caco_2_RF_RandomSplitter.ipynb

{
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install PyTDC\n",
        "!pip install deepchem\n",
        "!pip install rdkit-pypi\n",
        "!pip install scipy\n",
        "!python -m venv new_env\n",
        "!source new_env/bin/activate\n",
        "!pip install tensorflow scipy pandas rdkit\n",
        "!pip install scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pa2cMHWWgvmI",
        "outputId": "a5e29ec5-844c-47e4-9d89-f1fb88ea52d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.manifold import TSNE\n",
        "from tdc.single_pred import ADME\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Descriptors\n",
        "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
        "from sklearn.decomposition import PCA\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit.DataStructs import BulkTanimotoSimilarity\n",
        "\n",
        "# Load datasets\n",
        "data = ADME(name = 'Caco2_Wang')\n",
        "df = data.get_data()\n",
        "external_df = pd.read_csv('external.csv')\n",
        "\n",
        "def keep_largest_component(smiles):\n",
        "    components = smiles.split('.')\n",
        "    largest_component = max(components, key=len)\n",
        "    return largest_component\n",
        "\n",
        "# Apply the function to clean the smiles in the dataset and add it as a new column to the original dataset\n",
        "df['clean_SMILES'] = df['Drug'].apply(keep_largest_component)\n",
        "external_df['clean_SMILES'] = external_df['SMILES'].apply(keep_largest_component)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VEBCyML0gxeR"
      },
      "outputs": [],
      "source": [
        "# Generating 2D_Descriptors from RDkit\n",
        "descriptor_calculator = MoleculeDescriptors.MolecularDescriptorCalculator([desc[0] for desc in Descriptors._descList])\n",
        "# Create a list to store descriptor values for each molecule\n",
        "descriptor_values = []\n",
        "\n",
        "# Calculate descriptors for each SMILES string in the dataframe\n",
        "for smiles in df['clean_SMILES']:\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "    desc_values = descriptor_calculator.CalcDescriptors(mol)\n",
        "    descriptor_values.append(desc_values)\n",
        "\n",
        "# Create a new dataframe with descriptor values\n",
        "descriptor_data = pd.DataFrame(descriptor_values, columns=descriptor_calculator.GetDescriptorNames())\n",
        "\n",
        "# Concatenate the descriptor dataframe with the primary dataframe\n",
        "df_with_descriptors = pd.concat([df, descriptor_data], axis=1)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Generating Descriptors for external dataset:\n",
        "external_descriptor_calculator = MoleculeDescriptors.MolecularDescriptorCalculator([desc[0] for desc in Descriptors._descList])\n",
        "# Create a list to store descriptor values for each molecule\n",
        "external_descriptor_values = []\n",
        "\n",
        "# Calculate descriptors for each SMILES string in the dataframe\n",
        "for smiles in external_df['clean_SMILES']:\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "    desc_values = external_descriptor_calculator.CalcDescriptors(mol)\n",
        "    external_descriptor_values.append(desc_values)\n",
        "\n",
        "# Create a new dataframe with descriptor values\n",
        "external_descriptor_data = pd.DataFrame(external_descriptor_values, columns=external_descriptor_calculator.GetDescriptorNames())\n",
        "\n",
        "# Concatenate the descriptor dataframe with the external dataframe\n",
        "external_df_with_descriptors = pd.concat([external_df, external_descriptor_data], axis=1)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Remove rows with NaN values from both datasets\n",
        "df_with_descriptors_cleaned = df_with_descriptors.dropna(subset=descriptor_data.columns)\n",
        "external_df_with_descriptors_cleaned = external_df_with_descriptors.dropna(subset=external_descriptor_data.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yv2Hzpq-gz3k",
        "outputId": "f4950484-68cd-4474-f082-0ae26bab0ea2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Descriptor Data Shape: (729, 215)\n",
            "Test Descriptor Data Shape: (183, 215)\n"
          ]
        }
      ],
      "source": [
        "# Split the dataset by random splitter\n",
        "import numpy as np\n",
        "import deepchem as dc\n",
        "\n",
        "# Maybe Split based on some other metric\n",
        "dataset = df['clean_SMILES'].tolist()\n",
        "\n",
        "# Convert SMILES strings to a DeepChem dataset with the SMILES in the ids field\n",
        "Xs = np.zeros(len(dataset))\n",
        "Ys = np.ones(len(dataset))\n",
        "dataset = dc.data.DiskDataset.from_numpy(X=Xs, y=Ys, w=np.zeros(len(dataset)), ids=dataset)\n",
        "\n",
        "# Create a RandomSplitter\n",
        "random_splitter = dc.splits.RandomSplitter()\n",
        "\n",
        "# Split the dataset\n",
        "train, test = random_splitter.train_test_split(dataset)\n",
        "\n",
        "# Extract indices of molecules in the train and test datasets\n",
        "train_indices = np.where(np.isin(dataset.ids, train.ids))[0]\n",
        "test_indices = np.where(np.isin(dataset.ids, test.ids))[0]\n",
        "\n",
        "# Extract rows from descriptor_data using the indices\n",
        "train_descriptor_data = df_with_descriptors.iloc[train_indices]\n",
        "test_descriptor_data = df_with_descriptors.iloc[test_indices]\n",
        "\n",
        "# Display the shapes of the train and test sets\n",
        "print(\"Train Descriptor Data Shape:\", train_descriptor_data.shape)\n",
        "print(\"Test Descriptor Data Shape:\", test_descriptor_data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "go6rdHJCg15e",
        "outputId": "06972fa5-449d-4560-bf02-55668a829d29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MaxAbsEStateIndex  MaxEStateIndex  MinAbsEStateIndex  MinEStateIndex  \\\n",
              "0          10.180343       10.180343           0.142543       -0.933439   \n",
              "1          11.292841       11.292841           0.040533       -0.040533   \n",
              "2          10.554213       10.554213           0.080556       -0.538553   \n",
              "3          10.370278       10.370278           0.099537       -0.099537   \n",
              "4          11.075595       11.075595           0.113574       -0.908859   \n",
              "\n",
              "        qed        SPS    MolWt  HeavyAtomMolWt  ExactMolWt  \\\n",
              "0  0.509593  20.619048  290.271         276.159  290.079038   \n",
              "1  0.330873  11.333333  245.366         222.182  245.177964   \n",
              "2  0.798144  41.045455  299.370         278.202  299.152144   \n",
              "3  0.422306  19.375000  113.120         106.064  113.058912   \n",
              "4  0.721043  47.760000  337.463         310.247  337.204179   \n",
              "\n",
              "   NumValenceElectrons  ...  fr_sulfonamd  fr_sulfone  fr_term_acetylene  \\\n",
              "0                  110  ...             0           0                  0   \n",
              "1                   98  ...             0           0                  0   \n",
              "2                  116  ...             0           0                  0   \n",
              "3                   44  ...             0           0                  0   \n",
              "4                  132  ...             0           0                  1   \n",
              "\n",
              "   fr_tetrazole  fr_thiazole  fr_thiocyan  fr_thiophene  fr_unbrch_alkane  \\\n",
              "0             0            0            0             0                 0   \n",
              "1             0            0            0             0                 1   \n",
              "2             0            0            0             0                 0   \n",
              "3             0            0            0             0                 0   \n",
              "4             0            0            0             0                 0   \n",
              "\n",
              "   fr_urea        SPS  \n",
              "0        0  20.619048  \n",
              "1        0  11.333333  \n",
              "2        0  41.045455  \n",
              "3        0  19.375000  \n",
              "4        0  47.760000  \n",
              "\n",
              "[5 rows x 211 columns]"
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              "      <th></th>\n",
              "      <th>MaxAbsEStateIndex</th>\n",
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              "      <th>ExactMolWt</th>\n",
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              "      <th>...</th>\n",
              "      <th>fr_sulfonamd</th>\n",
              "      <th>fr_sulfone</th>\n",
              "      <th>fr_term_acetylene</th>\n",
              "      <th>fr_tetrazole</th>\n",
              "      <th>fr_thiazole</th>\n",
              "      <th>fr_thiocyan</th>\n",
              "      <th>fr_thiophene</th>\n",
              "      <th>fr_unbrch_alkane</th>\n",
              "      <th>fr_urea</th>\n",
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              "      <td>0.142543</td>\n",
              "      <td>-0.933439</td>\n",
              "      <td>0.509593</td>\n",
              "      <td>20.619048</td>\n",
              "      <td>290.271</td>\n",
              "      <td>276.159</td>\n",
              "      <td>290.079038</td>\n",
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              "      <th>1</th>\n",
              "      <td>11.292841</td>\n",
              "      <td>11.292841</td>\n",
              "      <td>0.040533</td>\n",
              "      <td>-0.040533</td>\n",
              "      <td>0.330873</td>\n",
              "      <td>11.333333</td>\n",
              "      <td>245.366</td>\n",
              "      <td>222.182</td>\n",
              "      <td>245.177964</td>\n",
              "      <td>98</td>\n",
              "      <td>...</td>\n",
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              "      <th>2</th>\n",
              "      <td>10.554213</td>\n",
              "      <td>10.554213</td>\n",
              "      <td>0.080556</td>\n",
              "      <td>-0.538553</td>\n",
              "      <td>0.798144</td>\n",
              "      <td>41.045455</td>\n",
              "      <td>299.370</td>\n",
              "      <td>278.202</td>\n",
              "      <td>299.152144</td>\n",
              "      <td>116</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.370278</td>\n",
              "      <td>10.370278</td>\n",
              "      <td>0.099537</td>\n",
              "      <td>-0.099537</td>\n",
              "      <td>0.422306</td>\n",
              "      <td>19.375000</td>\n",
              "      <td>113.120</td>\n",
              "      <td>106.064</td>\n",
              "      <td>113.058912</td>\n",
              "      <td>44</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.075595</td>\n",
              "      <td>11.075595</td>\n",
              "      <td>0.113574</td>\n",
              "      <td>-0.908859</td>\n",
              "      <td>0.721043</td>\n",
              "      <td>47.760000</td>\n",
              "      <td>337.463</td>\n",
              "      <td>310.247</td>\n",
              "      <td>337.204179</td>\n",
              "      <td>132</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 211 columns</p>\n",
              "</div>\n",
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              "        document.querySelector('#df-de234dee-56f7-4f27-b166-0646384baf05 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-de234dee-56f7-4f27-b166-0646384baf05');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\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",
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              "    30% {\n",
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              "      border-left-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",
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              "    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",
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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",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-63204bee-7d77-4125-867b-d1525a35396e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# train and test set\n",
        "\n",
        "columns_to_exclude = ['Drug_ID', 'Drug', 'Y','clean_SMILES']\n",
        "\n",
        "X_train= train_descriptor_data.drop(columns=columns_to_exclude)\n",
        "X_test= test_descriptor_data.drop(columns=columns_to_exclude)\n",
        "y_train=train_descriptor_data[['Y']]\n",
        "y_test=test_descriptor_data[['Y']]\n",
        "\n",
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "I54Er94Cg30E",
        "outputId": "221f049f-6a8c-4395-c970-491f01188f65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MaxAbsEStateIndex  MaxEStateIndex  MinAbsEStateIndex  MinEStateIndex  \\\n",
              "0          13.269874       13.269874           0.083784       -0.615829   \n",
              "1          14.092790       14.092790           0.099761       -0.642866   \n",
              "2          13.800499       13.800499           0.049984       -3.828812   \n",
              "3          13.646430       13.646430           0.023279       -3.839793   \n",
              "4          13.681269       13.681269           0.209423       -0.258350   \n",
              "\n",
              "        qed        SPS    MolWt  HeavyAtomMolWt  ExactMolWt  \\\n",
              "0  0.472135  14.514286  465.517         442.333  465.191323   \n",
              "1  0.342034  21.850000  580.556         545.276  579.205547   \n",
              "2  0.229048  11.700000  554.720         520.448  554.246395   \n",
              "3  0.329437  11.527778  503.632         474.400  503.210344   \n",
              "4  0.379704  11.542857  488.979         463.779  488.172752   \n",
              "\n",
              "   NumValenceElectrons  ...  fr_sulfonamd  fr_sulfone  fr_term_acetylene  \\\n",
              "0                  174  ...             0           0                  0   \n",
              "1                  210  ...             0           0                  0   \n",
              "2                  206  ...             1           0                  0   \n",
              "3                  186  ...             1           0                  0   \n",
              "4                  178  ...             0           0                  0   \n",
              "\n",
              "   fr_tetrazole  fr_thiazole  fr_thiocyan  fr_thiophene  fr_unbrch_alkane  \\\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_urea        SPS  \n",
              "0        0  14.514286  \n",
              "1        0  21.850000  \n",
              "2        0  11.700000  \n",
              "3        0  11.527778  \n",
              "4        0  11.542857  \n",
              "\n",
              "[5 rows x 211 columns]"
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MaxAbsEStateIndex</th>\n",
              "      <th>MaxEStateIndex</th>\n",
              "      <th>MinAbsEStateIndex</th>\n",
              "      <th>MinEStateIndex</th>\n",
              "      <th>qed</th>\n",
              "      <th>SPS</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>HeavyAtomMolWt</th>\n",
              "      <th>ExactMolWt</th>\n",
              "      <th>NumValenceElectrons</th>\n",
              "      <th>...</th>\n",
              "      <th>fr_sulfonamd</th>\n",
              "      <th>fr_sulfone</th>\n",
              "      <th>fr_term_acetylene</th>\n",
              "      <th>fr_tetrazole</th>\n",
              "      <th>fr_thiazole</th>\n",
              "      <th>fr_thiocyan</th>\n",
              "      <th>fr_thiophene</th>\n",
              "      <th>fr_unbrch_alkane</th>\n",
              "      <th>fr_urea</th>\n",
              "      <th>SPS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.269874</td>\n",
              "      <td>13.269874</td>\n",
              "      <td>0.083784</td>\n",
              "      <td>-0.615829</td>\n",
              "      <td>0.472135</td>\n",
              "      <td>14.514286</td>\n",
              "      <td>465.517</td>\n",
              "      <td>442.333</td>\n",
              "      <td>465.191323</td>\n",
              "      <td>174</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>14.514286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14.092790</td>\n",
              "      <td>14.092790</td>\n",
              "      <td>0.099761</td>\n",
              "      <td>-0.642866</td>\n",
              "      <td>0.342034</td>\n",
              "      <td>21.850000</td>\n",
              "      <td>580.556</td>\n",
              "      <td>545.276</td>\n",
              "      <td>579.205547</td>\n",
              "      <td>210</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>21.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.800499</td>\n",
              "      <td>13.800499</td>\n",
              "      <td>0.049984</td>\n",
              "      <td>-3.828812</td>\n",
              "      <td>0.229048</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>554.720</td>\n",
              "      <td>520.448</td>\n",
              "      <td>554.246395</td>\n",
              "      <td>206</td>\n",
              "      <td>...</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.646430</td>\n",
              "      <td>13.646430</td>\n",
              "      <td>0.023279</td>\n",
              "      <td>-3.839793</td>\n",
              "      <td>0.329437</td>\n",
              "      <td>11.527778</td>\n",
              "      <td>503.632</td>\n",
              "      <td>474.400</td>\n",
              "      <td>503.210344</td>\n",
              "      <td>186</td>\n",
              "      <td>...</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11.527778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.681269</td>\n",
              "      <td>13.681269</td>\n",
              "      <td>0.209423</td>\n",
              "      <td>-0.258350</td>\n",
              "      <td>0.379704</td>\n",
              "      <td>11.542857</td>\n",
              "      <td>488.979</td>\n",
              "      <td>463.779</td>\n",
              "      <td>488.172752</td>\n",
              "      <td>178</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>11.542857</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 211 columns</p>\n",
              "</div>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Input from external data\n",
        "columns_to_exclude = ['Molecule ID', 'SMILES', 'Y','clean_SMILES']\n",
        "\n",
        "X_external=external_df_with_descriptors_cleaned .drop(columns=columns_to_exclude)\n",
        "y_external=external_df_with_descriptors_cleaned [['Y']]\n",
        "\n",
        "\n",
        "X_external.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0F2LCB6cg53D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4addcb9-df29-4219-fa13-1c7ea53ca031"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial model performance:\n",
            "MAE: 0.28725615982792485\n",
            "MSE: 0.13263596697939883\n",
            "R-squared: 0.7654603815832222\n",
            "MedAE: 0.23427894400000326\n"
          ]
        }
      ],
      "source": [
        "# RandomForestRegressor Model\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
        "\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "model=model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate the MAE, MSE, and R-squared\n",
        "initial_RF_mae = mean_absolute_error(y_test, y_pred)\n",
        "initial_RF_mse = mean_squared_error(y_test, y_pred)\n",
        "initial_RF_r2 = r2_score(y_test, y_pred)\n",
        "initial_RF_medae = median_absolute_error(y_test, y_pred)  # Calculate Median Absolute Error\n",
        "\n",
        "print(\"Initial model performance:\")\n",
        "print(f\"MAE: {initial_RF_mae}\")\n",
        "print(f\"MSE: {initial_RF_mse}\")\n",
        "print(f\"R-squared: {initial_RF_r2}\")\n",
        "print(f\"MedAE: {initial_RF_medae}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "38kKaqf2g76U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "035dd15f-8b3a-4f73-c5d2-b8604a7ebaef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -0.3483\n",
            "P-value: 0.7278\n",
            "There is no significant difference between the groups.\n"
          ]
        }
      ],
      "source": [
        "# statistical test to see if the predicted values are similar to the actual values from the test split\n",
        "\n",
        "from scipy import stats\n",
        "\n",
        "# Example data for two groups\n",
        "\n",
        "# Perform independent two-sample t-test\n",
        "t_statistic, p_value = stats.ttest_ind(y_pred, y_test['Y'])\n",
        "\n",
        "# Print the results\n",
        "print(f\"T-statistic: {t_statistic:.4f}\")\n",
        "print(f\"P-value: {p_value:.4f}\")\n",
        "\n",
        "# Interpret the results\n",
        "alpha = 0.05  # significance level\n",
        "if p_value < alpha:\n",
        "    print(\"There is a significant difference between the groups.\")\n",
        "else:\n",
        "    print(\"There is no significant difference between the groups.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PAq-gQCWg-Av",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "72c43522-8bad-44b7-9e03-eac48c442639"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# R^2 vs feature selection\n",
        "\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize cross-validation\n",
        "kf = KFold(n_splits=10)\n",
        "\n",
        "# Define the range of the number of features to select\n",
        "feature_range = np.arange(5, 201, 5)\n",
        "\n",
        "# Initialize arrays to store performance metrics\n",
        "r2_values = []\n",
        "\n",
        "# Loop over different numbers of selected features\n",
        "for top_n in feature_range:\n",
        "    # Initialize feature importance storage\n",
        "    feature_importance_aggregated = np.zeros(X_train.shape[1])\n",
        "\n",
        "    # Cross-validation for feature importance extraction\n",
        "    for train_index, test_index in kf.split(X_train):\n",
        "        X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index]\n",
        "        y_train_fold, y_val_fold = y_train.iloc[train_index], y_train.iloc[test_index]\n",
        "\n",
        "        # Train the model\n",
        "        model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "        model.fit(X_train_fold, y_train_fold)\n",
        "\n",
        "        # Aggregate feature importances\n",
        "        feature_importance_aggregated += model.feature_importances_\n",
        "\n",
        "    # Average the feature importances across all folds\n",
        "    feature_importance_aggregated /= kf.get_n_splits()\n",
        "\n",
        "    # Identify top features based on aggregated importance\n",
        "    sorted_indices_aggregated = np.argsort(feature_importance_aggregated)[::-1]\n",
        "    selected_features = X_train.columns[sorted_indices_aggregated[:top_n]]\n",
        "    X_train_selected = X_train[selected_features]\n",
        "    X_test_selected = X_test[selected_features]\n",
        "\n",
        "    # Refit the model with selected features on the entire training dataset\n",
        "    model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "    model = model.fit(X_train_selected, y_train)\n",
        "\n",
        "    # Make predictions on the test set and calculate R-squared\n",
        "    y_pred_selected = model.predict(X_test_selected)\n",
        "    r2 = r2_score(y_test, y_pred_selected)\n",
        "    r2_values.append(r2)\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(feature_range, r2_values, marker='o')\n",
        "plt.title('R-squared vs. Number of Selected Features')\n",
        "plt.xlabel('Number of Selected Features')\n",
        "plt.ylabel('R-squared')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ag7ufn_hAHC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "c017741a-159a-4566-df87-aec881ae874c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Feature selection for 2nd Random using multiple folds:\n",
        "# Cross-validation can be used to aggregate feature importances across multiple subsets of the data.\n",
        "# This provides a more robust estimation of feature importance that is less likely to be biased by any particular subset of the training data.\n",
        "\n",
        "# Taking this approach accounts for the primary dataset's slight skew and protects against data leakage\n",
        "\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Initialize cross-validation and feature importance storage\n",
        "kf = KFold(n_splits=10)\n",
        "feature_importance_aggregated = np.zeros(X_train.shape[1])\n",
        "\n",
        "# Cross-validation for feature importance extraction\n",
        "for train_index, test_index in kf.split(X_train):\n",
        "    X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index]\n",
        "    y_train_fold, y_val_fold = y_train.iloc[train_index], y_train.iloc[test_index]\n",
        "\n",
        "    # Train the model\n",
        "    model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "    model.fit(X_train_fold, y_train_fold)\n",
        "\n",
        "    # Aggregate feature importances\n",
        "    feature_importance_aggregated += model.feature_importances_\n",
        "\n",
        "# Average the feature importances across all folds\n",
        "feature_importance_aggregated /= kf.get_n_splits()\n",
        "\n",
        "# Identify top features based on aggregated importance\n",
        "top_n = 60  # Top 60 features\n",
        "sorted_indices_aggregated = np.argsort(feature_importance_aggregated)[::-1]\n",
        "selected_features = X_train.columns[sorted_indices_aggregated[:top_n]]\n",
        "\n",
        "# Visualization of aggregated feature importances\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "ax.barh(range(top_n), feature_importance_aggregated[sorted_indices_aggregated[:top_n]][::-1])\n",
        "ax.set_yticks(range(top_n))\n",
        "ax.set_yticklabels(selected_features[::-1])  # Reverse order for labels\n",
        "plt.xlabel('Feature Importance (Aggregated)')\n",
        "plt.ylabel('Features')\n",
        "plt.title('Top 60 Aggregated Feature Importances')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C8d21oq4hCUQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7869ca01-911a-47e0-90e0-8058b7e8ec53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected features model performance:\n",
            "MAE: 0.28829177197874284\n",
            "MSE: 0.134394615997029\n",
            "R-squared: 0.7623505699769322\n",
            "MedAE: 0.25481594200000046\n"
          ]
        }
      ],
      "source": [
        "# RF model with selected features\n",
        "X_train_selected_ = X_train[selected_features]\n",
        "X_test_selected = X_test[selected_features]\n",
        "\n",
        "\n",
        "# Refit the model with selected features on the entire training dataset\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "model = model.fit(X_train_selected_, y_train)\n",
        "\n",
        "# Make predictions on the test set and calculate evaluation metrics\n",
        "y_pred_selected = model.predict(X_test_selected)\n",
        "\n",
        "\n",
        "# Calculate the MAE, MSE, R-squared and MedAE\n",
        "selected_RF_mae = mean_absolute_error(y_test, y_pred_selected)\n",
        "selected_RF_mse = mean_squared_error(y_test, y_pred_selected)\n",
        "selected_RF_r2 = r2_score(y_test, y_pred_selected)\n",
        "selected_RF_medae = median_absolute_error(y_test, y_pred_selected)\n",
        "\n",
        "print(\"Selected features model performance:\")\n",
        "print(f\"MAE: {selected_RF_mae}\")\n",
        "print(f\"MSE: {selected_RF_mse}\")\n",
        "print(f\"R-squared: {selected_RF_r2}\")\n",
        "print(f\"MedAE: {selected_RF_medae}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rrJeOSPthI4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cca5cd13-a003-45cd-bd83-a259f4178094"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -0.3104\n",
            "P-value: 0.7564\n",
            "There is no significant difference between the groups.\n"
          ]
        }
      ],
      "source": [
        "# statistical test to see if the predicted values are similar to the actual values from the test split\n",
        "\n",
        "from scipy import stats\n",
        "\n",
        "# Example data for two groups\n",
        "\n",
        "# Perform independent two-sample t-test\n",
        "t_statistic, p_value = stats.ttest_ind(y_pred_selected, y_test['Y'])\n",
        "\n",
        "# Print the results\n",
        "print(f\"T-statistic: {t_statistic:.4f}\")\n",
        "print(f\"P-value: {p_value:.4f}\")\n",
        "\n",
        "# Interpret the results\n",
        "alpha = 0.05  # significance level\n",
        "if p_value < alpha:\n",
        "    print(\"There is a significant difference between the groups.\")\n",
        "else:\n",
        "    print(\"There is no significant difference between the groups.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IZOXQrlihKq8"
      },
      "outputs": [],
      "source": [
        "# Optimized RF model\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [None, 10, 20],\n",
        "    'min_samples_split': [2, 5, 10]\n",
        "}\n",
        "\n",
        "# Create the RandomForestRegressor\n",
        "model = RandomForestRegressor(random_state=0)\n",
        "\n",
        "# Use GridSearchCV to find the best hyperparameters\n",
        "grid_search = GridSearchCV(model, param_grid, cv=10, scoring='r2')\n",
        "grid_search.fit(X_train_selected_, y_train)\n",
        "\n",
        "# Get the best model\n",
        "best_model = grid_search.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CXORPTArhMfw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f6104fd-3b7d-4004-c25b-140c245ec73c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected features model performance:\n",
            "MAE: 0.28933367249108266\n",
            "MSE: 0.136198662566665\n",
            "R-squared: 0.7591604820717848\n",
            "MedAE: 0.2689412427470357\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the test set and calculate evaluation metrics\n",
        "y_pred_selected = best_model.predict(X_test_selected)\n",
        "\n",
        "\n",
        "# Calculate the MAE, MSE, R-squared and MedAE\n",
        "best_selected_RF_mae = mean_absolute_error(y_test, y_pred_selected)\n",
        "best_selected_RF_mse = mean_squared_error(y_test, y_pred_selected)\n",
        "best_selected_RF_r2 = r2_score(y_test, y_pred_selected)\n",
        "best_selected_RF_medae = median_absolute_error(y_test, y_pred_selected)\n",
        "\n",
        "print(\"Selected features model performance:\")\n",
        "print(f\"MAE: {best_selected_RF_mae}\")\n",
        "print(f\"MSE: {best_selected_RF_mse}\")\n",
        "print(f\"R-squared: {best_selected_RF_r2}\")\n",
        "print(f\"MedAE: {best_selected_RF_medae}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "efPK-e5JhOD2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14ca204a-8937-40e7-8ac4-3ed859d4ed87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -0.2408\n",
            "P-value: 0.8099\n",
            "There is no significant difference between the groups.\n"
          ]
        }
      ],
      "source": [
        "# statistical test to see if the predicted values are similar to the actual values from the test split\n",
        "\n",
        "from scipy import stats\n",
        "\n",
        "# Example data for two groups\n",
        "\n",
        "# Perform independent two-sample t-test\n",
        "t_statistic, p_value = stats.ttest_ind(y_pred_selected, y_test['Y'])\n",
        "\n",
        "# Print the results\n",
        "print(f\"T-statistic: {t_statistic:.4f}\")\n",
        "print(f\"P-value: {p_value:.4f}\")\n",
        "\n",
        "# Interpret the results\n",
        "alpha = 0.05  # significance level\n",
        "if p_value < alpha:\n",
        "    print(\"There is a significant difference between the groups.\")\n",
        "else:\n",
        "    print(\"There is no significant difference between the groups.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AOwzTK1fhXwi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f27779f-24ed-43d0-9f11-e56d85869fa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected features model performance:\n",
            "MAE: 0.4814672225771834\n",
            "MSE: 0.39453762103356693\n",
            "R-squared: 0.30073972695050566\n",
            "MedAE: 0.373758137240332\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the external data and calculate evaluation metrics\n",
        "X_external_selected= X_external[selected_features]\n",
        "\n",
        "y_pred_selected_external = best_model.predict(X_external_selected)\n",
        "\n",
        "\n",
        "# Calculate the MAE, MSE, R-squared and MedAE\n",
        "selected_RF_mae_external = mean_absolute_error(y_external, y_pred_selected_external)\n",
        "selected_RF_mse_external = mean_squared_error(y_external, y_pred_selected_external)\n",
        "selected_RF_r2_external = r2_score(y_external, y_pred_selected_external)\n",
        "selected_RF_medae_external = median_absolute_error(y_external, y_pred_selected_external)\n",
        "\n",
        "print(\"Selected features model performance:\")\n",
        "print(f\"MAE: {selected_RF_mae_external}\")\n",
        "print(f\"MSE: {selected_RF_mse_external}\")\n",
        "print(f\"R-squared: {selected_RF_r2_external}\")\n",
        "print(f\"MedAE: {selected_RF_medae_external}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "15Ifhpt7haMk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9769ea7f-007a-428e-f717-9241862e347d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: 6.9849\n",
            "P-value: 0.0000\n",
            "There is a significant difference between the groups.\n"
          ]
        }
      ],
      "source": [
        "# statistical test to see if the predicted values are similar to the actual values from the external data\n",
        "\n",
        "from scipy import stats\n",
        "\n",
        "# Example data for two groups\n",
        "\n",
        "# Perform independent two-sample t-test\n",
        "t_statistic, p_value = stats.ttest_ind(y_pred_selected_external, y_external['Y'])\n",
        "\n",
        "# Print the results\n",
        "print(f\"T-statistic: {t_statistic:.4f}\")\n",
        "print(f\"P-value: {p_value:.4f}\")\n",
        "\n",
        "# Interpret the results\n",
        "alpha = 0.05  # significance level\n",
        "if p_value < alpha:\n",
        "    print(\"There is a significant difference between the groups.\")\n",
        "else:\n",
        "    print(\"There is no significant difference between the groups.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ozHtPBVfhdzT"
      },
      "outputs": [],
      "source": [
        "# Define a function to calculate MACCS keys\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import MACCSkeys\n",
        "def calculate_maccs_keys(smiles_list):\n",
        "    keys = []\n",
        "    for smiles in smiles_list:\n",
        "        mol = Chem.MolFromSmiles(smiles)\n",
        "        maccs_key = MACCSkeys.GenMACCSKeys(mol)\n",
        "        keys.append(list(maccs_key))\n",
        "    return pd.DataFrame(keys, columns=[f'MACCS_{i}' for i in range(167)])\n",
        "\n",
        "# Generate MACCS keys for the primary dataset\n",
        "maccs_key_data = calculate_maccs_keys(df['clean_SMILES'])\n",
        "\n",
        "# Concatenate the MACCS key dataframe with the primary dataframe\n",
        "df_with_maccs = pd.concat([df, maccs_key_data], axis=1)\n",
        "\n",
        "\n",
        "# Remove rows with NaN values from both datasets\n",
        "df_with_maccs_cleaned = df_with_maccs.dropna(subset=maccs_key_data.columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mFMdDpReheaE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31613b0c-39bf-413d-9622-b681a0c4ecce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Descriptor Data Shape: (729, 171)\n",
            "Test Descriptor Data Shape: (183, 171)\n"
          ]
        }
      ],
      "source": [
        "# train and test for Maccs\n",
        "# Extract rows from descriptor_data using the indices\n",
        "train_maccs_data = df_with_maccs_cleaned.iloc[train_indices]\n",
        "test_maccs_data = df_with_maccs_cleaned.iloc[test_indices]\n",
        "\n",
        "# Display the shapes of the train and test sets\n",
        "print(\"Train Descriptor Data Shape:\", train_maccs_data.shape)\n",
        "print(\"Test Descriptor Data Shape:\", test_maccs_data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6_k_HJH3hgOw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "cb567757-b684-40c1-9317-41efd0e37e20"
      },
      "outputs": [
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              "  .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-10ce1253-3a46-458b-b584-d9ef1ad44ec0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "# train and test set for Maccs\n",
        "\n",
        "columns_to_exclude = ['Drug_ID', 'Drug', 'Y','clean_SMILES']\n",
        "\n",
        "\n",
        "X_train_maccs= train_maccs_data.drop(columns=columns_to_exclude)\n",
        "X_test_maccs= test_maccs_data.drop(columns=columns_to_exclude)\n",
        "y_train_maccs=train_maccs_data[['Y']]\n",
        "y_test_maccs=test_maccs_data[['Y']]\n",
        "\n",
        "X_train_maccs.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VlRU7nxJhiDe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4adcaee6-1c08-458a-ad2a-2ec104ac55aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial model performance:\n",
            "MAE: 0.3146318741013409\n",
            "MSE: 0.15917826568749804\n",
            "R-squared: 0.7185257472402717\n",
            "MedAE: 0.25624858299999875\n"
          ]
        }
      ],
      "source": [
        "# RandomForestRegressor Model with Maccs\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
        "\n",
        "model_maccs = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "model_maccs=model_maccs.fit(X_train_maccs, y_train_maccs)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred_maccs = model_maccs.predict(X_test_maccs)\n",
        "\n",
        "# Calculate the MAE, MSE, and R-squared\n",
        "initial_RF_mae_maccs = mean_absolute_error(y_test, y_pred_maccs)\n",
        "initial_RF_mse_maccs = mean_squared_error(y_test, y_pred_maccs)\n",
        "initial_RF_r2_maccs = r2_score(y_test, y_pred_maccs)\n",
        "initial_RF_medae_maccs = median_absolute_error(y_test, y_pred_maccs)  # Calculate Median Absolute Error\n",
        "\n",
        "print(\"Initial model performance:\")\n",
        "print(f\"MAE: {initial_RF_mae_maccs}\")\n",
        "print(f\"MSE: {initial_RF_mse_maccs}\")\n",
        "print(f\"R-squared: {initial_RF_r2_maccs}\")\n",
        "print(f\"MedAE: {initial_RF_medae_maccs}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "emYGRasLhj25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35df1bc3-aa15-402a-c07a-b77a5230a4e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -0.8940\n",
            "P-value: 0.3719\n",
            "There is no significant difference between the groups.\n"
          ]
        }
      ],
      "source": [
        "# statistical test to see if the predicted values are similar to the actual values from the test split\n",
        "\n",
        "from scipy import stats\n",
        "\n",
        "# Example data for two groups\n",
        "\n",
        "# Perform independent two-sample t-test\n",
        "t_statistic, p_value = stats.ttest_ind(y_pred_maccs, y_test['Y'])\n",
        "\n",
        "# Print the results\n",
        "print(f\"T-statistic: {t_statistic:.4f}\")\n",
        "print(f\"P-value: {p_value:.4f}\")\n",
        "\n",
        "# Interpret the results\n",
        "alpha = 0.05  # significance level\n",
        "if p_value < alpha:\n",
        "    print(\"There is a significant difference between the groups.\")\n",
        "else:\n",
        "    print(\"There is no significant difference between the groups.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oo-Th99_hlz9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "ab03cdd0-fb95-4473-caf8-85b8fb8784c7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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z5MiREn+Wjv77sOrRo0eJi0NYP0iwhsoLBQYG2v6ekpKiWbNm6aOPPirWw+o6b+jC19yBAwdkGEax33dWHh4etvtNnjxZ8+bN0/vvv68rr7xSN9xwg0aPHs00PcAJCE4AKu38Ny3Dhw/XFVdcodtuu0379u2Tv7+/1q5dq/79+9vdJy4uTs2bN9fDDz+soUOH6ssvv9SyZcv05JNPavbs2Vq1apW6dOlSoTpK+yT8wgUfUlNT1bdvXwUGBuqpp55STEyMvL29tWXLFj322GPFTpI//5NoK4vFoo4dO2revHklPuaFAeBijBw5UuPGjdO2bdvUuXNnffLJJ7rmmmts51dIReHm0KFD+uqrr7R8+XL95z//0csvv6wFCxZo/PjxF13DjBkz1K9fP/373/8ucQlmk8lkO0fjfBf23spsNldoe0nHLo/FYtHAgQM1derUEm+3Bi2rkn7OZSlv5OViBAYGauDAgRo4cKA8PDz0zjvvaMOGDerbt+9FvfYq2pPKsP77GT16tMaMGVPiPlUR6CtSy3vvvWf7MOZ8549ujhgxQmvXrtWUKVPUuXNn+fv7y2KxaMiQIcV+J5TE0d8/57vwNWexWGQymfT999+X+G/B39/f9veXXnpJY8eOtf2bf/DBBzV79mytX7++3NF+ABeH4ASgSpjNZs2ePVv9+/fXa6+9pscff1ydOnUqtrrb+W9iYmJi9Mgjj+iRRx7RgQMH1LlzZ7300ktavHixmjVrJqnok9iWLVva7nPq1KlioxDWEajU1FS7N/cXjgCsXr1ap0+f1pIlS3TVVVfZtsfFxTn8PGNiYrR9+3Zdc801Zb6BbtasmSwWiw4dOmT3Kfq+ffscfqzhw4frb3/7m2263v79+zVt2rRi+4WGhmrcuHEaN26cMjMzddVVV2nmzJlVEpz69u2rfv36ae7cuZo+fXqx20NCQvTHH38U234xoy9lOXjwoAzDsOv9/v37Jcl2sn9MTIwyMzM1YMCAKn1s68/0wIEDatu2rW17YmKiUlNTba/ZqtK9e3e98847io+Pl+T4a68kjvYkJiZGy5YtU0pKSpmjTiU9vnX1yMLCwnIfp1mzZtq5c2exn2VF/n2UxTrSGx4eXmYtZ86c0cqVKzVr1iy713dJU19L6/n5v3/OV5F/AzExMTIMQy1atHAoxHbs2FEdO3bUP/7xD61du1Z9+vTRggUL9Mwzzzj8mAAqjnOcAFSZfv36qUePHpo/f75ycnIUEhKiAQMG2H15e3srOztbOTk5dveNiYlRQECAbTniAQMGyMPDQ6+++qrdqMOFK6JZ7yvJ7twV63LJ57N+knv+8fLy8vSvf/3L4ec4YsQInThxQm+99Vax286ePausrCxJRdeXkaRXXnnFbp+S6i9NcHCwBg8erE8++UQfffSRPD09i11w9Pwlk6WiT6ZbtWplt6xzWlqa9u7dW+lpR9ZznUqafhUTE6O9e/farSy2fft2rVmzplKPVZ6TJ0/aVq+TpPT0dL377rvq3LmzLZSPGDFC69at07Jly4rdPzU1VQUFBZV67Ouuu05S8Z+hdQTIkRUTL5Sdna1169aVeNv3338v6c/pa46+9kriaE9uvvlmGYZR4lL45/+78fPzKxYUzGazbr75Zn3++efauXNnsfuf/xq57rrrdPLkSX322We2bdnZ2aVO8auowYMHKzAwUM8995zy8/NLraWk3wlSyf9OrddauvB5BwYGKiwsrNi5cxX5vXLTTTfJbDZr1qxZxWoxDMP27zw9Pb3Y67djx45yc3Or8qXcARTHiBOAKjVlyhTdcsstWrRoUannxezfv1/XXHONRowYoXbt2snd3V1ffPGFEhMTNXLkSElFn14/+uijmj17tv7yl7/ouuuu09atW/X999/bTVWTpEGDBqlp06a6++67NWXKFJnNZr399ttq2LChjh49atuvd+/eCgkJ0ZgxY/Tggw/KZDLpvffeq9B0sDvuuEOffPKJ7rvvPv3444/q06ePCgsLtXfvXn3yySdatmyZunfvrs6dO2vUqFH617/+pbS0NPXu3VsrV67UwYMHK9TPW2+9VaNHj9a//vUvDR48uNh0uXbt2qlfv37q1q2bQkNDtWnTJn322WeaOHGibZ8vvvhC48aN08KFC+2ut+Oovn37qm/fvvrpp5+K3XbXXXdp3rx5Gjx4sO6++24lJSVpwYIFat++fbFzyKrCJZdcorvvvlu//fabIiIi9PbbbysxMVELFy607TNlyhR9/fXX+stf/qKxY8eqW7duysrK0u+//67PPvtMhw8fLvYackSnTp00ZswYvfnmm7Zpnxs3btQ777yj4cOHF5uW6ojs7Gz17t1bPXv21JAhQ9SkSROlpqbqyy+/1C+//KLhw4fbpq46+toriaM96d+/v+644w698sorOnDggG262i+//KL+/fvbXlfdunXTDz/8oHnz5qlRo0Zq0aKFYmNjNWfOHP3444+KjY3VPffco3bt2iklJUVbtmzRDz/8YDvn8Z577tFrr72mO++8U5s3b1ZUVJTee++9KrvAdGBgoN544w3dcccd6tq1q0aOHGn7ffDdd9+pT58+eu211xQYGKirrrpKzz//vPLz8xUdHa3ly5eXOArdrVs3SdITTzyhkSNHysPDQ0OHDpWfn5/Gjx+vOXPmaPz48erevbt+/vln20ioI2JiYvTMM89o2rRpOnz4sIYPH66AgADFxcXpiy++0L333qtHH31Uq1at0sSJE3XLLbfokksuUUFBgd577z1baAVQzZy/kB+A2q6kZamtCgsLjZiYGCMmJsYoKCgo8f7JycnGhAkTjDZt2hh+fn5GUFCQERsba7c0sfVYs2bNMqKiogwfHx+jX79+xs6dO4sttWwYhrF582YjNjbW8PT0NJo2bWrMmzevxOXI16xZY/Ts2dPw8fExGjVqZEydOtVYtmxZsSWQ+/bta7Rv377E+vPy8oy5c+ca7du3N7y8vIyQkBCjW7duxqxZs4y0tDTbfmfPnjUefPBBo0GDBoafn58xdOhQ49ixYw4tR26Vnp5u+Pj4GJKMxYsXF7v9mWeeMXr06GEEBwcbPj4+Rps2bYxnn33WyMvLs+1j7UNJyyVfSOctR36+H3/80bbc84U/98WLFxstW7Y0PD09jc6dOxvLli0rdTnyC5egtx73wuWVS3qNNWvWzLj++uuNZcuWGZdddpnh5eVltGnTpsSlmTMyMoxp06YZrVq1Mjw9PY2wsDCjd+/exosvvmjrTWk1lSU/P9+YNWuW0aJFC8PDw8No0qSJMW3aNLtl0w3D8eXI8/PzjbfeessYPny40axZM8PLy8vw9fU1unTpYrzwwgtGbm6u3f6OvvZK+jfiSE8MwzAKCgqMF154wWjTpo3h6elpNGzY0Lj22mvtlkjfu3evcdVVV9lem+c/VmJiojFhwgSjSZMmhoeHhxEZGWlcc801xptvvmlXz5EjR4wbbrjB8PX1NcLCwoyHHnrIWLp0aYWWIy/pd9D5fvzxR2Pw4MFGUFCQ4e3tbcTExBhjx441Nm3aZNvn+PHjxo033mgEBwcbQUFBxi233GKcPHmyxH+nTz/9tBEdHW24ubnZ/W7Jzs427r77biMoKMgICAgwRowYYSQlJZW6HPmpU6dKrPfzzz83rrjiCsPPz8/w8/Mz2rRpY0yYMMHYt2+fYRiG8ccffxh33XWXERMTY3h7exuhoaFG//79jR9++KHMPgCoGibDqMSZtwDgQs2bN1e/fv20aNEiV5cCAADqCc5xAgAAAIByEJwAAAAAoBwEJwAAAAAoB+c4AQAAAEA5GHECAAAAgHIQnAAAAACgHPXuArgWi0UnT55UQECATCaTq8sBAAAA4CKGYSgjI0ONGjWSm1vZY0r1LjidPHlSTZo0cXUZAAAAAGqIY8eOqXHjxmXuU++CU0BAgKSi5gQGBjp0n/z8fC1fvlyDBg2Sh4dHdZZX79Fr56HXzkOvnYM+Ow+9dh567Tz02nlqUq/T09PVpEkTW0YoS70LTtbpeYGBgRUKTr6+vgoMDHT5D7euo9fOQ6+dh147B312HnrtPPTaeei189TEXjtyCg+LQwAAAABAOQhOAAAAAFAOghMAAAAAlIPgBAAAAADlIDgBAAAAQDkITgAAAABQDoITAAAAAJSD4AQAAAAA5SA4AQAAAEA5CE4AAAAAUA6CEwAAAACUg+AEAAAAAOUgOAEAAABAOdxdXQAAAEB1K7QY2hiXoqSMHIUHeKtHi1CZ3UyuLgtALUJwAgAAddrSnfGa9c1uxafl2LZFBXlrxtB2GtIhyoWVAahNmKoHAADqrKU743X/4i12oUmSEtJydP/iLVq6M95FlQGobQhOAACgTiq0GJr1zW4ZJdxm3Tbrm90qtJS0R8UeZ92h0/pq2wmtO3T6oo8HoGZiqh4AAKhzCgoten/D0WIjTeczJMWn5eiZ73ZrYNsItQr3V8MAL5lMjp/7xDRAoP4gOAEAnIYT9J2nvvXaMAwdOpWlNQeT9cuBZG3447Qycgscuu/CNYe1cM1hSVKAt7tahfurVUP/oj/D/RXT0F9NQn2L9c86DfDC8SXrNMA3RnclPAF1CMEJAOAUfDLvPNXZ60KLoQ1xKdqcbFKDuBT1ahVeZYGsomEvKT1Haw4l69cDp7XmYLIS0u1Hl/w8zcrKKyz3cbs2DdGZ7DwdOZ2ljJwCbT2aqq1HU+328XR3U8swP8WcC1Utw/z09HelTwM0qWga4MB2kXU6sAL1CcEJAGCnOkYqqvuT+ep8M1/bVGev7QOZWe8e2FRlgcyRsJeZW6ANf5zWrweTteZgsvYnZtodw9PdTT2ah6pPqzBd0SpMl0YGqO8LPyohLafEgGOSFBnkrU/v6yWzm0m5BYU6nJytg0mZRV+niv7841Smcgss2puQob0JGQ49H+s0wI1xKeoV06CSXQFQkxCcAAA21TFSUd4J+hf7yXx1vpmXateUt+rsdXUHstKOfd/iLbq+Y5QS03O07ViqCs5beMFkkjpGB9mCUrdmIfL2MNsdY8bQdrp/8RaZJLvjm8673doLL3ezLo0M0KWRAXbHKLQYOnHmrA6eyrCFqo1xKTp8Orvc5xafdtbBLgCo6QhOAABJVfPG+GxeoVKy83QmK08pWXk6k52nLUfOOHSC/rwV+9S9WaiCfD0U5OOhYJ+iP93NpS8AW90jWbVteuHag8kO9br/i6sVFeStQB8PBXi7K9DbQ4E+Hgr0dv/zT9s2D/l5mTXz68oFMsMwVGAxlF9oUX6BobxCiwosf/49t6BQT3y5s8yV7777/c8lw5s18LUFpd4xDRTs61lmT4Z0iNIbo7sW+zlGVuDnaHYzqWkDXzVt4Kur20RIktYdOq1Rb60v975PfPG7Vu1N0tVtwtXv0nCF+pVdL+qe2vThC8pGcAIAOLRs87QlvysxI1dp2fm2UGT980xW0baz+eWfT1Ka1388JOlQse3+Xu4KOheign3//DPA20MfbjharSNZteHE/4ycfK3ed0ordidq+e4Eh+5zNCVbR1PKHy1xlDWQXf7sCrmZTMorsPwZlgqrZmnue65soTt7NVeTUN8K33dIhygNbBdZpW9ee7QIVVSQd6nTAKWiEbGz+RZ9uyNe3+6Il8lUdD7V1W3CdXWbcLWJDChzBb+adD4ZKqe6P3ypjT/H2jy1muAEANDGuJQyRyok6Ux2vmZ8tavcY3mYTQr181SIr6dC/TxlMQyt/yOl3Pt1aBQomaS0s/lKzc5XRk7RimiZuQXKzC3QidSKTXmyvpnv8ewKNQzwthtZ+fPv7grw9rjg7+7y83LXzK931dgT/+PTzuqH3YlavjtR6/84XeFwMu3aNooO8VH62QKl5+Qr/WxRv61/T88pOPdnvtLPFjgciFOy8h3az9PsJg+zSe5mN1kshkOr33WIDqpUaLIyu5mq9Fwjs5up3GmAr4/qqshgb63ak6SVe5O0Jz5dm4+c0eYjZ/TCsn1qFOSt/m3CdU3bcPWOCbObZujq88lqotoWEmrziHh19bq6p1ZXN4ITANRTadn5WvfHaa09lKylOx0bqegYHagO0UG2UGT7089Tob6eCvX3lJ+n2e5T9EKLoSvmrir3BP2vJl5h9x9zQaFFGTkFSj2bfy5M5SnN9vd8bTl6Rqv3nSq35tNZ+Trt4Bt6R/154v9p9YoJq/RxHH1zYhiG9iVmaMWuRK3Yk6gdx9Psbm/Z0E8D20XomjYRevDDrUpML7vX469sWaE3Qb8eOKXR/91Y7n7PDe+grs1D5GF2k6fZTe5mkzzMbnbfu7uZ7F4fjk55Cw/wdrheZ3F0GmDXpiF6dPClOpl6Vj/uS9KqPUlacyhZJ9Ny9P6Go3p/w1F5e7ipd0yYrm4TLjeT9MQXxacvVvf5ZDVpJPVCtS3sOePcTucsAlOkKnpdW1975yM4AUAtVJlPA8/mFWrTkRStOVgUlnaeSJOlgrOo/u+6dhX+1N6RT+bPP0Hfyt3sppBzoawk6w6ddig4PTu8g5o18FN6Tr4yzo2gFP39vFGVc3+3jrpYR7vKM/btjbo0KtB2vR/rtX+ahvqWeW6WVP6bk4JCizYdOaMVuxO1Ynei3dQ6k0nq0iRYg9pHamC7CMU09LfdNvOGive6PL1iwsqclmYNZLf2aFrhY5c35c167B4tQit0XGepyDTARsE+uj22mW6Pbaac/EKtO3RaK/cmatWeJJ1My9GqvUlatTep1Me62Dfc1f1mvrrUtjfchmHo2+0nHTrfMPbZHxTo6yFvd7O8Pdzk7WGWt4dZPh5meVm/t7vNTZ7ubnp5xYFatQhMQaFFM2rwKL6jCE4AUMs4+mlgfqFFO46nae3BZK05lKwtR1KVV2ixO1ZMQz/1aRWmni0aaNa3u5SUnlstb16r4gT9Czn6hntkJd7MrzmYrNv/s6Hc/XILDe04nlZsBMjT7KYWYX5FF089F6pah/urRZifvD3M5a4i17NFA+1LTNeZ7D9Hyjzd3XRlq7CikaW2EWoY4FViTdXR68qGX1cf21kqMw3Q28Os/m3C1b9NuIxhRSOKK/ck6attJ4ots34+6xvurk+vkI+HWWY3k0ymohrMJpPczv/TTTKbikb4zG4mZeUUOPRm/mKXUK/KaV7OCnsXc97N2bxCbT+eqi1Hz5y7BtgZJWfmOXTf5Kw8JWc5tq+jzg9lDQO9FeTz53mi538F+ngo2NfT9r2/l3u5i8DM/Hq3OkYHKyM3X2nZ52YAnC2a4mudDWCdGXD+V2p2Xpkf1NWW5fsJTgBQi5T3aeATf2krGdLaQ6e1MS5FmRecOxIV5K3eMWHq06qBeseEKTLoz+lPbm6q1jevVX2CfnW+4e7ZsoFDoWzRuMsVl5ylg0mZOnBumepDpzKVk2/RvsQM7Uu0v+aPm0lqHOKjxFICqnXb+rjTkqRgXw9d3SZcg9pF6MrWDeXn5dh/29WxGEJ1BDJnHLs2MJlMahMZqDaRgWoc4qOHPtpW7n2sb0irwyOfblf3ZiFqHe6v1hH+ahUeoGYNfOVRziiqVHXTvLJyC3TsTLZ+2J3oUNj7908HNbBdpBqH+MrH01zq/uXXXPZ5N4Zh6FjKWW05esb2tSc+Q4UXpAKzm3TB51QlemZ4B7UO91dOgUU5+YXnfVn+/LPgz225+YX6IzlT246llXvsqg5lhqSE9Bz1mbuqyo55oaSMss+1dTWCEwDUEo6sfPfMt3vstgf7eqhXywbq3SpMfWIaqEWYX6mreDnjzWtVn6BfXTU7GsoujQzUpZGBdve1WAydSD3750VUkzJ1IKno+j/pOQU6muLYIhdPXt9WY3o3L3fKX1nPoao/ubUGsnUHk7T8lw0adGVsla2IVR1hrzZy9FyuuTd3VPtGQbIYhgotxrk/dd7fDRUahozztu9NSNf8Hw6Ue+yTqWf19QWLsXiYTWoR5qfW4QFFI6gR/modHqDmYb7yci8KKhWZ5pVfaNHJ1LM6lnJWx85k69i5lR6PnTmr4ynZOl3BN/zPL9uv55ftlySF+XupcYiPmoT6qkmIjxqH+KpJqI+ahPiqUbCPPN3//DdVXs3zR3ZWRKB3uaNJEYFe6to0pOirWbDaRAZqwLyfyv3wZVQlRsQdPS/w6eEd1DTU98+Rn/POE/3zq8A2WnThB22lMZmkkPNGqi5c9bT4dk8dTMrUhA+2lHvsmngu4/kITgBQSziy8p0kXdY4SNd3jFKfVmFqFxUotwr8p1wb37xW15v5yoYyNzdT0Ru2UF/1bxNu224Yhk5l5urdtYf12o/Fl12/UFiAV6VDU3Uyu5kU2yJUp/cYiq3i10Z1hL3axtEpqH/t1qTCvR/YLkIf/3aszGM3DPDSM8M66FByli3wH0zKVHZeofYnZhabRmh2M6lZA1+1auintYdOl/nBzqSPt2vhmjgdP5Oj+LSz5Z5jGeTjoRA/Dx1OLn/p/KahvjqTlaeM3AIlZ+YqOTNX246lFn+OJiky0FtNQnwVHeKtFbuTyqy5pNE/D7NJ7RsF2UJSl6YhahTkXexDqeoaEXf0NXJbBUOZo4vAfDA+tsKL4rQK96/V5zJaEZwAoBYwDENrDyU7tO/dV7TQsM7RlX6s2vjmtbrezFdlkDSZTAoP8FafVg0dCk41/ZNXVA9Xn0/21LD2GtQh0u5+Fouhk2lni6ajJhaNoFr/npFboD9OZemPU1nlPv7Z/EJtiDtj+97L3e28kSFfNQ0tGhkqGiHyVZCPh8Orcv74aD+5nbucwfEzZ3UsJfvcSNZZHT9zbiTrTLZy8i2KT8sp+jDksGN9C/H1UM+WDWxBqX2jILul40vj6hHx6loEpkeLiv//UBfOZZQITgBQo53KyNXnW47r49+OKS65/DcmEm+4q1pVB8navoocql9NO5/Mzc2kxiG+ahziq/6X2o+iJqbn6kBShr7YckJLtp4o9/Hv6NlUwzpHq0morxr6e5U7Il7RN9zBvp4K9vVUh+igYscyDEPJmXm2qYErdifq2x3x5dY8c2h7DetSuQ+jqmsUv7YtAlNdNTsbwQkAaphCi6GfD5zSRxuPauWeJBWcm8/i6+Emw2TS2bySL0bKG+7aoa588orqVRvOJzOZTIoM8lZkkLfc3dwcCk7XdWyk7s0r9juqqt5wm0wmNQzwUsOAovORwgO8HQpO4YEX92FUdY3i17ZFYM6vuTpe185AcAKAGuL4mWx9uum4Pt10TCfP+w+rS9Ngjbq8qa6/LEq/HDil+xcXnWDLG+7aqy588orqV5vOJ6vukdTqCAl1YfS3OheBqa5zXavzdV3dCE4AUI3KuzZIXoFFK/ck6sPfjumXA6dknPvfO9jXQzd2idbIy5vq0sgA2/684a47auNCHEBpnDGSWtUhgdHf0tXGc12dgeAEANWkrGuDtI4I0Me/HdPnm4/bLbvbO6aBRvZoqkHtIko9+Zg33HUHb05Ql9TGD3ZqY81wHYITAFSD0q4NEp+Wo/sW21/LIjzAS7d0b6wR3ZuoWQM/h47PG24ANVFt/GCntp93A+chOAFAFSvrQrXnu/rShhoV20z9L21YI6/XAwCVURs/2KnN593AeQhOAFDF1h1KduhCtfdcFVPr3lwAAFBfEZwAoAqcycrT6v1J+mFPklbuTnToPkkZ5YcrAABQMxCcAKASDMPQoVOZRUFpT6I2HzkjS3lz8y7AhWoBAKg9CE4A6r1Ci+HQicx5BRb9djhFP+xJ1Mo9STqakm13e5vIAF3TNlz9Lg3X3z/YqsT02nttEAAAYI/gBKBes18yvEjUecvQnsnK04/7krRyT5J+3n9KGbkFtv08zW7qGdNAA9qG6+o24Woc4mu7beYNXBsEAIC6hOAEoN4qb8nwmIZ+ikvOspuCF+bvqf6XhuuathG6snWY/LxK/jXKtUEAAKhbCE4A6iVHlgw/dCpLUtEUvAFtI3RN23B1ahwsNwdHirg2CAAAdUeNuHDI66+/rubNm8vb21uxsbHauHFjqfv269dPJpOp2Nf111/vxIoB1GZZuQV665c/HFoy/JVRnbX04av06OBL1aVpiMOhycp6bZBuYVwbBACA2szlI04ff/yxJk+erAULFig2Nlbz58/X4MGDtW/fPoWHhxfbf8mSJcrLy7N9f/r0aXXq1Em33HKLM8sG4GSOLuBQkoycfG06ckbr/zitDX+k6PcTaSp0cAk8o4Ir5QEAgLrJ5cFp3rx5uueeezRu3DhJ0oIFC/Tdd9/p7bff1uOPP15s/9BQ+1WoPvroI/n6+hKcgDqsvAUcLpR2Nl+bDqdoQ1yK1v9xWjtPpBVbKjzMz1PJWXnF7nshlgwHAACSi4NTXl6eNm/erGnTptm2ubm5acCAAVq3bp1Dx/jvf/+rkSNHys/Pr8Tbc3NzlZuba/s+PT1dkpSfn6/8/HyHHsO6n6P7o/LotfPUll4v25Wov3+0vdi5SAlpObp/8Ra9OrKTYluEatORM9p4+Iw2xKVoT0JGsZGiJiE+6tEiRLHNQ9WjRYgiA73V76WflZieW8aS4V7q0jjgontUW3pd29Fn56HXzkOvnYdeO09N6nVFajAZhusmopw8eVLR0dFau3atevXqZds+depU/fTTT9qwYUOZ99+4caNiY2O1YcMG9ejRo8R9Zs6cqVmzZhXb/sEHH8jX17eEewCoKSyGNGuLWal50p8LeZ/PkNkkFRrFb2/obahVoKGYwKI/Q7yK33v7aZPe3m891fP8+xf9WrzrEos6NWCuHgAAdVV2drZuu+02paWlKTAwsMx9XT5V72L897//VceOHUsNTZI0bdo0TZ482fZ9enq6mjRpokGDBpXbHKv8/HytWLFCAwcOlIeHx0XXjdLRa+epDb3eEJei1PWbytjDdC40SS3D/NSjRYh6NC/6iggsf4rddZK67krUM//bq4T0P0emo4K89cS1bTS4fcTFPYFzakOv6wL67Dz02nnotfPQa+epSb22zkZzhEuDU1hYmMxmsxITE+22JyYmKjIyssz7ZmVl6aOPPtJTTz1V5n5eXl7y8ir+UbOHh0eFf1CVuQ8qh147T03u9ensgvJ3kvT08Pa6o2fzSj3GXzo31rWXRVd64YmKqMm9rkvos/PQa+eh185Dr52nJvS6Io/v0uXIPT091a1bN61cudK2zWKxaOXKlXZT90ry6aefKjc3V6NHj67uMgG4QEGhRTtPpDm0b6uGARf1WGY3k3rFNNCwztHqFdOAJcMBAEAxLp+qN3nyZI0ZM0bdu3dXjx49NH/+fGVlZdlW2bvzzjsVHR2t2bNn293vv//9r4YPH64GDRq4omwA1cQwDH2/M0EvLtunP5Kzyty3aAGHohEiAACA6uTy4HTrrbfq1KlTmj59uhISEtS5c2ctXbpUERFF5xYcPXpUbm72A2P79u3Tr7/+quXLl7uiZADV5NcDyXp+2V7tOF400hTq56lr2oTrs83HJclu9TvrmNCMoe0YIQIAANXO5cFJkiZOnKiJEyeWeNvq1auLbbv00kvlwsUAgVqv0GJoQ1yKNieb1CAuRb1ahbs0fOw4nqrnl+7TrweTJUl+nmaNv7Kl7rmqpfy93HVN2/Bi13GKLOM6TgAAAFWtRgQnAM5jfzFZs949sKnMi8lWp0OnMjVv+X5993u8JMnDbNLons00oX8rhfn/uajLkA5RGtgu0ikLOAAAAJSE4ATUI0t3xuv+xVtKvZjsG6O7OiU8JaTl6J8r9+uTTcdVaDFkMkk3donWpAGXqEloyddXsy7gAAAA4AoEJ6CeKLQYmvXN7mKhSSo6d8gkadY3uzWwXWSlR3IKLUaZo0Kp2Xl646dDWrTmsHILLJKkAW3DNWVwG10aeXEr4wEAAFQnghNQT2yMS7E7R+hChqT4tBz9evCU+l4SXuHj208BLGKdAtj3knAtXBunBasPKT2n6NpMlzcP0WND2qh7c1bEAwAANR/BCagnkjJKD03nG/P2b4oK8laLMD81D/NTiwbn/gzzVZNQX3m5m4vdp6wpgPct3qJAb3dbYGoTGaCpQy5V/0vDZTJxjhIAAKgdCE5APWAYhvacTHd4//i0HMWn5WjtodN2291MUnSIj5o38CsKVg381KyBr578clepUwAlKT2nQNHB3np08KW6oVM0izoAAIBah+AE1HEnU8/q/774Xav3nSpzP+vFZL+eeIWOpmQpLjlbh5OzFHc6S4eTi76y8gp1LOWsjqWc1S8HkitUx5ybLtOVlzS8iGcCAADgOgQnoI4yDEMf/3ZMz363Rxm5BfJ0d9N1HaL01bYTRbeft+/5F5NtGOClhgFe6tYstNjxTmXmKu5Ulg6f/jNY7TieqpNlnDtllZKdV0XPDAAAwPkITkAddPxMtqYt+d02KtSlabBe+OtlahUeoCEdIip1MVmTyaTwAG+FB3grtuWfy4KvO3Rao95aX25N4QHeF/GMAAAAXIvgBNQhFouh9zce1Zz/7VFWXqG83N306KBLddcVLWznFVkvJrvuYJKW/7JBg66MVa9W4ZU+76hHi1BFBXkrIS2nxPOcrFMAe7Rg9TwAAFB7EZyAOuLo6Ww99vkOrfujaEGHy5uHaO7Nl6llQ/9i+5rdTIptEarTewzFXnCtpYoyu5k0Y2g73b94i0wqfQogC0IAAIDajOAE1HIWi6F31x3W3KX7dDa/UN4ebnpsSBuN6dVcbk4KK0M6ROmN0V0rNQUQAACgNiA4AbXY4eQsTf18hzbGpUiSYluE6vm/XqZmDfycXot1CuDGuBQlZeQoPKBoeh4jTQAAoC4gOAG1UKHF0MI1cXpx+T7l5Fvk62nWtGvb6PbYZk4bZSqJ2c2kXjENyt8RAACgliE4ATVUocUocfTm0KlMTf1shzYfOSNJ6h3TQHNvvkxNQn1dXDEAAEDdRXACaqClO+OLny8U6K3eMQ303e/xyi2wyN/LXf93XVuN6tFEJhPT4QAAAKoTwQmoYZbujNf9i7cUW9o7IT1HS7YWXbz2ytZhmnPzZYoO9nF+gQAAAPUQwQmoQQothmZ9s7vE6yFZBfl4aOHYy+VudnNaXQAAAPUd77yAGmRjXIrd9LySpJ3N12+HzzipIgAAAEgEJ6BGScooOzRVdD8AAABUDYITUIOEB3hX6X4AAACoGgQnoAZp3sC3zAvGmiRFBRUtTQ4AAADnITgBNURSeo5u/+8GFVpKXhrCGqdmDG1XZrgCAABA1SM4ATVAQlqORr65Xn+cylJ0sI+eHtZBUUH20/Eig7z1xuiuGtIhykVVAgAA1F8sRw64WHzaWY16c70On85WdLCPPrq3p5qE+uq22KbaGJeipIwchQcUTc9jpAkAAMA1CE6AC51MPatRb63XkdPZahziow/vKQpNkmR2M6lXTAMXVwgAAACJ4AS4zInUopGmoynZahJaFJoah/i6uiwAAACUgOAEuMDxM9ka9dZ6HUs5q6ahvvrw3p6KDvZxdVkAAAAoBcEJcLJjKUWh6fiZs2rWwFcf3tNTjQhNAAAANRrBCXCiYynZGvnmep1IPavmDYpGmqKCCE0AAAA1HcEJcJKjp4tGmk6knlXLMD99cE9PRV6w5DgAAABqJoIT4ARHTmdp1JvrdTItRy0b+unDe3oqIpDQBAAAUFsQnIBqdjg5S6PeWq/4tBzFnAtN4YQmAACAWoXgBFSjuOSikaaE9By1CvfXB/fEKjyA0AQAAFDbEJyAavLHqUyNemu9EtNz1TrcXx/c01MNA7xcXRYAAAAqgeAEVIODSZm67a31SsrI1SURRaEpzJ/QBAAAUFsRnIAqdjApQ6Pe2qBTGblqExmg98fHqgGhCQAAoFYjOAEXodBiaGNcipIychQe4K0QXw+N/u9GJWcSmgAAAOoSghNQSUt3xmvWN7sVn5Zj2+ZmkiyG1DYqUO+Pj1Won6cLKwQAAEBVITgBlbB0Z7zuX7xFxgXbLec23N2nOaEJAACgDnFzdQFAbVNoMTTrm93FQpOVSdJLK/ar0FLaHgAAAKhtCE5ABW2MS7GbnnchQ1J8Wo42xqU4rygAAABUK4ITUEFJGaWHpsrsBwAAgJqP4ARUUHiAd5XuBwAAgJqP4ARUUI8WoYoMLH2JcZOkqCBv9WgR6ryiAAAAUK0ITkAFmd1M6tg4uMTbTOf+nDG0ncxuphL3AQAAQO1DcAIqaPuxVK3ckyhJCvH1sLstMshbb4zuqiEdolxRGgAAAKoJ13ECKiCvwKKpn+2QxZCGd26kl0Z01sa4FCVl5Cg8oGh6HiNNAAAAdQ/BCaiAN1Yf0r7EDIX6eWr60PYyu5nUK6aBq8sCAABANWOqHuCgfQkZeu3HA5KkmTe0V6ifp4srAgAAgLMQnAAHFFoMTf18h/ILDQ1oG6Ghl3EOEwAAQH1CcAIcsHBNnLYfS1WAl7ueGd5BJhPnMQEAANQnBCegHEdPZ+vF5fskSf93fVtFBnFhWwAAgPqG4ASUwTAMPb5kh3LyLerVsoFGXt7E1SUBAADABQhOQBk+/u2Y1h46LW8PN825uSNT9AAAAOopghNQioS0HD373R5J0qODLlWzBn4urggAAACuQnACSmAYhv7x5U5l5BaoU5NgjevTwtUlAQAAwIUITkAJvt0Rrx/2JMrDbNLzN18msxtT9AAAAOozghNwgZSsPM38epckaUL/Vro0MsDFFQEAAMDVCE7ABZ7+drdOZ+Xp0ogAPdCvlavLAQAAQA1AcALO8+PeJH2x9YTcTNLcv14mT3f+iQAAAIDgBNhk5OTr/774XZJ0V58W6twk2LUFAQAAoMYgOAHnzF26V/FpOWoa6qtHBl3q6nIAAABQgxCcAEnr/zitxeuPSpLm3NxRPp5mF1cEAACAmoTghHovJ79Qj3++Q5I0qkcT9Y4Jc3FFAAAAqGkITqj3Xv5hvw6fzlZEoJemXdfW1eUAAACgBiI4oV7bcTxVb/38hyTp2eEdFejt4eKKAAAAUBMRnFBv5RVYNPWzHbIY0tBOjTSgXYSrSwIAAEANRXBCvfXvnw5pb0KGQnw9NGNoO1eXAwAAgBqM4IR66UBihl5ddVCSNGNoe4X5e7m4IgAAANRkBCfUO4UWQ1M/36G8QouubhOuYZ0bubokAAAA1HAEJ9Q776w9rK1HU+Xv5a5nhneQyWRydUkAAACo4dxdXQBQ3QothjbGpSgpI0cmSc8v3StJevzaNmoU7OPa4gAAAFArEJxQpy3dGa9Z3+xWfFqO3fZW4f66rUdTF1UFAACA2oapeqizlu6M1/2LtxQLTZJ0MClTy3cnuKAqAAAA1EYEJ9RJhRZDs77ZLaOU202SZn2zW4WW0vYAAAAA/kRwQp20el9SiSNNVoak+LQcbYxLcV5RAAAAqLU4xwl1Ql6BRduOperXg8n69cApbT2W6tD9kjJKD1cAAACAFcEJNUahxdCGuBRtTjapQVyKerUKl9mt5KXCDcPQ/sRM/XowWWsOJmv9H6eVnVdY4ccMD/C+2LIBAABQDxCcUCPYr35n1rsHNikqyFszhrbTkA5RkqSEtBxbUPr1YLJOZeTaHaOBn6d6twrTFa0aqGfLBhr55nolpOWUeJ6TSVJkkLd6tAit9ucGAACA2o/gBJezrn53YcBJSMvRfYu3qP+lDXXszFkdTMq0u93bw009WjTQFa0a6IpWDdUmMkBu541QzRjaTvcv3iKTZHds03m3lzaiBQAAAJyP4ASXKmv1O+u2H/edkiS5maSOjYNtQalrs2B5uZtLPfaQDlF6Y3TXYtdxirxgJAsAAAAoD8EJLrUxLqXM1e+sJg1orbG9WyjI16NCxx/SIUoD20VqY1yKkjJyFB5QND2PkSYAAABUBMEJLuXoqnbNw/wqHJqszG4m9YppUKn7AgAAABLXcYILFVoMrT102qF9Wf0OAAAArsSIE1wiIS1Hkz7epnV/lB2cWP0OAAAANQEjTnC65bsSdO0/f9a6P07L19OsO3o1k0l/rnZnxep3AAAAqCkYcYLT5OQX6tnv9ui99UckSR2iA/XKyC5q2dBffWIasPodAAAAaiyCE5xiX0KGHvxwq/YlZkiS7rmyhaYMbiNP96JBT+vqd+sOJmn5Lxs06MpY9WoVzkgTAAAAagSXT9V7/fXX1bx5c3l7eys2NlYbN24sc//U1FRNmDBBUVFR8vLy0iWXXKL//e9/TqoWFWUYht5dd1hDX/tV+xIzFObvpXfu6qEnrm9nC01WZjeTYluEqluYoViWDAcAAEAN4tIRp48//liTJ0/WggULFBsbq/nz52vw4MHat2+fwsPDi+2fl5engQMHKjw8XJ999pmio6N15MgRBQcHO794lCslK09TP9uhH/YkSpL6XdpQL97SSWH+Xi6uDAAAAKgYlwanefPm6Z577tG4ceMkSQsWLNB3332nt99+W48//nix/d9++22lpKRo7dq18vAouqZP8+bNnVkyHLT2YLImfbJNiem58jS76fFr22hcn+YymRhFAgAAQO3jsuCUl5enzZs3a9q0abZtbm5uGjBggNatW1fifb7++mv16tVLEyZM0FdffaWGDRvqtttu02OPPSaz2VzifXJzc5Wbm2v7Pj09XZKUn5+v/Px8h2q17ufo/vVZfqFF/1x5SG/+GifDkFqG+enlER3VLipQBQUF5d+fXjsNvXYeeu0c9Nl56LXz0GvnodfOU5N6XZEaTIZhGNVYS6lOnjyp6OhorV27Vr169bJtnzp1qn766Sdt2LCh2H3atGmjw4cP6/bbb9cDDzyggwcP6oEHHtCDDz6oGTNmlPg4M2fO1KxZs4pt/+CDD+Tr61t1TwhKzpHePWDWkcyiUaVe4Rbd2Nwir5IzLQAAAOBS2dnZuu2225SWlqbAwMAy961Vq+pZLBaFh4frzTfflNlsVrdu3XTixAm98MILpQanadOmafLkybbv09PT1aRJEw0aNKjc5ljl5+drxYoVGjhwoG2KYH1VaDG06cgZJWXkKjzAS92bhcjsZtJX205q3rd7lJVbqEBvdz0zrJ2u7RBZ4ePTa+eh185Dr52DPjsPvXYeeu089Np5alKvrbPRHOGy4BQWFiaz2azExES77YmJiYqMLPkNd1RUlDw8POym5bVt21YJCQnKy8uTp6dnsft4eXnJy6v4YgQeHh4V/kFV5j51ydKd8cWutRQR6KVmDfy0MS5FktSjeaheHtlZ0cE+F/VY9b3XzkSvnYdeOwd9dh567Tz02nnotfPUhF5X5PFdthy5p6enunXrppUrV9q2WSwWrVy50m7q3vn69OmjgwcPymKx2Lbt379fUVFRJYYmVJ2lO+N1/+ItdqFJkhLTc7UxLkUmSZMGXKIP7+150aEJAAAAqGlceh2nyZMn66233tI777yjPXv26P7771dWVpZtlb0777zTbvGI+++/XykpKXrooYe0f/9+fffdd3ruuec0YcIEVz2FeqHQYmjWN7tV1slwoX6emnh1K669BAAAgDrJpec43XrrrTp16pSmT5+uhIQEde7cWUuXLlVERIQk6ejRo3Jz+zPbNWnSRMuWLdOkSZN02WWXKTo6Wg899JAee+wxVz2FemFjXEqxkaYLnc7K08a4FPWKaeCkqgAAAADncfniEBMnTtTEiRNLvG316tXFtvXq1Uvr16+v5qpwvqSMskNTRfcDAAAAahuXTtVD7RAe4F2l+wEAAAC1DcEJ5erRIlRRQd4q7ewlk6SoIG/1aBHqzLIAAAAApyE4oVxmN5NmDG1X4m3WMDVjaDsWhgAAAECdRXCCQ4Z0iNIbo7vKx8Nstz0yyFtvjO6qIR2iXFQZAAAAUP1cvjgEao8hHaK0aM1hrY9L0R09m+q6jo3Uo0UoI00AAACo8whOqJCT55YlH9opmnOaAAAAUG8wVQ8OK7QYOpl6VpLUOMTHxdUAAAAAzkNwgsOSMnJUYDHk7mZSRCBLjwMAAKD+IDjBYSfOFI02RQV7c14TAAAA6hWCExx2/Fxwig5mmh4AAADqF4ITHHYi1RqcfF1cCQAAAOBcBCc47PiZbEksDAEAAID6h+AEh9mm6hGcAAAAUM8QnOCwEyxFDgAAgHqK4ASHGIZhW1WvMec4AQAAoJ4hOMEhyZl5yi2wyGSSIoO4hhMAAADqF4ITHGKdphcZ6C1Pd142AAAAqF94BwyHWFfU4xpOAAAAqI8ITnDICVbUAwAAQD1GcIJDrEuRs6IeAAAA6iOCExxiPccpmhX1AAAAUA8RnOCQE4w4AQAAoB4jOKFchmH8uTgEwQkAAAD1EMEJ5Uo7m6+svEJJrKoHAACA+onghHJZF4YI8/eSt4fZxdUAAAAAzkdwQrmOsxQ5AAAA6jmCE8plXVGPhSEAAABQXxGcUC7rwhCNOb8JAAAA9RTBCeU6wVQ9AAAA1HMEJ5SLqXoAAACo7whOKJdtcYhgXxdXAgAAALgGwQllysjJV9rZfElM1QMAAED9RXBCmazT9IJ9PeTv5e7iagAAAADXIDihTLaFIVhRDwAAAPUYwQllYmEIAAAAgOCEcrAwBAAAAEBwQjm4hhMAAABAcEI5jp/JlsRUPQAAANRvBCeUyXqOE4tDAAAAoD4jOKFUOfmFSs7MkyQ1CeEcJwAAANRfDl+YJyQkRCaTyaF9U1JSKl0Qag7rwhD+Xu4K9OEaTgAAAKi/HH43PH/+fNvfT58+rWeeeUaDBw9Wr169JEnr1q3TsmXL9OSTT1Z5kXCN86fpORqaAQAAgLrI4eA0ZswY299vvvlmPfXUU5o4caJt24MPPqjXXntNP/zwgyZNmlS1VcIlrCvqsTAEAAAA6rtKneO0bNkyDRkypNj2IUOG6IcffrjoolAzWFfUYylyAAAA1HeVCk4NGjTQV199VWz7V199pQYNGlx0UagZrFP1GHECAABAfVepM/5nzZql8ePHa/Xq1YqNjZUkbdiwQUuXLtVbb71VpQXCdayLQ0QHs6IeAAAA6rdKBaexY8eqbdu2euWVV7RkyRJJUtu2bfXrr7/aghRqP+s5TkzVAwAAQH1X6TWmY2Nj9f7771dlLahB8gosSszIkcRUPQAAAKDSF8A9dOiQ/vGPf+i2225TUlKSJOn777/Xrl27qqw4uE582lkZhuTt4aYGfp6uLgcAAABwqUoFp59++kkdO3bUhg0b9PnnnyszM1OStH37ds2YMaNKC4RrWKfpNeIaTgAAAEDlgtPjjz+uZ555RitWrJCn55+jEVdffbXWr19fZcXBdY7bruHEwhAAAABApYLT77//rhtvvLHY9vDwcCUnJ190UXC946nWFfU4vwkAAACoVHAKDg5WfHx8se1bt25VdHT0RRcF1ztxhms4AQAAAFaVCk4jR47UY489poSEBJlMJlksFq1Zs0aPPvqo7rzzzqquES5w/Ey2JIITAAAAIFUyOD333HNq06aNmjRposzMTLVr105XXXWVevfurX/84x9VXSNc4ART9QAAAACbCl/HyTAMJSQk6JVXXtH06dP1+++/KzMzU126dFHr1q2ro0Y4WUGhRQlp1ms4sTgEAAAAUKng1KpVK+3atUutW7dWkyZNqqMuuFBiRq4KLIY8zCaFB3i5uhwAAADA5So8Vc/NzU2tW7fW6dOnq6Me1ADnX8PJzY1rOAEAAACVOsdpzpw5mjJlinbu3FnV9aAGsC4MwflNAAAAQJEKT9WTpDvvvFPZ2dnq1KmTPD095eNj/wY7JSWlSoqDa1hHnAhOAAAAQJFKBaf58+dXcRmoSawr6rEwBAAAAFCkUsFpzJgxVV0HapDj1hEnruEEAAAASKpkcDpfTk6O8vLy7LYFBgZe7GHhQn+OOBGcAAAAAKmSi0NkZWVp4sSJCg8Pl5+fn0JCQuy+UHtZLAbnOAEAAAAXqFRwmjp1qlatWqU33nhDXl5e+s9//qNZs2apUaNGevfdd6u6RjhRcmau8gotcjNJkUHeri4HAAAAqBEqNVXvm2++0bvvvqt+/fpp3LhxuvLKK9WqVSs1a9ZM77//vm6//faqrhNOcvzcNL2oIB95mCuVqwEAAIA6p1LvjFNSUtSyZUtJReczWZcfv+KKK/Tzzz9XXXVwuuNM0wMAAACKqVRwatmypeLi4iRJbdq00SeffCKpaCQqODi4yoqD851gRT0AAACgmEoFp3Hjxmn79u2SpMcff1yvv/66vL29NWnSJE2ZMqVKC4RznUjNlsSKegAAAMD5KnWO06RJk2x/HzBggPbu3avNmzerVatWuuyyy6qsODgfU/UAAACA4i76Ok6S1KxZMzVr1qwqDgUXs07Vaxzi6+JKAAAAgJqjUsHpqaeeKvP26dOnV6oYuJZhGH+OODFVDwAAALCpVHD64osv7L7Pz89XXFyc3N3dFRMTQ3Cqpc5k5+tsfqEkKYprOAEAAAA2lQpOW7duLbYtPT1dY8eO1Y033njRRcE1rNP0wgO85O1hdnE1AAAAQM1RZVc4DQwM1KxZs/Tkk09W1SHhZMfPFK2oxzQ9AAAAwF6VBSdJSktLU1paWlUeEk50IpWFIQAAAICSVGqq3iuvvGL3vWEYio+P13vvvadrr722SgqD87EUOQAAAFCySgWnl19+2e57Nzc3NWzYUGPGjNG0adOqpDA4HyvqAQAAACWrVHCKi4ur6jpQA/w5VY/gBAAAAJyvSs9xQu1mXRyiMVP1AAAAADuVGnG68cYbZTKZHNp3yZIllXkIOFl6Tr4ycgokMVUPAAAAuFClRpyCgoK0cuVKbdq0ybZt8+bNWrVqlQIDAxUUFGT7Qu1gvYZTqJ+nfD0rlacBAACAOqtS75AjIiI0YsQILViwQGZz0YVSCwsL9cADDygwMFAvvPBClRaJ6seKegAAAEDpKjXi9Pbbb+vRRx+1hSZJMpvNmjx5st5+++0qKw7Oc8J6fhPT9AAAAIBiKhWcCgoKtHfv3mLb9+7dK4vFctFFwfkYcQIAAABKV6mpeuPGjdPdd9+tQ4cOqUePHpKkDRs2aM6cORo3blyVFgjnsC5FzsIQAAAAQHGVCk4vvviiIiMj9dJLLyk+Pl6SFBUVpSlTpuiRRx6p0gLhHH9ew8nXxZUAAAAANU+lgpObm5umTp2qqVOnKj09XZIUGBhYpYXBuZiqBwAAAJSuUuc4nT17VtnZRYsJBAYG6syZM5o/f76WL19epcXBObLzCpSSlSeJqXoAAABASSoVnIYNG6Z3331XkpSamqoePXropZde0rBhw/TGG29UaYGoftZrOAV4uyvIx8PF1QAAAAA1T6WC05YtW3TllVdKkj777DNFRkbqyJEjevfdd/XKK69U+Hivv/66mjdvLm9vb8XGxmrjxo2l7rto0SKZTCa7L29v78o8DZxzPJVpegAAAEBZKhWcsrOzFRAQIElavny5brrpJrm5ualnz546cuRIhY718ccfa/LkyZoxY4a2bNmiTp06afDgwUpKSir1PoGBgYqPj7d9VfQxYc864sTCEAAAAEDJKhWcWrVqpS+//FLHjh3TsmXLNGjQIElSUlJShReJmDdvnu655x6NGzdO7dq104IFC+Tr61vmhXRNJpMiIyNtXxEREZV5GjjnuC04MeIEAAAAlKRSq+pNnz5dt912myZNmqSrr75avXr1klQ0+tSlSxeHj5OXl6fNmzdr2rRptm1ubm4aMGCA1q1bV+r9MjMz1axZM1ksFnXt2lXPPfec2rdvX+K+ubm5ys3NtX1vXQUwPz9f+fn5DtVp3c/R/WubYylZkqSoQE+XP8e63uuahF47D712DvrsPPTaeei189Br56lJva5IDSbDMIzKPEhCQoLi4+PVqVMnubkVDVxt3LhRgYGBatOmjUPHOHnypKKjo7V27Vpb+JKkqVOn6qefftKGDRuK3WfdunU6cOCALrvsMqWlpenFF1/Uzz//rF27dqlx48bF9p85c6ZmzZpVbPsHH3wgX1+mpknSy7+bdTjTpHGXFKpzg0q9HAAAAIBaJzs7W7fddpvS0tLKnTlX6eBk9eGHH+qGG26Qn59fhe9bmeB0ofz8fLVt21ajRo3S008/Xez2kkacmjRpouTkZIenFebn52vFihUaOHCgPDzq3qpzfZ7/SUkZuVpyX6w6Rge5tJa63uuahF47D712DvrsPPTaeei189Br56lJvU5PT1dYWJhDwalSU/XO97e//U2xsbFq2bJlhe8bFhYms9msxMREu+2JiYmKjIx06BgeHh7q0qWLDh48WOLtXl5e8vLyKvF+Ff1BVeY+NV1uQaGSMoqCZfOGgTXm+dXFXtdU9Np56LVz0GfnodfOQ6+dh147T03odUUev1KLQ5zvYgasPD091a1bN61cudK2zWKxaOXKlXYjUGUpLCzU77//rqioqErXUZ+dTM2RJPl4mBXiyy8JAAAAoCQXPeJ0obNnz8rHx/HV2SZPnqwxY8aoe/fu6tGjh+bPn6+srCyNGzdOknTnnXcqOjpas2fPliQ99dRT6tmzp1q1aqXU1FS98MILOnLkiMaPH1/VT6VesC5FHh3iI5PJ5OJqAAAAgJrpooPT999/r+joaOXm5uq1117TCy+8oISEBIfvf+utt+rUqVOaPn26EhIS1LlzZy1dutS2xPjRo0dti09I0pkzZ3TPPfcoISFBISEh6tatm9auXat27dpd7FOpl06kZktiKXIAAACgLBUKTrm5uZo5c6ZWrFghT09PTZ06VcOHD9fChQv1xBNPyGw2a9KkSRUuYuLEiZo4cWKJt61evdru+5dfflkvv/xyhR8DJbNewyk6mOAEAAAAlKZCwWn69On697//rQEDBmjt2rW65ZZbNG7cOK1fv17z5s3TLbfcIrPZXF21ohqcsF38lqXZAQAAgNJUKDh9+umnevfdd3XDDTdo586duuyyy1RQUKDt27dzfkwtdfy8c5wAAAAAlKxCq+odP35c3bp1kyR16NBBXl5emjRpEqGpFjuRylQ9AAAAoDwVCk6FhYXy9PS0fe/u7i5/f/8qLwrOUVBoUUJ60XLkTRhxAgAAAEpVoal6hmFo7NixtgvK5uTk6L777pOfn5/dfkuWLKm6ClFt4tNyVGgx5Gl2U5h/8YsEAwAAAChSoeA0ZswYu+9Hjx5dpcXAuWzT9EJ85ObGdEsAAACgNBUKTgsXLqyuOuACJ1iKHAAAAHBIhc5xQt3CNZwAAAAAxxCc6rETqdmSpMYsDAEAAACUieBUj3ENJwAAAMAxBKd6zLo4ROMQXxdXAgAAANRsBKd6ymIxdDKVEScAAADAEQSneiopI1f5hYbMbiZFBHANJwAAAKAsBKd6yrowRFSQt9zNvAwAAACAsvCOuZ5iKXIAAADAcQSneooV9QAAAADHEZzqKVbUAwAAABxHcKqnrCNOjZmqBwAAAJSL4FRPnThTtDhEY6bqAQAAAOUiONVDhmHYpupxjhMAAABQPoJTPXQ6K085+RaZTFJUEMEJAAAAKA/BqR46ce78pogAb3m68xIAAAAAysO75nqIpcgBAACAiiE41UMnUlkYAgAAAKgIglM9ZJ2qF81S5AAAAIBDCE71EFP1AAAAgIohONVD1qXIG4f4urgSAAAAoHYgONUzhmH8OeLEVD0AAADAIQSneib9bIEycwsksTgEAAAA4CiCUz1z/NyKemH+nvL2MLu4GgAAAKB2IDjVM0zTAwAAACqO4FTPWJciZ2EIAAAAwHEEp3rGuqIeS5EDAAAAjiM41TPHzxSd48RUPQAAAMBxBKd65s9rOBGcAAAAAEcRnOoZ2+IQBCcAAADAYQSneiQrt0Cp2fmSmKoHAAAAVATBqR6xTtML8vFQgLeHi6sBAAAAag+CUz3CwhAAAABA5RCc6pE/r+FEcAIAAAAqguBUj7AwBAAAAFA5BKd65LhtKXJfF1cCAAAA1C4Ep3rEOlWPc5wAAACAiiE41SPHOccJAAAAqBSCUz2Rk1+o5MxcSQQnAAAAoKIITvXEyXPnN/l5mhXkwzWcAAAAgIogONUT56+oZzKZXFwNAAAAULsQnOqJE6yoBwAAAFQawameOH4mWxIr6gEAAACVQXCqJ06woh4AAABQaQSnesI6VS+a4AQAAABUGMGpnjjOxW8BAACASiM41QN5BRYlpudIYnEIAAAAoDIITvVAQlqOLIbk5e6mMH9PV5cDAAAA1DoEp3rgeOq5FfW4hhMAAABQKQSneuAE5zcBAAAAF4XgVA8cZylyAAAA4KIQnOoB61LkLAwBAAAAVA7BqR5gqh4AAABwcQhO9YB1cQim6gEAAACVQ3Cq4wothuJTi67hFE1wAgAAACqF4FTHJabnqMBiyN3NpPAAb1eXAwAAANRKBKc6zrowRKNgH5nduIYTAAAAUBkEpzqOhSEAAACAi0dwquOOnylaGILzmwAAAIDKIzjVcX9ew4ngBAAAAFQWwamOO85UPQAAAOCiEZzqOOs5To1DfF1cCQAAAFB7EZzqMMMwmKoHAAAAVAGCUx12KjNXuQUWuZmkyCCu4QQAAABUFsGpDrNO04sM9JaHmR81AAAAUFm8m67DrNP0WIocAAAAuDgEpzrsOAtDAAAAAFWC4FSHnWApcgAAAKBKEJzqsONnsiUxVQ8AAAC4WASnOoylyAEAAICqQXCqowzDYKoeAAAAUEUITnVUana+svIKJUmNCE4AAADARSE41VHWaXoNA7zk7WF2cTUAAABA7UZwqoMKLYZW7kmUJAV6u6vQYri4IgAAAKB2IzjVMUt3xuuKuav08g8HJEmHTmXpirmrtHRnvIsrAwAAAGovglMdsnRnvO5fvEXxaTl22xPScnT/4i2EJwAAAKCSCE51RKHF0KxvdqukSXnWbbO+2c20PQAAAKASCE51xMa4lGIjTeczJMWn5WhjXIrzigIAAADqCIJTHZGUUXpoqsx+AAAAAP5EcKojwgO8q3Q/AAAAAH8iONURPVqEKirIW6ZSbjdJigryVo8Woc4sCwAAAKgTCE51hNnNpBlD25V4mzVMzRjaTma30qIVAAAAgNIQnOqQIR2i9M+RnYttjwzy1huju2pIhyjnFwUAAADUATUiOL3++utq3ry5vL29FRsbq40bNzp0v48++kgmk0nDhw+v3gJrkcggH0lSkI+H/nlrZ314T0/9+tjVhCYAAADgIrg8OH388ceaPHmyZsyYoS1btqhTp04aPHiwkpKSyrzf4cOH9eijj+rKK690UqW1w2+Hi5Ybv6JVmIZ1iVavmAZMzwMAAAAuksuD07x583TPPfdo3LhxateunRYsWCBfX1+9/fbbpd6nsLBQt99+u2bNmqWWLVs6sdqazxqcujcPcXElAAAAQN3h7soHz8vL0+bNmzVt2jTbNjc3Nw0YMEDr1q0r9X5PPfWUwsPDdffdd+uXX34p8zFyc3OVm5tr+z49PV2SlJ+fr/z8fIfqtO7n6P6uUmgxtOnwGUlSl8aBNb7ektSWXtcF9Np56LVz0GfnodfOQ6+dh147T03qdUVqcGlwSk5OVmFhoSIiIuy2R0REaO/evSXe59dff9V///tfbdu2zaHHmD17tmbNmlVs+/Lly+Xr61uhelesWFGh/Z3tRJaUmesuL7OhuK2/6sg2V1dUeTW913UJvXYeeu0c9Nl56LXz0GvnodfOUxN6nZ2d7fC+Lg1OFZWRkaE77rhDb731lsLCwhy6z7Rp0zR58mTb9+np6WrSpIkGDRqkwMBAh46Rn5+vFStWaODAgfLw8KhU7c7w3vqj0o696tEiTH+5vpury6mU2tLruoBeOw+9dg767Dz02nnotfPQa+epSb22zkZzhEuDU1hYmMxmsxITE+22JyYmKjIystj+hw4d0uHDhzV06FDbNovFIklyd3fXvn37FBMTY3cfLy8veXl5FTuWh4dHhX9QlbmPM205liZJ6tGiQY2u0xE1vdd1Cb12HnrtHPTZeei189Br56HXzlMTel2Rx3fp4hCenp7q1q2bVq5cadtmsVi0cuVK9erVq9j+bdq00e+//65t27bZvm644Qb1799f27ZtU5MmTZxZfo1iGIZtYYjLW4S6uBoAAACgbnH5VL3JkydrzJgx6t69u3r06KH58+crKytL48aNkyTdeeedio6O1uzZs+Xt7a0OHTrY3T84OFiSim2vb46fOavE9Fx5mE3q3CTY1eUAAAAAdYrLg9Ott96qU6dOafr06UpISFDnzp21dOlS24IRR48elZuby1dNr/E2xhWNNnWMDpK3h9nF1QAAAAB1i8uDkyRNnDhREydOLPG21atXl3nfRYsWVX1BtZBtml5zpukBAAAAVY2hnDqC4AQAAABUH4JTHXA6M1eHTmVJkro1C3FxNQAAAEDdQ3CqAzYdOSNJuiTCXyF+ni6uBgAAAKh7CE51wG/nFobozjQ9AAAAoFoQnOqA386NOPUgOAEAAADVguBUy2XnFWjXiTRJXPgWAAAAqC4Ep1pu29FUFVgMNQryVnSwj6vLAQAAAOokglMtt9G6DDmjTQAAAEC1ITjVcpsOF53fxMIQAAAAQPUhONViBYUWbTnKwhAAAABAdSM41WK749OVnVeoIB8PtQ73d3U5AAAAQJ1FcKrFNlqv39QsRG5uJhdXAwAAANRdBKdajPObAAAAAOcgONVShmHot3Mr6vVoEeLiagAAAIC6jeBUS8UlZ+l0Vp483d3UITrI1eUAAAAAdRrBqZayjjZ1bhIsL3ezi6sBAAAA6jaCUy3127nzmy5vzjQ9AAAAoLoRnGop64jT5SwMAQAAAFQ7glMtlJSeoyOns2UySV2bMeIEAAAAVDeCUy1knabXNjJQgd4eLq4GAAAAqPsITrXQn9P0GG0CAAAAnIHgVAvZglMLzm8CAAAAnIHgVMtk5ORrT3y6JBaGAAAAAJyF4FTLbDmaKoshNQ31VUSgt6vLAQAAAOoFglMt81tc0TS97pzfBAAAADgNwamWsZ7f1INpegAAAIDTEJxqkdyCQm07lipJ6k5wAgAAAJyG4FSL7DyRrtwCi0L9PBXT0M/V5QAAAAD1BsGpFrFO0+veLEQmk8nF1QAAAAD1B8GpFtlkPb+J6zcBAAAATkVwqiUsFkO/HT4jifObAAAAAGcjONUSB09lKu1svnw8zGrfKNDV5QAAAAD1CsGplth47vpNXZsFy8PMjw0AAABwJt6B1xKbbAtDME0PAAAAcDaCUy1hPb+JhSEAAAAA5yM41QInUs/qROpZmd1M6twk2NXlAAAAAPUOwakWsE7T69AoUH5e7i6uBgAAAKh/CE61gO3CtyxDDgAAALgEwakW+C2u6PymywlOAAAAgEsQnGq41Ow87UvMkCR1bx7i4moAAACA+ongVMNtPlI02tSyoZ/C/L1cXA0AAABQPxGcariN585vupzrNwEAAAAuQ3Cq4Tadu37T5Vy/CQAAAHAZglMNlpNfqB3HUyVJl3N+EwAAAOAyBKcabPuxVOUXGgoP8FLTUF9XlwMAAADUWwSnGsx6/abLm4fKZDK5uBoAAACg/iI41WC/Wc9vYpoeAAAA4FIEpxqq0GJoy7mlyLtz4VsAAADApQhONdTehHRl5BbI38tdbaMCXV0OAAAAUK8RnGqo3+KKzm/q2ixEZjfObwIAAABcieBUQ/12bppeD85vAgAAAFyO4FQDGYZhG3Hi/CYAAADA9QhONdCxlLNKysiVh9mkzk2CXV0OAAAAUO8RnGqgjeeu39QxOkjeHmYXVwMAAACA4FQDbbJe+LYF0/QAAACAmoDgVANZR5wub0ZwAgAAAGoCglMNczozV3+cypIkdWdFPQAAAKBGIDjVML8dLlqG/JIIfwX7erq4GgAAAAASwanGsZ3fxDLkAAAAQI1BcKphfjsXnHqwMAQAAABQYxCcapCs3ALtPJkuiQvfAgAAADUJwakG2XYsVYUWQ9HBPooO9nF1OQAAAADOITjVIBvjiqbpsZoeAAAAULMQnGqQTUdYGAIAAACoiQhONUR+oUVbjqRKIjgBAAAANQ3BqYbYfTJdZ/MLFeTjodbh/q4uBwAAAMB5CE41hHUZ8u7NQuTmZnJxNQAAAADOR3CqIazB6XKu3wQAAADUOASnGsAwDG06fEaSdDkr6gEAAAA1DsGpBvgjOUuns/Lk5e6mDtFBri4HAAAAwAUITjXAb+eu39SpSbC83M0urgYAAADAhQhONcBv56bp9WAZcgAAAKBGIjjVALYV9Ti/CQAAAKiRCE4ulpieo6Mp2XIzSd2aEZwAAACAmojg5GLW0aY2kYEK8PZwcTUAAAAASkJwcqFCi6Fvtp+UJDUJ9VGhxXBxRQAAAABKQnBykaU743XF3FVatitRkrRsV6KumLtKS3fGu7gyAAAAABciOLnA0p3xun/xFsWn5dhtT0jL0f2LtxCeAAAAgBqG4ORkhRZDs77ZrZIm5Vm3zfpmN9P2AAAAgBqE4ORkG+NSio00nc+QFJ+Wo43nLooLAAAAwPUITk6WlFF6aKrMfgAAAACqH8HJycIDvKt0PwAAAADVj+DkZD1ahCoqyFumUm43SYoK8laPFqHOLAsAAABAGQhOTmZ2M2nG0HaSVCw8Wb+fMbSdzG6lRSsAAAAAzkZwcoEhHaL0xuiuigyyn44XGeStN0Z31ZAOUS6qDAAAAEBJ3F1dQH01pEOUBraL1Ma4FCVl5Cg8oGh6HiNNAAAAQM1DcHIhs5tJvWIauLoMAAAAAOVgqh4AAAAAlIPgBAAAAADlIDgBAAAAQDlqRHB6/fXX1bx5c3l7eys2NlYbN24sdd8lS5aoe/fuCg4Olp+fnzp37qz33nvPidUCAAAAqG9cHpw+/vhjTZ48WTNmzNCWLVvUqVMnDR48WElJSSXuHxoaqieeeELr1q3Tjh07NG7cOI0bN07Lli1zcuUAAAAA6guXB6d58+bpnnvu0bhx49SuXTstWLBAvr6+evvtt0vcv1+/frrxxhvVtm1bxcTE6KGHHtJll12mX3/91cmVAwAAAKgvXLoceV5enjZv3qxp06bZtrm5uWnAgAFat25dufc3DEOrVq3Svn37NHfu3BL3yc3NVW5uru379PR0SVJ+fr7y8/MdqtO6n6P7o/LotfPQa+eh185Bn52HXjsPvXYeeu08NanXFanBZBiGUY21lOnkyZOKjo7W2rVr1atXL9v2qVOn6qefftKGDRtKvF9aWpqio6OVm5srs9msf/3rX7rrrrtK3HfmzJmaNWtWse0ffPCBfH19q+aJAAAAAKh1srOzddtttyktLU2BgYFl7lsrL4AbEBCgbdu2KTMzUytXrtTkyZPVsmVL9evXr9i+06ZN0+TJk23fp6enq0mTJho0aFC5zbHKz8/XihUrNHDgQHl4eFTV00AJ6LXz0GvnodfOQZ+dh147D712HnrtPDWp19bZaI5waXAKCwuT2WxWYmKi3fbExERFRkaWej83Nze1atVKktS5c2ft2bNHs2fPLjE4eXl5ycvLq9h2Dw+PCv+gKnMfVA69dh567Tz02jnos/PQa+eh185Dr52nJvS6Io/v0sUhPD091a1bN61cudK2zWKxaOXKlXZT98pjsVjszmMCAAAAgKrk8ql6kydP1pgxY9S9e3f16NFD8+fPV1ZWlsaNGydJuvPOOxUdHa3Zs2dLkmbPnq3u3bsrJiZGubm5+t///qf33ntPb7zxhiufBgAAAIA6zOXB6dZbb9WpU6c0ffp0JSQkqHPnzlq6dKkiIiIkSUePHpWb258DY1lZWXrggQd0/Phx+fj4qE2bNlq8eLFuvfVWVz0FAAAAAHWcy4OTJE2cOFETJ04s8bbVq1fbff/MM8/omWeeqfRjWRcRrMiJYPn5+crOzlZ6errL52HWdfTaeei189Br56DPzkOvnYdeOw+9dp6a1GtrJnBkofEaEZycKSMjQ5LUpEkTF1cCAAAAoCbIyMhQUFBQmfu49DpOrmCxWHTy5EkFBATIZDI5dB/rEubHjh1zeAlzVA69dh567Tz02jnos/PQa+eh185Dr52nJvXaMAxlZGSoUaNGdqcHlaTejTi5ubmpcePGlbpvYGCgy3+49QW9dh567Tz02jnos/PQa+eh185Dr52npvS6vJEmK5cuRw4AAAAAtQHBCQAAAADKQXBygJeXl2bMmCEvLy9Xl1Ln0WvnodfOQ6+dgz47D712HnrtPPTaeWprr+vd4hAAAAAAUFGMOAEAAABAOQhOAAAAAFAOghMAAAAAlIPgBAAAAADlIDiV4/XXX1fz5s3l7e2t2NhYbdy40dUl1XqzZ8/W5ZdfroCAAIWHh2v48OHat2+f3T45OTmaMGGCGjRoIH9/f918881KTEx0UcV1x5w5c2QymfTwww/bttHrqnPixAmNHj1aDRo0kI+Pjzp27KhNmzbZbjcMQ9OnT1dUVJR8fHw0YMAAHThwwIUV106FhYV68skn1aJFC/n4+CgmJkZPP/20zl/riF5Xzs8//6yhQ4eqUaNGMplM+vLLL+1ud6SvKSkpuv322xUYGKjg4GDdfffdyszMdOKzqB3K6nV+fr4ee+wxdezYUX5+fmrUqJHuvPNOnTx50u4Y9Nox5b2uz3fffffJZDJp/vz5dtvpdfkc6fOePXt0ww03KCgoSH5+frr88st19OhR2+01/T0JwakMH3/8sSZPnqwZM2Zoy5Yt6tSpkwYPHqykpCRXl1ar/fTTT5owYYLWr1+vFStWKD8/X4MGDVJWVpZtn0mTJumbb77Rp59+qp9++kknT57UTTfd5MKqa7/ffvtN//73v3XZZZfZbafXVePMmTPq06ePPDw89P3332v37t166aWXFBISYtvn+eef1yuvvKIFCxZow4YN8vPz0+DBg5WTk+PCymufuXPn6o033tBrr72mPXv2aO7cuXr++ef16quv2vah15WTlZWlTp066fXXXy/xdkf6evvtt2vXrl1asWKFvv32W/3888+69957nfUUao2yep2dna0tW7boySef1JYtW7RkyRLt27dPN9xwg91+9Nox5b2urb744gutX79ejRo1KnYbvS5feX0+dOiQrrjiCrVp00arV6/Wjh079OSTT8rb29u2T41/T2KgVD169DAmTJhg+76wsNBo1KiRMXv2bBdWVfckJSUZkoyffvrJMAzDSE1NNTw8PIxPP/3Uts+ePXsMSca6detcVWatlpGRYbRu3dpYsWKF0bdvX+Ohhx4yDINeV6XHHnvMuOKKK0q93WKxGJGRkcYLL7xg25aammp4eXkZH374oTNKrDOuv/5646677rLbdtNNNxm33367YRj0uqpIMr744gvb9470dffu3YYk47fffrPt8/333xsmk8k4ceKE02qvbS7sdUk2btxoSDKOHDliGAa9rqzSen38+HEjOjra2Llzp9GsWTPj5Zdftt1GryuupD7feuutxujRo0u9T214T8KIUyny8vK0efNmDRgwwLbNzc1NAwYM0Lp161xYWd2TlpYmSQoNDZUkbd68Wfn5+Xa9b9OmjZo2bUrvK2nChAm6/vrr7Xoq0euq9PXXX6t79+665ZZbFB4eri5duuitt96y3R4XF6eEhAS7XgcFBSk2NpZeV1Dv3r21cuVK7d+/X5K0fft2/frrr7r22msl0evq4khf161bp+DgYHXv3t22z4ABA+Tm5qYNGzY4vea6JC0tTSaTScHBwZLodVWyWCy64447NGXKFLVv377Y7fT64lksFn333Xe65JJLNHjwYIWHhys2NtZuOl9teE9CcCpFcnKyCgsLFRERYbc9IiJCCQkJLqqq7rFYLHr44YfVp08fdejQQZKUkJAgT09P238OVvS+cj766CNt2bJFs2fPLnYbva46f/zxh9544w21bt1ay5Yt0/33368HH3xQ77zzjiTZ+snvlIv3+OOPa+TIkWrTpo08PDzUpUsXPfzww7r99tsl0evq4khfExISFB4ebne7u7u7QkND6f1FyMnJ0WOPPaZRo0YpMDBQEr2uSnPnzpW7u7sefPDBEm+n1xcvKSlJmZmZmjNnjoYMGaLly5frxhtv1E033aSffvpJUu14T+Lu6gJQv02YMEE7d+7Ur7/+6upS6qRjx47poYce0ooVK+zmEKPqWSwWde/eXc8995wkqUuXLtq5c6cWLFigMWPGuLi6uuWTTz7R+++/rw8++EDt27fXtm3b9PDDD6tRo0b0GnVOfn6+RowYIcMw9MYbb7i6nDpn8+bN+uc//6ktW7bIZDK5upw6y2KxSJKGDRumSZMmSZI6d+6stWvXasGCBerbt68ry3MYI06lCAsLk9lsLraSR2JioiIjI11UVd0yceJEffvtt/rxxx/VuHFj2/bIyEjl5eUpNTXVbn96X3GbN29WUlKSunbtKnd3d7m7u+unn37SK6+8Ind3d0VERNDrKhIVFaV27drZbWvbtq1ttSBrP/mdcvGmTJliG3Xq2LGj7rjjDk2aNMk2qkqvq4cjfY2MjCy2gFJBQYFSUlLofSVYQ9ORI0e0YsUK22iTRK+ryi+//KKkpCQ1bdrU9v/kkSNH9Mgjj6h58+aS6HVVCAsLk7u7e7n/T9b09yQEp1J4enqqW7duWrlypW2bxWLRypUr1atXLxdWVvsZhqGJEyfqiy++0KpVq9SiRQu727t16yYPDw+73u/bt09Hjx6l9xV0zTXX6Pfff9e2bdtsX927d9ftt99u+zu9rhp9+vQptqz+/v371axZM0lSixYtFBkZadfr9PR0bdiwgV5XUHZ2ttzc7P/7MpvNtk806XX1cKSvvXr1UmpqqjZv3mzbZ9WqVbJYLIqNjXV6zbWZNTQdOHBAP/zwgxo0aGB3O72uGnfccYd27Nhh9/9ko0aNNGXKFC1btkwSva4Knp6euvzyy8v8f7JWvP9z9eoUNdlHH31keHl5GYsWLTJ2795t3HvvvUZwcLCRkJDg6tJqtfvvv98ICgoyVq9ebcTHx9u+srOzbfvcd999RtOmTY1Vq1YZmzZtMnr16mX06tXLhVXXHeevqmcY9LqqbNy40XB3dzeeffZZ48CBA8b7779v+Pr6GosXL7btM2fOHCM4ONj46quvjB07dhjDhg0zWrRoYZw9e9aFldc+Y8aMMaKjo41vv/3WiIuLM5YsWWKEhYUZU6dOte1DrysnIyPD2Lp1q7F161ZDkjFv3jxj69attpXcHOnrkCFDjC5duhgbNmwwfv31V6N169bGqFGjXPWUaqyyep2Xl2fccMMNRuPGjY1t27bZ/V+Zm5trOwa9dkx5r+sLXbiqnmHQa0eU1+clS5YYHh4exptvvmkcOHDAePXVVw2z2Wz88ssvtmPU9PckBKdyvPrqq0bTpk0NT09Po0ePHsb69etdXVKtJ6nEr4ULF9r2OXv2rPHAAw8YISEhhq+vr3HjjTca8fHxriu6DrkwONHrqvPNN98YHTp0MLy8vIw2bdoYb775pt3tFovFePLJJ42IiAjDy8vLuOaaa4x9+/a5qNraKz093XjooYeMpk2bGt7e3kbLli2NJ554wu4NJb2unB9//LHE389jxowxDMOxvp4+fdoYNWqU4e/vbwQGBhrjxo0zMjIyXPBsarayeh0XF1fq/5U//vij7Rj02jHlva4vVFJwotflc6TP//3vf41WrVoZ3t7eRqdOnYwvv/zS7hg1/T2JyTDOu9Q6AAAAAKAYznECAAAAgHIQnAAAAACgHAQnAAAAACgHwQkAAAAAykFwAgAAAIByEJwAAAAAoBwEJwAAAAAoB8EJAAAAAMpBcAKAeuTw4cMymUzatm2bq0ux2bt3r3r27Clvb2917ty52h5n5syZ1Xr8i1GTawMAFCE4AYATjR07ViaTSXPmzLHb/uWXX8pkMrmoKteaMWOG/Pz8tG/fPq1cubLEfU6dOqX7779fTZs2lZeXlyIjIzV48GCtWbPGydX+yZlhxxp4L/waPXp0lT1G8+bNNX/+/Co7HgDUNe6uLgAA6htvb2/NnTtXf/vb3xQSEuLqcqpEXl6ePD09K3XfQ4cO6frrr1ezZs1K3efmm29WXl6e3nnnHbVs2VKJiYlauXKlTp8+XdmSa6UffvhB7du3t33v4+PjwmpKdjGvBQCoyRhxAgAnGzBggCIjIzV79uxS9ylpNGP+/Plq3ry57fuxY8dq+PDheu655xQREaHg4GA99dRTKigo0JQpUxQaGqrGjRtr4cKFxY6/d+9e9e7dW97e3urQoYN++uknu9t37typa6+9Vv7+/oqIiNAdd9yh5ORk2+39+vXTxIkT9fDDDyssLEyDBw8u8XlYLBY99dRTaty4sby8vNS5c2ctXbrUdrvJZNLmzZv11FNPyWQyaebMmcWOkZqaql9++UVz585V//791axZM/Xo0UPTpk3TDTfcYLff+PHj1bBhQwUGBurqq6/W9u3bS+2xJP3nP/9R27Zt5e3trTZt2uhf//qX3e3Hjx/XqFGjFBoaKj8/P3Xv3l0bNmzQokWLNGvWLG3fvt02+rNo0SKH65gzZ44iIiIUEBCgu+++Wzk5OWXWadWgQQNFRkbavoKCghx6zEOHDmnYsGGKiIiQv7+/Lr/8cv3www+22/v166cjR45o0qRJtucjVex1+Oyzz6pRo0a69NJLJUnHjh3TiBEjFBwcrNDQUA0bNkyHDx+23W/16tXq0aOH/Pz8FBwcrD59+ujIkSMO9QEAXIHgBABOZjab9dxzz+nVV1/V8ePHL+pYq1at0smTJ/Xzzz9r3rx5mjFjhv7yl78oJCREGzZs0H333ae//e1vxR5nypQpeuSRR7R161b16tVLQ4cOtY3epKam6uqrr1aXLl20adMmLV26VImJiRoxYoTdMd555x15enpqzZo1WrBgQYn1/fOf/9RLL72kF198UTt27NDgwYN1ww036MCBA5Kk+Ph4tW/fXo888oji4+P16KOPFjuGv7+//P399eWXXyo3N7fUXtxyyy1KSkrS999/r82bN6tr16665pprlJKSUuL+77//vqZPn65nn31We/bs0XPPPacnn3xS77zzjiQpMzNTffv21YkTJ/T1119r+/btmjp1qiwWi2699VY98sgjat++veLj4xUfH69bb73VoTo++eQTzZw5U88995w2bdqkqKioYoGtosp7zMzMTF133XVauXKltm7dqiFDhmjo0KE6evSoJGnJkiVq3LixnnrqKdvzqYiVK1dq3759WrFihb799lvl5+dr8ODBCggI0C+//KI1a9bI399fQ4YMUV5engoKCjR8+HD17dtXO3bs0Lp163TvvffW2+mqAGoJAwDgNGPGjDGGDRtmGIZh9OzZ07jrrrsMwzCML774wjj/V/KMGTOMTp062d335ZdfNpo1a2Z3rGbNmhmFhYW2bZdeeqlx5ZVX2r4vKCgw/Pz8jA8//NAwDMOIi4szJBlz5syx7ZOfn280btzYmDt3rmEYhvH0008bgwYNsnvsY8eOGZKMffv2GYZhGH379jW6dOlS7vNt1KiR8eyzz9ptu/zyy40HHnjA9n2nTp2MGTNmlHmczz77zAgJCTG8vb2N3r17G9OmTTO2b99uu/2XX34xAgMDjZycHLv7xcTEGP/+978Nwyje05iYGOODDz6w2//pp582evXqZRiGYfz73/82AgICjNOnT5dYU0k/I0fq6NWrl93zNwzDiI2NLXas81l/bj4+Poafn5/ta8uWLQ49Zknat29vvPrqq7bvmzVrZrz88svlPseSXocRERFGbm6ubdt7771nXHrppYbFYrFty83NNXx8fIxly5YZp0+fNiQZq1evLrU+AKhpGHECABeZO3eu3nnnHe3Zs6fSx2jfvr3c3P78VR4REaGOHTvavjebzWrQoIGSkpLs7terVy/b393d3dW9e3dbHdu3b9ePP/5oG+nx9/dXmzZtJBVN+bLq1q1bmbWlp6fr5MmT6tOnj932Pn36VPg533zzzTp58qS+/vprDRkyRKtXr1bXrl1t0+O2b9+uzMxMNWjQwK7uuLg4u5qtsrKydOjQId199912+z/zzDO2/bdt26YuXbooNDTU4TodqWPPnj2KjY21u9/5P4+yfPzxx9q2bZvtq127dg49ZmZmph599FG1bdtWwcHB8vf31549e2wjTherY8eOduc1bd++XQcPHlRAQICtntDQUOXk5OjQoUMKDQ3V2LFjNXjwYA0dOlT//Oc/KzzKBQDOxuIQAOAiV111lQYPHqxp06Zp7Nixdre5ubnJMAy7bfn5+cWO4eHhYfe9yWQqcZvFYnG4rszMTA0dOlRz584tdltUVJTt735+fg4fsyp4e3tr4MCBGjhwoJ588kmNHz9eM2bM0NixY5WZmamoqCitXr262P2Cg4OLbcvMzJQkvfXWW8VCjNlsllS5hRcqWkdFNWnSRK1atarwYz766KNasWKFXnzxRbVq1Uo+Pj7661//qry8vDIfz9HX4YWvhczMTHXr1k3vv/9+sX0bNmwoSVq4cKEefPBBLV26VB9//LH+8Y9/aMWKFerZs2eZNQGAqxCcAMCF5syZo86dO9tOqLdq2LChEhISZBiG7byPqrz20vr163XVVVdJkgoKCrR582ZNnDhRktS1a1d9/vnnat68udzdK//fRGBgoBo1aqQ1a9aob9++tu1r1qxRjx49Lu4JSGrXrp2+/PJLSUU1JyQkyN3d3W7hgtJERESoUaNG+uOPP3T77beXuM9ll12m//znP0pJSSlx1MnT01OFhYV22xypo23bttqwYYPuvPNO27b169eXW3NpHHnMNWvWaOzYsbrxxhslFQWb8xdqKO35VPZ12LVrV3388ccKDw9XYGBgqft16dJFXbp00bRp09SrVy998MEHBCcANRZT9QDAhTp27Kjbb79dr7zyit32fv366dSpU3r++ed16NAhvf766/r++++r7HFff/11ffHFF9q7d68mTJigM2fO6K677pIkTZgwQSkpKRo1apR+++03HTp0SMuWLdO4ceOKvbEuz5QpUzR37lx9/PHH2rdvnx5//HFt27ZNDz30kMPHOH36tK6++motXrxYO3bsUFxcnD799FM9//zzGjZsmKSilQp79eql4cOHa/ny5Tp8+LDWrl2rJ554Qps2bSrxuLNmzdLs2bP1yiuvaP/+/fr999+1cOFCzZs3T5I0atQoRUZGavjw4VqzZo3++OMPff7551q3bp2kousexcXFadu2bUpOTlZubq5DdTz00EN6++23tXDhQu3fv18zZszQrl27KtTX8znymK1bt9aSJUu0bds2bd++XbfddluxUcjmzZvr559/1okTJ2wrKFb2dXj77bcrLCxMw4YN0y+//KK4uDitXr1aDz74oI4fP664uDhNmzZN69at05EjR7R8+XIdOHBAbdu2rXQfAKC6EZwAwMWeeuqpYm9i27Ztq3/96196/fXX1alTJ23cuLHEFecqa86cOZozZ446deqkX3/9VV9//bXCwsIkyTZKVFhYqEGDBqljx456+OGHFRwcbHc+lSMefPBBTZ48WY888og6duyopUuX6uuvv1br1q0dPoa/v79iY2P18ssv66qrrlKHDh305JNP6p577tFrr70mqWg64v/+9z9dddVVGjdunC655BKNHDlSR44cUURERInHHT9+vP7zn/9o4cKF6tixo/r27atFixapRYsWkopGYJYvX67w8HBdd9116tixo+bMmWObynfzzTdryJAh6t+/vxo2bKgPP/zQoTpuvfVWPfnkk5o6daq6deumI0eO6P77769QX8/nyGPOmzdPISEh6t27t4YOHarBgwera9eudsd56qmndPjwYcXExNim01X2dejr66uff/5ZTZs21U033aS2bdvall0PDAyUr6+v9u7dq5tvvlmXXHKJ7r33Xk2YMEF/+9vfKt0HAKhuJuPCycsAAAAAADuMOAEAAABAOQhOAAAAAFAOghMAAAAAlIPgBAAAAADlIDgBAAAAQDkITgAAAABQDoITAAAAAJSD4AQAAAAA5SA4AQAAAEA5CE4AAAAAUA6CEwAAAACU4/8BOKKxn4Ktqf8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plot maccs\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize cross-validation\n",
        "kf = KFold(n_splits=10)\n",
        "\n",
        "# Define the range of the number of features to select\n",
        "feature_range_maccs = np.arange(5, 165, 5)\n",
        "\n",
        "# Initialize arrays to store performance metrics\n",
        "r2_values_maccs = []\n",
        "\n",
        "# Loop over different numbers of selected features\n",
        "for top_n in feature_range_maccs:\n",
        "    # Initialize feature importance storage\n",
        "    feature_importance_aggregated = np.zeros(X_train_maccs.shape[1])\n",
        "\n",
        "    # Cross-validation for feature importance extraction\n",
        "    for train_index, test_index in kf.split(X_train_maccs):\n",
        "        X_train_fold, X_val_fold = X_train_maccs.iloc[train_index], X_train_maccs.iloc[test_index]\n",
        "        y_train_fold, y_val_fold = y_train_maccs.iloc[train_index], y_train_maccs.iloc[test_index]\n",
        "\n",
        "        # Train the model\n",
        "        model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "        model.fit(X_train_fold, y_train_fold)\n",
        "\n",
        "        # Aggregate feature importances\n",
        "        feature_importance_aggregated += model.feature_importances_\n",
        "\n",
        "    # Average the feature importances across all folds\n",
        "    feature_importance_aggregated /= kf.get_n_splits()\n",
        "\n",
        "    # Identify top features based on aggregated importance\n",
        "    sorted_indices_aggregated = np.argsort(feature_importance_aggregated)[::-1]\n",
        "    selected_features = X_train_maccs.columns[sorted_indices_aggregated[:top_n]]\n",
        "    X_train_selected = X_train_maccs[selected_features]\n",
        "    X_test_selected = X_test_maccs[selected_features]\n",
        "\n",
        "    # Refit the model with selected features on the entire training dataset\n",
        "    model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "    model = model.fit(X_train_selected, y_train_maccs)\n",
        "\n",
        "    # Make predictions on the test set and calculate R-squared\n",
        "    y_pred_selected = model.predict(X_test_selected)\n",
        "    r2 = r2_score(y_test_maccs, y_pred_selected)\n",
        "    r2_values_maccs.append(r2)\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(feature_range_maccs, r2_values_maccs, marker='o')\n",
        "plt.title('R-squared vs. Number of Selected Features')\n",
        "plt.xlabel('Number of Selected Features')\n",
        "plt.ylabel('R-squared')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WjO1dRhvhnok"
      },
      "outputs": [],
      "source": [
        "# RandomForestRegressor Model with ECFP\n",
        "import pandas as pd\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from tdc.single_pred import ADME\n",
        "# Generate ECFP for each SMILES string in the dataframe\n",
        "ecfp_list = []\n",
        "\n",
        "for smiles in df['clean_SMILES']:\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "\n",
        "    # Generate ECFP (you can adjust the radius as needed)\n",
        "    ecfp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=1024)\n",
        "    ecfp_array = list(ecfp)\n",
        "\n",
        "    ecfp_list.append(ecfp_array)\n",
        "\n",
        "# Create a new dataframe with ECFP values\n",
        "ecfp_df = pd.DataFrame(ecfp_list, columns=[f'ECFP_{i}' for i in range(1024)])\n",
        "\n",
        "# Concatenate the ECFP dataframe with the primary dataframe\n",
        "df_with_ecfp = pd.concat([df, ecfp_df], axis=1)\n",
        "# Remove rows with NaN values from both datasets\n",
        "df_with_ecfp_cleaned = df_with_ecfp.dropna(subset=ecfp_df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ty3k9865hp6Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60d8b625-a8c6-4368-ff0d-de6dac89ac75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Descriptor Data Shape: (729, 1028)\n",
            "Test Descriptor Data Shape: (183, 1028)\n"
          ]
        }
      ],
      "source": [
        "# train and test for ecfp\n",
        "# Extract rows from descriptor_data using the indices\n",
        "train_ecfp_data = df_with_ecfp_cleaned.iloc[train_indices]\n",
        "test_ecfp_data = df_with_ecfp_cleaned.iloc[test_indices]\n",
        "\n",
        "# Display the shapes of the train and test sets\n",
        "print(\"Train Descriptor Data Shape:\", train_ecfp_data.shape)\n",
        "print(\"Test Descriptor Data Shape:\", test_ecfp_data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mjpp_BylhruI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "3d03f88d-de09-45b4-e21c-6a5ef27d7139"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ECFP_0  ECFP_1  ECFP_2  ECFP_3  ECFP_4  ECFP_5  ECFP_6  ECFP_7  ECFP_8  \\\n",
              "0       0       0       0       0       0       0       0       0       0   \n",
              "1       0       1       0       0       0       0       0       0       0   \n",
              "2       0       0       0       0       0       0       0       0       0   \n",
              "3       0       0       0       0       0       0       0       0       0   \n",
              "4       0       0       0       0       0       0       0       1       0   \n",
              "\n",
              "   ECFP_9  ...  ECFP_1014  ECFP_1015  ECFP_1016  ECFP_1017  ECFP_1018  \\\n",
              "0       0  ...          0          0          0          0          0   \n",
              "1       0  ...          0          0          0          0          0   \n",
              "2       0  ...          0          0          0          0          0   \n",
              "3       0  ...          0          0          0          0          0   \n",
              "4       0  ...          0          0          0          0          0   \n",
              "\n",
              "   ECFP_1019  ECFP_1020  ECFP_1021  ECFP_1022  ECFP_1023  \n",
              "0          1          0          0          0          0  \n",
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              "      <th>ECFP_0</th>\n",
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              "      <th>ECFP_1014</th>\n",
              "      <th>ECFP_1015</th>\n",
              "      <th>ECFP_1016</th>\n",
              "      <th>ECFP_1017</th>\n",
              "      <th>ECFP_1018</th>\n",
              "      <th>ECFP_1019</th>\n",
              "      <th>ECFP_1020</th>\n",
              "      <th>ECFP_1021</th>\n",
              "      <th>ECFP_1022</th>\n",
              "      <th>ECFP_1023</th>\n",
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              "<p>5 rows × 1024 columns</p>\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "# train and test set for ecfp\n",
        "\n",
        "columns_to_exclude = ['Drug_ID', 'Drug', 'Y','clean_SMILES']\n",
        "\n",
        "\n",
        "X_train_ecfp= train_ecfp_data.drop(columns=columns_to_exclude)\n",
        "X_test_ecfp= test_ecfp_data.drop(columns=columns_to_exclude)\n",
        "y_train_ecfp=train_ecfp_data[['Y']]\n",
        "y_test_ecfp=test_ecfp_data[['Y']]\n",
        "\n",
        "X_train_ecfp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFF-IdXVhtbr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d34ff5a-77aa-4597-8243-e0b37c933770"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial model performance:\n",
            "MAE: 0.3551689575269845\n",
            "MSE: 0.1974021979898868\n",
            "R-squared: 0.6509345297088802\n",
            "MedAE: 0.30308488539999434\n"
          ]
        }
      ],
      "source": [
        "# RandomForestRegressor Model with ecfp\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
        "\n",
        "model_ecfp = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "model_ecfp=model_ecfp.fit(X_train_ecfp, y_train_ecfp)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred_ecfp = model_ecfp.predict(X_test_ecfp)\n",
        "\n",
        "# Calculate the MAE, MSE, and R-squared\n",
        "initial_RF_mae_ecfp = mean_absolute_error(y_test, y_pred_ecfp)\n",
        "initial_RF_mse_ecfp = mean_squared_error(y_test, y_pred_ecfp)\n",
        "initial_RF_r2_ecfp = r2_score(y_test, y_pred_ecfp)\n",
        "initial_RF_medae_ecfp = median_absolute_error(y_test, y_pred_ecfp)  # Calculate Median Absolute Error\n",
        "\n",
        "print(\"Initial model performance:\")\n",
        "print(f\"MAE: {initial_RF_mae_ecfp}\")\n",
        "print(f\"MSE: {initial_RF_mse_ecfp}\")\n",
        "print(f\"R-squared: {initial_RF_r2_ecfp}\")\n",
        "print(f\"MedAE: {initial_RF_medae_ecfp}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JdC5HXd5huzv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b61d4a85-7270-4aed-c465-43a52b6d419e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-statistic: -0.4474\n",
            "P-value: 0.6549\n",
            "Fail to reject the null hypothesis. There is no significant difference between the groups.\n"
          ]
        }
      ],
      "source": [
        "# statistical test to see if the predicted values are similar to the actual values from the test split\n",
        "\n",
        "from scipy import stats\n",
        "\n",
        "# Example data for two groups\n",
        "\n",
        "# Perform independent two-sample t-test\n",
        "t_statistic, p_value = stats.ttest_ind(y_pred_ecfp, y_test['Y'])\n",
        "\n",
        "# Print the results\n",
        "print(f\"T-statistic: {t_statistic:.4f}\")\n",
        "print(f\"P-value: {p_value:.4f}\")\n",
        "\n",
        "# Interpret the results\n",
        "alpha = 0.05  # significance level\n",
        "if p_value < alpha:\n",
        "    print(\"Reject the null hypothesis. There is a significant difference between the groups.\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis. There is no significant difference between the groups.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPIynep1hwVs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "7851c547-7a4b-4ce9-c6a6-0f3a9683195f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plot ecfp\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize cross-validation\n",
        "kf = KFold(n_splits=10)\n",
        "\n",
        "# Define the range of the number of features to select\n",
        "feature_range_ecfp = np.arange(50, 1000, 50)\n",
        "\n",
        "# Initialize arrays to store performance metrics\n",
        "r2_values_ecfp = []\n",
        "\n",
        "# Loop over different numbers of selected features\n",
        "for top_n in feature_range_ecfp:\n",
        "    # Initialize feature importance storage\n",
        "    feature_importance_aggregated = np.zeros(X_train_ecfp.shape[1])\n",
        "\n",
        "    # Cross-validation for feature importance extraction\n",
        "    for train_index, test_index in kf.split(X_train_ecfp):\n",
        "        X_train_fold, X_val_fold = X_train_ecfp.iloc[train_index], X_train_ecfp.iloc[test_index]\n",
        "        y_train_fold, y_val_fold = y_train_ecfp.iloc[train_index], y_train_ecfp.iloc[test_index]\n",
        "\n",
        "        # Train the model\n",
        "        model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "        model.fit(X_train_fold, y_train_fold)\n",
        "\n",
        "        # Aggregate feature importances\n",
        "        feature_importance_aggregated += model.feature_importances_\n",
        "\n",
        "    # Average the feature importances across all folds\n",
        "    feature_importance_aggregated /= kf.get_n_splits()\n",
        "\n",
        "    # Identify top features based on aggregated importance\n",
        "    sorted_indices_aggregated = np.argsort(feature_importance_aggregated)[::-1]\n",
        "    selected_features = X_train_ecfp.columns[sorted_indices_aggregated[:top_n]]\n",
        "    X_train_selected = X_train_ecfp[selected_features]\n",
        "    X_test_selected = X_test_ecfp[selected_features]\n",
        "\n",
        "    # Refit the model with selected features on the entire training dataset\n",
        "    model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
        "    model = model.fit(X_train_selected, y_train_ecfp)\n",
        "\n",
        "    # Make predictions on the test set and calculate R-squared\n",
        "    y_pred_selected = model.predict(X_test_selected)\n",
        "    r2 = r2_score(y_test_ecfp, y_pred_selected)\n",
        "    r2_values_ecfp.append(r2)\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(feature_range_ecfp, r2_values_ecfp, marker='o')\n",
        "plt.title('R-squared vs. Number of Selected Features')\n",
        "plt.xlabel('Number of Selected Features')\n",
        "plt.ylabel('R-squared')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yAqSk4s-iVRn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "dc291e6f-4b45-49fa-9609-311f3229ee1b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: [0.28933367249108266, 0.4814672225771834]\n",
            "RMSE: [0.136198662566665, 0.39453762103356693]\n",
            "R²: [0.7591604820717848, 0.30073972695050566]\n",
            "Pearson Correlation: [0.2689412427470357, 0.373758137240332]\n"
          ]
        }
      ],
      "source": [
        "models = ['Random (Internal)', 'Random (External)']\n",
        "mae_values = [best_selected_RF_mae, selected_RF_mae_external]\n",
        "rmse_values = [best_selected_RF_mse, selected_RF_mse_external]\n",
        "r2_values = [best_selected_RF_r2, selected_RF_r2_external]\n",
        "medae_values = [best_selected_RF_medae, selected_RF_medae_external]\n",
        "\n",
        "bar_width = 0.2\n",
        "index = np.arange(len(models))\n",
        "\n",
        "# Create grouped bar chart\n",
        "plt.bar(index, mae_values, width=bar_width, label='MAE', color='blue')\n",
        "plt.bar(index + bar_width, rmse_values, width=bar_width, label='RMSE', color='orange')\n",
        "plt.bar(index + 2 * bar_width, r2_values, width=bar_width, label='R²', color='green')\n",
        "plt.bar(index + 3 * bar_width, medae_values, width=bar_width, label='MedAE', color='red')\n",
        "\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Performance Metrics')\n",
        "plt.title('Metric Comparison Across Splits: MAE, RMSE, R², and MedAE')\n",
        "plt.xticks(index + 1.5 * bar_width, models, rotation=45, ha='right', fontsize=8)\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(f'MAE: {mae_values}')\n",
        "print(f'RMSE: {rmse_values}')\n",
        "print(f'R²: {r2_values}')\n",
        "print(f'Pearson Correlation: {medae_values}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YZiB3UDgpQyh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "a4af4b4e-8fc6-44fa-e29d-947604fc2168"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# on test set\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Scatter plot of actual vs. predicted values\n",
        "plt.scatter(y_test, y_pred_selected, color='blue', edgecolor='black')\n",
        "\n",
        "\n",
        "\n",
        "# Plot the dashed line from (-7.5, -7.5) to (-4, -4) in black\n",
        "plt.plot([-7.5, -4], [-7.5, -4], linestyle='--', color='black', linewidth=2)\n",
        "\n",
        "plt.xlabel('Actual Papp Values')\n",
        "plt.ylabel('Predicted Papp Values')\n",
        "plt.title('Actual vs. Predicted Papp Values for Selected Features Model')\n",
        "plt.legend(['y = x', 'Predictions', 'Dashed Line (-7.5, -7.5) to (-4, -4)'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g06xhvNBpS-7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "b8b7b51d-d9f3-400d-dd3e-273f4b189426"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Scatter plot of actual vs. predicted values\n",
        "plt.scatter(y_external, y_pred_selected_external, color='blue', edgecolor='black')\n",
        "\n",
        "\n",
        "\n",
        "# Plot the dashed line from (-7.5, -7.5) to (-4, -4) in black\n",
        "plt.plot([-7.5, -4], [-7.5, -4], linestyle='--', color='black', linewidth=2)\n",
        "\n",
        "plt.xlabel('Actual Papp Values')\n",
        "plt.ylabel('Predicted Papp Values')\n",
        "plt.title('Actual vs. Predicted Papp Values for Selected Features Model')\n",
        "plt.legend(['y = x', 'Predictions', 'Dashed Line (-7.5, -7.5) to (-4, -4)'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discussion\n",
        "\n",
        "The RF model can be used to predict Papp values. The best R-squared value of the model is around 0.76, and the P-test indicates that the predicted values on the test set have no significant difference from the actual values. During the experiments, we found that the prediction ability could be greatly affected by the type of descriptors used to construct the model. According to the results, the model constructed using 2D descriptors from RDKit works better than models using descriptors from ECFPs or MACCS Keys. This suggests that the 2D descriptors from RDKit can help capture more important features of the molecules contributing to the Papp value. We also tried a combination method that chose the top 50 most important descriptors from the above generation methods and applied these combined descriptors for model construction. The results indicate that the combined method didn't significantly improve the prediction ability of the model. In conclusion, all the descriptors we currently generated may not be enough to capture all the important features for predicting Papp values. Considering that the current descriptors are all 2D descriptors, more complex descriptors such as 3D descriptors could be applied in future explorations.\n"
      ],
      "metadata": {
        "id": "KBw8a9olu_Ny"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "gpuType": "A100"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}