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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/francescopatane96/Bioactivity-prediction-with-ML/blob/main/filtering_unwanted_substructures3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unwanted substructures:\n",
        "substructures can be reactive or toxic or they can interfere with certain assays. Filtering unwanted substructures can support assembling more efficient screening libraries, which can save time and resources.\n",
        "\n",
        "Examples of such unwanted features are nitro groups (mutagenic), sulfates and phosphates (likely resulting in unfavorable pharmacokinetic properties), 2-halopyridines and thiols (reactive). \n",
        "\n",
        "Pan Assay Interference Compounds (PAINS):\n",
        "PAINS are compounds that often occur as hits in HTS even though they actually are false positives. PAINS show activity at numerous targets rather than one specific target."
      ],
      "metadata": {
        "id": "pXWQZuaa19_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ebl1uvs2Vzz",
        "outputId": "1fd357f1-6d49-4afd-ea69-e9ddb3afb2a4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2022.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 36.8 MB 395 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from rdkit) (1.21.6)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from rdkit) (7.1.2)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2022.3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "eYx9Zu8n0-5g"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "import pandas as pd\n",
        "from tqdm.auto import tqdm\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import PandasTools\n",
        "from rdkit.Chem.FilterCatalog import FilterCatalog, FilterCatalogParams"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load data from Talktorial T2\n",
        "TNFB_data = pd.read_csv(\n",
        "    \"TNFB_compounds_lipinski.csv\",\n",
        "    index_col=0,\n",
        ")\n",
        "# Drop unnecessary information\n",
        "print(\"Dataframe shape:\", TNFB_data.shape)\n",
        "TNFB_data.drop(columns=[\"molecular_weight\", \"n_hbd\", \"n_hba\", \"logp\"], inplace=True)\n",
        "TNFB_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "5wN1gfYL2fEr",
        "outputId": "561f2912-efce-4aac-ebac-1790b3ec8de5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe shape: (49, 11)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id   IC50 units  \\\n",
              "0       CHEMBL336546   50.0    nM   \n",
              "1       CHEMBL131636  170.0    nM   \n",
              "2       CHEMBL134490  280.0    nM   \n",
              "3       CHEMBL135133  380.0    nM   \n",
              "4       CHEMBL334485  500.0    nM   \n",
              "\n",
              "                                              smiles     pIC50  \\\n",
              "0  O=C(Nc1cc(C(F)(F)F)cc(C(F)(F)F)c1)c1cnc(Cl)nc1...  7.301030   \n",
              "1  CCCCOC(=O)c1cc(NC(=O)c2cnc(Cl)nc2C(F)(F)F)cc(C...  6.769551   \n",
              "2  CCOC(=O)c1cc(NC(=O)c2cnc(Cl)nc2C(F)(F)F)cc(C(F...  6.552842   \n",
              "3       O=C(Nc1ccc(C(F)(F)F)cc1)c1cnc(Cl)nc1C(F)(F)F  6.420216   \n",
              "4         O=C(Nc1cc(Cl)cc(Cl)c1)c1cnc(Cl)nc1C(F)(F)F  6.301030   \n",
              "\n",
              "                                               ROMol  ro5_fulfilled  \n",
              "0  <img data-content=\"rdkit/molecule\" src=\"data:i...           True  \n",
              "1  <img data-content=\"rdkit/molecule\" src=\"data:i...           True  \n",
              "2  <img data-content=\"rdkit/molecule\" src=\"data:i...           True  \n",
              "3  <img data-content=\"rdkit/molecule\" src=\"data:i...           True  \n",
              "4  <img data-content=\"rdkit/molecule\" src=\"data:i...           True  "
            ],
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              "\n",
              "  <div id=\"df-a7217bc7-eba1-4454-aff7-9029ac6645a9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
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              "      <td>CHEMBL131636</td>\n",
              "      <td>170.0</td>\n",
              "      <td>nM</td>\n",
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              "      <th>2</th>\n",
              "      <td>CHEMBL134490</td>\n",
              "      <td>280.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>CCOC(=O)c1cc(NC(=O)c2cnc(Cl)nc2C(F)(F)F)cc(C(F...</td>\n",
              "      <td>6.552842</td>\n",
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              "      <th>3</th>\n",
              "      <td>CHEMBL135133</td>\n",
              "      <td>380.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>O=C(Nc1ccc(C(F)(F)F)cc1)c1cnc(Cl)nc1C(F)(F)F</td>\n",
              "      <td>6.420216</td>\n",
              "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL334485</td>\n",
              "      <td>500.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>O=C(Nc1cc(Cl)cc(Cl)c1)c1cnc(Cl)nc1C(F)(F)F</td>\n",
              "      <td>6.301030</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add molecule column\n",
        "PandasTools.AddMoleculeColumnToFrame(TNFB_data, smilesCol=\"smiles\")\n",
        "# Draw first 3 molecules\n",
        "Chem.Draw.MolsToGridImage(\n",
        "    list(TNFB_data.head(3).ROMol),\n",
        "    legends=list(TNFB_data.head(3).molecule_chembl_id),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "rzsqw1ha3Ha8",
        "outputId": "70c4655d-a4dc-4fd3-b5a3-f928e61a4d8f"
      },
      "execution_count": 5,
      "outputs": [
        {
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\n"
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filter for PAINS"
      ],
      "metadata": {
        "id": "u2dRkR2R3epc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize filter\n",
        "params = FilterCatalogParams()\n",
        "params.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS)\n",
        "catalog = FilterCatalog(params)"
      ],
      "metadata": {
        "id": "mtlZeOxU3hJj"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# search for PAINS\n",
        "matches = []\n",
        "clean = []\n",
        "for index, row in tqdm(TNFB_data.iterrows(), total=TNFB_data.shape[0]):\n",
        "    molecule = Chem.MolFromSmiles(row.smiles)\n",
        "    entry = catalog.GetFirstMatch(molecule)  # Get the first matching PAINS\n",
        "    if entry is not None:\n",
        "        # store PAINS information\n",
        "        matches.append(\n",
        "            {\n",
        "                \"chembl_id\": row.molecule_chembl_id,\n",
        "                \"rdkit_molecule\": molecule,\n",
        "                \"pains\": entry.GetDescription().capitalize(),\n",
        "            }\n",
        "        )\n",
        "    else:\n",
        "        # collect indices of molecules without PAINS\n",
        "        clean.append(index)\n",
        "\n",
        "matches = pd.DataFrame(matches)\n",
        "TNFB_data = TNFB_data.loc[clean]  # keep molecules without PAINS"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "26574b6d572c4e7bb2a65838080e4fbc",
            "8b7527e08e374036988e235fb0ee8669",
            "2d22fcd379764f819e99198b1c7d5827",
            "6f3f7a068670460baf509f43b3bff82a",
            "03f78a69559f4516bd53c49f1f4127af",
            "e39d346fb5234a32ace6a6adae97ccd1",
            "294c59d1cc4d42929138699c317764e7",
            "10fb9a54677c4260acc718fbe5059001",
            "48fb676cbee440b1a925388ff27dc477",
            "ace2b837064744449cc5140ede7ffd73",
            "8759431d32f844f4b8049eeaba48fa73"
          ]
        },
        "id": "X2kiOCOo3oe7",
        "outputId": "d3d464a7-9391-43fc-a2e8-6834f4ffca85"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/49 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26574b6d572c4e7bb2a65838080e4fbc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of compounds with PAINS: {len(matches)}\")\n",
        "print(f\"Number of compounds without PAINS: {len(TNFB_data)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msNTp6h_3_g6",
        "outputId": "5e10d490-26e9-4c45-cb9f-2d0782e7066a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of compounds with PAINS: 5\n",
            "Number of compounds without PAINS: 44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Chem.Draw.MolsToGridImage(\n",
        "    list(matches.head(5).rdkit_molecule),\n",
        "    legends=list(matches.head(5)[\"pains\"]),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "zAsR5xdo4Hy7",
        "outputId": "40a39698-af98-4a3b-f92b-cd21095dd4fe"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x400 at 0x7F74703DD510>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filter and highlight unwanted substructures"
      ],
      "metadata": {
        "id": "hNbb8xcZ4ZCa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "substructures = pd.read_csv(\"unwantedSubstructures.csv\", sep=\"\\s+\")\n",
        "substructures[\"rdkit_molecule\"] = substructures.smart.apply(Chem.MolFromSmarts)\n",
        "print(\"Number of unwanted substructures in collection:\", len(substructures))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zl3Lpz_Q4b3K",
        "outputId": "0591ff25-2a6d-4164-9057-3ba9afa6c395"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unwanted substructures in collection: 104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Chem.Draw.MolsToGridImage(\n",
        "    mols=substructures.rdkit_molecule.tolist()[2:5],\n",
        "    \n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "8XY7inLq7j5L",
        "outputId": "8aa90652-27a7-493b-931e-991560b60eb3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x7F746F816F90>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# search for unwanted substructure\n",
        "matches = []\n",
        "clean = []\n",
        "for index, row in tqdm(TNFB_data.iterrows(), total=TNFB_data.shape[0]):\n",
        "    molecule = Chem.MolFromSmiles(row.smiles)\n",
        "    match = False\n",
        "    for _, substructure in substructures.iterrows():\n",
        "        if molecule.HasSubstructMatch(substructure.rdkit_molecule):\n",
        "            matches.append(\n",
        "                {\n",
        "                    \"chembl_id\": row.molecule_chembl_id,\n",
        "                    \"rdkit_molecule\": molecule,\n",
        "                    \"substructure\": substructure.rdkit_molecule,\n",
        "                    \n",
        "                }\n",
        "            )\n",
        "            match = True\n",
        "    if not match:\n",
        "        clean.append(index)\n",
        "\n",
        "matches = pd.DataFrame(matches)\n",
        "TNFB_data = TNFB_data.loc[clean]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fdd03815f99c446082e62d1c63ac5790",
            "b2f1a2f355f1466cb7b816d5ddc09cf8",
            "ef4e97f1f2d54515bbf6f61ca297a50a",
            "89852215b25c4242a7a525078a8ac545",
            "fa5cde7ebf69481fa3f677be7d699f74",
            "d71ca387dc9145eebcea9bf8c29a7327",
            "6f978bdb3e484a968b30f1ff1453a908",
            "d4a374a8c13840ee891abdfca3a9a92b",
            "9399b196bee34090aa81de0405cf0f06",
            "19f9522521b64d9ebfed971d4d5f08ea",
            "8723d0f588294a48bc7150c5b5db1df9"
          ]
        },
        "id": "pvqK_Sgq946c",
        "outputId": "a4c50c17-b538-48aa-cb8d-2bc9016ab972"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/44 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fdd03815f99c446082e62d1c63ac5790"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of found unwanted substructure: {len(matches)}\")\n",
        "print(f\"Number of compounds without unwanted substructure: {len(TNFB_data)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AP-wfa4-RxL",
        "outputId": "eed99735-0ef8-42b0-bc0d-86441584ba19"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of found unwanted substructure: 37\n",
            "Number of compounds without unwanted substructure: 19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "to_highlight = [\n",
        "    row.rdkit_molecule.GetSubstructMatch(row.substructure) for _, row in matches.head(3).iterrows()\n",
        "]\n",
        "Chem.Draw.MolsToGridImage(\n",
        "    list(matches.head(3).rdkit_molecule),\n",
        "    highlightAtomLists=to_highlight,\n",
        "    \n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "46RQc5Mg-cSS",
        "outputId": "0f91dd75-3550-42ab-c308-8750b94bc13c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x7F746EC2F550>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Substructure statistics"
      ],
      "metadata": {
        "id": "vHbNd3uc-may"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "group_frequencies = groups.size()\n",
        "group_frequencies.sort_values(ascending=False, inplace=True)\n",
        "group_frequencies.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XEZb6VBj-myq",
        "outputId": "b44bc5f4-cf7c-421b-ac09-8b381080ffae"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-b12d81389525>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgroup_frequencies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mgroup_frequencies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mgroup_frequencies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'groups' is not defined"
          ]
        }
      ]
    }
  ]
}