.0           |       hd59e818_1        19.0 MB  conda-forge\n",
      "    cairo-1.16.0               |    h0ab9d94_1001         1.3 MB  conda-forge\n",
      "    conda-4.9.0                |   py38h5347e94_1         3.0 MB  conda-forge\n",
      "    fontconfig-2.13.1          |    h1027ab8_1000         269 KB  conda-forge\n",
      "    pixman-0.38.0              |    h01d97ff_1003         611 KB  conda-forge\n",
      "    pycairo-1.20.0             |   py38h26abc9d_1          70 KB  conda-forge\n",
      "    python_abi-3.8             |           1_cp38           4 KB  conda-forge\n",
      "    rdkit-2019.09.3            |   py38h3471270_0        22.4 MB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        47.0 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  boost              conda-forge/osx-64::boost-1.70.0-py38hbf1eeb5_1\n",
      "  boost-cpp          conda-forge/osx-64::boost-cpp-1.70.0-hd59e818_1\n",
      "  cairo              conda-forge/osx-64::cairo-1.16.0-h0ab9d94_1001\n",
      "  fontconfig         conda-forge/osx-64::fontconfig-2.13.1-h1027ab8_1000\n",
      "  pixman             conda-forge/osx-64::pixman-0.38.0-h01d97ff_1003\n",
      "  pycairo            conda-forge/osx-64::pycairo-1.20.0-py38h26abc9d_1\n",
      "  python_abi         conda-forge/osx-64::python_abi-3.8-1_cp38\n",
      "  rdkit              conda-forge/osx-64::rdkit-2019.09.3-py38h3471270_0\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  conda                       pkgs/main::conda-4.8.3-py38_0 --> conda-forge::conda-4.9.0-py38h5347e94_1\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "boost-1.70.0         | 347 KB    | ##################################### | 100% \n",
      "pixman-0.38.0        | 611 KB    | ##################################### | 100% \n",
      "python_abi-3.8       | 4 KB      | ##################################### | 100% \n",
      "boost-cpp-1.70.0     | 19.0 MB   | ##################################### | 100% \n",
      "pycairo-1.20.0       | 70 KB     | ##################################### | 100% \n",
      "conda-4.9.0          | 3.0 MB    | ##################################### | 100% \n",
      "cairo-1.16.0         | 1.3 MB    | ##################################### | 100% \n",
      "fontconfig-2.13.1    | 269 KB    | ##################################### | 100% \n",
      "rdkit-2019.09.3      | 22.4 MB   | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge rdkit"
   ]
  },
  {
   "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>molecule_chembl_id</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>class</th>\n",
       "      <th>canonical_smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL133897</td>\n",
       "      <td>750.0</td>\n",
       "      <td>active</td>\n",
       "      <td>CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>100.0</td>\n",
       "      <td>active</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL131588</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL130628</td>\n",
       "      <td>300.0</td>\n",
       "      <td>active</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL130478</td>\n",
       "      <td>800.0</td>\n",
       "      <td>active</td>\n",
       "      <td>CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C</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>4690</th>\n",
       "      <td>CHEMBL4293155</td>\n",
       "      <td>2440.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>CHEMBL4282558</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>CHEMBL4281727</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4693</th>\n",
       "      <td>CHEMBL4292349</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4694</th>\n",
       "      <td>CHEMBL4278260</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4695 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     molecule_chembl_id  standard_value         class  \\\n",
       "0          CHEMBL133897           750.0        active   \n",
       "1          CHEMBL336398           100.0        active   \n",
       "2          CHEMBL131588         50000.0      inactive   \n",
       "3          CHEMBL130628           300.0        active   \n",
       "4          CHEMBL130478           800.0        active   \n",
       "...                 ...             ...           ...   \n",
       "4690      CHEMBL4293155          2440.0  intermediate   \n",
       "4691      CHEMBL4282558          2540.0  intermediate   \n",
       "4692      CHEMBL4281727          3810.0  intermediate   \n",
       "4693      CHEMBL4292349          3460.0  intermediate   \n",
       "4694      CHEMBL4278260          2780.0  intermediate   \n",
       "\n",
       "                                       canonical_smiles  \n",
       "0                 CCOc1nn(-c2cccc(OCc3ccccc3)c2)c(=O)o1  \n",
       "1            O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1  \n",
       "2     CN(C(=O)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F)c1ccccc1  \n",
       "3         O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC(F)(F)F  \n",
       "4             CSc1nc(-c2ccc(OC(F)(F)F)cc2)nn1C(=O)N(C)C  \n",
       "...                                                 ...  \n",
       "4690  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...  \n",
       "4691  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...  \n",
       "4692  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...  \n",
       "4693  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...  \n",
       "4694  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...  \n",
       "\n",
       "[4695 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('acetylcholinesterase_bioactivity_data_curated.csv')\n",
    "df_no_smiles = df.drop(columns='canonical_smiles')\n",
    "smiles = []\n",
    "\n",
    "for i in df.canonical_smiles.tolist():\n",
    "  cpd = str(i).split('.')\n",
    "  cpd_longest = max(cpd, key = len)\n",
    "  smiles.append(cpd_longest)\n",
    "\n",
    "smiles = pd.Series(smiles, name = 'canonical_smiles')\n",
    "df_clean_smiles = pd.concat([df_no_smiles,smiles], axis=1)\n",
    "df_clean_smiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Lipinski descriptors\n",
    "Christopher Lipinski from Pfizer came up with a back of the envelope method for evaluating the druglikeness of compounds. Druglikeness is based on Absorption, Distribution, Metabolism, and Excretion (ADME) - pharmokinetic profile. Based on FDA approved orally adminstered drugs.\n",
    "\n",
    "Tells us the relative drug-likeness of the compound: whether it can be absorbed into th ebody, distributed to the proper tissues, become metabloized, and eventually be excreted (global features of the molecules)\n",
    "\n",
    "### Lipinski Rule\n",
    "    Molecular Weight < 500 Daltons\n",
    "    Octanol-water partition coefficient (LogP) < 5\n",
    "    Hydrogen bond donors < 5\n",
    "    Hydrogen bond acceptors < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors, Lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation\n",
    "def lipinski(smiles, verbose=False):\n",
    "\n",
    "    moldata= []\n",
    "    for elem in smiles:\n",
    "        mol=Chem.MolFromSmiles(elem) \n",
    "        moldata.append(mol)\n",
    "       \n",
    "    baseData= np.arange(1,1)\n",
    "    i=0  \n",
    "    for mol in moldata:        \n",
    "       \n",
    "        desc_MolWt = Descriptors.MolWt(mol)\n",
    "        desc_MolLogP = Descriptors.MolLogP(mol)\n",
    "        desc_NumHDonors = Lipinski.NumHDonors(mol)\n",
    "        desc_NumHAcceptors = Lipinski.NumHAcceptors(mol)\n",
    "           \n",
    "        row = np.array([desc_MolWt,\n",
    "                        desc_MolLogP,\n",
    "                        desc_NumHDonors,\n",
    "                        desc_NumHAcceptors])   \n",
    "    \n",
    "        if(i==0):\n",
    "            baseData=row\n",
    "        else:\n",
    "            baseData=np.vstack([baseData, row])\n",
    "        i=i+1      \n",
    "    \n",
    "    columnNames=[\"MW\",\"LogP\",\"NumHDonors\",\"NumHAcceptors\"]   \n",
    "    descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n",
    "    \n",
    "    return descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>555.601</td>\n",
       "      <td>7.18140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4693</th>\n",
       "      <td>521.702</td>\n",
       "      <td>6.32710</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4694</th>\n",
       "      <td>501.715</td>\n",
       "      <td>6.29058</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4695 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",
       "4690  511.150  7.07230         2.0            3.0\n",
       "4691  511.150  7.07230         2.0            3.0\n",
       "4692  555.601  7.18140         2.0            3.0\n",
       "4693  521.702  6.32710         2.0            5.0\n",
       "4694  501.715  6.29058         2.0            4.0\n",
       "\n",
       "[4695 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lipinski = lipinski(df_clean_smiles.canonical_smiles)\n",
    "df_lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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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>standard_value</th>\n",
       "      <th>class</th>\n",
       "      <th>MW</th>\n",
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       "      <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>750.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL336398</td>\n",
       "      <td>O=C(N1CCCCC1)n1nc(-c2ccc(Cl)cc2)nc1SCC1CC1</td>\n",
       "      <td>100.0</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",
       "    </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>50000.0</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",
       "    </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>300.0</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",
       "    </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>800.0</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",
       "    </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>4690</th>\n",
       "      <td>CHEMBL4293155</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...</td>\n",
       "      <td>2440.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>CHEMBL4282558</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>CHEMBL4281727</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>555.601</td>\n",
       "      <td>7.18140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4693</th>\n",
       "      <td>CHEMBL4292349</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>521.702</td>\n",
       "      <td>6.32710</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4694</th>\n",
       "      <td>CHEMBL4278260</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>501.715</td>\n",
       "      <td>6.29058</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4695 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",
       "4690      CHEMBL4293155  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...   \n",
       "4691      CHEMBL4282558  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...   \n",
       "4692      CHEMBL4281727  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...   \n",
       "4693      CHEMBL4292349  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...   \n",
       "4694      CHEMBL4278260  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...   \n",
       "\n",
       "      standard_value         class       MW     LogP  NumHDonors  \\\n",
       "0              750.0        active  312.325  2.80320         0.0   \n",
       "1              100.0        active  376.913  4.55460         0.0   \n",
       "2            50000.0      inactive  426.851  5.35740         0.0   \n",
       "3              300.0        active  404.845  4.70690         0.0   \n",
       "4              800.0        active  346.334  3.09530         0.0   \n",
       "...              ...           ...      ...      ...         ...   \n",
       "4690          2440.0  intermediate  511.150  7.07230         2.0   \n",
       "4691          2540.0  intermediate  511.150  7.07230         2.0   \n",
       "4692          3810.0  intermediate  555.601  7.18140         2.0   \n",
       "4693          3460.0  intermediate  521.702  6.32710         2.0   \n",
       "4694          2780.0  intermediate  501.715  6.29058         2.0   \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",
       "4690            3.0  \n",
       "4691            3.0  \n",
       "4692            3.0  \n",
       "4693            5.0  \n",
       "4694            4.0  \n",
       "\n",
       "[4695 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combine standard value with lipinksi descriptors\n",
    "df_combined = pd.concat([df,df_lipinski], axis=1) \n",
    "df_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IC50 to pIC50 = -log(IC50)\n",
    "Function to\n",
    "\n",
    "    Take the IC50 values from the standard_value column and converts it from nM to M by multiplying the value by 10^-9\n",
    "    \n",
    "    Take the molar value and apply -log10\n",
    "    \n",
    "    Delete the standard_value column and create a new pIC50 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/chaninlab/estrogen-receptor-alpha-qsar/blob/master/02_ER_alpha_RO5.ipynb\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def pIC50(input):\n",
    "    pIC50 = []\n",
    "\n",
    "    for i in input['standard_value_norm']:\n",
    "        molar = i*(10**-9) # Converts nM to M\n",
    "        pIC50.append(-np.log10(molar))\n",
    "\n",
    "    input['pIC50'] = pIC50\n",
    "    x = input.drop('standard_value_norm', 1)\n",
    "        \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.695000e+03\n",
       "mean     3.210931e+12\n",
       "std      1.189580e+14\n",
       "min      6.000000e-03\n",
       "25%      1.250000e+02\n",
       "50%      2.246000e+03\n",
       "75%      1.753000e+04\n",
       "max      5.888437e+15\n",
       "Name: standard_value, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.standard_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Values greater than 100,000,000 will be fixed at 100,000,000 otherwise the negative logarithmic value will become negative.\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>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>4690</th>\n",
       "      <td>CHEMBL4293155</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>CHEMBL4282558</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>CHEMBL4281727</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>555.601</td>\n",
       "      <td>7.18140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4693</th>\n",
       "      <td>CHEMBL4292349</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>521.702</td>\n",
       "      <td>6.32710</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4694</th>\n",
       "      <td>CHEMBL4278260</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>501.715</td>\n",
       "      <td>6.29058</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2780.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4695 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",
       "4690      CHEMBL4293155  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...   \n",
       "4691      CHEMBL4282558  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...   \n",
       "4692      CHEMBL4281727  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...   \n",
       "4693      CHEMBL4292349  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...   \n",
       "4694      CHEMBL4278260  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...   \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",
       "4690  intermediate  511.150  7.07230         2.0            3.0   \n",
       "4691  intermediate  511.150  7.07230         2.0            3.0   \n",
       "4692  intermediate  555.601  7.18140         2.0            3.0   \n",
       "4693  intermediate  521.702  6.32710         2.0            5.0   \n",
       "4694  intermediate  501.715  6.29058         2.0            4.0   \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",
       "4690               2440.0  \n",
       "4691               2540.0  \n",
       "4692               3810.0  \n",
       "4693               3460.0  \n",
       "4694               2780.0  \n",
       "\n",
       "[4695 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm = norm_value(df_combined)\n",
    "df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.695000e+03\n",
       "mean     3.276494e+05\n",
       "std      4.718369e+06\n",
       "min      6.000000e-03\n",
       "25%      1.250000e+02\n",
       "50%      2.246000e+03\n",
       "75%      1.753000e+04\n",
       "max      1.000000e+08\n",
       "Name: standard_value_norm, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.standard_value_norm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>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>4690</th>\n",
       "      <td>CHEMBL4293155</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.612610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>CHEMBL4282558</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>511.150</td>\n",
       "      <td>7.07230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.595166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>CHEMBL4281727</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>555.601</td>\n",
       "      <td>7.18140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.419075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4693</th>\n",
       "      <td>CHEMBL4292349</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>521.702</td>\n",
       "      <td>6.32710</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.460924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4694</th>\n",
       "      <td>CHEMBL4278260</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>501.715</td>\n",
       "      <td>6.29058</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.555955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4695 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",
       "4690      CHEMBL4293155  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3Cl)CC...   \n",
       "4691      CHEMBL4282558  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc(Cl)c3)...   \n",
       "4692      CHEMBL4281727  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(Br)cc3)...   \n",
       "4693      CHEMBL4292349  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3cccc([N+](=...   \n",
       "4694      CHEMBL4278260  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(C#N)cc3...   \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",
       "4690  intermediate  511.150  7.07230         2.0            3.0  5.612610  \n",
       "4691  intermediate  511.150  7.07230         2.0            3.0  5.595166  \n",
       "4692  intermediate  555.601  7.18140         2.0            3.0  5.419075  \n",
       "4693  intermediate  521.702  6.32710         2.0            5.0  5.460924  \n",
       "4694  intermediate  501.715  6.29058         2.0            4.0  5.555955  \n",
       "\n",
       "[4695 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pIC50(df_norm)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4695.000000\n",
       "mean        5.820864\n",
       "std         1.552813\n",
       "min         1.000000\n",
       "25%         4.756219\n",
       "50%         5.648590\n",
       "75%         6.903090\n",
       "max        11.221849\n",
       "Name: pIC50, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.pIC50.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('acetylcholinesterase_bioactivity_data_pIC50.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Intermediates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2.8032</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>4.5546</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>5.3574</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>4.7069</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>3.0953</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>4675</th>\n",
       "      <td>CHEMBL4284261</td>\n",
       "      <td>CCN(C)Cc1cc(N)ccc1O.Cl.Cl</td>\n",
       "      <td>inactive</td>\n",
       "      <td>180.251</td>\n",
       "      <td>1.4261</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.015428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>CHEMBL4276921</td>\n",
       "      <td>CN(C)Cc1cc(N)ccc1O.Cl.Cl</td>\n",
       "      <td>inactive</td>\n",
       "      <td>166.224</td>\n",
       "      <td>1.0360</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.813467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4677</th>\n",
       "      <td>CHEMBL4292574</td>\n",
       "      <td>CNCc1cc(N)ccc1O.Cl.Cl</td>\n",
       "      <td>inactive</td>\n",
       "      <td>152.197</td>\n",
       "      <td>0.6938</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.476904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>CHEMBL4292766</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3F)CC2...</td>\n",
       "      <td>active</td>\n",
       "      <td>494.695</td>\n",
       "      <td>6.5580</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.124939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>CHEMBL4284475</td>\n",
       "      <td>CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(F)cc3)C...</td>\n",
       "      <td>active</td>\n",
       "      <td>494.695</td>\n",
       "      <td>6.5580</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.008774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3549 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",
       "4675      CHEMBL4284261                          CCN(C)Cc1cc(N)ccc1O.Cl.Cl   \n",
       "4676      CHEMBL4276921                           CN(C)Cc1cc(N)ccc1O.Cl.Cl   \n",
       "4677      CHEMBL4292574                              CNCc1cc(N)ccc1O.Cl.Cl   \n",
       "4685      CHEMBL4292766  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccccc3F)CC2...   \n",
       "4687      CHEMBL4284475  CC(C)(C)c1cc(/C=C/C(=O)NCCC2CCN(Cc3ccc(F)cc3)C...   \n",
       "\n",
       "         class       MW    LogP  NumHDonors  NumHAcceptors     pIC50  \n",
       "0       active  312.325  2.8032         0.0            6.0  6.124939  \n",
       "1       active  376.913  4.5546         0.0            5.0  7.000000  \n",
       "2     inactive  426.851  5.3574         0.0            5.0  4.301030  \n",
       "3       active  404.845  4.7069         0.0            5.0  6.522879  \n",
       "4       active  346.334  3.0953         0.0            6.0  6.096910  \n",
       "...        ...      ...     ...         ...            ...       ...  \n",
       "4675  inactive  180.251  1.4261         2.0            3.0  3.015428  \n",
       "4676  inactive  166.224  1.0360         2.0            3.0  2.813467  \n",
       "4677  inactive  152.197  0.6938         3.0            3.0  3.476904  \n",
       "4685    active  494.695  6.5580         2.0            3.0  6.124939  \n",
       "4687    active  494.695  6.5580         2.0            3.0  6.008774  \n",
       "\n",
       "[3549 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2class = df_final[df_final['class'] != 'intermediate']\n",
    "df_2class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2class.to_csv('acetylcholinesterase_bioactivity_no_intermediates_pIC50.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (Chemical Space Analysis) with Lipinksi Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style='ticks')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.countplot(x='class', data=df_2class, edgecolor='black')\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_bioactivity_class.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Both bioactivity classes are spanning similar chemical spaces as evident by the scatter plot of MW vs LogP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.scatterplot(x='MW', y='LogP', data=df_2class, hue='class', size='pIC50', edgecolor='black', alpha=0.7)\n",
    "\n",
    "plt.xlabel('MW', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('LogP', fontsize=14, fontweight='bold')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)\n",
    "plt.savefig('plot_MW_vs_LogP.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots\n",
    "### pIC50 Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'pIC50', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('pIC50 value', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_ic50.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical analysis | Mann-Whitney U Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 samples\n",
    "  stat, p = mannwhitneyu(active, inactive)\n",
    "  #print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "\n",
    "# interpret\n",
    "  alpha = 0.05\n",
    "  if p > alpha:\n",
    "    interpretation = 'Same distribution (fail to reject H0)'\n",
    "  else:\n",
    "    interpretation = 'Different distribution (reject H0)'\n",
    "  \n",
    "  results = pd.DataFrame({'Descriptor':descriptor,\n",
    "                          'Statistics':stat,\n",
    "                          'p':p,\n",
    "                          'alpha':alpha,\n",
    "                          'Interpretation':interpretation}, index=[0])\n",
    "  filename = 'mannwhitneyu_' + descriptor + '.csv'\n",
    "  results.to_csv(filename)\n",
    "\n",
    "  return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics    p  alpha                      Interpretation\n",
       "0      pIC50         0.0  0.0   0.05  Different distribution (reject H0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('pIC50')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'MW', data = df_2class)\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('MW', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_MW.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>1058779.0</td>\n",
       "      <td>2.072256e-57</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics             p  alpha  \\\n",
       "0         MW   1058779.0  2.072256e-57   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('MW')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\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": 49,
   "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>1041929.0</td>\n",
       "      <td>2.355826e-61</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics             p  alpha  \\\n",
       "0       LogP   1041929.0  2.355826e-61   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('LogP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Num H Donors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\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": 51,
   "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>1361005.5</td>\n",
       "      <td>2.520096e-10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Descriptor  Statistics             p  alpha  \\\n",
       "0  NumHDonors   1361005.5  2.520096e-10   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHDonors')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Num of H Acceptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jkfTSpUtx48YNbN++HQ8ePEBKSgo2bNiA//znP5g+fTqePHmi+iEiw7R37154e3tj//79QkepNjQeSU+cOBEKhQIhISGlJugpmX4UKJ5U5caNG9pLSERVxvbt2wEA27Zt4zQPWqJxSTds2FCXOYioitu7d6/a8v79+1nUWqBxSb95OTcR0f8qGUWX4GhaOyp0JUphYSFOnz6N5ORkWFtbw9HREbVq1UKjRo10lY+IyKBpXNKPHz/G+PHjcffuXQDFs+JlZGQgIiICW7duRZcuXXQWkojIUFXo2x0pKSmYMGGC6u4LVlZWeP78OVauXKmzgERUNYwZM0Zt+W9/+5swQaoZjUv6/Pnz+Otf/4rZs2er1o0YMQLdunXjtzmICMOHD1db5vlo7dC4pGvWrInMzEwoFArVupcvX+Lhw4ecBY+IAPx3NM1RtPZoXNKDBw9GWloaPv74Y0gkEly5cgUDBgyATCbDwIEDdZmRiKqI4cOH49ChQxxFa5HGHxzOmTMHRkZGiIyMhFKpRE5ODkxNTTFixAh88cUXusxIRGSwNC5pU1NTzJs3D7NmzUJaWhoUCgWaNWsGCwsLXeYjIjJoGp/uAICTJ0/i2LFjaNu2LTp06ICVK1fixIkTuspGRGTwNC7pvXv3YsqUKaobxxYUFCAqKgrTp08vdTkoERFph8YlvWXLFlhYWGDixIkAADMzM9W6H374QWcBiYgMmcYl/fjxY3Tp0gWOjo6qdU5OTujcuTMePnyok3BERIZO45K2sbHBlStXcP78eRQVFeH169c4deoUrly5AhsbG11mJCIyWBp/u2PChAn48ssvMXbsWEgkEtWl4UqlEoGBgToLSERkyDQu6aFDh6Jhw4b4/vvvkZKSAgCws7NDYGAg+vfv/14hDh48iE2bNgEA+vbti3nz5r3X/oiIqosKTVXq4uICFxcXrQZ4+fIlli1bhuPHj0MqlWLUqFE4d+4cPvjgA60eh4ioKqpQSUdGRuL169eq6/IDAwPRt29fBAQEVDpAYWEhioqK8PLlS9SqVQsKhQI1atRQ20Yul0Mul6utk8lklT4mUXUWFxeHmJgYQY799OlTAEC9evUEOb6Hhwfc3NwEObauaFzSGzduxLp169CjRw/87W9/w6tXr3Du3DmcO3cO+fn5qq/mVZSFhQVmzpyJgQMHwtzcHD179kT37t3VtomIiEBYWFil9k9E+pOTkwNAuJKujjQu6T179qBhw4ZYsmQJAKBGjRo4fvw4/P39sWfPnkqX9M2bN7F//36cPHkSderUwZw5c7B161aMGzdOtU1AQAB8fX3VnieTyeDv71+pYxJVZ25uboKNJoOCggCg1M2qqfI0/gpednY22rRpg5YtW6rWNW/eHK1atUJWVlalAyQkJMDJyQkNGzaEmZkZhgwZgosXL6ptI5VKYWtrq/ZjbW1d6WMSEVUVGpd0ixYtcOnSJezbtw/p6elIS0tDZGQkLl++jBYtWlQ6QLt27XDu3Dm8ePECSqUScXFx6Ny5c6X3R0RUnWh8umP27NmYNm0aFi1aVOqxGTNmVDpAnz59cOPGDQwZMgSmpqbo3LkzJkyYUOn9ERFVJxqXtKurK6KiorB582akpKRAqVSiVatWGDt2LDp06PBeISZMmMBiJiIqQ4W+gmdvb49vvvmm1PqLFy+iV69eWgtFRETFKlTSb7p37x4OHDiAQ4cOQSaT8Wa0REQ6UKGSzs3NxdGjR3HgwAEkJycDKJ67o27dujoJR0Rk6N5Z0q9fv0ZcXBwOHjyIM2fOQKFQqCZXqlu3LoKCgngjWiIiHSm3pBcvXoxffvkFcrkcSqUSEokEDg4O8PDwwOrVq2FlZYVPPvlEX1mJiAxOuSW9Z88eSCQSmJmZYfz48Rg5ciQsLS0BAKtXr9ZLQCIiQ1ZuSTdu3BgZGRl49eoVNmzYgIsXL8LDwwPu7u76ykdEZNDKveIwPj4e27dvx7Bhw1C7dm1cvHgRy5cvh7u7OyQSCZ4/f44nT57oKysRkcF552XhvXr1wtdff42EhASsX78eHh4eMDMzg1KpxKNHj9CvXz9MnjxZH1mJiAyOxl/BMzMzg4eHBzw8PJCXl4fjx4/j8OHDuHjxIk6dOqXDiEREhqtSF7NYWFhg2LBhGDZsGDIzM3HkyBFt5yIiIryjpC9duqTRTjp16qSVMEREpK7ckh49ejQkEsk7dyKRSHhZOBGRDpRb0s7Ozmol/fvvvyMnJwfOzs46D0ZERO8o6a1bt6otT506FXFxcdiyZYtOQxERUTGN78xCRET6x5ImIhKxck93XLt2TW1ZLpeXuR4AunTposVYREQEvKOkR4wYUerbHUqlEiNHjlRbx293EBHpRrkl3bNnT33lICKiMpRb0j/++KO+chARURkqdFl4Wloabt++jYKCArX1EomEd2chItIBjUt627ZtWLVqlerWWf+LJU1EpH0al/T333+PoqIiODk5wcrKCkZG/PYeEZGuaVzSSqUSPXv2RHh4uC7zEBHRGzQeDo8dOxY3b97E5cuX33rKo7Li4uIwZMgQDBw4EF9//bVW901EVJVpPJLu27cvNm/erJoZz9TUVPWYRCLBf/7zn0oFePDgAYKDg7F37140bNgQAQEBiI+PR79+/Sq1PyKi6kTjkp4zZw6ePXsGoPjUx6tXr7QSICYmBp6enrC2tgYArFmzBjVq1FDbRi6Xq652LCGTySp9zM2bNyMlJaXSz6+qSl5zUFCQwEn0y87ODuPHjxc6BlGlaFzS6enpaN68OSIiItC4cWON5pnWRFpaGkxNTTFp0iQ8fvwYLi4umDVrlto2ERERCAsL08rxgOKySr7xB4xr1tPaPquCIoUxAOD3lAyBk+hPYf5ToSMQvReNS9rd3R3JycmwtLTUWkEDQGFhIS5fvowff/wRtWrVwuTJkxEdHY0hQ4aotgkICICvr6/a82QyGfz9/St9XOOa9VCruXuln09Vw4u0WKEjEL0XjUu6VatWOHHiBAYNGoRu3bqpnZKQSCQIDg6uVIBGjRrByckJDRo0AAD0798f165dUytpqVQKqVRaqf0TEVVlGpf0t99+CwBITU1Famqq2mPvU9Kurq6YN28e5HI5ateujTNnzsDdnSNcIiKgAiW9fPlyrZ7mKOHg4IBx48bh008/xevXr+Hs7IyhQ4dq/ThERFWRxiX95ukHbRs2bBiGDRums/0TEVVVGpf0mDFj3vqYRCJBRESEVgIREdF/aVzSFy9efOtjujgNQkREFSjp3bt3q35XKpUoKChAUlIStm7ditDQUJ2EIyIydBqXtIODQ6l1vXr1wq1bt/Dtt9/C2dlZq8GIiKgCJf3kyRO15aKiImRmZiIpKQkZGYZzBRsRkT5pXNLOzs5lnnsumcKUiIi0T+OS/stf/lJqXc2aNdG+fXvMmTNHq6GIiKiYxiUdFxenyxxERFQG3gOLiEjEyh1Jt2/fXqOdSCQS3LhxQyuBiIjov8otaRsbm7c+lp2djYKCAq0HIqouDPHmEoZ6YwlAdzeXKLekyzoPLZPJ8M033+DIkSMAgGbNmmHu3LlaD0ZU1aWkpOD279dhbaHxRz9VnrmyCADw7MEfAifRL1meQmf71vjd8/LlS2zevBnh4eHIz89H7dq1MWnSJAQEBKjd75CI/svawgR/79JA6BikY+HXcnS2b41KOjo6GmvWrEFmZiaMjIwwfPhwzJo1SzVRPxER6Ua5JX3p0iWEhITg999/BwD07t0bCxYsQNu2bfUSjojI0JVb0qNHj1ZdZWhkZIQHDx5g8uTJpbaTSCQ4ceKEbhISERmwd57uUCqVAIpvGPvw4UOdByIiov8qt6Rv3ryprxxERFQGXnFIRCRiLGkiIhFjSRMRiRhLmohIxFjSREQixpImIhIxljQRkYiJqqRXrlyJ+fPnCx2DiEg0RFPS58+fR3R0tNAxiIhERRQT3T59+hRr1qzBpEmTyrzKUS6XQy6Xq62TyWSVPl5ubi4K85/iRVpspfdBVUNh/lPk5poJcuzc3Fxk5yl0Oo0liYMsTwFFbq5O9i2Kkl68eDFmz56Nx48fl/l4REQEwsLC9JyKiEh4gpf03r17YWNjAycnJ0RFRZW5TUBAAHx9fdXWyWQy+Pv7V+qY9evXhyy3ALWau1fq+VR1vEiLRf369QU5dv369WGSl8lJ/w1A+LUc1NHR+0zwkj569CiysrLg4+ODP//8Ey9evMDy5cuxYMEC1TZSqRRSqVTAlEREwhC8pMPDw1W/R0VF4eLFi2oFTURkyETz7Q4iIipN8JH0m4YMGYIhQ4YIHYOISDQ4kiYiEjGWNBGRiLGkiYhEjCVNRCRiLGkiIhFjSRMRiRhLmohIxFjSREQixpImIhIxljQRkYiJ6rJwfTLESf+LFPkAACOTmgIn0Z/C/KcAGgt2fJmBTfqfV1AEALAwM6zxnyxPgTo62rdBlrSdnZ3QEQSRkpICALCzE6609K+xYH/ehvg+y/r/7zGbpob12utAd3/eBlnS48ePFzqCIIKCggAAISEhAicxDIb4PuN7TPsM698kRERVDEuaiEjEWNJERCLGkiYiEjGWNBGRiLGkiYhEjCVNRCRiLGkiIhFjSRMRiRhLmohIxFjSREQixpImIhIxUUywFBYWhmPHjgEA+vXrh7lz5wqciIhIHAQfSZ87dw4JCQmIjo7GgQMHcP36dcTExAgdi4hIFAQfSVtaWmL+/PkwMzMDALRq1QqPHj1S20Yul0Mul6utk8lkestIRCQUwUu6TZs2qt9TU1Nx7Ngx7Ny5U22biIgIhIWF6TsaEZHgBC/pErdv38bEiRMxd+5ctGjRQu2xgIAA+Pr6qq2TyWTw9/fXY0IiIv0TRUknJiZixowZWLBgAQYNGlTqcalUCqlUKkAyIiJhCV7Sjx8/xtSpU7FmzRo4OTkJHYeISFQEL+mtW7fi1atXWLFihWqdn58fRo0aJWAqIiJxELykFy5ciIULFwodg4hIlAT/njQREb0dS5qISMRY0kREIsaSJiISMZY0EZGIsaSJiESMJU1EJGIsaSIiEWNJExGJGEuaiEjEWNJERCLGkiYiEjGWNBGRiLGkiYhEjCVNRCRiLGkiIhFjSRMRiRhLmohIxFjSREQixpImIhIxljQRkYixpImIRIwlTUQkYixpIiIRY0kTEYmYKEr60KFD8PT0xIABAxAZGSl0HCIi0TAROkBGRgbWrFmDqKgomJmZwc/PD46OjmjdurXQ0YiIBCd4SZ87dw69e/dGvXr1AAAfffQRjh8/jmnTpqm2kcvlkMvlas+TyWR6zaktcXFxiImJEeTYKSkpAICgoCC9H9vDwwNubm56P64hMtT3GFA932eCl3RmZiYsLS1Vy1ZWVrh27ZraNhEREQgLC9N3tGqnQYMGQkegao7vMe0TvKSLioogkUhUy0qlUm0ZAAICAuDr66u2TiaTwd/fXy8ZtcnNza3a/U1P4sL3WPUieElbW1vj8uXLquWsrCxYWVmpbSOVSiGVSvUdjYhIcIJ/u+ODDz7A+fPnkZOTg5cvX+LXX39F3759hY5FRCQKgo+kGzdujNmzZ2PMmDF4/fo1hg0bhi5duggdi4hIFAQvaQDw9vaGt7e30DGIiERH8NMdRET0dixpIiIRY0kTEYkYS5qISMRY0kREIsaSJiISMVF8Ba8yCgsLAVTdiZaIiID/dlhJp/2vKlvSWVlZAFAl5+8gIvpfWVlZaN68ean1EqVSqRQgz3vLz89HcnIyLC0tYWxsLHScKqFkUqrIyEhYW1sLHYeqIb7HKq6wsBBZWVno1KkTatasWerxKjuSrlmzJnr06CF0jCrJ2toatra2QsegaozvsYopawRdgh8cEhGJGEuaiEjEWNJERCLGkjYgUqkU06ZN4w0USGf4HtO+KvvtDiIiQ8CRNBGRiLGkiYhEjCVNRCRiLGkDcPLkSYSHhwMAdu7ciZ07dwqciMTot99+w5dffqm1/QUFBeHhw4cAgPHjxyMjI0Nr+zYk/ODQAKxfvx4AMH36dIGTkCFxc3PD9u3beeXhe6qyl4UbOoVCga+++gq3b99GdnY27O3t8c9//hO7du3Czp07YWxsDFdXV/j6+mLXrlAQCkMAAA03SURBVF0AgL/85S949OgRAKBu3bpIS0vDokWLAAArVqyAtbU1hg8fjiVLluD27dsoLCzE+PHj4eXlJdjrJP25cOECwsLCAACdO3dGYmIicnJysHDhQvTr1w+3bt3C0qVL8eLFC+Tk5GDChAkYNWoUnj59ii+//BIpKSkwMzPD/Pnz8dtvvyEzMxMTJkxAZGQkhg4diu3bt2P69OlYunQpOnXqhMLCQri6uiI6OhoPHz5ESEgI8vPzUb9+ffzjH/9A06ZNBf4/Ig483VFFXb16Faampti9ezdiYmLw7NkzbN++HT/99BP27duHn3/+GdevX0d+fj78/Pzg5+eHoUOHqp7v5eWFmJgYFBYWQqlU4tdff8WgQYOwYcMGdOzYEVFRUYiMjMTGjRvx4MEDAV8pCeH169fYvXs3goKCsG7dOgDA3r17MWXKFOzfvx/bt2/HqlWrAADr1q1Ds2bNcOzYMaxatQpr167FhAkTYGVlhU2bNqF+/fqq/fr4+ODIkSMAgH//+99o164d6tSpg4ULFyI0NBTR0dH4+9//rho8EEfSVVbPnj1Rr149REZGIiUlBampqXB0dISrqyvq1KkDANi2bRuA4nPS/6tBgwZo164dLly4AFNTU7Rs2RKWlpY4d+4c8vPzsX//fgDAixcvcPv2bY5qDMyHH34IAGjTpg2ePn0KAJg/fz7OnDmD77//Hrdu3cKLFy8AAJcuXcI333wDALC3t8fu3bvfut9BgwZh5MiRmDt3Lg4fPozBgwcjNTUVDx48wOTJk1Xb5eXl6eqlVTks6SoqNjYW3377LcaMGYMhQ4YgNzcXderUUXtzZ2RkwNzc/K378PHxwdGjR2Fqagpvb28AQFFREVavXo2OHTsCALKzs1G3bl3dvhgSnRo1agAAJBKJat2sWbMglUrh6uoKT09PHD58GABgYmKitt3du3fRsmXLMvdraWmJli1b4sKFCzh//jwWL16MtLQ02Nra4uDBgwCKp+7Mzs7W1Uurcni6o4o6f/48Bg4ciKFDh0IqleLChQsoLCxEfHw8nj9/DoVCgc8//xzJyckwNjaGQqEotQ93d3dcunQJZ8+ehYeHBwCgd+/eqm9/ZGZmYvDgwXj8+LFeXxuJ09mzZzFjxgz0798fp0+fBlBcqD169FCdwrh79y7Gjx8PiUQCY2PjMu824uPjg5UrV8LR0RHm5uaws7PDn3/+icuXLwMA9u/fjzlz5ujvhYkcR9JV1PDhwzFnzhwcOXIEpqam6N69O/7880/83//9H/z8/FBUVAQPDw988MEHMDU1xbx589CoUSO1fdSsWRPdu3dHQUEBateuDQCYNm0avvrqK3h5eaGwsBBffPEFmjVrJsRLJJGZPn06Pv30U9SoUQPt2rVDkyZNkJ6ejhkzZmDhwoUYPHgwTExMsGrVKkgkEri4uGDChAnYsmWL2n48PDwQHBysKmIzMzOsW7cOy5Ytw6tXr2BhYYGVK1cK8RJFiV/BIyISMZ7uICISMZY0EZGIsaSJiESMJU1EJGIsaSIiEWNJk9bY29ur/XTo0AHOzs5Yvny52ve03dzcYG9vj6ysLL1li4mJQUpKCgAgPT0d9vb2+PjjjzV67vr162Fvb49Nmzap1t28eRPx8fE6yRoVFQV7e3ssXrxYJ/unqoUlTVplZGQEd3d3uLu748MPP4RCoUBERATWrl2r2sbZ2Rnu7u6qq9p07R//+AemTZsGuVwOADA3N4e7uzs++OADjZ5vZ2cHd3d3tGjRAgBw+PBhDB06FH/88YeuIhOp8GIW0ioTExN89913quVr165h+PDhiIuLU128sHTpUr1munPnjtpyw4YN1TK+y6BBgzBo0CDV8r1798q8gpNIFziSJp0qKCgAADRp0kS17n9Pd+Tn5yMkJAR9+vRB586dMWLECFy4cEG1vUKhwLJly9CnTx906tQJbm5ualexKZVKbNq0Ca6urujSpQu8vb1x4sQJAMWTAl28eBEAMHLkSKxfv17tdMfLly/RrVs3ODg4qCYMKtnW3t4ef/zxh9rpjqioKNV0nqGhoRg9ejQWLlwIe3t7REZGqp6/detW2Nvbq/0L4k13795FYGAgHBwc0Lt3b8yfPx/Pnj0rc9t3vf6kpCR8+umn6N69O3r27Ilx48YhLS1N9fiBAwfg6emJLl26wNnZGYsWLUJ+fn55f2wkIixp0iqFQoEpU6ZgypQpCAwMxMSJE9GwYUN8/vnnb33O9OnTsW3bNhgbG8PBwQG//fYbxo4di8TERADAli1bsH37dpiYmKBXr17IysrC6tWrcf36dQDATz/9hNDQUDx//hx//etfkZqaipkzZ+L69evo0KED6tWrB6B45kA7Ozu1Y5ubm+Ojjz5Cfn4+Tp06BQB49OgRkpKSVOfW32RjY6OaPKh169bo3r27ar7tkr8YACAuLg4A4OnpWer15ufnY9y4cUhISEDr1q1Rv359REdHY8GCBWX+/ynv9RcVFWHy5MlISkpC586d0bx5c5w5cwbTpk0DUHzufP78+cjKylLNlbFnzx6sXr36rX8eJC4sadKqoqIixMbGIjY2FgkJCcjLy4NEIsH9+/fL3D4xMRGnT59GixYtcOzYMezYsQNfffUVFAoFvv32WwCAg4MDJk2ahEOHDuGHH37AyJEjAUA1Wvz+++9hZGSEnTt3Ijw8HEuXLoWzszOys7MxZswYtG3bFgAwZ84ctdMWJXx8fAAAx48fV/1XqVRi8ODBpbZ1cnJS7cPHxwezZ89Gr1690LhxY1y6dAlyuRy5ubm4evUqWrVqpTr2mw4dOoRHjx7By8sL+/fvx8GDB9GnTx/Uq1cPr1+/LrV9ea//+fPnePLkCezs7BAaGop9+/Zh8eLFmDFjBhQKBR4+fAilUgl3d3esX78eUVFRmDdvHgYOHFjOnyKJCc9Jk1aZmZnht99+AwC8fPkSZ86cwezZs/HZZ58hNjYWjRs3Vts+KSkJQPGkO7Vq1QJQXH6LFy9WPebk5IT8/HyEhoYiMTERt27dAgC8evUKeXl5yMjIgI2NDVq1agUA+OSTT/DJJ59onNnR0RHW1taIj4/HixcvcOzYMRgZGammb30XIyMjeHp6Ijw8HKdOnUJhYSEKCwvLHEUD/z1H7uTkpPp/tnXr1rfuv7zXX6dOHXzyySc4cOAAPvzwQ7Rr1w59+/aFm5sbTExM4OjoiLZt2yI6OhpHjx5Ft27d4ObmppqKlsSPI2nSGXNzcwwYMABt2rTB69evceXKlVLbGBm9/S1YMkdxaGgoJk2ahCdPnmDSpEnw9/cHUHwuusSbH+QpFIoKfbBXUsj5+fnYu3cvrl27phoda+rNUx7lnep4M/eb03iWd474Xa9/xYoV+OGHH+Dn54eCggJs3LgRXl5ekMlksLCwwN69e7Fq1Sp4eHggJSUFy5cvV+2DxI8lTTr18uVL1QeEUqm01OPt27cHUPw95pIP7komf+/atSsAYMeOHZBIJAgJCcGgQYOQnp6uer6FhQWsrKyQnZ2NmzdvAgCio6PRtWtX1Qd8JWVfVFT01pwlI++Sm/aWdaqjRFn769SpE1q2bIkzZ84gISEB7dq1K3X+u0Tr1q0BFM/PDBTfqmrQoEHo06eP6i4obyrv9d+7dw8LFy7E+fPnERwcjCNHjmDgwIHIy8tDUlIS4uPjsWDBAhgZGSE0NBTx8fGwtLTE9evXkZub+9bXSOLB0x2kVSUfHJb8fuvWLWRnZ6NJkybo2bNnqe179uyJXr164eLFixg4cCCaNm2KxMREmJqaYsaMGQCKb6B7584dDB8+HGZmZqoyLin1wMBAhISEYPTo0ejUqRMSExOhVCrRt29fAFDdYy84OBi+vr4YMGBAqRytW7dGx44dcf36ddSoUQMfffTRW19jyf4iIyNx79491dzHXl5eqpJ/2ygaALy9vbF+/Xr88ssvGDJkCPLz85Geng5XV1fVh5xvKu/1W1paIj4+HllZWfj3v/+NWrVqITExETVr1kSnTp2QnZ2NX375Bb/++iv2798PuVyOrKws1QeWJH4cSZNWvfnB4enTp/H06VP06tULmzZtgpmZWantjYyMsHHjRowZMwZFRUVISkpCly5dEB4ejm7dugEAQkJCYG9vr7rTeclfAlevXgUAjBkzBjNnzoS5uTkSExPRokULrF27Fl26dAEABAQEoGnTprh//365984r+QDRzc0NFhYWb93u448/Rvfu3fH06VNkZGSo1r/5oWR5H8yZm5tj69at6N27N+7cuYPc3Fz4+vq+daL78l6/hYUFIiIi4Orqivv37yM5ORmdO3fGxo0b0aRJEzg4OOBf//oX2rVrh6SkJDx69Aj9+/fHhg0b3pqPxIWT/hNpyYkTJzB16lTV3daJtIGnO4je040bN7BhwwacP38eAODn5ydwIqpOeLqD6D2ZmZkhISEBxsbGGDNmDIYNGyZ0JKpGeLqDiEjEOJImIhIxljQRkYixpImIRIwlTUQkYixpIiIR+3+cPEfxEaAPtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>1407547.5</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Descriptor  Statistics         p  alpha  \\\n",
       "0  NumHAcceptors   1407547.5  0.000004   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHAcceptors')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation of Statistics\n",
    "Box Plots\n",
    "\n",
    "pIC50 values\n",
    "Taking a look at pIC50 values, the actives and inactives displayed statistically significant difference, which is to be expected since threshold values (IC50 < 1,000 nM = Actives while IC50 > 10,000 nM = Inactives, corresponding to pIC50 > 6 = Actives and pIC50 < 5 = Inactives) were used to define actives and inactives.\n",
    "\n",
    "Lipinski's descriptors\n",
    "\n",
    "All of the 4 Lipinski's descriptors exhibited statistically significant difference between the actives and inactives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: mannwhitneyu_NumHAcceptors.csv (deflated 11%)\n",
      "  adding: acetylcholinesterase_bioactivity_data_pIC50.csv (deflated 76%)\n",
      "  adding: acetylcholinesterase_bioactivity_no_intermediates_pIC50.csv (deflated 76%)\n",
      "  adding: mannwhitneyu_NumHDonors.csv (deflated 11%)\n",
      "  adding: mannwhitneyu_MW.csv (deflated 10%)\n",
      "  adding: plot_bioactivity_class.pdf (deflated 39%)\n",
      "  adding: plot_LogP.pdf (deflated 37%)\n",
      "  adding: plot_MW.pdf (deflated 38%)\n",
      "  adding: acetylcholinesterase_bioactivity_data_curated.csv (deflated 82%)\n",
      "  adding: plot_NumHDonors.pdf (deflated 38%)\n",
      "  adding: plot_NumHAcceptors.pdf (deflated 38%)\n",
      "  adding: acetylcholinesterase_bioactivity_data_preprocessed.csv (deflated 81%)\n",
      "  adding: plot_ic50.pdf (deflated 37%)\n",
      "  adding: mannwhitneyu_pIC50.csv (deflated 14%)\n",
      "  adding: acetylcholinesterase_bioactivity_data_raw.csv (deflated 90%)\n",
      "  adding: plot_MW_vs_LogP.pdf (deflated 2%)\n",
      "  adding: mannwhitneyu_LogP.csv (deflated 10%)\n"
     ]
    }
   ],
   "source": [
    "! zip -r results.zip . -i *.csv *.pdf"
   ]
  }
 ],
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