Functional_group_desc_v2.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/alihojatnia/rdkit/blob/main/Functional_group_desc_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "cBnYUjgHFm_U"
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      "source": [
        "%%capture\n",
        "!pip install rdkit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGO94Ym4b7I4"
      },
      "source": [
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "from rdkit import Chem"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "from rdkit.Chem import AllChem"
      ],
      "metadata": {
        "id": "XYPRnzBN7jCg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Day light smart functionagroups"
      ],
      "metadata": {
        "id": "ullMOZ2dDyUK"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hhMOOyX3b-6Y"
      },
      "source": [
        "### https://www.daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RDKit glbal feature which are related to the functional gruops"
      ],
      "metadata": {
        "id": "Bm8Yb8p_D3ve"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/rdkit/rdkit/blob/master/Data/FragmentDescriptors.csv"
      ],
      "metadata": {
        "id": "aA_NsQpuP-U1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://docs.chemaxon.com/display/docs/attachments/attachments_1829721_1_functionalgroups.cxsmi\n",
        "\n",
        "https://docs.chemaxon.com/display/docs/predefined-functional-groups-and-named-molecule-groups.md"
      ],
      "metadata": {
        "id": "UwifYeqptqJ-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Functional groups"
      ],
      "metadata": {
        "id": "hrO2oNdqthWu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smarts = [rdkit_smarts+daylight_smarts+chemaxon_smarts][0]\n",
        "print(len(smarts))\n",
        "#Drop Duplicate\n",
        "smarts = list(set(smarts))\n",
        "print(len(smarts))"
      ],
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        "colab": {
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        "outputId": "7c08f998-5b42-4544-f0da-fe2b91073f27",
        "id": "RYM1FSLesCAe"
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      "execution_count": null,
      "outputs": [
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          "text": [
            "397\n",
            "347\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#return would be a list of number of functiolan groups\n",
        "def check_for_smart(smiles,smarts):\n",
        "  results_of_smarts = []\n",
        "  mol = Chem.MolFromSmiles(smiles)\n",
        "  \n",
        "  for i in smarts:\n",
        "    smart_pattern = Chem.MolFromSmarts(i)\n",
        "    if mol.HasSubstructMatch(smart_pattern):\n",
        "      results_of_smarts.append(len(mol.GetSubstructMatches(smart_pattern)))\n",
        "    else:\n",
        "      results_of_smarts.append(0)\n",
        "  return results_of_smarts    \n",
        "\n",
        "#list_to_series\n",
        "def list_to_series(results_of_smarts:list,df:pd.DataFrame)->pd.Series: \n",
        "  return pd.Series(results_of_smarts, index=df.columns)\n"
      ],
      "metadata": {
        "id": "VhlljsmCNv9h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Smiles list\n",
        "\n",
        "freesolv_df = pd.read_csv('https://deepchemdata.s3.us-west-1.amazonaws.com/datasets/freesolv.csv.gz')\n",
        "sm = freesolv_df['smiles']\n",
        "\n",
        "# Cols with the len(smarts)\n",
        "cols = {str(i): 0 for i in range(len(smarts))}\n",
        "# Create datafarame\n",
        "df = pd.DataFrame(cols, index=[0])\n",
        "\n",
        "#for loop over smiles to raech to the FGs desc\n",
        "for i in range(len(sm)):\n",
        "  df = df.append(list_to_series(check_for_smart(sm[i],smarts),df), ignore_index=True)\n",
        "\n",
        "df = df.drop(0, axis=0)\n",
        "df"
      ],
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        },
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-adab3504-100e-4a31-9888-72fdbbae4fce button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-adab3504-100e-4a31-9888-72fdbbae4fce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Smarts"
      ],
      "metadata": {
        "id": "xC_wVc9szYk9"
      }
    }
  ]
}