fdb_calculating_pubchem_fingerprints_and_Tc_part3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Download PaDEL-Descriptor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'wget' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n",
      "'wget' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "#https://pubchem.ncbi.nlm.nih.gov/pc_fetch/pc_fetch.cgi to fetch smiles from pubchem id, also to convert pubchem to chemblids\n",
    "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n",
    "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'unzip' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!unzip padel.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Load bioactivity data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The curated ChEMBL bioactivity data that has been pre-processed from Parts 1 and 2 of this Project that essentially contain the pIC50 value will be used for  similarity analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 297 entries, 0 to 296\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   chembl_id           297 non-null    object \n",
      " 1   molecule_pref_name  244 non-null    object \n",
      " 2   target_chembl_id    297 non-null    object \n",
      " 3   target_pref_name    297 non-null    object \n",
      " 4   target_organism     243 non-null    object \n",
      " 5   class               297 non-null    object \n",
      " 6   pIC50               297 non-null    float64\n",
      " 7   pubchem_id          297 non-null    int64  \n",
      " 8   molecular_weight    297 non-null    float64\n",
      " 9   hbd_count           297 non-null    int64  \n",
      " 10  hba_count           297 non-null    int64  \n",
      " 11  xlogp               296 non-null    float64\n",
      " 12  natural             297 non-null    int64  \n",
      "dtypes: float64(3), int64(4), object(6)\n",
      "memory usage: 30.3+ KB\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chembl_id</th>\n",
       "      <th>molecule_pref_name</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>target_organism</th>\n",
       "      <th>class</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>pubchem_id</th>\n",
       "      <th>molecular_weight</th>\n",
       "      <th>hbd_count</th>\n",
       "      <th>hba_count</th>\n",
       "      <th>xlogp</th>\n",
       "      <th>natural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL165</td>\n",
       "      <td>RESVERATROL</td>\n",
       "      <td>CHEMBL399</td>\n",
       "      <td>HeLa</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>active</td>\n",
       "      <td>10.647817</td>\n",
       "      <td>445154</td>\n",
       "      <td>228.247</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL19224</td>\n",
       "      <td>PAPAVERINE</td>\n",
       "      <td>CHEMBL613633</td>\n",
       "      <td>Ileum</td>\n",
       "      <td>Rattus norvegicus</td>\n",
       "      <td>active</td>\n",
       "      <td>10.229148</td>\n",
       "      <td>4680</td>\n",
       "      <td>339.391</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL50</td>\n",
       "      <td>QUERCETIN</td>\n",
       "      <td>CHEMBL2362975</td>\n",
       "      <td>No relevant target</td>\n",
       "      <td>NaN</td>\n",
       "      <td>active</td>\n",
       "      <td>9.958607</td>\n",
       "      <td>5280343</td>\n",
       "      <td>302.238</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL107</td>\n",
       "      <td>COLCHICINE</td>\n",
       "      <td>CHEMBL3879801</td>\n",
       "      <td>NON-PROTEIN TARGET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>active</td>\n",
       "      <td>9.443697</td>\n",
       "      <td>6167</td>\n",
       "      <td>399.443</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL441687</td>\n",
       "      <td>GLYCYRRHIZIN</td>\n",
       "      <td>CHEMBL3746</td>\n",
       "      <td>11-beta-hydroxysteroid dehydrogenase 2</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>active</td>\n",
       "      <td>9.397940</td>\n",
       "      <td>14982</td>\n",
       "      <td>822.942</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      chembl_id molecule_pref_name target_chembl_id  \\\n",
       "0     CHEMBL165        RESVERATROL        CHEMBL399   \n",
       "1   CHEMBL19224         PAPAVERINE     CHEMBL613633   \n",
       "2      CHEMBL50          QUERCETIN    CHEMBL2362975   \n",
       "3     CHEMBL107         COLCHICINE    CHEMBL3879801   \n",
       "4  CHEMBL441687       GLYCYRRHIZIN       CHEMBL3746   \n",
       "\n",
       "                         target_pref_name    target_organism   class  \\\n",
       "0                                    HeLa       Homo sapiens  active   \n",
       "1                                   Ileum  Rattus norvegicus  active   \n",
       "2                      No relevant target                NaN  active   \n",
       "3                      NON-PROTEIN TARGET                NaN  active   \n",
       "4  11-beta-hydroxysteroid dehydrogenase 2       Homo sapiens  active   \n",
       "\n",
       "       pIC50  pubchem_id  molecular_weight  hbd_count  hba_count  xlogp  \\\n",
       "0  10.647817      445154           228.247          3          3    3.1   \n",
       "1  10.229148        4680           339.391          0          5    3.9   \n",
       "2   9.958607     5280343           302.238          5          7    1.5   \n",
       "3   9.443697        6167           399.443          1          6    1.0   \n",
       "4   9.397940       14982           822.942          8         16    3.7   \n",
       "\n",
       "   natural  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1=pd.read_csv('fdb_classification_bioactivity_pIC50_onlyactive_inactive.csv')\n",
    "df1.info()\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 297 entries, 0 to 296\n",
      "Data columns (total 3 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   molecule_pref_name  297 non-null    object\n",
      " 1   pubchem_id          297 non-null    int64 \n",
      " 2   smiles              297 non-null    object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 7.1+ KB\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_pref_name</th>\n",
       "      <th>pubchem_id</th>\n",
       "      <th>smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Aminopropan-2-ol</td>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PROTOCATECHUIC ACID</td>\n",
       "      <td>72</td>\n",
       "      <td>C1=CC(=C(C=C1C(=O)O)O)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PHENYL PROPIONIC ACID</td>\n",
       "      <td>107</td>\n",
       "      <td>C1=CC=C(C=C1)CCC(=O)O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAMMA-AMINOBUTYRIC ACID</td>\n",
       "      <td>119</td>\n",
       "      <td>C(CC(=O)O)CN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PARAHYDROXYBENZYL ALCOHOL</td>\n",
       "      <td>125</td>\n",
       "      <td>C1=CC(=CC=C1CO)O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          molecule_pref_name  pubchem_id                   smiles\n",
       "0         1-Aminopropan-2-ol           4                  CC(CN)O\n",
       "1        PROTOCATECHUIC ACID          72  C1=CC(=C(C=C1C(=O)O)O)O\n",
       "2      PHENYL PROPIONIC ACID         107    C1=CC=C(C=C1)CCC(=O)O\n",
       "3    GAMMA-AMINOBUTYRIC ACID         119             C(CC(=O)O)CN\n",
       "4  PARAHYDROXYBENZYL ALCOHOL         125         C1=CC(=CC=C1CO)O"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_smi=pd.read_csv('fdb_297_onlyactive_inactive_smiles.csv')\n",
    "df_smi.info()\n",
    "df_smi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_pref_name_x</th>\n",
       "      <th>pubchem_id</th>\n",
       "      <th>smiles</th>\n",
       "      <th>chembl_id</th>\n",
       "      <th>molecule_pref_name_y</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>target_organism</th>\n",
       "      <th>class</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>molecular_weight</th>\n",
       "      <th>hbd_count</th>\n",
       "      <th>hba_count</th>\n",
       "      <th>xlogp</th>\n",
       "      <th>natural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Aminopropan-2-ol</td>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>CHEMBL326602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHEMBL364</td>\n",
       "      <td>Plasmodium falciparum</td>\n",
       "      <td>Plasmodium falciparum</td>\n",
       "      <td>inactive</td>\n",
       "      <td>3.096910</td>\n",
       "      <td>75.111</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PROTOCATECHUIC ACID</td>\n",
       "      <td>72</td>\n",
       "      <td>C1=CC(=C(C=C1C(=O)O)O)O</td>\n",
       "      <td>CHEMBL37537</td>\n",
       "      <td>PROTOCATECHUIC ACID</td>\n",
       "      <td>CHEMBL1293263</td>\n",
       "      <td>M18 aspartyl aminopeptidase</td>\n",
       "      <td>Plasmodium falciparum 3D7</td>\n",
       "      <td>active</td>\n",
       "      <td>6.319302</td>\n",
       "      <td>154.121</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PHENYL PROPIONIC ACID</td>\n",
       "      <td>107</td>\n",
       "      <td>C1=CC=C(C=C1)CCC(=O)O</td>\n",
       "      <td>CHEMBL851</td>\n",
       "      <td>PHENYL PROPIONIC ACID</td>\n",
       "      <td>CHEMBL3879801</td>\n",
       "      <td>NON-PROTEIN TARGET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inactive</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>150.177</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAMMA-AMINOBUTYRIC ACID</td>\n",
       "      <td>119</td>\n",
       "      <td>C(CC(=O)O)CN</td>\n",
       "      <td>CHEMBL96</td>\n",
       "      <td>GAMMA-AMINOBUTYRIC ACID</td>\n",
       "      <td>CHEMBL1907607</td>\n",
       "      <td>GABA-A receptor; anion channel</td>\n",
       "      <td>Rattus norvegicus</td>\n",
       "      <td>active</td>\n",
       "      <td>7.886057</td>\n",
       "      <td>103.121</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PARAHYDROXYBENZYL ALCOHOL</td>\n",
       "      <td>125</td>\n",
       "      <td>C1=CC(=CC=C1CO)O</td>\n",
       "      <td>CHEMBL202132</td>\n",
       "      <td>PARAHYDROXYBENZYL ALCOHOL</td>\n",
       "      <td>CHEMBL398</td>\n",
       "      <td>KB</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>inactive</td>\n",
       "      <td>3.698970</td>\n",
       "      <td>124.139</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        molecule_pref_name_x  pubchem_id                   smiles  \\\n",
       "0         1-Aminopropan-2-ol           4                  CC(CN)O   \n",
       "1        PROTOCATECHUIC ACID          72  C1=CC(=C(C=C1C(=O)O)O)O   \n",
       "2      PHENYL PROPIONIC ACID         107    C1=CC=C(C=C1)CCC(=O)O   \n",
       "3    GAMMA-AMINOBUTYRIC ACID         119             C(CC(=O)O)CN   \n",
       "4  PARAHYDROXYBENZYL ALCOHOL         125         C1=CC(=CC=C1CO)O   \n",
       "\n",
       "      chembl_id       molecule_pref_name_y target_chembl_id  \\\n",
       "0  CHEMBL326602                        NaN        CHEMBL364   \n",
       "1   CHEMBL37537        PROTOCATECHUIC ACID    CHEMBL1293263   \n",
       "2     CHEMBL851      PHENYL PROPIONIC ACID    CHEMBL3879801   \n",
       "3      CHEMBL96    GAMMA-AMINOBUTYRIC ACID    CHEMBL1907607   \n",
       "4  CHEMBL202132  PARAHYDROXYBENZYL ALCOHOL        CHEMBL398   \n",
       "\n",
       "                 target_pref_name            target_organism     class  \\\n",
       "0           Plasmodium falciparum      Plasmodium falciparum  inactive   \n",
       "1     M18 aspartyl aminopeptidase  Plasmodium falciparum 3D7    active   \n",
       "2              NON-PROTEIN TARGET                        NaN  inactive   \n",
       "3  GABA-A receptor; anion channel          Rattus norvegicus    active   \n",
       "4                              KB               Homo sapiens  inactive   \n",
       "\n",
       "      pIC50  molecular_weight  hbd_count  hba_count  xlogp  natural  \n",
       "0  3.096910            75.111          2          2   -1.0        1  \n",
       "1  6.319302           154.121          3          4    1.1        1  \n",
       "2  4.000000           150.177          1          2    1.8        1  \n",
       "3  7.886057           103.121          2          3   -3.2        1  \n",
       "4  3.698970           124.139          2          2    0.2        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chem_pub=df_smi.merge(df1, on=['pubchem_id'], how=\"inner\")\n",
    "df_chem_pub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 297 entries, 0 to 296\n",
      "Data columns (total 15 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   molecule_pref_name_x  297 non-null    object \n",
      " 1   pubchem_id            297 non-null    int64  \n",
      " 2   smiles                297 non-null    object \n",
      " 3   chembl_id             297 non-null    object \n",
      " 4   molecule_pref_name_y  244 non-null    object \n",
      " 5   target_chembl_id      297 non-null    object \n",
      " 6   target_pref_name      297 non-null    object \n",
      " 7   target_organism       243 non-null    object \n",
      " 8   class                 297 non-null    object \n",
      " 9   pIC50                 297 non-null    float64\n",
      " 10  molecular_weight      297 non-null    float64\n",
      " 11  hbd_count             297 non-null    int64  \n",
      " 12  hba_count             297 non-null    int64  \n",
      " 13  xlogp                 296 non-null    float64\n",
      " 14  natural               297 non-null    int64  \n",
      "dtypes: float64(3), int64(4), object(8)\n",
      "memory usage: 37.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_chem_pub.to_csv('pubchem_chembl_smiles_fbd_activity.csv', index=False)\n",
    "df_chem_pub.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "selection = ['smiles','chembl_id']\n",
    "df2_selection = df_chem_pub[selection]\n",
    "df2_selection.to_csv('molecule.smi', sep='\\t', index=False, header= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C[C@@]12CC[C@H]3[C@]([C@@]14[C@H](O4)C(=O)O[C@H]2C5=COC=C5)(C(=O)C[C@@H]6[C@@]37COC(=O)C[C@@H]7OC6(C)C)C\tCHEMBL517449\r\n",
      "C1=CC(=C(C=C1C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)O)O)O\tCHEMBL50\r\n",
      "COC1=CC2=C(C=CN=C2C=C1)[C@H]([C@@H]3C[C@@H]4CCN3C[C@@H]4C=C)O\tCHEMBL170\r\n",
      "CC1=C2C[C@@H](CC[C@]2(CCC1)C)C(C)(C)O\tCHEMBL477900\r\n",
      "CC(=O)N[C@H]1CCC2=CC(=C(C(=C2C3=CC=C(C(=O)C=C13)OC)OC)OC)OC\tCHEMBL107\r\n"
     ]
    }
   ],
   "source": [
    "! cat molecule.smi | head -5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "382\r\n"
     ]
    }
   ],
   "source": [
    "! cat molecule.smi | wc -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## **Calculate pubchem fingerprint descriptors using Padel Descriptor Software**\n",
    "### **Calculate PaDEL descriptors**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "java -Xms1G -Xmx1G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.jar -removesalt -standardizenitro -fingerprints -descriptortypes ./PaDEL-Descriptor/PubchemFingerprinter.xml -dir ./ -file descriptors_output.csv\r\n"
     ]
    }
   ],
   "source": [
    "! cat padel.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing CHEMBL517449 in molecule.smi (1/382). \n",
      "Processing CHEMBL477900 in molecule.smi (4/382). \n",
      "Processing CHEMBL50 in molecule.smi (2/382). \n",
      "Processing CHEMBL170 in molecule.smi (3/382). \n",
      "Processing CHEMBL130 in molecule.smi (7/382). \n",
      "Processing CHEMBL107 in molecule.smi (5/382). \n",
      "Processing CHEMBL165 in molecule.smi (8/382). \n",
      "Processing CHEMBL150 in molecule.smi (6/382). \n",
      "Processing CHEMBL297453 in molecule.smi (9/382). \n",
      "Processing CHEMBL146 in molecule.smi (10/382). \n",
      "Processing CHEMBL108861 in molecule.smi (11/382). \n",
      "Processing CHEMBL47386 in molecule.smi (35/382). \n",
      "Processing CHEMBL19224 in molecule.smi (12/382). \n",
      "Processing CHEMBL44 in molecule.smi (13/382). \n",
      "Processing CHEMBL532 in molecule.smi (14/382). \n",
      "Processing CHEMBL545 in molecule.smi (15/382). \n",
      "Processing CHEMBL48310 in molecule.smi (16/382). \n",
      "Processing CHEMBL45967 in molecule.smi (17/382). \n",
      "Processing CHEMBL441687 in molecule.smi (18/382). \n",
      "Processing CHEMBL222021 in molecule.smi (19/382). \n",
      "Processing CHEMBL1157 in molecule.smi (20/382). \n",
      "Processing CHEMBL517016 in molecule.smi (21/382). \n",
      "Processing CHEMBL196 in molecule.smi (22/382). \n",
      "Processing CHEMBL311498 in molecule.smi (23/382). \n",
      "Processing CHEMBL31574 in molecule.smi (24/382). \n",
      "Processing CHEMBL583912 in molecule.smi (25/382). \n",
      "Processing CHEMBL250450 in molecule.smi (26/382). \n",
      "Processing CHEMBL151 in molecule.smi (27/382). \n",
      "Processing CHEMBL7983 in molecule.smi (28/382). \n",
      "Processing CHEMBL3 in molecule.smi (29/382). \n",
      "Processing CHEMBL117 in molecule.smi (30/382). \n",
      "Processing CHEMBL1672002 in molecule.smi (31/382). \n",
      "Processing CHEMBL416 in molecule.smi (32/382). \n",
      "Processing CHEMBL576 in molecule.smi (33/382). \n",
      "Processing CHEMBL508894 in molecule.smi (34/382). \n",
      "Processing CHEMBL16645 in molecule.smi (36/382). \n",
      "Processing CHEMBL28 in molecule.smi (37/382). Average speed: 3.92 s/mol.\n",
      "Processing CHEMBL7303 in molecule.smi (38/382). Average speed: 3.99 s/mol.\n",
      "Processing CHEMBL286727 in molecule.smi (39/382). Average speed: 2.18 s/mol.\n",
      "Processing CHEMBL125743 in molecule.smi (40/382). Average speed: 1.11 s/mol.\n",
      "Processing CHEMBL506678 in molecule.smi (41/382). Average speed: 0.89 s/mol.\n",
      "Processing CHEMBL36327 in molecule.smi (42/382). Average speed: 0.89 s/mol.\n",
      "Processing CHEMBL221542 in molecule.smi (43/382). Average speed: 0.75 s/mol.\n",
      "Processing CHEMBL465829 in molecule.smi (44/382). Average speed: 0.68 s/mol.\n",
      "Processing CHEMBL45005 in molecule.smi (45/382). Average speed: 0.61 s/mol.\n",
      "Processing CHEMBL294431 in molecule.smi (46/382). Average speed: 0.52 s/mol.\n",
      "Processing CHEMBL441343 in molecule.smi (47/382). Average speed: 0.50 s/mol.\n",
      "Processing CHEMBL210635 in molecule.smi (48/382). Average speed: 0.50 s/mol.\n",
      "Processing CHEMBL186141 in molecule.smi (50/382). Average speed: 0.39 s/mol.\n",
      "Processing CHEMBL93353 in molecule.smi (49/382). Average speed: 0.42 s/mol.\n",
      "Processing CHEMBL129795 in molecule.smi (51/382). Average speed: 0.37 s/mol.\n",
      "Processing CHEMBL120568 in molecule.smi (52/382). Average speed: 0.37 s/mol.\n",
      "Processing CHEMBL245067 in molecule.smi (53/382). Average speed: 0.34 s/mol.\n",
      "Processing CHEMBL486625 in molecule.smi (54/382). Average speed: 0.34 s/mol.\n",
      "Processing CHEMBL239243 in molecule.smi (55/382). Average speed: 0.33 s/mol.\n",
      "Processing CHEMBL8145 in molecule.smi (56/382). Average speed: 0.31 s/mol.\n",
      "Processing CHEMBL275638 in molecule.smi (57/382). Average speed: 0.29 s/mol.\n",
      "Processing CHEMBL82293 in molecule.smi (58/382). Average speed: 0.28 s/mol.\n",
      "Processing CHEMBL413552 in molecule.smi (60/382). Average speed: 0.27 s/mol.\n",
      "Processing CHEMBL15245 in molecule.smi (59/382). Average speed: 0.27 s/mol.\n",
      "Processing CHEMBL752 in molecule.smi (61/382). Average speed: 0.27 s/mol.\n",
      "Processing CHEMBL170190 in molecule.smi (62/382). Average speed: 0.27 s/mol.\n",
      "Processing CHEMBL13883 in molecule.smi (63/382). Average speed: 0.25 s/mol.\n",
      "Processing CHEMBL96 in molecule.smi (64/382). Average speed: 0.24 s/mol.\n",
      "Processing CHEMBL9352 in molecule.smi (65/382). Average speed: 0.24 s/mol.\n",
      "Processing CHEMBL267476 in molecule.smi (67/382). Average speed: 0.23 s/mol.\n",
      "Processing CHEMBL364713 in molecule.smi (66/382). Average speed: 0.23 s/mol.\n",
      "Processing CHEMBL25308 in molecule.smi (68/382). Average speed: 0.21 s/mol.\n",
      "Processing CHEMBL575060 in molecule.smi (69/382). Average speed: 0.22 s/mol.\n",
      "Processing CHEMBL1232207 in molecule.smi (71/382). Average speed: 0.21 s/mol.\n",
      "Processing CHEMBL70518 in molecule.smi (70/382). Average speed: 0.21 s/mol.\n",
      "Processing CHEMBL537 in molecule.smi (73/382). Average speed: 0.20 s/mol.\n",
      "Processing CHEMBL226507 in molecule.smi (72/382). Average speed: 0.20 s/mol.\n",
      "Processing CHEMBL14152 in molecule.smi (75/382). Average speed: 0.19 s/mol.\n",
      "Processing CHEMBL225303 in molecule.smi (74/382). Average speed: 0.19 s/mol.\n",
      "Processing CHEMBL14193 in molecule.smi (76/382). Average speed: 0.19 s/mol.\n",
      "Processing CHEMBL53566 in molecule.smi (77/382). Average speed: 0.18 s/mol.\n",
      "Processing CHEMBL8659 in molecule.smi (78/382). Average speed: 0.18 s/mol.\n",
      "Processing CHEMBL274323 in molecule.smi (79/382). Average speed: 0.18 s/mol.\n",
      "Processing CHEMBL930 in molecule.smi (80/382). Average speed: 0.18 s/mol.\n",
      "Processing CHEMBL66879 in molecule.smi (81/382). Average speed: 0.17 s/mol.\n",
      "Processing CHEMBL559945 in molecule.smi (82/382). Average speed: 0.17 s/mol.\n",
      "Processing CHEMBL28626 in molecule.smi (83/382). Average speed: 0.17 s/mol.\n",
      "Processing CHEMBL55415 in molecule.smi (84/382). Average speed: 0.17 s/mol.\n",
      "Processing CHEMBL14474 in molecule.smi (85/382). Average speed: 0.17 s/mol.\n",
      "Processing CHEMBL388558 in molecule.smi (86/382). Average speed: 0.16 s/mol.\n",
      "Processing CHEMBL1485 in molecule.smi (87/382). Average speed: 0.16 s/mol.\n",
      "Processing CHEMBL17962 in molecule.smi (89/382). Average speed: 0.16 s/mol.\n",
      "Processing CHEMBL54976 in molecule.smi (88/382). Average speed: 0.16 s/mol.\n",
      "Processing CHEMBL379064 in molecule.smi (90/382). Average speed: 0.16 s/mol.\n",
      "Processing CHEMBL12014 in molecule.smi (91/382). Average speed: 0.15 s/mol.\n",
      "Processing CHEMBL1591973 in molecule.smi (92/382). Average speed: 0.15 s/mol.\n",
      "Processing CHEMBL3186027 in molecule.smi (93/382). Average speed: 0.15 s/mol.\n",
      "Processing CHEMBL66926 in molecule.smi (94/382). Average speed: 0.15 s/mol.\n",
      "Processing CHEMBL294199 in molecule.smi (95/382). Average speed: 0.15 s/mol.\n",
      "Processing CHEMBL485259 in molecule.smi (96/382). Average speed: 0.15 s/mol.\n",
      "Processing CHEMBL45068 in molecule.smi (97/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL76447 in molecule.smi (99/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL29757 in molecule.smi (98/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL463088 in molecule.smi (100/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL242273 in molecule.smi (101/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL253896 in molecule.smi (102/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL541 in molecule.smi (103/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL49732 in molecule.smi (104/382). Average speed: 0.14 s/mol.\n",
      "Processing CHEMBL113 in molecule.smi (105/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL42710 in molecule.smi (107/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL333306 in molecule.smi (106/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL3356397 in molecule.smi (108/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL27246 in molecule.smi (109/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL73930 in molecule.smi (110/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL395827 in molecule.smi (111/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL1453648 in molecule.smi (112/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL111077 in molecule.smi (113/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL151649 in molecule.smi (114/382). Average speed: 0.13 s/mol.\n",
      "Processing CHEMBL105912 in molecule.smi (115/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL301523 in molecule.smi (117/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL25894 in molecule.smi (116/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL541939 in molecule.smi (118/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL510714 in molecule.smi (119/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL573781 in molecule.smi (120/382). Average speed: 0.12 s/mol.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing CHEMBL308187 in molecule.smi (121/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL328441 in molecule.smi (122/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL274467 in molecule.smi (123/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL201083 in molecule.smi (124/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL21932 in molecule.smi (125/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL450288 in molecule.smi (126/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL185885 in molecule.smi (127/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL66 in molecule.smi (128/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL1950582 in molecule.smi (129/382). Average speed: 0.12 s/mol.\n",
      "Processing CHEMBL232202 in molecule.smi (130/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL451532 in molecule.smi (131/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL25719 in molecule.smi (133/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL486422 in molecule.smi (132/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL108862 in molecule.smi (134/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL8320 in molecule.smi (135/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL328910 in molecule.smi (136/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL1369384 in molecule.smi (137/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL23194 in molecule.smi (138/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL3883497 in molecule.smi (139/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL14060 in molecule.smi (140/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL52267 in molecule.smi (141/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL424 in molecule.smi (142/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL15844 in molecule.smi (143/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL2268549 in molecule.smi (144/382). Average speed: 0.11 s/mol.\n",
      "Processing CHEMBL37537 in molecule.smi (145/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL851 in molecule.smi (146/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL55285 in molecule.smi (148/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL109341 in molecule.smi (147/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL43185 in molecule.smi (149/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL32571 in molecule.smi (150/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL1644111 in molecule.smi (151/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL452683 in molecule.smi (152/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL297569 in molecule.smi (153/382). Average speed: 0.10 s/mol.\n",
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      "Processing CHEMBL1093743 in molecule.smi (157/382). Average speed: 0.10 s/mol.\n",
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      "Processing CHEMBL1182 in molecule.smi (159/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL1661 in molecule.smi (160/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL13766 in molecule.smi (161/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL82411 in molecule.smi (162/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL202132 in molecule.smi (163/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL90039 in molecule.smi (164/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL108925 in molecule.smi (165/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL15134 in molecule.smi (166/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL347285 in molecule.smi (167/382). Average speed: 0.10 s/mol.\n",
      "Processing CHEMBL248594 in molecule.smi (168/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL358850 in molecule.smi (169/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL417016 in molecule.smi (170/382). Average speed: 0.09 s/mol.\n",
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      "Processing CHEMBL460657 in molecule.smi (172/382). Average speed: 0.09 s/mol.\n",
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      "Processing CHEMBL303697 in molecule.smi (179/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL14227 in molecule.smi (180/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL324794 in molecule.smi (181/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL54922 in molecule.smi (182/382). Average speed: 0.09 s/mol.\n",
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      "Processing CHEMBL108545 in molecule.smi (186/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL1229937 in molecule.smi (187/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL300520 in molecule.smi (188/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL491174 in molecule.smi (189/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL2071440 in molecule.smi (190/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL469654 in molecule.smi (192/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL281202 in molecule.smi (191/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL1239 in molecule.smi (193/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL396295 in molecule.smi (194/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL504 in molecule.smi (195/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL18104 in molecule.smi (197/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL128000 in molecule.smi (196/382). Average speed: 0.09 s/mol.\n",
      "Processing CHEMBL346919 in molecule.smi (198/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL610 in molecule.smi (199/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL30707 in molecule.smi (200/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL1232797 in molecule.smi (201/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL4210821 in molecule.smi (202/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL3126829 in molecule.smi (203/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL233248 in molecule.smi (204/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL365740 in molecule.smi (206/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL46403 in molecule.smi (205/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL383808 in molecule.smi (207/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL1276010 in molecule.smi (208/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL214321 in molecule.smi (209/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL24147 in molecule.smi (210/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL218693 in molecule.smi (211/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL293492 in molecule.smi (212/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL161577 in molecule.smi (214/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL863 in molecule.smi (213/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL558557 in molecule.smi (215/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL47244 in molecule.smi (216/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL561014 in molecule.smi (217/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL195895 in molecule.smi (218/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL1234268 in molecule.smi (219/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL401912 in molecule.smi (220/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL453509 in molecule.smi (222/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL539 in molecule.smi (221/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL370688 in molecule.smi (223/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL89306 in molecule.smi (224/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL462861 in molecule.smi (225/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL1547 in molecule.smi (226/382). Average speed: 0.08 s/mol.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing CHEMBL225153 in molecule.smi (227/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL470671 in molecule.smi (228/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL29411 in molecule.smi (229/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL249592 in molecule.smi (230/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL445206 in molecule.smi (231/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL73639 in molecule.smi (232/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL83159 in molecule.smi (233/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL925 in molecule.smi (234/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL1224557 in molecule.smi (235/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL247484 in molecule.smi (236/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL14021 in molecule.smi (237/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL46931 in molecule.smi (238/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL773 in molecule.smi (240/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL1974890 in molecule.smi (239/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL689 in molecule.smi (241/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL227934 in molecule.smi (242/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL25306 in molecule.smi (243/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL16217 in molecule.smi (245/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL452630 in molecule.smi (244/382). Average speed: 0.08 s/mol.\n",
      "Processing CHEMBL574688 in molecule.smi (246/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL88244 in molecule.smi (247/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL445740 in molecule.smi (248/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL401911 in molecule.smi (249/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL195593 in molecule.smi (250/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL472877 in molecule.smi (251/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL320358 in molecule.smi (252/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL29966 in molecule.smi (253/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3186705 in molecule.smi (255/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3187012 in molecule.smi (254/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL285123 in molecule.smi (256/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL11608 in molecule.smi (257/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL14092 in molecule.smi (258/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL108475 in molecule.smi (259/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1911053 in molecule.smi (260/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL460647 in molecule.smi (261/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL108299 in molecule.smi (263/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL263094 in molecule.smi (262/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL47127 in molecule.smi (264/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL15972 in molecule.smi (265/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL66693 in molecule.smi (266/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL276218 in molecule.smi (267/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL153339 in molecule.smi (268/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1814589 in molecule.smi (269/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL448502 in molecule.smi (270/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL205268 in molecule.smi (271/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL173521 in molecule.smi (272/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL366603 in molecule.smi (273/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL449062 in molecule.smi (274/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL256087 in molecule.smi (276/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL399036 in molecule.smi (275/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL481044 in molecule.smi (278/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL71595 in molecule.smi (277/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL108766 in molecule.smi (279/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL325372 in molecule.smi (280/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL253582 in molecule.smi (281/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL298312 in molecule.smi (283/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL350966 in molecule.smi (282/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL25424 in molecule.smi (284/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL304461 in molecule.smi (285/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL16293 in molecule.smi (286/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL18407 in molecule.smi (287/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL95973 in molecule.smi (288/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1232258 in molecule.smi (289/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1096927 in molecule.smi (290/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL486193 in molecule.smi (291/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1814588 in molecule.smi (293/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL272485 in molecule.smi (292/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL22585 in molecule.smi (294/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL330546 in molecule.smi (295/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL492828 in molecule.smi (297/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1200559 in molecule.smi (296/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL430341 in molecule.smi (298/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL430091 in molecule.smi (300/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL30018 in molecule.smi (299/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL242383 in molecule.smi (301/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL110309 in molecule.smi (302/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL333179 in molecule.smi (304/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL237994 in molecule.smi (303/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL107498 in molecule.smi (305/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1222250 in molecule.smi (306/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1261 in molecule.smi (307/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1233860 in molecule.smi (308/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL440161 in molecule.smi (309/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1614854 in molecule.smi (310/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2229207 in molecule.smi (311/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL191935 in molecule.smi (312/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL280331 in molecule.smi (313/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL442915 in molecule.smi (315/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL318196 in molecule.smi (314/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL170458 in molecule.smi (316/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL56395 in molecule.smi (317/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2059292 in molecule.smi (319/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2251610 in molecule.smi (318/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL446299 in molecule.smi (320/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL18602 in molecule.smi (321/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL154155 in molecule.smi (322/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2268550 in molecule.smi (323/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1076637 in molecule.smi (324/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL292303 in molecule.smi (325/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL31561 in molecule.smi (326/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL450072 in molecule.smi (327/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL14184 in molecule.smi (328/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL2105350 in molecule.smi (330/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL15605 in molecule.smi (329/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL504760 in molecule.smi (331/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL266158 in molecule.smi (332/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL1187 in molecule.smi (333/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL146755 in molecule.smi (334/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3617994 in molecule.smi (335/382). Average speed: 0.07 s/mol.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing CHEMBL1114 in molecule.smi (336/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL473366 in molecule.smi (337/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL276849 in molecule.smi (338/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL3183500 in molecule.smi (339/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL108030 in molecule.smi (341/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL264141 in molecule.smi (340/382). Average speed: 0.07 s/mol.\n",
      "Processing CHEMBL195827 in molecule.smi (342/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL379630 in molecule.smi (344/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL104875 in molecule.smi (345/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL361197 in molecule.smi (347/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL190927 in molecule.smi (346/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL298717 in molecule.smi (348/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL365316 in molecule.smi (343/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL721 in molecule.smi (349/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL108778 in molecule.smi (350/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL1162495 in molecule.smi (351/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL9113 in molecule.smi (352/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL251280 in molecule.smi (353/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL486795 in molecule.smi (354/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL1044 in molecule.smi (355/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL31422 in molecule.smi (356/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL291962 in molecule.smi (357/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL470874 in molecule.smi (358/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL123040 in molecule.smi (359/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL460025 in molecule.smi (360/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL2368547 in molecule.smi (362/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL1233058 in molecule.smi (361/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL324846 in molecule.smi (363/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL273782 in molecule.smi (364/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL3360549 in molecule.smi (365/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL195215 in molecule.smi (366/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL8085 in molecule.smi (367/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL510309 in molecule.smi (368/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL18549 in molecule.smi (369/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL18893 in molecule.smi (370/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL487213 in molecule.smi (371/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL277871 in molecule.smi (372/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL14253 in molecule.smi (373/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL118504 in molecule.smi (374/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL198877 in molecule.smi (376/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL573448 in molecule.smi (375/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL470670 in molecule.smi (377/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL107874 in molecule.smi (378/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL326602 in molecule.smi (379/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL271663 in molecule.smi (381/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL108436 in molecule.smi (380/382). Average speed: 0.06 s/mol.\n",
      "Processing CHEMBL192458 in molecule.smi (382/382). Average speed: 0.06 s/mol.\n",
      "Descriptor calculation completed in 23.678 secs . Average speed: 0.06 s/mol.\n"
     ]
    }
   ],
   "source": [
    "! bash padel.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_X = pd.read_csv('descriptors_output.csv')\n",
    "df3_X.rename(columns = {'Name':'chembl_id'}, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chembl_id</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>...</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>0</th>\n",
       "      <td>CHEMBL545</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>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>1</th>\n",
       "      <td>CHEMBL576</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>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>2</th>\n",
       "      <td>CHEMBL1157</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>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>3</th>\n",
       "      <td>CHEMBL3</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>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>4</th>\n",
       "      <td>CHEMBL196</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>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>377</th>\n",
       "      <td>CHEMBL3617994</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>378</th>\n",
       "      <td>CHEMBL430341</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>379</th>\n",
       "      <td>CHEMBL110309</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>380</th>\n",
       "      <td>CHEMBL2368547</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>381</th>\n",
       "      <td>CHEMBL506247</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>382 rows × 882 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         chembl_id  PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  \\\n",
       "0        CHEMBL545           1           0           0           0   \n",
       "1        CHEMBL576           1           0           0           0   \n",
       "2       CHEMBL1157           1           1           0           0   \n",
       "3          CHEMBL3           1           1           0           0   \n",
       "4        CHEMBL196           1           1           0           0   \n",
       "..             ...         ...         ...         ...         ...   \n",
       "377  CHEMBL3617994           1           1           1           0   \n",
       "378   CHEMBL430341           1           1           1           0   \n",
       "379   CHEMBL110309           1           1           1           1   \n",
       "380  CHEMBL2368547           1           1           1           1   \n",
       "381   CHEMBL506247           1           1           1           1   \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",
       "377           0           0           0           0           0  ...   \n",
       "378           0           0           0           0           0  ...   \n",
       "379           0           0           0           0           0  ...   \n",
       "380           0           0           0           0           0  ...   \n",
       "381           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",
       "377             0             0             0             0             0   \n",
       "378             0             0             0             0             0   \n",
       "379             0             0             0             0             0   \n",
       "380             0             0             0             0             0   \n",
       "381             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",
       "377             0             0             0             0             0  \n",
       "378             0             0             0             0             0  \n",
       "379             0             0             0             0             0  \n",
       "380             0             0             0             0             0  \n",
       "381             0             0             0             0             0  \n",
       "\n",
       "[382 rows x 882 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 297 entries, 0 to 296\n",
      "Columns: 883 entries, chembl_id to class\n",
      "dtypes: int64(881), object(2)\n",
      "memory usage: 2.0+ MB\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chembl_id</th>\n",
       "      <th>PubchemFP0</th>\n",
       "      <th>PubchemFP1</th>\n",
       "      <th>PubchemFP2</th>\n",
       "      <th>PubchemFP3</th>\n",
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       "      <th>PubchemFP8</th>\n",
       "      <th>...</th>\n",
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       "      <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",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL576</td>\n",
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       "      <th>1</th>\n",
       "      <td>CHEMBL1157</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL3</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL196</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",
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       "      <td>active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL48310</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>active</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 883 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chembl_id  PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  \\\n",
       "0    CHEMBL576           1           0           0           0           0   \n",
       "1   CHEMBL1157           1           1           0           0           0   \n",
       "2      CHEMBL3           1           1           0           0           0   \n",
       "3    CHEMBL196           1           1           0           0           0   \n",
       "4  CHEMBL48310           1           1           0           0           0   \n",
       "\n",
       "   PubchemFP5  PubchemFP6  PubchemFP7  PubchemFP8  ...  PubchemFP872  \\\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",
       "   PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  PubchemFP877  \\\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",
       "   PubchemFP878  PubchemFP879  PubchemFP880   class  \n",
       "0             0             0             0  active  \n",
       "1             0             0             0  active  \n",
       "2             0             0             0  active  \n",
       "3             0             0             0  active  \n",
       "4             0             0             0  active  \n",
       "\n",
       "[5 rows x 883 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection_2 = ['chembl_id','class']\n",
    "df3_selection = df_chem_pub[selection_2]\n",
    "df4_pca=df3_X.merge(df3_selection,on=['chembl_id'],how=\"inner\")\n",
    "df5_tc=df4_pca.drop(['chembl_id','class'], axis =1)\n",
    "df4_pca.info()\n",
    "df4_pca.head()\n",
    "#df5_tc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4> <b> Tanimoto Coefficient </b> </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>active</td>\n",
       "      <td>1100000001110011000000000000000000000000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>active</td>\n",
       "      <td>1100000001100000001110000000000000000000000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL48310</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>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>active</td>\n",
       "      <td>1100000001110000001100000000000000000000000000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 884 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chembl_id  PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  \\\n",
       "0    CHEMBL576           1           0           0           0           0   \n",
       "1   CHEMBL1157           1           1           0           0           0   \n",
       "2      CHEMBL3           1           1           0           0           0   \n",
       "3    CHEMBL196           1           1           0           0           0   \n",
       "4  CHEMBL48310           1           1           0           0           0   \n",
       "\n",
       "   PubchemFP5  PubchemFP6  PubchemFP7  PubchemFP8  ...  PubchemFP873  \\\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",
       "   PubchemFP874  PubchemFP875  PubchemFP876  PubchemFP877  PubchemFP878  \\\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",
       "   PubchemFP879  PubchemFP880   class  \\\n",
       "0             0             0  active   \n",
       "1             0             0  active   \n",
       "2             0             0  active   \n",
       "3             0             0  active   \n",
       "4             0             0  active   \n",
       "\n",
       "                                          pubchem_fp  \n",
       "0  1000000001100000001110000000000000000000000000...  \n",
       "1  1100000001100000001110000000000000000000000000...  \n",
       "2  1100000001110011000000000000000000000000000000...  \n",
       "3  1100000001100000001110000000000000000000000000...  \n",
       "4  1100000001110000001100000000000000000000000000...  \n",
       "\n",
       "[5 rows x 884 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4_pca['pubchem_fp'] = df5_tc[df5_tc.columns[:]].apply(lambda x: ''.join(x.dropna().astype(str)),axis=1)\n",
    "df4_pca.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4> <b> Making Pairs of Query and Target for Tanimoto Pairwise Similarity Calculation </b> </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Query</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL48310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL7303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Query       Target\n",
       "0  CHEMBL576   CHEMBL1157\n",
       "1  CHEMBL576      CHEMBL3\n",
       "2  CHEMBL576    CHEMBL196\n",
       "3  CHEMBL576  CHEMBL48310\n",
       "4  CHEMBL576   CHEMBL7303"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_pca= df_pca.set_index(\"chembl_id\")\n",
    "import itertools\n",
    "cc1 = list(itertools.combinations(df4_pca['chembl_id'],2))\n",
    "df_cc=pd.DataFrame(data=cc1,columns=['Query','Target'])\n",
    "df_cc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10000000011000000011100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001101000000000000000000000100000000000000000000000100000000000100000001000000000000000000000000000100000000000000000000000001000000000000010000000000000000000100100000000100000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000010000000000000000000000000000000000000100000000000000000001000000000000000000000000000000000000000000010000110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df4_pca['pubchem_fp'].[df_pca]\n",
    "df4_pca.loc[df4_pca['chembl_id'] == 'CHEMBL576', 'pubchem_fp'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4> <b> Install Rdkit package </b> </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! conda install -c rdkit rdkit -y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4> <b> Generating Bitvectors from Pubchem Fingerprints </b> </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import DataStructs\n",
    "for i in range(len(df_cc)):\n",
    "    ref = df_cc.Query[i]\n",
    "    bit=df4_pca.loc[df4_pca['chembl_id'] == ref, 'pubchem_fp'].iloc[0]\n",
    "    bitvect = DataStructs.CreateFromBitString(bit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_pref_name_x</th>\n",
       "      <th>pubchem_id</th>\n",
       "      <th>smiles</th>\n",
       "      <th>chembl_id</th>\n",
       "      <th>molecule_pref_name_y</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>target_organism</th>\n",
       "      <th>class</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>molecular_weight</th>\n",
       "      <th>hbd_count</th>\n",
       "      <th>hba_count</th>\n",
       "      <th>xlogp</th>\n",
       "      <th>natural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-Aminopropan-2-ol</td>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>CHEMBL326602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CHEMBL364</td>\n",
       "      <td>Plasmodium falciparum</td>\n",
       "      <td>Plasmodium falciparum</td>\n",
       "      <td>inactive</td>\n",
       "      <td>3.096910</td>\n",
       "      <td>75.111</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PROTOCATECHUIC ACID</td>\n",
       "      <td>72</td>\n",
       "      <td>C1=CC(=C(C=C1C(=O)O)O)O</td>\n",
       "      <td>CHEMBL37537</td>\n",
       "      <td>PROTOCATECHUIC ACID</td>\n",
       "      <td>CHEMBL1293263</td>\n",
       "      <td>M18 aspartyl aminopeptidase</td>\n",
       "      <td>Plasmodium falciparum 3D7</td>\n",
       "      <td>active</td>\n",
       "      <td>6.319302</td>\n",
       "      <td>154.121</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PHENYL PROPIONIC ACID</td>\n",
       "      <td>107</td>\n",
       "      <td>C1=CC=C(C=C1)CCC(=O)O</td>\n",
       "      <td>CHEMBL851</td>\n",
       "      <td>PHENYL PROPIONIC ACID</td>\n",
       "      <td>CHEMBL3879801</td>\n",
       "      <td>NON-PROTEIN TARGET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inactive</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>150.177</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAMMA-AMINOBUTYRIC ACID</td>\n",
       "      <td>119</td>\n",
       "      <td>C(CC(=O)O)CN</td>\n",
       "      <td>CHEMBL96</td>\n",
       "      <td>GAMMA-AMINOBUTYRIC ACID</td>\n",
       "      <td>CHEMBL1907607</td>\n",
       "      <td>GABA-A receptor; anion channel</td>\n",
       "      <td>Rattus norvegicus</td>\n",
       "      <td>active</td>\n",
       "      <td>7.886057</td>\n",
       "      <td>103.121</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PARAHYDROXYBENZYL ALCOHOL</td>\n",
       "      <td>125</td>\n",
       "      <td>C1=CC(=CC=C1CO)O</td>\n",
       "      <td>CHEMBL202132</td>\n",
       "      <td>PARAHYDROXYBENZYL ALCOHOL</td>\n",
       "      <td>CHEMBL398</td>\n",
       "      <td>KB</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>inactive</td>\n",
       "      <td>3.698970</td>\n",
       "      <td>124.139</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        molecule_pref_name_x  pubchem_id                   smiles  \\\n",
       "0         1-Aminopropan-2-ol           4                  CC(CN)O   \n",
       "1        PROTOCATECHUIC ACID          72  C1=CC(=C(C=C1C(=O)O)O)O   \n",
       "2      PHENYL PROPIONIC ACID         107    C1=CC=C(C=C1)CCC(=O)O   \n",
       "3    GAMMA-AMINOBUTYRIC ACID         119             C(CC(=O)O)CN   \n",
       "4  PARAHYDROXYBENZYL ALCOHOL         125         C1=CC(=CC=C1CO)O   \n",
       "\n",
       "      chembl_id       molecule_pref_name_y target_chembl_id  \\\n",
       "0  CHEMBL326602                        NaN        CHEMBL364   \n",
       "1   CHEMBL37537        PROTOCATECHUIC ACID    CHEMBL1293263   \n",
       "2     CHEMBL851      PHENYL PROPIONIC ACID    CHEMBL3879801   \n",
       "3      CHEMBL96    GAMMA-AMINOBUTYRIC ACID    CHEMBL1907607   \n",
       "4  CHEMBL202132  PARAHYDROXYBENZYL ALCOHOL        CHEMBL398   \n",
       "\n",
       "                 target_pref_name            target_organism     class  \\\n",
       "0           Plasmodium falciparum      Plasmodium falciparum  inactive   \n",
       "1     M18 aspartyl aminopeptidase  Plasmodium falciparum 3D7    active   \n",
       "2              NON-PROTEIN TARGET                        NaN  inactive   \n",
       "3  GABA-A receptor; anion channel          Rattus norvegicus    active   \n",
       "4                              KB               Homo sapiens  inactive   \n",
       "\n",
       "      pIC50  molecular_weight  hbd_count  hba_count  xlogp  natural  \n",
       "0  3.096910            75.111          2          2   -1.0        1  \n",
       "1  6.319302           154.121          3          4    1.1        1  \n",
       "2  4.000000           150.177          1          2    1.8        1  \n",
       "3  7.886057           103.121          2          3   -3.2        1  \n",
       "4  3.698970           124.139          2          2    0.2        1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chem_pub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4> <b> Displaying chemical strs. of 297 active and inactive flavor molecules  </b> </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91809\\Anaconda3\\lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py:188: UserWarning: Truncating the list of molecules to be displayed to 50. Change the maxMols value to display more.\n",
      "  % (maxMols))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=800x2600 at 0x23A26887AC8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import time\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "mols = [ Chem.MolFromSmiles(x) for x in df_chem_pub['smiles'] ]\n",
    "Chem.Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(200,200), legends=[str(x) for x in df_chem_pub['pubchem_id']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Query</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL48310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL576</td>\n",
       "      <td>CHEMBL7303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Query       Target\n",
       "0  CHEMBL576   CHEMBL1157\n",
       "1  CHEMBL576      CHEMBL3\n",
       "2  CHEMBL576    CHEMBL196\n",
       "3  CHEMBL576  CHEMBL48310\n",
       "4  CHEMBL576   CHEMBL7303"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(fp1)\n",
    "scores = []\n",
    "from rdkit import DataStructs\n",
    "for i in range(len(df_cc)):\n",
    "    ref = df_cc.Query[i]\n",
    "    comp=df_cc.Target[i]\n",
    "    bit_ref=df4_pca.loc[df4_pca['chembl_id'] == ref, 'pubchem_fp'].iloc[0]\n",
    "    bit_comp=df4_pca.loc[df4_pca['chembl_id'] == comp, 'pubchem_fp'].iloc[0]\n",
    "    bitvect_ref = DataStructs.CreateFromBitString(bit_ref)\n",
    "    bitvect_comp = DataStructs.CreateFromBitString(bit_comp)\n",
    "    fps_ref=bitvect_ref\n",
    "    fps_comp=bitvect_comp\n",
    "    #print(ref,comp)\n",
    "   # print(\"Tanimoto    :\", round(DataStructs.TanimotoSimilarity(fps_ref, fps_comp), 4))\n",
    "    scores.append(DataStructs.TanimotoSimilarity(fps_ref, fps_comp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43956, 43956)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scores), len(df_cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "sns.countplot(x='natural', data=df_fla,palette=['gold','cornflowerblue'])\n",
    "\n",
    "plt.xlabel('Type of Flavor molecule', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.savefig('plot_synthtic_nativecount_class.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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q57d6r03WdZsk/5nk17J6sJp0gc2fJnltjaVMoxD5o0kekDVCxXSzyd5eVf3Qb6O6np7kE0kemrWD1ST5vnT78n2mqh63mXtsV1UdVFVvTfKudIH9Wp+DpJuh/Mwkp1bVsVMq6fbpZomOe/mU7nUFVXWjJB9P8tKsE6yO3CbJq5N8qKpWC8bWu8+90s1a/sOsE+KN/ECS1yV572hPy71GVe2oqj9ONyv1UVk/WE26Gc0/le7n8SoTrmWmX5OqOipdIH58Vg9Wk6TSBeAfqqr7b+U+M/RjvfZ751LFAKMZ6R9KsivJ/8o6werI0UmenOQPBt7ilHQrR+z245urEABg72fmKgAAMGv9N3leluQzSb6UbunKK6VbyvI26ZbW3O36Sd5dVXdsrX16k/c8IMnb0i0ZmySXJvlIkjPSBQA3y55lEJMupPvLqjplyktDfn+6gGt3IHdekg8nOT/d8o53y8olZh+RLjx7XlU9IMnfZs/n8wvpwtBvp/tc3SUrH7r/WVV9pLX2gY2KqqqXpgvB+05O8vnRPW+clZ+z/ZP8eVVdu7X29I3usV1VdXC6AOQOvZcuTRc8nJHuc3ebdJ/n3Q5P8i9V9aDW2j9NuKx79trnt9b+a8L3WFVV/UCSd6f72Rn3tXTfU+eNXrtTukB8tzskOamq7tla+8KA+zwy3d6v/UDnS+m+/76e7vP+g+mWt97tnkneX1V3ba19feifa1FV1YFJ/jHJvVZ5+UvpAtfz0u3hed1034dXnVIts/6aHJruDQ03GbUvTPIf6ZZWPyDduDD+BpYDk7ypqm49z2U9oKICAAAU7ElEQVTXN9APf983lyo2MJrl/Y/p/n4Yd3mS/0ryP0kuSLfE/k3SfR3WXRagr7XWqurEdDOxk+SmVXXUkPEBAGBfIVwFAADm4ewkr0n3kPgDrbXL+ieMZmjeO91ylHccHT44yeur6vab3Ef0aemC2kvSzYL98/7SsFV1+3Rh5e4Zf/sleUH2BLLT8Lx0s1W/muTXk7xpfInWUYDz3HSzWnd7elW9I93n70rpQo0ntNZOGu+4qq6X5G+yJ/ypJH+SLnRdU1U9KlcMVt+W5Nf7D9dHywa/KMkPjx1+WlV9uLX29l4fJ6fbDzZJHpvkN8deOy7Jv61T1mr7zb4kK4PV7yR5fpLntta+MVZjJbnv6PyjR4cPSDcL+AcnHPb0v1c+PMG+1zT6PnlTVgar56Wbrfa68Z+v0f65v5Du++rg0eHvT/dzdex6e4JW1d3TLa08/izhTUmevdqyr6MZwn+ebunepPvZemW62Zvjxr83kuT3072RYLf7pJuV2XdWutmTbxg79u7s+Tqfmz1jR9+6exgP8IpcMVh9Q5LfX+NzsV+6MPPh6Zb/nogpfk3W84J04+nXkjw1ySv7e0JX1b2TvDZdsJx04e5zkvzMKv09MnuC58+ke6NG0s3of+A6dXxjndcGGy1VP/59ckZr7dy1zp+X0Zi+KyuD1a+n+1n+i9UC8tHM5Ack+fl0e2UP9Z/ZE64m3dLDwlUAgBHhKgAAMGt/l27vt0vXO2kUnr57FB78XZKfGL1023T78w3ah3DksCQXJbnPWjM3W2sfGwUC/51u1k+S3K2qbtVa++Qm7rUZ10g3w/LurbXTV6npwiSPr6rrZ0/IcFC6ZZW/L90yxw9YbT/S1toZVfUT6WbQ7Z65eef1/jyjB/Ev6B1+SWvtV1c7v7X26aq6X7olRh8y9tJfVtU/t9YuGjv3kiRfHN2nv3fuWevt17pKnfdN8nNjh76b5Gdba29cpcaW5F2jGV//luQWo5cOSfLi7Pm+moTr9dqfmmDf63laVi4DfG6Se7bW/rt/4ujr8OKq+miSf86egPXO6fZp/ePVbjDaz/ONWfkc4Vdaay9dq6jW2omjn9//lz2zeh9YVfdtrZ3Qq+mLY/fq74l7xjrfHxdlLGSrqvE3aly+me+roarq/yR52Ph9kjymtfaqta5prV2e5D1J3lNVT003w3q7dUzta7KBw5Kck+QerbXPrHGf94yWAv5I9syofVBV/Vpr7fzeud/bk7i3x+ol0/j6reJmWblCwCkzuOdWvCrJtcfan09y/9ba59a6YPS5flWSV1XV4H2E062QMO5O6cZ5AABiz1UAAGDGWmtnbhSs9s6/LN1+hheNHV5t9tNGfmujJXFba2emm4k57t5buNdmPHq1YLXnOb32NdLNWPqZ1YLV3VprF+SKf54fXu3ckV/KnrAt6ZaZfMJ6hY1Co59PMv5nODwrw89Je3Kv/dLVgtVxrbVz0s0aHJ+Z+WNVNWTf36H6+1dOffnb0azV/kzjx64WrI4bzXR+Su/wE6pqrTdhPzorw+MXrhfijd3nwnRB5IVjh39jo+sW1WiW41N7h5+9XrDa11o7e3yG+jbM82vy6LWC1bH7fDxd+LvbAZnuSgBb1R8DNhqPZ2705pAfGTt0cbo31qwZrPa11r66iVv2Pwcb7eMLALBPEa4CAAALb7RE47+PHbrbJrs4O8nLB577j7327TZ5r834WGvtXRud1Fr7cK64BOZfDVy6sj8b7barntV5ZK/9jPWWiR2r78Ikz+4dPm7j0javqq6blSHDpUmOH3LtaP/T8SVkK1f8M29Hfx/EWewt+pPpwvbdTm6tvWXgtS/NyhDl+7P2mwkeP/bxxUmeObTAUajzt2OH7j3aM3cZ3Scrg6Yvp1vGeB7m9TX5r9baPww8d5bj6VYd2Wt/eR5FbOBXeu0Xr7bs8wT1PwdHTvFeAABLR7gKAAAsjKo6sKquXVU3rKojx//LyqDq6NHekUP9S39PwHX0l3I9fBP32ax3bOLc/gylocsi96+79monVdVhWTmD6/x0S4cO9cas3MfymKq6yiauH+ru6ULR3d61yf0RX9NrH7v9kuaqX/9rh144mj35t73DV/h8VNUNktxk7NAJq+3vuIH3jn28X7o9HJdRf+b3K4a8AWHS5vw1+adN3GOW4+lW9WvqL1u+CPrfdy+b8v3O77UX8esGADA39lwFAADmYrS85j2SPDjJHZPcMiuXpN3IIUnO2vCszrpLpI5rrX2zqi5PFzYkydU3UdNmbWZPzv7M1UHXttYu6P15vm+NU+/Qa394M6FRa+3bVXXKWD/7J/nBJB8a2sdA/TrXXep5FSel26N195uN+/1tx/nZs19vMt3vnd22+/non3/HVc65R699+ugND5vReu0bpdvzddn0Z83/6zyKyHy/JoPH01wxqJzFz8RmHdhrX7TqWXNSVTdMcsTYoTNaa6dO856tte9U1Xey57nhQdO8HwDAshGuAgAAMzfaP+7FSW6/jW6unuHh6mZnIn0ne8LIK2/y2s3YTF39oHMzs9SG/Hn6M5M+u4n+d/t0VoZ905jttK06W2vfqqqvJLn+6NBVq+pqrbVvTaC285IcPdY+ZK0TJ2i7X7dPb9Bf0i0XPO7XRv9tR39/2mVx3V77k3OpYr5fk82MW5f12tMcT7eq/2xs5jORNzCv77nxcHURv24AAHNjWWAAAGCmquon08322k6wmmzu3zPf3ea9pmU7dU36z3SNXrs/U3aI/jXTCNAWuc6v9Nq3mFC/69nu52PI56K/l+wkXG0Kfc7CPPbVXc08vyaLOp7+//buPdjWsq4D+PcnCIKKAQoMqOENzRTzho42g5csyhJvXMJQI1BsmPIPtYumMTaTZqbgWGqmkbdS8TJNjKVpmoYBIiAOhRlp6ikP9w4Rgj798a7tefd79uXde6+99j77fD4za2Y/z3rf53nW+7xrnTnrt57fs1rDlar7bcgoFjec63VPW1xVlaSf1v2W9e4TAGB3IrgKAADMTFUdnm5PyP5+qdcmOSfJc9Olkb1XujSNe7XWau6R5O2zHu8epgblYcrQ1ZhGG0ObeZxfGJQXSrE7bcPrsVYLXYt9ptxHMv1xb5T1uMfHMCfTc/OgvNlT4M7inhumSh5eIwCAPZq0wAAAwCy9LPNXR30iyUmttTFf3C62VyjTcf2gvJq9EYfnrMcKq808zs8OygdX1dGttSum1P5Crs/813NAVvZ6xlyLawfl01pr715BH1vJtZmf7vnAJNs3aBx9e/KcrNV/DsrDNLwbbTjXs0ipffigPLxGAAB7NCtXAQCAWTq+9/etSZ43MrCa7PplL9P13UH5qFW08eBl2pyGNY2zqu6W+ffSrVPabzVJLs2ugbYXTantxax13sbM2XBv49XcG1vFtkH5oRsyCnMyTf8+KN97waM2zkbcc0cMytfMoE8AgN2G4CoAADATVbV3kvv1qi5srQ1XIS527p2TPGZdBsacLw3Kx0zmbJSqumuSo3tV30ty+SKHryWt5SWD8hNWeP7jM///wsP2Vq21dkeScwfVp1bVIdPqYwFrvR7D4y9e4Jh/GpR/eoV9bCXD1M9P2ohBZOvOyUakWb5i0O9DNmAMi2qtfTPJt3pV966qB6xzt8P9oi9b5/4AAHYrgqsAAMCsHJD5e/rdtIJzT8jm3wdvt9Za257kX3tVByU5bgVNnJj5e+l+qbX2f4sce9ugvO+CRy1sGNw6rqoOXsH5pw7Kn1/BuWO8NcmOXvmAJO+YRsOLBFSG43/eCtqrBY7f5Xq01q5M8u1e1aOq6tFj+9liPjUo/8pKfoQwLVt4TvqfDSv5XFi11tqNSf6tV/XQjZjTZQzvuxevc39HD8oXrXN/AAC7FcFVAABgVm5K8oNeedTqoEka199djwGxi78YlM+uqr2WO6mq9kvyO4Pq85Y45cZBefQeh621bUk+2avaJ8mrx5xbVQ9Pckq/uez6mtektXZDkt8eVB9fVWevts2q2r+q3p/kjAWe/uvM3yf10VX1rJFNn5nkyF75W0k+vcixbx2Uz52sKN/TfDrJVb3yfZL85gaNZSvOSf+zYZZ7n/b3S94nyU/MsO8xhnN9VlWtZ3rgY3p/35rkn9exLwCA3Y7gKgAAMBOtte8n+Wqv6seq6hlLnVNVd0nyviQPWs+x8UNvT3JLr/yoJH+01AlVdackf5b5KZ+3Z+mg5VWD8kpTmr5xUD6rqp671AlVdc8kH0jSX5F2QWvtX1bY97Jaa29J8leD6ldX1Tuq6oCVtDV5j1yS5BcX6euWdPPW97aqGu6lOmz3mCSvH1SfM0ltvJBzk/xXr/yEJO+tqv2X6meBfu9dVU9fyTmbSWutJXntoPo1VfX8sW1U1SGT981abcU56X82HFpVwxWU6+WCQfnJM+p3lNbaJZk/xv2SfHwl6YGr6rCRx/1I5geXP71EFgIAgD2S4CoAADBLw4DT+6rqBQutjqyqJ6VLATsXgN2+zmPb47XWrkvy0kH1r1XVh6vqyOHxkwDeJ7Jr4O/FrbVbl+jqssxfbfmsqvqTqnpqVR1VVUcOHv100mmt/W26oPucOyV5f1W9dhi8rM7TklyY5Md7T92U5KwlxrhWp2fX1V5nJPl6Vf1GVR212IlVdURVnVFVX0zy8ey6/+HQ7yW5ulc+JMk/VtWpw/SmVbVPVZ2ZbvXv3XtPXZLknMU6mARxT0i3l+6cE5N8uaqeP1m9vNjrOXTyPv9Ykmuya2rm3Upr7QNJ3tur2jvJeVX1vsVWE1bVXlX15Kr60yTfSLc6cq3j2Ipz8plB+SNVdVpVPbKq7j/4XDhwiv1+MvN/WPK0KbY9Lacn2dYrPzDJxVX1sklAdBdVdWBVvbCqPpfkLSP7eUrmf1/4sVWNFgBgC9tse0gAAABb2zlJXpTkvpPy3ZL8eZI/qKqL06WEPDDdqpnDe+d9OMl1Wf995vZ4rbV3VtXj0n2RP+c5SZ5dVZemC8RUutXEC60qe11r7aPL9HFbVf1xklf2qs+cPBayX5LhyqlfTRd0fNSkfOckr0ry8klQclu6++vo7Lzf5nwvyfNba/+x1DjXorW2o6p+Kl165Gf3nrpnktcleV1VfTPd9bw2yR1JDkt33y+2Uvt/F+nrlqo6McnfJ5nbf/Ze6VYPv2ny3rph0vcxSe4xaOLbSU5urd2+zGv6fFX90qTdu0yqj5q8xndW1WWTtnak2yP5oHTpvw9dqt3d1IvSpa19aq/ulCSnVNU30q3AvD7ddToi3X24aLBztbbgnLw7yW8lmQsWPiDdyviFvD5TSsk8eb9+JDuDzE+uqoNaa9dPo/1paK1tq6pnJvmbdO/lpPv38g3pPk++nC619y3prt+DJo+5H6ecP7KrfhaAW5N8cI1DBwDYcgRXAQCAmZl8gf30JH+X+fvpHZJksbSUH0r3hfeiq+qYrtbaGVX1nXTBz7lVxZXk0ZPHQm5P8orW2ptHdnN2kgdn/hf5KxnjzVV1bJL3J/mF3lP7Jjl2iVO3JzmhtfbZJY6ZitbajiTPqapT0wVUDx8cct/sGvhdyNfT7Wn7l0v0dXlVPSHdHqz9VbEHJzluibYvTXJ8a+1bI8aR1tqHqupr6VIs9/dNvnOSx04ey7lh+UM2t9barVV1XLoU1Wdl/kq/H508ZjWWLTMnrbXtVXVCuiwHB824+3dlZ3B17yTPnNRtGq21iyYpvc9P8sjeU3sleczksWqT1c4/36s6v7V281raBADYiqQFBgAAZqq1dmW6lanvyvx0lvMOS5fG9ZTW2omttdtmNT46rbXXpFtt98EssmJy4n/SrZp7yAoCq2mt3d5aOyHd3oZvS3JxuhWco+e6tbajtfaMJD+X5PNJfrDE4d9Jt1fmA2cRWO1rrb0nyf2TvCBd+tHF7vu+a5O8J11g9MGttQ9M9vtcqp+rkzwsXbDv6qWOTXJlktOSPHZsYLXXz2WTfk5Od90X26f1h6ckuTzJH076e8lK+tusWmt3tNZ+PcnD0wX5lwtC3ZguaPgz097DcivNSWvtU+l+ePHSdKs0r0n3ObPU+3sa/f5DupTlc05f5NAN1Vq7Jt2PXE5O8sUsf12uTvL7SV4+ovmTMj9d+JtWM0YAgK2ulvm/GQAAwLqpqnskeWK6vePuni6N5rYkl7bWvrmRY2Onqto33TwdmS4dZUu3AvTrSb64XDrZWamqg5L8ZLpV0QenCwpvT3JFa+0rGzm2vsn1fES6+/6wJPunC7jekOS7SS6fRsriqrpfulWLhyQ5IN0+s/+dbs5WFFBdpp+7Jnl8kvuku+77pUtDe12SryW5qrV207T626wm+9sek25e75luXnek+0y7KslXW2vfn9FYzMkqVNUpmb+f8yNaa1ds1HjGmOw9+8Ts/NxLukD/NUm+spL3elVdmO6+SZLPtNaeMs2xAgBsFYKrAAAAAOzxqmqvJF9Jt59zkpzXWnvhxo1odiZpxb/Qqzq2tfa5jRoPAMBmJi0wAAAAAHu8ycriV/aqnldVM9s/d4O9qvf3BQKrAACLE1wFAAAAgCSttY8mmQss7p35Qcctqaoel+RnJ8U7krxiA4cDALDpCa4CAAAAwE4vSTK3l/QvV9XDNnIwM/DG3t9vbq19dcNGAgCwG7DnKgAAAAD0VNVJ2bn36kWttQs2cjzrpaoOS3LmpNiSvLG1tmMDhwQAsOkJrgIAAAAAAACMIC0wAAAAAAAAwAiCqwAAAAAAAAAjCK4CAAAAAAAAjCC4CgAAAAAAADCC4CoAAAAAAADACIKrAAAAAAAAACMIrgIAAAAAAACMILgKAAAAAAAAMILgKgAAAAAAAMAIgqsAAAAAAAAAIwiuAgAAAAAAAIwguAoAAAAAAAAwguAqAAAAAAAAwAiCqwAAAAAAAAAjCK4CAAAAAAAAjCC4CgAAAAAAADCC4CoAAAAAAADACIKrAAAAAAAAACMIrgIAAAAAAACMILgKAAAAAAAAMILgKgAAAAAAAMAIgqsAAAAAAAAAIwiuAgAAAAAAAIwguAoAAAAAAAAwguAqAAAAAAAAwAiCqwAAAAAAAAAjCK4CAAAAAAAAjCC4CgAAAAAAADCC4CoAAAAAAADACIKrAAAAAAAAACMIrgIAAAAAAACM8P/O6zyLkI9PQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2100x2100 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(7,7), dpi=300)\n",
    "# An \"interface\" to matplotlib.axes.Axes.hist() method\n",
    "plt.hist(x=scores, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)\n",
    "#plt.grid(axis='y', alpha=0.75)\n",
    "\n",
    "plt.xlabel('Tanimoto Coefficient (Tc)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Frequency Plot')\n",
    "#plt.figure(figsize=(7,7))\n",
    "plt.savefig('plot_tc_nativecount_class.png')\n",
    "#plt.text(0.33, r'$\\mu=15')\n",
    "#maxfreq = n.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "mybins = [ x * 0.01 for x in range(101)]\n",
    "\n",
    "fig = plt.figure(figsize=(8,4), dpi=300)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Distribution\")\n",
    "plt.hist(scores, bins=mybins)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Cumulative Distribution\")\n",
    "plt.hist(scores, bins=mybins, density=True, cumulative=1)\n",
    "plt.plot([0,1],[0.95,0.95]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000    43956 (100.0000 %)\n",
      "0.050    43135 ( 98.1322 %)\n",
      "0.100    40620 ( 92.4106 %)\n",
      "0.150    35569 ( 80.9196 %)\n",
      "0.200    30069 ( 68.4070 %)\n",
      "0.250    24896 ( 56.6385 %)\n",
      "0.300    20322 ( 46.2326 %)\n",
      "0.350    16385 ( 37.2759 %)\n",
      "0.400    13308 ( 30.2757 %)\n",
      "0.450    10980 ( 24.9795 %)\n",
      "0.500     9445 ( 21.4874 %)\n",
      "0.550     7880 ( 17.9270 %)\n",
      "0.600     6021 ( 13.6978 %)\n",
      "0.650     4245 (  9.6574 %)\n",
      "0.700     2819 (  6.4132 %)\n",
      "0.750     1734 (  3.9449 %)\n",
      "0.800     1051 (  2.3910 %)\n",
      "0.850      636 (  1.4469 %)\n",
      "0.900      315 (  0.7166 %)\n",
      "0.950      121 (  0.2753 %)\n",
      "1.000       56 (  0.1274 %)\n"
     ]
    }
   ],
   "source": [
    "for i in range(21) :\n",
    "\n",
    "    thresh = i / 20\n",
    "    num_similar_pairs = len([x for x in scores if x >= thresh]) \n",
    "    prob = num_similar_pairs / len(scores) * 100\n",
    "    print(\"%.3f %8d (%8.4f %%)\" % (thresh, num_similar_pairs, round(prob,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average: 0.33088463035201027\n"
     ]
    }
   ],
   "source": [
    "print(\"Average:\", sum(scores)/len(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using molecular fingeprints. we can compute the similarity scores between molecules.  However, how should these scores be interpreted?  For example, the Tanimoto score between CID 60823 and CID 446155 is computed to be 0.662, but does it mean that the two compounds are similar?  How similar is similar?  The following analysis would help answer these questions.\n",
    "\n",
    "\n",
    "From the distribution of the similarity scores among 297 natural compounds, we observe the following:<br>\n",
    "- If you randomly select two compounds from this dataset, the similarity score between them (computed using the Tanimoto equation and PubChem fingerprints) is ~0.33 on average.\n",
    "- About  18 % of randomly selected compound pairs have a similarity score greater than 0.55.\n",
    "- About 9% of randomly selected compound pairs have a similarity score greater than 0.65."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If two compounds have a Tanimoto score of 0.33, it is close to the average Tanimoto score between randomly selected compounds from our dataset and there is a 50% chance that you will get a score of 0.33 or greater just by selecting two compounds from PubChem.  Therefore, it is reasonable to consider the two compounds are not similar.<br>\n",
    "\n",
    "The Tanimoto index may have a value ranging from 0 (for no similarity) to 1 (for identical molecules) and the midpoint of this value range is 0.5.  Because of this, a Tanimoto score of **0.55** may not sound great enough to consider two compounds to be similar.  However, according to the score distribution curve generated here, only **~17%** of randomly selected compound pairs will have a score greater than this.<br>\n",
    "\n",
    "In the previous section, we computed the similarity scores between some cholesterol-lowering drugs, and CID 60823 and CID 446155 had a Tanimoto score of **0.662**.  Based on the score distribution curve generated in the second section, we can say that the probablilty of two randomly selected compounds from PubChem having a Tanimoto score greater than 0.662 is **less than 9%**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code cell demonstrates how to find an appropriate similarity score threshold above which a given percentage of the compound pairs will be considered to be similar to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores.sort() # Sort the scores in an increasing order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# total compound pairs:    43956\n",
      "# 95% of compound pairs:   42637.32\n",
      "# score at 95% percentile: 0.7762237762237763\n"
     ]
    }
   ],
   "source": [
    "# to find a threshold for top 5% compound pairs (i.e., 95% percentile)\n",
    "print(\"# total compound pairs:   \", len(scores))\n",
    "print(\"# 95% of compound pairs:  \", len(scores) * 0.95)\n",
    "print(\"# score at 95% percentile:\", scores[ round(len(scores) * 0.95) ] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=4> <b> PCA and tSNE of active and inactive flavor molecules  </b> </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import time\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib.pyplot import figure\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "df4_pca.head()\n",
    "df5=df4_pca.drop(['chembl_id','class','pubchem_fp'], axis=1)\n",
    "crds = pca.fit_transform(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.489498</td>\n",
       "      <td>-7.237064</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.904657</td>\n",
       "      <td>-6.804352</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.635824</td>\n",
       "      <td>19.299477</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.144082</td>\n",
       "      <td>-4.545019</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.082286</td>\n",
       "      <td>-4.406270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PC1        PC2  class\n",
       "0   5.489498  -7.237064      0\n",
       "1   4.904657  -6.804352      0\n",
       "2  13.635824  19.299477      0\n",
       "3   2.144082  -4.545019      0\n",
       "4   2.082286  -4.406270      0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X = df.drop(['POPULATION'], axis = 1)\n",
    "# Y = df['POPULATION']\n",
    "# X = pd.get_dummies(X, prefix_sep='_')\n",
    "# Y = LabelEncoder().fit_transform(Y)\n",
    "X = StandardScaler().fit_transform(df5)\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "PCA_df = pd.DataFrame(data = X_pca, columns = ['PC1', 'PC2'])\n",
    "PCA_df = pd.concat([PCA_df, df4_pca['class']], axis = 1)\n",
    "PCA_df['class'] = LabelEncoder().fit_transform(PCA_df['class'])\n",
    "PCA_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(PCA_df['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.datasets import load_boston\n",
    "\n",
    "# boston = load_boston()\n",
    "#df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "n_components = 4\n",
    "\n",
    "pca = PCA(n_components=4)\n",
    "X_pca = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(8, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "classes = [0,1,2]\n",
    "colors = ['r', 'b',\"g\"]\n",
    "for clas, color in zip(classes, colors):\n",
    "    plt.scatter(PCA_df.loc[PCA_df['class'] == clas, 'PC1'], PCA_df.loc[PCA_df['class'] == clas, 'PC2'], c = color)\n",
    "    \n",
    "plt.xlabel('Principal Component 1', fontsize = 12)\n",
    "plt.ylabel('Principal Component 2', fontsize = 12)\n",
    "plt.title('PCA visualization of flavor molecules on basis of activity ', fontsize = 15)\n",
    "plt.legend(['Active', 'Inactive'])\n",
    "#plt.grid()\n",
    "plt.savefig('pca_fdb.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 297 samples in 0.027s...\n",
      "[t-SNE] Computed neighbors for 297 samples in 0.107s...\n",
      "[t-SNE] Computed conditional probabilities for sample 297 / 297\n",
      "[t-SNE] Mean sigma: 2.682502\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 56.885036\n",
      "[t-SNE] KL divergence after 1500 iterations: 0.288904\n",
      "t-SNE done! Time elapsed: 1.3642065525054932 seconds\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "time_start = time.time()\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=30, n_iter=1500)\n",
    "X_tsne = tsne.fit_transform(df5)\n",
    "print('t-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'active', 'inactive'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df4_pca['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = dict({'active':'red', 'inactive':'yellow','intermediate': 'green'})\n",
    "ax=sns.scatterplot(\n",
    "    x=X_tsne[:,0], y=X_tsne[:,1],\n",
    "    data=df4_pca,\n",
    "    hue=\"class\",\n",
    "    hue_order=[\"active\",\"inactive\"],\n",
    "    palette=color_dict,\n",
    "    legend=\"brief\",\n",
    "    alpha=0.8\n",
    ")\n",
    "ax.set(xlabel = \"tSNE1\",\n",
    "      ylabel = \"tSNE2\",\n",
    "      title = \"tSNE visualization of flavor molecules on basis of activity\")\n",
    "#plt.figure(figsize=(16,9))\n",
    "plt.gcf().set_size_inches(10, 8)\n",
    "plt.savefig(\"tsne_superpop_4.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flavor Molecules from both the classes have similar chemical space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}