2 CDD ML Exploratory Data Analysis (rdkit).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80bd5eb5",
   "metadata": {},
   "source": [
    "## Computational Drug Discovery - Exploratory Data Analysis\n",
    "\n",
    "In this Jupyter notebook, I will be building a machine learning model using the ChEMBL bioactivity data.\n",
    "\n",
    "In **Part 2**, I will be performing Descriptor Calculation and Exploratory Data Analysis. \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05b22c8a",
   "metadata": {},
   "source": [
    "## Load bioactivity data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbd8edf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d998d417",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/beta_secretase_bioactivity_data_curated.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e8fc91fe",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>413.0</td>\n",
       "      <td>active</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "   Unnamed: 0 molecule_chembl_id  \\\n",
       "0           0       CHEMBL406146   \n",
       "1           1        CHEMBL78946   \n",
       "2           2       CHEMBL324109   \n",
       "3           3       CHEMBL114147   \n",
       "4           4       CHEMBL419949   \n",
       "\n",
       "                                    canonical_smiles  standard_value  \\\n",
       "0  CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1  CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2  CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3  CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4  CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "\n",
       "          class  \n",
       "0        active  \n",
       "1        active  \n",
       "2        active  \n",
       "3  intermediate  \n",
       "4  intermediate  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932dbd11",
   "metadata": {},
   "source": [
    "## Calculate Lipinski descriptors\n",
    "\n",
    "Christopher Lipinski, a scientist at Pfizer, came up with a set of rule-of-thumb for evaluating the **druglikeness** of compounds. Such druglikeness is based on the Absorption, Distribution, Metabolism and Excretion (ADME) that is also known as the pharmacokinetic profile. Lipinski analyzed all orally active FDA-approved drugs in the formulation of what is to be known as the **Rule-of-Five** or **Lipinski's Rule**.\n",
    "\n",
    "The Lipinski's Rule stated the following:\n",
    "\n",
    "- Molecular weight < 500 Dalton\n",
    "- Octanol-water partition coefficient (LogP) < 5\n",
    "- Hydrogen bond donors < 5\n",
    "- Hydrogen bond acceptors < 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8421443d",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a80eb6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors, Lipinski"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e93c089",
   "metadata": {},
   "source": [
    "### Calculate descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02a8d624",
   "metadata": {},
   "outputs": [],
   "source": [
    "# inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation\n",
    "\n",
    "# function that takes smiles notation...\n",
    "# which is chemical information that tells the exact atomic details of the molecule\n",
    "\n",
    "def lipinski(smiles, verbose=False):\n",
    "    \n",
    "    moldata = []\n",
    "    for elem in smiles:\n",
    "        mol = Chem.MolFromSmiles(elem) \n",
    "        moldata.append(mol)\n",
    "        \n",
    "    baseData= np.arange(1,1)\n",
    "   \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",
    "        row = np.array([desc_MolWt,\n",
    "                        desc_MolLogP,\n",
    "                        desc_NumHDonors,\n",
    "                        desc_NumHAcceptors])   \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": 7,
   "id": "78a891c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lipinski = lipinski(df.canonical_smiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5176f715",
   "metadata": {},
   "source": [
    "### Combine Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61bfa36e",
   "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",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>999.085</td>\n",
       "      <td>-1.4355</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893.005</td>\n",
       "      <td>-1.7361</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>751.988</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <th>7229</th>\n",
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       "      <td>2.3125</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>7230</th>\n",
       "      <td>637.888</td>\n",
       "      <td>2.7026</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7231</th>\n",
       "      <td>651.915</td>\n",
       "      <td>3.0927</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>7232</th>\n",
       "      <td>556.176</td>\n",
       "      <td>6.7284</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>7233</th>\n",
       "      <td>562.154</td>\n",
       "      <td>7.8680</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
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       "<p>7234 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "           MW    LogP  NumHDonors  NumHAcceptors\n",
       "0     999.085 -1.4355        13.0           13.0\n",
       "1     893.005 -1.7361        12.0           12.0\n",
       "2     751.988  2.3535         8.0            9.0\n",
       "3     737.895  1.9626         8.0            8.0\n",
       "4     828.020  3.5739         8.0            8.0\n",
       "...       ...     ...         ...            ...\n",
       "7229  623.861  2.3125         7.0            8.0\n",
       "7230  637.888  2.7026         7.0            8.0\n",
       "7231  651.915  3.0927         7.0            8.0\n",
       "7232  556.176  6.7284         3.0            5.0\n",
       "7233  562.154  7.8680         4.0            5.0\n",
       "\n",
       "[7234 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lipinski data frame containing:\n",
    "# MW (molecular weight) which tells us size, \n",
    "# LogP which tells us solubility,\n",
    "# NumHDonors which tells us relative number of hyodrogen bond donors\n",
    "# NumHAcceptors which tells us relative number of hyodrogen bond acceptors\n",
    "df_lipinski"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3ed9184",
   "metadata": {},
   "outputs": [
    {
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       "      <td>9200.0</td>\n",
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       "      <th>7231</th>\n",
       "      <td>10421</td>\n",
       "      <td>CHEMBL4853052</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
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       "      <td>CHEMBL4862716</td>\n",
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      "text/plain": [
       "      Unnamed: 0 molecule_chembl_id  \\\n",
       "0              0       CHEMBL406146   \n",
       "1              1        CHEMBL78946   \n",
       "2              2       CHEMBL324109   \n",
       "3              3       CHEMBL114147   \n",
       "4              4       CHEMBL419949   \n",
       "...          ...                ...   \n",
       "7229       10419      CHEMBL4849016   \n",
       "7230       10420      CHEMBL4872824   \n",
       "7231       10421      CHEMBL4853052   \n",
       "7232       10424      CHEMBL4862716   \n",
       "7233       10425      CHEMBL4859103   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "...                                                 ...             ...   \n",
       "7229  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...          4400.0   \n",
       "7230  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...          9200.0   \n",
       "7231  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...         10000.0   \n",
       "7232  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...         10800.0   \n",
       "7233  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNC(=O)c4cc(...          2710.0   \n",
       "\n",
       "             class  \n",
       "0           active  \n",
       "1           active  \n",
       "2           active  \n",
       "3     intermediate  \n",
       "4     intermediate  \n",
       "...            ...  \n",
       "7229  intermediate  \n",
       "7230  intermediate  \n",
       "7231      inactive  \n",
       "7232      inactive  \n",
       "7233  intermediate  \n",
       "\n",
       "[7234 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f2608d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>molecule_chembl_id</th>\n",
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       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>460.0</td>\n",
       "      <td>active</td>\n",
       "      <td>751.988</td>\n",
       "      <td>2.3535</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>737.895</td>\n",
       "      <td>1.9626</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>5600.0</td>\n",
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       "    <tr>\n",
       "      <th>7229</th>\n",
       "      <td>10419</td>\n",
       "      <td>CHEMBL4849016</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
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       "    <tr>\n",
       "      <th>7230</th>\n",
       "      <td>10420</td>\n",
       "      <td>CHEMBL4872824</td>\n",
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       "    <tr>\n",
       "      <th>7231</th>\n",
       "      <td>10421</td>\n",
       "      <td>CHEMBL4853052</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>651.915</td>\n",
       "      <td>3.0927</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>7232</th>\n",
       "      <td>10424</td>\n",
       "      <td>CHEMBL4862716</td>\n",
       "      <td>CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>inactive</td>\n",
       "      <td>556.176</td>\n",
       "      <td>6.7284</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>7233</th>\n",
       "      <td>10425</td>\n",
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       "      <td>7.8680</td>\n",
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       "      Unnamed: 0 molecule_chembl_id  \\\n",
       "0              0       CHEMBL406146   \n",
       "1              1        CHEMBL78946   \n",
       "2              2       CHEMBL324109   \n",
       "3              3       CHEMBL114147   \n",
       "4              4       CHEMBL419949   \n",
       "...          ...                ...   \n",
       "7229       10419      CHEMBL4849016   \n",
       "7230       10420      CHEMBL4872824   \n",
       "7231       10421      CHEMBL4853052   \n",
       "7232       10424      CHEMBL4862716   \n",
       "7233       10425      CHEMBL4859103   \n",
       "\n",
       "                                       canonical_smiles  standard_value  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...           413.0   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...             2.0   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...           460.0   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...          9000.0   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...          5600.0   \n",
       "...                                                 ...             ...   \n",
       "7229  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...          4400.0   \n",
       "7230  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...          9200.0   \n",
       "7231  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...         10000.0   \n",
       "7232  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...         10800.0   \n",
       "7233  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNC(=O)c4cc(...          2710.0   \n",
       "\n",
       "             class       MW    LogP  NumHDonors  NumHAcceptors  \n",
       "0           active  999.085 -1.4355        13.0           13.0  \n",
       "1           active  893.005 -1.7361        12.0           12.0  \n",
       "2           active  751.988  2.3535         8.0            9.0  \n",
       "3     intermediate  737.895  1.9626         8.0            8.0  \n",
       "4     intermediate  828.020  3.5739         8.0            8.0  \n",
       "...            ...      ...     ...         ...            ...  \n",
       "7229  intermediate  623.861  2.3125         7.0            8.0  \n",
       "7230  intermediate  637.888  2.7026         7.0            8.0  \n",
       "7231      inactive  651.915  3.0927         7.0            8.0  \n",
       "7232      inactive  556.176  6.7284         3.0            5.0  \n",
       "7233  intermediate  562.154  7.8680         4.0            5.0  \n",
       "\n",
       "[7234 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined = pd.concat([df, df_lipinski], axis = 1)\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcaabb1a",
   "metadata": {},
   "source": [
    "### Convert IC50 to pIC50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ade71440",
   "metadata": {},
   "source": [
    "To allow **IC50** data to be more uniformly distributed, I will convert **IC50** to the negative logarithmic scale which is essentially **-log10(IC50)**.\n",
    "\n",
    "This custom function pIC50() will accept a DataFrame as input and will:\n",
    "\n",
    "- Take the IC50 values from the <mark>standard_value</mark> column and converts it from nM to M by multiplying the value by $10^{-9}$\n",
    "- Take the molar value and apply -log10\n",
    "- Delete the <mark>standard_value</mark> column and create a new <mark>pIC50</mark> column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eeaf1b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pIC50(input):\n",
    "    pIC50 = []\n",
    "    \n",
    "    for i in input['standard_value_norm']:\n",
    "        molar = i*(10**-9) # converts nM (nanomolar) to M (molar)\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": "markdown",
   "id": "15311321",
   "metadata": {},
   "source": [
    "Note: Values greater than 100,000,000 will be fixed at 100,000,000 otherwise the negative logarithmic value will become negative.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ccffdf1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7.234000e+03\n",
       "mean     4.157136e+04\n",
       "std      1.113669e+06\n",
       "min      2.000000e-04\n",
       "25%      2.920000e+01\n",
       "50%      2.637500e+02\n",
       "75%      3.300000e+03\n",
       "max      8.600000e+07\n",
       "Name: standard_value, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.standard_value.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d7acd5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-np.log10( (10**-9)* 100000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "416f28da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-np.log10( (10**-9)* 10000000000 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00af97ac",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aaaa44a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kiannahendricks/opt/anaconda3/envs/rdkit-env-test/lib/python3.7/site-packages/ipykernel_launcher.py:10: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
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       "      <th>3</th>\n",
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       "      <td>CHEMBL114147</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
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       "    <tr>\n",
       "      <th>7229</th>\n",
       "      <td>10419</td>\n",
       "      <td>CHEMBL4849016</td>\n",
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       "      <th>7230</th>\n",
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       "      <th>7231</th>\n",
       "      <td>10421</td>\n",
       "      <td>CHEMBL4853052</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>10000.0</td>\n",
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       "    <tr>\n",
       "      <th>7232</th>\n",
       "      <td>10424</td>\n",
       "      <td>CHEMBL4862716</td>\n",
       "      <td>CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>556.176</td>\n",
       "      <td>6.7284</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7233</th>\n",
       "      <td>10425</td>\n",
       "      <td>CHEMBL4859103</td>\n",
       "      <td>CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNC(=O)c4cc(...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>562.154</td>\n",
       "      <td>7.8680</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2710.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7234 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 molecule_chembl_id  \\\n",
       "0              0       CHEMBL406146   \n",
       "1              1        CHEMBL78946   \n",
       "2              2       CHEMBL324109   \n",
       "3              3       CHEMBL114147   \n",
       "4              4       CHEMBL419949   \n",
       "...          ...                ...   \n",
       "7229       10419      CHEMBL4849016   \n",
       "7230       10420      CHEMBL4872824   \n",
       "7231       10421      CHEMBL4853052   \n",
       "7232       10424      CHEMBL4862716   \n",
       "7233       10425      CHEMBL4859103   \n",
       "\n",
       "                                       canonical_smiles         class  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...        active   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...        active   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...        active   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...  intermediate   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...  intermediate   \n",
       "...                                                 ...           ...   \n",
       "7229  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  intermediate   \n",
       "7230  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  intermediate   \n",
       "7231  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...      inactive   \n",
       "7232  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...      inactive   \n",
       "7233  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNC(=O)c4cc(...  intermediate   \n",
       "\n",
       "           MW    LogP  NumHDonors  NumHAcceptors  standard_value_norm  \n",
       "0     999.085 -1.4355        13.0           13.0                413.0  \n",
       "1     893.005 -1.7361        12.0           12.0                  2.0  \n",
       "2     751.988  2.3535         8.0            9.0                460.0  \n",
       "3     737.895  1.9626         8.0            8.0               9000.0  \n",
       "4     828.020  3.5739         8.0            8.0               5600.0  \n",
       "...       ...     ...         ...            ...                  ...  \n",
       "7229  623.861  2.3125         7.0            8.0               4400.0  \n",
       "7230  637.888  2.7026         7.0            8.0               9200.0  \n",
       "7231  651.915  3.0927         7.0            8.0              10000.0  \n",
       "7232  556.176  6.7284         3.0            5.0              10800.0  \n",
       "7233  562.154  7.8680         4.0            5.0               2710.0  \n",
       "\n",
       "[7234 rows x 9 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply so that values in standard_value are normalized\n",
    "df_norm = norm_value(df_combined)\n",
    "df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49672435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7.234000e+03\n",
       "mean     4.157136e+04\n",
       "std      1.113669e+06\n",
       "min      2.000000e-04\n",
       "25%      2.920000e+01\n",
       "50%      2.637500e+02\n",
       "75%      3.300000e+03\n",
       "max      8.600000e+07\n",
       "Name: standard_value_norm, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.standard_value_norm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7fe2a9b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kiannahendricks/opt/anaconda3/envs/rdkit-env-test/lib/python3.7/site-packages/ipykernel_launcher.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
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       "      <th>molecule_chembl_id</th>\n",
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       "      <th>MW</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL78946</td>\n",
       "      <td>CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>8.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL324109</td>\n",
       "      <td>CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...</td>\n",
       "      <td>active</td>\n",
       "      <td>751.988</td>\n",
       "      <td>2.3535</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.337242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CHEMBL114147</td>\n",
       "      <td>CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>737.895</td>\n",
       "      <td>1.9626</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.045757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL419949</td>\n",
       "      <td>CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>828.020</td>\n",
       "      <td>3.5739</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>5.251812</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7229</th>\n",
       "      <td>10419</td>\n",
       "      <td>CHEMBL4849016</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>intermediate</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7230</th>\n",
       "      <td>10420</td>\n",
       "      <td>CHEMBL4872824</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>637.888</td>\n",
       "      <td>2.7026</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>5.036212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7231</th>\n",
       "      <td>10421</td>\n",
       "      <td>CHEMBL4853052</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>651.915</td>\n",
       "      <td>3.0927</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7232</th>\n",
       "      <td>10424</td>\n",
       "      <td>CHEMBL4862716</td>\n",
       "      <td>CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>556.176</td>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>7233</th>\n",
       "      <td>10425</td>\n",
       "      <td>CHEMBL4859103</td>\n",
       "      <td>CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNC(=O)c4cc(...</td>\n",
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       "      <td>562.154</td>\n",
       "      <td>7.8680</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.567031</td>\n",
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       "</table>\n",
       "<p>7234 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 molecule_chembl_id  \\\n",
       "0              0       CHEMBL406146   \n",
       "1              1        CHEMBL78946   \n",
       "2              2       CHEMBL324109   \n",
       "3              3       CHEMBL114147   \n",
       "4              4       CHEMBL419949   \n",
       "...          ...                ...   \n",
       "7229       10419      CHEMBL4849016   \n",
       "7230       10420      CHEMBL4872824   \n",
       "7231       10421      CHEMBL4853052   \n",
       "7232       10424      CHEMBL4862716   \n",
       "7233       10425      CHEMBL4859103   \n",
       "\n",
       "                                       canonical_smiles         class  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...        active   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...        active   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...        active   \n",
       "3     CC(=O)NCC(=O)N[C@@H](Cc1ccccc1)[C@@H](O)CC(=O)...  intermediate   \n",
       "4     CC(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1...  intermediate   \n",
       "...                                                 ...           ...   \n",
       "7229  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  intermediate   \n",
       "7230  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  intermediate   \n",
       "7231  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...      inactive   \n",
       "7232  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...      inactive   \n",
       "7233  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNC(=O)c4cc(...  intermediate   \n",
       "\n",
       "           MW    LogP  NumHDonors  NumHAcceptors     pIC50  \n",
       "0     999.085 -1.4355        13.0           13.0  6.384050  \n",
       "1     893.005 -1.7361        12.0           12.0  8.698970  \n",
       "2     751.988  2.3535         8.0            9.0  6.337242  \n",
       "3     737.895  1.9626         8.0            8.0  5.045757  \n",
       "4     828.020  3.5739         8.0            8.0  5.251812  \n",
       "...       ...     ...         ...            ...       ...  \n",
       "7229  623.861  2.3125         7.0            8.0  5.356547  \n",
       "7230  637.888  2.7026         7.0            8.0  5.036212  \n",
       "7231  651.915  3.0927         7.0            8.0  5.000000  \n",
       "7232  556.176  6.7284         3.0            5.0  4.966576  \n",
       "7233  562.154  7.8680         4.0            5.0  5.567031  \n",
       "\n",
       "[7234 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pIC50(df_norm)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "50e1ce28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7234.000000\n",
       "mean        6.477377\n",
       "std         1.391441\n",
       "min         1.065502\n",
       "25%         5.481486\n",
       "50%         6.578808\n",
       "75%         7.534617\n",
       "max        12.698970\n",
       "Name: pIC50, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.pIC50.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d0d0cc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('beta_secretase_bioactivity_data_3class_pIC50.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e075f9",
   "metadata": {},
   "source": [
    "### Removing the 'intermediate' bioactivity class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a158f01",
   "metadata": {},
   "source": [
    "I will remove the intermediate class from the data set to allow for simple comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f62afac",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>CHEMBL143239</td>\n",
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       "      <td>7.698970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7224</th>\n",
       "      <td>10414</td>\n",
       "      <td>CHEMBL4877632</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>675.849</td>\n",
       "      <td>2.28890</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.031517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7226</th>\n",
       "      <td>10416</td>\n",
       "      <td>CHEMBL4866494</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>675.849</td>\n",
       "      <td>2.50892</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.886057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7228</th>\n",
       "      <td>10418</td>\n",
       "      <td>CHEMBL4864653</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>671.905</td>\n",
       "      <td>3.38110</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.853872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7231</th>\n",
       "      <td>10421</td>\n",
       "      <td>CHEMBL4853052</td>\n",
       "      <td>CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>651.915</td>\n",
       "      <td>3.09270</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7232</th>\n",
       "      <td>10424</td>\n",
       "      <td>CHEMBL4862716</td>\n",
       "      <td>CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>556.176</td>\n",
       "      <td>6.72840</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.966576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5838 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 molecule_chembl_id  \\\n",
       "0              0       CHEMBL406146   \n",
       "1              1        CHEMBL78946   \n",
       "2              2       CHEMBL324109   \n",
       "5              5       CHEMBL116826   \n",
       "6              6       CHEMBL143239   \n",
       "...          ...                ...   \n",
       "7224       10414      CHEMBL4877632   \n",
       "7226       10416      CHEMBL4866494   \n",
       "7228       10418      CHEMBL4864653   \n",
       "7231       10421      CHEMBL4853052   \n",
       "7232       10424      CHEMBL4862716   \n",
       "\n",
       "                                       canonical_smiles     class       MW  \\\n",
       "0     CC(C)C[C@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CCC(=O...    active  999.085   \n",
       "1     CC(C)C[C@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N...    active  893.005   \n",
       "2     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...    active  751.988   \n",
       "5     CCC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(C)=O)[C@@H]...  inactive  767.987   \n",
       "6     CC(C)[C@H](NC(=O)C[C@H](O)[C@H](Cc1cc(F)cc(F)c...    active  717.722   \n",
       "...                                                 ...       ...      ...   \n",
       "7224  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  inactive  675.849   \n",
       "7226  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  inactive  675.849   \n",
       "7228  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  inactive  671.905   \n",
       "7231  CC[C@H](C)[C@H](NC(=O)[C@@H](N)CCC(=O)O)C(=O)N...  inactive  651.915   \n",
       "7232  CC1=CC2Cc3nc4cc(Cl)ccc4c(NCCCCCCCCCNS(=O)(=O)c...  inactive  556.176   \n",
       "\n",
       "         LogP  NumHDonors  NumHAcceptors     pIC50  \n",
       "0    -1.43550        13.0           13.0  6.384050  \n",
       "1    -1.73610        12.0           12.0  8.698970  \n",
       "2     2.35350         8.0            9.0  6.337242  \n",
       "5     1.36900         8.0            9.0  4.468521  \n",
       "6     4.31960         7.0            7.0  7.698970  \n",
       "...       ...         ...            ...       ...  \n",
       "7224  2.28890         7.0           10.0  4.031517  \n",
       "7226  2.50892         8.0            9.0  4.886057  \n",
       "7228  3.38110         7.0            8.0  4.853872  \n",
       "7231  3.09270         7.0            8.0  5.000000  \n",
       "7232  6.72840         3.0            5.0  4.966576  \n",
       "\n",
       "[5838 rows x 9 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2class = df_final[df_final['class'] != 'intermediate']\n",
    "df_2class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "515e7867",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2class.to_csv('beta_secretase_bioactivity_data_2class_pIC50.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2103282",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (Chemical Space Analysis) via Lipinski descriptors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "188589ff",
   "metadata": {},
   "source": [
    "### Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68413d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style = 'ticks')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d9cc3786",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create color palette variable\n",
    "palette = ['#953019', '#9EBC9F']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98b67b6",
   "metadata": {},
   "source": [
    "### Frequency plot of the 2 bioactivity classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ff6ce67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5.5, 5.5))\n",
    "\n",
    "sns.countplot(x = 'class', data = df_2class, edgecolor = 'black', palette = palette)\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",
   "id": "bb23ea97",
   "metadata": {},
   "source": [
    "### Scatter plot of MW versus LogP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4d51f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5.5, 5.5))\n",
    "\n",
    "sns.scatterplot(x = 'MW', y = 'LogP', data = df_2class, hue = 'class', size = 'pIC50', edgecolor = 'black', palette = palette, 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",
    "\n",
    "plt.savefig('plot_MW_vs_LogP.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd6b606",
   "metadata": {},
   "source": [
    "### Box plot of pIC50 value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "02914217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'pIC50', data = df_2class, palette = palette)\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",
   "id": "4e627738",
   "metadata": {},
   "source": [
    "### Statistical Analysis: Mann-Whitney U Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "46c63e17",
   "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",
    "\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": 38,
   "id": "992ed808",
   "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>5664461.0</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   5664461.0  0.0   0.05  Different distribution (reject H0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('pIC50')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "610d9787",
   "metadata": {},
   "source": [
    "### MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c2ef3c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'MW', data = df_2class, palette = palette)\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": 40,
   "id": "b6a7bf3b",
   "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>3126754.5</td>\n",
       "      <td>2.023387e-08</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   3126754.5  2.023387e-08   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('MW')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512683a6",
   "metadata": {},
   "source": [
    "### LogP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "09c38fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'LogP', data = df_2class, palette = palette)\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": 42,
   "id": "9d1cc1bb",
   "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>3142493.0</td>\n",
       "      <td>3.425826e-09</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   3142493.0  3.425826e-09   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('LogP')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a289c143",
   "metadata": {},
   "source": [
    "### NumHDonors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a1c2428a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'NumHDonors', data = df_2class, palette = palette)\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": 44,
   "id": "c82af3ca",
   "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>2595935.0</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Descriptor  Statistics         p  alpha                      Interpretation\n",
       "0  NumHDonors   2595935.0  0.000002   0.05  Different distribution (reject H0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHDonors')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3548a09a",
   "metadata": {},
   "source": [
    "### NumHAcceptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fa873dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "\n",
    "sns.boxplot(x = 'class', y = 'NumHAcceptors', data = df_2class, palette = palette)\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": 46,
   "id": "d4df2156",
   "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>3155363.5</td>\n",
       "      <td>3.596305e-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  NumHAcceptors   3155363.5  3.596305e-10   0.05   \n",
       "\n",
       "                       Interpretation  \n",
       "0  Different distribution (reject H0)  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney('NumHAcceptors')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff4bf99",
   "metadata": {},
   "source": [
    "### Interpretation of Statistical Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440c0b6a",
   "metadata": {},
   "source": [
    "**Box Plots**\n",
    "\n",
    "**pIC50**\n",
    "When looking at the pIC50 values, the actives and inactives displayed a statistically significant difference. This is expected because the threshold values were defined as IC50 < 1,000 nM = Actives while IC50 > 10,000 nM = Inactives. This corresponds to pIC50 > 6 = Actives and pIC50 < 5 = Inactives. \n",
    "\n",
    "**Lipinski's descriptors**\n",
    "ALl of the 4 Lipinski's descriptrs displayed a statisitcally significance difference between actives and inactives.\n",
    "\n"
   ]
  }
 ],
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