3_model_building.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style='ticks')\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Unnamed: 0</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>bioactivity</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>0</td>\n",
       "      <td>CHEMBL330863</td>\n",
       "      <td>COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...</td>\n",
       "      <td>active</td>\n",
       "      <td>576.742</td>\n",
       "      <td>5.28050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.892790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL124660</td>\n",
       "      <td>COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...</td>\n",
       "      <td>active</td>\n",
       "      <td>562.715</td>\n",
       "      <td>5.03450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.657577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>493.615</td>\n",
       "      <td>4.59032</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.522879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>CHEMBL124035</td>\n",
       "      <td>COCCOc1cc2ncnc(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC...</td>\n",
       "      <td>active</td>\n",
       "      <td>495.580</td>\n",
       "      <td>3.80490</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.346787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>CHEMBL125898</td>\n",
       "      <td>COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...</td>\n",
       "      <td>active</td>\n",
       "      <td>564.687</td>\n",
       "      <td>3.88080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.397940</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>2864</th>\n",
       "      <td>3298</td>\n",
       "      <td>CHEMBL4218162</td>\n",
       "      <td>Nc1ncnc2c1c(-c1ccc(Oc3ccccc3)cc1)cn2[C@H]1CC[C...</td>\n",
       "      <td>active</td>\n",
       "      <td>405.550</td>\n",
       "      <td>3.47700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.301030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2865</th>\n",
       "      <td>3299</td>\n",
       "      <td>CHEMBL4218371</td>\n",
       "      <td>CC(C)n1cnc2c(Nc3ccc(Br)cc3)nc(N[C@H]3CC[C@H](N...</td>\n",
       "      <td>active</td>\n",
       "      <td>506.658</td>\n",
       "      <td>4.88200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2866</th>\n",
       "      <td>3301</td>\n",
       "      <td>CHEMBL4216401</td>\n",
       "      <td>N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Br)cc3)c3ncn(C4CCC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>509.622</td>\n",
       "      <td>2.76000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.397940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2867</th>\n",
       "      <td>3302</td>\n",
       "      <td>CHEMBL4207535</td>\n",
       "      <td>N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Cl)cc3)c3ncn(C4CCC...</td>\n",
       "      <td>active</td>\n",
       "      <td>495.595</td>\n",
       "      <td>2.36990</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.301030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2868</th>\n",
       "      <td>3303</td>\n",
       "      <td>CHEMBL4215850</td>\n",
       "      <td>CC(O)CNc1nc(Nc2ccc(Cl)cc2)c2ncn(C3CCCC3)c2n1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>480.580</td>\n",
       "      <td>2.63270</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.763715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2869 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 molecule_chembl_id  \\\n",
       "0              0       CHEMBL330863   \n",
       "1              1       CHEMBL124660   \n",
       "2              4          CHEMBL941   \n",
       "3              5       CHEMBL124035   \n",
       "4              6       CHEMBL125898   \n",
       "...          ...                ...   \n",
       "2864        3298      CHEMBL4218162   \n",
       "2865        3299      CHEMBL4218371   \n",
       "2866        3301      CHEMBL4216401   \n",
       "2867        3302      CHEMBL4207535   \n",
       "2868        3303      CHEMBL4215850   \n",
       "\n",
       "                                       canonical_smiles bioactivity       MW  \\\n",
       "0     COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...      active  576.742   \n",
       "1     COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...      active  562.715   \n",
       "2     Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc...    inactive  493.615   \n",
       "3     COCCOc1cc2ncnc(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC...      active  495.580   \n",
       "4     COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...      active  564.687   \n",
       "...                                                 ...         ...      ...   \n",
       "2864  Nc1ncnc2c1c(-c1ccc(Oc3ccccc3)cc1)cn2[C@H]1CC[C...      active  405.550   \n",
       "2865  CC(C)n1cnc2c(Nc3ccc(Br)cc3)nc(N[C@H]3CC[C@H](N...      active  506.658   \n",
       "2866  N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Br)cc3)c3ncn(C4CCC...    inactive  509.622   \n",
       "2867  N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Cl)cc3)c3ncn(C4CCC...      active  495.595   \n",
       "2868       CC(O)CNc1nc(Nc2ccc(Cl)cc2)c2ncn(C3CCCC3)c2n1    inactive  480.580   \n",
       "\n",
       "         LogP  NumHDonors  NumHAcceptors     pIC50  \n",
       "0     5.28050         1.0            8.0  6.892790  \n",
       "1     5.03450         1.0            8.0  6.657577  \n",
       "2     4.59032         2.0            7.0  4.522879  \n",
       "3     3.80490         1.0            8.0  6.346787  \n",
       "4     3.88080         1.0            9.0  7.397940  \n",
       "...       ...         ...            ...       ...  \n",
       "2864  3.47700         1.0            7.0  8.301030  \n",
       "2865  4.88200         2.0            8.0  8.000000  \n",
       "2866  2.76000         1.0           11.0  8.397940  \n",
       "2867  2.36990         1.0           11.0  8.301030  \n",
       "2868  2.63270         2.0           10.0  5.763715  \n",
       "\n",
       "[2869 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#open the file we saved in last part(data processing)\n",
    "df=pd.read_csv('D:\\data_eda.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets create a countplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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='bioactivity', data=df, edgecolor='black')\n",
    "\n",
    "plt.xlabel('Bioactivity class', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=14, fontweight='bold')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'logP')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.scatterplot(x='MW',y='LogP',data=df,hue='bioactivity',size='pIC50')\n",
    "plt.xlabel('MW', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('logP', fontsize=14, fontweight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'pIC50')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.boxplot(x='bioactivity',y='pIC50',data=df)\n",
    "plt.xlabel('Bioactivity', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('pIC50', fontsize=14, fontweight='bold')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Molecular fingerprints are a way of encoding the structure of a molecule. The most common type of fingerprint is a series of binary digits (bits) that represent the presence or absence of particular substructures in the molecule.\n",
    "\n",
    "lets generate molecular fingerprint data using canonical smiles present in our dataset.\n",
    "I used paDEL-Descriptors software (http://www.yapcwsoft.com/dd/padeldescriptor/)\n",
    "\n",
    "PubChem Substructure Fingerprint Description\n",
    "\n",
    "\n",
    "Section 1: Hierarchic Element Counts - These bits test for the \n",
    "presence or count of individual chemical atoms \n",
    "represented by their atomic symbol.\n",
    "\n",
    "Bit Position\tBit Substructure\n",
    "0\t>= 4 H\n",
    "1 \t>= 8 H\n",
    "2\t>= 16 H\n",
    "3\t>= 32 H\n",
    "4\t>= 1 Li\n",
    "5\t>= 2 Li\n",
    "6\t>= 1 B\n",
    "7\t>= 2 B\n",
    "8\t>= 4 B\n",
    "9\t>= 2 C\n",
    "10\t>= 4 C\n",
    "11\t>= 8 C\n",
    "12\t>= 16 C\n",
    "13\t>= 32 C\n",
    "14\t>= 1 N\n",
    "15\t>= 2 N\n",
    "16\t>= 4 N\n",
    "17\t>= 8 N\n",
    "18\t>= 1 O\n",
    "19\t>= 2 O\n",
    "20\t>= 4 O\n",
    "21\t>= 8 O\n",
    "22\t>= 16 O\n",
    "23\t>= 1 F\n",
    "24\t>= 2 F\n",
    "25\t>= 4 F\n",
    "26\t>= 1 Na\n",
    "27\t>= 2 Na\n",
    "28\t>= 1 Si\n",
    "29\t>= 2 Si\n",
    "30\t>= 1 P\n",
    "31\t>= 2 P\n",
    "32\t>= 4 P\n",
    "33\t>= 1 S\n",
    "34\t>= 2 S\n",
    "35\t>= 4 S\n",
    "36\t>= 8 S\n",
    "37\t>= 1 Cl\n",
    "38\t>= 2 Cl\n",
    "39\t>= 4 Cl\n",
    "40\t>= 8 Cl\n",
    "41\t>= 1 K\n",
    "42\t>= 2 K\n",
    "43\t>= 1 Br\n",
    "44\t>= 2 Br\n",
    "45\t>= 4 Br\n",
    "46\t>= 1 I\n",
    "47\t>= 2 I\n",
    "48\t>= 4 I\n",
    "49 \t>= 1 Be\n",
    "50\t>= 1 Mg\n",
    "51\t>= 1 Al\n",
    "52\t>= 1 Ca\n",
    "53\t>= 1 Sc\n",
    "54\t>= 1 Ti\n",
    "55\t>= 1 V\n",
    "56\t>= 1 Cr\n",
    "57\t>= 1 Mn\n",
    "58\t>= 1 Fe\n",
    "59\t>= 1 Co\n",
    "60\t>= 1 Ni\n",
    "61\t>= 1 Cu\n",
    "62\t>= 1 Zn\n",
    "63\t>= 1 Ga\n",
    "64\t>= 1 Ge\n",
    "65\t>= 1 As\n",
    "66\t>= 1 Se\n",
    "67\t>= 1 Kr\n",
    "68\t>= 1 Rb\n",
    "69\t>= 1 Sr\n",
    "70\t>= 1 Y\n",
    "71\t>= 1 Zr\n",
    "72\t>= 1 Nb\n",
    "73\t>= 1 Mo\n",
    "74\t>= 1 Ru\n",
    "75\t>= 1 Rh\n",
    "76\t>= 1 Pd\n",
    "77\t>= 1 Ag\n",
    "78\t>= 1 Cd\n",
    "79\t>= 1 In\n",
    "80\t>= 1 Sn\n",
    "81\t>= 1 Sb\n",
    "82\t>= 1 Te\n",
    "83\t>= 1 Xe\n",
    "84\t>= 1 Cs\n",
    "85\t>= 1 Ba\n",
    "86\t>= 1 Lu\n",
    "87\t>= 1 Hf\n",
    "88\t>= 1 Ta\n",
    "89\t>= 1 W\n",
    "90\t>= 1 Re\n",
    "91\t>= 1 Os\n",
    "92\t>= 1 Ir\n",
    "93\t>= 1 Pt\n",
    "94\t>= 1 Au\n",
    "95\t>= 1 Hg\n",
    "96\t>= 1 Tl\n",
    "97\t>= 1 Pb\n",
    "98\t>= 1 Bi\n",
    "99\t>= 1 La\n",
    "100\t>= 1 Ce\n",
    "101\t>= 1 Pr\n",
    "102\t>= 1 Nd\n",
    "103\t>= 1 Pm\n",
    "104\t>= 1 Sm\n",
    "105\t>= 1 Eu\n",
    "106\t>= 1 Gd\n",
    "107\t>= 1 Tb\n",
    "108\t>= 1 Dy\n",
    "109\t>= 1 Ho\n",
    "110\t>= 1 Er\n",
    "111\t>= 1 Tm\n",
    "112\t>= 1 Yb\n",
    "113\t>= 1 Tc\n",
    "114\t>= 1 U\n",
    "\n",
    "\n",
    "\n",
    "Section 2: Rings in a canonic Extended Smallest Set of Smallest \n",
    "Rings (ESSSR) ring set - These bits test for the presence \n",
    "or count of the described chemical ring system.  An ESSSR \n",
    "ring is any ring which does not share three consecutive \n",
    "atoms with any other ring in the chemical structure.  For \n",
    "example, naphthalene has three ESSSR rings (two phenyl \n",
    "fragments and the 10-membered envelope), while biphenyl \n",
    "will yield a count of only two ESSSR rings.\n",
    "\n",
    "Bit Position\tBit Substructure\n",
    "115\t>= 1 any ring size 3\n",
    "116\t>= 1 saturated or aromatic carbon-only ring size 3\n",
    "117\t>= 1 saturated or aromatic nitrogen-containing ring size 3\n",
    "118\t>= 1 saturated or aromatic heteroatom-containing ring size 3\n",
    "119\t>= 1 unsaturated non-aromatic carbon-only ring size 3\n",
    "120\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 3\n",
    "121\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 3\n",
    "122\t>= 2 any ring size 3\n",
    "123\t>= 2 saturated or aromatic carbon-only ring size 3\n",
    "124\t>= 2 saturated or aromatic nitrogen-containing ring size 3\n",
    "125\t>= 2 saturated or aromatic heteroatom-containing ring size 3\n",
    "126\t>= 2 unsaturated non-aromatic carbon-only ring size 3\n",
    "127\t>= 2 unsaturated non-aromatic nitrogen-containing ring size 3\n",
    "128\t>= 2 unsaturated non-aromatic heteroatom-containing ring size 3\n",
    "\n",
    "129\t>= 1 any ring size 4\n",
    "130\t>= 1 saturated or aromatic carbon-only ring size 4\n",
    "131\t>= 1 saturated or aromatic nitrogen-containing ring size 4\n",
    "132\t>= 1 saturated or aromatic heteroatom-containing ring size 4\n",
    "133\t>= 1 unsaturated non-aromatic carbon-only ring size 4\n",
    "134\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 4\n",
    "135\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 4\n",
    "136\t>= 2 any ring size 4\n",
    "137\t>= 2 saturated or aromatic carbon-only ring size 4\n",
    "138\t>= 2 saturated or aromatic nitrogen-containing ring size 4\n",
    "139\t>= 2 saturated or aromatic heteroatom-containing ring size 4\n",
    "140\t>= 2 unsaturated non-aromatic carbon-only ring size 4\n",
    "141\t>= 2 unsaturated non-aromatic nitrogen-containing ring size 4\n",
    "142\t>= 2 unsaturated non-aromatic heteroatom-containing ring size 4\n",
    "\n",
    "143\t>= 1 any ring size 5\n",
    "144\t>= 1 saturated or aromatic carbon-only ring size 5\n",
    "145\t>= 1 saturated or aromatic nitrogen-containing ring size 5\n",
    "146\t>= 1 saturated or aromatic heteroatom-containing ring size 5\n",
    "147\t>= 1 unsaturated non-aromatic carbon-only ring size 5\n",
    "148\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 5\n",
    "149\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 5\n",
    "150\t>= 2 any ring size 5\n",
    "151\t>= 2 saturated or aromatic carbon-only ring size 5\n",
    "152\t>= 2 saturated or aromatic nitrogen-containing ring size 5\n",
    "153\t>= 2 saturated or aromatic heteroatom-containing ring size 5\n",
    "154\t>= 2 unsaturated non-aromatic carbon-only ring size 5\n",
    "155\t>= 2 unsaturated non-aromatic nitrogen-containing ring size 5\n",
    "156\t>= 2 unsaturated non-aromatic heteroatom-containing ring size 5\n",
    "157\t>= 3 any ring size 5\n",
    "158\t>= 3 saturated or aromatic carbon-only ring size 5\n",
    "159\t>= 3 saturated or aromatic nitrogen-containing ring size 5\n",
    "160\t>= 3 saturated or aromatic heteroatom-containing ring size 5\n",
    "161\t>= 3 unsaturated non-aromatic carbon-only ring size 5\n",
    "162\t>= 3 unsaturated non-aromatic nitrogen-containing ring size 5\n",
    "163\t>= 3 unsaturated non-aromatic heteroatom-containing ring size 5\n",
    "164\t>= 4 any ring size 5\n",
    "165\t>= 4 saturated or aromatic carbon-only ring size 5\n",
    "166\t>= 4 saturated or aromatic nitrogen-containing ring size 5\n",
    "167\t>= 4 saturated or aromatic heteroatom-containing ring size 5\n",
    "168\t>= 4 unsaturated non-aromatic carbon-only ring size 5\n",
    "169\t>= 4 unsaturated non-aromatic nitrogen-containing ring size 5\n",
    "170\t>= 4 unsaturated non-aromatic heteroatom-containing ring size 5\n",
    "171\t>= 5 any ring size 5\n",
    "172\t>= 5 saturated or aromatic carbon-only ring size 5\n",
    "173\t>= 5 saturated or aromatic nitrogen-containing ring size 5\n",
    "174\t>= 5 saturated or aromatic heteroatom-containing ring size 5\n",
    "175\t>= 5 unsaturated non-aromatic carbon-only ring size 5\n",
    "176\t>= 5 unsaturated non-aromatic nitrogen-containing ring size 5\n",
    "177\t>= 5 unsaturated non-aromatic heteroatom-containing ring size 5\n",
    "\n",
    "178\t>= 1 any ring size 6\n",
    "179\t>= 1 saturated or aromatic carbon-only ring size 6\n",
    "180\t>= 1 saturated or aromatic nitrogen-containing ring size 6\n",
    "181\t>= 1 saturated or aromatic heteroatom-containing ring size 6\n",
    "182\t>= 1 unsaturated non-aromatic carbon-only ring size 6\n",
    "183\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 6\n",
    "184\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 6\n",
    "185\t>= 2 any ring size 6\n",
    "186\t>= 2 saturated or aromatic carbon-only ring size 6\n",
    "187\t>= 2 saturated or aromatic nitrogen-containing ring size 6\n",
    "188\t>= 2 saturated or aromatic heteroatom-containing ring size 6\n",
    "189\t>= 2 unsaturated non-aromatic carbon-only ring size 6\n",
    "190\t>= 2 unsaturated non-aromatic nitrogen-containing ring size 6\n",
    "191\t>= 2 unsaturated non-aromatic heteroatom-containing ring size 6\n",
    "192\t>= 3 any ring size 6\n",
    "193\t>= 3 saturated or aromatic carbon-only ring size 6\n",
    "194\t>= 3 saturated or aromatic nitrogen-containing ring size 6\n",
    "195\t>= 3 saturated or aromatic heteroatom-containing ring size 6\n",
    "196\t>= 3 unsaturated non-aromatic carbon-only ring size 6\n",
    "197\t>= 3 unsaturated non-aromatic nitrogen-containing ring size 6\n",
    "198\t>= 3 unsaturated non-aromatic heteroatom-containing ring size 6\n",
    "199\t>= 4 any ring size 6\n",
    "200\t>= 4 saturated or aromatic carbon-only ring size 6\n",
    "201\t>= 4 saturated or aromatic nitrogen-containing ring size 6\n",
    "202\t>= 4 saturated or aromatic heteroatom-containing ring size 6\n",
    "203\t>= 4 unsaturated non-aromatic carbon-only ring size 6\n",
    "204\t>= 4 unsaturated non-aromatic nitrogen-containing ring size 6\n",
    "205\t>= 4 unsaturated non-aromatic heteroatom-containing ring size 6\n",
    "206\t>= 5 any ring size 6\n",
    "207\t>= 5 saturated or aromatic carbon-only ring size 6\n",
    "208\t>= 5 saturated or aromatic nitrogen-containing ring size 6\n",
    "209\t>= 5 saturated or aromatic heteroatom-containing ring size 6\n",
    "210\t>= 5 unsaturated non-aromatic carbon-only ring size 6\n",
    "211\t>= 5 unsaturated non-aromatic nitrogen-containing ring size 6\n",
    "212\t>= 5 unsaturated non-aromatic heteroatom-containing ring size 6\n",
    "\n",
    "213\t>= 1 any ring size 7\n",
    "214\t>= 1 saturated or aromatic carbon-only ring size 7\n",
    "215\t>= 1 saturated or aromatic nitrogen-containing ring size 7\n",
    "216\t>= 1 saturated or aromatic heteroatom-containing ring size 7\n",
    "217\t>= 1 unsaturated non-aromatic carbon-only ring size 7\n",
    "218\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 7\n",
    "219\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 7\n",
    "220\t>= 2 any ring size 7\n",
    "221\t>= 2 saturated or aromatic carbon-only ring size 7\n",
    "222\t>= 2 saturated or aromatic nitrogen-containing ring size 7\n",
    "223\t>= 2 saturated or aromatic heteroatom-containing ring size 7\n",
    "224\t>= 2 unsaturated non-aromatic carbon-only ring size 7\n",
    "225\t>= 2 unsaturated non-aromatic nitrogen-containing ring size 7\n",
    "226\t>= 2 unsaturated non-aromatic heteroatom-containing ring size 7\n",
    "\n",
    "227\t>= 1 any ring size 8\n",
    "228\t>= 1 saturated or aromatic carbon-only ring size 8\n",
    "229\t>= 1 saturated or aromatic nitrogen-containing ring size 8\n",
    "230\t>= 1 saturated or aromatic heteroatom-containing ring size 8\n",
    "231\t>= 1 unsaturated non-aromatic carbon-only ring size 8\n",
    "232\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 8\n",
    "233\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 8\n",
    "234\t>= 2 any ring size 8\n",
    "235\t>= 2 saturated or aromatic carbon-only ring size 8\n",
    "236\t>= 2 saturated or aromatic nitrogen-containing ring size 8\n",
    "237\t>= 2 saturated or aromatic heteroatom-containing ring size 8\n",
    "238\t>= 2 unsaturated non-aromatic carbon-only ring size 8\n",
    "239\t>= 2 unsaturated non-aromatic nitrogen-containing ring size 8\n",
    "240\t>= 2 unsaturated non-aromatic heteroatom-containing ring size 8\n",
    "\n",
    "241\t>= 1 any ring size 9\n",
    "242\t>= 1 saturated or aromatic carbon-only ring size 9\n",
    "243\t>= 1 saturated or aromatic nitrogen-containing ring size 9\n",
    "244\t>= 1 saturated or aromatic heteroatom-containing ring size 9\n",
    "245\t>= 1 unsaturated non-aromatic carbon-only ring size 9\n",
    "246\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 9\n",
    "247\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 9\n",
    "\n",
    "248\t>= 1 any ring size 10\n",
    "249\t>= 1 saturated or aromatic carbon-only ring size 10\n",
    "250\t>= 1 saturated or aromatic nitrogen-containing ring size 10\n",
    "251\t>= 1 saturated or aromatic heteroatom-containing ring size 10\n",
    "252\t>= 1 unsaturated non-aromatic carbon-only ring size 10\n",
    "253\t>= 1 unsaturated non-aromatic nitrogen-containing ring size 10\n",
    "254\t>= 1 unsaturated non-aromatic heteroatom-containing ring size 10\n",
    "\n",
    "255\t>= 1 aromatic ring\n",
    "256\t>= 1 hetero-aromatic ring\n",
    "257\t>= 2 aromatic rings\n",
    "258\t>= 2 hetero-aromatic rings\n",
    "259\t>= 3 aromatic rings\n",
    "260\t>= 3 hetero-aromatic rings\n",
    "261\t>= 4 aromatic rings\n",
    "262\t>= 4 hetero-aromatic rings\n",
    "\n",
    "\n",
    "\n",
    "Section 3: Simple atom pairs - These bits test for the presence of \n",
    "patterns of bonded atom pairs, regardless of bond order \n",
    "or count.\n",
    "\n",
    "Bit Position\tBit Substructure\n",
    "263\tLi-H\n",
    "264\tLi-Li\n",
    "265\tLi-B\n",
    "266\tLi-C\n",
    "267\tLi-O\n",
    "268\tLi-F\n",
    "269\tLi-P\n",
    "270\tLi-S\n",
    "271\tLi-Cl\n",
    "272\tB-H\n",
    "273\tB-B\n",
    "274\tB-C\n",
    "275\tB-N\n",
    "276\tB-O\n",
    "277\tB-F\n",
    "278\tB-Si\n",
    "279\tB-P\n",
    "280\tB-S\n",
    "281\tB-Cl\n",
    "282\tB-Br\n",
    "283\tC-H\n",
    "284\tC-C\n",
    "285\tC-N\n",
    "286\tC-O\n",
    "287\tC-F\n",
    "288\tC-Na\n",
    "289\tC-Mg\n",
    "290\tC-Al\n",
    "291\tC-Si\n",
    "292\tC-P\n",
    "293\tC-S\n",
    "294\tC-Cl\n",
    "295\tC-As\n",
    "296\tC-Se\n",
    "297\tC-Br\n",
    "298\tC-I\n",
    "299\tN-H\n",
    "300\tN-N\n",
    "301\tN-O\n",
    "302\tN-F\n",
    "303\tN-Si\n",
    "304\tN-P\n",
    "305\tN-S\n",
    "306\tN-Cl\n",
    "307\tN-Br\n",
    "308\tO-H\n",
    "309\tO-O\n",
    "310\tO-Mg\n",
    "311\tO-Na\n",
    "312\tO-Al\n",
    "313\tO-Si\n",
    "314\tO-P\n",
    "315\tO-K\n",
    "316\tF-P\n",
    "317\tF-S\n",
    "318\tAl-H\n",
    "319\tAl-Cl\n",
    "320\tSi-H\n",
    "321\tSi-Si\n",
    "322\tSi-Cl\n",
    "323\tP-H\n",
    "324\tP-P\n",
    "325\tAs-H\n",
    "326\tAs-As\n",
    "\n",
    "\n",
    "\n",
    "Section 4: Simple atom nearest neighbors - These bits test for the \n",
    "presence of atom nearest neighbor patterns, regardless of \n",
    "bond order (denoted by \"~\") or count, but where bond \n",
    "aromaticity (denoted by \":\") is significant.\n",
    "\n",
    "Bit Position\tBit Substructure\n",
    "327\tC(~Br)(~C)\n",
    "328\tC(~Br)(~C)(~C)\n",
    "329\tC(~Br)(~H)\n",
    "330\tC(~Br)(:C)\n",
    "331\tC(~Br)(:N)\n",
    "332\tC(~C)(~C)\n",
    "333\tC(~C)(~C)(~C)\n",
    "334\tC(~C)(~C)(~C)(~C)\n",
    "335\tC(~C)(~C)(~C)(~H)\n",
    "336\tC(~C)(~C)(~C)(~N)\n",
    "337\tC(~C)(~C)(~C)(~O)\n",
    "338\tC(~C)(~C)(~H)(~N)\n",
    "339\tC(~C)(~C)(~H)(~O)\n",
    "340\tC(~C)(~C)(~N)\n",
    "341\tC(~C)(~C)(~O)\n",
    "342\tC(~C)(~Cl)\n",
    "343\tC(~C)(~Cl)(~H)\n",
    "344\tC(~C)(~H)\n",
    "345\tC(~C)(~H)(~N)\n",
    "346\tC(~C)(~H)(~O)\n",
    "347\tC(~C)(~H)(~O)(~O)\n",
    "348\tC(~C)(~H)(~P)\n",
    "349\tC(~C)(~H)(~S)\n",
    "350\tC(~C)(~I)\n",
    "351\tC(~C)(~N)\n",
    "352\tC(~C)(~O)\n",
    "353\tC(~C)(~S)\n",
    "354\tC(~C)(~Si)\n",
    "355\tC(~C)(:C)\n",
    "356\tC(~C)(:C)(:C)\n",
    "357\tC(~C)(:C)(:N)\n",
    "358\tC(~C)(:N)\n",
    "359\tC(~C)(:N)(:N)\n",
    "360\tC(~Cl)(~Cl)\n",
    "361\tC(~Cl)(~H)\n",
    "362\tC(~Cl)(:C)\n",
    "363\tC(~F)(~F)\n",
    "364\tC(~F)(:C)\n",
    "365\tC(~H)(~N)\n",
    "366\tC(~H)(~O)\n",
    "367\tC(~H)(~O)(~O)\n",
    "368\tC(~H)(~S)\n",
    "369\tC(~H)(~Si)\n",
    "370\tC(~H)(:C)\n",
    "371\tC(~H)(:C)(:C)\n",
    "372\tC(~H)(:C)(:N)\n",
    "373\tC(~H)(:N)\n",
    "374\tC(~H)(~H)(~H)\n",
    "375\tC(~N)(~N)\n",
    "376\tC(~N)(:C)\n",
    "377\tC(~N)(:C)(:C)\n",
    "378\tC(~N)(:C)(:N)\n",
    "379\tC(~N)(:N)\n",
    "380\tC(~O)(~O)\n",
    "381\tC(~O)(:C)\n",
    "382\tC(~O)(:C)(:C)\n",
    "383\tC(~S)(:C)\n",
    "384\tC(:C)(:C)\n",
    "385\tC(:C)(:C)(:C)\n",
    "386\tC(:C)(:C)(:N)\n",
    "387\tC(:C)(:N)\n",
    "388\tC(:C)(:N)(:N)\n",
    "389\tC(:N)(:N)\n",
    "390\tN(~C)(~C)\n",
    "391\tN(~C)(~C)(~C)\n",
    "392\tN(~C)(~C)(~H)\n",
    "393\tN(~C)(~H)\n",
    "394\tN(~C)(~H)(~N)\n",
    "395\tN(~C)(~O)\n",
    "396\tN(~C)(:C)\n",
    "397\tN(~C)(:C)(:C)\n",
    "398\tN(~H)(~N)\n",
    "399\tN(~H)(:C)\n",
    "400\tN(~H)(:C)(:C)\n",
    "401\tN(~O)(~O)\n",
    "402\tN(~O)(:O)\n",
    "403\tN(:C)(:C)\n",
    "404\tN(:C)(:C)(:C)\n",
    "405\tO(~C)(~C)\n",
    "406\tO(~C)(~H)\n",
    "407\tO(~C)(~P)\n",
    "408\tO(~H)(~S)\n",
    "409\tO(:C)(:C)\n",
    "410\tP(~C)(~C)\n",
    "411\tP(~O)(~O)\n",
    "412\tS(~C)(~C)\n",
    "413\tS(~C)(~H)\n",
    "414\tS(~C)(~O)\n",
    "415\tSi(~C)(~C)\n",
    "\n",
    "\n",
    "\n",
    "Section 5: Detailed atom neighborhoods - These bits test for the \n",
    "presence of detailed atom neighborhood patterns, \n",
    "regardless of count, but where bond orders are specific, \n",
    "bond aromaticity matches both single and double bonds, \n",
    "and where \"-\", \"=\", and \"#\" matches a single bond, double \n",
    "bond, and triple bond order, respectively.\n",
    "\n",
    "Bit Position\tBit Substructure\n",
    "416\tC=C\n",
    "417\tC#C\n",
    "418\tC=N\n",
    "419\tC#N\n",
    "420\tC=O\n",
    "421\tC=S\n",
    "422\tN=N\n",
    "423\tN=O\n",
    "424\tN=P\n",
    "425\tP=O\n",
    "426\tP=P\n",
    "427\tC(#C)(-C)\n",
    "428\tC(#C)(-H)\n",
    "429\tC(#N)(-C)\n",
    "430\tC(-C)(-C)(=C)\n",
    "431\tC(-C)(-C)(=N)\n",
    "432\tC(-C)(-C)(=O)\n",
    "433\tC(-C)(-Cl)(=O)\n",
    "434\tC(-C)(-H)(=C)\n",
    "435\tC(-C)(-H)(=N)\n",
    "436\tC(-C)(-H)(=O)\n",
    "437\tC(-C)(-N)(=C)\n",
    "438\tC(-C)(-N)(=N)\n",
    "439\tC(-C)(-N)(=O)\n",
    "440\tC(-C)(-O)(=O)\n",
    "441\tC(-C)(=C)\n",
    "442\tC(-C)(=N)\n",
    "443\tC(-C)(=O)\n",
    "444\tC(-Cl)(=O)\n",
    "445\tC(-H)(-N)(=C)\n",
    "446\tC(-H)(=C)\n",
    "447\tC(-H)(=N)\n",
    "448\tC(-H)(=O)\n",
    "449\tC(-N)(=C)\n",
    "450\tC(-N)(=N)\n",
    "451\tC(-N)(=O)\n",
    "452\tC(-O)(=O)\n",
    "453\tN(-C)(=C)\n",
    "454\tN(-C)(=O)\n",
    "455\tN(-O)(=O)\n",
    "456\tP(-O)(=O)\n",
    "457\tS(-C)(=O)\n",
    "458\tS(-O)(=O)\n",
    "459\tS(=O)(=O)\n",
    "\n",
    "\n",
    "\n",
    "Section 6: Simple SMARTS patterns - These bits test for the \n",
    "presence of simple SMARTS patterns, regardless of count, \n",
    "but where bond orders are specific and bond aromaticity \n",
    "matches both single and double bonds.\n",
    "\n",
    "Bit Position\tBit Substructure\n",
    "460\tC-C-C#C\n",
    "461\tO-C-C=N\n",
    "462\tO-C-C=O\n",
    "463\tN:C-S-[#1]\n",
    "464\tN-C-C=C\n",
    "465\tO=S-C-C\n",
    "466\tN#C-C=C\n",
    "467\tC=N-N-C\n",
    "468\tO=S-C-N\n",
    "469\tS-S-C:C\n",
    "470\tC:C-C=C\n",
    "471\tS:C:C:C\n",
    "472\tC:N:C-C\n",
    "473\tS-C:N:C\n",
    "474\tS:C:C:N\n",
    "475\tS-C=N-C\n",
    "476\tC-O-C=C\n",
    "477\tN-N-C:C\n",
    "478\tS-C=N-[#1]\n",
    "479\tS-C-S-C\n",
    "480\tC:S:C-C\n",
    "481\tO-S-C:C\n",
    "482\tC:N-C:C\n",
    "483\tN-S-C:C\n",
    "484\tN-C:N:C\n",
    "485\tN:C:C:N\n",
    "486\tN-C:N:N\n",
    "487\tN-C=N-C\n",
    "488\tN-C=N-[#1]\n",
    "489\tN-C-S-C\n",
    "490\tC-C-C=C\n",
    "491\tC-N:C-[#1]\n",
    "492\tN-C:O:C\n",
    "493\tO=C-C:C\n",
    "494\tO=C-C:N\n",
    "495\tC-N-C:C\n",
    "496\tN:N-C-[#1]\n",
    "497\tO-C:C:N\n",
    "498\tO-C=C-C\n",
    "499\tN-C:C:N\n",
    "500\tC-S-C:C\n",
    "501\tCl-C:C-C\n",
    "502\tN-C=C-[#1]\n",
    "503\tCl-C:C-[#1]\n",
    "504\tN:C:N-C\n",
    "505\tCl-C:C-O\n",
    "506\tC-C:N:C\n",
    "507\tC-C-S-C\n",
    "508\tS=C-N-C\n",
    "509\tBr-C:C-C\n",
    "510\t[#1]-N-N-[#1]\n",
    "511\tS=C-N-[#1]\n",
    "512\tC-[As]-O-[#1]\n",
    "513\tS:C:C-[#1]\n",
    "514\tO-N-C-C\n",
    "515\tN-N-C-C\n",
    "516\t[#1]-C=C-[#1]\n",
    "517\tN-N-C-N\n",
    "518\tO=C-N-N\n",
    "519\tN=C-N-C\n",
    "520\tC=C-C:C\n",
    "521\tC:N-C-[#1]\n",
    "522\tC-N-N-[#1]\n",
    "523\tN:C:C-C\n",
    "524\tC-C=C-C\n",
    "525\t[As]-C:C-[#1]\n",
    "526\tCl-C:C-Cl\n",
    "527\tC:C:N-[#1]\n",
    "528\t[#1]-N-C-[#1]\n",
    "529\tCl-C-C-Cl\n",
    "530\tN:C-C:C\n",
    "531\tS-C:C-C\n",
    "532\tS-C:C-[#1]\n",
    "533\tS-C:C-N\n",
    "534\tS-C:C-O\n",
    "535\tO=C-C-C\n",
    "536\tO=C-C-N\n",
    "537\tO=C-C-O\n",
    "538\tN=C-C-C\n",
    "539\tN=C-C-[#1]\n",
    "540\tC-N-C-[#1]\n",
    "541\tO-C:C-C\n",
    "542\tO-C:C-[#1]\n",
    "543\tO-C:C-N\n",
    "544\tO-C:C-O\n",
    "545\tN-C:C-C\n",
    "546\tN-C:C-[#1]\n",
    "547\tN-C:C-N\n",
    "548\tO-C-C:C\n",
    "549\tN-C-C:C\n",
    "550\tCl-C-C-C\n",
    "551\tCl-C-C-O\n",
    "552\tC:C-C:C\n",
    "553\tO=C-C=C\n",
    "554\tBr-C-C-C\n",
    "555\tN=C-C=C\n",
    "556\tC=C-C-C\n",
    "557\tN:C-O-[#1]\n",
    "558\tO=N-C:C\n",
    "559\tO-C-N-[#1]\n",
    "560\tN-C-N-C\n",
    "561\tCl-C-C=O\n",
    "562\tBr-C-C=O\n",
    "563\tO-C-O-C\n",
    "564\tC=C-C=C\n",
    "565\tC:C-O-C\n",
    "566\tO-C-C-N\n",
    "567\tO-C-C-O\n",
    "568\tN#C-C-C\n",
    "569\tN-C-C-N\n",
    "570\tC:C-C-C\n",
    "571\t[#1]-C-O-[#1]\n",
    "572\tN:C:N:C\n",
    "573\tO-C-C=C\n",
    "574\tO-C-C:C-C\n",
    "575\tO-C-C:C-O\n",
    "576\tN=C-C:C-[#1]\n",
    "577\tC:C-N-C:C\n",
    "578\tC-C:C-C:C\n",
    "579\tO=C-C-C-C\n",
    "580\tO=C-C-C-N\n",
    "581\tO=C-C-C-O\n",
    "582\tC-C-C-C-C\n",
    "583\tCl-C:C-O-C\n",
    "584\tC:C-C=C-C\n",
    "585\tC-C:C-N-C\n",
    "586\tC-S-C-C-C\n",
    "587\tN-C:C-O-[#1]\n",
    "588\tO=C-C-C=O\n",
    "589\tC-C:C-O-C\n",
    "590\tC-C:C-O-[#1]\n",
    "591\tCl-C-C-C-C\n",
    "592\tN-C-C-C-C\n",
    "593\tN-C-C-C-N\n",
    "594\tC-O-C-C=C\n",
    "595\tC:C-C-C-C\n",
    "596\tN=C-N-C-C\n",
    "597\tO=C-C-C:C\n",
    "598\tCl-C:C:C-C\n",
    "599\t[#1]-C-C=C-[#1]\n",
    "600\tN-C:C:C-C\n",
    "601\tN-C:C:C-N\n",
    "602\tO=C-C-N-C\n",
    "603\tC-C:C:C-C\n",
    "604\tC-O-C-C:C\n",
    "605\tO=C-C-O-C\n",
    "606\tO-C:C-C-C\n",
    "607\tN-C-C-C:C\n",
    "608\tC-C-C-C:C\n",
    "609\tCl-C-C-N-C\n",
    "610\tC-O-C-O-C\n",
    "611\tN-C-C-N-C\n",
    "612\tN-C-O-C-C\n",
    "613\tC-N-C-C-C\n",
    "614\tC-C-O-C-C\n",
    "615\tN-C-C-O-C\n",
    "616\tC:C:N:N:C\n",
    "617\tC-C-C-O-[#1]\n",
    "618\tC:C-C-C:C\n",
    "619\tO-C-C=C-C\n",
    "620\tC:C-O-C-C\n",
    "621\tN-C:C:C:N\n",
    "622\tO=C-O-C:C\n",
    "623\tO=C-C:C-C\n",
    "624\tO=C-C:C-N\n",
    "625\tO=C-C:C-O\n",
    "626\tC-O-C:C-C\n",
    "627\tO=[As]-C:C:C\n",
    "628\tC-N-C-C:C\n",
    "629\tS-C:C:C-N\n",
    "630\tO-C:C-O-C\n",
    "631\tO-C:C-O-[#1]\n",
    "632\tC-C-O-C:C\n",
    "633\tN-C-C:C-C\n",
    "634\tC-C-C:C-C\n",
    "635\tN-N-C-N-[#1]\n",
    "636\tC-N-C-N-C\n",
    "637\tO-C-C-C-C\n",
    "638\tO-C-C-C-N\n",
    "639\tO-C-C-C-O\n",
    "640\tC=C-C-C-C\n",
    "641\tO-C-C-C=C\n",
    "642\tO-C-C-C=O\n",
    "643\t[#1]-C-C-N-[#1]\n",
    "644\tC-C=N-N-C\n",
    "645\tO=C-N-C-C\n",
    "646\tO=C-N-C-[#1]\n",
    "647\tO=C-N-C-N\n",
    "648\tO=N-C:C-N\n",
    "649\tO=N-C:C-O\n",
    "650\tO=C-N-C=O\n",
    "651\tO-C:C:C-C\n",
    "652\tO-C:C:C-N\n",
    "653\tO-C:C:C-O\n",
    "654\tN-C-N-C-C\n",
    "655\tO-C-C-C:C\n",
    "656\tC-C-N-C-C\n",
    "657\tC-N-C:C-C\n",
    "658\tC-C-S-C-C\n",
    "659\tO-C-C-N-C\n",
    "660\tC-C=C-C-C\n",
    "661\tO-C-O-C-C\n",
    "662\tO-C-C-O-C\n",
    "663\tO-C-C-O-[#1]\n",
    "664\tC-C=C-C=C\n",
    "665\tN-C:C-C-C\n",
    "666\tC=C-C-O-C\n",
    "667\tC=C-C-O-[#1]\n",
    "668\tC-C:C-C-C\n",
    "669\tCl-C:C-C=O\n",
    "670\tBr-C:C:C-C\n",
    "671\tO=C-C=C-C\n",
    "672\tO=C-C=C-[#1]\n",
    "673\tO=C-C=C-N\n",
    "674\tN-C-N-C:C\n",
    "675\tBr-C-C-C:C\n",
    "676\tN#C-C-C-C\n",
    "677\tC-C=C-C:C\n",
    "678\tC-C-C=C-C\n",
    "679\tC-C-C-C-C-C\n",
    "680\tO-C-C-C-C-C\n",
    "681\tO-C-C-C-C-O\n",
    "682\tO-C-C-C-C-N\n",
    "683\tN-C-C-C-C-C\n",
    "684\tO=C-C-C-C-C\n",
    "685\tO=C-C-C-C-N\n",
    "686\tO=C-C-C-C-O\n",
    "687\tO=C-C-C-C=O\n",
    "688\tC-C-C-C-C-C-C\n",
    "689\tO-C-C-C-C-C-C\n",
    "690\tO-C-C-C-C-C-O\n",
    "691\tO-C-C-C-C-C-N\n",
    "692\tO=C-C-C-C-C-C\n",
    "693\tO=C-C-C-C-C-O\n",
    "694\tO=C-C-C-C-C=O\n",
    "695\tO=C-C-C-C-C-N\n",
    "696\tC-C-C-C-C-C-C-C\n",
    "697\tC-C-C-C-C-C(C)-C\n",
    "698\tO-C-C-C-C-C-C-C\n",
    "699\tO-C-C-C-C-C(C)-C\n",
    "700\tO-C-C-C-C-C-O-C\n",
    "701\tO-C-C-C-C-C(O)-C\n",
    "702\tO-C-C-C-C-C-N-C\n",
    "703\tO-C-C-C-C-C(N)-C\n",
    "704\tO=C-C-C-C-C-C-C\n",
    "705\tO=C-C-C-C-C(O)-C\n",
    "706\tO=C-C-C-C-C(=O)-C\n",
    "707\tO=C-C-C-C-C(N)-C\n",
    "708\tC-C(C)-C-C\n",
    "709\tC-C(C)-C-C-C\n",
    "710\tC-C-C(C)-C-C\n",
    "711\tC-C(C)(C)-C-C\n",
    "712\tC-C(C)-C(C)-C\n",
    "\n",
    "\n",
    "\n",
    "Section 7: Complex SMARTS patterns - These bits test for the \n",
    "presence of complex SMARTS patterns, regardless of count, \n",
    "but where bond orders and bond aromaticity are specific.\n",
    "\n",
    "Bit Position\tBit Substructure\n",
    "713\tCc1ccc(C)cc1\n",
    "714\tCc1ccc(O)cc1\n",
    "715\tCc1ccc(S)cc1\n",
    "716\tCc1ccc(N)cc1\n",
    "717\tCc1ccc(Cl)cc1\n",
    "718\tCc1ccc(Br)cc1\n",
    "719\tOc1ccc(O)cc1\n",
    "720\tOc1ccc(S)cc1\n",
    "721\tOc1ccc(N)cc1\n",
    "722\tOc1ccc(Cl)cc1\n",
    "723\tOc1ccc(Br)cc1\n",
    "724\tSc1ccc(S)cc1\n",
    "725\tSc1ccc(N)cc1\n",
    "726\tSc1ccc(Cl)cc1\n",
    "727\tSc1ccc(Br)cc1\n",
    "728\tNc1ccc(N)cc1\n",
    "729\tNc1ccc(Cl)cc1\n",
    "730\tNc1ccc(Br)cc1\n",
    "731\tClc1ccc(Cl)cc1\n",
    "732\tClc1ccc(Br)cc1\n",
    "733\tBrc1ccc(Br)cc1\n",
    "734\tCc1cc(C)ccc1\n",
    "735\tCc1cc(O)ccc1\n",
    "736\tCc1cc(S)ccc1\n",
    "737\tCc1cc(N)ccc1\n",
    "738\tCc1cc(Cl)ccc1\n",
    "739\tCc1cc(Br)ccc1\n",
    "740\tOc1cc(O)ccc1\n",
    "741\tOc1cc(S)ccc1\n",
    "742\tOc1cc(N)ccc1\n",
    "743\tOc1cc(Cl)ccc1\n",
    "744\tOc1cc(Br)ccc1\n",
    "745\tSc1cc(S)ccc1\n",
    "746\tSc1cc(N)ccc1\n",
    "747\tSc1cc(Cl)ccc1\n",
    "748\tSc1cc(Br)ccc1\n",
    "749\tNc1cc(N)ccc1\n",
    "750\tNc1cc(Cl)ccc1\n",
    "751\tNc1cc(Br)ccc1\n",
    "752\tClc1cc(Cl)ccc1\n",
    "753\tClc1cc(Br)ccc1\n",
    "754\tBrc1cc(Br)ccc1\n",
    "755\tCc1c(C)cccc1\n",
    "756\tCc1c(O)cccc1\n",
    "757\tCc1c(S)cccc1\n",
    "758\tCc1c(N)cccc1\n",
    "759\tCc1c(Cl)cccc1\n",
    "760\tCc1c(Br)cccc1\n",
    "761\tOc1c(O)cccc1\n",
    "762\tOc1c(S)cccc1\n",
    "763\tOc1c(N)cccc1\n",
    "764\tOc1c(Cl)cccc1\n",
    "765\tOc1c(Br)cccc1\n",
    "766\tSc1c(S)cccc1\n",
    "767\tSc1c(N)cccc1\n",
    "768\tSc1c(Cl)cccc1\n",
    "769\tSc1c(Br)cccc1\n",
    "770\tNc1c(N)cccc1\n",
    "771\tNc1c(Cl)cccc1\n",
    "772\tNc1c(Br)cccc1\n",
    "773\tClc1c(Cl)cccc1\n",
    "774\tClc1c(Br)cccc1\n",
    "775\tBrc1c(Br)cccc1\n",
    "776\tCC1CCC(C)CC1\n",
    "777\tCC1CCC(O)CC1\n",
    "778\tCC1CCC(S)CC1\n",
    "779\tCC1CCC(N)CC1\n",
    "780\tCC1CCC(Cl)CC1\n",
    "781\tCC1CCC(Br)CC1\n",
    "782\tOC1CCC(O)CC1\n",
    "783\tOC1CCC(S)CC1\n",
    "784\tOC1CCC(N)CC1\n",
    "785\tOC1CCC(Cl)CC1\n",
    "786\tOC1CCC(Br)CC1\n",
    "787\tSC1CCC(S)CC1\n",
    "788\tSC1CCC(N)CC1\n",
    "789\tSC1CCC(Cl)CC1\n",
    "790\tSC1CCC(Br)CC1\n",
    "791\tNC1CCC(N)CC1\n",
    "792\tNC1CCC(Cl)CC1\n",
    "793\tNC1CCC(Br)CC1\n",
    "794\tClC1CCC(Cl)CC1\n",
    "795\tClC1CCC(Br)CC1\n",
    "796\tBrC1CCC(Br)CC1\n",
    "797\tCC1CC(C)CCC1\n",
    "798\tCC1CC(O)CCC1\n",
    "799\tCC1CC(S)CCC1\n",
    "800\tCC1CC(N)CCC1\n",
    "801\tCC1CC(Cl)CCC1\n",
    "802\tCC1CC(Br)CCC1\n",
    "803\tOC1CC(O)CCC1\n",
    "804\tOC1CC(S)CCC1\n",
    "805\tOC1CC(N)CCC1\n",
    "806\tOC1CC(Cl)CCC1\n",
    "807\tOC1CC(Br)CCC1\n",
    "808\tSC1CC(S)CCC1\n",
    "809\tSC1CC(N)CCC1\n",
    "810\tSC1CC(Cl)CCC1\n",
    "811\tSC1CC(Br)CCC1\n",
    "812\tNC1CC(N)CCC1\n",
    "813\tNC1CC(Cl)CCC1\n",
    "814\tNC1CC(Br)CCC1\n",
    "815\tClC1CC(Cl)CCC1\n",
    "816\tClC1CC(Br)CCC1\n",
    "817\tBrC1CC(Br)CCC1\n",
    "818\tCC1C(C)CCCC1\n",
    "819\tCC1C(O)CCCC1\n",
    "820\tCC1C(S)CCCC1\n",
    "821\tCC1C(N)CCCC1\n",
    "822\tCC1C(Cl)CCCC1\n",
    "823\tCC1C(Br)CCCC1\n",
    "824\tOC1C(O)CCCC1\n",
    "825\tOC1C(S)CCCC1\n",
    "826\tOC1C(N)CCCC1\n",
    "827\tOC1C(Cl)CCCC1\n",
    "828\tOC1C(Br)CCCC1\n",
    "829\tSC1C(S)CCCC1\n",
    "830\tSC1C(N)CCCC1\n",
    "831\tSC1C(Cl)CCCC1\n",
    "832\tSC1C(Br)CCCC1\n",
    "833\tNC1C(N)CCCC1\n",
    "834\tNC1C(Cl)CCCC1\n",
    "835\tNC1C(Br)CCCC1\n",
    "836\tClC1C(Cl)CCCC1\n",
    "837\tClC1C(Br)CCCC1\n",
    "838\tBrC1C(Br)CCCC1\n",
    "839\tCC1CC(C)CC1\n",
    "840\tCC1CC(O)CC1\n",
    "841\tCC1CC(S)CC1\n",
    "842\tCC1CC(N)CC1\n",
    "843\tCC1CC(Cl)CC1\n",
    "844\tCC1CC(Br)CC1\n",
    "845\tOC1CC(O)CC1\n",
    "846\tOC1CC(S)CC1\n",
    "847\tOC1CC(N)CC1\n",
    "848\tOC1CC(Cl)CC1\n",
    "849\tOC1CC(Br)CC1\n",
    "850\tSC1CC(S)CC1\n",
    "851\tSC1CC(N)CC1\n",
    "852\tSC1CC(Cl)CC1\n",
    "853\tSC1CC(Br)CC1\n",
    "854\tNC1CC(N)CC1\n",
    "855\tNC1CC(Cl)CC1\n",
    "856\tNC1CC(Br)CC1\n",
    "857\tClC1CC(Cl)CC1\n",
    "858\tClC1CC(Br)CC1\n",
    "859\tBrC1CC(Br)CC1\n",
    "860\tCC1C(C)CCC1\n",
    "861\tCC1C(O)CCC1\n",
    "862\tCC1C(S)CCC1\n",
    "863\tCC1C(N)CCC1\n",
    "864\tCC1C(Cl)CCC1\n",
    "865\tCC1C(Br)CCC1\n",
    "866\tOC1C(O)CCC1\n",
    "867\tOC1C(S)CCC1\n",
    "868\tOC1C(N)CCC1\n",
    "869\tOC1C(Cl)CCC1\n",
    "870\tOC1C(Br)CCC1\n",
    "871\tSC1C(S)CCC1\n",
    "872\tSC1C(N)CCC1\n",
    "873\tSC1C(Cl)CCC1\n",
    "874\tSC1C(Br)CCC1\n",
    "875\tNC1C(N)CCC1\n",
    "876\tNC1C(Cl)CC1\n",
    "877\tNC1C(Br)CCC1\n",
    "878\tClC1C(Cl)CCC1\n",
    "879\tClC1C(Br)CCC1\n",
    "880\tBrC1C(Br)CCC1\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a (.smi) file to calculate fingerprint using paDEL software\n",
    "selection = ['canonical_smiles','molecule_chembl_id']\n",
    "df3_selection = df[selection]\n",
    "df3_selection.to_csv('for_padel.smi', sep='\\t', index=False, header=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_selection.to_csv('curr_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>PubchemFP0</th>\n",
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       "      <th>PubchemFP6</th>\n",
       "      <th>PubchemFP7</th>\n",
       "      <th>PubchemFP8</th>\n",
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       "      <th>PubchemFP871</th>\n",
       "      <th>PubchemFP872</th>\n",
       "      <th>PubchemFP873</th>\n",
       "      <th>PubchemFP874</th>\n",
       "      <th>PubchemFP875</th>\n",
       "      <th>PubchemFP876</th>\n",
       "      <th>PubchemFP877</th>\n",
       "      <th>PubchemFP878</th>\n",
       "      <th>PubchemFP879</th>\n",
       "      <th>PubchemFP880</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL330863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL124660</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL124035</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL125898</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789</th>\n",
       "      <td>CHEMBL4218162</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2790</th>\n",
       "      <td>CHEMBL4218371</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2791</th>\n",
       "      <td>CHEMBL4216401</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2792</th>\n",
       "      <td>CHEMBL4207535</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>CHEMBL4215850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2794 rows × 882 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  \\\n",
       "0      CHEMBL330863           1           1           1           1   \n",
       "1      CHEMBL124660           1           1           1           1   \n",
       "2         CHEMBL941           1           1           1           0   \n",
       "3      CHEMBL124035           1           1           1           1   \n",
       "4      CHEMBL125898           1           1           1           1   \n",
       "...             ...         ...         ...         ...         ...   \n",
       "2789  CHEMBL4218162           1           1           1           1   \n",
       "2790  CHEMBL4218371           1           1           1           0   \n",
       "2791  CHEMBL4216401           1           1           1           0   \n",
       "2792  CHEMBL4207535           1           1           1           0   \n",
       "2793  CHEMBL4215850           1           1           1           0   \n",
       "\n",
       "      PubchemFP4  PubchemFP5  PubchemFP6  PubchemFP7  PubchemFP8  ...  \\\n",
       "0              0           0           0           0           0  ...   \n",
       "1              0           0           0           0           0  ...   \n",
       "2              0           0           0           0           0  ...   \n",
       "3              0           0           0           0           0  ...   \n",
       "4              0           0           0           0           0  ...   \n",
       "...          ...         ...         ...         ...         ...  ...   \n",
       "2789           0           0           0           0           0  ...   \n",
       "2790           0           0           0           0           0  ...   \n",
       "2791           0           0           0           0           0  ...   \n",
       "2792           0           0           0           0           0  ...   \n",
       "2793           0           0           0           0           0  ...   \n",
       "\n",
       "      PubchemFP871  PubchemFP872  PubchemFP873  PubchemFP874  PubchemFP875  \\\n",
       "0                0             0             0             0             0   \n",
       "1                0             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "3                0             0             0             0             0   \n",
       "4                0             0             0             0             0   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "2789             0             0             0             0             0   \n",
       "2790             0             0             0             0             0   \n",
       "2791             0             0             0             0             0   \n",
       "2792             0             0             0             0             0   \n",
       "2793             0             0             0             0             0   \n",
       "\n",
       "      PubchemFP876  PubchemFP877  PubchemFP878  PubchemFP879  PubchemFP880  \n",
       "0                0             0             0             0             0  \n",
       "1                0             0             0             0             0  \n",
       "2                0             0             0             0             0  \n",
       "3                0             0             0             0             0  \n",
       "4                0             0             0             0             0  \n",
       "...            ...           ...           ...           ...           ...  \n",
       "2789             0             0             0             0             0  \n",
       "2790             0             0             0             0             0  \n",
       "2791             0             0             0             0             0  \n",
       "2792             0             0             0             0             0  \n",
       "2793             0             0             0             0             0  \n",
       "\n",
       "[2794 rows x 882 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#open the file \n",
    "data=pd.read_csv('D:\\datafinal_md.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>PubchemFP0</th>\n",
       "      <th>PubchemFP1</th>\n",
       "      <th>PubchemFP2</th>\n",
       "      <th>PubchemFP3</th>\n",
       "      <th>PubchemFP4</th>\n",
       "      <th>PubchemFP5</th>\n",
       "      <th>PubchemFP6</th>\n",
       "      <th>PubchemFP7</th>\n",
       "      <th>PubchemFP8</th>\n",
       "      <th>PubchemFP9</th>\n",
       "      <th>...</th>\n",
       "      <th>PubchemFP871</th>\n",
       "      <th>PubchemFP872</th>\n",
       "      <th>PubchemFP873</th>\n",
       "      <th>PubchemFP874</th>\n",
       "      <th>PubchemFP875</th>\n",
       "      <th>PubchemFP876</th>\n",
       "      <th>PubchemFP877</th>\n",
       "      <th>PubchemFP878</th>\n",
       "      <th>PubchemFP879</th>\n",
       "      <th>PubchemFP880</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.000000</td>\n",
       "      <td>2794.000000</td>\n",
       "      <td>2794.000000</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999642</td>\n",
       "      <td>0.880100</td>\n",
       "      <td>0.215820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018919</td>\n",
       "      <td>0.324902</td>\n",
       "      <td>0.411463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 881 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PubchemFP0   PubchemFP1   PubchemFP2   PubchemFP3  PubchemFP4  \\\n",
       "count      2794.0  2794.000000  2794.000000  2794.000000      2794.0   \n",
       "mean          1.0     0.999642     0.880100     0.215820         0.0   \n",
       "std           0.0     0.018919     0.324902     0.411463         0.0   \n",
       "min           1.0     0.000000     0.000000     0.000000         0.0   \n",
       "25%           1.0     1.000000     1.000000     0.000000         0.0   \n",
       "50%           1.0     1.000000     1.000000     0.000000         0.0   \n",
       "75%           1.0     1.000000     1.000000     0.000000         0.0   \n",
       "max           1.0     1.000000     1.000000     1.000000         0.0   \n",
       "\n",
       "       PubchemFP5  PubchemFP6  PubchemFP7  PubchemFP8  PubchemFP9  ...  \\\n",
       "count      2794.0      2794.0      2794.0      2794.0      2794.0  ...   \n",
       "mean          0.0         0.0         0.0         0.0         1.0  ...   \n",
       "std           0.0         0.0         0.0         0.0         0.0  ...   \n",
       "min           0.0         0.0         0.0         0.0         1.0  ...   \n",
       "25%           0.0         0.0         0.0         0.0         1.0  ...   \n",
       "50%           0.0         0.0         0.0         0.0         1.0  ...   \n",
       "75%           0.0         0.0         0.0         0.0         1.0  ...   \n",
       "max           0.0         0.0         0.0         0.0         1.0  ...   \n",
       "\n",
       "       PubchemFP871  PubchemFP872  PubchemFP873  PubchemFP874  PubchemFP875  \\\n",
       "count        2794.0        2794.0        2794.0        2794.0        2794.0   \n",
       "mean            0.0           0.0           0.0           0.0           0.0   \n",
       "std             0.0           0.0           0.0           0.0           0.0   \n",
       "min             0.0           0.0           0.0           0.0           0.0   \n",
       "25%             0.0           0.0           0.0           0.0           0.0   \n",
       "50%             0.0           0.0           0.0           0.0           0.0   \n",
       "75%             0.0           0.0           0.0           0.0           0.0   \n",
       "max             0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "       PubchemFP876  PubchemFP877  PubchemFP878  PubchemFP879  PubchemFP880  \n",
       "count        2794.0        2794.0        2794.0        2794.0        2794.0  \n",
       "mean            0.0           0.0           0.0           0.0           0.0  \n",
       "std             0.0           0.0           0.0           0.0           0.0  \n",
       "min             0.0           0.0           0.0           0.0           0.0  \n",
       "25%             0.0           0.0           0.0           0.0           0.0  \n",
       "50%             0.0           0.0           0.0           0.0           0.0  \n",
       "75%             0.0           0.0           0.0           0.0           0.0  \n",
       "max             0.0           0.0           0.0           0.0           0.0  \n",
       "\n",
       "[8 rows x 881 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Finally we have 881 features!!!\n",
    "## lets train our model to predict potency\n",
    "## I'll use Random foresr Regressor . A random forest fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_X=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>canonical_smiles</th>\n",
       "      <th>bioactivity</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>0</td>\n",
       "      <td>CHEMBL330863</td>\n",
       "      <td>COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...</td>\n",
       "      <td>active</td>\n",
       "      <td>576.742</td>\n",
       "      <td>5.28050</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6.892790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL124660</td>\n",
       "      <td>COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...</td>\n",
       "      <td>active</td>\n",
       "      <td>562.715</td>\n",
       "      <td>5.03450</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6.657577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL941</td>\n",
       "      <td>Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>493.615</td>\n",
       "      <td>4.59032</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4.522879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>CHEMBL124035</td>\n",
       "      <td>COCCOc1cc2ncnc(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC...</td>\n",
       "      <td>active</td>\n",
       "      <td>495.580</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>CHEMBL125898</td>\n",
       "      <td>COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...</td>\n",
       "      <td>active</td>\n",
       "      <td>564.687</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789</th>\n",
       "      <td>3298</td>\n",
       "      <td>CHEMBL4218162</td>\n",
       "      <td>Nc1ncnc2c1c(-c1ccc(Oc3ccccc3)cc1)cn2[C@H]1CC[C...</td>\n",
       "      <td>active</td>\n",
       "      <td>405.550</td>\n",
       "      <td>3.47700</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8.301030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2790</th>\n",
       "      <td>3299</td>\n",
       "      <td>CHEMBL4218371</td>\n",
       "      <td>CC(C)n1cnc2c(Nc3ccc(Br)cc3)nc(N[C@H]3CC[C@H](N...</td>\n",
       "      <td>active</td>\n",
       "      <td>506.658</td>\n",
       "      <td>4.88200</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2791</th>\n",
       "      <td>3301</td>\n",
       "      <td>CHEMBL4216401</td>\n",
       "      <td>N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Br)cc3)c3ncn(C4CCC...</td>\n",
       "      <td>inactive</td>\n",
       "      <td>509.622</td>\n",
       "      <td>2.76000</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>8.397940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2792</th>\n",
       "      <td>3302</td>\n",
       "      <td>CHEMBL4207535</td>\n",
       "      <td>N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Cl)cc3)c3ncn(C4CCC...</td>\n",
       "      <td>active</td>\n",
       "      <td>495.595</td>\n",
       "      <td>2.36990</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>8.301030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>3303</td>\n",
       "      <td>CHEMBL4215850</td>\n",
       "      <td>CC(O)CNc1nc(Nc2ccc(Cl)cc2)c2ncn(C3CCCC3)c2n1</td>\n",
       "      <td>inactive</td>\n",
       "      <td>480.580</td>\n",
       "      <td>2.63270</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5.763715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2794 rows × 9 columns</p>\n",
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      ],
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       "      Unnamed: 0 molecule_chembl_id  \\\n",
       "0              0       CHEMBL330863   \n",
       "1              1       CHEMBL124660   \n",
       "2              4          CHEMBL941   \n",
       "3              5       CHEMBL124035   \n",
       "4              6       CHEMBL125898   \n",
       "...          ...                ...   \n",
       "2789        3298      CHEMBL4218162   \n",
       "2790        3299      CHEMBL4218371   \n",
       "2791        3301      CHEMBL4216401   \n",
       "2792        3302      CHEMBL4207535   \n",
       "2793        3303      CHEMBL4215850   \n",
       "\n",
       "                                       canonical_smiles bioactivity       MW  \\\n",
       "0     COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...      active  576.742   \n",
       "1     COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...      active  562.715   \n",
       "2     Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc...    inactive  493.615   \n",
       "3     COCCOc1cc2ncnc(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC...      active  495.580   \n",
       "4     COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc...      active  564.687   \n",
       "...                                                 ...         ...      ...   \n",
       "2789  Nc1ncnc2c1c(-c1ccc(Oc3ccccc3)cc1)cn2[C@H]1CC[C...      active  405.550   \n",
       "2790  CC(C)n1cnc2c(Nc3ccc(Br)cc3)nc(N[C@H]3CC[C@H](N...      active  506.658   \n",
       "2791  N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Br)cc3)c3ncn(C4CCC...    inactive  509.622   \n",
       "2792  N[C@H]1CC[C@H](Nc2nc(Nc3ccc(Cl)cc3)c3ncn(C4CCC...      active  495.595   \n",
       "2793       CC(O)CNc1nc(Nc2ccc(Cl)cc2)c2ncn(C3CCCC3)c2n1    inactive  480.580   \n",
       "\n",
       "         LogP  NumHDonors  NumHAcceptors     pIC50  \n",
       "0     5.28050           1              8  6.892790  \n",
       "1     5.03450           1              8  6.657577  \n",
       "2     4.59032           2              7  4.522879  \n",
       "3     3.80490           1              8  6.346787  \n",
       "4     3.88080           1              9  7.397940  \n",
       "...       ...         ...            ...       ...  \n",
       "2789  3.47700           1              7  8.301030  \n",
       "2790  4.88200           2              8  8.000000  \n",
       "2791  2.76000           1             11  8.397940  \n",
       "2792  2.36990           1             11  8.301030  \n",
       "2793  2.63270           2             10  5.763715  \n",
       "\n",
       "[2794 rows x 9 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data=pd.read_csv('D:\\data_eda_ytrain.csv')\n",
    "y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       6.892790\n",
       "1       6.657577\n",
       "2       4.522879\n",
       "3       6.346787\n",
       "4       7.397940\n",
       "          ...   \n",
       "2789    8.301030\n",
       "2790    8.000000\n",
       "2791    8.397940\n",
       "2792    8.301030\n",
       "2793    5.763715\n",
       "Name: pIC50, Length: 2794, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dependent variable\n",
    "data_y=y_data['pIC50']\n",
    "data_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2792</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2794 rows × 881 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  PubchemFP5  \\\n",
       "0              1           1           1           1           0           0   \n",
       "1              1           1           1           1           0           0   \n",
       "2              1           1           1           0           0           0   \n",
       "3              1           1           1           1           0           0   \n",
       "4              1           1           1           1           0           0   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "2789           1           1           1           1           0           0   \n",
       "2790           1           1           1           0           0           0   \n",
       "2791           1           1           1           0           0           0   \n",
       "2792           1           1           1           0           0           0   \n",
       "2793           1           1           1           0           0           0   \n",
       "\n",
       "      PubchemFP6  PubchemFP7  PubchemFP8  PubchemFP9  ...  PubchemFP871  \\\n",
       "0              0           0           0           1  ...             0   \n",
       "1              0           0           0           1  ...             0   \n",
       "2              0           0           0           1  ...             0   \n",
       "3              0           0           0           1  ...             0   \n",
       "4              0           0           0           1  ...             0   \n",
       "...          ...         ...         ...         ...  ...           ...   \n",
       "2789           0           0           0           1  ...             0   \n",
       "2790           0           0           0           1  ...             0   \n",
       "2791           0           0           0           1  ...             0   \n",
       "2792           0           0           0           1  ...             0   \n",
       "2793           0           0           0           1  ...             0   \n",
       "\n",
       "      PubchemFP872  PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  \\\n",
       "0                0             0             0             0             0   \n",
       "1                0             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "3                0             0             0             0             0   \n",
       "4                0             0             0             0             0   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "2789             0             0             0             0             0   \n",
       "2790             0             0             0             0             0   \n",
       "2791             0             0             0             0             0   \n",
       "2792             0             0             0             0             0   \n",
       "2793             0             0             0             0             0   \n",
       "\n",
       "      PubchemFP877  PubchemFP878  PubchemFP879  PubchemFP880  \n",
       "0                0             0             0             0  \n",
       "1                0             0             0             0  \n",
       "2                0             0             0             0  \n",
       "3                0             0             0             0  \n",
       "4                0             0             0             0  \n",
       "...            ...           ...           ...           ...  \n",
       "2789             0             0             0             0  \n",
       "2790             0             0             0             0  \n",
       "2791             0             0             0             0  \n",
       "2792             0             0             0             0  \n",
       "2793             0             0             0             0  \n",
       "\n",
       "[2794 rows x 881 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df3_X.drop('Name',axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       6.892790\n",
       "1       6.657577\n",
       "2       4.522879\n",
       "3       6.346787\n",
       "4       7.397940\n",
       "          ...   \n",
       "2789    8.301030\n",
       "2790    8.000000\n",
       "2791    8.397940\n",
       "2792    8.301030\n",
       "2793    5.763715\n",
       "Name: pIC50, Length: 2794, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data_y\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we have a dataset with boolean features and we want to remove all features that are either one or zero (on or off) in more than 80% of the samples. Boolean features are Bernoulli random variables, and the variance of such variables is given by      Var[X] = p(1 - p)          so we can select using the threshold .8 * (1 - .8):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature selection\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))    \n",
    "x = selection.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## out of 881 features now we have 145 features !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2794, 145)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### splitting the data into train and test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(x,y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2235, 145), (2235,))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((559, 145), (559,))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape,Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = RandomForestRegressor(n_estimators=120)\n",
    "model.fit(X_train, Y_train)\n",
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7684043261573292"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = model.score(X_test, Y_test)\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets visualize the predicted and actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jash trivedi\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(color_codes=True)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "ax = sns.regplot(Y_test, Y_pred, scatter_kws={'alpha':0.4})\n",
    "ax.set_xlabel('Experimental pIC50', fontsize='large', fontweight='bold')\n",
    "ax.set_ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
    "ax.set_xlim(3,12)\n",
    "ax.set_ylim(3,12)\n",
    "ax.figure.set_size_inches(10,10)\n",
    "plt.show"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}