hacking_solubility.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solubility Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### part of the data analyisis and model prediction by team: C. Di Paola, J. Manson and K. Makobe\n",
    "### revised, adapted and completed (also including a bigger data set) by: C. Di Paola"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import necessary initial libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "np.set_printoptions(threshold=np.inf)\n",
    "#%load_ext autotime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read files for analyis and prediction\n",
    "\n",
    "We need solubility data and DRAGON 2D descriptors from the training set and DRAGON 2D descriptors for prediction data set\n",
    "\n",
    "'bd' files are coming from bigger solubility training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/fm1/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#solub=pd.read_excel('soldata.xls')\n",
    "solub_train_data=pd.read_excel('soldata_trainingset.xls')\n",
    "solub_train_descriptors=pd.read_excel('Solubility_training_descriptors_cleaned.xlsx')\n",
    "solub_pred_descriptors=pd.read_excel('Solubility_prediction_descriptors_cleaned.xlsx')\n",
    "solub_pred_data=pd.read_excel('soldata_prediction_withSvalues.xlsx')\n",
    "solub_train_bd_1=pd.read_csv('./LargerDataset/solubility_combined_values_noNA_rows_1.csv')\n",
    "#solub_tran_bigdata_2=pd.read_csv('./LargerDataset/solubility_combined_values_noNA_rows_2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis (Pre-processing data)\n",
    "\n",
    "### * data shape and formatting for training data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Substance</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>assays</th>\n",
       "      <th>Ionic Strength (M)</th>\n",
       "      <th>S0 (mM)</th>\n",
       "      <th>SD of S0 (mM)</th>\n",
       "      <th>Kinetic Solubility (mM)</th>\n",
       "      <th>SD of Kinetic Solubility (mM)</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_naphthol</td>\n",
       "      <td>25.89</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.17121</td>\n",
       "      <td>10432.300</td>\n",
       "      <td>408.616</td>\n",
       "      <td>18026.0</td>\n",
       "      <td>11884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c1ccc2c(cccc2O)c1</td>\n",
       "      <td>InChI=1/C10H8O/c11-10-7-3-5-8-4-1-2-6-9(8)10/h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2_amino_5_bromobenzoic_acid</td>\n",
       "      <td>25.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.16295</td>\n",
       "      <td>842.692</td>\n",
       "      <td>14.6303</td>\n",
       "      <td>2562.0</td>\n",
       "      <td>863.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brc1cc(C(O)=O)c(N)cc1</td>\n",
       "      <td>InChI=1/C7H6BrNO2/c8-4-1-2-6(9)5(3-4)7(10)11/h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4_iodophenol</td>\n",
       "      <td>25.74</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.218635</td>\n",
       "      <td>19312.000</td>\n",
       "      <td>604.678</td>\n",
       "      <td>25091.0</td>\n",
       "      <td>8427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c1cc(ccc1O)I</td>\n",
       "      <td>InChI=1/C6H5IO/c7-5-1-3-6(8)4-2-5/h1-4,8H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5_bromo_2_4_dihydroxybenzoic_acid</td>\n",
       "      <td>25.05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.186497</td>\n",
       "      <td>2397.220</td>\n",
       "      <td>40.1944</td>\n",
       "      <td>8726.0</td>\n",
       "      <td>753.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oc1c(Br)cc(C(O)=O)c(O)c1</td>\n",
       "      <td>InChI=1/C7H5BrO4/c8-4-1-3(7(11)12)5(9)2-6(4)10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5_fluorouracil</td>\n",
       "      <td>25.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No precipitation detected. Kinetic solubility ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fc1c(=O)[nH]c(=O)[nH]c1</td>\n",
       "      <td>InChI=1/C4H3FN2O2/c5-2-1-6-4(9)7-3(2)8/h1H,(H2...</td>\n",
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      "text/plain": [
       "                            Substance  Temperature  assays  \\\n",
       "0                          1_naphthol        25.89     4.0   \n",
       "1         2_amino_5_bromobenzoic_acid        25.00     5.0   \n",
       "2                        4_iodophenol        25.74     4.0   \n",
       "3   5_bromo_2_4_dihydroxybenzoic_acid        25.05     5.0   \n",
       "4                      5_fluorouracil        25.10     NaN   \n",
       "\n",
       "                                  Ionic Strength (M)    S0 (mM) SD of S0 (mM)  \\\n",
       "0                                            0.17121  10432.300       408.616   \n",
       "1                                            0.16295    842.692       14.6303   \n",
       "2                                           0.218635  19312.000       604.678   \n",
       "3                                           0.186497   2397.220       40.1944   \n",
       "4  No precipitation detected. Kinetic solubility ...        NaN           NaN   \n",
       "\n",
       "   Kinetic Solubility (mM) SD of Kinetic Solubility (mM)  Unnamed: 8  \\\n",
       "0                  18026.0                         11884         NaN   \n",
       "1                   2562.0                         863.3         NaN   \n",
       "2                  25091.0                          8427         NaN   \n",
       "3                   8726.0                         753.8         NaN   \n",
       "4                      NaN                           NaN         NaN   \n",
       "\n",
       "   Unnamed: 9                    SMILES  \\\n",
       "0         NaN         c1ccc2c(cccc2O)c1   \n",
       "1         NaN     Brc1cc(C(O)=O)c(N)cc1   \n",
       "2         NaN              c1cc(ccc1O)I   \n",
       "3         NaN  Oc1c(Br)cc(C(O)=O)c(O)c1   \n",
       "4         NaN   Fc1c(=O)[nH]c(=O)[nH]c1   \n",
       "\n",
       "                                               InChI  \n",
       "0  InChI=1/C10H8O/c11-10-7-3-5-8-4-1-2-6-9(8)10/h...  \n",
       "1  InChI=1/C7H6BrNO2/c8-4-1-2-6(9)5(3-4)7(10)11/h...  \n",
       "2          InChI=1/C6H5IO/c7-5-1-3-6(8)4-2-5/h1-4,8H  \n",
       "3  InChI=1/C7H5BrO4/c8-4-1-3(7(11)12)5(9)2-6(4)10...  \n",
       "4  InChI=1/C4H3FN2O2/c5-2-1-6-4(9)7-3(2)8/h1H,(H2...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "solub_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>No.</th>\n",
       "      <th>NAME</th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>Sv</th>\n",
       "      <th>Mv</th>\n",
       "      <th>Me</th>\n",
       "      <th>Mp</th>\n",
       "      <th>Mi</th>\n",
       "      <th>nBM</th>\n",
       "      <th>...</th>\n",
       "      <th>Depressant-50</th>\n",
       "      <th>Psychotic-80</th>\n",
       "      <th>Hypertens-80</th>\n",
       "      <th>Hypertens-50</th>\n",
       "      <th>Hypnotic-80</th>\n",
       "      <th>Hypnotic-50</th>\n",
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       "      <td>1-Naphthol                                    ...</td>\n",
       "      <td>144.18</td>\n",
       "      <td>7.588</td>\n",
       "      <td>12.822</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.711</td>\n",
       "      <td>1.098</td>\n",
       "      <td>11</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2_amino_5_bromo_benzoic_acid                  ...</td>\n",
       "      <td>216.04</td>\n",
       "      <td>12.708</td>\n",
       "      <td>12.057</td>\n",
       "      <td>0.709</td>\n",
       "      <td>1.037</td>\n",
       "      <td>0.738</td>\n",
       "      <td>1.118</td>\n",
       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4-Iodophenol                                  ...</td>\n",
       "      <td>220.01</td>\n",
       "      <td>16.924</td>\n",
       "      <td>9.612</td>\n",
       "      <td>0.739</td>\n",
       "      <td>1.004</td>\n",
       "      <td>0.877</td>\n",
       "      <td>1.090</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5_bromo_2_4_dihydroxybenzoic_acid             ...</td>\n",
       "      <td>233.02</td>\n",
       "      <td>13.707</td>\n",
       "      <td>12.465</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1.070</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1.113</td>\n",
       "      <td>7</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5_fluorouracil                                ...</td>\n",
       "      <td>130.09</td>\n",
       "      <td>10.841</td>\n",
       "      <td>8.383</td>\n",
       "      <td>0.699</td>\n",
       "      <td>1.105</td>\n",
       "      <td>0.635</td>\n",
       "      <td>1.181</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "<p>5 rows × 1468 columns</p>\n",
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      "text/plain": [
       "   No.                                               NAME      MW     AMW  \\\n",
       "0    1  1-Naphthol                                    ...  144.18   7.588   \n",
       "1    2  2_amino_5_bromo_benzoic_acid                  ...  216.04  12.708   \n",
       "2    3  4-Iodophenol                                  ...  220.01  16.924   \n",
       "3    4  5_bromo_2_4_dihydroxybenzoic_acid             ...  233.02  13.707   \n",
       "4    5  5_fluorouracil                                ...  130.09  10.841   \n",
       "\n",
       "       Sv     Mv     Me     Mp     Mi  nBM  ...  Depressant-50  Psychotic-80  \\\n",
       "0  12.822  0.675  0.993  0.711  1.098   11  ...              0             0   \n",
       "1  12.057  0.709  1.037  0.738  1.118    7  ...              0             0   \n",
       "2   9.612  0.739  1.004  0.877  1.090    6  ...              0             0   \n",
       "3  12.465  0.733  1.070  0.733  1.113    7  ...              0             0   \n",
       "4   8.383  0.699  1.105  0.635  1.181    3  ...              0             0   \n",
       "\n",
       "   Hypertens-80  Hypertens-50  Hypnotic-80  Hypnotic-50  Neoplastic-80  \\\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",
       "   Neoplastic-50  Infective-80  Infective-50  \n",
       "0              0             0             0  \n",
       "1              0             1             0  \n",
       "2              0             0             0  \n",
       "3              0             0             0  \n",
       "4              0             0             0  \n",
       "\n",
       "[5 rows x 1468 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "solub_train_descriptors.head()"
   ]
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DE_100</td>\n",
       "      <td>Delaney</td>\n",
       "      <td>Nc1cccc2ccccc12</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.20</td>\n",
       "      <td>7.160</td>\n",
       "      <td>13.129</td>\n",
       "      <td>19.636</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>DE_1000</td>\n",
       "      <td>Delaney</td>\n",
       "      <td>CCCc1ccccc1</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.21</td>\n",
       "      <td>5.724</td>\n",
       "      <td>12.161</td>\n",
       "      <td>20.302</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>DE_1001</td>\n",
       "      <td>Delaney</td>\n",
       "      <td>CCCC1CCCC1</td>\n",
       "      <td>-4.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.24</td>\n",
       "      <td>4.677</td>\n",
       "      <td>12.214</td>\n",
       "      <td>23.069</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>DE_1005</td>\n",
       "      <td>Delaney</td>\n",
       "      <td>CC(C)(C)c1ccc(O)cc1</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.24</td>\n",
       "      <td>6.010</td>\n",
       "      <td>14.402</td>\n",
       "      <td>24.513</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Compound_Identifier   Source               SMILES LogS.M.  \\\n",
       "0               DE_10  Delaney       C1CCc2ccccc2C1   -4.37   \n",
       "1              DE_100  Delaney      Nc1cccc2ccccc12   -1.92   \n",
       "2             DE_1000  Delaney          CCCc1ccccc1   -3.37   \n",
       "3             DE_1001  Delaney           CCCC1CCCC1   -4.74   \n",
       "4             DE_1005  Delaney  CC(C)(C)c1ccc(O)cc1   -2.41   \n",
       "\n",
       "   Solubility.microgram.mL.  Solubility.micromolar.      MW    AMW      Sv  \\\n",
       "0                       NaN                     NaN  132.22  6.010  13.161   \n",
       "1                       NaN                     NaN  143.20  7.160  13.129   \n",
       "2                       NaN                     NaN  120.21  5.724  12.161   \n",
       "3                       NaN                     NaN  112.24  4.677  12.214   \n",
       "4                       NaN                     NaN  150.24  6.010  14.402   \n",
       "\n",
       "       Se  ...  Psychotic-80  Psychotic-50  Hypertens-80  Hypertens-50  \\\n",
       "0  21.302  ...             0             0             0             0   \n",
       "1  19.636  ...             0             0             0             0   \n",
       "2  20.302  ...             0             0             0             0   \n",
       "3  23.069  ...             0             0             0             0   \n",
       "4  24.513  ...             0             0             0             0   \n",
       "\n",
       "   Hypnotic-80  Hypnotic-50  Neoplastic-80  Neoplastic-50  Infective-80  \\\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             1   \n",
       "\n",
       "   Infective-50  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 2262 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_train_bd_1.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "solub_train_bd_1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_train_bd_1['LogS.M.']=pd.to_numeric(solub_train_bd_1['LogS.M.'], errors='coerce')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "solub_train_bd_1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>name</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>MW</th>\n",
       "      <th>Solubility (from findings) (micro M)</th>\n",
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       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acebutolol</td>\n",
       "      <td>Acebutolol                                    ...</td>\n",
       "      <td>336.48</td>\n",
       "      <td>2113.05</td>\n",
       "      <td>711</td>\n",
       "      <td>CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amoxicillin</td>\n",
       "      <td>Amoxicillin                                   ...</td>\n",
       "      <td>365.45</td>\n",
       "      <td>10671.8</td>\n",
       "      <td>3900</td>\n",
       "      <td>O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bendroflumethiazide</td>\n",
       "      <td>Bendroflumethiazide                           ...</td>\n",
       "      <td>421.46</td>\n",
       "      <td>50.3</td>\n",
       "      <td>21.2</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...</td>\n",
       "      <td>InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzocaine</td>\n",
       "      <td>Benzocaine                                    ...</td>\n",
       "      <td>165.21</td>\n",
       "      <td>4721.26</td>\n",
       "      <td>780</td>\n",
       "      <td>NC1=CC=C(C(OCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benzthiazide</td>\n",
       "      <td>Benzthiazide                                  ...</td>\n",
       "      <td>431.98</td>\n",
       "      <td>14.82</td>\n",
       "      <td>6.4</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...</td>\n",
       "      <td>InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name                                         Unnamed: 1  \\\n",
       "0           Acebutolol  Acebutolol                                    ...   \n",
       "1          Amoxicillin  Amoxicillin                                   ...   \n",
       "2  Bendroflumethiazide  Bendroflumethiazide                           ...   \n",
       "3           Benzocaine  Benzocaine                                    ...   \n",
       "4         Benzthiazide  Benzthiazide                                  ...   \n",
       "\n",
       "       MW Solubility (from findings) (micro M)  \\\n",
       "0  336.48                              2113.05   \n",
       "1  365.45                              10671.8   \n",
       "2  421.46                                 50.3   \n",
       "3  165.21                              4721.26   \n",
       "4  431.98                                14.82   \n",
       "\n",
       "  Solubility (from findings) (micro g/mL)  \\\n",
       "0                                     711   \n",
       "1                                    3900   \n",
       "2                                    21.2   \n",
       "3                                     780   \n",
       "4                                     6.4   \n",
       "\n",
       "                                              SMILES  \\\n",
       "0        CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1   \n",
       "1  O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...   \n",
       "2  O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...   \n",
       "3                             NC1=CC=C(C(OCC)=O)C=C1   \n",
       "4  O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...   \n",
       "\n",
       "                                               InChI  \n",
       "0  InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...  \n",
       "1  InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...  \n",
       "2  InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...  \n",
       "3  InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...  \n",
       "4  InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_pred_data['Solubility (from findings) (micro M)']=solub_pred_data['Solubility (from findings) (micro M)'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                                        object\n",
       "Unnamed: 1                                  object\n",
       "MW                                         float64\n",
       "Solubility (from findings) (micro M)        object\n",
       "Solubility (from findings) (micro g/mL)     object\n",
       "SMILES                                      object\n",
       "InChI                                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_pred_data=solub_pred_data[~solub_pred_data['Solubility (from findings) (micro M)'].str.contains('forms')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Acebutolol</td>\n",
       "      <td>Acebutolol                                    ...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Amoxicillin                                   ...</td>\n",
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       "      <th>2</th>\n",
       "      <td>Bendroflumethiazide</td>\n",
       "      <td>Bendroflumethiazide                           ...</td>\n",
       "      <td>421.46</td>\n",
       "      <td>50.3</td>\n",
       "      <td>21.2</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...</td>\n",
       "      <td>InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzocaine</td>\n",
       "      <td>Benzocaine                                    ...</td>\n",
       "      <td>165.21</td>\n",
       "      <td>4721.26</td>\n",
       "      <td>780</td>\n",
       "      <td>NC1=CC=C(C(OCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benzthiazide</td>\n",
       "      <td>Benzthiazide                                  ...</td>\n",
       "      <td>431.98</td>\n",
       "      <td>14.82</td>\n",
       "      <td>6.4</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...</td>\n",
       "      <td>InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...</td>\n",
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      ],
      "text/plain": [
       "                  name                                         Unnamed: 1  \\\n",
       "0           Acebutolol  Acebutolol                                    ...   \n",
       "1          Amoxicillin  Amoxicillin                                   ...   \n",
       "2  Bendroflumethiazide  Bendroflumethiazide                           ...   \n",
       "3           Benzocaine  Benzocaine                                    ...   \n",
       "4         Benzthiazide  Benzthiazide                                  ...   \n",
       "\n",
       "       MW Solubility (from findings) (micro M)  \\\n",
       "0  336.48                              2113.05   \n",
       "1  365.45                             10671.77   \n",
       "2  421.46                                 50.3   \n",
       "3  165.21                              4721.26   \n",
       "4  431.98                                14.82   \n",
       "\n",
       "  Solubility (from findings) (micro g/mL)  \\\n",
       "0                                     711   \n",
       "1                                    3900   \n",
       "2                                    21.2   \n",
       "3                                     780   \n",
       "4                                     6.4   \n",
       "\n",
       "                                              SMILES  \\\n",
       "0        CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1   \n",
       "1  O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...   \n",
       "2  O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...   \n",
       "3                             NC1=CC=C(C(OCC)=O)C=C1   \n",
       "4  O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...   \n",
       "\n",
       "                                               InChI  \n",
       "0  InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...  \n",
       "1  InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...  \n",
       "2  InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...  \n",
       "3  InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...  \n",
       "4  InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_pred_data.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>MW</th>\n",
       "      <th>Solubility (from findings) (micro M)</th>\n",
       "      <th>Solubility (from findings) (micro g/mL)</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acebutolol</td>\n",
       "      <td>Acebutolol                                    ...</td>\n",
       "      <td>336.48</td>\n",
       "      <td>2113.05</td>\n",
       "      <td>711</td>\n",
       "      <td>CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amoxicillin</td>\n",
       "      <td>Amoxicillin                                   ...</td>\n",
       "      <td>365.45</td>\n",
       "      <td>10671.77</td>\n",
       "      <td>3900</td>\n",
       "      <td>O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...</td>\n",
       "      <td>InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bendroflumethiazide</td>\n",
       "      <td>Bendroflumethiazide                           ...</td>\n",
       "      <td>421.46</td>\n",
       "      <td>50.3</td>\n",
       "      <td>21.2</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...</td>\n",
       "      <td>InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzocaine</td>\n",
       "      <td>Benzocaine                                    ...</td>\n",
       "      <td>165.21</td>\n",
       "      <td>4721.26</td>\n",
       "      <td>780</td>\n",
       "      <td>NC1=CC=C(C(OCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benzthiazide</td>\n",
       "      <td>Benzthiazide                                  ...</td>\n",
       "      <td>431.98</td>\n",
       "      <td>14.82</td>\n",
       "      <td>6.4</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...</td>\n",
       "      <td>InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name                                         Unnamed: 1  \\\n",
       "0           Acebutolol  Acebutolol                                    ...   \n",
       "1          Amoxicillin  Amoxicillin                                   ...   \n",
       "2  Bendroflumethiazide  Bendroflumethiazide                           ...   \n",
       "3           Benzocaine  Benzocaine                                    ...   \n",
       "4         Benzthiazide  Benzthiazide                                  ...   \n",
       "\n",
       "       MW Solubility (from findings) (micro M)  \\\n",
       "0  336.48                              2113.05   \n",
       "1  365.45                             10671.77   \n",
       "2  421.46                                 50.3   \n",
       "3  165.21                              4721.26   \n",
       "4  431.98                                14.82   \n",
       "\n",
       "  Solubility (from findings) (micro g/mL)  \\\n",
       "0                                     711   \n",
       "1                                    3900   \n",
       "2                                    21.2   \n",
       "3                                     780   \n",
       "4                                     6.4   \n",
       "\n",
       "                                              SMILES  \\\n",
       "0        CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1   \n",
       "1  O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...   \n",
       "2  O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...   \n",
       "3                             NC1=CC=C(C(OCC)=O)C=C1   \n",
       "4  O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...   \n",
       "\n",
       "                                               InChI  \n",
       "0  InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...  \n",
       "1  InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...  \n",
       "2  InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...  \n",
       "3  InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...  \n",
       "4  InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_pred_data['Solubility (from findings) (micro M)']=pd.to_numeric(solub_pred_data['Solubility (from findings) (micro M)'], errors='coerce')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "solub_pred_data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * **Searching for missing solubility (S0 in micro Molar) data in the form of null/NaN values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    94\n",
      "True     11\n",
      "Name: S0 (mM), dtype: int64\n",
      "False    94\n",
      "True     11\n",
      "Name: S0 (mM), dtype: int64\n",
      "False    32\n",
      "True      4\n",
      "Name: Solubility (from findings) (micro M), dtype: int64\n",
      "False    32\n",
      "True      4\n",
      "Name: Solubility (from findings) (micro M), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(solub_train_data['S0 (mM)'].isna().value_counts()) ## specific fro NaN search\n",
    "print(solub_train_data['S0 (mM)'].isnull().value_counts()) ## null data general\n",
    "print(solub_pred_data['Solubility (from findings) (micro M)'].isnull().value_counts())\n",
    "print(solub_pred_data['Solubility (from findings) (micro M)'].isna().value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * **check the data are in the rigth format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 12)\n",
      "Substance     object\n",
      "S0 (mM)      float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(solub_train_data.shape)\n",
    "print(solub_train_data[['Substance','S0 (mM)']].dtypes)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(solub_train_descriptors.shape)\n",
    "print(solub_train_descriptors.dtypes) # truncated list of data types\n",
    "#print(solub_train_descriptors.info(verbose=True)) # full list of data types"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(solub_pred_descriptors.shape)\n",
    "print(solub_pred_descriptors.dtypes) # truncated list of data types\n",
    "#print(solub_pred_descriptors.info(verbose=True)) # full list of data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 7)\n",
      "name                                        object\n",
      "Unnamed: 1                                  object\n",
      "MW                                         float64\n",
      "Solubility (from findings) (micro M)       float64\n",
      "Solubility (from findings) (micro g/mL)     object\n",
      "SMILES                                      object\n",
      "InChI                                       object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(solub_pred_data.shape)\n",
    "print(solub_pred_data.dtypes)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(solub_train_bd_1.shape)\n",
    "print(solub_train_bd_1.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * **Need to scale descriptors data to the same range of value [0,1]: MIN-MAX SCALER does this for us (from scikit-learn lib)**\n",
    "This can be done also in pipeline afterwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "%matplotlib inline\n",
    "min_max_scaler = preprocessing.MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_train_descrit=solub_train_descriptors.columns\n",
    "columns_pred_descrit=solub_pred_descriptors.columns\n",
    "columns_train_bd_descrit1=solub_train_bd_1.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_all_minmax = min_max_scaler.fit_transform(solub_train_descriptors[columns_train_descrit[2:]])\n",
    "solub_train_descriptors_values=pd.DataFrame(x_train_all_minmax, columns=columns_train_descrit[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>Sv</th>\n",
       "      <th>Mv</th>\n",
       "      <th>Me</th>\n",
       "      <th>Mp</th>\n",
       "      <th>Mi</th>\n",
       "      <th>nBM</th>\n",
       "      <th>RBN</th>\n",
       "      <th>RBF</th>\n",
       "      <th>...</th>\n",
       "      <th>Depressant-50</th>\n",
       "      <th>Psychotic-80</th>\n",
       "      <th>Hypertens-80</th>\n",
       "      <th>Hypertens-50</th>\n",
       "      <th>Hypnotic-80</th>\n",
       "      <th>Hypnotic-50</th>\n",
       "      <th>Neoplastic-80</th>\n",
       "      <th>Neoplastic-50</th>\n",
       "      <th>Infective-80</th>\n",
       "      <th>Infective-50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.322169</td>\n",
       "      <td>0.245792</td>\n",
       "      <td>0.435356</td>\n",
       "      <td>0.469429</td>\n",
       "      <td>0.329740</td>\n",
       "      <td>0.295363</td>\n",
       "      <td>0.424655</td>\n",
       "      <td>0.491449</td>\n",
       "      <td>0.278751</td>\n",
       "      <td>0.393447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059406</td>\n",
       "      <td>0.158416</td>\n",
       "      <td>0.594059</td>\n",
       "      <td>0.207921</td>\n",
       "      <td>0.415842</td>\n",
       "      <td>0.108911</td>\n",
       "      <td>0.712871</td>\n",
       "      <td>0.247525</td>\n",
       "      <td>0.782178</td>\n",
       "      <td>0.207921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.170704</td>\n",
       "      <td>0.162795</td>\n",
       "      <td>0.220415</td>\n",
       "      <td>0.217406</td>\n",
       "      <td>0.210968</td>\n",
       "      <td>0.148663</td>\n",
       "      <td>0.175305</td>\n",
       "      <td>0.179383</td>\n",
       "      <td>0.196975</td>\n",
       "      <td>0.225203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.237562</td>\n",
       "      <td>0.366952</td>\n",
       "      <td>0.493522</td>\n",
       "      <td>0.407844</td>\n",
       "      <td>0.495325</td>\n",
       "      <td>0.313081</td>\n",
       "      <td>0.454679</td>\n",
       "      <td>0.433727</td>\n",
       "      <td>0.414824</td>\n",
       "      <td>0.407844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.220879</td>\n",
       "      <td>0.152591</td>\n",
       "      <td>0.295478</td>\n",
       "      <td>0.322275</td>\n",
       "      <td>0.162963</td>\n",
       "      <td>0.199313</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.253219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.305828</td>\n",
       "      <td>0.214999</td>\n",
       "      <td>0.415792</td>\n",
       "      <td>0.445498</td>\n",
       "      <td>0.303704</td>\n",
       "      <td>0.281787</td>\n",
       "      <td>0.395604</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.377682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.419389</td>\n",
       "      <td>0.305963</td>\n",
       "      <td>0.592755</td>\n",
       "      <td>0.620853</td>\n",
       "      <td>0.451852</td>\n",
       "      <td>0.347079</td>\n",
       "      <td>0.527473</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.536481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1466 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               MW         AMW          Sv          Mv          Me          Mp  \\\n",
       "count  101.000000  101.000000  101.000000  101.000000  101.000000  101.000000   \n",
       "mean     0.322169    0.245792    0.435356    0.469429    0.329740    0.295363   \n",
       "std      0.170704    0.162795    0.220415    0.217406    0.210968    0.148663   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.220879    0.152591    0.295478    0.322275    0.162963    0.199313   \n",
       "50%      0.305828    0.214999    0.415792    0.445498    0.303704    0.281787   \n",
       "75%      0.419389    0.305963    0.592755    0.620853    0.451852    0.347079   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "               Mi         nBM         RBN         RBF  ...  Depressant-50  \\\n",
       "count  101.000000  101.000000  101.000000  101.000000  ...     101.000000   \n",
       "mean     0.424655    0.491449    0.278751    0.393447  ...       0.059406   \n",
       "std      0.175305    0.179383    0.196975    0.225203  ...       0.237562   \n",
       "min      0.000000    0.000000    0.000000    0.000000  ...       0.000000   \n",
       "25%      0.307692    0.363636    0.153846    0.253219  ...       0.000000   \n",
       "50%      0.395604    0.454545    0.230769    0.377682  ...       0.000000   \n",
       "75%      0.527473    0.590909    0.384615    0.536481  ...       0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000  ...       1.000000   \n",
       "\n",
       "       Psychotic-80  Hypertens-80  Hypertens-50  Hypnotic-80  Hypnotic-50  \\\n",
       "count    101.000000    101.000000    101.000000   101.000000   101.000000   \n",
       "mean       0.158416      0.594059      0.207921     0.415842     0.108911   \n",
       "std        0.366952      0.493522      0.407844     0.495325     0.313081   \n",
       "min        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "25%        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "50%        0.000000      1.000000      0.000000     0.000000     0.000000   \n",
       "75%        0.000000      1.000000      0.000000     1.000000     0.000000   \n",
       "max        1.000000      1.000000      1.000000     1.000000     1.000000   \n",
       "\n",
       "       Neoplastic-80  Neoplastic-50  Infective-80  Infective-50  \n",
       "count     101.000000     101.000000    101.000000    101.000000  \n",
       "mean        0.712871       0.247525      0.782178      0.207921  \n",
       "std         0.454679       0.433727      0.414824      0.407844  \n",
       "min         0.000000       0.000000      0.000000      0.000000  \n",
       "25%         0.000000       0.000000      1.000000      0.000000  \n",
       "50%         1.000000       0.000000      1.000000      0.000000  \n",
       "75%         1.000000       0.000000      1.000000      0.000000  \n",
       "max         1.000000       1.000000      1.000000      1.000000  \n",
       "\n",
       "[8 rows x 1466 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_train_descriptors_values.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pred_all_minmax = min_max_scaler.fit_transform(solub_pred_descriptors[columns_pred_descrit[2:]])\n",
    "solub_pred_descriptors_values=pd.DataFrame(x_pred_all_minmax, columns=columns_pred_descrit[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>Sv</th>\n",
       "      <th>Mv</th>\n",
       "      <th>Me</th>\n",
       "      <th>Mp</th>\n",
       "      <th>Mi</th>\n",
       "      <th>nBM</th>\n",
       "      <th>RBN</th>\n",
       "      <th>RBF</th>\n",
       "      <th>...</th>\n",
       "      <th>Inflammat-80</th>\n",
       "      <th>Depressant-80</th>\n",
       "      <th>Psychotic-80</th>\n",
       "      <th>Hypertens-80</th>\n",
       "      <th>Hypertens-50</th>\n",
       "      <th>Hypnotic-80</th>\n",
       "      <th>Hypnotic-50</th>\n",
       "      <th>Neoplastic-80</th>\n",
       "      <th>Neoplastic-50</th>\n",
       "      <th>Infective-50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.431442</td>\n",
       "      <td>0.370234</td>\n",
       "      <td>0.378107</td>\n",
       "      <td>0.479992</td>\n",
       "      <td>0.419215</td>\n",
       "      <td>0.410227</td>\n",
       "      <td>0.546274</td>\n",
       "      <td>0.408482</td>\n",
       "      <td>0.295455</td>\n",
       "      <td>0.482904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.468750</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.218750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.259477</td>\n",
       "      <td>0.268941</td>\n",
       "      <td>0.235210</td>\n",
       "      <td>0.310569</td>\n",
       "      <td>0.267304</td>\n",
       "      <td>0.260742</td>\n",
       "      <td>0.243490</td>\n",
       "      <td>0.268020</td>\n",
       "      <td>0.266298</td>\n",
       "      <td>0.260128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507007</td>\n",
       "      <td>0.482559</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.296145</td>\n",
       "      <td>0.470929</td>\n",
       "      <td>0.439941</td>\n",
       "      <td>0.420013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.291934</td>\n",
       "      <td>0.115574</td>\n",
       "      <td>0.220477</td>\n",
       "      <td>0.207317</td>\n",
       "      <td>0.242021</td>\n",
       "      <td>0.215152</td>\n",
       "      <td>0.360577</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.394978</td>\n",
       "      <td>0.358668</td>\n",
       "      <td>0.333328</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0.351064</td>\n",
       "      <td>0.375758</td>\n",
       "      <td>0.605769</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.467647</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.602383</td>\n",
       "      <td>0.607312</td>\n",
       "      <td>0.510609</td>\n",
       "      <td>0.740854</td>\n",
       "      <td>0.609043</td>\n",
       "      <td>0.563636</td>\n",
       "      <td>0.677885</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.626471</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              MW        AMW         Sv         Mv         Me         Mp  \\\n",
       "count  32.000000  32.000000  32.000000  32.000000  32.000000  32.000000   \n",
       "mean    0.431442   0.370234   0.378107   0.479992   0.419215   0.410227   \n",
       "std     0.259477   0.268941   0.235210   0.310569   0.267304   0.260742   \n",
       "min     0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "25%     0.291934   0.115574   0.220477   0.207317   0.242021   0.215152   \n",
       "50%     0.394978   0.358668   0.333328   0.512195   0.351064   0.375758   \n",
       "75%     0.602383   0.607312   0.510609   0.740854   0.609043   0.563636   \n",
       "max     1.000000   1.000000   1.000000   1.000000   1.000000   1.000000   \n",
       "\n",
       "              Mi        nBM        RBN        RBF  ...  Inflammat-80  \\\n",
       "count  32.000000  32.000000  32.000000  32.000000  ...     32.000000   \n",
       "mean    0.546274   0.408482   0.295455   0.482904  ...      0.468750   \n",
       "std     0.243490   0.268020   0.266298   0.260128  ...      0.507007   \n",
       "min     0.000000   0.000000   0.000000   0.000000  ...      0.000000   \n",
       "25%     0.360577   0.214286   0.090909   0.300000  ...      0.000000   \n",
       "50%     0.605769   0.428571   0.227273   0.467647  ...      0.000000   \n",
       "75%     0.677885   0.571429   0.363636   0.626471  ...      1.000000   \n",
       "max     1.000000   1.000000   1.000000   1.000000  ...      1.000000   \n",
       "\n",
       "       Depressant-80  Psychotic-80  Hypertens-80  Hypertens-50  Hypnotic-80  \\\n",
       "count      32.000000     32.000000     32.000000     32.000000    32.000000   \n",
       "mean        0.343750      0.187500      0.562500      0.187500     0.437500   \n",
       "std         0.482559      0.396558      0.504016      0.396558     0.504016   \n",
       "min         0.000000      0.000000      0.000000      0.000000     0.000000   \n",
       "25%         0.000000      0.000000      0.000000      0.000000     0.000000   \n",
       "50%         0.000000      0.000000      1.000000      0.000000     0.000000   \n",
       "75%         1.000000      0.000000      1.000000      0.000000     1.000000   \n",
       "max         1.000000      1.000000      1.000000      1.000000     1.000000   \n",
       "\n",
       "       Hypnotic-50  Neoplastic-80  Neoplastic-50  Infective-50  \n",
       "count    32.000000      32.000000      32.000000     32.000000  \n",
       "mean      0.093750       0.687500       0.250000      0.218750  \n",
       "std       0.296145       0.470929       0.439941      0.420013  \n",
       "min       0.000000       0.000000       0.000000      0.000000  \n",
       "25%       0.000000       0.000000       0.000000      0.000000  \n",
       "50%       0.000000       1.000000       0.000000      0.000000  \n",
       "75%       0.000000       1.000000       0.250000      0.000000  \n",
       "max       1.000000       1.000000       1.000000      1.000000  \n",
       "\n",
       "[8 rows x 1169 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_descriptors_values.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound_Identifier', 'Source', 'SMILES', 'LogS.M.',\n",
       "       'Solubility.microgram.mL.', 'Solubility.micromolar.', 'MW', 'AMW', 'Sv',\n",
       "       'Se',\n",
       "       ...\n",
       "       'Psychotic-80', 'Psychotic-50', 'Hypertens-80', 'Hypertens-50',\n",
       "       'Hypnotic-80', 'Hypnotic-50', 'Neoplastic-80', 'Neoplastic-50',\n",
       "       'Infective-80', 'Infective-50'],\n",
       "      dtype='object', length=2262)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_train_bd_descrit1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_bd_all_minmax1 = min_max_scaler.fit_transform(solub_train_bd_1[columns_train_bd_descrit1[6:]])\n",
    "solub_train_bd_descriptors1_values=pd.DataFrame(x_train_bd_all_minmax1, columns=columns_train_bd_descrit1[6:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>Sv</th>\n",
       "      <th>Se</th>\n",
       "      <th>Sp</th>\n",
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       "      <th>Me</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
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       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
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       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.231589</td>\n",
       "      <td>0.127291</td>\n",
       "      <td>0.466913</td>\n",
       "      <td>0.378471</td>\n",
       "      <td>0.467823</td>\n",
       "      <td>0.375610</td>\n",
       "      <td>0.261054</td>\n",
       "      <td>0.274092</td>\n",
       "      <td>0.214316</td>\n",
       "      <td>0.439265</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271033</td>\n",
       "      <td>0.028633</td>\n",
       "      <td>0.847700</td>\n",
       "      <td>0.353000</td>\n",
       "      <td>0.617667</td>\n",
       "      <td>0.068533</td>\n",
       "      <td>0.925900</td>\n",
       "      <td>0.577867</td>\n",
       "      <td>0.944000</td>\n",
       "      <td>0.449167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.054541</td>\n",
       "      <td>0.049994</td>\n",
       "      <td>0.118557</td>\n",
       "      <td>0.111389</td>\n",
       "      <td>0.122512</td>\n",
       "      <td>0.113756</td>\n",
       "      <td>0.081363</td>\n",
       "      <td>0.100715</td>\n",
       "      <td>0.072415</td>\n",
       "      <td>0.077386</td>\n",
       "      <td>...</td>\n",
       "      <td>0.444501</td>\n",
       "      <td>0.166776</td>\n",
       "      <td>0.359317</td>\n",
       "      <td>0.477911</td>\n",
       "      <td>0.485965</td>\n",
       "      <td>0.252663</td>\n",
       "      <td>0.261938</td>\n",
       "      <td>0.493908</td>\n",
       "      <td>0.229926</td>\n",
       "      <td>0.497418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.202779</td>\n",
       "      <td>0.096054</td>\n",
       "      <td>0.398379</td>\n",
       "      <td>0.310242</td>\n",
       "      <td>0.395632</td>\n",
       "      <td>0.304381</td>\n",
       "      <td>0.207024</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.168654</td>\n",
       "      <td>0.391813</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.234240</td>\n",
       "      <td>0.120085</td>\n",
       "      <td>0.471586</td>\n",
       "      <td>0.380554</td>\n",
       "      <td>0.471766</td>\n",
       "      <td>0.376591</td>\n",
       "      <td>0.256932</td>\n",
       "      <td>0.260417</td>\n",
       "      <td>0.206133</td>\n",
       "      <td>0.438596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.262012</td>\n",
       "      <td>0.149591</td>\n",
       "      <td>0.540656</td>\n",
       "      <td>0.450128</td>\n",
       "      <td>0.545771</td>\n",
       "      <td>0.449988</td>\n",
       "      <td>0.308688</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.250426</td>\n",
       "      <td>0.479532</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 2256 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MW           AMW            Sv            Se            Sp  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean       0.231589      0.127291      0.466913      0.378471      0.467823   \n",
       "std        0.054541      0.049994      0.118557      0.111389      0.122512   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.202779      0.096054      0.398379      0.310242      0.395632   \n",
       "50%        0.234240      0.120085      0.471586      0.380554      0.471766   \n",
       "75%        0.262012      0.149591      0.540656      0.450128      0.545771   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "                 Si            Mv            Me            Mp            Mi  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean       0.375610      0.261054      0.274092      0.214316      0.439265   \n",
       "std        0.113756      0.081363      0.100715      0.072415      0.077386   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.304381      0.207024      0.208333      0.168654      0.391813   \n",
       "50%        0.376591      0.256932      0.260417      0.206133      0.438596   \n",
       "75%        0.449988      0.308688      0.328125      0.250426      0.479532   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       ...  Psychotic-80  Psychotic-50  Hypertens-80  Hypertens-50  \\\n",
       "count  ...  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   ...      0.271033      0.028633      0.847700      0.353000   \n",
       "std    ...      0.444501      0.166776      0.359317      0.477911   \n",
       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...      0.000000      0.000000      1.000000      0.000000   \n",
       "50%    ...      0.000000      0.000000      1.000000      0.000000   \n",
       "75%    ...      1.000000      0.000000      1.000000      1.000000   \n",
       "max    ...      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "        Hypnotic-80   Hypnotic-50  Neoplastic-80  Neoplastic-50  Infective-80  \\\n",
       "count  30000.000000  30000.000000   30000.000000   30000.000000  30000.000000   \n",
       "mean       0.617667      0.068533       0.925900       0.577867      0.944000   \n",
       "std        0.485965      0.252663       0.261938       0.493908      0.229926   \n",
       "min        0.000000      0.000000       0.000000       0.000000      0.000000   \n",
       "25%        0.000000      0.000000       1.000000       0.000000      1.000000   \n",
       "50%        1.000000      0.000000       1.000000       1.000000      1.000000   \n",
       "75%        1.000000      0.000000       1.000000       1.000000      1.000000   \n",
       "max        1.000000      1.000000       1.000000       1.000000      1.000000   \n",
       "\n",
       "       Infective-50  \n",
       "count  30000.000000  \n",
       "mean       0.449167  \n",
       "std        0.497418  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 2256 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_train_bd_descriptors1_values.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "corr_train=solub_train_descriptors_values.corr()\n",
    "corr_pred=solub_pred_descriptors_values.corr()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "corr_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * **Correlation heatmap: load seaborn lib**"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# execute this cell as a code if you want a correlation heatmap\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.heatmap(corr_train,cmap='binary')\n",
    "plt.title('Correlation of the training descriptors')\n",
    "plt.subplot(1,2,2)\n",
    "sns.heatmap(corr_pred,cmap='binary')\n",
    "plt.title('Correlation of the prediction descriptors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * **Uniforming training (small and bigger set) and pediction data to the same descriptors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_same_descriptors=solub_train_bd_descriptors1_values.columns.isin(solub_pred_descriptors_values.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_train_bd_descriptors1_temp=solub_train_bd_descriptors1_values.loc[:,bool_same_descriptors]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_same_descriptors1=solub_train_bd_descriptors1_temp.columns.isin(solub_train_descriptors_values.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_train_bd_descriptors1_new=solub_train_bd_descriptors1_temp.loc[:,bool_same_descriptors1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_same_descriptors2=solub_train_descriptors_values.columns.isin(solub_train_bd_descriptors1_new.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_train_descriptors_new=solub_train_descriptors_values.loc[:,bool_same_descriptors2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_same_descriptors3=solub_pred_descriptors_values.columns.isin(solub_train_bd_descriptors1_new.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_pred_descriptors_new=solub_pred_descriptors_values.loc[:,bool_same_descriptors3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30000, 651)\n",
      "(101, 651)\n",
      "(32, 651)\n"
     ]
    }
   ],
   "source": [
    "print(solub_train_bd_descriptors1_new.shape)\n",
    "print(solub_train_descriptors_new.shape)\n",
    "print(solub_pred_descriptors_new.shape)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "a11=solub_train_bd_descriptors1_values.columns.isin(solub_pred_descriptors_values.columns)\n",
    "a111=solub_train_bd_descriptors1_values.loc[:,a11]\n",
    "a22=a111.columns.isin(solub_train_descriptors_values.columns)\n",
    "a222=a111.loc[:,a22]\n",
    "a33=solub_train_descriptors_values.columns.isin(a222.columns)\n",
    "a333=solub_train_descriptors_values.loc[:,a33]\n",
    "a44=solub_pred_descriptors_values.columns.isin(a222.columns)\n",
    "a444=solub_pred_descriptors_values.loc[:,a44]\n",
    "uniqueValues, occurCount = np.unique(a11, return_counts=True)\n",
    "print(uniqueValues, occurCount)\n",
    "uniqueValues, occurCount = np.unique(a22, return_counts=True)\n",
    "print(uniqueValues, occurCount)\n",
    "uniqueValues, occurCount = np.unique(a33, return_counts=True)\n",
    "print(uniqueValues, occurCount)\n",
    "uniqueValues, occurCount = np.unique(a44, return_counts=True)\n",
    "print(uniqueValues, occurCount)\n",
    "a11\n",
    "print(a111.shape)\n",
    "print(a222.shape)\n",
    "print(a333.shape)\n",
    "print(a444.shape)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "corr_train1=solub_train_descriptors_new.corr()\n",
    "corr_pred1=solub_pred_descriptors_new.corr()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# execute this cell as code if you want the correlation heatmap\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.heatmap(corr_train1,cmap='RdBu')\n",
    "plt.title('Correlation of the training descriptors')\n",
    "plt.subplot(1,2,2)\n",
    "sns.heatmap(corr_pred1,cmap='RdBu')\n",
    "plt.title('Correlation of the prediction descriptors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * **Clean data from different isomer forms (only DRAGON 2D descriptors available)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** clean the training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Substance</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>assays</th>\n",
       "      <th>Ionic Strength (M)</th>\n",
       "      <th>S0 (mM)</th>\n",
       "      <th>SD of S0 (mM)</th>\n",
       "      <th>Kinetic Solubility (mM)</th>\n",
       "      <th>SD of Kinetic Solubility (mM)</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chlorprothixene_form_I</td>\n",
       "      <td>25.48</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.153915</td>\n",
       "      <td>0.177964</td>\n",
       "      <td>0.00812546</td>\n",
       "      <td>1.554</td>\n",
       "      <td>0.1589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN(C)CC\\C=C1\\c2ccccc2Sc2ccc(cc12)Cl</td>\n",
       "      <td>InChI=1/C18H18ClNS/c1-20(2)11-5-7-14-15-6-3-4-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>chlorprothixene_form_II</td>\n",
       "      <td>26.11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.153385</td>\n",
       "      <td>1.361050</td>\n",
       "      <td>0.12461</td>\n",
       "      <td>1.562</td>\n",
       "      <td>0.1519</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN(C)CC\\C=C1\\c2ccccc2Sc2ccc(cc12)Cl</td>\n",
       "      <td>InChI=1/C18H18ClNS/c1-20(2)11-5-7-14-15-6-3-4-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>phthalic_acid_form_I</td>\n",
       "      <td>25.10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.280791</td>\n",
       "      <td>32158.400000</td>\n",
       "      <td>2311.89</td>\n",
       "      <td>52659.000</td>\n",
       "      <td>17955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C(c1c(C(=O)O)cccc1)(=O)O</td>\n",
       "      <td>InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>phthalic_acid_form_II</td>\n",
       "      <td>24.86</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.261149</td>\n",
       "      <td>24823.500000</td>\n",
       "      <td>864.304</td>\n",
       "      <td>52659.000</td>\n",
       "      <td>17955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C(c1c(C(=O)O)cccc1)(=O)O</td>\n",
       "      <td>InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>sulindac_form_I</td>\n",
       "      <td>24.84</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.160486</td>\n",
       "      <td>210.015000</td>\n",
       "      <td>23.1367</td>\n",
       "      <td>231.500</td>\n",
       "      <td>26.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(CC(O)=O)c2cc(F)ccc2C\\1=C\\c1ccc(cc1)S(C)=O</td>\n",
       "      <td>InChI=1/C20H17FO3S/c1-12-17(9-13-3-6-15(7-4-13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>sulindac_form_II</td>\n",
       "      <td>24.83</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.15972</td>\n",
       "      <td>31.902500</td>\n",
       "      <td>4.12412</td>\n",
       "      <td>234.700</td>\n",
       "      <td>29.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(CC(O)=O)c2cc(F)ccc2C\\1=C\\c1ccc(cc1)S(C)=O</td>\n",
       "      <td>InChI=1/C20H17FO3S/c1-12-17(9-13-3-6-15(7-4-13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>trichloromethiazide_form_I</td>\n",
       "      <td>25.66</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.163679</td>\n",
       "      <td>660.645000</td>\n",
       "      <td>17.5235</td>\n",
       "      <td>4530.000</td>\n",
       "      <td>2054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NS(=O)(=O)c1cc2c(NC(NS2(=O)=O)C(Cl)Cl)cc1Cl</td>\n",
       "      <td>InChI=1/C8H12Cl3N3O4Rn2/c9-3-1-4-6(2-5(3)19(12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>trichloromethiazide_form_II</td>\n",
       "      <td>25.67</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.16193</td>\n",
       "      <td>295.843000</td>\n",
       "      <td>0</td>\n",
       "      <td>4530.000</td>\n",
       "      <td>2054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NS(=O)(=O)c1cc2c(NC(NS2(=O)=O)C(Cl)Cl)cc1Cl</td>\n",
       "      <td>InChI=1/C8H12Cl3N3O4Rn2/c9-3-1-4-6(2-5(3)19(12...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Substance  Temperature  assays Ionic Strength (M)  \\\n",
       "24        chlorprothixene_form_I        25.48     9.0           0.153915   \n",
       "25       chlorprothixene_form_II        26.11     9.0           0.153385   \n",
       "76          phthalic_acid_form_I        25.10     7.0           0.280791   \n",
       "77         phthalic_acid_form_II        24.86     7.0           0.261149   \n",
       "92               sulindac_form_I        24.84    20.0           0.160486   \n",
       "93              sulindac_form_II        24.83    19.0            0.15972   \n",
       "98    trichloromethiazide_form_I        25.66     3.0           0.163679   \n",
       "99   trichloromethiazide_form_II        25.67     3.0            0.16193   \n",
       "\n",
       "         S0 (mM) SD of S0 (mM)  Kinetic Solubility (mM)  \\\n",
       "24      0.177964    0.00812546                    1.554   \n",
       "25      1.361050       0.12461                    1.562   \n",
       "76  32158.400000       2311.89                52659.000   \n",
       "77  24823.500000       864.304                52659.000   \n",
       "92    210.015000       23.1367                  231.500   \n",
       "93     31.902500       4.12412                  234.700   \n",
       "98    660.645000       17.5235                 4530.000   \n",
       "99    295.843000             0                 4530.000   \n",
       "\n",
       "   SD of Kinetic Solubility (mM)  Unnamed: 8  Unnamed: 9  \\\n",
       "24                        0.1589         NaN         NaN   \n",
       "25                        0.1519         NaN         NaN   \n",
       "76                         17955         NaN         NaN   \n",
       "77                         17955         NaN         NaN   \n",
       "92                         26.48         NaN         NaN   \n",
       "93                         29.93         NaN         NaN   \n",
       "98                          2054         NaN         NaN   \n",
       "99                          2054         NaN         NaN   \n",
       "\n",
       "                                             SMILES  \\\n",
       "24              CN(C)CC\\C=C1\\c2ccccc2Sc2ccc(cc12)Cl   \n",
       "25              CN(C)CC\\C=C1\\c2ccccc2Sc2ccc(cc12)Cl   \n",
       "76                         C(c1c(C(=O)O)cccc1)(=O)O   \n",
       "77                         C(c1c(C(=O)O)cccc1)(=O)O   \n",
       "92  CC1=C(CC(O)=O)c2cc(F)ccc2C\\1=C\\c1ccc(cc1)S(C)=O   \n",
       "93  CC1=C(CC(O)=O)c2cc(F)ccc2C\\1=C\\c1ccc(cc1)S(C)=O   \n",
       "98      NS(=O)(=O)c1cc2c(NC(NS2(=O)=O)C(Cl)Cl)cc1Cl   \n",
       "99      NS(=O)(=O)c1cc2c(NC(NS2(=O)=O)C(Cl)Cl)cc1Cl   \n",
       "\n",
       "                                                InChI  \n",
       "24  InChI=1/C18H18ClNS/c1-20(2)11-5-7-14-15-6-3-4-...  \n",
       "25  InChI=1/C18H18ClNS/c1-20(2)11-5-7-14-15-6-3-4-...  \n",
       "76  InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...  \n",
       "77  InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...  \n",
       "92  InChI=1/C20H17FO3S/c1-12-17(9-13-3-6-15(7-4-13...  \n",
       "93  InChI=1/C20H17FO3S/c1-12-17(9-13-3-6-15(7-4-13...  \n",
       "98  InChI=1/C8H12Cl3N3O4Rn2/c9-3-1-4-6(2-5(3)19(12...  \n",
       "99  InChI=1/C8H12Cl3N3O4Rn2/c9-3-1-4-6(2-5(3)19(12...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_train_data[solub_train_data['Substance'].str.contains('form')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_train_data1=solub_train_data[~solub_train_data['Substance'].str.contains('form_II')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        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>Substance</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>assays</th>\n",
       "      <th>Ionic Strength (M)</th>\n",
       "      <th>S0 (mM)</th>\n",
       "      <th>SD of S0 (mM)</th>\n",
       "      <th>Kinetic Solubility (mM)</th>\n",
       "      <th>SD of Kinetic Solubility (mM)</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chlorprothixene_form_I</td>\n",
       "      <td>25.48</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.153915</td>\n",
       "      <td>0.177964</td>\n",
       "      <td>0.00812546</td>\n",
       "      <td>1.554</td>\n",
       "      <td>0.1589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CN(C)CC\\C=C1\\c2ccccc2Sc2ccc(cc12)Cl</td>\n",
       "      <td>InChI=1/C18H18ClNS/c1-20(2)11-5-7-14-15-6-3-4-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>phthalic_acid_form_I</td>\n",
       "      <td>25.10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.280791</td>\n",
       "      <td>32158.400000</td>\n",
       "      <td>2311.89</td>\n",
       "      <td>52659.000</td>\n",
       "      <td>17955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C(c1c(C(=O)O)cccc1)(=O)O</td>\n",
       "      <td>InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>sulindac_form_I</td>\n",
       "      <td>24.84</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.160486</td>\n",
       "      <td>210.015000</td>\n",
       "      <td>23.1367</td>\n",
       "      <td>231.500</td>\n",
       "      <td>26.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC1=C(CC(O)=O)c2cc(F)ccc2C\\1=C\\c1ccc(cc1)S(C)=O</td>\n",
       "      <td>InChI=1/C20H17FO3S/c1-12-17(9-13-3-6-15(7-4-13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>trichloromethiazide_form_I</td>\n",
       "      <td>25.66</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.163679</td>\n",
       "      <td>660.645000</td>\n",
       "      <td>17.5235</td>\n",
       "      <td>4530.000</td>\n",
       "      <td>2054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NS(=O)(=O)c1cc2c(NC(NS2(=O)=O)C(Cl)Cl)cc1Cl</td>\n",
       "      <td>InChI=1/C8H12Cl3N3O4Rn2/c9-3-1-4-6(2-5(3)19(12...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Substance  Temperature  assays Ionic Strength (M)  \\\n",
       "24       chlorprothixene_form_I        25.48     9.0           0.153915   \n",
       "76         phthalic_acid_form_I        25.10     7.0           0.280791   \n",
       "92              sulindac_form_I        24.84    20.0           0.160486   \n",
       "98   trichloromethiazide_form_I        25.66     3.0           0.163679   \n",
       "\n",
       "         S0 (mM) SD of S0 (mM)  Kinetic Solubility (mM)  \\\n",
       "24      0.177964    0.00812546                    1.554   \n",
       "76  32158.400000       2311.89                52659.000   \n",
       "92    210.015000       23.1367                  231.500   \n",
       "98    660.645000       17.5235                 4530.000   \n",
       "\n",
       "   SD of Kinetic Solubility (mM)  Unnamed: 8  Unnamed: 9  \\\n",
       "24                        0.1589         NaN         NaN   \n",
       "76                         17955         NaN         NaN   \n",
       "92                         26.48         NaN         NaN   \n",
       "98                          2054         NaN         NaN   \n",
       "\n",
       "                                             SMILES  \\\n",
       "24              CN(C)CC\\C=C1\\c2ccccc2Sc2ccc(cc12)Cl   \n",
       "76                         C(c1c(C(=O)O)cccc1)(=O)O   \n",
       "92  CC1=C(CC(O)=O)c2cc(F)ccc2C\\1=C\\c1ccc(cc1)S(C)=O   \n",
       "98      NS(=O)(=O)c1cc2c(NC(NS2(=O)=O)C(Cl)Cl)cc1Cl   \n",
       "\n",
       "                                                InChI  \n",
       "24  InChI=1/C18H18ClNS/c1-20(2)11-5-7-14-15-6-3-4-...  \n",
       "76  InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...  \n",
       "92  InChI=1/C20H17FO3S/c1-12-17(9-13-3-6-15(7-4-13...  \n",
       "98  InChI=1/C8H12Cl3N3O4Rn2/c9-3-1-4-6(2-5(3)19(12...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_train_data1[solub_train_data1['Substance'].str.contains('form')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/fm1/lib/python3.7/site-packages/pandas/core/generic.py:6586: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "solub_train_data1['Substance'].replace(regex=True,inplace=True,to_replace=r'_form_I',value=r'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Substance</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>assays</th>\n",
       "      <th>Ionic Strength (M)</th>\n",
       "      <th>S0 (mM)</th>\n",
       "      <th>SD of S0 (mM)</th>\n",
       "      <th>Kinetic Solubility (mM)</th>\n",
       "      <th>SD of Kinetic Solubility (mM)</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>phthalic_acid</td>\n",
       "      <td>25.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.280791</td>\n",
       "      <td>32158.4</td>\n",
       "      <td>2311.89</td>\n",
       "      <td>52659.0</td>\n",
       "      <td>17955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C(c1c(C(=O)O)cccc1)(=O)O</td>\n",
       "      <td>InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Substance  Temperature  assays Ionic Strength (M)  S0 (mM)  \\\n",
       "76   phthalic_acid         25.1     7.0           0.280791  32158.4   \n",
       "\n",
       "   SD of S0 (mM)  Kinetic Solubility (mM) SD of Kinetic Solubility (mM)  \\\n",
       "76       2311.89                  52659.0                         17955   \n",
       "\n",
       "    Unnamed: 8  Unnamed: 9                    SMILES  \\\n",
       "76         NaN         NaN  C(c1c(C(=O)O)cccc1)(=O)O   \n",
       "\n",
       "                                                InChI  \n",
       "76  InChI=1/C8H6O4/c9-7(10)5-3-1-2-4-6(5)8(11)12/h...  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_train_data1[solub_train_data1['Substance'].str.contains('phthalic')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_train_data1.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 12)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_train_data1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** clean the reference/prediction set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>name</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>MW</th>\n",
       "      <th>Solubility (from findings) (micro M)</th>\n",
       "      <th>Solubility (from findings) (micro g/mL)</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Diflunisal_I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.21</td>\n",
       "      <td>103.91</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Diflunisal_II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.21</td>\n",
       "      <td>30.37</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Diflunisal_III</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.21</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.29</td>\n",
       "      <td>used this one in scoring (least soluble)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Diflunisal_IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.21</td>\n",
       "      <td>13.83</td>\n",
       "      <td>3.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Trazodone_I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371.91</td>\n",
       "      <td>1236.86</td>\n",
       "      <td>460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Trazodone_II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371.91</td>\n",
       "      <td>341.48</td>\n",
       "      <td>127</td>\n",
       "      <td>used this one in scoring (least soluble)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name Unnamed: 1      MW  Solubility (from findings) (micro M)  \\\n",
       "9     Diflunisal_I        NaN  250.21                                103.91   \n",
       "10   Diflunisal_II        NaN  250.21                                 30.37   \n",
       "11  Diflunisal_III        NaN  250.21                                  1.16   \n",
       "12   Diflunisal_IV        NaN  250.21                                 13.83   \n",
       "34     Trazodone_I        NaN  371.91                               1236.86   \n",
       "35    Trazodone_II        NaN  371.91                                341.48   \n",
       "\n",
       "   Solubility (from findings) (micro g/mL)  \\\n",
       "9                                       26   \n",
       "10                                     7.6   \n",
       "11                                    0.29   \n",
       "12                                    3.46   \n",
       "34                                     460   \n",
       "35                                     127   \n",
       "\n",
       "                                      SMILES InChI  \n",
       "9                                        NaN   NaN  \n",
       "10                                       NaN   NaN  \n",
       "11  used this one in scoring (least soluble)   NaN  \n",
       "12                                       NaN   NaN  \n",
       "34                                       NaN   NaN  \n",
       "35  used this one in scoring (least soluble)   NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data[solub_pred_data['name'].str.contains('_I')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/fm1/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "/anaconda3/envs/fm1/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "solub_pred_data1=solub_pred_data[~solub_pred_data['name'].str.contains('_II')]\n",
    "solub_pred_data1=solub_pred_data1[~solub_pred_data['name'].str.contains('_III')]\n",
    "solub_pred_data1=solub_pred_data1[~solub_pred_data['name'].str.contains('_IV')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>MW</th>\n",
       "      <th>Solubility (from findings) (micro M)</th>\n",
       "      <th>Solubility (from findings) (micro g/mL)</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acebutolol</td>\n",
       "      <td>Acebutolol                                    ...</td>\n",
       "      <td>336.48</td>\n",
       "      <td>2113.050</td>\n",
       "      <td>711</td>\n",
       "      <td>CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amoxicillin</td>\n",
       "      <td>Amoxicillin                                   ...</td>\n",
       "      <td>365.45</td>\n",
       "      <td>10671.770</td>\n",
       "      <td>3900</td>\n",
       "      <td>O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...</td>\n",
       "      <td>InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bendroflumethiazide</td>\n",
       "      <td>Bendroflumethiazide                           ...</td>\n",
       "      <td>421.46</td>\n",
       "      <td>50.300</td>\n",
       "      <td>21.2</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...</td>\n",
       "      <td>InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzocaine</td>\n",
       "      <td>Benzocaine                                    ...</td>\n",
       "      <td>165.21</td>\n",
       "      <td>4721.260</td>\n",
       "      <td>780</td>\n",
       "      <td>NC1=CC=C(C(OCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benzthiazide</td>\n",
       "      <td>Benzthiazide                                  ...</td>\n",
       "      <td>431.98</td>\n",
       "      <td>14.820</td>\n",
       "      <td>6.4</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...</td>\n",
       "      <td>InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2-chloromandelic_acid</td>\n",
       "      <td>2-chloromandelic acid                         ...</td>\n",
       "      <td>186.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>too soluble</td>\n",
       "      <td>OC(C(O)C1=CC=CC=C1Cl)=O</td>\n",
       "      <td>InChI=1/C8H7ClO3/c9-6-4-2-1-3-5(6)7(10)8(11)12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clozapine</td>\n",
       "      <td>Clozapine                                     ...</td>\n",
       "      <td>326.86</td>\n",
       "      <td>577.920</td>\n",
       "      <td>188.9</td>\n",
       "      <td>ClC(C=C1)=CC2=C1NC(C=CC=C3)=C3C(N4CCN(C)CC4)=N2</td>\n",
       "      <td>InChI=1/C18H19ClN4/c1-22-8-10-23(11-9-22)18-14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dibucaine</td>\n",
       "      <td>Dibucaine                                     ...</td>\n",
       "      <td>343.52</td>\n",
       "      <td>40.750</td>\n",
       "      <td>14</td>\n",
       "      <td>CCN(CC)CCNC(C1=C(C=CC=C2)C2=NC(OCCCC)=C1)=O</td>\n",
       "      <td>InChI=1/C20H29N3O2/c1-4-7-14-25-19-15-17(16-10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Diethylstilbestrol</td>\n",
       "      <td>Diethylstilbestrol                            ...</td>\n",
       "      <td>268.38</td>\n",
       "      <td>37.260</td>\n",
       "      <td>10</td>\n",
       "      <td>CC/C(C1=CC=C(O)C=C1)=C(C2=CC=C(O)C=C2)/CC</td>\n",
       "      <td>InChI=1/C18H20O2/c1-3-17(13-5-9-15(19)10-6-13)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Diflunisal_I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.21</td>\n",
       "      <td>103.910</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dipyridamole</td>\n",
       "      <td>Dipyridamole                                  ...</td>\n",
       "      <td>504.72</td>\n",
       "      <td>6.860</td>\n",
       "      <td>3.46</td>\n",
       "      <td>OCCN(CCO)C1=NC2=C(N3CCCCC3)N=C(N(CCO)CCO)N=C2C...</td>\n",
       "      <td>InChI=1/C24H40N8O4/c33-15-11-31(12-16-34)23-26...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ephedrine</td>\n",
       "      <td>Ephedrine                                     ...</td>\n",
       "      <td>165.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>too soluble</td>\n",
       "      <td>CN[C@H](C)[C@@H](O)C1=CC=CC=C1</td>\n",
       "      <td>InChI=1/C10H15NO/c1-8(11-2)10(12)9-6-4-3-5-7-9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Folic_Acid</td>\n",
       "      <td>Folic Acid                                    ...</td>\n",
       "      <td>441.45</td>\n",
       "      <td>5.660</td>\n",
       "      <td>2.5</td>\n",
       "      <td>OC([C@H](CCC(O)=O)NC(C(C=C1)=CC=C1NCC2=NC3=C(O...</td>\n",
       "      <td>InChI=1/C19H19N7O6/c20-19-25-15-14(17(30)26-19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Furosemide</td>\n",
       "      <td>Furosemide                                    ...</td>\n",
       "      <td>330.77</td>\n",
       "      <td>59.260</td>\n",
       "      <td>19.6</td>\n",
       "      <td>OC(C1=CC(S(N)(=O)=O)=C(Cl)C=C1NCC2=CC=CO2)=O</td>\n",
       "      <td>InChI=1/C12H13ClN2O5Rn/c13-9-5-10(15-6-7-2-1-3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hydrochlorothiazide</td>\n",
       "      <td>Hydrochlorothiazide                           ...</td>\n",
       "      <td>297.77</td>\n",
       "      <td>2098.940</td>\n",
       "      <td>625</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2NCN1)=O</td>\n",
       "      <td>InChI=1/C7H12ClN3O4Rn2/c8-4-1-5-7(2-6(4)16(9,1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Imipramine</td>\n",
       "      <td>Imipramine                                    ...</td>\n",
       "      <td>280.45</td>\n",
       "      <td>78.450</td>\n",
       "      <td>22</td>\n",
       "      <td>CN(C)CCCN1C2=C(C=CC=C2)CCC3=C1C=CC=C3</td>\n",
       "      <td>InChI=1/C19H24N2/c1-20(2)14-7-15-21-18-10-5-3-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Indomethacin</td>\n",
       "      <td>Indomethacin                                  ...</td>\n",
       "      <td>357.81</td>\n",
       "      <td>1145.860</td>\n",
       "      <td>410</td>\n",
       "      <td>COC1=CC=C2C(C(CC(O)=O)=C(C)N2C(C3=CC=C(Cl)C=C3...</td>\n",
       "      <td>InChI=1/C19H16ClNO4/c1-11-15(10-18(22)23)16-9-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ketoprofen</td>\n",
       "      <td>Ketoprofen                                    ...</td>\n",
       "      <td>254.30</td>\n",
       "      <td>617.380</td>\n",
       "      <td>157</td>\n",
       "      <td>O=C(C1=CC=CC=C1)C2=CC=CC(C(C(O)=O)C)=C2</td>\n",
       "      <td>InChI=1/C16H14O3/c1-11(16(18)19)13-8-5-9-14(10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lidocaine</td>\n",
       "      <td>Lidocaine                                     ...</td>\n",
       "      <td>234.38</td>\n",
       "      <td>13354.380</td>\n",
       "      <td>3130</td>\n",
       "      <td>CCN(CC)CC(NC1=C(C)C=CC=C1C)=O</td>\n",
       "      <td>InChI=1/C14H22N2O/c1-5-16(6-2)10-13(17)15-14-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Marbofloxacin</td>\n",
       "      <td>Marbofloxacin                                 ...</td>\n",
       "      <td>362.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>too soluble</td>\n",
       "      <td>OC(C1=CN(N(C)CO2)C3=C2C(N4CCN(C)CC4)=C(F)C=C3C...</td>\n",
       "      <td>InChI=1/C17H19FN4O4/c1-19-3-5-21(6-4-19)14-12(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Meclofenamic_acid</td>\n",
       "      <td>Meclofenamic acid</td>\n",
       "      <td>296.16</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.16</td>\n",
       "      <td>ClC(C=CC(C)=C1Cl)=C1NC2=CC=CC=C2C(O)=O</td>\n",
       "      <td>InChI=1/C14H11Cl2NO2/c1-8-6-7-10(15)13(12(8)16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Naphthoic_acid</td>\n",
       "      <td>Naphthoic acid                                ...</td>\n",
       "      <td>172.19</td>\n",
       "      <td>168.190</td>\n",
       "      <td>28.96</td>\n",
       "      <td>OC(C1=CC2=CC=CC=C2C=C1)=O</td>\n",
       "      <td>InChI=1/C11H8O2/c12-11(13)10-6-5-8-3-1-2-4-9(8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Probenecid</td>\n",
       "      <td>Probenecid                                    ...</td>\n",
       "      <td>285.40</td>\n",
       "      <td>13.670</td>\n",
       "      <td>3.9</td>\n",
       "      <td>CN[C@@H](C)[C@H](O)C1=CC=CC=C1</td>\n",
       "      <td>InChI=1/C13H21NO4Rn/c1-3-9-14(10-4-2)19(17,18)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pseudoephedrine</td>\n",
       "      <td>Pseudophedrine</td>\n",
       "      <td>165.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>too soluble</td>\n",
       "      <td>OC(C1=CC=C(S(N(CCC)CCC)(=O)=O)C=C1)=O</td>\n",
       "      <td>InChI=1/C10H15NO/c1-8(11-2)10(12)9-6-4-3-5-7-9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Pyrimethamine</td>\n",
       "      <td>Pyrimethamine                                 ...</td>\n",
       "      <td>248.74</td>\n",
       "      <td>77.990</td>\n",
       "      <td>19.4</td>\n",
       "      <td>ClC(C=C1)=CC=C1C2=C(CC)N=C(N)N=C2N</td>\n",
       "      <td>InChI=1/C12H13ClN4/c1-2-9-10(11(14)17-12(15)16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Salicylic_acid</td>\n",
       "      <td>Salicylic acid                                ...</td>\n",
       "      <td>138.13</td>\n",
       "      <td>11728.080</td>\n",
       "      <td>1620</td>\n",
       "      <td>OC(C1=CC=CC=C1O)=O</td>\n",
       "      <td>InChI=1/C7H6O3/c8-6-4-2-1-3-5(6)7(9)10/h1-4,8H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sulfamerazine</td>\n",
       "      <td>Sulfamerazine                                 ...</td>\n",
       "      <td>264.34</td>\n",
       "      <td>756.600</td>\n",
       "      <td>200</td>\n",
       "      <td>NC1=CC=C(S(NC2=NC(C)=CC=N2)(=O)=O)C=C1</td>\n",
       "      <td>InChI=1/C11H14N4O2Rn/c1-8-6-7-13-11(14-8)15-18...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Sulfamethizole</td>\n",
       "      <td>Sulfamethizole                                ...</td>\n",
       "      <td>270.37</td>\n",
       "      <td>1664.390</td>\n",
       "      <td>450</td>\n",
       "      <td>NC1=CC=C(S(NC2=NN=C(C)S2)(=O)=O)C=C1</td>\n",
       "      <td>InChI=1/C9H12N4O2RnS/c1-6-11-12-9(17-6)13-16(1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Terfenadine</td>\n",
       "      <td>Terfenadine                                   ...</td>\n",
       "      <td>471.74</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.00856</td>\n",
       "      <td>OC(C1=CC=CC=C1)(C2=CC=CC=C2)C3CCN(CCCC(O)C4=CC...</td>\n",
       "      <td>InChI=1/C32H41NO2/c1-31(2,3)26-18-16-25(17-19-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Thiabendazole</td>\n",
       "      <td>Thiabendazole                                 ...</td>\n",
       "      <td>201.27</td>\n",
       "      <td>327.920</td>\n",
       "      <td>66</td>\n",
       "      <td>C12=CC=CC=C1NC(C3=CSC=N3)=N2</td>\n",
       "      <td>InChI=1/C10H7N3S/c1-2-4-8-7(3-1)12-10(13-8)9-5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Tolbutamide</td>\n",
       "      <td>Tolbutamide                                   ...</td>\n",
       "      <td>270.39</td>\n",
       "      <td>343.950</td>\n",
       "      <td>93</td>\n",
       "      <td>CC1=CC=C(S(NC(NCCCC)=O)(=O)=O)C=C1</td>\n",
       "      <td>InChI=1/C12H18N2O3S/c1-3-4-9-13-12(15)14-18(16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Trazodone_I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371.91</td>\n",
       "      <td>1236.860</td>\n",
       "      <td>460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name                                         Unnamed: 1  \\\n",
       "0              Acebutolol  Acebutolol                                    ...   \n",
       "1             Amoxicillin  Amoxicillin                                   ...   \n",
       "2     Bendroflumethiazide  Bendroflumethiazide                           ...   \n",
       "3              Benzocaine  Benzocaine                                    ...   \n",
       "4            Benzthiazide  Benzthiazide                                  ...   \n",
       "5   2-chloromandelic_acid  2-chloromandelic acid                         ...   \n",
       "6               Clozapine  Clozapine                                     ...   \n",
       "7               Dibucaine  Dibucaine                                     ...   \n",
       "8      Diethylstilbestrol  Diethylstilbestrol                            ...   \n",
       "9            Diflunisal_I                                                NaN   \n",
       "13           Dipyridamole  Dipyridamole                                  ...   \n",
       "14              Ephedrine  Ephedrine                                     ...   \n",
       "15             Folic_Acid  Folic Acid                                    ...   \n",
       "16             Furosemide  Furosemide                                    ...   \n",
       "17    Hydrochlorothiazide  Hydrochlorothiazide                           ...   \n",
       "18             Imipramine  Imipramine                                    ...   \n",
       "19           Indomethacin  Indomethacin                                  ...   \n",
       "20             Ketoprofen  Ketoprofen                                    ...   \n",
       "21              Lidocaine  Lidocaine                                     ...   \n",
       "22          Marbofloxacin  Marbofloxacin                                 ...   \n",
       "23      Meclofenamic_acid                                  Meclofenamic acid   \n",
       "24         Naphthoic_acid  Naphthoic acid                                ...   \n",
       "25             Probenecid  Probenecid                                    ...   \n",
       "26        Pseudoephedrine                                     Pseudophedrine   \n",
       "27          Pyrimethamine  Pyrimethamine                                 ...   \n",
       "28         Salicylic_acid  Salicylic acid                                ...   \n",
       "29          Sulfamerazine  Sulfamerazine                                 ...   \n",
       "30         Sulfamethizole  Sulfamethizole                                ...   \n",
       "31            Terfenadine  Terfenadine                                   ...   \n",
       "32          Thiabendazole  Thiabendazole                                 ...   \n",
       "33            Tolbutamide  Tolbutamide                                   ...   \n",
       "34            Trazodone_I                                                NaN   \n",
       "\n",
       "        MW  Solubility (from findings) (micro M)  \\\n",
       "0   336.48                              2113.050   \n",
       "1   365.45                             10671.770   \n",
       "2   421.46                                50.300   \n",
       "3   165.21                              4721.260   \n",
       "4   431.98                                14.820   \n",
       "5   186.60                                   NaN   \n",
       "6   326.86                               577.920   \n",
       "7   343.52                                40.750   \n",
       "8   268.38                                37.260   \n",
       "9   250.21                               103.910   \n",
       "13  504.72                                 6.860   \n",
       "14  165.26                                   NaN   \n",
       "15  441.45                                 5.660   \n",
       "16  330.77                                59.260   \n",
       "17  297.77                              2098.940   \n",
       "18  280.45                                78.450   \n",
       "19  357.81                              1145.860   \n",
       "20  254.30                               617.380   \n",
       "21  234.38                             13354.380   \n",
       "22  362.40                                   NaN   \n",
       "23  296.16                                 0.540   \n",
       "24  172.19                               168.190   \n",
       "25  285.40                                13.670   \n",
       "26  165.26                                   NaN   \n",
       "27  248.74                                77.990   \n",
       "28  138.13                             11728.080   \n",
       "29  264.34                               756.600   \n",
       "30  270.37                              1664.390   \n",
       "31  471.74                                 0.018   \n",
       "32  201.27                               327.920   \n",
       "33  270.39                               343.950   \n",
       "34  371.91                              1236.860   \n",
       "\n",
       "   Solubility (from findings) (micro g/mL)  \\\n",
       "0                                      711   \n",
       "1                                     3900   \n",
       "2                                     21.2   \n",
       "3                                      780   \n",
       "4                                      6.4   \n",
       "5                              too soluble   \n",
       "6                                    188.9   \n",
       "7                                       14   \n",
       "8                                       10   \n",
       "9                                       26   \n",
       "13                                    3.46   \n",
       "14                             too soluble   \n",
       "15                                     2.5   \n",
       "16                                    19.6   \n",
       "17                                     625   \n",
       "18                                      22   \n",
       "19                                     410   \n",
       "20                                     157   \n",
       "21                                    3130   \n",
       "22                             too soluble   \n",
       "23                                    0.16   \n",
       "24                                   28.96   \n",
       "25                                     3.9   \n",
       "26                             too soluble   \n",
       "27                                    19.4   \n",
       "28                                    1620   \n",
       "29                                     200   \n",
       "30                                     450   \n",
       "31                                 0.00856   \n",
       "32                                      66   \n",
       "33                                      93   \n",
       "34                                     460   \n",
       "\n",
       "                                               SMILES  \\\n",
       "0         CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1   \n",
       "1   O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...   \n",
       "2   O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...   \n",
       "3                              NC1=CC=C(C(OCC)=O)C=C1   \n",
       "4   O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...   \n",
       "5                             OC(C(O)C1=CC=CC=C1Cl)=O   \n",
       "6     ClC(C=C1)=CC2=C1NC(C=CC=C3)=C3C(N4CCN(C)CC4)=N2   \n",
       "7         CCN(CC)CCNC(C1=C(C=CC=C2)C2=NC(OCCCC)=C1)=O   \n",
       "8           CC/C(C1=CC=C(O)C=C1)=C(C2=CC=C(O)C=C2)/CC   \n",
       "9                                                 NaN   \n",
       "13  OCCN(CCO)C1=NC2=C(N3CCCCC3)N=C(N(CCO)CCO)N=C2C...   \n",
       "14                     CN[C@H](C)[C@@H](O)C1=CC=CC=C1   \n",
       "15  OC([C@H](CCC(O)=O)NC(C(C=C1)=CC=C1NCC2=NC3=C(O...   \n",
       "16       OC(C1=CC(S(N)(=O)=O)=C(Cl)C=C1NCC2=CC=CO2)=O   \n",
       "17            O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2NCN1)=O   \n",
       "18              CN(C)CCCN1C2=C(C=CC=C2)CCC3=C1C=CC=C3   \n",
       "19  COC1=CC=C2C(C(CC(O)=O)=C(C)N2C(C3=CC=C(Cl)C=C3...   \n",
       "20            O=C(C1=CC=CC=C1)C2=CC=CC(C(C(O)=O)C)=C2   \n",
       "21                      CCN(CC)CC(NC1=C(C)C=CC=C1C)=O   \n",
       "22  OC(C1=CN(N(C)CO2)C3=C2C(N4CCN(C)CC4)=C(F)C=C3C...   \n",
       "23             ClC(C=CC(C)=C1Cl)=C1NC2=CC=CC=C2C(O)=O   \n",
       "24                          OC(C1=CC2=CC=CC=C2C=C1)=O   \n",
       "25                     CN[C@@H](C)[C@H](O)C1=CC=CC=C1   \n",
       "26              OC(C1=CC=C(S(N(CCC)CCC)(=O)=O)C=C1)=O   \n",
       "27                 ClC(C=C1)=CC=C1C2=C(CC)N=C(N)N=C2N   \n",
       "28                                 OC(C1=CC=CC=C1O)=O   \n",
       "29             NC1=CC=C(S(NC2=NC(C)=CC=N2)(=O)=O)C=C1   \n",
       "30               NC1=CC=C(S(NC2=NN=C(C)S2)(=O)=O)C=C1   \n",
       "31  OC(C1=CC=CC=C1)(C2=CC=CC=C2)C3CCN(CCCC(O)C4=CC...   \n",
       "32                       C12=CC=CC=C1NC(C3=CSC=N3)=N2   \n",
       "33                 CC1=CC=C(S(NC(NCCCC)=O)(=O)=O)C=C1   \n",
       "34                                                NaN   \n",
       "\n",
       "                                                InChI  \n",
       "0   InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...  \n",
       "1   InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...  \n",
       "2   InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...  \n",
       "3   InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...  \n",
       "4   InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...  \n",
       "5   InChI=1/C8H7ClO3/c9-6-4-2-1-3-5(6)7(10)8(11)12...  \n",
       "6   InChI=1/C18H19ClN4/c1-22-8-10-23(11-9-22)18-14...  \n",
       "7   InChI=1/C20H29N3O2/c1-4-7-14-25-19-15-17(16-10...  \n",
       "8   InChI=1/C18H20O2/c1-3-17(13-5-9-15(19)10-6-13)...  \n",
       "9                                                 NaN  \n",
       "13  InChI=1/C24H40N8O4/c33-15-11-31(12-16-34)23-26...  \n",
       "14  InChI=1/C10H15NO/c1-8(11-2)10(12)9-6-4-3-5-7-9...  \n",
       "15  InChI=1/C19H19N7O6/c20-19-25-15-14(17(30)26-19...  \n",
       "16  InChI=1/C12H13ClN2O5Rn/c13-9-5-10(15-6-7-2-1-3...  \n",
       "17  InChI=1/C7H12ClN3O4Rn2/c8-4-1-5-7(2-6(4)16(9,1...  \n",
       "18  InChI=1/C19H24N2/c1-20(2)14-7-15-21-18-10-5-3-...  \n",
       "19  InChI=1/C19H16ClNO4/c1-11-15(10-18(22)23)16-9-...  \n",
       "20  InChI=1/C16H14O3/c1-11(16(18)19)13-8-5-9-14(10...  \n",
       "21  InChI=1/C14H22N2O/c1-5-16(6-2)10-13(17)15-14-1...  \n",
       "22  InChI=1/C17H19FN4O4/c1-19-3-5-21(6-4-19)14-12(...  \n",
       "23  InChI=1/C14H11Cl2NO2/c1-8-6-7-10(15)13(12(8)16...  \n",
       "24  InChI=1/C11H8O2/c12-11(13)10-6-5-8-3-1-2-4-9(8...  \n",
       "25  InChI=1/C13H21NO4Rn/c1-3-9-14(10-4-2)19(17,18)...  \n",
       "26  InChI=1/C10H15NO/c1-8(11-2)10(12)9-6-4-3-5-7-9...  \n",
       "27  InChI=1/C12H13ClN4/c1-2-9-10(11(14)17-12(15)16...  \n",
       "28  InChI=1/C7H6O3/c8-6-4-2-1-3-5(6)7(9)10/h1-4,8H...  \n",
       "29  InChI=1/C11H14N4O2Rn/c1-8-6-7-13-11(14-8)15-18...  \n",
       "30  InChI=1/C9H12N4O2RnS/c1-6-11-12-9(17-6)13-16(1...  \n",
       "31  InChI=1/C32H41NO2/c1-31(2,3)26-18-16-25(17-19-...  \n",
       "32  InChI=1/C10H7N3S/c1-2-4-8-7(3-1)12-10(13-8)9-5...  \n",
       "33  InChI=1/C12H18N2O3S/c1-3-4-9-13-12(15)14-18(16...  \n",
       "34                                                NaN  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "solub_pred_data1.replace(regex=True,inplace=True,to_replace=r'_I',value=r'')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "solub_pred_data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 7)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data1.reset_index(drop=True,inplace=True)\n",
    "solub_pred_data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>MW</th>\n",
       "      <th>Solubility (from findings) (micro M)</th>\n",
       "      <th>Solubility (from findings) (micro g/mL)</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>InChI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acebutolol</td>\n",
       "      <td>Acebutolol                                    ...</td>\n",
       "      <td>336.48</td>\n",
       "      <td>2113.05</td>\n",
       "      <td>711</td>\n",
       "      <td>CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amoxicillin</td>\n",
       "      <td>Amoxicillin                                   ...</td>\n",
       "      <td>365.45</td>\n",
       "      <td>10671.77</td>\n",
       "      <td>3900</td>\n",
       "      <td>O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...</td>\n",
       "      <td>InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bendroflumethiazide</td>\n",
       "      <td>Bendroflumethiazide                           ...</td>\n",
       "      <td>421.46</td>\n",
       "      <td>50.30</td>\n",
       "      <td>21.2</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...</td>\n",
       "      <td>InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzocaine</td>\n",
       "      <td>Benzocaine                                    ...</td>\n",
       "      <td>165.21</td>\n",
       "      <td>4721.26</td>\n",
       "      <td>780</td>\n",
       "      <td>NC1=CC=C(C(OCC)=O)C=C1</td>\n",
       "      <td>InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benzthiazide</td>\n",
       "      <td>Benzthiazide                                  ...</td>\n",
       "      <td>431.98</td>\n",
       "      <td>14.82</td>\n",
       "      <td>6.4</td>\n",
       "      <td>O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...</td>\n",
       "      <td>InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name                                         Unnamed: 1  \\\n",
       "0           Acebutolol  Acebutolol                                    ...   \n",
       "1          Amoxicillin  Amoxicillin                                   ...   \n",
       "2  Bendroflumethiazide  Bendroflumethiazide                           ...   \n",
       "3           Benzocaine  Benzocaine                                    ...   \n",
       "4         Benzthiazide  Benzthiazide                                  ...   \n",
       "\n",
       "       MW  Solubility (from findings) (micro M)  \\\n",
       "0  336.48                               2113.05   \n",
       "1  365.45                              10671.77   \n",
       "2  421.46                                 50.30   \n",
       "3  165.21                               4721.26   \n",
       "4  431.98                                 14.82   \n",
       "\n",
       "  Solubility (from findings) (micro g/mL)  \\\n",
       "0                                     711   \n",
       "1                                    3900   \n",
       "2                                    21.2   \n",
       "3                                     780   \n",
       "4                                     6.4   \n",
       "\n",
       "                                              SMILES  \\\n",
       "0        CC(C)NCC(O)COC1=C(C(C)=O)C=C(NC(CCC)=O)C=C1   \n",
       "1  O=C(O)[C@H]1C(C)(C)S[C@@H](N21)[C@H](NC([C@@H]...   \n",
       "2  O=S1(C2=CC(S(N)(=O)=O)=C(C(F)(F)F)C=C2NC(CC3=C...   \n",
       "3                             NC1=CC=C(C(OCC)=O)C=C1   \n",
       "4  O=S1(C2=CC(S(N)(=O)=O)=C(Cl)C=C2N=C(CSCC3=CC=C...   \n",
       "\n",
       "                                               InChI  \n",
       "0  InChI=1/C18H28N2O4/c1-5-6-18(23)20-14-7-8-17(1...  \n",
       "1  InChI=1/C16H19N3O5S/c1-16(2)11(15(23)24)19-13(...  \n",
       "2  InChI=1/C15H18F3N3O4Rn2/c16-15(17,18)10-7-11-1...  \n",
       "3  InChI=1/C9H11NO2/c1-2-12-9(11)7-3-5-8(10)6-4-7...  \n",
       "4  InChI=1/C15H18ClN3O4Rn2S/c16-11-6-12-14(7-13(1...  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solub_pred_data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * **clean data from null/NaN values of solubility S0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "S0_pred_descrip=pd.merge(solub_pred_data1[['name','Solubility (from findings) (micro M)']], solub_pred_descriptors_new,left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 653)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_pred_descrip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "S0_pred_descrip.dropna(subset=['Solubility (from findings) (micro M)'], axis=0, inplace=True)\n",
    "S0_pred_descrip.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 653)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_pred_descrip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>name</th>\n",
       "      <th>Solubility (from findings) (micro M)</th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>Sv</th>\n",
       "      <th>Mv</th>\n",
       "      <th>Me</th>\n",
       "      <th>Mp</th>\n",
       "      <th>Mi</th>\n",
       "      <th>nBM</th>\n",
       "      <th>...</th>\n",
       "      <th>Inflammat-80</th>\n",
       "      <th>Depressant-80</th>\n",
       "      <th>Psychotic-80</th>\n",
       "      <th>Hypertens-80</th>\n",
       "      <th>Hypertens-50</th>\n",
       "      <th>Hypnotic-80</th>\n",
       "      <th>Hypnotic-50</th>\n",
       "      <th>Neoplastic-80</th>\n",
       "      <th>Neoplastic-50</th>\n",
       "      <th>Infective-50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acebutolol</td>\n",
       "      <td>2113.05</td>\n",
       "      <td>0.541068</td>\n",
       "      <td>0.078122</td>\n",
       "      <td>0.555280</td>\n",
       "      <td>0.042683</td>\n",
       "      <td>0.255319</td>\n",
       "      <td>0.024242</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amoxicillin</td>\n",
       "      <td>10671.77</td>\n",
       "      <td>0.620093</td>\n",
       "      <td>0.389087</td>\n",
       "      <td>0.505370</td>\n",
       "      <td>0.439024</td>\n",
       "      <td>0.521277</td>\n",
       "      <td>0.327273</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bendroflumethiazide</td>\n",
       "      <td>50.30</td>\n",
       "      <td>0.772880</td>\n",
       "      <td>0.723606</td>\n",
       "      <td>0.508143</td>\n",
       "      <td>0.737805</td>\n",
       "      <td>0.904255</td>\n",
       "      <td>0.509091</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benzocaine</td>\n",
       "      <td>4721.26</td>\n",
       "      <td>0.073870</td>\n",
       "      <td>0.198780</td>\n",
       "      <td>0.098068</td>\n",
       "      <td>0.286585</td>\n",
       "      <td>0.340426</td>\n",
       "      <td>0.206061</td>\n",
       "      <td>0.596154</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benzthiazide</td>\n",
       "      <td>14.82</td>\n",
       "      <td>0.801577</td>\n",
       "      <td>0.811557</td>\n",
       "      <td>0.518008</td>\n",
       "      <td>0.896341</td>\n",
       "      <td>0.648936</td>\n",
       "      <td>0.903030</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 653 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name  Solubility (from findings) (micro M)        MW  \\\n",
       "0           Acebutolol                               2113.05  0.541068   \n",
       "1          Amoxicillin                              10671.77  0.620093   \n",
       "2  Bendroflumethiazide                                 50.30  0.772880   \n",
       "3           Benzocaine                               4721.26  0.073870   \n",
       "4         Benzthiazide                                 14.82  0.801577   \n",
       "\n",
       "        AMW        Sv        Mv        Me        Mp        Mi       nBM  ...  \\\n",
       "0  0.078122  0.555280  0.042683  0.255319  0.024242  0.769231  0.142857  ...   \n",
       "1  0.389087  0.505370  0.439024  0.521277  0.327273  0.615385  0.214286  ...   \n",
       "2  0.723606  0.508143  0.737805  0.904255  0.509091  0.961538  0.714286  ...   \n",
       "3  0.198780  0.098068  0.286585  0.340426  0.206061  0.596154  0.071429  ...   \n",
       "4  0.811557  0.518008  0.896341  0.648936  0.903030  0.269231  0.785714  ...   \n",
       "\n",
       "   Inflammat-80  Depressant-80  Psychotic-80  Hypertens-80  Hypertens-50  \\\n",
       "0           1.0            1.0           0.0           1.0           1.0   \n",
       "1           0.0            0.0           0.0           1.0           0.0   \n",
       "2           1.0            0.0           0.0           1.0           1.0   \n",
       "3           0.0            0.0           0.0           0.0           0.0   \n",
       "4           1.0            0.0           1.0           1.0           1.0   \n",
       "\n",
       "   Hypnotic-80  Hypnotic-50  Neoplastic-80  Neoplastic-50  Infective-50  \n",
       "0          0.0          0.0            1.0            1.0           0.0  \n",
       "1          0.0          0.0            1.0            1.0           0.0  \n",
       "2          0.0          0.0            1.0            0.0           0.0  \n",
       "3          1.0          0.0            0.0            0.0           0.0  \n",
       "4          0.0          0.0            1.0            0.0           0.0  \n",
       "\n",
       "[5 rows x 653 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_pred_descrip.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "S0_train_descrip=pd.merge(solub_train_data1[['Substance','S0 (mM)']], solub_train_descriptors_new,left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 653)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_train_descrip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "S0_train_descrip.dropna(subset=['S0 (mM)'], axis=0, inplace=True)\n",
    "S0_train_descrip.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 653)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_train_descrip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Substance</th>\n",
       "      <th>S0 (mM)</th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>Sv</th>\n",
       "      <th>Mv</th>\n",
       "      <th>Me</th>\n",
       "      <th>Mp</th>\n",
       "      <th>Mi</th>\n",
       "      <th>nBM</th>\n",
       "      <th>...</th>\n",
       "      <th>Inflammat-80</th>\n",
       "      <th>Depressant-80</th>\n",
       "      <th>Psychotic-80</th>\n",
       "      <th>Hypertens-80</th>\n",
       "      <th>Hypertens-50</th>\n",
       "      <th>Hypnotic-80</th>\n",
       "      <th>Hypnotic-50</th>\n",
       "      <th>Neoplastic-80</th>\n",
       "      <th>Neoplastic-50</th>\n",
       "      <th>Infective-50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_naphthol</td>\n",
       "      <td>10432.300</td>\n",
       "      <td>0.054753</td>\n",
       "      <td>0.189654</td>\n",
       "      <td>0.133119</td>\n",
       "      <td>0.620853</td>\n",
       "      <td>0.170370</td>\n",
       "      <td>0.429553</td>\n",
       "      <td>0.087912</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2_amino_5_bromobenzoic_acid</td>\n",
       "      <td>842.692</td>\n",
       "      <td>0.190287</td>\n",
       "      <td>0.634060</td>\n",
       "      <td>0.110178</td>\n",
       "      <td>0.781991</td>\n",
       "      <td>0.496296</td>\n",
       "      <td>0.522337</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4_iodophenol</td>\n",
       "      <td>19312.000</td>\n",
       "      <td>0.197774</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036856</td>\n",
       "      <td>0.924171</td>\n",
       "      <td>0.251852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5_bromo_2_4_dihydroxybenzoic_acid</td>\n",
       "      <td>2397.220</td>\n",
       "      <td>0.222312</td>\n",
       "      <td>0.720771</td>\n",
       "      <td>0.122413</td>\n",
       "      <td>0.895735</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.505155</td>\n",
       "      <td>0.252747</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acetaminophen</td>\n",
       "      <td>86329.600</td>\n",
       "      <td>0.067955</td>\n",
       "      <td>0.187137</td>\n",
       "      <td>0.125202</td>\n",
       "      <td>0.398104</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.213058</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 653 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Substance    S0 (mM)        MW       AMW  \\\n",
       "0                          1_naphthol  10432.300  0.054753  0.189654   \n",
       "1         2_amino_5_bromobenzoic_acid    842.692  0.190287  0.634060   \n",
       "2                        4_iodophenol  19312.000  0.197774  1.000000   \n",
       "3   5_bromo_2_4_dihydroxybenzoic_acid   2397.220  0.222312  0.720771   \n",
       "4                       acetaminophen  86329.600  0.067955  0.187137   \n",
       "\n",
       "         Sv        Mv        Me        Mp        Mi       nBM  ...  \\\n",
       "0  0.133119  0.620853  0.170370  0.429553  0.087912  0.500000  ...   \n",
       "1  0.110178  0.781991  0.496296  0.522337  0.307692  0.318182  ...   \n",
       "2  0.036856  0.924171  0.251852  1.000000  0.000000  0.272727  ...   \n",
       "3  0.122413  0.895735  0.740741  0.505155  0.252747  0.318182  ...   \n",
       "4  0.125202  0.398104  0.333333  0.213058  0.428571  0.318182  ...   \n",
       "\n",
       "   Inflammat-80  Depressant-80  Psychotic-80  Hypertens-80  Hypertens-50  \\\n",
       "0           0.0            0.0           0.0           0.0           0.0   \n",
       "1           0.0            0.0           0.0           0.0           0.0   \n",
       "2           0.0            0.0           0.0           0.0           0.0   \n",
       "3           0.0            0.0           0.0           0.0           0.0   \n",
       "4           0.0            0.0           0.0           0.0           0.0   \n",
       "\n",
       "   Hypnotic-80  Hypnotic-50  Neoplastic-80  Neoplastic-50  Infective-50  \n",
       "0          0.0          0.0            0.0            0.0           0.0  \n",
       "1          0.0          0.0            0.0            0.0           0.0  \n",
       "2          0.0          0.0            0.0            0.0           0.0  \n",
       "3          0.0          0.0            0.0            0.0           0.0  \n",
       "4          0.0          0.0            0.0            0.0           0.0  \n",
       "\n",
       "[5 rows x 653 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_train_descrip.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "S0_train_bd_descrip=pd.merge(solub_train_bd_1['LogS.M.'], solub_train_bd_descriptors1_new,left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 652)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_train_bd_descrip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "S0_train_bd_descrip.dropna(subset=['LogS.M.'], axis=0, inplace=True)\n",
    "S0_train_bd_descrip.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29999, 652)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_train_bd_descrip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>LogS.M.</th>\n",
       "      <th>MW</th>\n",
       "      <th>AMW</th>\n",
       "      <th>Sv</th>\n",
       "      <th>Mv</th>\n",
       "      <th>Me</th>\n",
       "      <th>Mp</th>\n",
       "      <th>Mi</th>\n",
       "      <th>nBM</th>\n",
       "      <th>RBN</th>\n",
       "      <th>...</th>\n",
       "      <th>Inflammat-80</th>\n",
       "      <th>Depressant-80</th>\n",
       "      <th>Psychotic-80</th>\n",
       "      <th>Hypertens-80</th>\n",
       "      <th>Hypertens-50</th>\n",
       "      <th>Hypnotic-80</th>\n",
       "      <th>Hypnotic-50</th>\n",
       "      <th>Neoplastic-80</th>\n",
       "      <th>Neoplastic-50</th>\n",
       "      <th>Infective-50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.37</td>\n",
       "      <td>0.068533</td>\n",
       "      <td>0.047387</td>\n",
       "      <td>0.190299</td>\n",
       "      <td>0.164510</td>\n",
       "      <td>0.036458</td>\n",
       "      <td>0.183986</td>\n",
       "      <td>0.362573</td>\n",
       "      <td>0.206897</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.92</td>\n",
       "      <td>0.078684</td>\n",
       "      <td>0.088269</td>\n",
       "      <td>0.189525</td>\n",
       "      <td>0.271719</td>\n",
       "      <td>0.109375</td>\n",
       "      <td>0.253833</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.37</td>\n",
       "      <td>0.057430</td>\n",
       "      <td>0.037220</td>\n",
       "      <td>0.166106</td>\n",
       "      <td>0.129390</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.156729</td>\n",
       "      <td>0.397661</td>\n",
       "      <td>0.206897</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.74</td>\n",
       "      <td>0.050061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.167388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.056218</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.41</td>\n",
       "      <td>0.085192</td>\n",
       "      <td>0.047387</td>\n",
       "      <td>0.220322</td>\n",
       "      <td>0.123845</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.131175</td>\n",
       "      <td>0.432749</td>\n",
       "      <td>0.206897</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 652 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LogS.M.        MW       AMW        Sv        Mv        Me        Mp  \\\n",
       "0    -4.37  0.068533  0.047387  0.190299  0.164510  0.036458  0.183986   \n",
       "1    -1.92  0.078684  0.088269  0.189525  0.271719  0.109375  0.253833   \n",
       "2    -3.37  0.057430  0.037220  0.166106  0.129390  0.031250  0.156729   \n",
       "3    -4.74  0.050061  0.000000  0.167388  0.000000  0.000000  0.056218   \n",
       "4    -2.41  0.085192  0.047387  0.220322  0.123845  0.104167  0.131175   \n",
       "\n",
       "         Mi       nBM       RBN  ...  Inflammat-80  Depressant-80  \\\n",
       "0  0.362573  0.206897  0.000000  ...           0.0            0.0   \n",
       "1  0.333333  0.379310  0.000000  ...           0.0            0.0   \n",
       "2  0.397661  0.206897  0.111111  ...           0.0            0.0   \n",
       "3  0.508772  0.000000  0.111111  ...           0.0            0.0   \n",
       "4  0.432749  0.206897  0.055556  ...           0.0            0.0   \n",
       "\n",
       "   Psychotic-80  Hypertens-80  Hypertens-50  Hypnotic-80  Hypnotic-50  \\\n",
       "0           0.0           0.0           0.0          0.0          0.0   \n",
       "1           0.0           0.0           0.0          0.0          0.0   \n",
       "2           0.0           0.0           0.0          0.0          0.0   \n",
       "3           0.0           0.0           0.0          0.0          0.0   \n",
       "4           0.0           0.0           0.0          0.0          0.0   \n",
       "\n",
       "   Neoplastic-80  Neoplastic-50  Infective-50  \n",
       "0            0.0            0.0           0.0  \n",
       "1            0.0            0.0           0.0  \n",
       "2            0.0            0.0           0.0  \n",
       "3            0.0            0.0           0.0  \n",
       "4            0.0            0.0           0.0  \n",
       "\n",
       "[5 rows x 652 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_train_bd_descrip.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# execute cell as code for corrrelation heatmap\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.heatmap(corr_S0_descrip,cmap='binary')\n",
    "plt.title('Correlation of S0 wrt the training descriptors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "na_free = solub2['S0 (mM)'].dropna()\n",
    "remove1=solub2['S0 (mM)'].index.isin(na_free.index)\n",
    "print(remove1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * create final training and prediction sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LogS.M.', 'MW', 'AMW', 'Sv', 'Mv', 'Me', 'Mp', 'Mi', 'nBM', 'RBN',\n",
       "       ...\n",
       "       'Inflammat-80', 'Depressant-80', 'Psychotic-80', 'Hypertens-80',\n",
       "       'Hypertens-50', 'Hypnotic-80', 'Hypnotic-50', 'Neoplastic-80',\n",
       "       'Neoplastic-50', 'Infective-50'],\n",
       "      dtype='object', length=652)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S0_train_bd_descrip.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_descriptors=S0_train_descrip.columns[2:]\n",
    "columns_descriptors1=S0_pred_descrip.columns[2:]\n",
    "columns_descriptors2=S0_train_bd_descrip.columns[1:]\n",
    "#print(columns_descriptors==columns_descriptors1)\n",
    "X_train=S0_train_descrip[columns_descriptors]\n",
    "X_train_bd=S0_train_bd_descrip[columns_descriptors2]\n",
    "X_pred=S0_pred_descrip[columns_descriptors1]\n",
    "y_train=S0_train_descrip['S0 (mM)']\n",
    "y_train_bd_log=S0_train_bd_descrip['LogS.M.']\n",
    "y_ref=S0_pred_descrip['Solubility (from findings) (micro M)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 651)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 651)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29999, 651)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_bd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90,)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28,)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ref.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29999,)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_bd_log.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * All solubility data in LogS(Molar) for homogeneity and small values practicality (best convergency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_log=np.log10(y_train*1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    90.000000\n",
       "mean     -3.375543\n",
       "std       1.218324\n",
       "min      -6.749668\n",
       "25%      -4.082716\n",
       "50%      -3.215998\n",
       "75%      -2.649274\n",
       "max      -1.063840\n",
       "Name: S0 (mM), dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_log.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_ref_log=np.log10(y_ref*1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.000000\n",
       "mean     -3.786838\n",
       "std       1.338111\n",
       "min      -7.744727\n",
       "25%      -4.399594\n",
       "50%      -3.629216\n",
       "75%      -2.875446\n",
       "max      -1.874376\n",
       "Name: Solubility (from findings) (micro M), dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ref_log.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    29999.000000\n",
       "mean        -4.262177\n",
       "std          0.844041\n",
       "min        -11.620000\n",
       "25%         -4.691304\n",
       "50%         -3.923516\n",
       "75%         -3.824074\n",
       "max          1.070000\n",
       "Name: LogS.M., dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_bd_log.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model prediction with SciKit.learn\n",
    "### * ** Import the necessary libs **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.feature_selection import f_regression, chi2\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "from scipy.stats import randint as sp_randint\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * ** PCA on the smaller data set and choice of the best number of components (cumulative variance~0.988-0.99 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of components:   70\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#Fitting the PCA algorithm with our Data\n",
    "pca = PCA().fit(X_train)\n",
    "#Plotting the Cumulative Summation of the Explained Variance\n",
    "a_boolean=(np.cumsum(pca.explained_variance_ratio_)< 0.99) & (np.cumsum(pca.explained_variance_ratio_)>0.988)\n",
    "#print(a_boolean)\n",
    "#print(np.cumsum(pca.explained_variance_ratio_))\n",
    "a1=[i for i, x in enumerate(a_boolean) if x]\n",
    "n_comp=a1[0]+1\n",
    "print('Optimal number of components: %4d' %(n_comp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best n. components:   70, with comulative variance (%): 0.988\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percen1=np.cumsum(pca.explained_variance_ratio_)[a1[0]]\n",
    "print('Best n. components: %4d, with comulative variance (%%): %1.3f' %(n_comp, percen1))\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.scatter(a1[0],np.cumsum(pca.explained_variance_ratio_)[a1[0]],c='r')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Variance (%)') #for each component\n",
    "plt.title('Solubility Descriptors Data set Variance')\n",
    "plt.show()\n",
    "#pca = PCA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * ** PCA on the bigger data set and choice of the best number of components (cumulative variance~0.988-0.99 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of components:  222\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#Fitting the PCA algorithm with our Data\n",
    "pca = PCA().fit(X_train_bd)\n",
    "#Plotting the Cumulative Summation of the Explained Variance\n",
    "a_boolean=(np.cumsum(pca.explained_variance_ratio_)< 0.99) & (np.cumsum(pca.explained_variance_ratio_)>0.988)\n",
    "#print(a_boolean)\n",
    "#print(np.cumsum(pca.explained_variance_ratio_))\n",
    "a1=[i for i, x in enumerate(a_boolean) if x]\n",
    "n_comp1=a1[0]+1\n",
    "print('Optimal number of components: %4d' %(n_comp1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best n. components:  222, with comulative variance (%): 0.988\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percen1=np.cumsum(pca.explained_variance_ratio_)[a1[0]]\n",
    "print('Best n. components: %4d, with comulative variance (%%): %1.3f' %(n_comp1, percen1))\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.scatter(a1[0],np.cumsum(pca.explained_variance_ratio_)[a1[0]],c='r')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Variance (%)') #for each component\n",
    "plt.title('Solubility Descriptors Data set Variance')\n",
    "plt.show()\n",
    "#pca = PCA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * ** set of functions defining the pipeline of choice: 1) ANOVA+Ridge regressot 2) ANOVA+AdaBooster regressor\n",
    "### * Also used SelectKBEST as ANOVA filter and 1) Randomized or 2) Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_ridge(cv_dat,n_comp,X_train,y_train):\n",
    "    np.seterr(divide='ignore', invalid='ignore')\n",
    "    i=n_comp\n",
    "    reg = Ridge()\n",
    "    int1=int(i/10)\n",
    "    print(int1)\n",
    "    anova_filter = SelectKBest(f_regression, k=i)\n",
    "    pipe = Pipeline(steps=[('anova', anova_filter),('regressor', reg)])\n",
    "    #pipe = Pipeline(steps=[('anova', anova_filter),('regressor', reg)])\n",
    "    pipe.fit(X_train, y_train)\n",
    "    pipe.score(X_train, y_train)\n",
    "    param_dist = {\"anova__score_func\": [mutual_info_regression, f_regression],\n",
    "                \"anova__k\": sp_randint(i-int1, i),\n",
    "                \"regressor__alpha\": [0.01,0.1,1.0,10.0,100.0]}\n",
    "    \n",
    "    test = RandomizedSearchCV(pipe,\n",
    "                              param_distributions = param_dist,\n",
    "                              cv=cv_dat,\n",
    "                              n_iter=100)\n",
    "\n",
    "    test.fit(X_train, y_train)\n",
    "    print(test.score(X_train,y_train))\n",
    "    print(test.best_estimator_)\n",
    "    #print(test.best_score_)\n",
    "    mask = anova_filter.get_support() #list of booleans\n",
    "    new_features = [] # The list of your K best features\n",
    "\n",
    "    for bool, feature in zip(mask, X_train.columns.values):\n",
    "        if bool:\n",
    "            new_features.append(feature)\n",
    "    print(i,new_features)\n",
    "    return new_features,test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_ridge(cv_dat,n_comp,X_train,y_train):\n",
    "    np.seterr(divide='ignore', invalid='ignore')\n",
    "    i=n_comp\n",
    "    reg = Ridge()\n",
    "    anova_filter = SelectKBest(f_regression, k=i)\n",
    "    pipe = Pipeline(steps=[('anova', anova_filter),('regressor', reg)])\n",
    "    #pipe = Pipeline(steps=[('anova', anova_filter),('regressor', reg)])\n",
    "    pipe.fit(X_train, y_train)\n",
    "    pipe.score(X_train, y_train)\n",
    "    param_dist = {\"anova__score_func\": [mutual_info_regression, f_regression],\n",
    "                \"regressor__alpha\": [0.01,0.1,1.0,10.0,100.0]}\n",
    "    \n",
    "    test = GridSearchCV(pipe,\n",
    "                        param_grid = param_dist,\n",
    "                        cv=cv_dat)\n",
    "\n",
    "    test.fit(X_train, y_train)\n",
    "    print(test.score(X_train,y_train))\n",
    "    print(test.best_estimator_)\n",
    "    #print(test.best_score_)\n",
    "    mask = anova_filter.get_support() #list of booleans\n",
    "    new_features = [] # The list of your K best features\n",
    "\n",
    "    for bool1, feature in zip(mask, X_train.columns.values):\n",
    "        if bool1:\n",
    "            new_features.append(feature)\n",
    "    print(i,new_features)\n",
    "    return new_features,test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_adab(cv_dat,n_comp,X_train,y_train):\n",
    "    np.seterr(divide='ignore', invalid='ignore')\n",
    "    i=n_comp\n",
    "    reg = AdaBoostRegressor()\n",
    "    int1=int(i/10)\n",
    "    print(int1)\n",
    "    anova_filter = SelectKBest(f_regression, k=i)\n",
    "    pipe = Pipeline(steps=[('anova', anova_filter),('regressor', reg)])\n",
    "    pipe.fit(X_train, y_train)\n",
    "    pipe.score(X_train, y_train)\n",
    "    param_dist = {\"anova__score_func\": [mutual_info_regression, f_regression],\n",
    "                \"anova__k\": sp_randint(i-int1, i),\n",
    "                \"regressor__n_estimators\": [50, 100],\n",
    "                'regressor__learning_rate' : [0.01,0.05,0.1,0.3,1],\n",
    "                'regressor__loss' : ['linear', 'square', 'exponential']}\n",
    "              \n",
    "    test = RandomizedSearchCV(pipe,\n",
    "                              param_distributions = param_dist,\n",
    "                              cv=cv_dat,\n",
    "                              n_iter=100)\n",
    "\n",
    "    test.fit(X_train, y_train)\n",
    "    print(test.score(X_train,y_train))\n",
    "    print(test.best_estimator_)\n",
    "    #print(test.best_score_)\n",
    "    mask = anova_filter.get_support() #list of booleans\n",
    "    new_features = [] # The list of your K best features\n",
    "\n",
    "    for bool1, feature in zip(mask, X_train.columns.values):\n",
    "        if bool1:\n",
    "            new_features.append(feature)\n",
    "    print(i,new_features)\n",
    "    np.seterr(divide='warn', invalid='warn')\n",
    "    return new_features,test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_adab(cv_dat,n_comp,X_train,y_train):\n",
    "    np.seterr(divide='ignore', invalid='ignore')\n",
    "    i=n_comp\n",
    "    reg = AdaBoostRegressor()\n",
    "    anova_filter = SelectKBest(f_regression, k=i)\n",
    "    pipe = Pipeline(steps=[('anova', anova_filter),('regressor', reg)])\n",
    "    pipe.fit(X_train, y_train)\n",
    "    pipe.score(X_train, y_train)\n",
    "    param_dist = {\"anova__score_func\": [mutual_info_regression, f_regression],\n",
    "                \"regressor__n_estimators\": [50, 100],\n",
    "                'regressor__learning_rate' : [0.01,0.05,0.1,0.3,1],\n",
    "                'regressor__loss' : ['linear', 'square', 'exponential']}\n",
    "              \n",
    "    test = GridSearchCV(pipe,\n",
    "                        param_grid = param_dist,\n",
    "                        cv=cv_dat)\n",
    "\n",
    "    test.fit(X_train, y_train)\n",
    "    print(test.score(X_train,y_train))\n",
    "    print(test.best_estimator_)\n",
    "    #print(test.best_score_)\n",
    "    mask = anova_filter.get_support() #list of booleans\n",
    "    new_features = [] # The list of your K best features\n",
    "\n",
    "    for bool1, feature in zip(mask, X_train.columns.values):\n",
    "        if bool1:\n",
    "            new_features.append(feature)\n",
    "    print(i,new_features)\n",
    "    np.seterr(divide='warn', invalid='warn')\n",
    "    return new_features,test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * model training Random and Grid AdaBoost regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "0.9233874395593822\n",
      "Pipeline(memory=None,\n",
      "         steps=[('anova',\n",
      "                 SelectKBest(k=67,\n",
      "                             score_func=<function mutual_info_regression at 0x1a227e3f28>)),\n",
      "                ('regressor',\n",
      "                 AdaBoostRegressor(base_estimator=None, learning_rate=1,\n",
      "                                   loss='exponential', n_estimators=50,\n",
      "                                   random_state=None))],\n",
      "         verbose=False)\n",
      "70 ['MW', 'Sv', 'nBM', 'nAB', 'nCsp2', 'nBnz', 'D/Dtr06', 'Xt', 'MSD', 'piPC02', 'piPC07', 'piPC09', 'piID', 'PCR', 'PCD', 'IDDE', 'VE1_A', 'J_D', 'VE1_X', 'VE2_X', 'VE2_D/Dt', 'Chi_Dz(Z)', 'J_Dz(Z)', 'VE1_B(m)', 'AVS_B(v)', 'SpMaxA_B(s)', 'VE1_B(s)', 'ATS8m', 'ATS7e', 'GATS7p', 'GATS8p', 'GATS7i', 'GATS8i', 'SpMax3_Bh(m)', 'SpMax4_Bh(m)', 'SpMax5_Bh(m)', 'SpMax2_Bh(v)', 'SpMax3_Bh(v)', 'SpMax4_Bh(v)', 'SpMin2_Bh(m)', 'SpMin3_Bh(m)', 'SpMin2_Bh(v)', 'SpMin3_Bh(v)', 'P_VSA_MR_6', 'P_VSA_m_2', 'P_VSA_v_3', 'P_VSA_e_2', 'P_VSA_s_4', 'Eta_FL', 'SpMaxA_EA(ed)', 'SM11_AEA(bo)', 'SM12_AEA(ri)', 'SM13_AEA(ri)', 'SM15_AEA(ri)', 'nCbH', 'nCb-', 'C-025', 'SaasC', 'NaasC', 'CATS2D_07_AL', 'CATS2D_08_AL', 'CATS2D_07_NL', 'CATS2D_01_LL', 'CATS2D_05_LL', 'CATS2D_06_LL', 'CATS2D_07_LL', 'B07[C-C]', 'B08[C-C]', 'MLOGP2', 'ALOGP2']\n",
      "CPU times: user 3min 16s, sys: 1.14 s, total: 3min 17s\n",
      "Wall time: 3min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,radab_cv3=random_adab(cv_dat,n_comp,X_train,y_train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9161258054085376\n",
      "Pipeline(memory=None,\n",
      "         steps=[('anova',\n",
      "                 SelectKBest(k=70,\n",
      "                             score_func=<function mutual_info_regression at 0x1a227e3f28>)),\n",
      "                ('regressor',\n",
      "                 AdaBoostRegressor(base_estimator=None, learning_rate=1,\n",
      "                                   loss='linear', n_estimators=50,\n",
      "                                   random_state=None))],\n",
      "         verbose=False)\n",
      "70 ['MW', 'Sv', 'nBM', 'nAB', 'nCsp2', 'nBnz', 'D/Dtr06', 'Xt', 'MSD', 'piPC02', 'piPC07', 'piPC09', 'piID', 'PCR', 'PCD', 'IDDE', 'VE1_A', 'J_D', 'VE1_X', 'VE2_X', 'VE2_D/Dt', 'Chi_Dz(Z)', 'J_Dz(Z)', 'VE1_B(m)', 'AVS_B(v)', 'SpMaxA_B(s)', 'VE1_B(s)', 'ATS8m', 'ATS7e', 'GATS7p', 'GATS8p', 'GATS7i', 'GATS8i', 'SpMax3_Bh(m)', 'SpMax4_Bh(m)', 'SpMax5_Bh(m)', 'SpMax2_Bh(v)', 'SpMax3_Bh(v)', 'SpMax4_Bh(v)', 'SpMin2_Bh(m)', 'SpMin3_Bh(m)', 'SpMin2_Bh(v)', 'SpMin3_Bh(v)', 'P_VSA_MR_6', 'P_VSA_m_2', 'P_VSA_v_3', 'P_VSA_e_2', 'P_VSA_s_4', 'Eta_FL', 'SpMaxA_EA(ed)', 'SM11_AEA(bo)', 'SM12_AEA(ri)', 'SM13_AEA(ri)', 'SM15_AEA(ri)', 'nCbH', 'nCb-', 'C-025', 'SaasC', 'NaasC', 'CATS2D_07_AL', 'CATS2D_08_AL', 'CATS2D_07_NL', 'CATS2D_01_LL', 'CATS2D_05_LL', 'CATS2D_06_LL', 'CATS2D_07_LL', 'B07[C-C]', 'B08[C-C]', 'MLOGP2', 'ALOGP2']\n",
      "CPU times: user 1min 57s, sys: 952 ms, total: 1min 58s\n",
      "Wall time: 1min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,gadab_cv3=grid_adab(cv_dat,n_comp,X_train,y_train_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * model training Random and Grid Ridge regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "0.7359649309749998\n",
      "Pipeline(memory=None,\n",
      "         steps=[('anova',\n",
      "                 SelectKBest(k=64,\n",
      "                             score_func=<function mutual_info_regression at 0x1a227e3f28>)),\n",
      "                ('regressor',\n",
      "                 Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
      "                       max_iter=None, normalize=False, random_state=None,\n",
      "                       solver='auto', tol=0.001))],\n",
      "         verbose=False)\n",
      "70 ['MW', 'Sv', 'nBM', 'nAB', 'nCsp2', 'nBnz', 'D/Dtr06', 'Xt', 'MSD', 'piPC02', 'piPC07', 'piPC09', 'piID', 'PCR', 'PCD', 'IDDE', 'VE1_A', 'J_D', 'VE1_X', 'VE2_X', 'VE2_D/Dt', 'Chi_Dz(Z)', 'J_Dz(Z)', 'VE1_B(m)', 'AVS_B(v)', 'SpMaxA_B(s)', 'VE1_B(s)', 'ATS8m', 'ATS7e', 'GATS7p', 'GATS8p', 'GATS7i', 'GATS8i', 'SpMax3_Bh(m)', 'SpMax4_Bh(m)', 'SpMax5_Bh(m)', 'SpMax2_Bh(v)', 'SpMax3_Bh(v)', 'SpMax4_Bh(v)', 'SpMin2_Bh(m)', 'SpMin3_Bh(m)', 'SpMin2_Bh(v)', 'SpMin3_Bh(v)', 'P_VSA_MR_6', 'P_VSA_m_2', 'P_VSA_v_3', 'P_VSA_e_2', 'P_VSA_s_4', 'Eta_FL', 'SpMaxA_EA(ed)', 'SM11_AEA(bo)', 'SM12_AEA(ri)', 'SM13_AEA(ri)', 'SM15_AEA(ri)', 'nCbH', 'nCb-', 'C-025', 'SaasC', 'NaasC', 'CATS2D_07_AL', 'CATS2D_08_AL', 'CATS2D_07_NL', 'CATS2D_01_LL', 'CATS2D_05_LL', 'CATS2D_06_LL', 'CATS2D_07_LL', 'B07[C-C]', 'B08[C-C]', 'MLOGP2', 'ALOGP2']\n",
      "CPU times: user 3min 3s, sys: 1.37 s, total: 3min 5s\n",
      "Wall time: 2min 40s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,rridge_cv3=random_ridge(cv_dat,n_comp,X_train,y_train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5550841442083445\n",
      "Pipeline(memory=None,\n",
      "         steps=[('anova',\n",
      "                 SelectKBest(k=70,\n",
      "                             score_func=<function f_regression at 0x1a2267b8c8>)),\n",
      "                ('regressor',\n",
      "                 Ridge(alpha=10.0, copy_X=True, fit_intercept=True,\n",
      "                       max_iter=None, normalize=False, random_state=None,\n",
      "                       solver='auto', tol=0.001))],\n",
      "         verbose=False)\n",
      "70 ['MW', 'Sv', 'nBM', 'nAB', 'nCsp2', 'nBnz', 'D/Dtr06', 'Xt', 'MSD', 'piPC02', 'piPC07', 'piPC09', 'piID', 'PCR', 'PCD', 'IDDE', 'VE1_A', 'J_D', 'VE1_X', 'VE2_X', 'VE2_D/Dt', 'Chi_Dz(Z)', 'J_Dz(Z)', 'VE1_B(m)', 'AVS_B(v)', 'SpMaxA_B(s)', 'VE1_B(s)', 'ATS8m', 'ATS7e', 'GATS7p', 'GATS8p', 'GATS7i', 'GATS8i', 'SpMax3_Bh(m)', 'SpMax4_Bh(m)', 'SpMax5_Bh(m)', 'SpMax2_Bh(v)', 'SpMax3_Bh(v)', 'SpMax4_Bh(v)', 'SpMin2_Bh(m)', 'SpMin3_Bh(m)', 'SpMin2_Bh(v)', 'SpMin3_Bh(v)', 'P_VSA_MR_6', 'P_VSA_m_2', 'P_VSA_v_3', 'P_VSA_e_2', 'P_VSA_s_4', 'Eta_FL', 'SpMaxA_EA(ed)', 'SM11_AEA(bo)', 'SM12_AEA(ri)', 'SM13_AEA(ri)', 'SM15_AEA(ri)', 'nCbH', 'nCb-', 'C-025', 'SaasC', 'NaasC', 'CATS2D_07_AL', 'CATS2D_08_AL', 'CATS2D_07_NL', 'CATS2D_01_LL', 'CATS2D_05_LL', 'CATS2D_06_LL', 'CATS2D_07_LL', 'B07[C-C]', 'B08[C-C]', 'MLOGP2', 'ALOGP2']\n",
      "CPU times: user 17.3 s, sys: 87.9 ms, total: 17.4 s\n",
      "Wall time: 14.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,gridge_cv3=grid_ridge(cv_dat,n_comp,X_train,y_train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * model prediction using Random and Grid Ridge and AdaBoost regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.07714772 -3.23092522 -3.6716648  -1.97868469 -3.68034836 -4.56515795\n",
      " -3.97365454 -4.60656656 -3.66238015 -3.14289917 -3.89039775 -3.26911881\n",
      " -2.85244619 -4.12276228 -5.68642371 -3.97365454 -2.84529922 -4.69484255\n",
      " -2.84529922 -3.04108358 -3.32267502 -1.73827403 -3.01706193 -2.84529922\n",
      " -4.57920589 -3.3161582  -3.01706193 -3.04971463]\n"
     ]
    }
   ],
   "source": [
    "y_hat_radab=radab_cv3.predict(X_pred)\n",
    "print(y_hat_radab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.95193634 -2.98461207 -3.4449466  -1.87095789 -3.52547554 -4.49960029\n",
      " -3.83803355 -4.94151908 -3.23671925 -3.14077825 -3.49114053 -3.37041274\n",
      " -2.82864344 -4.0964556  -5.11858384 -3.94999584 -2.43194298 -4.73059287\n",
      " -2.73154768 -2.43194298 -3.37041274 -1.87095789 -2.98461207 -3.07433113\n",
      " -4.38658023 -3.50808542 -2.48927794 -3.37041274]\n"
     ]
    }
   ],
   "source": [
    "y_hat_gadab=gadab_cv3.predict(X_pred)\n",
    "print(y_hat_gadab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.54561243 -3.07264783 -3.78159854 -1.57601451 -4.13606748 -4.23689394\n",
      " -3.51878949 -5.07609271 -4.59993351 -1.93120314 -5.25394878 -3.99131808\n",
      " -2.41372704 -4.08582636 -5.41886908 -4.2886815  -2.29976324 -5.97359464\n",
      " -2.32490619 -3.16072991 -3.49515464 -1.02368661 -3.20359283 -3.40870638\n",
      " -6.06677704 -3.83643246 -2.59469355 -2.4595981 ]\n"
     ]
    }
   ],
   "source": [
    "y_hat_rridge=rridge_cv3.predict(X_pred)\n",
    "print(y_hat_rridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.79931241 -2.89428319 -3.9259164  -2.01120072 -4.12169198 -4.18781882\n",
      " -3.67650352 -4.10449569 -3.65937919 -3.42810845 -4.61966641 -3.48912557\n",
      " -2.54044389 -4.11624762 -5.13614467 -4.33257575 -2.62197585 -4.82430836\n",
      " -2.94587582 -2.68554652 -3.44533427 -1.56032738 -3.13813699 -3.0019764\n",
      " -5.43642304 -3.25607461 -2.59508994 -3.31006833]\n"
     ]
    }
   ],
   "source": [
    "y_hat_gridge=gridge_cv3.predict(X_pred)\n",
    "print(y_hat_gridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentage1(y_ref,y_hat,per_lim,tot1):\n",
    "    percent=(y_ref-y_hat)/y_ref*100.\n",
    "    count1=(percent<=per_lim) & (percent>=-per_lim)\n",
    "    percent1=100.-((tot1-count1.sum())/tot1*100.)\n",
    "    percent=round(percent,2)\n",
    "    percent1=round(percent1,2)\n",
    "    return percent,percent1,count1.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35.71, 53.57, 82.14, 92.86]\n",
      "[21.43, 50.0, 85.71, 92.86]\n",
      "                Random AdaBoost (%)  Random Ridge (%)\n",
      "|error| <= 10%                35.71             21.43\n",
      "|error| <= 20%                53.57             50.00\n",
      "|error| <= 40%                82.14             85.71\n",
      "|error| <= 60%                92.86             92.86\n"
     ]
    }
   ],
   "source": [
    "radab_list=[]\n",
    "rridge_list=[]\n",
    "for i in [10.,20.,40.,60.]:\n",
    "    likeness,per_int,coun1=percentage1(y_ref_log,y_hat_radab,i,S0_pred_descrip['name'].size)\n",
    "    radab_list.append(per_int)\n",
    "print(radab_list)\n",
    "for i in [10.,20.,40.,60.]:\n",
    "    likeness,per_int,coun1=percentage1(y_ref_log,y_hat_rridge,i,S0_pred_descrip['name'].size)\n",
    "    rridge_list.append(per_int)\n",
    "print(rridge_list)\n",
    "idx_rename = {0:'|error| <= 10%',1:'|error| <= 20%', 2:'|error| <= 40%',3:'|error| <= 60%'} \n",
    "sum_results1=pd.DataFrame.from_dict({'Random AdaBoost (%)': radab_list, 'Random Ridge (%)': rridge_list})\n",
    "sum_results=sum_results1.rename(index=idx_rename)\n",
    "print(sum_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Random AdaBoost (%)</th>\n",
       "      <th>Random Ridge (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 10%</th>\n",
       "      <td>35.71</td>\n",
       "      <td>21.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 20%</th>\n",
       "      <td>53.57</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 40%</th>\n",
       "      <td>82.14</td>\n",
       "      <td>85.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 60%</th>\n",
       "      <td>92.86</td>\n",
       "      <td>92.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Random AdaBoost (%)  Random Ridge (%)\n",
       "|error| <= 10%                35.71             21.43\n",
       "|error| <= 20%                53.57             50.00\n",
       "|error| <= 40%                82.14             85.71\n",
       "|error| <= 60%                92.86             92.86"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(np.log10(y_ref*1.e-6),np.log10(y_ref*1.e-6))\n",
    "plt.plot(range(0,S0_pred_descrip['name'].size),y_ref_log-y_ref_log,'k-',label='Reference')\n",
    "plt.scatter(range(0,S0_pred_descrip['name'].size),(y_ref_log-y_hat_rridge)\n",
    "            /y_ref_log*100.,label='Random Ridge')\n",
    "plt.scatter(range(0,S0_pred_descrip['name'].size),(y_ref_log-y_hat_radab)\n",
    "            /y_ref_log*100.,label='Random AdaBoost')\n",
    "plt.xlabel('molecule index')\n",
    "plt.ylabel('error in % of (y_ref-y_hat)')\n",
    "plt.legend()\n",
    "#plt.scatter(y_hat_radab1,y_ref)\n",
    "sum_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17.86, 50.0, 85.71, 96.43]\n",
      "[32.14, 60.71, 89.29, 96.43]\n",
      "                Grid AdaBoost (%)  Grid Ridge (%)\n",
      "|error| <= 10%              17.86           32.14\n",
      "|error| <= 20%              50.00           60.71\n",
      "|error| <= 40%              85.71           89.29\n",
      "|error| <= 60%              96.43           96.43\n"
     ]
    }
   ],
   "source": [
    "gadab_list=[]\n",
    "gridge_list=[]\n",
    "for i in [10.,20.,40.,60.]:\n",
    "    likeness,per_int,coun1=percentage1(y_ref_log,y_hat_gadab,i,S0_pred_descrip['name'].size)\n",
    "    gadab_list.append(per_int)\n",
    "print(gadab_list)\n",
    "for i in [10.,20.,40.,60.]:\n",
    "    likeness,per_int,coun1=percentage1(y_ref_log,y_hat_gridge,i,S0_pred_descrip['name'].size)\n",
    "    gridge_list.append(per_int)\n",
    "print(gridge_list)\n",
    "idx_rename = {0:'|error| <= 10%',1:'|error| <= 20%', 2:'|error| <= 40%',3:'|error| <= 60%'} \n",
    "sum_results1=pd.DataFrame.from_dict({'Grid AdaBoost (%)': gadab_list, 'Grid Ridge (%)': gridge_list})\n",
    "sum_results=sum_results1.rename(index=idx_rename)\n",
    "print(sum_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Grid AdaBoost (%)</th>\n",
       "      <th>Grid Ridge (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 10%</th>\n",
       "      <td>17.86</td>\n",
       "      <td>32.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 20%</th>\n",
       "      <td>50.00</td>\n",
       "      <td>60.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 40%</th>\n",
       "      <td>85.71</td>\n",
       "      <td>89.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|error| &lt;= 60%</th>\n",
       "      <td>96.43</td>\n",
       "      <td>96.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Grid AdaBoost (%)  Grid Ridge (%)\n",
       "|error| <= 10%              17.86           32.14\n",
       "|error| <= 20%              50.00           60.71\n",
       "|error| <= 40%              85.71           89.29\n",
       "|error| <= 60%              96.43           96.43"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(np.log10(y_ref*1.e-6),np.log10(y_ref*1.e-6))\n",
    "plt.plot(range(0,S0_pred_descrip['name'].size),y_ref_log-y_ref_log,'k-',label='Reference')\n",
    "plt.scatter(range(0,S0_pred_descrip['name'].size),(y_ref_log-y_hat_gridge)\n",
    "            /y_ref_log*100.,label='Grid Ridge')\n",
    "plt.scatter(range(0,S0_pred_descrip['name'].size),(y_ref_log-y_hat_gadab)\n",
    "            /y_ref_log*100.,label='Grid AdaBoost')\n",
    "plt.xlabel('molecule index')\n",
    "plt.ylabel('error in % of (y_ref-y_hat)')\n",
    "plt.legend()\n",
    "#plt.scatter(y_hat_radab1,y_ref)\n",
    "sum_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, Rdige and AdaBooster regressors show comparable performances.\n",
    "In particular: \n",
    "* with RandomizedSearch under 20% of error AdaBooster shows a better prediction power\n",
    "* with GridSearch under 20% of error Ridge is a better regressor\n",
    "\n",
    "In general, even with the smaller training data set we obtaion decent predictions (inside 20% of error more than 60% of molecules solubility has been predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,radab_bd_cv3=random_adab(cv_dat,n_comp,X_train_bd,y_train_bd_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_bd.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boolean_test=y_train_bd_log.isna()\n",
    "y_train_bd_log[boolean_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,gadab_bd_cv3=grid_adab(cv_dat,n_comp,X_train_bd,y_train_bd_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,rridge_bd_cv3=random_ridge(cv_dat,n_comp,X_train_bd,y_train_bd_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "cv_dat=3\n",
    "feature1,gridge_bd_cv3=grid_ridge(cv_dat,n_comp,X_train_bd,y_train_bd_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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