1-.ipynb

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   "source": [
    "This project is an implementation of the paper **\"Classification of thyroid hormone receptor agonists and antagonists using statistical learning approaches\".** A QSAR classification model is built for differentiating between Thyroid receptor (TR) agonists and antagonists. The dataset is collected from the literature. "
   ]
  },
  {
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   "execution_count": 1,
   "id": "ca9ac42c-d5fe-46ff-a4a7-3a6cdac16f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit.Chem import Descriptors\n",
    "import matplotlib.pyplot as plt\n",
    "from rdkit.Chem import MACCSkeys\n",
    "from rdkit.Chem import AllChem\n",
    "import numpy as np\n",
    "from rdkit.Chem import Draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2332873b-6b40-4cc8-932a-fb17b6af7d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"dataset.csv\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "4e088131-6228-4a7f-ba49-60a22a45f191",
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       "      <th>label</th>\n",
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      "text/plain": [
       "                                              smiles label\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     -\n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     -\n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     -\n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     -\n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     -"
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "9344aff6-225b-408c-b538-bd7a19e7a589",
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    {
     "data": {
      "text/plain": [
       "(259, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
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   "cell_type": "markdown",
   "id": "8acc86b2-88c5-4ca9-8ebc-9700c77f2ef3",
   "metadata": {},
   "source": [
    "+ The dataset is comprised of 259 molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afd7900b-40b5-46ca-a86b-9c4b7b702fdc",
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    {
     "data": {
      "text/plain": [
       "0      -\n",
       "1      -\n",
       "2      -\n",
       "3      -\n",
       "4      -\n",
       "      ..\n",
       "254    +\n",
       "255    +\n",
       "256    +\n",
       "257    +\n",
       "258    +\n",
       "Name: label, Length: 259, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aaabe32-11ec-48e9-8c2f-cb30cac5e698",
   "metadata": {},
   "source": [
    "+ The label is designated as (+) and (-) indicating an **agonist** and **antagonist** respectively. We will change them to (1) and (0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbdd54e7-3aa8-4c04-9393-b3a453ebf8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"label\"].replace(to_replace=\"-\",value=\"0\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9678d768-86e4-481e-98a5-ab9090b70927",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"label\"].replace(to_replace=\"+\",value=\"1\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e6cbae4-8319-43f8-8ef2-bc5c1669473b",
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    {
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       "      <th>label</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "                                              smiles label\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0\n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0\n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0\n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0\n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0"
      ]
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     "metadata": {},
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    "df.head()"
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   "execution_count": 9,
   "id": "6dfd51d5-69be-441e-860e-7be8e2b209df",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 259 entries, 0 to 258\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   smiles  259 non-null    object\n",
      " 1   label   258 non-null    object\n",
      "dtypes: object(2)\n",
      "memory usage: 4.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df7f3de-e558-4b0e-bd36-ec5f8c3c6ec0",
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   "source": [
    "+ The label column has one missing value. After checking, it should be filled with (1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e46e387-51d6-46f2-bb14-3318dcbc9138",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(value=\"1\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b79676e-8f84-403c-bfe3-c2b4301ba974",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 259 entries, 0 to 258\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   smiles  259 non-null    object\n",
      " 1   label   259 non-null    object\n",
      "dtypes: object(2)\n",
      "memory usage: 4.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "973c54e3-5d23-4ca3-8386-d5e19c902fa2",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "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.countplot(x=df[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14b5c95c-5ea2-4e37-9f82-98a4a87d0a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    198\n",
       "0     61\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9367fbd9-818b-4459-a764-fe4b336863c5",
   "metadata": {},
   "source": [
    "+ The dataset contains more agonists than antagonists."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2193622f-911a-4b5d-ae0a-4912a2a956c2",
   "metadata": {},
   "source": [
    "### Chemical Space analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e93311a-e97d-40e8-bf44-69afdc9dbe74",
   "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>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles label\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0\n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0\n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0\n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0\n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77774ec9-720d-4787-924c-6c40eb41b393",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit ERROR: [13:58:40] SMILES Parse Error: syntax error while parsing: ClC[14]=C(C(Cl)=CC(CC(O)=O)=C@14)OC[4]=CC=C(NC(C[S=I]H(C)CC)=O)C(Br)=C@5\n",
      "[13:58:40] SMILES Parse Error: syntax error while parsing: ClC[14]=C(C(Cl)=CC(CC(O)=O)=C@14)OC[4]=CC=C(NC(C[S=I]H(C)CC)=O)C(Br)=C@5\n",
      "RDKit ERROR: [13:58:40] SMILES Parse Error: Failed parsing SMILES 'ClC[14]=C(C(Cl)=CC(CC(O)=O)=C@14)OC[4]=CC=C(NC(C[S=I]H(C)CC)=O)C(Br)=C@5' for input: 'ClC[14]=C(C(Cl)=CC(CC(O)=O)=C@14)OC[4]=CC=C(NC(C[S=I]H(C)CC)=O)C(Br)=C@5'\n",
      "[13:58:40] SMILES Parse Error: Failed parsing SMILES 'ClC[14]=C(C(Cl)=CC(CC(O)=O)=C@14)OC[4]=CC=C(NC(C[S=I]H(C)CC)=O)C(Br)=C@5' for input: 'ClC[14]=C(C(Cl)=CC(CC(O)=O)=C@14)OC[4]=CC=C(NC(C[S=I]H(C)CC)=O)C(Br)=C@5'\n"
     ]
    }
   ],
   "source": [
    "PandasTools.AddMoleculeColumnToFrame(df,\"smiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a74e789-ae96-4d94-8b1e-e86267e28154",
   "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>label</th>\n",
       "      <th>ROMol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...</td>\n",
       "      <td>0</td>\n",
       "      <td><img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "      <td><img data-content=\"rdkit/molecule\" 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\" alt=\"Mol\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...</td>\n",
       "      <td>0</td>\n",
       "      <td><img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...</td>\n",
       "      <td>0</td>\n",
       "      <td><img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...</td>\n",
       "      <td>0</td>\n",
       "      <td><img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles label  \\\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0   \n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0   \n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0   \n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0   \n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0   \n",
       "\n",
       "                                               ROMol  \n",
       "0  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "1  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "2  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "3  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "4  <img data-content=\"rdkit/molecule\" src=\"data:i...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7a86844-f2b9-4ff0-ab69-690a70857c93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 259 entries, 0 to 258\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   smiles  259 non-null    object\n",
      " 1   label   259 non-null    object\n",
      " 2   ROMol   258 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 6.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ae964d45-4f4b-495b-86b0-31cfe06e8374",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7122f2ef-b6dc-4a1a-974d-47f69ce7305a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 258 entries, 0 to 258\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   smiles  258 non-null    object\n",
      " 1   label   258 non-null    object\n",
      " 2   ROMol   258 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 8.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "383b4b50-a8a4-4354-ac44-e4c7b4b42394",
   "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>label</th>\n",
       "      <th>ROMol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" 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\" alt=\"Mol\"/&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles label  \\\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0   \n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0   \n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0   \n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0   \n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0   \n",
       "\n",
       "                                               ROMol  \n",
       "0  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "1  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "2  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "3  <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
       "4  <img data-content=\"rdkit/molecule\" src=\"data:i...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16277fa2-0598-47d2-8269-1d4acb06c479",
   "metadata": {},
   "source": [
    "+ We will calculate some descriptors to analyze chemical space of agonists and antagonists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb1a2b1a-52eb-4d57-a514-16d1ebd5514d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Molwt\"] = df[\"ROMol\"].apply(Descriptors.MolWt)\n",
    "df[\"LogP\"] = df[\"ROMol\"].apply(Descriptors.MolLogP)\n",
    "df[\"HBA\"] = df[\"ROMol\"].apply(Descriptors.NumHAcceptors)\n",
    "df[\"HBD\"] = df[\"ROMol\"].apply(Descriptors.NumHDonors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8fc70879-6eb4-4edb-9d67-71e6903f67dd",
   "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>label</th>\n",
       "      <th>ROMol</th>\n",
       "      <th>Molwt</th>\n",
       "      <th>LogP</th>\n",
       "      <th>HBA</th>\n",
       "      <th>HBD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>581.838</td>\n",
       "      <td>8.88724</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>473.525</td>\n",
       "      <td>5.49464</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>398.528</td>\n",
       "      <td>4.79444</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>438.593</td>\n",
       "      <td>5.57464</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>468.663</td>\n",
       "      <td>6.74494</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles label  \\\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0   \n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0   \n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0   \n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0   \n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0   \n",
       "\n",
       "                                               ROMol    Molwt     LogP  HBA  \\\n",
       "0  <img data-content=\"rdkit/molecule\" src=\"data:i...  581.838  8.88724    4   \n",
       "1  <img data-content=\"rdkit/molecule\" src=\"data:i...  473.525  5.49464    5   \n",
       "2  <img data-content=\"rdkit/molecule\" src=\"data:i...  398.528  4.79444    5   \n",
       "3  <img data-content=\"rdkit/molecule\" src=\"data:i...  438.593  5.57464    5   \n",
       "4  <img data-content=\"rdkit/molecule\" src=\"data:i...  468.663  6.74494    5   \n",
       "\n",
       "   HBD  \n",
       "0    2  \n",
       "1    2  \n",
       "2    2  \n",
       "3    2  \n",
       "4    2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee842f75-cb94-4bd8-bb3d-28b7d0855027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='Molwt'>"
      ]
     },
     "execution_count": 23,
     "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.boxplot(x=df[\"label\"],y=df[\"Molwt\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e6e4b4-cef3-4c78-ad7b-675a577d330b",
   "metadata": {},
   "source": [
    "+ Both Agonists and antagonists almost share similar Molwt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b4a06e3b-0868-42b5-ac7d-920f0d56812a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='LogP'>"
      ]
     },
     "execution_count": 24,
     "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.boxplot(x=df[\"label\"],y=df[\"LogP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c7342c0d-5ad2-4ab1-a414-4a7935a323bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='HBA'>"
      ]
     },
     "execution_count": 25,
     "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.boxplot(x=df[\"label\"],y=df[\"HBA\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d9f24825-c188-41bc-8f9c-8129c33a906f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='HBD'>"
      ]
     },
     "execution_count": 26,
     "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.boxplot(x=df[\"label\"],y=df[\"HBD\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4037d1e1-a1c1-4dae-b8ba-4a33c072898a",
   "metadata": {},
   "source": [
    "+ Agonists tend to have more Hb doners."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ec218900-53d0-4b60-80b1-cd6d11ada65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Molwt', ylabel='LogP'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.scatterplot(x=df[\"Molwt\"],y=df[\"LogP\"],hue=df[\"label\"],alpha=1,edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "068dcb7e-1d05-4edb-a39b-0b2ceff5df9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7c73792e-8589-480d-a64a-6bfeeaa171f2",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mannwhitneyu' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/ng/whnp3t611lq2k8dzf4hzhtnw0000gn/T/ipykernel_8363/3225316430.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmannwhitneyu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"HBA\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"HBD\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'mannwhitneyu' is not defined"
     ]
    }
   ],
   "source": [
    "mannwhitneyu(df[\"HBA\"],df[\"HBD\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "246e8606-c986-432e-830d-8a7140412c63",
   "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>Molwt</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581.838</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>473.525</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>398.528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>438.593</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>468.663</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>433.255</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>449.254</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>447.282</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>410.217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>408.245</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Molwt label\n",
       "0    581.838     0\n",
       "1    473.525     0\n",
       "2    398.528     0\n",
       "3    438.593     0\n",
       "4    468.663     0\n",
       "..       ...   ...\n",
       "254  433.255     1\n",
       "255  449.254     1\n",
       "256  447.282     1\n",
       "257  410.217     1\n",
       "258  408.245     1\n",
       "\n",
       "[258 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Molwt\",\"label\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46e09023-039d-48d4-a1a1-46b0241c0caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mannwhitney(descriptor, verbose=False):\n",
    "  # https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/\n",
    "  from numpy.random import seed\n",
    "  from numpy.random import randn\n",
    "  from scipy.stats import mannwhitneyu\n",
    "\n",
    "# seed the random number generator\n",
    "  seed(1)\n",
    "\n",
    "# actives and inactives\n",
    "  selection = [descriptor, 'label']\n",
    "  df_ = df[selection]\n",
    "  agonist = df_[df_['label'] == \"1\"]\n",
    "  agonist = agonist[descriptor]\n",
    "\n",
    "  selection = [descriptor, 'label']\n",
    "  df_ = df[selection]\n",
    "  antagonist = df_[df_['label'] == \"0\"]\n",
    "  antagonist = antagonist[descriptor]\n",
    "\n",
    "# compare samples\n",
    "  stat, p = mannwhitneyu(agonist, antagonist)\n",
    "  #print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "\n",
    "# interpret\n",
    "  alpha = 0.05\n",
    "  if p > alpha:\n",
    "    interpretation = 'Same distribution (fail to reject H0)'\n",
    "  else:\n",
    "    interpretation = 'Different distribution (reject H0)'\n",
    "  \n",
    "  results = pd.DataFrame({'Descriptor':descriptor,\n",
    "                          'Statistics':stat,\n",
    "                          'p':p,\n",
    "                          'alpha':alpha,\n",
    "                          'Interpretation':interpretation}, index=[0])\n",
    "  return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d8fe47f3-4837-45f7-a46c-47700a68e2d1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Descriptor</th>\n",
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       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Molwt</td>\n",
       "      <td>5940.5</td>\n",
       "      <td>0.447278</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Same distribution (fail to reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics         p  alpha  \\\n",
       "0      Molwt      5940.5  0.447278   0.05   \n",
       "\n",
       "                          Interpretation  \n",
       "0  Same distribution (fail to reject H0)  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney(\"Molwt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "712301d9-af41-4894-bc3a-a9bbe7212ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
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       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogP</td>\n",
       "      <td>5324.0</td>\n",
       "      <td>0.089622</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Same distribution (fail to reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "  Descriptor  Statistics         p  alpha  \\\n",
       "0       LogP      5324.0  0.089622   0.05   \n",
       "\n",
       "                          Interpretation  \n",
       "0  Same distribution (fail to reject H0)  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitney(\"LogP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf5dfc21-f319-4153-9728-61ca3d7895e4",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HBA</td>\n",
       "      <td>5101.5</td>\n",
       "      <td>0.033536</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics         p  alpha                      Interpretation\n",
       "0        HBA      5101.5  0.033536   0.05  Different distribution (reject H0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "mannwhitney(\"HBA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3a578337-eb2e-4500-9f50-95d77d4f7f3b",
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Descriptor</th>\n",
       "      <th>Statistics</th>\n",
       "      <th>p</th>\n",
       "      <th>alpha</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HBD</td>\n",
       "      <td>4366.5</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Different distribution (reject H0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Descriptor  Statistics         p  alpha                      Interpretation\n",
       "0        HBD      4366.5  0.000207   0.05  Different distribution (reject H0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "mannwhitney(\"HBD\")"
   ]
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  {
   "cell_type": "markdown",
   "id": "0ed02d78-0569-4fb7-8db6-c81a2712ac02",
   "metadata": {},
   "source": [
    "### Molecular Descriptors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6534d014-8be4-4179-8ab1-242bd0201dc4",
   "metadata": {},
   "source": [
    "+ In the paper, DRAGON software (currently not available) was used for descriptors Generation. Here morgan fingerprints and MACCS will be generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b94526c0-7273-4429-9583-924cb1232a4a",
   "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>label</th>\n",
       "      <th>ROMol</th>\n",
       "      <th>Molwt</th>\n",
       "      <th>LogP</th>\n",
       "      <th>HBA</th>\n",
       "      <th>HBD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>581.838</td>\n",
       "      <td>8.88724</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>473.525</td>\n",
       "      <td>5.49464</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>398.528</td>\n",
       "      <td>4.79444</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>438.593</td>\n",
       "      <td>5.57464</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>468.663</td>\n",
       "      <td>6.74494</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles label  \\\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0   \n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0   \n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0   \n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0   \n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0   \n",
       "\n",
       "                                               ROMol    Molwt     LogP  HBA  \\\n",
       "0  <img data-content=\"rdkit/molecule\" src=\"data:i...  581.838  8.88724    4   \n",
       "1  <img data-content=\"rdkit/molecule\" src=\"data:i...  473.525  5.49464    5   \n",
       "2  <img data-content=\"rdkit/molecule\" src=\"data:i...  398.528  4.79444    5   \n",
       "3  <img data-content=\"rdkit/molecule\" src=\"data:i...  438.593  5.57464    5   \n",
       "4  <img data-content=\"rdkit/molecule\" src=\"data:i...  468.663  6.74494    5   \n",
       "\n",
       "   HBD  \n",
       "0    2  \n",
       "1    2  \n",
       "2    2  \n",
       "3    2  \n",
       "4    2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6a8ed2ab-73ab-4eb5-9675-71163b87eac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfp = []\n",
    "for mol in df.ROMol:\n",
    "    molH = AllChem.AddHs(mol)\n",
    "    mfp.append(AllChem.GetMorganFingerprintAsBitVect(molH,2,1024))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15dd9069-917c-45e4-ba68-c5fe2bbbecaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfp = np.asarray(mfp,dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4e605bf6-3a4e-4063-991b-1259f07870ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "maccs = []\n",
    "for mol in df.ROMol:\n",
    "    molH = AllChem.AddHs(mol)\n",
    "    maccs.append(MACCSkeys.GenMACCSKeys(molH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "05b46ebd-56c3-4c2d-940e-ccbaf3b603dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "maccs = np.asarray(maccs,dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "64c3bf2f-0184-4077-9de9-0c9b9bc9e705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 1., 1., 0.],\n",
       "       [0., 0., 0., ..., 1., 1., 0.],\n",
       "       [0., 0., 0., ..., 1., 1., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 1., 0.],\n",
       "       [0., 0., 0., ..., 1., 1., 0.],\n",
       "       [0., 0., 0., ..., 1., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maccs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2661fe-6104-4ddb-96b1-9b8d71d7d507",
   "metadata": {},
   "source": [
    "### Feature Selection "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7ad017-a33d-495a-8648-ec515409f3ba",
   "metadata": {},
   "source": [
    "In order to reduce the information obviously redundant, the descriptors with > 95% zeros are excluded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ef2b721f-2698-47c6-9021-5a5893aa1d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e2729e9a-b4e9-4c21-a14f-cf0ad08a1f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = VarianceThreshold(threshold=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0712b09d-3792-4d1f-aa64-92c364e07f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfp = var.fit_transform(mfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "539d099a-45b4-4c21-bf78-bdd225fee2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mfp[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9ff335-d477-42d8-a47d-a39f754e7a28",
   "metadata": {},
   "source": [
    "+ The number of morganfingerprints decreases from 1024 to 146. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9e275fba-322a-4694-b963-b50f12065f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0', 'x5', 'x20', 'x26', 'x29', 'x36', 'x41', 'x53', 'x70', 'x73',\n",
       "       'x76', 'x77', 'x81', 'x88', 'x114', 'x119', 'x122', 'x133', 'x139',\n",
       "       'x145', 'x146', 'x171', 'x174', 'x175', 'x182', 'x186', 'x189',\n",
       "       'x192', 'x197', 'x201', 'x213', 'x219', 'x223', 'x228', 'x248',\n",
       "       'x264', 'x267', 'x276', 'x282', 'x285', 'x301', 'x313', 'x314',\n",
       "       'x317', 'x333', 'x341', 'x347', 'x350', 'x353', 'x365', 'x366',\n",
       "       'x378', 'x400', 'x407', 'x416', 'x429', 'x434', 'x436', 'x449',\n",
       "       'x453', 'x466', 'x482', 'x486', 'x494', 'x504', 'x505', 'x518',\n",
       "       'x519', 'x546', 'x547', 'x548', 'x561', 'x579', 'x586', 'x592',\n",
       "       'x593', 'x595', 'x596', 'x607', 'x608', 'x617', 'x619', 'x632',\n",
       "       'x635', 'x649', 'x656', 'x659', 'x667', 'x669', 'x674', 'x675',\n",
       "       'x677', 'x685', 'x692', 'x701', 'x708', 'x714', 'x715', 'x723',\n",
       "       'x725', 'x728', 'x753', 'x754', 'x759', 'x771', 'x774', 'x782',\n",
       "       'x783', 'x790', 'x799', 'x807', 'x809', 'x816', 'x828', 'x831',\n",
       "       'x832', 'x838', 'x847', 'x848', 'x854', 'x855', 'x861', 'x871',\n",
       "       'x879', 'x881', 'x887', 'x893', 'x904', 'x906', 'x918', 'x926',\n",
       "       'x932', 'x935', 'x939', 'x952', 'x957', 'x960', 'x977', 'x981',\n",
       "       'x990', 'x1005', 'x1009', 'x1011', 'x1013', 'x1021', 'x1022'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0c3e96de-0f27-4e46-bda4-ca709713d3dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "maccs = var.fit_transform(maccs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "30a7b0fd-d1c5-45a9-82a8-9bacedf98209",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(maccs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61f1274-b89d-4800-8230-891f4eae1534",
   "metadata": {},
   "source": [
    "### Self Organizing Map (SOM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b938ee08-5ebb-42b6-af7f-f78368efa7f7",
   "metadata": {},
   "source": [
    "+ SOM was used for clustering of molecules and data was split into training and test sets according to molecules distribution. We will use Morgan FP to generate SOM and visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "302e1b53-2c99-4536-82f6-9aed71a3ff9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from minisom import MiniSom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bf84ed21-c464-42c0-baa9-1bbc9db5c563",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8x8 SOM\n",
    "\n",
    "som = MiniSom(6,6,len(mfp[0]),sigma=0.5,learning_rate=0.5,random_seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b06a0371-3610-42ce-b77e-81f9fde071d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "som.train_random(mfp,num_iteration=20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "001e1354-7d9c-4623-a7f0-098024256ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y =[]\n",
    "\n",
    "for row in mfp:\n",
    "    x_val,y_val = som.winner(row)\n",
    "    x.append(x_val)\n",
    "    y.append(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ac6c3165-648f-4a29-b7ae-aed3a17b0be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfp_ = pd.DataFrame(mfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1c3f49ac-9c64-4f1b-ba0a-34ea1b79c628",
   "metadata": {},
   "outputs": [
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     },
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   ],
   "source": [
    "mfp_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff579598-ba88-4ab3-8235-9cf7bd664174",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfp_[\"label\"] = df[\"label\"]\n",
    "mfp_[\"X\"] = x\n",
    "mfp_[\"Y\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e6993874-2974-4f15-8743-4d776b1faa3b",
   "metadata": {},
   "outputs": [
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       "      <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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>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>...</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9  ...  139  140  141  142  \\\n",
       "0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  ...  0.0  1.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  0.0  ...  0.0  1.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  1.0  ...  1.0  1.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  1.0  ...  1.0  1.0  0.0  0.0   \n",
       "4  0.0  0.0  1.0  0.0  0.0  1.0  1.0  0.0  0.0  1.0  ...  1.0  1.0  0.0  0.0   \n",
       "\n",
       "   143  144  145  label  X  Y  \n",
       "0  0.0  0.0  1.0      0  4  4  \n",
       "1  1.0  0.0  0.0      0  3  3  \n",
       "2  0.0  0.0  0.0      0  2  4  \n",
       "3  0.0  0.0  0.0      0  2  4  \n",
       "4  0.0  0.0  0.0      0  2  4  \n",
       "\n",
       "[5 rows x 149 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfp_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "163dc342-b16f-4eb9-abc0-e677db45b937",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adapted from the MiniSom example notebook\n",
    "def depict_som(cluster_df, x_dim, y_dim, x_column=\"X\", y_column=\"Y\"):\n",
    "    \"\"\"\n",
    "    Draw a SOM with each cell depicted as a pie chart\n",
    "    :param cluster_df: data frame with SOM output, should have columns active, X, and Y\n",
    "    :param x_dim: X dimension of the SOM\n",
    "    :param y_dim: Y dimension of the SOM\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    required_colums = [x_column, y_column, \"label\"]\n",
    "    for col in required_colums:\n",
    "        if col not in cluster_df.columns:\n",
    "            print(f\"Error {col} not in dataframe columns\", file=sys.stderr)\n",
    "            sys.exit(1)\n",
    "    cell_dict = {}\n",
    "    for k, v in [x for x in cluster_df.groupby([x_column, y_column])]:\n",
    "        cell_dict[k] = Counter(v[\"label\"])\n",
    "        cell_names = cluster_df[\"label\"].unique()\n",
    "    plt.figure(figsize=(x_dim, y_dim))\n",
    "    the_grid = GridSpec(x_dim, y_dim)\n",
    "    for position in cell_dict.keys():\n",
    "        label_fracs = [cell_dict[position][l] for l in cell_names]\n",
    "        plt.subplot(the_grid[(x_dim - 1) - position[1], position[0]], aspect=1)\n",
    "        patches, texts = plt.pie(label_fracs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0ab9fbc1-cad8-423c-944d-ce92714ffe9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 35 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depict_som(mfp_,8,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0e5ccede-fa41-429e-a627-9fadce75ae1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"X\"] = x\n",
    "df[\"Y\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9e24497a-6315-420a-9f92-8258096e3b1f",
   "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>label</th>\n",
       "      <th>ROMol</th>\n",
       "      <th>Molwt</th>\n",
       "      <th>LogP</th>\n",
       "      <th>HBA</th>\n",
       "      <th>HBD</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>581.838</td>\n",
       "      <td>8.88724</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>473.525</td>\n",
       "      <td>5.49464</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>398.528</td>\n",
       "      <td>4.79444</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>438.593</td>\n",
       "      <td>5.57464</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/&gt;</td>\n",
       "      <td>468.663</td>\n",
       "      <td>6.74494</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles label  \\\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0   \n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0   \n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0   \n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0   \n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0   \n",
       "\n",
       "                                               ROMol    Molwt     LogP  HBA  \\\n",
       "0  <img data-content=\"rdkit/molecule\" src=\"data:i...  581.838  8.88724    4   \n",
       "1  <img data-content=\"rdkit/molecule\" src=\"data:i...  473.525  5.49464    5   \n",
       "2  <img data-content=\"rdkit/molecule\" src=\"data:i...  398.528  4.79444    5   \n",
       "3  <img data-content=\"rdkit/molecule\" src=\"data:i...  438.593  5.57464    5   \n",
       "4  <img data-content=\"rdkit/molecule\" src=\"data:i...  468.663  6.74494    5   \n",
       "\n",
       "   HBD  X  Y  \n",
       "0    2  4  4  \n",
       "1    2  3  3  \n",
       "2    2  2  4  \n",
       "3    2  2  4  \n",
       "4    2  2  4  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3202ee02-910b-455a-95af-aa9c2673226c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"ROMol\",\"Molwt\",\"LogP\",\"HBA\",\"HBA\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4564487a-9fde-48ed-9f48-78968c7ad081",
   "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>label</th>\n",
       "      <th>HBD</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles label  HBD  X  Y\n",
       "0  OC1=CC=C([C@@H](CCCCCCCCCCC(N(C)CCCC)=O)C2=C(C...     0    2  4  4\n",
       "1  OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0    2  3  3\n",
       "2  OC1=CC=C(N(C)C2=C(C)C=C(C[C@H](S3)C(NC3=O)=O)C...     0    2  2  4\n",
       "3  OC1=CC=C(N(CC2CC2)C3=C(C)C=C(C[C@H](S4)C(NC4=O...     0    2  2  4\n",
       "4  OC1=CC=C(N(CCCCCC)C2=C(C)C=C(C[C@H](S3)C(NC3=O...     0    2  2  4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "65449563-76a5-4637-a72f-dd7d6d54f32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uRvoxDaOBvwBXYqJcFc0rlsrLAecAfyDdvToSuAC4Lgz8KTbaZg3jLYs8j9untPQD8jxeg4msPY9Wg2yxVN4TuBL5/LDFf4ED8vKJXSyVDwMGAnNYNPsBsFsY+O9YtJmYYqm8L3A5tZU4r5WXgANzUwbdePsDl1H7iKcWXgAOwkSfWrSZmGKpfCxwITCLRbPDgL3y8jxivBORwZhNja8Bu9oqEW4lyBZL5SbgZmDf1Ma6ZiJwUBj4dyvZ75XqvOstwF5KLsYD+4WB/5CS/V6parwN2EPJxQRE44NK9nvHeP2B25GRnQYTgH0w0cNK9nulOu96B7CbkotxwN5h4D+mZL93jNeMTNXsp+RhFLA3JnoqraHUQbb6YD4IDEjbmF5oBQ4PA/9GZT/TUSyVG4B7kSkQTVqBI8PAH6TsZzqqD+YjwDbKrlqQF+btyn6mx3izAI8BWyp7agEOwER/V/YzHdV+fBDwlV1NAXYPAz/7l4nx5gAeIpt+3BsT3ZvGSKoUrmrw+Tv6ARakrYOKpbLWKKsnrkE/wIJovKZYKm+Vga9fKJbKjcA96AdYkJX6m4ulctq53ngYrxG4D/0HE0TjrRhvpwx8/UK1Hx9AP8ACNAP3FkvlLHy1I/14Pxn1479a1jgs7fOYNk/2WPQ+SbqiAbipWCovm5XDYql8FrJimRUNwJ3FUnmxDH2eSPrFnzg0ArdWFxCz4mTSL4zEoRG4DeMtnqHPs8lmwNNGM3KvLpOhz3PJYDBQqVD5+9TNnz94yp83A+6vZkslInGQrQa685Nen4LZkJFQQdtRsVReAzhL208XzAfcVyyVm7UdFUvlFZHV56yZE3lhqvcjxlsJyXDImrmQeUN1iqXyhsCpWfjqhAfcUf2q1cV4GwOnaLupVJh60dTdXzlj6sGbVP/TXEgWSiIS/WGqD8YtSHJvPdgIyWvU5hpkRFIP1kPyjNWoPhi3Av01/fTA5mh/Jcjn5a1AP1U/3bMVxjtU00H1ebyC+u3gXA/JTdXDeAUkq0f1pVypMOGEKUe8fk3Ljht0+qdti6Vyoq/2pJ2yNbBhwmttcUZ10U2F6pzhulr2a+TU6k4rLXYG1lG0Xwumulijxa7A2or2a+FsjKcZ5HcC1lC0XwtnVbOMtNgN5X5srfDTvlNO/fTh1o3+r5ufXJLk6zJpkD0y4XU2mR/d+eATFG3XykLA3or2/6Rou1YWRFdjHvpxYWBPRfv1mNLqzDLopTcCnKlom6mVhpEDJp8/6qXWVVbp4WeLITvmYhE7yFYXK7JdUewelWBfLJUXAtbXsJ0AlSmD6lxsXjQerGJVtsvWe6TehorGYqm8GrI/Pw8coGLVeGsg50ioMKnS9Nmmky+tvFcp1rKAd1Bc+0lGsrslvE6D9Yul8hIKdndEee4nBmsUS+V5Fexmm0LVM+tXX2y2yZPGjTBenPMtaiVPGjdWuldjjx5rZWxl1nfXnXTV3F9V5l+kxks2K5bKsXa0JgmW3c1X1AuNeZqdFGwmpYCcfmWbes9TdmYtBZt9QeNmCjaT0ghsqmBXReN3lblf++2kq4s/MVecF0MjsGYcP0mCrMaNkgaN9tR7wasz6ynYzFsA0mhPX7hXf6NgMw0a7fm1bYMfty7y8vqTrlhtArPMnuDy1eP8OFaQLZbKHjLBnSdivVV6o1gqz4XdQ0NsYPXroVgqz49M4ucJq/2I8RZEFpzyhO17dQFA4/M8DStatSZTLDYPKuLVlhWf33LyRetPpSlpHvpqcX4cdyQb51zYrLA9z7WgZXs2sHqTYf9vZgPbbVrAsj0b2NaYtxcl2G9T4p1WXfFgy0bP7TnlzE2gkGbNJdb6Qay8tr0an21atPDji/Hao8vkStNIy8kOVo4wXJTvR+7a+MKn/QpTUx9zNrnS9LVljfXafNATttuUR402j+MDixsstm4Y/PqqDZ+nPnh8YqU5tHyvWtv1OLoy+5vfVOZt/HPTvali2MRK8/A4GmMF2fObb25FdlvlidfllEVrTEh64ez8PO6gxn+8eUDT07PNy9jVCgVrn6tDLWvM16HLgu025VHjZMv2rB0s/duGD8Yc1PTUJr3/shZusmNGsPY3m7swfrWjmh61Yaoljsa4OzRGx/x9FkSW7YVISYqaPicaaG3ZvuHV149uenjSsoURaxQKdN6OZ4MfLdsbbdmeDUbn3J4NRlu2950tQzdO3W75AxufqhQKqVMXv7fSoHZGW7Zngx/i/DjenKyJvkfKUOSJN20aq9bcGtHb79YsfPThnc3nPf9x/31/vLzf1Wsv1zBig0KB2Wy2pQNWKyaEgf8VcihxnrDaj9VT7UdbtZkeuxqlOKeVmlQjmG/hscxqo4ip7eKSnwF5qy0W63lMksI1NME1mrymYPOzrv7jIvww8sKmQc990H//jx/qb1bYoPHdTRoLlSwWWN5SsJm3ftRozzAFm2mwqjEM/ApSusgK5ZZ1bXwxWWsPACZqwf7LKS19LshqtOeVtv8zOz+PO7rx4Zde6//HYS/3P3bB3Zue33SWwpTlFHz2xBAFmxovpzRo9GPe7lWNoP+SLUODWgYsndJEBXjOQlM6k7d7NdbzmCTIWpk5tsRH2H5zAk1MfWD7hldee6bfn19+p//BDSc137/hfIUxaxYKddlO/CYmek/B7pMKNpPyQRj4GsUH86TxPUwUKti19jyGlYUXH1/pn+ZzfzAm+spWezqQp5gzFBN9GeeC+EHDRK8jFWTzwLUaJZg/CXZ87cp+V82xfMPXmvOstXKHhtEw8F8CPtawnYBblew+TzdTP3XgFiW7zyNzs1Z4uvX/vk1x+X222tGJf2NRY0pi16ZLOjK7JuF1NpmA3sMJcJWi7VqZgNRQ0+IKRdu1MgG4QcWyvIDzoHEclvOa2ggDvxX4my17g6YOSFoSaBR6/dhKPmLOaBIMepIG2fuAeteWvw4TjVa0Pwj7K6VxuRATpRlZ9MbNQKxPHwWuDANfM9PhRuo/CroCE/2kaP8GLGn8sLLEUhMrzZ8kuPRiTDTWRhu64Qrqf6+ek6QfkwVZE01Czse0/qleI5+gfIgvJppKfQ+1/hK4SNNBGPgTgEM0ffTCB2jX3jLReOAwVR898x7KNdSqaYfW6nv9u3WNuPOqIdpfDCaaQH2fx49I+HWbfCHHRM+TorhYCirAQdU/ui4mehq4Td3P9LQCh2Mi9fzAMPD/CVyv7acLWoEDw8CfqO7JRE+h9LneCy3AAdVBiSph4N+JlMpOzaCp2y8a4+ctwH7Vl5kuJnqI+iyCTQUOxUSJdhGmXS0vAa+ntBGX8zBRlucnHAa8nKE/gDOrgSErTgLeztAfwF/CwM9yAfUEwEayfRzOxEQa6XfdcQgWprjerCyz3ORKU1jjz0sZP4/7kf003gmY6IWkF6cLsvL22prsRF+DiXSnCTpjosnA75Epiiy4FhNlWmo9DPyxwFZkl21waRj452bkSzDRGERjVmsJAzHRBRn5AiAM/DHIAdep0xpfbF0lrOFnZ2Oii9P6ioX049ZkNz/7N0x0ZRoD6fM+ZavtJujvrrkYEx2l7KNrRON6gOYbuwKchonqUqQyDPxvkMN/tHfXnBcGfn2KG5poBKJRe9R+DiY6SdlHl1T78Xek3Io9aOqAnnYyVpBRuknjIzEmGo7Up9P8SmgBjsVEp6Q1ZCe5XoLQhsAlyFybTb4DdsVEf7ZsNx4m+gHYAp25vbGIxkxHPp0JA/9b5Oa9HPuLmiOBHcLAP8Oy3XiYaCRS+eJK7GscAWyPif5i2W4sqoF2XVLkWA+urPibqZWGrhbAvgW2xUTZfol0xkRfAxujk0f+E7BD2hFsG4VKxfJ9Zrz1kMTrFSxYuxc4uhrg8oPxtkYCUVqNrUiu72nKqVqxKZbKGyEpXstaMPd34Ngw8DXTmOJjvE0QjWm3k4IkqR+vnKoVm2KpvBMQkOBevbP5vOc3aHy37fjDFuBO4OS83asYbydkgJf2gO9WJGifjImsnXBmP8gCGK8Jqfh6JPGLoI1HOvMaTJS3gyHaEY37ICWCNyReddtxwINI/mTeDjH5hWKp3AzsCRxL/PpUExGNA8PAz3pxtHaM1w/YC9G4RsyrJyIr+pdgojcst8waxVK5EdH4Z2CVWq/buOHNt2/vd+FyiMZzMdFHSk1Mj/GagQORxb+45ZrGIAO6qzVijk6Q7Yjxfg1shzykawHLM21A+hmZBxwKDAYeqU5uzzgYbyngD0gxwFWQ0V9jh19MRBaVhgLPAg9nkoJmkWKpvAqycNTWj8sxbT9OAN5ANA4BHg8Df3S2rUyJ8VYDtqRd47JMq3E8onEYovExTGT7PGNViqXyysAOyHTCisjor+O9OgZZOHu3gdanP5tln3/MgM/jssDuwAbAqsAiTNuPE4EPkYNn/oXEHLV0Sf0g2xnjzYKUeGkCJgFjqseZzTwYrz/TlhoZW90aONNQLJU792NU3eI58zDtvToRuVdnKo3FUrk/4CHrM5OVd9/VB+nHWZHpgBZgQpb9mH2QdTgcjj5EPY7uczgcjj6DC7IOh8OhiAuyDofDoYgLsg6Hw6GIC7IOh8OhiAuyDofDoYgLsg6Hw6GIC7IOh8OhiAuyDofDoYgLsg6Hw6GIC7IOh8OhiAuyDofDoYgLsg6Hw6GIC7IOh8OhiAuyDofDoYgLsg6Hw6FIU6bepEzLKshJ7I3AZKSK6euYaHSmbVGiWCovhZS8mItOGme4cixdUCyVG5CSJSsg/Qhy4vzXwPth4P9Yr7bZolgqNwHLIAUW5wXmRqq0vgN8HAb+TFHJo1gqLwusDMyJ3KuTkHt1WBj4M1bJmS4olsoFpB87axyBaBybRTt0KyMYb0Gk2OA2wJrIDdsdnyF1k+4HHsdEk/UaZo9iqbwQonFretZYoZPGMPCnZNLIlBRL5UWAPWivDdW/h59/C/wTKaL4ZBj4M0o/zooUjTwU6cd+3fx0ElIf6h3gYeCRMPCnZtLIlBRL5XmRYoPbIRrn7uanFaQm3WCkeus/w8CfIUqoFEvlxYB9kVpta9I+EOhMR433Af/Q6ketarUrAacixQWbE1j4DrgKuDyvRdyqBelOA3Yj2RfBt8CVwBVZvVHjUiyVlwH+glQ6bezl513xDXABMCgM/Ek222aLYqm8HHAEcAAwTwITXyHl4a8KA3+ixaZZo1gqrwb8CSkuOEsvP++Kj4HrgOvCwM9lAdBiqbwmEnN+T7J79WukH6+2rdFukJWyvKcBp5MsuHZmOHAIJnrGgi0rVMtkn4l0qI3pli+AQ8LAf9aCLStUiyReiJR0t6HxK+DIMPAft2DLCsVSeXbkoTqIeOXcu2M4cEoY+HdbsGWFapHEs4GTSBZ4OvMpcFAY+C9YsGWF6r16DnAidtaYPgEODAP/JQu2AJtB1ngrI58Wq9sxOA03AsdjovEKtmumWCqvCtwOrKZgfhBwQr1HCsVSeSXgHmQeyzZXAyfVe8RXLW/+AFKe3jYXhIF/moLdWBRL5bWQe/U3lk1XkK/Mk3PQj2sjMefXlk23AlcAJRtfYHaCrPE2Bh5HFnu0+C+wHSb6SdFHtxRL5U2Bx5AJdC1eAfx6LZAVS+WNgCeRMthavAhsGwZ+XV6YxVJ5S2S+WLMfb0O+TuoyV1sslbcGHgJmU3TzHLBDvaa6iqXytkg/zqro5l/ATmHgj0tjJH2QNd4GwDPodmgbQ4HNsp6nrQafp9Ht0DaGAJtnffNmFGDbqEugrT6Yj2JnKqs3ngJ2rYPGLYEn6H7hziYvA1tl/fVVfYk8RjYaXwC2CQP/56QG0s1hGG9poEw2ARZgLeABjGdjDq0mqmkuT5BNgAX4P2S1MzOKpfLyiMYsAizARsA91RSbTCiWygsiI8wsAixIRs2DGWtcCcl4yCL4AGwA3JWRL+CXqZ6HyE7jxsi0S2KSB1kJdDfTfYqEFlsiCzLqVHNCb0F3GqQrtimWyn/MxJPxmh/qd9Y5s/NzZsGgygDghAz93QLMn6E/kLS+o7JwVM3tvQ2YPQt/HdixWCrvn4WjDhqzGtS1sWuxVN476cVpRrJHA5ukuD4NF1ZH0docB2yYgZ+uuKhYKhcz8HPGmg2f7P5G/8NG/bbw3nsZ+OvIedUUKlWKpfIxwLbafrrhb8VSeckM/JyMfOnVg8uqudTanAaskYGfrriimhMfm2RB1ngLAUGia+0wO7JSrUb1pjlP00cvzIGs4uphvAWAEkBzoWXJe/qdu1zQdP1zoLlDZRr6I3m4alSne/6m6aMXZkX5WakGcdW/Yy/MDVyi6aCas32Gpo9emBe4NMmFSUeyx5L9kL0z22A8jVSqNo4ju3nY7vCrc1BaHEKHua1CgeY9mp7bdHD/I4ctyKjvFP12ZM9iqWw7Bacjx5AsAd8mexRLZc0R2BFkN0fZHbsUS+VFFe2fSHbz6d2xW3XbfCziB1nZcHBo7Ot0UJnvKpbK/ZAAlAd05p+N1wAc3tU/LVCI1nq1/zGFHRtefk3F97Q0oKSxulV2Pw3bCdhFw2h1w8FBGrZj0gQcpmG4WCrPQT76sRGIvVaSZCS7HTBfgus02B3jaYxSBtDzOQtZskc16NtmALBEd//YUKjMf1nz1Wv9vfn855uZqn3+wM5Kq/Db0P3+/KzZTsnuzmS/oNcdh1YXi22zC9kv6HXHvnE1JvmDbJrgGi3mQmeH2aYKNpMyNzo7zHbu7QeFAoUNG9/Z5I3+h366XOGrUKENbSwKrKNgd3sFm0lZvVgqL6xgd3MFm0lZGFhRwe6mCjaTsjAxdwomCbL1WsHsDo329AWNa9f6w9kLk1Z8pt/J8x3T+NDLCu1oQyPIbqlgMykFZGRtm75wr87QGuMFWcmNXT3WNfqsadNY9VNAc0EtCVY1YrwmYu73LhSY48TmBzb4Z7+TXp6dnzV2o2mMgBZQsJkGq6PO6nzsSjZtWsBqQKweAKNxb6Qh1vMYdyS7KLp7vpNguwOWID/zP23Y1rgYCU9lWq5hxAZv9D/sp3Ub3n3XcptWsGms+nDWe8W9M7bXMpah/ivunbGdKbIcWRcX6J1Yz2PcIJu34AP2U8n6gsZUD3tzoWWJu5vPW/6ipuuet5hTa3tXXda79GrB9gClL9yrM7zGuG+IrLde1oLt1cy+oHF0WgOFAs27Nb2wyc6NL35foZC6wsMUmn6C79Oa6Yi17d7/7X/Ua/MRpd7RNIWmsTY1Dul/RGUexo6wZtACk2mKLPfjDP88xg2yeTz53Xab+oLGH2wZaixU5pcjRtPRxORPLTSnI1ZGVLMyacKC/LR6oZD+k7WJyR/ZaFMb8xeiyUAW21lrponJH1g2OcM/j3FHSCOQwoB54nPL9r4C8lazya5GKVqZN43fWrYX2jCyYcPbH9kIsFVGWrLTxhfYeMPZJcy5PRvEeh5jZhdEkwHbCx5pGWbTWPW096wPSukNqxqrZLVttlaspoeFgR8BX6a1s13j/yILzWnjFYu2wEQRUhImTwy1aSwM/J+wP5BKS6znMclcn9U/ogU02tMXNL6qYDMNGnWjBqc1sF7DezYXcjTyjPvCvTpDa0wSZPP0cE4FNPbX50njFHRusn8o2EzKGOBNBbspC3BWKgvwk62jGMcBb1my1RHNDSJxGY9OP+bpeRwPvB3ngiRB9iHyMxn9ZHV+0TYPAnkpYf1EGPga5XYeJj/z689hohYFu0+RYs7yN4UvPmsoWDv74BkljXeRn+fxLkyk0ZZ7keKGeeCBuMUVE5zCFY1GROeBQRpGw8AfhVQzzQMqGjHRKKTkTB64UsNoGPjDgb8nvX67xv/ZTI+6wqKtdkz0IxmXK+oBlTOew8D/mvzcq7Gfx6T5lwOp/6rmu+h+8g5UtF0rb4WB/7SifUP9swyGYKJnFe2fTsKR3u8a3rCVo/kqJnrekq2uUD3AvkZewUQaUwVtXKxou1ZeCQM/9tRFwsoI0dvAtYmutUMFOAoTqQX6MPBfB67Xsl8DFaTEjx7Sj5er+uidkzSNV0ezlyW5drnC17YOoT7Vkp2uMdFgUozYLdACHK/pIAz8F4G7NX30wlTkAPjYpNlJdDL1S624Wnlk0MZJSC5iPbiiemNpY5Dc4HpwBybSyCrozAXE3IAxL9GP/QpTY5+C3wXXZHSvHov9PNxauQgTDcnAzzHYz6eulSAM/ESplCmq1UbjkRPZs542+JRqXSptwsAfi1RIyFrjx0jROH1MNA6pTJC1xldROkm/M9WFw6OIoXGLxmE28k9fQnmE9wsm+oluKl0o8y7yolYnDPwfSVCZwAJvA39NenG6PfEmeg7tT9pp+R4YUA3wmRAG/rNIva+s+A4YEAb+hMw8muhxstX4ObCj0kp0l4SBfx/wp1p/v23D4J9Tuvwa2BUTpT7XoWakH0/JzJ9oHICJMsvECQP/EWSePSuGI89j4kyc9AePmOga5ObVHgl9B2yJiWzvje6VMPCvRHnusMo3wBZh4Fvd414TJroS+HMGnn4EfExk9RSRWggD/3Jq/Apao+GTeVK4GoFozP7T1kR/A87MwNNXwOaYKMzA1zSEgX8+2YyehwObh4GfauegndOdTHQZsC+gcZgzSBL3Rsqrlz0SBv5AYH8kqVyDN4CNwsCPlehsFRNdDByMXt7lMGAtTPS+kv1eCQP/QmSaq9usimamTvYYH6vESAeGAb+t572Kic5FpmK0voaGABtiog+V7PdKGPhnI1V6035xdMdgYMMw8D9Oa8jeEXomuhNYGbCZjjMVOAdYGxNlP7rrRBj4twOrAP+2aHYK8lZeJwz8TyzaTYaJbkZO27eZHteKpOBsgInqtZD4C2Hg34JU+OhS4zoNH3xcKMQuI96KZGqsh4nqtZDYjoluQCp82Fw8nYSsFayXk368DulHm2dCTEK+dtZPO4Jto1CxduZyB4x3EDJvsnQKK08Dp2EijcNRUlGtrHowcsOlWYF+Cjg1DPw3bLTLOsbbBUl/WiyhhSnIzrKLMFEW5cVjUyyVN0deAKu3/bdzm256fp+mf21So4kpSPpUkIeBwHRIyajDgROQKgNJmAo8BpyFifJ2QFRbyagjkYynxROaqQCPI8+j1QOidIIsgPEakLLTByIF7Wo54fwr4H7gpjx2ZmeKpXIj7Rq3oDaNw6lqtN2ZKhivGfCR6aCtgDlquOorJMf4Bkz0jWLrrFB9SPcAdgc2e77f8e8s2fDdur1c9hnyUF6CiayMeFSRYLsl8om9FbWdtxsCtwPXY6Kv9Rpnh2Kp3ATsBBwAbAbMWsNlIbKD9WattRC9INsRCbgrIEXWVkVKgzQjc3/fIAegDJsRHsjuqD6ovWkcGgZ+vfL80mO8fsB6yOh9vg7/m4ocK/gxMBQTfVa3NqakWCo3D+n/x9XnL4xZC/kS6zilNgFZH3itHgs+1jBeI1Knai1kim9O5F79Gcm1HYr0Y+aLk7aoDoC60ziCaswJA19dYzZB1uFwOPootmtHORwOh6MDLsg6HA6HIi7IOhwOhyIuyDocDociLsg6HA6HIi7IOhwOhyIuyDocDociLsg6HA6HIi7IOhwOhyIuyDocDociLsg6HA6HIi7IOhwOhyIuyDocDociLsg6HA6HIi7IOhwOhyIuyDocDociLsg6HA6HIk3qHozXBKyJlIFoK83iAY3AZKTcxTCkHMQQTPSpeptsI3WwOmuci2k1DqVd44xXnkX6cQ1gA2BdYElgHqA/8BPwNVLb6zXg2TxUM42NlNfp2I+r0N6Pk5i2HwfPkCVoRONaTKtxTto1jkCex9cQjTNcPxZL5Y4a16a9/ExHjW39+D9bVWm7Q7OQ4tLAUcA+wAIxrnwduBG4FRNp1Y23g/GWoV3j/DGuHIZovG0G0Lgc7Rp/FePKN4CrgDsw0WSFltnDeMsjGvcmnsbXgBsQjT9rNM0axlsBOBrYC5g3xpWDkXv1Dkw0UaNptiiWyr9GNO5JPI3/Q/rx72HgT7LdLvtB1ni/Ai5BHso00xGjgL8AV2OifBUiM958SKnsPUmn8UfgLODaHGqcHymVnbYfvwYuBK7BRC02mmYN4y0AXA78gXQafwBOx0TXW2mXTYy3IHAFsBtQSGHpO0TjjVbaZZFiqbwQcCWwC+k0fgucFgb+zVYaVsVukDXeTsB1wIL2jPIccHBuPrGNtwtwDfFG573xH+Cg3Hx+Gu9AJMDGGQ30SNi64H2bTr70tDDw8zEdZLzdkZH2fBat/hM4JDclwo23FxJ8rPUj8DRwKCYabtFmYoql8j7IS2Qei2b/ARwaBr6VMuh2gqyUGL4WODS9sS4ZD+yHiR5Sst87onEQcLCSh3HAPpjoUSX7vSPzrtcCh9g0W6kwZuPJl40dXllgTuCAMPAftmk/FjJ/fiOwn5KHMcCemOhJJfu9IxpvQaY/NBgD7I6JnlKy3yvVedfbgD2UXIwG/hAG/j/TGkqfXSAP5j3oBViA2YH7MN4+ij66RzTej16ABZgDeLA6+sge4/UHHsJygAUYOHW3t4ZXFlgUWUS6r1gqb2vbR03Ios9D6AVYEI2PYLxdFX10j/TjI+gFWBCNj2G8nRV9dEuxVO4PPIZegAWYG3iiWCrvmNZQuiBrvAJwM5DFDdUI3Irxfp+Br3ZE4+1AFn4bgdswXuqOTcAgYHvbRr9snf+/V7X8fsMO/6kJuL9YKq9t21ePGK8BuBMYkIG3ZuAujJfty0S+tu4GtsvAWzNwN8bbKgNfv1AslRuB+4CtM3DXD7i3WCpvkcZI2pHsH4F9U9qIQ1sQWjJDn22rlVnRBNyO8RbPzKPxjgP2t222pVL4fqfJ5yzXxT/NDpSLpfLStn32wJ/IZjDQRlugXSRDnyeRzWCgjX5IoF0oQ5+nADtk6K8/cHexVE68BpM8yBqvCPwt8fXJmRO4qTrC1EVStAJ1P9MzFzJvqI/x1kQWuaxTmnro56PwukuJWgAZJWTRjysA56r7mZ65gWwyDoz3G+DsTHxNy7zIYrc6xVJ5ZSTjKGvmQ9YqEpEsyLZPE8yR1HFKNgcOV/XQrnE2VT/dsxXG05znbuMqFDalvN1afPH+lk3X6eVnawMH2PY9DTJNcAswi6qf7vExnvWvhGmQaYJbkFFXPdgR42nOAbdNE9yKjJ7rwc7FUjnRHHDSkey2wO8SXmuLczCe5oMzANhY0X4t/LW6kKGDzG+vZ9vslErj8D0mn7l6jT8/p7pSrMXOKGiMyXnVFX8tdgV6e6Fpc351gViL3ZEdXPXkgmqwj0XSIPunhNfZZH5054PzoHFBdFeJj7VtsFKh9bApJ4waz6xz1njJYsiGBy3y0I+LIhsetMiDxiXQnfPOg8YiCea84wdZ4y2FfK7nAZ2UKpmLrfdIvQ0tjUsAm9g2+0Lrqi/8p3WN1WJedoDtdgBgvBWB9VVsx0erH1cGfqtiOz4qGoul8mrI1FIeiJ3imGQkuzPptq7Z5LcYbzEFu7so2EzK+kor1HthuR9/rvT7+NApJyb5NF+nWCprTP3UJ1e1azatbse2TZ40bobxbO68aiNPGrcslspenAuSBNm8vFHa0GhP3jRqzEVtZtNYpcLkPSafUZlMc5I55P7IyV62yVM/FpATvmyTJ40N9A2Na8S9IC71nnzujEZ7+oJGqw/DAy0bv/pmZdnlU5jY1FZbOtAX+tFpzJ5Y7YkXZI03J7BsrGv0sfvmNN7cQJZJ8rVgW+N8xDvSr0eiymxvnTz1sI1Smvk/K41pQ07YWtSqzfTY7seFgSw3AtSCVY3FUnkx4h0jmgWxNMYdyS5AfuZj27B54hfYPV3LFrY1Wps3q1QYt9Pkc+au0JB292Csea4asP03s4HtNjmN9SFWm+LmtdUrEbgnbOeR1iuhuydst8laPuMIfvXeNg1DpgCpjr6bQtOX4FtqFZDPe9X24l5fuFdneI1xH7apMX+fBVNybs8GttsUO6G6OxYt/LjOKc332jDVLMf0WiOP96rtChF94V6d4TXG/cSLYv4+C8ZYttcXNP5k2Z4NfrRsry/0o9NYH2JpjLnwFX2HlGjIE29atWaikUg5kTxhV6MUkstbTap3Ldv7AvsPfFps9+PnyGHvecK2xk+BvNXBi6UxyWLF0ATXaKLRnplbo9QTsx3U0mK3PaJxmFWb6bHdj61I4dE8YVVjGPgtSFHOPBFLowuy2dlMg0Z7/qVgMw3/U7DZF/rRacwe9SD7jwTXaPEF8J6C3Txp/Bz4UMHuAwo2k/IRJnpfwW6e+vFDTPS5gt08aXxPqYhknjS+HbfAYoIDYqJXgQ9iX6fDbSqltE30EvCxdbvJuFVJ42tIAM8DdyjZ/TfyIs4DtyjZfZaU6XMW0dL4DFJaPg/ELheeNIH8ioTX2WQyKU4rr4ErFW3XyiR0T53Pg8ZxwA0qluXllAeNE9CqdCHzslep2I7HOOAmDcPVeVmr+X0JGUuCF0nSIHsr9R8hXI+JvlG0fzP1HyFcV83o0LNf/xHCQEykmbFyPTBS0X4tXI2JbKeodWQQ9c/6uRITaaYGXgN8r2i/Fi4PAz92SlnC8jPRz8jZkfY/Y2sjBE5V9WCi8eiWOe+Nz4DTVT1IP56s6qNnvkGpvtgvmGgs2qWKeuYjtOtSmShCiprWi/eBczQdhIE/GjhS00cvvEvCOnEpCilG/0LeoFlTAQ7CRPr5gSZ6mqwKGk5Lm8bx6p5MdBdwm7qf6akAB2TUj48jZd2zphU4sPoy08VEjwB3qfuZnhakHydqOwoD/wHAyvbCmEwFDggDf1KSi9Me6vFn4J2UNuJyESb6T4b+TkQng6EnAkz0fIb+jiT7fjy7+hLLiuPQydLoib9iolcy9HcM2S/YGkw0OEN/RyEbFLLkrDDwX0t6cbogK6OQLYFPUtmpnZuAUka+BBONQTRm1bE3YKLTMvIlmGgCcjpLVtkG16L8eTkdJhqN9GNWawlXYSKTkS/BRKMQjRppVF1xOSbKtNR6GPg/AlsAX2XkcmAY+BekMZB2JEt18Wkj4O3UtnrmKuBQlXSm3jDRCKRyrfYuqcup1/yh5DdujP6I9mxMdGSd+nE4cq9qj2gvwkTHKPvoGhN9gWjUHtEGmOh4ZR9dEgZ+iGjUHtydFwb+SWmNFCoVS/e68eYALgSOwO6Zs6OAYzHRnRZtJkM0XoQEQpsafwSOxkT3WLSZDOPNBVwGHGjZ8ljgGExUj/nfaRGNA0lQFK8XvgeOxET13+hhPA+4BDjIsuXvgCMw0UOW7camWCrPDVyK/UKc3wKHh4H/qA1j9oJsG8b7HfJZv5QFa48BhyunasXHeJsjGpe0YO1h5KatdwrOtBhvOyTFa3EL1h5HAmy90/6mxXhbIzm6NjTeDxyFieqdZjQtxtsW0WijSsS9yGAgVwcoFUtlH0nVs1Fw9G7g6DDwR1mwBWgEWQDj9Qf2BY4FVol59VTkobykuvMqnxhvFto1rhzz6qnIC+QSTPSy7aZZQ/rxQGRB5Tcxr54M3AdcgYmG2G6aNYw3K7A/0o8rxrx6CvAI0o//tdwyexhvNto1/jrm1ZMRjQMzXuCKRbFUno32e3WFmJdPRgY7A8PAt36v6gTZjhhvLWBrpPjY2sASnX4xBZnrHAoMAR7J3aiuN4y3Nu0a16J7ja/RrlFzk4F9jLcmsDmy6LA2MG+nX0xFFlz+hxw+88QM2I/rAFvRfq92Ljc/BVl7aLtXH87bqK5XjPdb2jWuxfQaJyPz8m336sPKGymsUyyV10UWANdGNHYexU+mvR8HAw/bHLl2Rj/IdkbmiuZCqjJMAn7ERInyz3JL39A4O1KMsRHRl7ezW9MjRTXnRPpxIqLTdnWD+tIHNBZL5XmAOeigMQz8zDRmH2QdDoejD5E+hcvhcDgc3eKCrMPhcCjigqzD4XAo4oKsw+FwKOKCrMPhcCjigqzD4XAo4oKsw+FwKOKCrMPhcCjigqzD4XAo4oKsw+FwKOKCrMPhcCjigqzD4XAo4oKsw+FwKOKCrMPhcCjigqzD4XAo4oKsw+FwKNKUqTfjLYTU/PJorxowEngLE03ItC1aiMZVmbYywkjgTUz0cz2bZg3jLYz0Y0eNI5B+nFk0LoLUbmvTOJH2fpxYz6Y5aqdYKi+K9GPH6g8jgLfCwM+kH3UrIxhvHmBPYBuk1k531SRbgA+QmkL3A09joha9hlnEePMiGttqfPWk8X3aNT4zA2n8FdNqXLibX06lXeN9wD8xUWsmbUyL8eYD9qK9/tVC3fyyTeNgROOzM4zGPkCxVJ4f2Bup8bUWsGA3P52K1N1r68d/h4Gv0o9a1WqXA05BxM6SwMJXwBXANZhovM2mWcN4y9OusX8CC18iGq/N7SjeeL8GTkaCTxKNX9CuMZ8jXOOtiPTjnkC/BBZC4HJgUG419gGKpfJKQAnYHWhOYOIz4DLg+jDwrdbjsxtkjdcInAScTbKHsjOfAQdhouct2LKDaDwF+AvJHsrOfIJofNGCLTsYrwm5Yc/EjsaPgQNzVf5cNJ4GnEGyh7IzHwEHYKJXLdhy1EixVG5G7tNTsTP9+R5wgM3S4PaCrPFWAG4H1rFj8BcqwNXAyXUfKcio5zbg/yxbbgWuBEp1n+8z3m+QflzLsuVWZMR3at0r9xpvJUTjmpYttwKXAqfNbBVf80ixVF4N6cdVLZtuAS4CzgoDf0paY3aCrPHWBZ4E5klvrFteALavW+lp460PlIG5Fb08B+yAicYq+uge420IPIEsTGrxb2BHTDRO0Uf3GG9j4HFkQUuLZ4GdcjvVNRNQLJU3Bx5BSn1r8SSwaxj4qQZ36YOs8dZBbqo50xmqif8CW2R+88pL5Flg9gy8vQJsmfk8rbxEniEbjS8CW2f+ZWK8jYCngVkz8PY8sE3dv0xmQoql8mbIgCfJek9c/glsn2aeNl2erPGWAP5BNgEWYF3g3ox8CcZbCnmjZRF8ANYH7snIl2C8ZZCbNiuNGwF3ZeRLkMXYx8kmwAJsAvw9I199hmKpvCLwKNkEWJAshZvTGEi7GeEmYN6UNuLiY7xDM/FkvALyB9acBumK7THeQZl4atc4dyb+2tkJ4+2fiSfjNQC3oDsN0hW7YLx9MvY501IslRuRNRHNKYKu2KtYKu+W9OLkQdZ4hwNbJL4+HQOro2htjgQ2zcBPV1yC8RbLwM8xwMYZ+OmKyzDeohn4OQ7YIAM/XXFFdfOGIz1/xv6ic61cXc3BjU2yIGu8BZDVt3oxJ3CVqgfZuXWhqo+e8ZCMAz1kV9MFqj56Zm4k40APeVGdp+qjZ+ZB8i8dKSiWyksBpo5NmB8YmOTCpCPZo8luHrY7tq+m4mhxDNnNUXbHjtW0MS2OBWZTtF8LO1c3dmhxPNnNw3bHrhhv2Tq3YUbnT9jJvU/DnsVSOfYXdPwgK0nch8W+ToejVawar5l8aCwAR6lYNl4/4BAV2/EoINMy9jHeLMDBKrbj0QAcUe9GzKgUS+XZgAPq3Q5ks8Mf416UZCS7Dd3vB86aPTCextttO2A+BbtJ2LMa9G0zAPiVgt0k7F19edtmB7Jf0OuOvau7BR3x2Zn6fzm3sV+xVC7EuSBJkP1dgmu0mBtYTcFunjTOi/0dLZAvjfMBGlM/edK4IKA59TMzk6d+XBRYLs4FSYKs7e2WadFoj9OYPU6jozvy9neL1Z54QVZyKteIdY0+djtAcipndo2NwOpWbaZnbavWZIpF4wsgDXkLFrmnWCrPgs5XThoUg6wMlTX3fCfB9ifY4tQ/q6AztjUWqf+Ke2dsa1yK+q9Gd8ZNF8RnWbIuLtA7sfoxbpDNW/AB+21yGuuD0+joijz+zWK1KW6QzWNNMNttchrrQ1/Q6LIL4pPHfozVprgC8niikO02OY31oS9odJUT4pPHfozVprhBdgSQ+hBby3xh2d7XyKG9ecK2xuHIAdN5QkOjYgG7RNjW2BfI498sVptiZhdEk5DiY3liqFVrcsbpe1Ztpse2xvHAh1Ztpse2xjFI2Zs8YVdjHyAM/FFIHbU8Easfk8x35O1G0WiP05g9TqOjO/L2d1MPsv9NcI0WU9HpgL6gMU8F/yYDwxTs5qkfJwFv1LsRMyh56sefgbfiXJAkyD6E3DB54GlMNErB7gPIg58HnsREkYLd+8nP/PoTSnXN7iM/8+uPuZpfibmX/KwhPBi3FE2CU7iiUUgQygPXq1g10Y/Awyq246Ol8XukjEce0NL4DfCYiu346GjsA4SBPxx4qt7tqBK7H5PmoCU6vNYyHyI1m7S4WNF2rbyH1BfTIg8a38ZETyvaz4PGNzHRs/VuxAxOHvpxcBj4L8a9KGFlhOh14MZE19rjGEykl6JjoteAW9Xs904FfY3/A+5Qs987olETE71C1kUbp0VfYx8gDPz/AA/WsQktyCH3sUmzm+JEJBexHlyPif6ZgZ/jkbzZenAdJvp3Bn6OA0Zm4KcrrsZEz2fg5xjgmwz8dMUVmCj26MfRJUcCP9TJ98Aw8P+X5MIUhRSjMcip81knfH8BnJSJJ1lwyqYy7rR8DpyciScT/UR9qkB8CpQy8STrCLFPtLfAx8BpdfA7UxIG/ndoVdHomfeBs5JenG5fsIwmT0xlIx6jgAFKK9FdY6J/kFXAE35ENI7LzKOJngBOzcyfjEYGZLrabqJHgTMy8wffA9tjogkZ+pzpCQP/fuCvGbr8Btg+bkZBR9IfvmCiS8nmAf0R2BoTvZOBr2kx0UVk84D+AGyFibLfcWaiAPhLBp6+B7bERB9k4GtaTHQe2Tyg3wFbYKK87aqbKQgD/yyyqSQ9Atg8DPxP0xixc8KNPKCHAFpv7Q+ATaqLUfVBHtDD0Dvk4z1gY0ykkZRfGyY6Byn4p6XxXWAjTPSGkv3eMdFZSHFKrYNH3gY2xESxEtYd8QgDv4SsmWjl7L8BbBQGfuoBj71jxEx0E3ISvc1J/hbgb8AamKj+ZyaY6AakptjLFq22AAGwJiZ636LdZJjoOqRqgs0dYS3A+YjG+o/uTHQNUv3C5k6iqcC5wNqYKG9nJsyUhIF/OVKlwObgawpggHXCwP/MhsFCpWJ53UpK1ByBTCEslsLSC8ApmChPW+oEKVFzJLJws2gKS88hGgfbaJZVROPRwCnAIiks/QfROMRKu2wiZXiOQebcF05h6V+Ixrztse8TFEvlJmRUexLpKmk/A5wSBv4bFpr1C/aDbBtSY2ln4ECk2mS/Gq76EdnueVNdpwZqRTTugmjclNo0/oBovLGuUwO1Ihp3pV1jLeXJv6dd4+t6jbOE8frRrnETatP4He0a39BrnKNWiqVyf+APwAHARtTWj98i23ZvDAP/bY126QXZjshNvDIytF8VqRPWhMynfIMcDjIUE32u3xgletc4FNEY1quJqRGNqyAaV2FajSNp15jHM0Brw3j9mVbjnIjGiYjGtnt1xtXYB6gG3FWRflyZaftxBNV7tbplV5VsgqzD4XD0UfJYP8fhcDhmGlyQdTgcDkVckHU4HA5FXJB1OBwORVyQdTgcDkVckHU4HA5FXJB1OBwORVyQdTgcDkVckHU4HA5FXJB1OBwORVyQdTgcDkVckHU4HA5FXJB1OBwORVyQdTgcDkVckHU4HA5FXJB1OBwORVyQdTgcDkWa1D1IQb62sixtJT08oBGYjJT0eB0pBzEEE32t3ibbTK+xrfxMR41StkQ0jqhTS5MjRQc792NHjSOYVuPIOrU0OaKxrfRMW9mSNo1tJXZmNo1tJXbaNHbux2/q1NJUFEvlRYEtqv9bEpgDiXcTgDHAYOCfwKth4E/WbItmIcXFkKq1+xOvouurwI3AnZhIq6a6HYy3OKJxP+JpfAXReNcMoHEJpDLvvsSrWvsycANwNyZSvYlTY7wladcYp2rtS4jGe2YAjUXaNS5U41UV4EVE472YaIpO4+xQLJWXRzRuBaxY42XjkcrYDwG3hYFvXaNGSXAPuBA4iNqqRXbHt8DpmOgmK+2yifHmBi5CqmKm+Rr4BjgNE91ioVV2Md48iMb9SadxJHAqJrrNSrtsYrx5gYuRwJNG4wighInusNIumxjvV8BAYB9ktJqUr5Gy53daaZdFiqXyQsC5yPOYRuPnwJlh4FvVaDfIGm8b5K23mD2jPAUciom+smgzOcbbDrieeCPX3ngSOCw3UyXGGwAMIt7ItTeeAA7PzVSJ8XZANNY6qquFxxGN+ZhGMN5OwHXAghatPopo/NaizcQUS+VDgb8Bc1s0+yxweBj4n9kwZifIypzkpcCx6Y11yRhgL0xUVrLfO6LxcuBoJQ+jgT0x0VNK9ntHNF6FTIFo8BOwByZ6Rsl+78ic5NXA4UoefgL+gImeVbLfO8ZrAq4FDlHyMArYDRP9W8l+TRRL5auR6QENRgFbhIH/elpD6YOs3LS3AXunbUwvTAH2wUT3KfuZHtH4d2APZU9TkJfJA8p+pkcezDuBPyh7mowE2oeV/UyPaLwX2FnZ0yQk0D6m7Gd6jNcM3AfspOxpErArJnpC2U+XKAfYNqwEWhspXIPQD7Ag87t3Yjw/A1/tGK+ALFJpB1gQjXdhvG0z8NWOaLwZ/QAL0A+4B+NtlYGvdkTjbegHWID+wH0Yb4sMfLUjXyJ3oB9gQTQ+gPF+l4GvacgowALMCzxbLJXXSGMkXZA13iHAwalsxENGW5K5kBWHIxPqWdEWaG3O+fZG26pzVrQF2jgr+Wk5BtgrQ3/9EY0250N743hg9wz99QfuxXjzZ+WwWCr/lWwCbBttgXbJpAaSB1lJXxqY+PrkeMjimj6S9nJRJr6mZW5kcU0f4y2NZINkzTzIV5A+xlsWuCATX9PyK2ThSR/jLY+ssGfN/MA1WTiqjihPzcJXJ+YlhcY0I9kbkUTterANxstiBH0TksRcD7bDeAeoepBP6JuA2VX9dM/2GE93BN0+FTKbqp/u2Qnj6Y6gZZrgZmBWVT/dsyvGy2KqaRDpUrTSsF2xVN4lyYXJgqzMp2U7pzY952K8/mrWJVVrMzX7tXEexuunaH8AsKmi/Vo4v7pYo8WOwEaK9mvhguqimxa/BzZQtF8LQXWBWIViqbwz8H9a9mvkvGKpHFtj0pHsCQmvs8lC6M6x/UnRdq0sgu6CWx40Lobuglse7tUlgF0V7edB41JIsNfiL4q2a2UFEjyP8YOsbLPcMvZ1OujkARpvKWBzFdvx0dK4DJD5ynA36Ez9GG8F6j+KbUNL44rA+iq246NyrxZL5aWQ80DywI5xL0gykt0l4XUarK+0Cr8LUFCwm4SNMJ7NXUltaI6s4rKp0gp1njRuVt3Ga5vdFGwmZcvqlnPbZJsK1zObFUvlWPEvSbCs97xIZ9ZWsOk0ZksBORHKNnnS2EDf0Limgt28fDmDZIzEyptNEmQ1bpQ0aLTHacwepzE/NtNgtT3VUWO9F6A7EyvoxwuyxpsDWC7WNfrYfXPKKWJLW7WZHtsa5wWKVm2mx26wkOmHLDet1ILtflyQeEczZoHtkWwRGT3miVhflnFHsguSn7nKNmzPV/YFjRpzvGlxGuOTtwAL9jXWKxe/J2LlzsfN3dPM2UyK7VxZvdzb5NhuU1/oxzxqnMWyvb5wr1rbRDKg4dWhvyqMSX0o9xSavoXaj1CJG2RbYv4+C6bm3J4NbLepL/RjHjXaPnW/L9yrVipOrN/wzrtXNl+5ZqFg5St1PFxS84/jThdEMX+fBWMs23Ma64PTGJ++oHFcehOVyqDmS7EUYEFK1tRMzIWv6Fvgu1jX6POWVWtycv+PVm2mx7bGL5FDwvPEm5bthcBYyzbTYrcf4TNiPvAZYFvjcFJ+ARzV+OgrcxZ+XslSe0D+7jWTJIVrWIJrNBmqYNNpzB67Gk1UQaog5wnbGluBN6zaTI9VjWHgj0eKqyZidn4ed0LT/ctYbBJIlduaSRJkNR74NGi0x2nMHqcxPzbToNGexKV8Lm++emhjoWIz42EK8HycC5IE2frVoJqer4B3FezmSeOXwPsKdvOk8TPgYwW7edL4CSb6VMFunjR+iIlCBbuxRo5tFAsjh2/eMGxdy215FRMpzskCmOgldB6IJNxe/WSyzQvEnHdR5Lbqp69t/oPMW+YBLY3PInN6eeBWJbvPICXJ88CtSnaHIAUqY3Fb84VfFwrWU8piFwFNetDLlQmvs8kUtE5klwf+KhXb8ZiMVB21j2i8WsV2PCahVSFBXsB50DgRrUoXJmohHxonoFSxJAz8FqRSdM1s1TDk9SUbvrM9ih1Hgn5MGmRvQj7V68nNmOhrRfs3UP8Rwo2YaKSi/euBbxTt19YGyVrR4jrqnxFzHSb6XtH+tYCm/Vq4BhNpZuVcQo1ZPw20tlzefLVGtY/LkvRjwsoI0QTg0ETX2mE4cLKqBxONQ4oo1osvgJKqBxONAf6o6qNnQuA0VQ8mioAjVH30zGfAGaoeTPQTcLSqj575GDhL00EY+GOp8Xk4pemeV2YtTF7echO+AIIkF6YopBg9hdQVqgeHVAOELlJT/nZ1P11zCCbSz/M00aPAnep+pqcCHFR9meliooeAe9T9TE+bRv1cVhPdBzyg7md6WoEDMdHP2o7CwL8ReK6n38zFuOjQxvJvFNwfkbQf0x6+fQLwYUobcbkcE8WefE7BcWS/0HcJJkqctpKAY4FPMvQHcDEm+k+G/o4m+8XMCzFRrHSflBxJ9ouZ52OilzP0tzvwXnf/OKj5sjcbChXbp3adjIn+kfTidEFWPsW2ILuOvZOs61KZaDSi8cuMPN4OnJSRL8FEo5AzMrNaib8FOCUjX4LMF24JaM7jd+RGTJRt+WqZL9wC0JzH78ggTHRmRr4ACAP/O6Rs0nSBdsXCF5+u2/Ce7VI8J2Oii9IYSF9GxkRfIXWUPkhtq2duBPZTSvXpGdmGuhHwkbKnQcinVz00hohG7RHtNcDBddL4GaJRI1+1I1cChyn76BrJxd0I+FzZ0+XUaa67u0B7W78LfyoUYh961ROpAyxAoVKxdK/LYdeXAAfZMfgLEXAiJrrJst34SP2iS4EDLFuOgOMx0a2W7cZHNF4O7GfZ8mjgOExUrznudow3D3AFsI9lyz8Bx2Kiv1u2Gx85mP1K7Fd0HgUcg4nusmw3NsVSeQHgDmCrXRufH3Jx8yBbpXjGASdgIispafaCbBvG2wZJf7JxKv3TyAJQvdPFpsV4PpL+tIgFa/8ADlVOR4uP8QYgGm0cDF0GDqsevpMfjLcD8vVgY9vl48Dhyil38THe75EUrwUtWHsE+KNyyl1siqXyvu/2P/CY2QuTbATZR5GXiLWpM/tBFsB4syEj2mOJX66mFdlVcQkmSrSdLhOMNztS5vkYYNmYV7ciL5CBmOhftptmDSk31KYx7iEbrciWz4GY6N+2m2YN0XgIcq8uFfPqVuBJ5F7NchEvHsabE0m5PIb4ZYdaEI0DM17Ei4fEnGOQfow7+GkB/g38BRMlPoymO3SCbEeMtyGwNVLDaS1ggU6/aEXmOoch2+cetPkWyQTjbQRsRe8ah9KuMV+j854wXgGZ5+uosXMJ71Yk06SjxnyNzntCNG7MtBrn6/SrVmTtoeO9OqNp3ARZAOxNY8d+zNcXSG8YbyVE45ZIn3ZVLuYj5EyEZ4H/VBfxVdAPsp2R4m9zIVUZJgHfZZIrmSV9U+O3meSDZonxFgLmRDRORPrRaZyRMF4TUohxDqAZOX93bDVrKBOyD7IOh8PRh0ifwuVwOByObnFB1uFwOBRxQdbhcDgUcUHW4XA4FHFB1uFwOBRxQdbhcDgUcUHW4XA4FHFB1uFwOBRxQdbhcDgUcUHW4XA4FPl/+i4umLeiHwgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 35 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depict_som(df,8,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08520e40-33f0-4751-b14f-188922b906e0",
   "metadata": {},
   "source": [
    "+ Agonists are shown as orange and Antagonists are shown as blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "73f3f68c-73dc-4a5c-8279-753c4d58a4d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_cell = df.query(\"X==3 and Y==3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0827eb5c-b3fa-4b5b-a1dc-55863f4d94ff",
   "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>label</th>\n",
       "      <th>HBD</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OC1=C(C2=CC=C([N+]([O-])=O)C=C2)C=C(CC3=C(C)C=...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CC1=CC(OCC(O)=O)=CC(C)=C1CC2=CC(C(C)C)=C(O)C(C...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>OC1=C(C2=CC=C(N(=O)=O)C=C2)C=C(CC3=C(C)C=C(OCC...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>OC1=C(C#CC2=CC=C(N(=O)=O)C=C2)C=C(CC3=C(C)C=C(...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>CC1=CC(OCC(O)=O)=CC(C)=C1CC2=CC(C3=CC=C(N(=O)=...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>CC1=CC(OCC(O)=O)=CC(C)=C1CC2=CC(C#CC3=CC=C(N(=...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                smiles label  HBD  X  Y\n",
       "1    OC1=C(C(C)C)C=C(CC2=C(C)C=C(OCC(O)=O)C=C2C)C=C...     0    2  3  3\n",
       "9    OC1=C(C2=CC=C([N+]([O-])=O)C=C2)C=C(CC3=C(C)C=...     0    2  3  3\n",
       "27   CC1=CC(OCC(O)=O)=CC(C)=C1CC2=CC(C(C)C)=C(O)C(C...     0    2  3  3\n",
       "30   OC1=C(C2=CC=C(N(=O)=O)C=C2)C=C(CC3=C(C)C=C(OCC...     0    2  3  3\n",
       "31   OC1=C(C#CC2=CC=C(N(=O)=O)C=C2)C=C(CC3=C(C)C=C(...     0    2  3  3\n",
       "138  CC1=CC(OCC(O)=O)=CC(C)=C1CC2=CC(C3=CC=C(N(=O)=...     1    2  3  3\n",
       "139  CC1=CC(OCC(O)=O)=CC(C)=C1CC2=CC(C#CC3=CC=C(N(=...     1    2  3  3"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7fbd881b-0629-44a1-8f55-ed6320eebb9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolsToGridImage([Chem.MolFromSmiles(x) for x in my_cell.smiles],molsPerRow=4,legends=list(my_cell.label))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b269a600-8f2c-4c4b-a3dd-943d4f738859",
   "metadata": {},
   "source": [
    "+ We will not use SOM for splitting the data. We will adopt similar way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "184356b9-475b-4a39-9fb9-31328860649b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1    2    3    4    5    6    7    8    9    ...  103  104  105  106  \\\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  1.0  1.0  1.0   \n",
       "1  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  1.0  1.0  1.0   \n",
       "2  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  1.0  1.0  0.0  1.0   \n",
       "3  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  1.0  1.0  1.0  1.0   \n",
       "4  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  1.0  1.0  1.0  1.0   \n",
       "\n",
       "   107  108  109  110  111  112  \n",
       "0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "1  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "2  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "3  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "4  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "\n",
       "[5 rows x 113 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maccs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "86bd1449-ca7d-4f99-bc1b-97ac6061c49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "maccs[\"label\"] = df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "93c7d1cb-a256-4cbe-8653-43791a2b18b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "maccs.to_csv(\"maccs.csv\",header=True,index=False,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d285c17f-d86c-47f7-a893-7789940b8ac6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1c232c7-8641-4ad6-a2ba-ccb8f7787624",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8110cf3d-2aa6-45cb-b9e1-0513a7a8e0e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1221439c-daa2-4a6a-bf89-f158ed40e012",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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