notebooks/10_eda_logD_logd74_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA of logd74.tsv\n",
    "\n",
    "## Summary\n",
    "1. There are 4200 experimental records\n",
    "2. All the SMILES are unique.\n",
    "3. All the experiments were held at pH=7.4\n",
    "4. Temperature is not mentioned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import seaborn as sns\n",
    "\n",
    "import sys\n",
    "\n",
    "DATA_PATH = \"../data/raw/logd/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>ID</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>logD7.4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-logD7.4-100</td>\n",
       "      <td>Fc1cc2c(N(C=C(C(O)=O)C2=O)C2CC2)cc1N1CCNCC1</td>\n",
       "      <td>-0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-logD7.4-101</td>\n",
       "      <td>S1(=O)(=O)N(CCN1C)c1cc2c([nH]cc2CCN(C)C)cc1</td>\n",
       "      <td>-0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A-logD7.4-104</td>\n",
       "      <td>OC=1C(=O)C=CN(CCCO)C=1C</td>\n",
       "      <td>-0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A-logD7.4-105</td>\n",
       "      <td>Fc1cc2c(N(C=C(C(O)=O)C2=O)c2ccccc2C)cc1N1CCNCC1</td>\n",
       "      <td>-0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A-logD7.4-106</td>\n",
       "      <td>OC=1C(=O)C=CN(CCC(=O)NCC)C=1C</td>\n",
       "      <td>-0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID                                           SMILES  logD7.4\n",
       "0  A-logD7.4-100      Fc1cc2c(N(C=C(C(O)=O)C2=O)C2CC2)cc1N1CCNCC1    -0.96\n",
       "1  A-logD7.4-101      S1(=O)(=O)N(CCN1C)c1cc2c([nH]cc2CCN(C)C)cc1    -0.92\n",
       "2  A-logD7.4-104                          OC=1C(=O)C=CN(CCCO)C=1C    -0.90\n",
       "3  A-logD7.4-105  Fc1cc2c(N(C=C(C(O)=O)C2=O)c2ccccc2C)cc1N1CCNCC1    -0.83\n",
       "4  A-logD7.4-106                    OC=1C(=O)C=CN(CCC(=O)NCC)C=1C    -0.82"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data = pd.read_csv(DATA_PATH + \"logd74.tsv\", delimiter=\"\\t\", low_memory=False)\n",
    "logd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "logd_data = logd_data.drop(columns=[\"ID\"])\n",
    "logd_data = logd_data.rename(columns={\"logD7.4\": \"logD\", \"SMILES\": \"smiles\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>smiles</th>\n",
       "      <th>logD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fc1cc2c(N(C=C(C(O)=O)C2=O)C2CC2)cc1N1CCNCC1</td>\n",
       "      <td>-0.96</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S1(=O)(=O)N(CCN1C)c1cc2c([nH]cc2CCN(C)C)cc1</td>\n",
       "      <td>-0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC=1C(=O)C=CN(CCCO)C=1C</td>\n",
       "      <td>-0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fc1cc2c(N(C=C(C(O)=O)C2=O)c2ccccc2C)cc1N1CCNCC1</td>\n",
       "      <td>-0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC=1C(=O)C=CN(CCC(=O)NCC)C=1C</td>\n",
       "      <td>-0.82</td>\n",
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      ],
      "text/plain": [
       "                                            smiles  logD\n",
       "0      Fc1cc2c(N(C=C(C(O)=O)C2=O)C2CC2)cc1N1CCNCC1 -0.96\n",
       "1      S1(=O)(=O)N(CCN1C)c1cc2c([nH]cc2CCN(C)C)cc1 -0.92\n",
       "2                          OC=1C(=O)C=CN(CCCO)C=1C -0.90\n",
       "3  Fc1cc2c(N(C=C(C(O)=O)C2=O)c2ccccc2C)cc1N1CCNCC1 -0.83\n",
       "4                    OC=1C(=O)C=CN(CCC(=O)NCC)C=1C -0.82"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1130, 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data.logD.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1112,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data.smiles.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1127, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data = logd_data.drop_duplicates()\n",
    "logd_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_of_records</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smiles</th>\n",
       "      <th></th>\n",
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       "      <th>O=C1CC[C@@]2([C@@H]3[C@H]([C@@H]4CC[C@H](O)[C@]4(CC3)C)CCC2=C1)C</th>\n",
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       "      <th>Clc1cc(N2CCN(CC2)CCCN2N=C3N(C=CC=C3)C2=O)ccc1</th>\n",
       "      <td>2</td>\n",
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       "      <th>Clc1cc(O)ccc1</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>OC(=O)C(C)c1cc(ccc1)C(=O)c1ccccc1</th>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
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       "      <th>Clc1ccc(cc1)C(=O)n1c2c(cc(OC)cc2)c(CC(O)=O)c1C</th>\n",
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       "    <tr>\n",
       "      <th>S1(=O)(=O)N(C)C(C(=O)Nc2ncccc2)=C(O)c2c1cccc2</th>\n",
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       "      <th>Clc1ccc(cc1S(=O)(=O)N)C1(O)NC(=O)c2c1cccc2</th>\n",
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       "    <tr>\n",
       "      <th>F[C@@]12[C@H]([C@@H]3C[C@@H](C)[C@](O)(C(=O)CO)[C@]3(C[C@@H]1O)C)CCC1=CC(=O)C=C[C@@]12C</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Clc1cc2N(c3c(Sc2cc1)cccc3)CCCN(C)C</th>\n",
       "      <td>2</td>\n",
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       "      <th>O(CC(O)CNC(C)C)c1c2c(ccc1)cccc2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.039200</td>\n",
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       "    <tr>\n",
       "      <th>O=C1CC[C@@]2([C@@H]3[C@H]([C@@H]4CC[C@](O)(C(=O)CO)[C@]4(C[C@@H]3O)C)CCC2=C1)C</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N(CCC=C1c2c(CCc3c1cccc3)cccc2)(C)C</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n1c2c(cccc2)ccc1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.007200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O(C)c1cc2c(cc(cc2)[C@@H](C(O)=O)C)cc1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.006050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC=1C(=O)C=CN(CCO)C=1C</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brc1[nH]c2c3c1C[C@@H]1N(C[C@@H](C=C1c3ccc2)C(=O)N[C@@]1(O[C@]2(O)N([C@H](CC(C)C)C(=O)N3[C@H]2CCC3)C1=O)C(C)C)C</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC=1C(=O)C=CN(CCNC(=O)C)C=1C</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC=1C(=O)C=CN(CCN)C=1C</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC=1C(=O)C=CN(CCCOCC)C=1CC</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                                    count_of_records       var\n",
       "smiles                                                                        \n",
       "O=C1CC[C@@]2([C@@H]3[C@H]([C@@H]4CC[C@H](O)[C@]...                 2  0.000200\n",
       "Clc1cc(N2CCN(CC2)CCCN2N=C3N(C=CC=C3)C2=O)ccc1                      2  0.092450\n",
       "Clc1cc(O)ccc1                                                      2  0.001800\n",
       "OC(=O)C(C)c1cc(ccc1)C(=O)c1ccccc1                                  2  0.004232\n",
       "Clc1ccc(cc1)C(=O)n1c2c(cc(OC)cc2)c(CC(O)=O)c1C                     2  0.432450\n",
       "S1(=O)(=O)N(C)C(C(=O)Nc2ncccc2)=C(O)c2c1cccc2                      2  0.057800\n",
       "Clc1ccc(cc1S(=O)(=O)N)C1(O)NC(=O)c2c1cccc2                         2  0.054450\n",
       "N(CCCN1c2c(CCc3c1cccc3)cccc2)(C)C                                  2  0.000450\n",
       "F[C@@]12[C@H]([C@@H]3C[C@@H](C)[C@](O)(C(=O)CO)...                 2  0.004050\n",
       "Clc1cc2N(c3c(Sc2cc1)cccc3)CCCN(C)C                                 2  0.006050\n",
       "O(CC(O)CNC(C)C)c1c2c(ccc1)cccc2                                    2  0.039200\n",
       "O=C1CC[C@@]2([C@@H]3[C@H]([C@@H]4CC[C@](O)(C(=O...                 2  0.000050\n",
       "N(CCC=C1c2c(CCc3c1cccc3)cccc2)(C)C                                 2  0.000450\n",
       "n1c2c(cccc2)ccc1                                                   2  0.007200\n",
       "O(C)c1cc2c(cc(cc2)[C@@H](C(O)=O)C)cc1                              2  0.006050\n",
       "OC=1C(=O)C=CN(CCO)C=1C                                             1       NaN\n",
       "Brc1[nH]c2c3c1C[C@@H]1N(C[C@@H](C=C1c3ccc2)C(=O...                 1       NaN\n",
       "OC=1C(=O)C=CN(CCNC(=O)C)C=1C                                       1       NaN\n",
       "OC=1C(=O)C=CN(CCN)C=1C                                             1       NaN\n",
       "OC=1C(=O)C=CN(CCCOCC)C=1CC                                         1       NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data = logd_data.groupby(['smiles']).agg(count_of_records=('logD', 'count'), \n",
    "                                       var=('logD', 'var')).sort_values(by=['count_of_records'], ascending=False)\n",
    "grouped_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28bfa5e8080>"
      ]
     },
     "execution_count": 41,
     "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": [
    "sns.distplot(grouped_data['var'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>smiles</th>\n",
       "      <th>logD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brc1[nH]c2c3c1C[C@@H]1N(C[C@@H](C=C1c3ccc2)C(=...</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brc1c2nccnc2ccc1NC=1NCCN=1</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brc1cc(Br)cc(Br)c1NC=1NCCN=1</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brc1cc(Br)cc(C(F)(F)F)c1NC=1NCCN=1</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brc1cc(Cl)c(NC=2NCCN=2)c(Cl)c1</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles  logD\n",
       "0  Brc1[nH]c2c3c1C[C@@H]1N(C[C@@H](C=C1c3ccc2)C(=...  4.20\n",
       "1                         Brc1c2nccnc2ccc1NC=1NCCN=1  0.31\n",
       "2                       Brc1cc(Br)cc(Br)c1NC=1NCCN=1  2.51\n",
       "3                 Brc1cc(Br)cc(C(F)(F)F)c1NC=1NCCN=1  2.80\n",
       "4                     Brc1cc(Cl)c(NC=2NCCN=2)c(Cl)c1  2.31"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data = logd_data.groupby(['smiles']).mean().reset_index()\n",
    "logd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1112, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1112"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logd_data.smiles.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all the experiments were held with pH=7.4\n",
    "logd_data['pH'] = pd.Series([7.4 for i in range(1112)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28bfa9395f8>"
      ]
     },
     "execution_count": 47,
     "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": [
    "sns.distplot(logd_data.logD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "logd_data.to_csv(\"../data/1_filtering/logd/logd_logd74_dataset.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chemvae",
   "language": "python",
   "name": "chemvae"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}