FlavorMiner.ipynb

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      "source": [
        "# FlavorMiner: Instructions to Run\n",
        "\n",
        "This script contains the code necessary to run FlavorMiner. In oreder to run this code the user needs to organize the input data in an excel file containing either the PubChem ID or the isomeric smiles of the input compounds.\n",
        "\n",
        "Name the column containing the isomeric smiles \"Isomeric Smiles\". This way there is no need to manipulate the code to run the analyzes."
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        "IMPORTANT: This script requires scikit-learn version 1.2.2. The models trained to perform the respective predictions were trained and saved in joblib files with this version, and a different version with end up with and error at the moment to run the code. Use the code below to install it, if necessary"
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        "!pip install -U scikit-learn==1.2.2"
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      "source": [
        "# 1. Upload the data\n",
        "\n",
        "The first step to use FlavorMiner is to upload the excel file containing the data. To do this in Google Colab go to *Files> Upload>Select the file>Open*.\n",
        "\n",
        "Then click the three dots on the right of the uploaded file and copy the path."
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              "                                       Compound name  Fold Change  ID Level  \\\n",
              "0  Tetrahydroxychal-\\ncone 4’-O-(2’’-\\nO-acetyl-6...         14.1         3   \n",
              "1                                    Tumonoic Acid H         12.2         3   \n",
              "2                                       Acetophenone          6.8         3   \n",
              "3                                Hydroxyacetophenone          6.1         3   \n",
              "4                                    Anhydrofructose          0.2         3   \n",
              "\n",
              "  PubChem ID                                    Isomeric Smiles  \\\n",
              "0  42607557   CC(=O)OC1[C@H]([C@@H](C(O[C@H]1OC2=C(C(=C(C=C2...   \n",
              "1  25058105   CCCCCCCC[C@H](C)C(=O)N1CCC[C@H]1C(=O)O[C@H](C(...   \n",
              "2      7410                                   CC(=O)C1=CC=CC=C1   \n",
              "3      7469                                CC(=O)C1=CC=C(C=C1)O   \n",
              "4   14299935                             C1C(=O)C(C(C(O1)CO)O)O   \n",
              "\n",
              "  Processing stage  \n",
              "0     Fermentation  \n",
              "1     Fermentation  \n",
              "2     Fermentation  \n",
              "3     Fermentation  \n",
              "4     Fermentation  "
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      "source": [
        "import pandas as pd\n",
        "\n",
        "'''URL of the Excel file'''\n",
        "\n",
        "file_url = 'https://github.com/FabioHerrera97/FlavorMiner/raw/main/Data/Cocoa%20metabolomics%20data.xlsx' # Copy here the path of the input file\n",
        "\n",
        "''' Read the Excel file'''\n",
        "\n",
        "data = pd.read_excel(file_url)\n",
        "\n",
        "''' Print the contents of the DataFrame'''\n",
        "\n",
        "data.head()"
      ]
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        "# 2. Matching the input data with the flavor database.\n",
        "\n",
        "IMPORTANT: This match is performed with the isomeric smiles, not the name of the compounds due to the lack of standardization in compounds name.\n",
        "\n",
        "This part of the code takes the provided isomeric smiles and perform a database match to exclude from the prediction those entries with already reported flavor profiles. This avoids adding unnnecessary uncertainty when there is experimental data available."
      ],
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        "''' Read the Excel file'''\n",
        "\n",
        "database = pd.read_excel('https://github.com/FabioHerrera97/FlavorMiner/raw/main/Data/LabelDataBase.xlsx')\n",
        "\n",
        "''' Print the contents of the DataFrame'''\n",
        "\n",
        "database.head()"
      ],
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              "                 Compound Name pubChem ID  flavor profile  \\\n",
              "0           1-Aminopropan-2-ol           4          fishy   \n",
              "1          2-Deoxyhexopyranose          40          sweet   \n",
              "2   3-Methyl-2-oxovaleric acid          47          fruit   \n",
              "3  3-Methyl-2-oxobutanoic acid          49         fruity   \n",
              "4          2-ketoglutaric acid          51       odorless   \n",
              "\n",
              "         Isomeric Smiles    Source        Labels  Bitter  Floral  Fruity  \\\n",
              "0                CC(CN)O  flavorDB    Off_flavor       0       0       0   \n",
              "1    C1C(C(C(OC1O)CO)O)O  flavorDB         Sweet       0       0       0   \n",
              "2      CCC(C)C(=O)C(=O)O  flavorDB        Fruity       0       0       1   \n",
              "3       CC(C)C(=O)C(=O)O  flavorDB        Fruity       0       0       1   \n",
              "4  C(CC(=O)O)C(=O)C(=O)O  flavorDB  Unclassified       0       0       0   \n",
              "\n",
              "   Off_flavor  Nutty  Sour  Sweet  Unclassified  \n",
              "0           1      0     0      0             0  \n",
              "1           0      0     0      1             0  \n",
              "2           0      0     0      0             0  \n",
              "3           0      0     0      0             0  \n",
              "4           0      0     0      0             1  "
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              "      <th>flavor profile</th>\n",
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              "      <td>1-Aminopropan-2-ol</td>\n",
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              "      <td>C1C(C(C(OC1O)CO)O)O</td>\n",
              "      <td>flavorDB</td>\n",
              "      <td>Sweet</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3-Methyl-2-oxovaleric acid</td>\n",
              "      <td>47</td>\n",
              "      <td>fruit</td>\n",
              "      <td>CCC(C)C(=O)C(=O)O</td>\n",
              "      <td>flavorDB</td>\n",
              "      <td>Fruity</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3-Methyl-2-oxobutanoic acid</td>\n",
              "      <td>49</td>\n",
              "      <td>fruity</td>\n",
              "      <td>CC(C)C(=O)C(=O)O</td>\n",
              "      <td>flavorDB</td>\n",
              "      <td>Fruity</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2-ketoglutaric acid</td>\n",
              "      <td>51</td>\n",
              "      <td>odorless</td>\n",
              "      <td>C(CC(=O)O)C(=O)C(=O)O</td>\n",
              "      <td>flavorDB</td>\n",
              "      <td>Unclassified</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-19bdaf37-1069-494c-94df-f540c020d230 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-19bdaf37-1069-494c-94df-f540c020d230');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-997bbf32-e735-440b-b390-7e73399304e1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-997bbf32-e735-440b-b390-7e73399304e1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-997bbf32-e735-440b-b390-7e73399304e1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db_matches = data[data['Isomeric Smiles'].isin(database['Isomeric Smiles'])]\n",
        "db_matches.head()"
      ],
      "metadata": {
        "id": "iZr7y2ZBFceE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2bfc4c0a-9b7d-4ddc-8a11-8d8a8aff242a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Compound name  Fold Change  ID Level PubChem ID  \\\n",
              "2          Acetophenone          6.8         3      7410    \n",
              "3   Hydroxyacetophenone          6.1         3      7469    \n",
              "5         Gluconic acid          0.7         3      10690   \n",
              "6             Raffinose          0.1         3    39242     \n",
              "10       Methylcatechol         11.9         3       460    \n",
              "\n",
              "                                      Isomeric Smiles Processing stage  \n",
              "2                                   CC(=O)C1=CC=CC=C1     Fermentation  \n",
              "3                                CC(=O)C1=CC=C(C=C1)O     Fermentation  \n",
              "5         C([C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O)O     Fermentation  \n",
              "6   C([C@@H]1[C@@H]([C@@H]([C@H]([C@H](O1)OC[C@@H]...     Fermentation  \n",
              "10                                     COC1=CC=CC=C1O     Fermentation  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c80225bf-834d-4f01-bc2f-1861799c3f69\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Compound name</th>\n",
              "      <th>Fold Change</th>\n",
              "      <th>ID Level</th>\n",
              "      <th>PubChem ID</th>\n",
              "      <th>Isomeric Smiles</th>\n",
              "      <th>Processing stage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Acetophenone</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3</td>\n",
              "      <td>7410</td>\n",
              "      <td>CC(=O)C1=CC=CC=C1</td>\n",
              "      <td>Fermentation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hydroxyacetophenone</td>\n",
              "      <td>6.1</td>\n",
              "      <td>3</td>\n",
              "      <td>7469</td>\n",
              "      <td>CC(=O)C1=CC=C(C=C1)O</td>\n",
              "      <td>Fermentation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gluconic acid</td>\n",
              "      <td>0.7</td>\n",
              "      <td>3</td>\n",
              "      <td>10690</td>\n",
              "      <td>C([C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O)O</td>\n",
              "      <td>Fermentation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Raffinose</td>\n",
              "      <td>0.1</td>\n",
              "      <td>3</td>\n",
              "      <td>39242</td>\n",
              "      <td>C([C@@H]1[C@@H]([C@@H]([C@H]([C@H](O1)OC[C@@H]...</td>\n",
              "      <td>Fermentation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Methylcatechol</td>\n",
              "      <td>11.9</td>\n",
              "      <td>3</td>\n",
              "      <td>460</td>\n",
              "      <td>COC1=CC=CC=C1O</td>\n",
              "      <td>Fermentation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-c80225bf-834d-4f01-bc2f-1861799c3f69 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c80225bf-834d-4f01-bc2f-1861799c3f69');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "    </g>\n",
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              "  </button>\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    }\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this matches the flavor profile is assigned according the report in the database as well as a probability of 100% as their flavor profile is experimentally validated. Additionally, the source of the data recorded in the database is provided."
      ],
      "metadata": {
        "id": "sBLOWAnjdeTY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns = ['Bitter', 'Floral', 'Fruity', 'Off_flavor', 'Nutty', 'Sour', 'Sweet', 'Source']\n",
        "\n",
        "result_dict = {col: [] for col in columns}\n",
        "\n",
        "for smiles in db_matches['Isomeric Smiles']:\n",
        "    for col in columns:\n",
        "        values = database[database['Isomeric Smiles'] == smiles][col].values\n",
        "        result_dict[col].append(values[0])\n",
        "\n",
        "for col in columns:\n",
        "    db_matches = db_matches.copy()\n",
        "    db_matches[col] = result_dict[col]\n",
        "\n",
        "db_matches.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "pkANbXty5ONz",
        "outputId": "670eac28-9551-4476-f864-d2126833f005"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Compound name  Fold Change  ID Level PubChem ID  \\\n",
              "2          Acetophenone          6.8         3      7410    \n",
              "3   Hydroxyacetophenone          6.1         3      7469    \n",
              "5         Gluconic acid          0.7         3      10690   \n",
              "6             Raffinose          0.1         3    39242     \n",
              "10       Methylcatechol         11.9         3       460    \n",
              "\n",
              "                                      Isomeric Smiles Processing stage  \\\n",
              "2                                   CC(=O)C1=CC=CC=C1     Fermentation   \n",
              "3                                CC(=O)C1=CC=C(C=C1)O     Fermentation   \n",
              "5         C([C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O)O     Fermentation   \n",
              "6   C([C@@H]1[C@@H]([C@@H]([C@H]([C@H](O1)OC[C@@H]...     Fermentation   \n",
              "10                                     COC1=CC=CC=C1O     Fermentation   \n",
              "\n",
              "    Bitter  Floral  Fruity  Off_flavor  Nutty  Sour  Sweet    Source  \n",
              "2        1       1       1           1      1     0      1  flavorDB  \n",
              "3        1       1       1           0      0     0      1  flavorDB  \n",
              "5        0       0       0           0      0     0      1  flavorDB  \n",
              "6        0       0       0           0      0     0      1  flavorDB  \n",
              "10       0       1       0           1      1     0      1  flavorDB  "
            ],
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              "      <th>Isomeric Smiles</th>\n",
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              "      <th>2</th>\n",
              "      <td>Acetophenone</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3</td>\n",
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              "      <td>6.1</td>\n",
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              "      <th>5</th>\n",
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              "      <td>0.7</td>\n",
              "      <td>3</td>\n",
              "      <td>10690</td>\n",
              "      <td>C([C@H]([C@H]([C@@H]([C@H](C(=O)O)O)O)O)O)O</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Raffinose</td>\n",
              "      <td>0.1</td>\n",
              "      <td>3</td>\n",
              "      <td>39242</td>\n",
              "      <td>C([C@@H]1[C@@H]([C@@H]([C@H]([C@H](O1)OC[C@@H]...</td>\n",
              "      <td>Fermentation</td>\n",
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              "      <td>flavorDB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Methylcatechol</td>\n",
              "      <td>11.9</td>\n",
              "      <td>3</td>\n",
              "      <td>460</td>\n",
              "      <td>COC1=CC=CC=C1O</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This data can be stored in an excel file. Run the following cell if you want to store it."
      ],
      "metadata": {
        "id": "Rpc_Fxo5KGme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "db_matches.to_excel('Database matches.xlsx', index=False)"
      ],
      "metadata": {
        "id": "T2FPmj4mkulN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Obtaining the data without reported flavor profile\n",
        "\n",
        "The next step is obtaining the set of compounds without database matches. The structure of this molecules will be used for the prediction."
      ],
      "metadata": {
        "id": "sXeYhUtaI9R7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "non_matches = data[~data['Isomeric Smiles'].isin(database['Isomeric Smiles'])]\n",
        "non_matches.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Os8Tq7Rlrv3b",
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            "text/plain": [
              "                                       Compound name  Fold Change  ID Level  \\\n",
              "0  Tetrahydroxychal-\\ncone 4’-O-(2’’-\\nO-acetyl-6...         14.1         3   \n",
              "1                                    Tumonoic Acid H         12.2         3   \n",
              "4                                    Anhydrofructose          0.2         3   \n",
              "7                                          Sorbosone          0.5         3   \n",
              "8                                      Maltotetraose          0.2         4   \n",
              "\n",
              "  PubChem ID                                    Isomeric Smiles  \\\n",
              "0  42607557   CC(=O)OC1[C@H]([C@@H](C(O[C@H]1OC2=C(C(=C(C=C2...   \n",
              "1  25058105   CCCCCCCC[C@H](C)C(=O)N1CCC[C@H]1C(=O)O[C@H](C(...   \n",
              "4   14299935                             C1C(=O)C(C(C(O1)CO)O)O   \n",
              "7   24755546             C([C@H]([C@@H]([C@H](C(=O)C=O)O)O)O)O    \n",
              "8    446495   C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O[C@@H]2[...   \n",
              "\n",
              "  Processing stage  \n",
              "0     Fermentation  \n",
              "1     Fermentation  \n",
              "4     Fermentation  \n",
              "7     Fermentation  \n",
              "8     Fermentation  "
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              "      <th>Compound name</th>\n",
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              "      <td>Tetrahydroxychal-\\ncone 4’-O-(2’’-\\nO-acetyl-6...</td>\n",
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              "      <td>3</td>\n",
              "      <td>42607557</td>\n",
              "      <td>CC(=O)OC1[C@H]([C@@H](C(O[C@H]1OC2=C(C(=C(C=C2...</td>\n",
              "      <td>Fermentation</td>\n",
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              "      <th>1</th>\n",
              "      <td>Tumonoic Acid H</td>\n",
              "      <td>12.2</td>\n",
              "      <td>3</td>\n",
              "      <td>25058105</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Featurizing the compounds\n",
        "\n",
        "In order to run the prediction it is required to transform the molecular structure into the mathematical representation that the trained algorithms require: RDKit molecular descriptors and Extended Connectivity Fingerprint.\n",
        "\n",
        "For this, RDKit python library needs to be installed, as it is not a default library in Google Colab."
      ],
      "metadata": {
        "id": "TES3n141ML1p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit-pypi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGMY6uMcAXGh",
        "outputId": "b4accbb0-73d6-485e-f5cb-038085a438cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rdkit-pypi in /usr/local/lib/python3.10/dist-packages (2022.9.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit-pypi) (1.23.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit-pypi) (9.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first representation obtained are the 200 RDKit molecular descriptors. This descriptors are obtained using the isomeric smiles as input."
      ],
      "metadata": {
        "id": "oQcp5RrVNR-0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "''' Import the RDKit libraries for molecular descriptor calculation'''\n",
        "\n",
        "from rdkit.Chem import Descriptors\n",
        "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
        "from rdkit import Chem\n",
        "\n",
        "''' Create a fucntion that extract all the RDKit molecular descriptors'''\n",
        "\n",
        "def RDkit_descriptors(smiles):\n",
        "    mols = [Chem.MolFromSmiles(i) for i in smiles]\n",
        "    calc = MoleculeDescriptors.MolecularDescriptorCalculator([x[0] for x in Descriptors._descList])\n",
        "    desc_names = calc.GetDescriptorNames()\n",
        "\n",
        "    Mol_descriptors =[]\n",
        "\n",
        "    for mol in mols:\n",
        "        descriptors = calc.CalcDescriptors(mol)\n",
        "        Mol_descriptors.append(descriptors)\n",
        "    return Mol_descriptors,desc_names\n",
        "\n",
        "''' Call the fucntion that extract all the RDKit molecular descriptors'''\n",
        "\n",
        "Mol_descriptors,desc_names = RDkit_descriptors(non_matches ['Isomeric Smiles'])\n",
        "\n",
        "df_RDKit_descriptors = pd.DataFrame(Mol_descriptors,columns=desc_names)\n",
        "\n",
        "df_RDKit_descriptors.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "N2zNUzjfAh4Z",
        "outputId": "b5cbb3bc-cb53-4b82-ed0c-641e806df4af"
      },
      "execution_count": null,
      "outputs": [
        {
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              "   MaxEStateIndex  MinEStateIndex  MaxAbsEStateIndex  MinAbsEStateIndex  \\\n",
              "0       12.671459       -1.761259          12.671459           0.050808   \n",
              "1       13.036577       -1.319601          13.036577           0.053305   \n",
              "2       10.649343       -1.415833          10.649343           0.246574   \n",
              "3       10.402816       -1.981806          10.402816           0.168670   \n",
              "4       10.858559       -2.020271          10.858559           0.783569   \n",
              "\n",
              "        qed    MolWt  HeavyAtomMolWt  ExactMolWt  NumValenceElectrons  \\\n",
              "0  0.098362  606.580         576.340  606.173726                  230   \n",
              "1  0.267956  483.646         438.286  483.319603                  196   \n",
              "2  0.402243  162.141         152.061  162.052823                   64   \n",
              "3  0.260576  178.140         168.060  178.047738                   70   \n",
              "4  0.102836  666.579         624.243  666.221858                  264   \n",
              "\n",
              "   NumRadicalElectrons  ...  fr_sulfide  fr_sulfonamd  fr_sulfone  \\\n",
              "0                    0  ...           0             0           0   \n",
              "1                    0  ...           0             0           0   \n",
              "2                    0  ...           0             0           0   \n",
              "3                    0  ...           0             0           0   \n",
              "4                    0  ...           0             0           0   \n",
              "\n",
              "   fr_term_acetylene  fr_tetrazole  fr_thiazole  fr_thiocyan  fr_thiophene  \\\n",
              "0                  0             0            0            0             0   \n",
              "1                  0             0            0            0             0   \n",
              "2                  0             0            0            0             0   \n",
              "3                  0             0            0            0             0   \n",
              "4                  0             0            0            0             0   \n",
              "\n",
              "   fr_unbrch_alkane  fr_urea  \n",
              "0                 0        0  \n",
              "1                 4        0  \n",
              "2                 0        0  \n",
              "3                 0        0  \n",
              "4                 0        0  \n",
              "\n",
              "[5 rows x 208 columns]"
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              "      <th></th>\n",
              "      <th>MaxEStateIndex</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.036577</td>\n",
              "      <td>-1.319601</td>\n",
              "      <td>13.036577</td>\n",
              "      <td>0.053305</td>\n",
              "      <td>0.267956</td>\n",
              "      <td>483.646</td>\n",
              "      <td>438.286</td>\n",
              "      <td>483.319603</td>\n",
              "      <td>196</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.649343</td>\n",
              "      <td>-1.415833</td>\n",
              "      <td>10.649343</td>\n",
              "      <td>0.246574</td>\n",
              "      <td>0.402243</td>\n",
              "      <td>162.141</td>\n",
              "      <td>152.061</td>\n",
              "      <td>162.052823</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.402816</td>\n",
              "      <td>-1.981806</td>\n",
              "      <td>10.402816</td>\n",
              "      <td>0.168670</td>\n",
              "      <td>0.260576</td>\n",
              "      <td>178.140</td>\n",
              "      <td>168.060</td>\n",
              "      <td>178.047738</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.858559</td>\n",
              "      <td>-2.020271</td>\n",
              "      <td>10.858559</td>\n",
              "      <td>0.783569</td>\n",
              "      <td>0.102836</td>\n",
              "      <td>666.579</td>\n",
              "      <td>624.243</td>\n",
              "      <td>666.221858</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 208 columns</p>\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before training the respective algorithms, the descriptors were cleaned by colineality and variance. Therefore, not all the 200 molecular descriptors will be accepted as input by the respective algorithms.\n",
        "\n",
        "It is necessary to filter the input descriptors. For this, the file containing the test data is used to filter the input columns."
      ],
      "metadata": {
        "id": "CRleF30gOI85"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RDKit_test = pd.read_excel('https://github.com/FabioHerrera97/FlavorMiner/raw/main/Data/RDKit_test.xlsx')\n",
        "\n",
        "x_RDKit_data = RDKit_test.drop(['Bitter', 'Floral', 'Fruity', 'Off_flavor', 'Nutty', 'Sour', 'Sweet'], axis=1)\n",
        "\n",
        "input_columns_names_RDKit = x_RDKit_data.columns.tolist()\n",
        "input_columns_names_RDKit\n",
        "\n",
        "input_RDKit = df_RDKit_descriptors[input_columns_names_RDKit]"
      ],
      "metadata": {
        "id": "URq1evOB4VUt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first representation obtained are the Extended Connectivity Fingerprint, with a radio of 2. This results in a binary vector with a lenth of 1024 bits.  "
      ],
      "metadata": {
        "id": "Ze05VOVEPihY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from rdkit import Chem\n",
        "from rdkit.Chem import Descriptors\n",
        "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "\n",
        "''' Generate a molecule from each isomeric smiles'''\n",
        "\n",
        "smiles_non_matches = non_matches ['Isomeric Smiles']\n",
        "mols = [Chem.MolFromSmiles(i) for i in smiles_non_matches]\n",
        "\n",
        "''' Configure the extended connectivity fingerprint'''\n",
        "\n",
        "radius=2\n",
        "nBits=1024\n",
        "\n",
        "''' Calculate the ECFP for each molecule'''\n",
        "\n",
        "ECFP2 = [AllChem.GetMorganFingerprintAsBitVect(mol,radius=radius, nBits=nBits) for mol in mols]\n",
        "\n",
        "ecfp2_name = [f'Bit_{i}' for i in range(nBits)]\n",
        "ecfp2_bits = [list(l) for l in ECFP2]\n",
        "df_ecfp_2 = pd.DataFrame(ecfp2_bits, index = non_matches.index, columns = ecfp2_name)\n",
        "df_ecfp_2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "c5_ye9zKB23C",
        "outputId": "3d773e94-f20c-4552-e186-598f70d243ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Bit_0  Bit_1  Bit_2  Bit_3  Bit_4  Bit_5  Bit_6  Bit_7  Bit_8  Bit_9  ...  \\\n",
              "0      0      0      0      0      0      0      0      0      0      0  ...   \n",
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              "\n",
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              "      <td>...</td>\n",
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c54b73a0-3e58-45e3-909c-ac11d2eb578a');\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7baa0715-227d-47cd-b8f6-a3b366f3c6f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before training the respective algorithms, the descriptors were cleaned by colineality and variance. Therefore, not all the 200 molecular descriptors will be accepted as input by the respective algorithms.\n",
        "\n",
        "It is necessary to filter the input descriptors. For this, the file containing the test data is used to filter the input columns."
      ],
      "metadata": {
        "id": "7ZjaeQVsQ8K0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns_ecfp = pd.read_excel('https://github.com/FabioHerrera97/FlavorMiner/raw/main/Data/ECFP_test.xlsx')\n",
        "\n",
        "x_ECFP_data = columns_ecfp.drop(['Bitter', 'Floral', 'Fruity', 'Off_flavor', 'Nutty', 'Sour', 'Sweet'], axis=1)\n",
        "\n",
        "input_bits = x_ECFP_data.columns.tolist()\n",
        "\n",
        "input_ECFP = df_ecfp_2[input_bits]\n",
        "\n",
        "input_ECFP.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "ly_AgxLc2C48",
        "outputId": "1fb0c21b-e739-4815-df01-904f3929785a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Bit_1  Bit_10  Bit_1004  Bit_1009  Bit_1010  Bit_1017  Bit_1019  Bit_102  \\\n",
              "0      0       0         0         1         0         1         1        1   \n",
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              "\n",
              "   Bit_1020  Bit_104  ...  Bit_953  Bit_960  Bit_962  Bit_964  Bit_965  \\\n",
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              "7         0        0  ...        0        0        0        0        0   \n",
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              "\n",
              "   Bit_967  Bit_980  Bit_981  Bit_992  Bit_998  \n",
              "0        1        0        0        0        0  \n",
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              "[5 rows x 266 columns]"
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Performing the prediction on those compounds with unknown flavor profiles.\n",
        "\n",
        "The first step to perform the prediction is to import the models. These models are stored in a Zenodo repository ([https://zenodo.org/records/8435106](https://)). These model were stored in Zeno because they exceded the maximum size allowed by GitHub."
      ],
      "metadata": {
        "id": "H9zB2LwyRF8a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After importing the models, the prediction is run. Similarly, the probability is calculated as measure of the confidence of the prediction for each entry.\n",
        "\n",
        "This process is first performed for the models trained with Extended Connectivity Fingerprint."
      ],
      "metadata": {
        "id": "4HFY4Ear3jQz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "import numpy as np\n",
        "\n",
        "ecfp_model_urls = [\n",
        "    'https://zenodo.org/records/8435106/files/Bitter_Random_Forest_SMOTE.sav?download=1',\n",
        "    'https://zenodo.org/records/8435106/files/Sour_KNN_SMOTE.sav?download=1',\n",
        "    'https://zenodo.org/records/8435106/files/Fruity_Random_Forest_SMOTE.sav?download=1',\n",
        "    'https://zenodo.org/records/10033243/files/Sweet_updated_RF_Final.sav?download=1',\n",
        "]\n",
        "\n",
        "# Download the model files from Zenodo and load them\n",
        "ecfp_models = []\n",
        "\n",
        "for model_url in ecfp_model_urls:\n",
        "    model_file_name = model_url.split('/')[-1]\n",
        "    !wget -O {model_file_name} {model_url}\n",
        "    model = joblib.load(model_file_name)\n",
        "    ecfp_models.append(model)\n",
        "\n",
        "predictions_ecfp = []\n",
        "positive_probabilities_ecfp = []\n",
        "\n",
        "for model in ecfp_models:\n",
        "    probabilities = model.predict_proba(input_ECFP)\n",
        "    positive_probabilities = probabilities[:, 0]\n",
        "    predictions = model.predict(input_ECFP)\n",
        "    predictions_ecfp.append(predictions)\n",
        "    positive_probabilities_ecfp.append(positive_probabilities)\n",
        "\n",
        "Bitter, Sour, Fruity, Sweet = predictions_ecfp\n",
        "Bitter_pos_proba, Sour_pos_proba, Fruity_pos_proba, Sweet_pos_proba = positive_probabilities_ecfp\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKAGrnjdZkDt",
        "outputId": "c8f0ad69-8ecf-4094-ebdb-7f4a4f72c860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-23 12:26:53--  https://zenodo.org/records/8435106/files/Bitter_Random_Forest_SMOTE.sav?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.33.206, 188.185.10.78, 188.185.22.33, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.33.206|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 26226873 (25M) [application/octet-stream]\n",
            "Saving to: ‘Bitter_Random_Forest_SMOTE.sav?download=1’\n",
            "\n",
            "Bitter_Random_Fores 100%[===================>]  25.01M  18.1MB/s    in 1.4s    \n",
            "\n",
            "2023-10-23 12:26:55 (18.1 MB/s) - ‘Bitter_Random_Forest_SMOTE.sav?download=1’ saved [26226873/26226873]\n",
            "\n",
            "--2023-10-23 12:26:56--  https://zenodo.org/records/8435106/files/Sour_KNN_SMOTE.sav?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.33.206, 188.185.10.78, 188.185.22.33, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.33.206|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 44378116 (42M) [application/octet-stream]\n",
            "Saving to: ‘Sour_KNN_SMOTE.sav?download=1’\n",
            "\n",
            "Sour_KNN_SMOTE.sav? 100%[===================>]  42.32M  20.6MB/s    in 2.1s    \n",
            "\n",
            "2023-10-23 12:26:58 (20.6 MB/s) - ‘Sour_KNN_SMOTE.sav?download=1’ saved [44378116/44378116]\n",
            "\n",
            "--2023-10-23 12:26:58--  https://zenodo.org/records/8435106/files/Fruity_Random_Forest_SMOTE.sav?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.10.78, 188.185.33.206, 188.185.22.33, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.10.78|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 21180681 (20M) [application/octet-stream]\n",
            "Saving to: ‘Fruity_Random_Forest_SMOTE.sav?download=1’\n",
            "\n",
            "Fruity_Random_Fores 100%[===================>]  20.20M  15.7MB/s    in 1.3s    \n",
            "\n",
            "2023-10-23 12:27:00 (15.7 MB/s) - ‘Fruity_Random_Forest_SMOTE.sav?download=1’ saved [21180681/21180681]\n",
            "\n",
            "--2023-10-23 12:27:01--  https://zenodo.org/records/10033243/files/Sweet_updated_RF_Final.sav?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.10.78, 188.185.33.206, 188.185.22.33, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.10.78|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 147982457 (141M) [application/octet-stream]\n",
            "Saving to: ‘Sweet_updated_RF_Final.sav?download=1’\n",
            "\n",
            "Sweet_updated_RF_Fi 100%[===================>] 141.13M  35.3MB/s    in 4.8s    \n",
            "\n",
            "2023-10-23 12:27:06 (29.7 MB/s) - ‘Sweet_updated_RF_Final.sav?download=1’ saved [147982457/147982457]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, the process is repeated for the models trained with RDKit molecular descriptors."
      ],
      "metadata": {
        "id": "A4eHSS4Q4cxX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Zenodo model file URLs\n",
        "\n",
        "rdkit_model_urls = [\n",
        "    'https://zenodo.org/records/8435106/files/Off_flavor_Random_Forest_SMOTE_RDKit.sav?download=1',\n",
        "    'https://zenodo.org/records/8435106/files/Floral_Random_Forest_SMOTE_RDKit.sav?download=1',\n",
        "    'https://zenodo.org/records/8435106/files/Nutty_Random_Forest_SMOTE_RDKit.sav?download=1'\n",
        "]\n",
        "\n",
        "# Download the model files from Zenodo and load them\n",
        "\n",
        "rdkit_models = []\n",
        "positive_probabilities_rdkit = []\n",
        "negative_probabilities_rdkit = []\n",
        "predictions_rdkit = []\n",
        "\n",
        "for model_url in rdkit_model_urls:\n",
        "    model_file_name = model_url.split('/')[-1]\n",
        "    !wget -O {model_file_name} {model_url}\n",
        "    model = joblib.load(model_file_name)\n",
        "    rdkit_models.append(model)\n",
        "\n",
        "for model in rdkit_models:\n",
        "    probabilities = model.predict_proba(input_RDKit)\n",
        "    positive_probabilities = probabilities[:, 1]\n",
        "    negative_probabilities = probabilities[:, 0]\n",
        "    predictions = model.predict(input_RDKit)\n",
        "    predictions_rdkit.append(predictions)\n",
        "    positive_probabilities_rdkit.append(positive_probabilities)\n",
        "    negative_probabilities_rdkit.append(negative_probabilities)\n",
        "\n",
        "Off_flavor, Floral, Nutty = predictions_rdkit\n",
        "Off_flavor_pos_proba, Floral_pos_proba, Nutty_pos_proba = positive_probabilities_rdkit\n",
        "Off_flavor_neg_proba, Floral_neg_proba, Nutty_neg_proba = negative_probabilities_rdkit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GulPu4KPzV-y",
        "outputId": "2f298075-0c97-4b76-bbe3-005d392636db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-23 12:26:41--  https://zenodo.org/records/8435106/files/Off_flavor_Random_Forest_SMOTE_RDKit.sav?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.33.206, 188.185.10.78, 188.185.22.33, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.33.206|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4409049 (4.2M) [application/octet-stream]\n",
            "Saving to: ‘Off_flavor_Random_Forest_SMOTE_RDKit.sav?download=1’\n",
            "\n",
            "Off_flavor_Random_F 100%[===================>]   4.20M  5.14MB/s    in 0.8s    \n",
            "\n",
            "2023-10-23 12:26:43 (5.14 MB/s) - ‘Off_flavor_Random_Forest_SMOTE_RDKit.sav?download=1’ saved [4409049/4409049]\n",
            "\n",
            "--2023-10-23 12:26:43--  https://zenodo.org/records/8435106/files/Floral_Random_Forest_SMOTE_RDKit.sav?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.33.206, 188.185.10.78, 188.185.22.33, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.33.206|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4456569 (4.2M) [application/octet-stream]\n",
            "Saving to: ‘Floral_Random_Forest_SMOTE_RDKit.sav?download=1’\n",
            "\n",
            "Floral_Random_Fores 100%[===================>]   4.25M  5.46MB/s    in 0.8s    \n",
            "\n",
            "2023-10-23 12:26:44 (5.46 MB/s) - ‘Floral_Random_Forest_SMOTE_RDKit.sav?download=1’ saved [4456569/4456569]\n",
            "\n",
            "--2023-10-23 12:26:44--  https://zenodo.org/records/8435106/files/Nutty_Random_Forest_SMOTE_RDKit.sav?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 188.185.33.206, 188.185.10.78, 188.185.22.33, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.185.33.206|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9908281 (9.4M) [application/octet-stream]\n",
            "Saving to: ‘Nutty_Random_Forest_SMOTE_RDKit.sav?download=1’\n",
            "\n",
            "Nutty_Random_Forest 100%[===================>]   9.45M  9.64MB/s    in 1.0s    \n",
            "\n",
            "2023-10-23 12:26:47 (9.64 MB/s) - ‘Nutty_Random_Forest_SMOTE_RDKit.sav?download=1’ saved [9908281/9908281]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Results saving and visualization\n",
        "\n",
        "The results of the predictions are then organized in a table. This table contains the preexisting information in the input data,the flavor profile of the compounds and the source of this flavor profile (wether it was a database match or a prediction)\n",
        "\n",
        "First, the predicted labels as well as a column indicating that this data is the result of a prediction are added to the data with uknown flavor profiles.\n"
      ],
      "metadata": {
        "id": "NNE7AdYO4oVX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Source = [\"prediction\"] * (len(non_matches))"
      ],
      "metadata": {
        "id": "5piJPyCc25Uh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary with the lists as values\n",
        "new_columns = {\n",
        "    'Bitter': Bitter,\n",
        "    'Floral': Floral,\n",
        "    'Fruity': Fruity,\n",
        "    'Off_flavor': Off_flavor,\n",
        "    'Nutty': Nutty,\n",
        "    'Sour': Sour,\n",
        "    'Sweet': Sweet,\n",
        "    'Source': Source\n",
        "}\n",
        "\n",
        "new_non_matches = non_matches.copy()  # Create a copy to avoid modifying the original DataFrame\n",
        "\n",
        "for column_name, values in new_columns.items():\n",
        "    new_non_matches[column_name] = values\n",
        "\n",
        "new_non_matches.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "vBwaiVtD3MCh",
        "outputId": "50cf3852-8276-42bb-bd29-20745fdbcceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Compound name  Fold Change  ID Level  \\\n",
              "0  Tetrahydroxychal-\\ncone 4’-O-(2’’-\\nO-acetyl-6...         14.1         3   \n",
              "1                                    Tumonoic Acid H         12.2         3   \n",
              "4                                    Anhydrofructose          0.2         3   \n",
              "7                                          Sorbosone          0.5         3   \n",
              "8                                      Maltotetraose          0.2         4   \n",
              "\n",
              "  PubChem ID                                    Isomeric Smiles  \\\n",
              "0  42607557   CC(=O)OC1[C@H]([C@@H](C(O[C@H]1OC2=C(C(=C(C=C2...   \n",
              "1  25058105   CCCCCCCC[C@H](C)C(=O)N1CCC[C@H]1C(=O)O[C@H](C(...   \n",
              "4   14299935                             C1C(=O)C(C(C(O1)CO)O)O   \n",
              "7   24755546             C([C@H]([C@@H]([C@H](C(=O)C=O)O)O)O)O    \n",
              "8    446495   C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O[C@@H]2[...   \n",
              "\n",
              "  Processing stage  Bitter  Floral  Fruity  Off_flavor  Nutty  Sour  Sweet  \\\n",
              "0     Fermentation       0       0       0           0      0     0      1   \n",
              "1     Fermentation       0       0       0           0      0     0      0   \n",
              "4     Fermentation       1       0       0           0      0     0      1   \n",
              "7     Fermentation       0       0       0           0      0     0      1   \n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "Second, the database matches are combined with predictions and plot using radar curve. This radar curve contains the frequency of compounds containing in their flavor profiles the 7 target flavors."
      ],
      "metadata": {
        "id": "RCyLbD3iOBzJ"
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    {
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        "combined_data = pd.concat([db_matches, new_non_matches], ignore_index=True)\n",
        "\n",
        "combined_data.sample(5)"
      ],
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        "id": "wQoUtVlS5tea",
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "      Compound name  Fold Change  ID Level PubChem ID  \\\n",
              "25          Maltose          0.0         2       6255   \n",
              "126  Deoxyestradiol         11.6         3      66426   \n",
              "57         Fructose          0.6         1    2723872   \n",
              "17          Proline          3.3         2     145742   \n",
              "131  Occidentoside           0.3         1    6440594   \n",
              "\n",
              "                                       Isomeric Smiles Processing stage  \\\n",
              "25   C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O[C@@H]2[...     Fermentation   \n",
              "126  C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@@H]2O)CCC4=C3...     Fermentation   \n",
              "57               C1[C@H]([C@H]([C@@H](C(O1)(CO)O)O)O)O           Drying   \n",
              "17                                 C1C[C@H](NC1)C(=O)O     Fermentation   \n",
              "131  C1[C@H](OC2=CC(=CC(=C2C1=O)O)O)C3=CC=C(C=C3)OC...     Fermentation   \n",
              "\n",
              "     Bitter  Floral  Fruity  Off_flavor  Nutty  Sour  Sweet      Source  \n",
              "25        0       0       0           0      0     0      1    flavorDB  \n",
              "126       0       1       0           0      0     1      0  prediction  \n",
              "57        0       0       0           0      0     0      1    flavorDB  \n",
              "17        0       0       0           0      0     0      0    flavorDB  \n",
              "131       0       0       0           0      0     0      0  prediction  "
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              "      <th>57</th>\n",
              "      <td>Fructose</td>\n",
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              "      <td>1</td>\n",
              "      <td>2723872</td>\n",
              "      <td>C1[C@H]([C@H]([C@@H](C(O1)(CO)O)O)O)O</td>\n",
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              "      <th>17</th>\n",
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              "      <td>0</td>\n",
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              "      <th>131</th>\n",
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              "      <td>C1[C@H](OC2=CC(=CC(=C2C1=O)O)O)C3=CC=C(C=C3)OC...</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>prediction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6de3e056-ca82-4cff-86ed-e846e313ebcd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6de3e056-ca82-4cff-86ed-e846e313ebcd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6de3e056-ca82-4cff-86ed-e846e313ebcd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cf3faf7e-60d2-4fa4-b954-818f7ea9d12a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cf3faf7e-60d2-4fa4-b954-818f7ea9d12a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf3faf7e-60d2-4fa4-b954-818f7ea9d12a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "categories = ['Bitter', 'Floral', 'Fruity', 'Nutty', 'Sweet', 'Off_flavor', 'Sour']\n",
        "values = [combined_data[col].sum() for col in categories]\n",
        "\n",
        "num_categories = len(categories)\n",
        "angles = np.linspace(0, 2 * np.pi, num_categories, endpoint=False).tolist()\n",
        "angles += angles[:1]\n",
        "\n",
        "values += values[:1]\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "ax = plt.subplot(111, polar=True)\n",
        "ax.fill(angles, values, 'b', alpha=0.25)\n",
        "ax.set_xticks(angles[:-1])\n",
        "ax.set_xticklabels(categories)\n",
        "\n",
        "plt.savefig('Flavor profile.jpeg', format='jpeg', dpi=2000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "apJFYUpQ8UQN",
        "outputId": "39a566b0-6ffa-4da7-a823-ae45f2d185a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Third, the data is stored in an excel file. Even though this data is combined, the dataframe contains a column name Source indicating if the result id the product of a prediction or a database hit.\n",
        "\n",
        "Additionally, the results of the 7 binary variables associated with the flavor are combined into a single variable, and these binary variables are dropped."
      ],
      "metadata": {
        "id": "Mv36xy2TP-uM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_combined_data = combined_data.copy()\n",
        "\n",
        "columns=['Bitter', 'Floral', 'Fruity', 'Off_flavor', 'Nutty', 'Sour', 'Sweet']\n",
        "\n",
        "flavor_profiles = []\n",
        "\n",
        "for col in columns:\n",
        "    flavor_profiles.append(col)\n",
        "\n",
        "new_combined_data['Flavor_profile'] = new_combined_data[flavor_profiles].apply(lambda x: ', '.join(x.index[x == 1]), axis=1)\n",
        "\n",
        "new_combined_data = new_combined_data.drop(columns=flavor_profiles)\n",
        "\n",
        "new_combined_data.to_excel('Flavor profile.xlsx', index=False)"
      ],
      "metadata": {
        "id": "J6dsAvB8YXfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, a new dataframe containing only data resulted from the prediction is created. This dataframe contains a column with the probability calculated during the prediction. This probability is offered a measure of the confidence of the prediction.\n",
        "\n",
        "This final probability is calculated as the average probability of the flavor notes predicted as positive for each entry. The results are sorted in descending oreder and stored in an excel file"
      ],
      "metadata": {
        "id": "pA9cclYUSGcl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_columns_2 = {\n",
        "    'Bitter': Bitter,\n",
        "    'Floral': Floral,\n",
        "    'Fruity': Fruity,\n",
        "    'Off_flavor': Off_flavor,\n",
        "    'Nutty': Nutty,\n",
        "    'Sour': Sour,\n",
        "    'Sweet': Sweet,\n",
        "\n",
        "    'Bitter probability': Bitter_pos_proba,\n",
        "    'Sour probability': Sour_pos_proba,\n",
        "    'Fruity probability': Fruity_pos_proba,\n",
        "    'Sweet probability': Sweet_pos_proba,\n",
        "    'Off_flavor probability': Off_flavor_pos_proba,\n",
        "    'Floral probability': Floral_pos_proba,\n",
        "    'Nutty probability': Nutty_pos_proba,\n",
        "}\n",
        "\n",
        "probability_data = non_matches.copy()  # Create a copy to avoid modifying the original DataFrame\n",
        "\n",
        "for column_name, values in new_columns_2.items():\n",
        "    probability_data [column_name] = values\n",
        "\n",
        "probability_data .head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "Xbn0XWCwgQZQ",
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Compound name  Fold Change  ID Level  \\\n",
              "0  Tetrahydroxychal-\\ncone 4’-O-(2’’-\\nO-acetyl-6...         14.1         3   \n",
              "1                                    Tumonoic Acid H         12.2         3   \n",
              "4                                    Anhydrofructose          0.2         3   \n",
              "7                                          Sorbosone          0.5         3   \n",
              "8                                      Maltotetraose          0.2         4   \n",
              "\n",
              "  PubChem ID                                    Isomeric Smiles  \\\n",
              "0  42607557   CC(=O)OC1[C@H]([C@@H](C(O[C@H]1OC2=C(C(=C(C=C2...   \n",
              "1  25058105   CCCCCCCC[C@H](C)C(=O)N1CCC[C@H]1C(=O)O[C@H](C(...   \n",
              "4   14299935                             C1C(=O)C(C(C(O1)CO)O)O   \n",
              "7   24755546             C([C@H]([C@@H]([C@H](C(=O)C=O)O)O)O)O    \n",
              "8    446495   C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O[C@@H]2[...   \n",
              "\n",
              "  Processing stage  Bitter  Floral  Fruity  Off_flavor  ...  \\\n",
              "0     Fermentation       0       0       0           0  ...   \n",
              "1     Fermentation       0       0       0           0  ...   \n",
              "4     Fermentation       1       0       0           0  ...   \n",
              "7     Fermentation       0       0       0           0  ...   \n",
              "8     Fermentation       0       0       0           0  ...   \n",
              "\n",
              "   Off_flavor positive probability  Floral positive probability  \\\n",
              "0                         0.370535                     0.268308   \n",
              "1                         0.235785                     0.375867   \n",
              "4                         0.011649                     0.000895   \n",
              "7                         0.009728                     0.000630   \n",
              "8                         0.122890                     0.020851   \n",
              "\n",
              "   Nutty positive probability  Bitter negative probability  \\\n",
              "0                    0.320639                     0.771270   \n",
              "1                    0.308707                     0.632766   \n",
              "4                    0.085790                     0.438211   \n",
              "7                    0.038224                     0.995125   \n",
              "8                    0.004951                     0.997822   \n",
              "\n",
              "   Sour negative probability  Fruity negative probability  \\\n",
              "0                   1.000000                     0.966250   \n",
              "1                   0.571429                     0.684990   \n",
              "4                   1.000000                     0.925228   \n",
              "7                   1.000000                     1.000000   \n",
              "8                   1.000000                     1.000000   \n",
              "\n",
              "   Sweet negative probability  Off_flavor negative probability  \\\n",
              "0                    0.349446                         0.629465   \n",
              "1                    0.927560                         0.764215   \n",
              "4                    0.382275                         0.988351   \n",
              "7                    0.016793                         0.990272   \n",
              "8                    0.000349                         0.877110   \n",
              "\n",
              "   Floral negative probability  Nutty negative probability  \n",
              "0                     0.731692                    0.679361  \n",
              "1                     0.624133                    0.691293  \n",
              "4                     0.999105                    0.914210  \n",
              "7                     0.999370                    0.961776  \n",
              "8                     0.979149                    0.995049  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <th></th>\n",
              "      <th>Compound name</th>\n",
              "      <th>Fold Change</th>\n",
              "      <th>ID Level</th>\n",
              "      <th>PubChem ID</th>\n",
              "      <th>Isomeric Smiles</th>\n",
              "      <th>Processing stage</th>\n",
              "      <th>Bitter</th>\n",
              "      <th>Floral</th>\n",
              "      <th>Fruity</th>\n",
              "      <th>Off_flavor</th>\n",
              "      <th>...</th>\n",
              "      <th>Off_flavor positive probability</th>\n",
              "      <th>Floral positive probability</th>\n",
              "      <th>Nutty positive probability</th>\n",
              "      <th>Bitter negative probability</th>\n",
              "      <th>Sour negative probability</th>\n",
              "      <th>Fruity negative probability</th>\n",
              "      <th>Sweet negative probability</th>\n",
              "      <th>Off_flavor negative probability</th>\n",
              "      <th>Floral negative probability</th>\n",
              "      <th>Nutty negative probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tetrahydroxychal-\\ncone 4’-O-(2’’-\\nO-acetyl-6...</td>\n",
              "      <td>14.1</td>\n",
              "      <td>3</td>\n",
              "      <td>42607557</td>\n",
              "      <td>CC(=O)OC1[C@H]([C@@H](C(O[C@H]1OC2=C(C(=C(C=C2...</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.370535</td>\n",
              "      <td>0.268308</td>\n",
              "      <td>0.320639</td>\n",
              "      <td>0.771270</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.966250</td>\n",
              "      <td>0.349446</td>\n",
              "      <td>0.629465</td>\n",
              "      <td>0.731692</td>\n",
              "      <td>0.679361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tumonoic Acid H</td>\n",
              "      <td>12.2</td>\n",
              "      <td>3</td>\n",
              "      <td>25058105</td>\n",
              "      <td>CCCCCCCC[C@H](C)C(=O)N1CCC[C@H]1C(=O)O[C@H](C(...</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.235785</td>\n",
              "      <td>0.375867</td>\n",
              "      <td>0.308707</td>\n",
              "      <td>0.632766</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.684990</td>\n",
              "      <td>0.927560</td>\n",
              "      <td>0.764215</td>\n",
              "      <td>0.624133</td>\n",
              "      <td>0.691293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Anhydrofructose</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3</td>\n",
              "      <td>14299935</td>\n",
              "      <td>C1C(=O)C(C(C(O1)CO)O)O</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.011649</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>0.085790</td>\n",
              "      <td>0.438211</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.925228</td>\n",
              "      <td>0.382275</td>\n",
              "      <td>0.988351</td>\n",
              "      <td>0.999105</td>\n",
              "      <td>0.914210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sorbosone</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3</td>\n",
              "      <td>24755546</td>\n",
              "      <td>C([C@H]([C@@H]([C@H](C(=O)C=O)O)O)O)O</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.009728</td>\n",
              "      <td>0.000630</td>\n",
              "      <td>0.038224</td>\n",
              "      <td>0.995125</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.016793</td>\n",
              "      <td>0.990272</td>\n",
              "      <td>0.999370</td>\n",
              "      <td>0.961776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Maltotetraose</td>\n",
              "      <td>0.2</td>\n",
              "      <td>4</td>\n",
              "      <td>446495</td>\n",
              "      <td>C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O[C@@H]2[...</td>\n",
              "      <td>Fermentation</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.122890</td>\n",
              "      <td>0.020851</td>\n",
              "      <td>0.004951</td>\n",
              "      <td>0.997822</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000349</td>\n",
              "      <td>0.877110</td>\n",
              "      <td>0.979149</td>\n",
              "      <td>0.995049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_check = ['Bitter', 'Sour', 'Fruity', 'Sweet', 'Off_flavor', 'Floral', 'Nutty']\n",
        "\n",
        "# Create a mask for entries with value 1 in any of the flavor columns\n",
        "flavor_mask = probability_data[columns_to_check].eq(1).any(axis=1)\n",
        "\n",
        "# Create a 'profile' column containing names of columns set to 1\n",
        "probability_data['Profile'] = probability_data[columns_to_check].apply(\n",
        "    lambda row: ', '.join([col for col, val in zip(columns_to_check, row) if val == 1]), axis=1)\n",
        "\n",
        "# Calculate average positive and negative probabilities for entries with value 1 in any flavor column\n",
        "probability_data['Average Positive Probability'] = probability_data.loc[flavor_mask][[f\"{col} probability\" for col in columns_to_check]].mean(axis=1)"
      ],
      "metadata": {
        "id": "R4RcL23_khH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "probability_data.to_excel('Probability data.xlsx', index=False)"
      ],
      "metadata": {
        "id": "winpds7dtctz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}