QSAR_V1_09012024.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DIFACQUIM/Cursos/blob/main/QSAR_V1_09012024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c11acc27",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "c11acc27"
      },
      "source": [
        "# ***Construcción de un modelo QSAR***\n",
        "\n",
        "\n",
        "---\n",
        "Script realizado por Raziel Cedillo-González.\n",
        "Última actualización: 09-01-2024.\n",
        "Contacto: brayanraziel1997@gmail.com\n",
        "\n",
        "---\n",
        "\n",
        "El flujo de trabajo presentado más abajo recopila la experiencia y trabajo del Dr. Pat Walters (https://github.com/PatWalters/practical_cheminformatics_tutorials). En este script se puede cambiar el formato de entrada, los descriptores o el modelo de machine learning al cambiar una sola línea de código. Cabe destacar que este código no toma lugar de una validación rigurosa pero proporciona un punto de partida rápido."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***Tabla de Contenido***\n",
        "Previo a ejecutar este código se requiere el curado de la base de datos y estandarización de SMILES\n",
        "\n",
        "- [0 - ***Instalación e importación de librerías***](#0)\n",
        "- [1 - ***Lectura de archivos***](#1)\n",
        "- [2 - ***Cálculo de descriptores (EFPC)***](#2)\n",
        "- [3 - ***Transformación de moléculas (molfeat)***](#3)\n",
        "- [4 - ***Cálculo de fingerprints***](#4)\n",
        "- [5 - ***Segmentación de datos: entrenamiento y prueba***](#5)\n",
        "- [6 - ***Regresión (sklearn)***](#6)\n",
        "- [7 - ***Construcción y visualización del modelo***](#7)\n",
        "- [8 - ***Residuales***](#8)\n",
        "- [9 - ***Predicción***](#9)"
      ],
      "metadata": {
        "id": "0X5KDqaF5_JS"
      },
      "id": "0X5KDqaF5_JS"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name='0'></a>\n",
        "# **Instalación e importación de librerías**"
      ],
      "metadata": {
        "id": "RPa9LY0L6EIJ"
      },
      "id": "RPa9LY0L6EIJ"
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.utils import io\n",
        "import tqdm.notebook\n",
        "import os, sys, random, subprocess, statistics\n",
        "total = 100\n",
        "with tqdm.notebook.tqdm(total=total) as pbar:\n",
        "    with io.capture_output() as captured:\n",
        "        pbar.update(20)\n",
        "        !pip install rdkit, pandas\n",
        "        !pip install datamol # Biblioteca que proporciona utilidades y funciones para trabajar con datos moleculares, como moléculas químicas y conjuntos de datos químicos.\n",
        "        !pip install molfeat\n",
        "        !pip install numpy\n",
        "        !pip install sklearn #Biblioteca de ML, proporciona herramientas para realizar tareas comunes: clasificación, regresión, clustering y selección de modelos.\n",
        "        !pip install yellowbrick\n",
        "        pbar.update(30)\n",
        "        import pandas as pd\n",
        "        import datamol as dm\n",
        "        from molfeat.calc import FPCalculator #Esta clase se utiliza para calcular descriptores moleculares\n",
        "        from molfeat.trans import MoleculeTransformer # Esta clase se utiliza para transformar las moléculas aplicando ciertas operaciones.\n",
        "        from molfeat.calc import FP_FUNCS\n",
        "        pbar.update(30)\n",
        "        import numpy as np\n",
        "        from sklearn.model_selection import train_test_split #Se utiliza para dividir un conjunto de datos en conjuntos de entrenamiento y prueba.\n",
        "        from sklearn.ensemble import HistGradientBoostingRegressor # Es una implementación de Gradient Boosting para problemas de regresión en scikit-learn.\n",
        "        from yellowbrick.regressor import prediction_error, residuals_plot # Se utiliza para (1) visualizar y (2) gráficar errores de predicción en problemas de regresión.\n",
        "\n",
        "        #RDKit\n",
        "        !pip install rdkit\n",
        "        from rdkit import Chem, DataStructs\n",
        "        from rdkit.Chem import AllChem, Draw, PandasTools, Descriptors, Descriptors3D, rdMolDescriptors, Scaffolds\n",
        "        PandasTools.RenderImagesInAllDataFrames(images=True) #Para visualizar moléculas\n",
        "        from rdkit.Chem.Draw import IPythonConsole\n",
        "\n",
        "        pbar.update(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2361f0d210924117934a3f12f38f934d",
            "6e4c62bd96344fbbbb23c005f20d4d87",
            "aa388c983c0d4431864bacac187461aa",
            "cf546c4babec4786b4ea1765da60e4ac",
            "a91097f3f6714b249b4aa09b21b79e5a",
            "e1da59ec19484ceb8af5240a063d50d7",
            "0ca16a5a319e4ad480e6720d1ff7ee90",
            "e4893520b2904734a1b11c44100b6d78",
            "c7ecdea6e52d49988a74ce07bf85a985",
            "744a99a9c571471e9797121eb9c8cfda",
            "c6c83a44007746369d77f0f2cd6c387f"
          ]
        },
        "id": "ZyztYVng6JYp",
        "outputId": "5a87096b-a39e-48d7-8667-32555eb3ad22"
      },
      "id": "ZyztYVng6JYp",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2361f0d210924117934a3f12f38f934d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41037477",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "41037477"
      },
      "source": [
        "<a name='1'></a>\n",
        "##**1.** Lectura de archivos.\n",
        "Se leerá el archivo (csv o xlsx) que cuente con la información de las moléculas de interés (SMILES, ID/NAME, propiedad).\n",
        "\n",
        "Ejemplo:\n",
        "```\n",
        "filename =\"https://raw.githubusercontent.com/PatWalters/yamc/main/data/Carbonic.smi\"\n",
        "df = pd.read_csv(filename,sep=\" \",names=[\"SMILES\",\"Name\",\"pIC50\"])\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Leer archivo\n",
        "url = \"https://raw.githubusercontent.com/DIFACQUIM/Cursos/main/Datasets/compounds_ChEMBL_G9a.csv\"\n",
        "df = pd.read_csv(url, index_col=0)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2EAgcTkhiBmB",
        "outputId": "3d61a843-cfc9-4bee-f78a-0a4e64fe3b70"
      },
      "id": "2EAgcTkhiBmB",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                             SMILES  \\\n",
              "0        CHEMBL4170225  COc1cc2c(=NC3CCN(C)CC3)cc(-c3ccc(C)[nH]3)[nH]c...   \n",
              "1        CHEMBL4162206  COc1cc2c(=NC3CCN(C(C)C)CC3)cc(C3CCCCC3)[nH]c2c...   \n",
              "2        CHEMBL3109639               COc1cc2c(cc1OCCCN1CCCC1)N=C(N)C2(C)C   \n",
              "3        CHEMBL3109631             COc1cc2c(cc1OCC1CCN(C)C1)N=C(N)C21CCC1   \n",
              "4        CHEMBL5187766              COc1cc2c(=NCCN)cc[nH]c2cc1OCCCN1CCCC1   \n",
              "..                 ...                                                ...   \n",
              "425      CHEMBL1213958  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "426      CHEMBL1214630  COc1cc2[nH]c(N3CCCN(C)CC3)nc(=NC3CCN(C)C3)c2cc1OC   \n",
              "427      CHEMBL1214145  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "428      CHEMBL1213959  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "429      CHEMBL1213829  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "\n",
              "        pIC50  \n",
              "0    9.301030  \n",
              "1    9.154902  \n",
              "2    9.045757  \n",
              "3    9.000000  \n",
              "4    8.795880  \n",
              "..        ...  \n",
              "425  3.055517  \n",
              "426  3.040959  \n",
              "427  2.823909  \n",
              "428  2.468521  \n",
              "429  1.821023  \n",
              "\n",
              "[430 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf1911e1-0afd-4451-b200-bb1d05c98d2d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>pIC50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL4170225</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)cc(-c3ccc(C)[nH]3)[nH]c...</td>\n",
              "      <td>9.301030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL4162206</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C(C)C)CC3)cc(C3CCCCC3)[nH]c2c...</td>\n",
              "      <td>9.154902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3109639</td>\n",
              "      <td>COc1cc2c(cc1OCCCN1CCCC1)N=C(N)C2(C)C</td>\n",
              "      <td>9.045757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL3109631</td>\n",
              "      <td>COc1cc2c(cc1OCC1CCN(C)C1)N=C(N)C21CCC1</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL5187766</td>\n",
              "      <td>COc1cc2c(=NCCN)cc[nH]c2cc1OCCCN1CCCC1</td>\n",
              "      <td>8.795880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>425</th>\n",
              "      <td>CHEMBL1213958</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>3.055517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>CHEMBL1214630</td>\n",
              "      <td>COc1cc2[nH]c(N3CCCN(C)CC3)nc(=NC3CCN(C)C3)c2cc1OC</td>\n",
              "      <td>3.040959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>CHEMBL1214145</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>2.823909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>428</th>\n",
              "      <td>CHEMBL1213959</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>2.468521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>429</th>\n",
              "      <td>CHEMBL1213829</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>1.821023</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>430 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf1911e1-0afd-4451-b200-bb1d05c98d2d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bf1911e1-0afd-4451-b200-bb1d05c98d2d 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-bf1911e1-0afd-4451-b200-bb1d05c98d2d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-527bbb09-ccfc-4c80-b1ac-7d4110ca50a6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-527bbb09-ccfc-4c80-b1ac-7d4110ca50a6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-527bbb09-ccfc-4c80-b1ac-7d4110ca50a6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ef70d7ea-221f-40c9-a746-318a3df56223\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ef70d7ea-221f-40c9-a746-318a3df56223 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_visualization=df.copy()\n",
        "df_visualization['ROMol'] = df_visualization['SMILES'].apply(Chem.MolFromSmiles)\n",
        "df_visualization[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "99pFnTsieryt",
        "outputId": "1c9edd4e-9685-44dd-d6b1-71d601f6c02e"
      },
      "id": "99pFnTsieryt",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id                                             SMILES  \\\n",
              "0      CHEMBL4170225  COc1cc2c(=NC3CCN(C)CC3)cc(-c3ccc(C)[nH]3)[nH]c...   \n",
              "1      CHEMBL4162206  COc1cc2c(=NC3CCN(C(C)C)CC3)cc(C3CCCCC3)[nH]c2c...   \n",
              "2      CHEMBL3109639               COc1cc2c(cc1OCCCN1CCCC1)N=C(N)C2(C)C   \n",
              "\n",
              "      pIC50                                             ROMol  \n",
              "0  9.301030  <rdkit.Chem.rdchem.Mol object at 0x78e11c6b09e0>  \n",
              "1  9.154902  <rdkit.Chem.rdchem.Mol object at 0x78e11c6b1a80>  \n",
              "2  9.045757  <rdkit.Chem.rdchem.Mol object at 0x78e11c6b1af0>  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4ca0d8b-703f-4e3e-b56a-162b16338973\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>ROMol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL4170225</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)cc(-c3ccc(C)[nH]3)[nH]c...</td>\n",
              "      <td>9.301030</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL4162206</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C(C)C)CC3)cc(C3CCCCC3)[nH]c2c...</td>\n",
              "      <td>9.154902</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3109639</td>\n",
              "      <td>COc1cc2c(cc1OCCCN1CCCC1)N=C(N)C2(C)C</td>\n",
              "      <td>9.045757</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4ca0d8b-703f-4e3e-b56a-162b16338973')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a4ca0d8b-703f-4e3e-b56a-162b16338973 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-a4ca0d8b-703f-4e3e-b56a-162b16338973');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-10ba2645-571e-4413-be10-9fb26206f9a7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10ba2645-571e-4413-be10-9fb26206f9a7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-10ba2645-571e-4413-be10-9fb26206f9a7 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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8488ccfb",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "8488ccfb"
      },
      "source": [
        "<a name='2'></a>\n",
        "## **2.** Selección de descriptores.\n",
        "Se emplea el paquete [molfeat](https://m2d2.io/blog/posts/introtaining-molfeat-a-hub-of-molecular-featurizers/), el cual tiene varios tipos de descriptores disponibles."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FP_FUNCS.keys() #Descriptores que se pueden calcular con esta libreria"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8Iv_VGqChzD",
        "outputId": "9cc8882c-69ea-432a-d101-991799c6b110"
      },
      "id": "J8Iv_VGqChzD",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['maccs', 'avalon', 'ecfp', 'fcfp', 'topological', 'atompair', 'rdkit', 'pattern', 'layered', 'map4', 'secfp', 'erg', 'estate', 'avalon-count', 'rdkit-count', 'ecfp-count', 'fcfp-count', 'topological-count', 'atompair-count'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "6ff42711",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "6ff42711"
      },
      "outputs": [],
      "source": [
        "calc = FPCalculator(\"ecfp\") #Cálculo de FPS"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "98a1b30f",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "98a1b30f"
      },
      "source": [
        "<a name='3'></a>\n",
        "\n",
        "##**3.** Transformación de moléculas (molfeat).\n",
        "\n",
        "Este objeto toma una lista de SMILES como input y regresa los descriptores seleccionados. Es muy flexible y puede correr en paralelo.[URL](https://molfeat-docs.datamol.io/stable/tutorials/types_of_featurizers.html)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "4e9bd7ae",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "4e9bd7ae"
      },
      "outputs": [],
      "source": [
        "trans = MoleculeTransformer(calc)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6bda1548",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "6bda1548"
      },
      "source": [
        "<a name='4'></a>\n",
        "\n",
        "## **4.** Cálculo de fingerprints.\n",
        "Tenga en cuenta el uso de la función [datamol](https://datamol.io), que silencia el registro de mensajes del RDKit. Esta es una versión más amigable de la función rd_shut_the_hell_up en [useful_rdkit_utils](https://github.com/PatWalters/useful_rdkit_utils)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "f8cedeaa",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "f8cedeaa",
        "outputId": "2dfb7f4d-020f-476f-f530-166d833d824f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(430, 4)\n",
            "CPU times: user 352 ms, sys: 18.5 ms, total: 370 ms\n",
            "Wall time: 411 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                             SMILES  \\\n",
              "0        CHEMBL4170225  COc1cc2c(=NC3CCN(C)CC3)cc(-c3ccc(C)[nH]3)[nH]c...   \n",
              "1        CHEMBL4162206  COc1cc2c(=NC3CCN(C(C)C)CC3)cc(C3CCCCC3)[nH]c2c...   \n",
              "2        CHEMBL3109639               COc1cc2c(cc1OCCCN1CCCC1)N=C(N)C2(C)C   \n",
              "3        CHEMBL3109631             COc1cc2c(cc1OCC1CCN(C)C1)N=C(N)C21CCC1   \n",
              "4        CHEMBL5187766              COc1cc2c(=NCCN)cc[nH]c2cc1OCCCN1CCCC1   \n",
              "..                 ...                                                ...   \n",
              "425      CHEMBL1213958  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "426      CHEMBL1214630  COc1cc2[nH]c(N3CCCN(C)CC3)nc(=NC3CCN(C)C3)c2cc1OC   \n",
              "427      CHEMBL1214145  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "428      CHEMBL1213959  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "429      CHEMBL1213829  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "\n",
              "        pIC50                                                 fp  \n",
              "0    9.301030  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "1    9.154902  [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "2    9.045757  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "3    9.000000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "4    8.795880  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "..        ...                                                ...  \n",
              "425  3.055517  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...  \n",
              "426  3.040959  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...  \n",
              "427  2.823909  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...  \n",
              "428  2.468521  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...  \n",
              "429  1.821023  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...  \n",
              "\n",
              "[430 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00365310-52ed-46fd-9a93-ff9a5986af61\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL4170225</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)cc(-c3ccc(C)[nH]3)[nH]c...</td>\n",
              "      <td>9.301030</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL4162206</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C(C)C)CC3)cc(C3CCCCC3)[nH]c2c...</td>\n",
              "      <td>9.154902</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3109639</td>\n",
              "      <td>COc1cc2c(cc1OCCCN1CCCC1)N=C(N)C2(C)C</td>\n",
              "      <td>9.045757</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL3109631</td>\n",
              "      <td>COc1cc2c(cc1OCC1CCN(C)C1)N=C(N)C21CCC1</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL5187766</td>\n",
              "      <td>COc1cc2c(=NCCN)cc[nH]c2cc1OCCCN1CCCC1</td>\n",
              "      <td>8.795880</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>425</th>\n",
              "      <td>CHEMBL1213958</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>3.055517</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>CHEMBL1214630</td>\n",
              "      <td>COc1cc2[nH]c(N3CCCN(C)CC3)nc(=NC3CCN(C)C3)c2cc1OC</td>\n",
              "      <td>3.040959</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>CHEMBL1214145</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>2.823909</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>428</th>\n",
              "      <td>CHEMBL1213959</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>2.468521</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>429</th>\n",
              "      <td>CHEMBL1213829</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>1.821023</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>430 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00365310-52ed-46fd-9a93-ff9a5986af61')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-00365310-52ed-46fd-9a93-ff9a5986af61 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-00365310-52ed-46fd-9a93-ff9a5986af61');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-72dd8547-3607-4aba-bd43-3c4f34cf9dbd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-72dd8547-3607-4aba-bd43-3c4f34cf9dbd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-72dd8547-3607-4aba-bd43-3c4f34cf9dbd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ed3f9e21-ec2f-4296-8de5-faecca123326\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ed3f9e21-ec2f-4296-8de5-faecca123326 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "%%time\n",
        "with dm.without_rdkit_log():\n",
        "    df['fp'] = trans.transform(df.SMILES.values)\n",
        "print(df.shape)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec6f5322",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "ec6f5322"
      },
      "source": [
        "<a name='5'></a>\n",
        "##**5.** Segmentación de datos: entrenamiento y prueba.\n",
        "Es buena opción realizar con dataframes, ya que de esta manera no tienes que recordar el orden en que se recupera la información (entrenamiento_x, prueba_x,entrenamiento_y y prueba_y) con [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "37a488a6",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "37a488a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "dd81d1c7-d99d-49c5-a40b-00d39451cfd6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                             SMILES  \\\n",
              "412      CHEMBL1213828  COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...   \n",
              "320      CHEMBL4472432        N=C1Nc2cc(OCCCN3CCCC3)c(OCC3CC3)cc2C12CCCC2   \n",
              "39       CHEMBL1829298  COc1cc2c(=NC3CCN(C(C)C)CC3)[nH]c(N3CCCN(c4cccc...   \n",
              "296      CHEMBL3948437  CN1CCN(c2cc(NCCN3CCCCC3)c3c4c(onc24)-c2ccccc2C...   \n",
              "386      CHEMBL3109628                       COc1ccc2c(c1)C1(CCC1)C(=N)N2   \n",
              "..                 ...                                                ...   \n",
              "218      CHEMBL3122204                         c1ccc([Se][Se]c2ccccc2)cc1   \n",
              "393      CHEMBL2315525  CN1C(=O)C2(S)CC3(C45CC6(S)C(=O)N(C)C(S)(CO)C(=...   \n",
              "341       CHEMBL568963    COc1cc2[nH]c(N3CCCN(C)CC3)nc(=NC3CCCCC3)c2cc1OC   \n",
              "424      CHEMBL1214186   N=c1[nH]cnc2c1ncn2C1OC(CC(N)CCC(N)C(=O)O)C(O)C1O   \n",
              "405      CHEMBL1950960               CN1C(=O)C23Cc4ccccc4N2C(=O)C1(CO)SS3   \n",
              "\n",
              "        pIC50                                                 fp  \n",
              "412  4.022276  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...  \n",
              "320  5.508638  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "39   8.000000  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "296  5.728158  [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...  \n",
              "386  4.739929  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "..        ...                                                ...  \n",
              "218  6.259637  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "393  4.567031  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "341  5.411504  [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...  \n",
              "424  3.293282  [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "405  4.259637  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "\n",
              "[344 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-be3572da-55cc-428a-a5bb-00aaf1c27528\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>CHEMBL1213828</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(N3CCCN(C)CC3)nc2c...</td>\n",
              "      <td>4.022276</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>CHEMBL4472432</td>\n",
              "      <td>N=C1Nc2cc(OCCCN3CCCC3)c(OCC3CC3)cc2C12CCCC2</td>\n",
              "      <td>5.508638</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>CHEMBL1829298</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C(C)C)CC3)[nH]c(N3CCCN(c4cccc...</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>CHEMBL3948437</td>\n",
              "      <td>CN1CCN(c2cc(NCCN3CCCCC3)c3c4c(onc24)-c2ccccc2C...</td>\n",
              "      <td>5.728158</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>CHEMBL3109628</td>\n",
              "      <td>COc1ccc2c(c1)C1(CCC1)C(=N)N2</td>\n",
              "      <td>4.739929</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>CHEMBL3122204</td>\n",
              "      <td>c1ccc([Se][Se]c2ccccc2)cc1</td>\n",
              "      <td>6.259637</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>CHEMBL2315525</td>\n",
              "      <td>CN1C(=O)C2(S)CC3(C45CC6(S)C(=O)N(C)C(S)(CO)C(=...</td>\n",
              "      <td>4.567031</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>CHEMBL568963</td>\n",
              "      <td>COc1cc2[nH]c(N3CCCN(C)CC3)nc(=NC3CCCCC3)c2cc1OC</td>\n",
              "      <td>5.411504</td>\n",
              "      <td>[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>CHEMBL1214186</td>\n",
              "      <td>N=c1[nH]cnc2c1ncn2C1OC(CC(N)CCC(N)C(=O)O)C(O)C1O</td>\n",
              "      <td>3.293282</td>\n",
              "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>CHEMBL1950960</td>\n",
              "      <td>CN1C(=O)C23Cc4ccccc4N2C(=O)C1(CO)SS3</td>\n",
              "      <td>4.259637</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>344 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be3572da-55cc-428a-a5bb-00aaf1c27528')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-be3572da-55cc-428a-a5bb-00aaf1c27528 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-be3572da-55cc-428a-a5bb-00aaf1c27528');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0f72138b-524c-4d2e-8242-8c01bcb97c30\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0f72138b-524c-4d2e-8242-8c01bcb97c30')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0f72138b-524c-4d2e-8242-8c01bcb97c30 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_75f038ff-cae0-4ff7-8b94-a4c286e9adf8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_75f038ff-cae0-4ff7-8b94-a4c286e9adf8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "train, test = train_test_split(df, test_size=0.2, train_size=0.8)\n",
        "train"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "H1EUnM20Q9AN",
        "outputId": "9629fbb4-27e4-4a81-959f-1946d4b80b57"
      },
      "id": "H1EUnM20Q9AN",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    molecule_chembl_id                                             SMILES  \\\n",
              "239      CHEMBL4216799  COc1cc2c(=NC3CCCC3)cc(-c3ccc(C)o3)[nH]c2cc1OCC...   \n",
              "61       CHEMBL4759665  CN=c1cc(C)[nH]c(=Nc2ccc(OC)c(OCCCN3CCCC3)c2)[nH]1   \n",
              "181      CHEMBL4170228  COc1cc2c(=NC3CCN(C)CC3)[nH]c(-c3ccc(C)o3)nc2cc...   \n",
              "330      CHEMBL4204903  COc1cc2c(=NC3CC4(CCN(C)CC4)C3)cc(-c3ccc(C)o3)[...   \n",
              "243      CHEMBL4080189  COc1cc2[nH]c(=NCCCCCCO)[nH]c(=NC3CCN(C)CC3)c2c...   \n",
              "..                 ...                                                ...   \n",
              "207      CHEMBL4063767   CCCN(CC)c1nc(=NC2CCN(C)CC2)c2cc(OC)c(OC)cc2[nH]1   \n",
              "117      CHEMBL4212375  CNC(=O)CCN=c1cc(-c2ccc(C)o2)[nH]c2cc(OCCCN3CCC...   \n",
              "267      CHEMBL5207069          COc1cc2c(=NCC(N)=O)cc[nH]c2cc1OCCCN1CCCC1   \n",
              "244      CHEMBL3335713                                     Br[Se]c1ccccc1   \n",
              "138      CHEMBL4217332  COc1cc2c(=NC3CCN(C)C(=O)C3)cc(-c3ccc(C)o3)[nH]...   \n",
              "\n",
              "        pIC50                                                 fp  \n",
              "239  6.133713  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "61   7.772113  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "181  6.473661  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "330  5.462181  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "243  6.103474  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "..        ...                                                ...  \n",
              "207  6.317855  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "117  6.978811  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "267  5.964090  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "244  6.102373  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "138  6.835647  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...  \n",
              "\n",
              "[86 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4a36c70a-b2bd-4e85-bfdb-23b9bf7cb659\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>CHEMBL4216799</td>\n",
              "      <td>COc1cc2c(=NC3CCCC3)cc(-c3ccc(C)o3)[nH]c2cc1OCC...</td>\n",
              "      <td>6.133713</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>CHEMBL4759665</td>\n",
              "      <td>CN=c1cc(C)[nH]c(=Nc2ccc(OC)c(OCCCN3CCCC3)c2)[nH]1</td>\n",
              "      <td>7.772113</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>CHEMBL4170228</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)CC3)[nH]c(-c3ccc(C)o3)nc2cc...</td>\n",
              "      <td>6.473661</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330</th>\n",
              "      <td>CHEMBL4204903</td>\n",
              "      <td>COc1cc2c(=NC3CC4(CCN(C)CC4)C3)cc(-c3ccc(C)o3)[...</td>\n",
              "      <td>5.462181</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>CHEMBL4080189</td>\n",
              "      <td>COc1cc2[nH]c(=NCCCCCCO)[nH]c(=NC3CCN(C)CC3)c2c...</td>\n",
              "      <td>6.103474</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>CHEMBL4063767</td>\n",
              "      <td>CCCN(CC)c1nc(=NC2CCN(C)CC2)c2cc(OC)c(OC)cc2[nH]1</td>\n",
              "      <td>6.317855</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>CHEMBL4212375</td>\n",
              "      <td>CNC(=O)CCN=c1cc(-c2ccc(C)o2)[nH]c2cc(OCCCN3CCC...</td>\n",
              "      <td>6.978811</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>CHEMBL5207069</td>\n",
              "      <td>COc1cc2c(=NCC(N)=O)cc[nH]c2cc1OCCCN1CCCC1</td>\n",
              "      <td>5.964090</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>CHEMBL3335713</td>\n",
              "      <td>Br[Se]c1ccccc1</td>\n",
              "      <td>6.102373</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>CHEMBL4217332</td>\n",
              "      <td>COc1cc2c(=NC3CCN(C)C(=O)C3)cc(-c3ccc(C)o3)[nH]...</td>\n",
              "      <td>6.835647</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>86 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a36c70a-b2bd-4e85-bfdb-23b9bf7cb659')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4a36c70a-b2bd-4e85-bfdb-23b9bf7cb659 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-4a36c70a-b2bd-4e85-bfdb-23b9bf7cb659');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6045bcef-bbc0-4f44-89e5-3767323af499\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6045bcef-bbc0-4f44-89e5-3767323af499')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6045bcef-bbc0-4f44-89e5-3767323af499 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5f20f179-e4ec-4557-a3a6-4d4fbd9c318f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5f20f179-e4ec-4557-a3a6-4d4fbd9c318f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0ff42cd",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "d0ff42cd"
      },
      "source": [
        "<a name='6'></a>\n",
        "\n",
        "##**6.** Regresión (sklearn).\n",
        "En este caso se utilizó\n",
        "[HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html)\n",
        "que es la implementación scikit-learn de\n",
        "[LightGBM](https://lightgbm.readthedocs.io/en/latest/Python-Intro.html)\n",
        ". Puede conectar fácilmente cualquier regresor compatible con scikit-learn como [RandomForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) o [XGBoost](https://xgboost.readthedocs.io/en/stable/python/python_intro.html)\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "from lightgbm import LGBMRegressor\n",
        "model = LGBMRegressor()\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model = RandomForestRegressor()\n",
        "from xgboost import XGBRegressor\n",
        "model = XGBRegressor()\n",
        "```\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "outputs": [],
      "source": [
        "model = HistGradientBoostingRegressor()"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "mRWYIuwg5IHh"
      },
      "id": "mRWYIuwg5IHh"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name='7'></a>\n",
        "\n",
        "##**7.** Construcción y visualización del modelo.\n",
        "Usa [YellowBrick](https://www.scikit-yb.org/en/latest/) para construir el modelo y visualizar su empeño. La **perdida** reportada en el gráfico siguiente es la [$R^2$](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) para el modelo."
      ],
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "P6P6syzp5IHi"
      },
      "id": "P6P6syzp5IHi"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "c082ffd0",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "c082ffd0",
        "outputId": "85039701-2e31-4f61-c580-50a74dd1f2d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 441 ms, sys: 42.1 ms, total: 483 ms\n",
            "Wall time: 475 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "visualizer = prediction_error(model,np.stack(train.fp),train.pIC50,np.stack(test.fp),test.pIC50)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Un R2 de 0.83 indica que el modelo explica aproximadamente el 81.2% de la variabilidad en la variable objetivo en el conjunto de prueba. Esto se considera bastante bueno y sugiere que el modelo tiene un buen poder predictivo."
      ],
      "metadata": {
        "id": "YqwCwlNLBrBJ"
      },
      "id": "YqwCwlNLBrBJ"
    },
    {
      "cell_type": "markdown",
      "id": "ec329324",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "ec329324"
      },
      "source": [
        "<a name='8'></a>\n",
        "##**8.** Residuales.\n",
        "Gráfica los residuales del set de entrenamiento y prueba. Los residuales son la diferencia entre los valores reales de la variable objetivo y las predicciones realizadas por el modelo. Los residuales son una medida de cuánto se desvían las predicciones del modelo de los valores reales. Si un modelo es preciso, los residuales deberían ser cercanos a cero.\n",
        "Una gran diferencia entre la R2 de entrenamiento y prueba podría indicar sobreajuste (overfitting), donde el modelo se ha adaptado demasiado a los datos de entrenamiento específicos y no generaliza bien a nuevos datos.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "e5853f4b",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "e5853f4b",
        "outputId": "eab8c71e-3f4f-425f-c8bb-bb856e53d118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "viz = residuals_plot(model,np.stack(train.fp), train.pIC50, np.stack(test.fp), test.pIC50, is_fitted=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name='9'></a>\n",
        "##**9.** Predicción.\n"
      ],
      "metadata": {
        "id": "ssxjXMkoC_ai"
      },
      "id": "ssxjXMkoC_ai"
    },
    {
      "cell_type": "code",
      "source": [
        "#Leer archivo\n",
        "url = \"https://raw.githubusercontent.com/DIFACQUIM/Cursos/main/Datasets/Fingerprints.csv\"\n",
        "df_prediction = pd.read_csv(url)\n",
        "df_prediction=df_prediction[['ID', 'smiles']]\n",
        "df_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5-mkjuCyQrxS",
        "outputId": "b1d71f00-483b-40ec-85a4-2aff3c05a4c8"
      },
      "id": "5-mkjuCyQrxS",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      ID                                             smiles\n",
              "0          CHEMBL3087498  Brc1c2c(N)ncnc2n([C@]2[C@](O)[C@](O)[C@@](CN(C...\n",
              "1          CHEMBL2171169  O=C(NCCCN(C(C)C)C[C@@]1[C@@](O)[C@@](O)[C@](n2...\n",
              "2            CHEMBL83747  Brc1c(O)ccc(C/C(=N\\O)/C(=O)NCCSSCCNC(=O)/C(=N/...\n",
              "3          CHEMBL4215326  O(C)c1c(OC2CC3(CN(C)C3)C2)cc2nc(-c3oc(C)cc3)cc...\n",
              "4          CHEMBL1201129         O=C1N([C@@]2O[C@](CO)[C@@](O)C2)C=NC(N)=N1\n",
              "..                   ...                                                ...\n",
              "194            F447-0397  S(=O)(=O)(Nc1cc2c(C(=O)O)cc(N3CCN(c4ccc(OC)cc4...\n",
              "195  FC.CSC.04E fr 16-20  Clc1cc(NS(=O)(=O)c2ccc(NS(=O)(=O)c3cc4c(cc3)cc...\n",
              "196      FC.CSC.10C fr 4       S(=O)(=O)(Nc1cc(OC)cc(OC)c1)c1cc2c(cc1)cccc2\n",
              "197                DLP18         O=C(Nc1nocc1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1\n",
              "198                DLP19         O=C(Nc1sccn1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1\n",
              "\n",
              "[199 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3335741-03b4-43a8-9146-403d3b462385\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>smiles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL3087498</td>\n",
              "      <td>Brc1c2c(N)ncnc2n([C@]2[C@](O)[C@](O)[C@@](CN(C...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL2171169</td>\n",
              "      <td>O=C(NCCCN(C(C)C)C[C@@]1[C@@](O)[C@@](O)[C@](n2...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL83747</td>\n",
              "      <td>Brc1c(O)ccc(C/C(=N\\O)/C(=O)NCCSSCCNC(=O)/C(=N/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL4215326</td>\n",
              "      <td>O(C)c1c(OC2CC3(CN(C)C3)C2)cc2nc(-c3oc(C)cc3)cc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL1201129</td>\n",
              "      <td>O=C1N([C@@]2O[C@](CO)[C@@](O)C2)C=NC(N)=N1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>F447-0397</td>\n",
              "      <td>S(=O)(=O)(Nc1cc2c(C(=O)O)cc(N3CCN(c4ccc(OC)cc4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>FC.CSC.04E fr 16-20</td>\n",
              "      <td>Clc1cc(NS(=O)(=O)c2ccc(NS(=O)(=O)c3cc4c(cc3)cc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>FC.CSC.10C fr 4</td>\n",
              "      <td>S(=O)(=O)(Nc1cc(OC)cc(OC)c1)c1cc2c(cc1)cccc2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>DLP18</td>\n",
              "      <td>O=C(Nc1nocc1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>DLP19</td>\n",
              "      <td>O=C(Nc1sccn1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>199 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3335741-03b4-43a8-9146-403d3b462385')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c3335741-03b4-43a8-9146-403d3b462385 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-c3335741-03b4-43a8-9146-403d3b462385');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-50b88161-53de-4690-8eda-5a3f96f9d4c0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50b88161-53de-4690-8eda-5a3f96f9d4c0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-50b88161-53de-4690-8eda-5a3f96f9d4c0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b0cb91e6-6f94-4322-a7e4-5fad071bb794\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_prediction')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b0cb91e6-6f94-4322-a7e4-5fad071bb794 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_prediction');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_prediction['fp'] = trans.transform(df_prediction.smiles.values)\n",
        "df_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CqZ63IeW_zvX",
        "outputId": "88470a46-f9c1-4c8a-f210-dd0083765181"
      },
      "id": "CqZ63IeW_zvX",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      ID                                             smiles  \\\n",
              "0          CHEMBL3087498  Brc1c2c(N)ncnc2n([C@]2[C@](O)[C@](O)[C@@](CN(C...   \n",
              "1          CHEMBL2171169  O=C(NCCCN(C(C)C)C[C@@]1[C@@](O)[C@@](O)[C@](n2...   \n",
              "2            CHEMBL83747  Brc1c(O)ccc(C/C(=N\\O)/C(=O)NCCSSCCNC(=O)/C(=N/...   \n",
              "3          CHEMBL4215326  O(C)c1c(OC2CC3(CN(C)C3)C2)cc2nc(-c3oc(C)cc3)cc...   \n",
              "4          CHEMBL1201129         O=C1N([C@@]2O[C@](CO)[C@@](O)C2)C=NC(N)=N1   \n",
              "..                   ...                                                ...   \n",
              "194            F447-0397  S(=O)(=O)(Nc1cc2c(C(=O)O)cc(N3CCN(c4ccc(OC)cc4...   \n",
              "195  FC.CSC.04E fr 16-20  Clc1cc(NS(=O)(=O)c2ccc(NS(=O)(=O)c3cc4c(cc3)cc...   \n",
              "196      FC.CSC.10C fr 4       S(=O)(=O)(Nc1cc(OC)cc(OC)c1)c1cc2c(cc1)cccc2   \n",
              "197                DLP18         O=C(Nc1nocc1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1   \n",
              "198                DLP19         O=C(Nc1sccn1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1   \n",
              "\n",
              "                                                    fp  \n",
              "0    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "1    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "2    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "3    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "4    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "..                                                 ...  \n",
              "194  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "195  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "196  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "197  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "198  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...  \n",
              "\n",
              "[199 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4db63f30-7cc9-4c83-8bdb-261fcb3e207b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>smiles</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL3087498</td>\n",
              "      <td>Brc1c2c(N)ncnc2n([C@]2[C@](O)[C@](O)[C@@](CN(C...</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL2171169</td>\n",
              "      <td>O=C(NCCCN(C(C)C)C[C@@]1[C@@](O)[C@@](O)[C@](n2...</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL83747</td>\n",
              "      <td>Brc1c(O)ccc(C/C(=N\\O)/C(=O)NCCSSCCNC(=O)/C(=N/...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL4215326</td>\n",
              "      <td>O(C)c1c(OC2CC3(CN(C)C3)C2)cc2nc(-c3oc(C)cc3)cc...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL1201129</td>\n",
              "      <td>O=C1N([C@@]2O[C@](CO)[C@@](O)C2)C=NC(N)=N1</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>F447-0397</td>\n",
              "      <td>S(=O)(=O)(Nc1cc2c(C(=O)O)cc(N3CCN(c4ccc(OC)cc4...</td>\n",
              "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>FC.CSC.04E fr 16-20</td>\n",
              "      <td>Clc1cc(NS(=O)(=O)c2ccc(NS(=O)(=O)c3cc4c(cc3)cc...</td>\n",
              "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>FC.CSC.10C fr 4</td>\n",
              "      <td>S(=O)(=O)(Nc1cc(OC)cc(OC)c1)c1cc2c(cc1)cccc2</td>\n",
              "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>DLP18</td>\n",
              "      <td>O=C(Nc1nocc1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>DLP19</td>\n",
              "      <td>O=C(Nc1sccn1)CN1CCN(c2ccc(C(=O)OCC)cc2)CC1</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>199 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4db63f30-7cc9-4c83-8bdb-261fcb3e207b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4db63f30-7cc9-4c83-8bdb-261fcb3e207b 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-4db63f30-7cc9-4c83-8bdb-261fcb3e207b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5403b591-4bb7-45f6-ac9a-ae9f4030c589\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5403b591-4bb7-45f6-ac9a-ae9f4030c589')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5403b591-4bb7-45f6-ac9a-ae9f4030c589 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_99f7b67f-8c65-4884-8f41-8dacd7416aa3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_prediction')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_99f7b67f-8c65-4884-8f41-8dacd7416aa3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_prediction');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_prediction['predicted_ic50'] = model.predict(np.stack(df_prediction.fp))\n",
        "df_prediction=df_prediction.sort_values(by='predicted_ic50', ascending=False)\n",
        "df_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oypwzKd8_4mF",
        "outputId": "bda833d9-f942-4d54-e79f-3fcd40ee7f1d"
      },
      "id": "oypwzKd8_4mF",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ID                                             smiles  \\\n",
              "113  CHEMBL4454542  O(CCCN1CCCC1)c1c(OC)cc2C(NC3CCN(C(C)C)CC3)=NCC...   \n",
              "60   CHEMBL4162206  O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)cc(C...   \n",
              "99   CHEMBL1231795  O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)nc(C...   \n",
              "20   CHEMBL4204482  O(CCCN1CCCC1)c1c(OC)cc2c(NC3CC4(C3)CCN(C(C)C)C...   \n",
              "34   CHEMBL4173626  O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)cc(-...   \n",
              "..             ...                                                ...   \n",
              "154  CHEMBL3109085  S(N[C@](C(=O)O)Cc1c2c([nH]c1)cccc2)c1c([N+](=O...   \n",
              "190   Panobinostat      O=C(NO)/C=C/c1ccc(CNCCc2c(C)[nH]c3c2cccc3)cc1   \n",
              "193   CSC027694519            O=C(N)c1c(C)cc(NCc2oc(Cc3ccccc3)nn2)cc1   \n",
              "100  CHEMBL4878570  O=C(NO)C1CCN(CC(O)COC(Cn2c3c(c4c2cccc4)cccc3)C...   \n",
              "148  CHEMBL4649221  Fc1cc(-c2nc(C3C(=O)c4c(N(CC(=O)Nc5cc6OCOc6cc5)...   \n",
              "\n",
              "                                                    fp  predicted_ic50  \\\n",
              "113  [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        7.060800   \n",
              "60   [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        7.002007   \n",
              "99   [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        6.896079   \n",
              "20   [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        6.863930   \n",
              "34   [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        6.837579   \n",
              "..                                                 ...             ...   \n",
              "154  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...        4.641793   \n",
              "190  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...        4.472556   \n",
              "193  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...        4.415326   \n",
              "100  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...        4.412415   \n",
              "148  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...        4.108194   \n",
              "\n",
              "                                                ROMol  \n",
              "113  <rdkit.Chem.rdchem.Mol object at 0x78e11a1ae9d0>  \n",
              "60   <rdkit.Chem.rdchem.Mol object at 0x78e11a1ae030>  \n",
              "99   <rdkit.Chem.rdchem.Mol object at 0x78e11a1ae570>  \n",
              "20   <rdkit.Chem.rdchem.Mol object at 0x78e11a1aef10>  \n",
              "34   <rdkit.Chem.rdchem.Mol object at 0x78e11a1ae500>  \n",
              "..                                                ...  \n",
              "154  <rdkit.Chem.rdchem.Mol object at 0x78e11a1c3840>  \n",
              "190  <rdkit.Chem.rdchem.Mol object at 0x78e11a1c38b0>  \n",
              "193  <rdkit.Chem.rdchem.Mol object at 0x78e11a1c3920>  \n",
              "100  <rdkit.Chem.rdchem.Mol object at 0x78e11a1c3990>  \n",
              "148  <rdkit.Chem.rdchem.Mol object at 0x78e11a1c3a00>  \n",
              "\n",
              "[199 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1294177-aeb1-457b-9f22-cfcee7e91623\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>smiles</th>\n",
              "      <th>fp</th>\n",
              "      <th>predicted_ic50</th>\n",
              "      <th>ROMol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>CHEMBL4454542</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2C(NC3CCN(C(C)C)CC3)=NCC...</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>7.060800</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>CHEMBL4162206</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)cc(C...</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>7.002007</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>CHEMBL1231795</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)nc(C...</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>6.896079</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>CHEMBL4204482</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2c(NC3CC4(C3)CCN(C(C)C)C...</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>6.863930</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>CHEMBL4173626</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)cc(-...</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>6.837579</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>CHEMBL3109085</td>\n",
              "      <td>S(N[C@](C(=O)O)Cc1c2c([nH]c1)cccc2)c1c([N+](=O...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>4.641793</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Panobinostat</td>\n",
              "      <td>O=C(NO)/C=C/c1ccc(CNCCc2c(C)[nH]c3c2cccc3)cc1</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>4.472556</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>CSC027694519</td>\n",
              "      <td>O=C(N)c1c(C)cc(NCc2oc(Cc3ccccc3)nn2)cc1</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>4.415326</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>CHEMBL4878570</td>\n",
              "      <td>O=C(NO)C1CCN(CC(O)COC(Cn2c3c(c4c2cccc4)cccc3)C...</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>4.412415</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>CHEMBL4649221</td>\n",
              "      <td>Fc1cc(-c2nc(C3C(=O)c4c(N(CC(=O)Nc5cc6OCOc6cc5)...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>4.108194</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>199 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1294177-aeb1-457b-9f22-cfcee7e91623')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f1294177-aeb1-457b-9f22-cfcee7e91623 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-f1294177-aeb1-457b-9f22-cfcee7e91623');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b4c4dff2-4704-4a07-8334-ae855d89493d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b4c4dff2-4704-4a07-8334-ae855d89493d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b4c4dff2-4704-4a07-8334-ae855d89493d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2d9a4f70-39b7-4b01-88ba-4a5b9b610067\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_prediction')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2d9a4f70-39b7-4b01-88ba-4a5b9b610067 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_prediction');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_prediction['ROMol'] = df_prediction['smiles'].apply(Chem.MolFromSmiles)#.apply(Chem.AddHs)\n",
        "df_prediction[0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "MXJq3pncdq3J",
        "outputId": "9e47ba35-a40a-4744-d2ba-c74a211b36d8"
      },
      "id": "MXJq3pncdq3J",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ID                                             smiles  \\\n",
              "113  CHEMBL4454542  O(CCCN1CCCC1)c1c(OC)cc2C(NC3CCN(C(C)C)CC3)=NCC...   \n",
              "60   CHEMBL4162206  O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)cc(C...   \n",
              "99   CHEMBL1231795  O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)nc(C...   \n",
              "\n",
              "                                                    fp  predicted_ic50  \\\n",
              "113  [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        7.060800   \n",
              "60   [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        7.002007   \n",
              "99   [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...        6.896079   \n",
              "\n",
              "                                                ROMol  \n",
              "113  <rdkit.Chem.rdchem.Mol object at 0x78e118904660>  \n",
              "60   <rdkit.Chem.rdchem.Mol object at 0x78e1189067a0>  \n",
              "99   <rdkit.Chem.rdchem.Mol object at 0x78e1189065e0>  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-65e303dd-fd92-46da-9c9e-38147fdb1b21\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>smiles</th>\n",
              "      <th>fp</th>\n",
              "      <th>predicted_ic50</th>\n",
              "      <th>ROMol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>CHEMBL4454542</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2C(NC3CCN(C(C)C)CC3)=NCC...</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>7.060800</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>CHEMBL4162206</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)cc(C...</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>7.002007</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>CHEMBL1231795</td>\n",
              "      <td>O(CCCN1CCCC1)c1c(OC)cc2c(NC3CCN(C(C)C)CC3)nc(C...</td>\n",
              "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
              "      <td>6.896079</td>\n",
              "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65e303dd-fd92-46da-9c9e-38147fdb1b21')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-65e303dd-fd92-46da-9c9e-38147fdb1b21 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-65e303dd-fd92-46da-9c9e-38147fdb1b21');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-32c26061-8b3d-4ad9-b504-49641376e347\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32c26061-8b3d-4ad9-b504-49641376e347')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-32c26061-8b3d-4ad9-b504-49641376e347 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": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name='10'></a>\n",
        "\n",
        "# **10.** - Referencias y fuentes de consulta:\n",
        "\n",
        "\n",
        "*   Script original de Patt Walters: https://colab.research.google.com/github/PatWalters/practical_cheminformatics_tutorials/blob/main/ml_models/QSAR_in_8_lines.ipynb\n",
        "* Blog en GitHub de Patt Walters: https://github.com/PatWalters/practical_cheminformatics_tutorials?tab=readme-ov-file\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "HwmLjnO6gNHJ"
      },
      "id": "HwmLjnO6gNHJ"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.16"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "2361f0d210924117934a3f12f38f934d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_6e4c62bd96344fbbbb23c005f20d4d87",
              "IPY_MODEL_aa388c983c0d4431864bacac187461aa",
              "IPY_MODEL_cf546c4babec4786b4ea1765da60e4ac"
            ],
            "layout": "IPY_MODEL_a91097f3f6714b249b4aa09b21b79e5a"
          }
        },
        "6e4c62bd96344fbbbb23c005f20d4d87": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e1da59ec19484ceb8af5240a063d50d7",
            "placeholder": "​",
            "style": "IPY_MODEL_0ca16a5a319e4ad480e6720d1ff7ee90",
            "value": "100%"
          }
        },
        "aa388c983c0d4431864bacac187461aa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e4893520b2904734a1b11c44100b6d78",
            "max": 100,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_c7ecdea6e52d49988a74ce07bf85a985",
            "value": 100
          }
        },
        "cf546c4babec4786b4ea1765da60e4ac": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_744a99a9c571471e9797121eb9c8cfda",
            "placeholder": "​",
            "style": "IPY_MODEL_c6c83a44007746369d77f0f2cd6c387f",
            "value": " 100/100 [00:41&lt;00:00,  2.67it/s]"
          }
        },
        "a91097f3f6714b249b4aa09b21b79e5a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e1da59ec19484ceb8af5240a063d50d7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0ca16a5a319e4ad480e6720d1ff7ee90": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e4893520b2904734a1b11c44100b6d78": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c7ecdea6e52d49988a74ce07bf85a985": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "744a99a9c571471e9797121eb9c8cfda": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c6c83a44007746369d77f0f2cd6c387f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}