1_local.ipynb

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      "source": [
        "This notebook provide you a way to obtain a csv file for later QSAR building"
      ]
    },
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      "execution_count": 2,
      "metadata": {
        "id": "oWYkkuPUiiK0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "GxL-9LTNiiK1"
      },
      "source": [
        "Read structure"
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        "id": "GCcE8J5XwjtB"
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        "df3 = pd.read_csv('structures.txt')"
      ]
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        "Next the txt file will be save as a csv file, then import back again, to beautify the structure."
      ]
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "   bioactivity\n",
              "0          8.5\n",
              "1          6.9"
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          },
          "metadata": {},
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      ],
      "source": [
        "selection=['bioactivity']\n",
        "df8[selection]"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
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          "height": 112
        },
        "id": "WljdMv7JiiK6",
        "outputId": "f99ba8f0-f62c-4396-a693-17bec5d5d859"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "   bioactivity\n",
              "0          8.5\n",
              "1          6.9"
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          },
          "metadata": {},
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      ],
      "source": [
        "df8_selection = df8[selection]\n",
        "df8_selection"
      ]
    },
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      "cell_type": "markdown",
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        "id": "I5sZ1ML4iiK6"
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      "source": [
        "Combine structure and bioactivity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
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        "id": "vuxKbqwgiiK6",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "                                              smiles  bioactivity\n",
              "0  CN(N=C1)C=C1C2=CC=C(OC(F)=C3C4=CN(CC5=CC=CC=C5...          8.5\n",
              "1  CN(N=C1)C=C1C2=CC=C(OC(C)=C3C4=CN(CC5=CC=CC=C5...          6.9"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df10 = pd.concat([df4_selection, df8_selection], axis=1)\n",
        "df10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVpUG1_ZiiK6"
      },
      "source": [
        "Classification of bioactivities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "_b6XgvAMiiK6"
      },
      "outputs": [],
      "source": [
        "bioactivity_class = []\n",
        "for i in df10.bioactivity:\n",
        "  if float(i) >= 7:\n",
        "    bioactivity_class.append(\"active\")\n",
        "  elif float(i) <= 6:\n",
        "    bioactivity_class.append(\"inactive\")\n",
        "  else:\n",
        "    bioactivity_class.append(\"intermediate\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJnPRs1GiiK7"
      },
      "source": [
        "Combine "
      ]
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    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
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        "id": "RZyuT8GpiiK7",
        "outputId": "c9156679-8cea-44e2-cb2f-3c084482b852"
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-7f1d8c07-197d-4a72-98f2-2c6475543c69 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-7f1d8c07-197d-4a72-98f2-2c6475543c69');\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",
              "  "
            ],
            "text/plain": [
              "                                              smiles  ...  bioactivity_class\n",
              "0  CN(N=C1)C=C1C2=CC=C(OC(F)=C3C4=CN(CC5=CC=CC=C5...  ...             active\n",
              "1  CN(N=C1)C=C1C2=CC=C(OC(C)=C3C4=CN(CC5=CC=CC=C5...  ...       intermediate\n",
              "\n",
              "[2 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "bioactivity_class = pd.Series(bioactivity_class, name='bioactivity_class')\n",
        "df11 = pd.concat([df10, bioactivity_class], axis=1)\n",
        "df11"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slttXsfniiK7"
      },
      "source": [
        "Save file as csv file, here we kept index"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df12 = df11[df11.bioactivity_class != 'intermediate']\n",
        "df12"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "u_-rin5Qijt0",
        "outputId": "e64ed101-ab17-4b8f-e753-17923ed84d6e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>smiles</th>\n",
              "      <th>bioactivity</th>\n",
              "      <th>bioactivity_class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CN(N=C1)C=C1C2=CC=C(OC(F)=C3C4=CN(CC5=CC=CC=C5...</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-3c428c5e-1d8e-4899-82f5-61116c28a2b2 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-3c428c5e-1d8e-4899-82f5-61116c28a2b2');\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",
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              "  "
            ],
            "text/plain": [
              "                                              smiles  ...  bioactivity_class\n",
              "0  CN(N=C1)C=C1C2=CC=C(OC(F)=C3C4=CN(CC5=CC=CC=C5...  ...             active\n",
              "\n",
              "[1 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1z_b7U2tkgWC"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df12.to_csv('sars-cov-2-local_bioactivity_data_preprocessed.csv')"
      ],
      "metadata": {
        "id": "Fxt9rdKykVNP"
      },
      "execution_count": 21,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "1_local.ipynb",
      "provenance": []
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