Run ChemAxon KNIME Nodes in Jupyter.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References:\n",
    "\n",
    "1. Download the Run Jupyter in KNIME and Run KNIME in Jupyter workflows from the KNIME Hub here(url: https://hub.knime.com/mpattadkal/spaces/Public/latest/Jupyter%20Webinar/)\n",
    "\n",
    "2. Read another blog post on KNIME and Jupyter by Greg Landrum\n",
    "https://www.knime.com/blog/knime-and-jupyter\n",
    "\n",
    "3. Watch a recording of our webinar Enhancing Jupyter Notebook with visual workflows Webinar\n",
    "https://www.youtube.com/watch?v=1Rr8Q27k7cQ&t=1161s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import knime\n",
    "#pip install knime "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the Workspace and select the Workflows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Provide knime.exe path for execution of workflow\n",
    "knime.executable_path = \"C:\\Program Files\\KNIME\\knime.exe\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CXCALC_PATH'] = 'C:\\Program Files\\ChemAxon\\MarvinSuite\\bin\\cxcalc.exe'\n",
    "os.environ['mview'] = 'C:\\Program Files\\ChemAxon\\MarvinSuite\\MarvinView.exe'\n",
    "os.environ['STANDARDIZE_PATH'] = 'C:\\Program Files\\ChemAxon\\JChemSuite\\bin\\standardizer.exe'\n",
    "import win32com.shell.shell as shell\n",
    "ASADMIN = 'asadmin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up the workspace and visualize the Deployment workflow\n",
    "workspace = \"C:\\\\Users\\sunee\\knime-workspace\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select the Deployment workflow for prediction on external data\n",
    "workflow_deployment = \"ChemAxon Example Workflows\\\\KNIME_project8\"\n",
    "\n",
    "knime.Workflow(workflow_path=workflow_deployment,workspace_path=workspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<knime.LocalWorkflow at 0x16f1f67abe0>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select the Deployment workflow for prediction on external data\n",
    "\n",
    "workflow = \"ChemAxon Example Workflows\\KNIME_project8\"\n",
    "knime.Workflow(workflow_path=workflow_deployment,workspace_path=workspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "with knime.Workflow(workflow_path=workflow_deployment,workspace_path=workspace) as wf:\n",
    "    wf.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets review the outputfile using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Molecule</th>\n",
       "      <th>Mass</th>\n",
       "      <th>Grouped dot-disconnected formula</th>\n",
       "      <th>Mass spectrum</th>\n",
       "      <th>Donor sites</th>\n",
       "      <th>Donor count</th>\n",
       "      <th>Acceptor sites</th>\n",
       "      <th>Acceptor count</th>\n",
       "      <th>Molecule (HBDA)</th>\n",
       "      <th>Molecule (logP)</th>\n",
       "      <th>...</th>\n",
       "      <th>Dreiding energy (kcal/mol)</th>\n",
       "      <th>MMFF94 energy (kcal/mol)</th>\n",
       "      <th>Minimal projection area</th>\n",
       "      <th>Maximal projection area</th>\n",
       "      <th>Minimal projection radius</th>\n",
       "      <th>Maximal projection radius</th>\n",
       "      <th>Length perpendicular to the min area</th>\n",
       "      <th>Length perpendicular to the max area</th>\n",
       "      <th>van der Waals volume</th>\n",
       "      <th>Molecule (Geometry)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...</td>\n",
       "      <td>354.454</td>\n",
       "      <td>C20H26N4O2</td>\n",
       "      <td>354: 1.00 355: 0.23 356: 0.03</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...</td>\n",
       "      <td>C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...</td>\n",
       "      <td>...</td>\n",
       "      <td>101.54</td>\n",
       "      <td>76.15</td>\n",
       "      <td>61.157349</td>\n",
       "      <td>94.836021</td>\n",
       "      <td>5.891842</td>\n",
       "      <td>6.294975</td>\n",
       "      <td>11.727164</td>\n",
       "      <td>9.179482</td>\n",
       "      <td>335.798263</td>\n",
       "      <td>C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...</td>\n",
       "      <td>354.454</td>\n",
       "      <td>C20H26N4O2</td>\n",
       "      <td>354: 1.00 355: 0.23 356: 0.03</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...</td>\n",
       "      <td>C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...</td>\n",
       "      <td>...</td>\n",
       "      <td>88.86</td>\n",
       "      <td>54.00</td>\n",
       "      <td>50.927742</td>\n",
       "      <td>110.511226</td>\n",
       "      <td>5.505449</td>\n",
       "      <td>7.231766</td>\n",
       "      <td>14.088040</td>\n",
       "      <td>6.850647</td>\n",
       "      <td>335.906105</td>\n",
       "      <td>C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...</td>\n",
       "      <td>368.481</td>\n",
       "      <td>C21H28N4O2</td>\n",
       "      <td>368: 1.00 369: 0.25 370: 0.03</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...</td>\n",
       "      <td>CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...</td>\n",
       "      <td>...</td>\n",
       "      <td>100.76</td>\n",
       "      <td>75.36</td>\n",
       "      <td>65.252304</td>\n",
       "      <td>98.431582</td>\n",
       "      <td>5.901988</td>\n",
       "      <td>6.439623</td>\n",
       "      <td>12.258243</td>\n",
       "      <td>8.525495</td>\n",
       "      <td>352.631170</td>\n",
       "      <td>CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...</td>\n",
       "      <td>368.481</td>\n",
       "      <td>C21H28N4O2</td>\n",
       "      <td>368: 1.00 369: 0.25 370: 0.03</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...</td>\n",
       "      <td>CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...</td>\n",
       "      <td>...</td>\n",
       "      <td>89.35</td>\n",
       "      <td>51.71</td>\n",
       "      <td>55.032987</td>\n",
       "      <td>114.221564</td>\n",
       "      <td>5.545892</td>\n",
       "      <td>7.244920</td>\n",
       "      <td>14.083390</td>\n",
       "      <td>6.604394</td>\n",
       "      <td>352.853856</td>\n",
       "      <td>CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...</td>\n",
       "      <td>382.508</td>\n",
       "      <td>C22H30N4O2</td>\n",
       "      <td>382: 1.00 383: 0.26 384: 0.04</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...</td>\n",
       "      <td>CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...</td>\n",
       "      <td>...</td>\n",
       "      <td>103.71</td>\n",
       "      <td>74.16</td>\n",
       "      <td>66.615679</td>\n",
       "      <td>104.056359</td>\n",
       "      <td>5.823607</td>\n",
       "      <td>7.036128</td>\n",
       "      <td>11.785796</td>\n",
       "      <td>8.547878</td>\n",
       "      <td>369.720801</td>\n",
       "      <td>CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
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      ],
      "text/plain": [
       "                                            Molecule     Mass  \\\n",
       "0  C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...  354.454   \n",
       "1  C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...  354.454   \n",
       "2  CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...  368.481   \n",
       "3  CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...  368.481   \n",
       "4  CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...  382.508   \n",
       "\n",
       "  Grouped dot-disconnected formula                   Mass spectrum  \\\n",
       "0                       C20H26N4O2  354: 1.00 355: 0.23 356: 0.03    \n",
       "1                       C20H26N4O2  354: 1.00 355: 0.23 356: 0.03    \n",
       "2                       C21H28N4O2  368: 1.00 369: 0.25 370: 0.03    \n",
       "3                       C21H28N4O2  368: 1.00 369: 0.25 370: 0.03    \n",
       "4                       C22H30N4O2  382: 1.00 383: 0.26 384: 0.04    \n",
       "\n",
       "   Donor sites  Donor count  Acceptor sites  Acceptor count  \\\n",
       "0            2            2               6               4   \n",
       "1            2            2               6               4   \n",
       "2            2            2               6               4   \n",
       "3            2            2               6               4   \n",
       "4            2            2               6               4   \n",
       "\n",
       "                                     Molecule (HBDA)  \\\n",
       "0  C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...   \n",
       "1  C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...   \n",
       "2  CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...   \n",
       "3  CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...   \n",
       "4  CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...   \n",
       "\n",
       "                                     Molecule (logP)  ...  \\\n",
       "0  C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...  ...   \n",
       "1  C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...  ...   \n",
       "2  CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...  ...   \n",
       "3  CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...  ...   \n",
       "4  CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...  ...   \n",
       "\n",
       "   Dreiding energy (kcal/mol)  MMFF94 energy (kcal/mol)  \\\n",
       "0                      101.54                     76.15   \n",
       "1                       88.86                     54.00   \n",
       "2                      100.76                     75.36   \n",
       "3                       89.35                     51.71   \n",
       "4                      103.71                     74.16   \n",
       "\n",
       "   Minimal projection area  Maximal projection area  \\\n",
       "0                61.157349                94.836021   \n",
       "1                50.927742               110.511226   \n",
       "2                65.252304                98.431582   \n",
       "3                55.032987               114.221564   \n",
       "4                66.615679               104.056359   \n",
       "\n",
       "   Minimal projection radius Maximal projection radius  \\\n",
       "0                   5.891842                  6.294975   \n",
       "1                   5.505449                  7.231766   \n",
       "2                   5.901988                  6.439623   \n",
       "3                   5.545892                  7.244920   \n",
       "4                   5.823607                  7.036128   \n",
       "\n",
       "  Length perpendicular to the min area  Length perpendicular to the max area  \\\n",
       "0                            11.727164                              9.179482   \n",
       "1                            14.088040                              6.850647   \n",
       "2                            12.258243                              8.525495   \n",
       "3                            14.083390                              6.604394   \n",
       "4                            11.785796                              8.547878   \n",
       "\n",
       "   van der Waals volume                                Molecule (Geometry)  \n",
       "0            335.798263  C[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNCC...  \n",
       "1            335.906105  C[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(C...  \n",
       "2            352.631170  CC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CNC...  \n",
       "3            352.853856  CC[C:5]1=NC2=C(CNC3=CN(CCC3)[C:1](=[O:2])C(C)(...  \n",
       "4            369.720801  CCC[C:5]1=NC2=C(CN([C:1](=[O:2])C(C)(C)C)C3=CN...  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = pd.read_csv(\"test1.csv\")\n",
    "data_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets generate plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Mass', ylabel='Donor count'>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import seaborn\n",
    "import seaborn as sns\n",
    "\n",
    "# Apply the default theme\n",
    "sns.set_theme()\n",
    "\n",
    "# Create a visualization\n",
    "sns.scatterplot(data=data_set, x=\"Mass\", y=\"Donor count\", hue=\"Donor count\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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