notebooks/protac_degradation_predictor_tutorial.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMLv05e/gGMLdaS7UI/GPTc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ribesstefano/PROTAC-Degradation-Predictor/blob/main/notebooks/protac_degradation_predictor_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PROTAC-Degradation-Predictor: Tutorial\n",
        "\n",
        "This notebook includes a short tutorial and a collection of code snippets for predicting protein degradation by PROTACs.\n",
        "\n",
        "The underlined deep learning models have been trained and evaluated as reported in [_\"Modeling PROTAC Degradation Activity with Machine Learning\"_](https://arxiv.org/abs/2406.02637)."
      ],
      "metadata": {
        "id": "N_0Bfod51dDY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clone Repository and Install Package"
      ],
      "metadata": {
        "id": "nLP8GZMp2cSh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNmrlIcqvOu1",
        "outputId": "1c06335e-0d72-4790-f575-679b084a88a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'PROTAC-Degradation-Predictor'...\n",
            "remote: Enumerating objects: 1052, done.\u001b[K\n",
            "remote: Counting objects: 100% (262/262), done.\u001b[K\n",
            "remote: Compressing objects: 100% (136/136), done.\u001b[K\n",
            "remote: Total 1052 (delta 117), reused 244 (delta 102), pack-reused 790\u001b[K\n",
            "Receiving objects: 100% (1052/1052), 284.74 MiB | 13.45 MiB/s, done.\n",
            "Resolving deltas: 100% (523/523), done.\n",
            "Updating files: 100% (156/156), done.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m802.3/802.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.9/34.9 MB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))': /simple/optuna/\u001b[0m\u001b[33m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m380.1/380.1 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m868.8/868.8 kB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.4/233.4 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for protac-degradation-predictor (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ribesstefano/PROTAC-Degradation-Predictor.git\n",
        "!pip install ./PROTAC-Degradation-Predictor  -qqq"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup\n",
        "\n",
        "Let's import the required packages:"
      ],
      "metadata": {
        "id": "Kvq5ljqLKwzI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import protac_degradation_predictor as pdp\n",
        "import torch\n",
        "from rdkit import Chem"
      ],
      "metadata": {
        "id": "hkucKE6SKweE"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Curated Dataset\n",
        "\n",
        "The curated dataset can be loaded via the following code:"
      ],
      "metadata": {
        "id": "egrArJi_KdBU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "protac_df = pdp.load_curated_dataset()\n",
        "display(protac_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "_jzpk3GcKjqI",
        "outputId": "7b02b3ff-a85f-40b0-b2ec-dc4c72f92fbf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Compound ID Uniprot                                             Smiles  \\\n",
              "0            1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
              "1            2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
              "2            3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
              "3            4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
              "4            5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
              "\n",
              "  E3 Ligase                                              InChI  \\\n",
              "0       VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
              "1       VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
              "2       VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
              "3       VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
              "4       VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
              "\n",
              "                     InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
              "0  SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
              "1  HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
              "2  ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
              "3  FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
              "4  PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
              "\n",
              "   Ring Count  Rotatable Bond Count  ...  Name  Assay (DC50/Dmax)  Exact Mass  \\\n",
              "0          10                    24  ...   NaN                NaN         NaN   \n",
              "1          10                    25  ...   NaN                NaN         NaN   \n",
              "2          10                    26  ...   NaN                NaN         NaN   \n",
              "3          10                    27  ...   NaN                NaN         NaN   \n",
              "4          10                    28  ...   NaN                NaN         NaN   \n",
              "\n",
              "  XLogP3  Target (Parsed)                                       POI Sequence  \\\n",
              "0    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
              "1    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
              "2    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
              "3    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
              "4    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
              "\n",
              "   E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
              "0             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
              "1             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
              "2             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
              "3             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
              "4             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
              "\n",
              "  Cell Line Identifier Active - OR  \n",
              "0               MOLT-4         NaN  \n",
              "1               MOLT-4         NaN  \n",
              "2               MOLT-4         NaN  \n",
              "3               MOLT-4         NaN  \n",
              "4               MOLT-4        True  \n",
              "\n",
              "[5 rows x 35 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Compound ID</th>\n",
              "      <th>Uniprot</th>\n",
              "      <th>Smiles</th>\n",
              "      <th>E3 Ligase</th>\n",
              "      <th>InChI</th>\n",
              "      <th>InChI Key</th>\n",
              "      <th>Molecular Weight</th>\n",
              "      <th>Heavy Atom Count</th>\n",
              "      <th>Ring Count</th>\n",
              "      <th>Rotatable Bond Count</th>\n",
              "      <th>...</th>\n",
              "      <th>Name</th>\n",
              "      <th>Assay (DC50/Dmax)</th>\n",
              "      <th>Exact Mass</th>\n",
              "      <th>XLogP3</th>\n",
              "      <th>Target (Parsed)</th>\n",
              "      <th>POI Sequence</th>\n",
              "      <th>E3 Ligase Uniprot</th>\n",
              "      <th>E3 Ligase Sequence</th>\n",
              "      <th>Cell Line Identifier</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Q07817</td>\n",
              "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
              "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
              "      <td>1486.282</td>\n",
              "      <td>101</td>\n",
              "      <td>10</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
              "      <td>P40337</td>\n",
              "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
              "      <td>MOLT-4</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Q07817</td>\n",
              "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
              "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
              "      <td>1500.309</td>\n",
              "      <td>102</td>\n",
              "      <td>10</td>\n",
              "      <td>25</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
              "      <td>P40337</td>\n",
              "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
              "      <td>MOLT-4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Q07817</td>\n",
              "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
              "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
              "      <td>1514.336</td>\n",
              "      <td>103</td>\n",
              "      <td>10</td>\n",
              "      <td>26</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
              "      <td>P40337</td>\n",
              "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
              "      <td>MOLT-4</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Q07817</td>\n",
              "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
              "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
              "      <td>1528.363</td>\n",
              "      <td>104</td>\n",
              "      <td>10</td>\n",
              "      <td>27</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
              "      <td>P40337</td>\n",
              "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
              "      <td>MOLT-4</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Q07817</td>\n",
              "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
              "      <td>VHL</td>\n",
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              "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
              "      <td>1542.390</td>\n",
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              "      <td>10</td>\n",
              "      <td>28</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
              "      <td>P40337</td>\n",
              "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
              "      <td>MOLT-4</td>\n",
              "      <td>True</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset includes entries for which either (or both) $pDC_{50}$ or $D_{max}$ information about PROTAC are available. We can isolate data for which we know both information:"
      ],
      "metadata": {
        "id": "PH-Z81ffLBAw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pDC50_threshold = 6.0\n",
        "Dmax_threshold = 0.6\n",
        "\n",
        "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')\n",
        "protac_df['Active'] = protac_df.apply(\n",
        "    lambda x: pdp.is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1\n",
        ")\n",
        "\n",
        "protac_df = protac_df[~protac_df['Active'].isna()]\n",
        "\n",
        "relevant_cols = ['Uniprot', 'Cell Line Identifier', 'Smiles', 'E3 Ligase', 'DC50 (nM)', 'Dmax (%)', 'Active']\n",
        "display(protac_df[relevant_cols])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "QyLc-ytILAEt",
        "outputId": "3b4e9004-3109-4f0b-c848-dc29043ab49b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     Uniprot Cell Line Identifier  \\\n",
              "4     Q07817               MOLT-4   \n",
              "7     Q07817               MOLT-4   \n",
              "60    P00533             H1975/WR   \n",
              "69    P00533             H1975/WR   \n",
              "72    Q9NWZ3   PH1-PBMCs-hiPSC4F1   \n",
              "...      ...                  ...   \n",
              "2121  O60885              HEK293T   \n",
              "2122  O60885              HEK293T   \n",
              "2125  O60885              HEK293T   \n",
              "2126  O60885              HEK293T   \n",
              "2127  O60885              HEK293T   \n",
              "\n",
              "                                                 Smiles E3 Ligase  DC50 (nM)  \\\n",
              "4     Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...       VHL       53.0   \n",
              "7     Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...       VHL       93.0   \n",
              "60    C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...       VHL       25.3   \n",
              "69    C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...       VHL        5.9   \n",
              "72    COc1cc2c(OC[C@@H]3CCC(=O)N3)ncc(C#CCCCCCCCCCCC...       VHL     3000.0   \n",
              "...                                                 ...       ...        ...   \n",
              "2121  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B      820.0   \n",
              "2122  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...     FEM1B      250.0   \n",
              "2125  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B     1100.0   \n",
              "2126  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B     3600.0   \n",
              "2127  Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...     FEM1B     1600.0   \n",
              "\n",
              "      Dmax (%) Active  \n",
              "4        100.0   True  \n",
              "7         90.0   True  \n",
              "60        90.0   True  \n",
              "69       100.0   True  \n",
              "72        50.0  False  \n",
              "...        ...    ...  \n",
              "2121      81.0   True  \n",
              "2122      94.0   True  \n",
              "2125      85.0  False  \n",
              "2126      60.0  False  \n",
              "2127      80.0  False  \n",
              "\n",
              "[857 rows x 7 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Uniprot</th>\n",
              "      <th>Cell Line Identifier</th>\n",
              "      <th>Smiles</th>\n",
              "      <th>E3 Ligase</th>\n",
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              "      <th>4</th>\n",
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              "      <td>MOLT-4</td>\n",
              "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>53.0</td>\n",
              "      <td>100.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>93.0</td>\n",
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              "      <td>True</td>\n",
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              "      <th>60</th>\n",
              "      <td>P00533</td>\n",
              "      <td>H1975/WR</td>\n",
              "      <td>C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>25.3</td>\n",
              "      <td>90.0</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>P00533</td>\n",
              "      <td>H1975/WR</td>\n",
              "      <td>C=CC(=O)Nc1cccc(-n2c(=O)cc(C)c3cnc(Nc4ccc(N5CC...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>5.9</td>\n",
              "      <td>100.0</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Q9NWZ3</td>\n",
              "      <td>PH1-PBMCs-hiPSC4F1</td>\n",
              "      <td>COc1cc2c(OC[C@@H]3CCC(=O)N3)ncc(C#CCCCCCCCCCCC...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2121</th>\n",
              "      <td>O60885</td>\n",
              "      <td>HEK293T</td>\n",
              "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
              "      <td>FEM1B</td>\n",
              "      <td>820.0</td>\n",
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              "      <th>2122</th>\n",
              "      <td>O60885</td>\n",
              "      <td>HEK293T</td>\n",
              "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCC...</td>\n",
              "      <td>FEM1B</td>\n",
              "      <td>250.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>True</td>\n",
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              "      <th>2125</th>\n",
              "      <td>O60885</td>\n",
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              "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
              "      <td>FEM1B</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>85.0</td>\n",
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              "    <tr>\n",
              "      <th>2126</th>\n",
              "      <td>O60885</td>\n",
              "      <td>HEK293T</td>\n",
              "      <td>Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=N[C@@H](CC(=O)NCCO...</td>\n",
              "      <td>FEM1B</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>False</td>\n",
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              "      <th>2127</th>\n",
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              "      <td>FEM1B</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>False</td>\n",
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              "  </tbody>\n",
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              "<p>857 rows × 7 columns</p>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "\n",
              "<div id=\"df-866077b7-1a9f-401e-800b-fcd731517f46\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-866077b7-1a9f-401e-800b-fcd731517f46')\"\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-866077b7-1a9f-401e-800b-fcd731517f46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(protac_df[relevant_cols])\",\n  \"rows\": 857,\n  \"fields\": [\n    {\n      \"column\": \"Uniprot\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 68,\n        \"samples\": [\n          \"Q15022\",\n          \"Q07820\",\n          \"Q16288\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cell Line Identifier\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"OVCAR-8\",\n          \"MOLT4/P\",\n          \"SW480\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Smiles\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 571,\n        \"samples\": [\n          \"CN(c1ccc(C#N)c(Cl)c1)[C@H]1CC[C@H](NC(=O)c2ccc(N3CCN(C4CN(c5ccc6c(c5)C(=O)N(C5CCC(=O)NC5=O)C6=O)C4)CC3)cc2)CC1\",\n          \"CN1CC(Nc2cnn(C)c(=O)c2Br)CC(c2ccc(C(=O)N(C)CCCN(C)c3ccc(CCCOc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)cc3)cc2)C1\",\n          \"COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(CCOCCOCCOc2cc(-c3scnc3C)ccc2CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)C2(C#N)CC2)C(C)(C)C)CC1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"E3 Ligase\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Ubr1\",\n          \"cIAP1\",\n          \"VHL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DC50 (nM)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9644.906857535914,\n        \"min\": 0.01,\n        \"max\": 100000.0,\n        \"num_unique_values\": 346,\n        \"samples\": [\n          1160.0,\n          790.0,\n          74.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dmax (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.440753372765272,\n        \"min\": 0.03,\n        \"max\": 100.0,\n        \"num_unique_values\": 155,\n        \"samples\": [\n          25.3,\n          66.5,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Active\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting Protein Degradation\n",
        "\n",
        "First, let's get the device to run the models onto."
      ],
      "metadata": {
        "id": "HZpq9ETn2gwy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "print(f'Device to offload computations to: {device.upper()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3jDe_gd-R-f",
        "outputId": "57d80625-b10b-493f-ef53-db0ae6e402e5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device to offload computations to: CPU\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can now have a simple example involving the following PROTAC molecule:"
      ],
      "metadata": {
        "id": "RZWIgEQJHStH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = protac_df.sample(1, random_state=42)\n",
        "display(example[relevant_cols])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "IwznJ4mQMOHv",
        "outputId": "93a62b03-bf95-4d99-e86f-48759f27c999"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     Uniprot Cell Line Identifier  \\\n",
              "1871  Q92769    HCT116-53BPI(+/-)   \n",
              "\n",
              "                                                 Smiles E3 Ligase  DC50 (nM)  \\\n",
              "1871  Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...       VHL        NaN   \n",
              "\n",
              "      Dmax (%) Active  \n",
              "1871      23.0  False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c68267f-cd7d-46b0-a4a2-27815297e8ce\" 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>Uniprot</th>\n",
              "      <th>Cell Line Identifier</th>\n",
              "      <th>Smiles</th>\n",
              "      <th>E3 Ligase</th>\n",
              "      <th>DC50 (nM)</th>\n",
              "      <th>Dmax (%)</th>\n",
              "      <th>Active</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1871</th>\n",
              "      <td>Q92769</td>\n",
              "      <td>HCT116-53BPI(+/-)</td>\n",
              "      <td>Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O...</td>\n",
              "      <td>VHL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>False</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-7c68267f-cd7d-46b0-a4a2-27815297e8ce')\"\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",
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              "  </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-7c68267f-cd7d-46b0-a4a2-27815297e8ce 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-7c68267f-cd7d-46b0-a4a2-27815297e8ce');\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",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(example[relevant_cols])\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Uniprot\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Q92769\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cell Line Identifier\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"HCT116-53BPI(+/-)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Smiles\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)COCCCCCCCCCOCC(=O)Nc2ccc(C(=O)Nc3ccc(F)cc3N)cc2)C(C)(C)C)cc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"E3 Ligase\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"VHL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DC50 (nM)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dmax (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 23.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Active\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "protac_smiles = example['Smiles'].iloc[0]\n",
        "print(f'PROTAC SMILES: {protac_smiles}')\n",
        "display(Chem.MolFromSmiles(protac_smiles))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "QCNDkTBQHmzl",
        "outputId": "21017e98-f022-4ff1-f9a2-235510874d61"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PROTAC SMILES: Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)COCCCCCCCCCOCC(=O)Nc2ccc(C(=O)Nc3ccc(F)cc3N)cc2)C(C)(C)C)cc1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e54519a0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The \"biological context\" will be:\n",
        "- a VHL E3 ligase\n",
        "- protein P04637 (Uniprot) as POI\n",
        "- in HeLa cell line"
      ],
      "metadata": {
        "id": "rPgch0R9H9Uo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "e3_ligase = example['E3 Ligase'].iloc[0]\n",
        "target_uniprot = example['Uniprot'].iloc[0]\n",
        "cell_line = example['Cell Line Identifier'].iloc[0]\n",
        "\n",
        "print(f'E3 Ligase: {e3_ligase}')\n",
        "print(f'POI:       {target_uniprot}')\n",
        "print(f'Cell line: {cell_line}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rz58I--GITr4",
        "outputId": "1c704022-79a0-4a61-c4fd-85132ffb1b19"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "E3 Ligase: VHL\n",
            "POI:       Q92769\n",
            "Cell line: HCT116-53BPI(+/-)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given the PROTAC SMILES and its biological context, we can predict its activity.\n",
        "\n",
        "Please note that a PROTAC has been defined active when both the following conditions are true:\n",
        "- $pDC_{50} \\ge 6.0$\n",
        "- $D_{max} \\ge 60\\%$"
      ],
      "metadata": {
        "id": "J2Z8JL9XIU8u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_activity = example['Active'].iloc[0]\n",
        "\n",
        "active_protac = pdp.is_protac_active(\n",
        "    protac_smiles,\n",
        "    e3_ligase,\n",
        "    target_uniprot,\n",
        "    cell_line,\n",
        "    device=device,\n",
        "    proba_threshold=0.5, # Default value\n",
        ")\n",
        "print(f'The given PROTAC is predicted {\"active\" if active_protac else \"inactive\"} (True activity: {\"active\" if true_activity else \"inactive\"}).')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EaIP3Q4jysSC",
        "outputId": "c5b1621d-40af-4feb-9b08-c9933b5a4ffd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator StandardScaler from version 1.2.0 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The given PROTAC is predicted inactive (True activity: inactive).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If one is interested in the _probability_ of the PROTAC being active, we can run the function `get_protac_active_proba`.\n",
        "\n",
        "The function returns the predictions of three models, along side with their mean and majority vote prediction (both as Numpy arrays):"
      ],
      "metadata": {
        "id": "89MZyDJZJJBM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = pdp.get_protac_active_proba(\n",
        "    protac_smiles,\n",
        "    e3_ligase,\n",
        "    target_uniprot,\n",
        "    cell_line,\n",
        "    device=device,\n",
        ")\n",
        "\n",
        "active_protac_prob = prediction[\"mean\"]\n",
        "\n",
        "if not active_protac:\n",
        "    active_protac_prob = 1 - active_protac_prob\n",
        "\n",
        "print(f'The given PROTAC is {\"active\" if active_protac else \"inactive\"} with probability: {active_protac_prob:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqnMQA1GI0W4",
        "outputId": "f1bb3334-eb54-4286-822e-befe3a403344"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The given PROTAC is inactive with probability: 0.818\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One can also inspect the single predictions by each model, like so:"
      ],
      "metadata": {
        "id": "b9zp2gXcPm7g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i, pred in enumerate(prediction['preds']):\n",
        "    print(f'Model n.{i} prediction: {pred[0]:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIo_rY0dPmX_",
        "outputId": "2ad85970-e1b1-4813-a537-0e145f2f6854"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model n.0 prediction: 0.1702\n",
            "Model n.1 prediction: 0.3745\n",
            "Model n.2 prediction: 0.0007\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Batch Processing\n",
        "\n",
        "To speed up computation, multiple PROTACs can be processed in \"one shot\" by simply passing them as lists, as follows:"
      ],
      "metadata": {
        "id": "kkztgPOYOe9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 16\n",
        "\n",
        "examples = protac_df.sample(batch_size, random_state=42)"
      ],
      "metadata": {
        "id": "DyDmOyZhO-by"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = pdp.get_protac_active_proba(\n",
        "    protac_smiles=examples['Smiles'].tolist(),\n",
        "    e3_ligase=examples['E3 Ligase'].tolist(),\n",
        "    target_uniprot=examples['Uniprot'].tolist(),\n",
        "    cell_line=examples['Cell Line Identifier'].tolist(),\n",
        "    device=device,\n",
        ")['majority_vote']\n",
        "\n",
        "for smiles, active in zip(examples['Smiles'].tolist(), predictions):\n",
        "    display(Chem.MolFromSmiles(smiles))\n",
        "    print(f'{\"Active\" if active else \"Inactive\"}')\n",
        "    print('-' * 80)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fdZyWYm4RB-V",
        "outputId": "e84fd4f8-6db2-4d78-d04e-459a2500b692"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator StandardScaler from version 1.2.0 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd4c80>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd4c80>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd4c80>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd5380>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd51c0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Active\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd4f20>"
            ],
            "image/png": 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sxGIxty9InVJTcfMmnj5Fnz54nit02p49+Ogj9OyJ8+dhaNhI48REuLnBygpZWRAK1RIfpT1oGm1mWBbcZsi1H6qroa9fX/Pk5OSePXtWVVWdPHlyeGMbYHTpgqQkJCbC1bUpQ9Yu+/YBQGAg1q6FgwO8vGBvr9CBHh4QixEdjZEjVRogpX3oFFMzs2DBsw/z5yMnB7NnY+NGzJuHul5mlEgkgYGBVVVVM2bMaDSHAhCJAKCqqinj1To5OTh3Dk+fPpt2VzCHAuCqZXFZmKJeRJ+NNjNVVbh69dnniAgsXgxnZxw4gG3bsHQpN0QtNjEZYW8v5/H09PTi4+P19fW///57Rfo2MAAAiURlwWuJRYsQHq70UZMn47ffvrpxI7K4+Apd7ES9iI5GmxmWRVkZuEliiQRGRgCePbpr1QoeHnB25tnaDhsxIjQ0NDQ0VE9Pr7q6eo9ie7G5u+8aOHCeVJqosui1g5UVunR5to8Uyyp6lL09eLzL6ekphw4dUl1slFbS2NbOVJ0+/fTZh7lziVhMFi4kFy6QqVNJWVmdzY8ePcowjFAoPH/+fKN9jxgxAsDx48ebMF6tc/UqKS8nxcVk4kTi40OmTVP0QJZluZ8rb29vVQZIaR86Gm1mFi36+4O7O5YsActi0yYYG9fZfNSoUUFBQTKZbPz48VlZWQ33bWBgAKBKtx+Ovv02jIwwdiwOHkRsLCIjUV7e+FEymWzo0KG5ublCoTA+Pt7S0nLAgAELFizYsWPHxYsXdfz/UoqORrWeXC739fUF0L9/f6lUWl+z7OzsLl26ANi7d68SvWdnk2++IWFhJD+/CWJtNrZvJwAxMyMA2b+/8fbTpk0DYGhoWPu/LxIKhW5ubhMnTvzqq6+io6NVHz7VvNA02hLk5uba29sDWLx48UtfVVVVRUZG+vn5CYVCAPr6+qdPn1ai6xkzSGkpKSggs2c3ZcSaVlxMDAwIwxT36xc9YcKChhuHhYVxuRKAgYHBtWvXnjx5Eh0dHRYWFhgY2KVLF/4LpV/s7e0vXbqknqugmgmaRluIuLg4PT09hmEiIyMJIXK5PCYm5oMPPjB+/jRAT09v9OjRP/zwA8uya9euZVm2oe7Onyfvv0/u3fs7e7asNEoImT8/rXv3xQKBgM/nZ2dn19fs5MmTfD6fYRiGYXg8XlRU1KttKisrr1+/vnv3bm5Hlrlz56oycKrZoWm05di0aRM3XJo0aZL98/chGYbp37//Dz/8UFBQUNuyvLycEHL06NGqqqqXOqn+4w8yaBABCECGDiXTpxO5nEil5OOP1Xoxqnf06FEAJiYmANatW1dnm1u3bnG/Q3p6egDCw8Mb7vPWrVsALC0tq6urVRAy1UzRNNqiTJgwQfh8raKDg0NwcHBqamqdLeVy+cyZMwsLCx89esQ9UT1z5oynp2eEtzcBiL4+0dcnQiE5fZqsWEFWrCD19KO9pFKplZUVgBEjRly+fPnVBtnZ2e3atQNgZGQE4MMPP1Sk265duwKIjj6lbDxr167t27fv/Pnzf/zxx6tXr3I/dZRWoGm0RcnNzZ0/f359eeFVUqnUzc1t586dPXv25JKvpbFxVatWz0ajw4eTlBRVx6xBQ4cONTIyEggEXbp0CQwMDAsLi46Orr3B37hxY+1wdcCAARKJRJE+t2494+5+edw45SI5efIkj8cT/nO5vq2trZ+fX3BwcEREhFgspiPcZouuqdd1Fy5c6NOnj5OTU1FRkUwmk8vlBwYNmmhpieXL8Ty3tkhxcXGDBw+uqamRy+UvfWVnZ9e1a1cPD4/o6OiUlJQOHTpcvXrV2tpakW6zsuDgAKHwWWlXRSQmJvbv37+kpMTDw2Py5MlisVgsFiclJUn/ueezlZVVTExMD23f0bpF0nQepzTv6vPlpzwez8/P78aNG5qOSOUePnxoY2MDYMaMGcXFxRcuXNi+ffv8+fO9vLy44SfHwcFBJBKJxWKlOn/3XQKQH39UqHFeXp6TkxOev0dV+zqaVCoVi8W//PLL0qVLR4wYYWhoyDDMZ599puyVUmpA0yhFTp48CcDR0TE5OVnTsahDaSkZMEDavftnQ4YMkclkL33Lsuz9+/ejoqKCgoIYhjEwMCgtLVWq/z17SPv2ZPfuxltWV1cPGjQIADeR1bt374qKilebHThwwM3NjUvrNTU1SgVDqQFNoxTZt28fgMmTJ2s6EHWQy8mIEQQg3bqxxcWN5Edvb28AERERSp1CJiM+PqSggFy8SH7/vd5mLMtOmTIFz6ewHB0dc3Jy6mz51Vdf1aba2NhYpYKh1IAuBqUgkeh37Ohqa9te04E0gUpud+j6hYfj5ElYWeHwYcbMzKThxlya+/nnn5WKQSCAoyPCwlBVhawsnD6NOpfpfv311z///LOenl5FRYWJiUl0dDT3nOFVgYGBDMNwj0r30VJ9zQ+dYqIQGoqVKxESgtBQTYfyBoqKivr27VtaWmpqatqtW7euXbv27DnL1dWmQwfwePjzT3ToAEdHREVh61YsX46BAxvvs6SkxNbWtrq6OiMjo23btooHs3gxnJxQUICSEqxdCwCWlvD1vde69eauXbt269YtLS0tMDCQ++tjGObo0aMjGywH7enpeeXKFQCmpqY5OTlceQSqudD0cJjSvHnzCEA2btR0HG9AKpW+++67AHi8v2+w7OweAcTIiLz9Npk+/VnxLGUnabjtrb777jsF27MsYVkSFETkcuLjQ5YvJz4+xNKSAMTLa09tbNwdOveG0/r16xvtdsuWLQDMzMwAcAvVqOaDjkYpTJmC/fuxb9+zAu/aaM6cOVu3bm3Tps3FixcrKirEYnFiYvKtW6vFYmRmcg3g6wu5HJcuKbcx6rFjx0aPHu3m5paQkKBI+y++QHo6goLw9ttIT4eBAWxtAeDJE6SkJF+/Hi0Wi2/duvXgwQOGYTZu3BgfH88tP2tYYWGhra2tXC5nWdbf3//YsWNKXAOlaprO45TmrV1L/PxIXJxaT5qcTPr0ITIZWbmSvLBO9XWsX78egEgkiqvrGgoLyfnzZMkSIpeTuXOVrg1Qu9gpPj6+0cYREQQgQiFptDiJh4cHgGPHjikeib+/PwA+ny8UCvPy8hQ/kFI1OsWkiz777NmHhQsREgIeD8eP4+BBdYfRqxe2bn3TTn777bfPP/+cYZhdu3b169fv1QYWFvD2hr09GAaLF0NPT7n+hULh+PHjocBE05UrmbNmAcCmTejfv5FuuckrpeaLAgMDARgbG8tksp07dyp+IKVqNI3qIokECQlISEB1NQBkZSEjQwNh9OiBJ0/eaPP3xMTEiRMnyuXy0NDQSZMmNdBy/nzweAgNxbZtSEpS7ixcytu4ceOXX3558ODBu3fvvrrwKT093d+/Z48eG4OCambPbrzPyZMn8/n8EydOFBcXKxiGn5/funXr3N3dBQLBuXPnlLsGSpVoGtVFNTW4fx/37z/bbzQo6Nlscnq6EnsTNYmlS3HqFAA8eaL0sfn5+f7+/iUlJePGjVuxYoUihwgEkEqxf79yJ+rXr1/Hjh0Zhvnmm28CAgLc3d0NDQ3d3NzGjx8fGhp6/Pjx+Pj40aNH5+XlmZr+Gham0GSDnZ3d4MGDJRLJ//73PwXDEIlEtra2XLUE7gafai40/VSB0oDaepiffkq++oqUl5P//peMGEHs7Un//mqq5VRRQTIzSUICWbiQtG9PjIzq226qbiUlJf3790f9K3/q9OefBCCOjkTZpUAlJSVbtmxZsWLFmDFjnJycGIZ56e+IYRh3d/eSkhLF+1R2Z6fr169zC0Y3bdqkXPSUitE0qouSkv7+kJZG5HJCCPnlF2JrSwBibEx27JA1Ute5Kfj7Ex6PAMTcnABEqc1Ndu3aZWBgYG5uXt/KnzqxLHF3l3t7p1y40Ph8UQPKysquXLmyY8eOefPm+fj4GBoa9u/f/8GDB0p1Ul5ebmxszDBMenp6o40zMzO5GrLTp09/zaAplaFplPpbURGZOZMAxNs7wtfXNyMjQ6WnCwh4Nq/NMAQgvr5KHHv48GEA7dq1U/aky5YtAzBjxgxlD1QF7nnumjVrGm5WVlbWrVs3AIMGDaLl8pohmkaplx09Wty6tQ0AMzOzXbt2qe5Ep04RgJiaEoDY2FQOHvzbkydPFDy2pqaGG51duXJFqZMmJSUBMDU1raysVD7kJvbrr78CaNu27fXr11/diYBTU1MzatQoAC4uLoWFhWqOkFIETaNUHXJzc7nVO23atFFuJ1FlyGSkSxdZ165XOnT4t6mpKZRZLEQICQoKAjBvntK141xcXACo9BdCQffu3TM2NuYqmfL5/I4dO9bWaU5ISOCKOXGXaWlpWd9GBpTG0TRK1cvHxwfAJ598orpTLFiwAC9sWdytWzfFj42PT/XyetCuHavUbW5OTo6lpaWBgcHmzZuVDrdJFRUVde7cmXs04ebmJhAIXpq2MjY25jK+np7en3/+qdloqQbQF56oerm6ugLgNhdSEe6VckIIAJFIdOfOnXv37il4rIdHp9LS9o8fM7/9pujpJBLJmDFjCgsLPTw8pmh06WtNTc3kyZOTk5ONjIzkcvkvv/xSWVmZkJAQGRkZEhLi5+fXsWPHioqKlJQUKyurWbNmcWVJqWZK03mcar4mTJgA4ODBgyo9S5cuXQCYmZlZWVkpWzc6PJwARMGNj1iWnTx5Mhqs7Kk2c+bMASASiQC0adOmztm8xMRET09PAGPHjlV/hJTi6GiUqldBQQEAS0tLlZ5l/fr1ly9fNjIyys/Pf/DggVLHTp4MY2MYGyvUeNWqVfv372+4sudrS5Gk/FTwU2p16p6CPY023rx58w8//CAUCiUSiUgkioqKcnBweLVZUFBQXFwcj8eLjo4uLCxs2oCpJkTTKFUv7k9X1Wl0yJAhnp6en3zyCZQvkGxvj48+QmAgAKxe3VDLQ4cOrVy5ksfj7d+/nysL0oQeSx9/l/vdENMhD6sfiqvEqdWpT+VP62t8+vTpRYsWMQzDsizDMDt37qyzFAAAbuxsYmIilUq5F7yo5ommUape6kmjHK7Ae1RUVFlZmVIHCgT43/9QXY2CApw+jTqHszdu3OB2mV+3bl3D1ZFfz+WKyxMsJtgL7YeYDgEw69Esmzs2bcRtfO/5BmUGRRyIuHHjRlVVFYCkpCSuCIC+vn5NTU1ISAiXK+s0ZswYExOTkpIS0KL3zdvLk4MUVUudadTR0XHAgAEXLlyIior64IMPFD+QYTB3LjZuRHU13n8fAExN4e6O3r0Xu7h07Nq1q7W19ahRoyorKz/66CPuxYAmZy2wfix9XPuPJjwTM75Zriw3V5Z7o/JG0aQiAHw+38nJKT8/v7i42NjYuLy8fOzYsdwmS/UxNDQcM2bM3r179fT0Ll68mJ6e3rFjR1XET70hWraZqptcLtfT0+Pz+VKp9NUl5KqwY8eOWbNmvffee2fOnFHwEELw+edYuxarVuH+fWRnQyxGTg4sLfMLC/+xrfy7777766+/ctXmmxwLdlnWMn1Gv62w7VP50y/bfAkgQ5ohrhKn56XHrYgTi8UpKSlyudzBwUEikTg5ObEse+7cuUb3Ajl79qyvry+XdlevXr18+XJVxE+9IZpGqbo9ffrUxsbG2tr66dN6H/M1LWU3Prp4EQsXYtkyjB6N8nLEx8PLCwDy85GQUHT7dkRCQkJ8fPzjx49btWp1/vz5Vq1aqfwa6ieVSq9du+br6yuVSu/du2dqaspVg24Yy7KOjo6ZmZkAOnXqlJKSop6fNEo5mn1RgGqeSkpKgoKC+Hy+mZnZw4cP1XZexTc+SksjVlYEIF991Xi3zWcd+tixYwF8++23ih/y+eef4/kKhatXr6ouNuq10TRK/UNhYWFISIiFhQWeb7hmamq6c+dO9Zz96NGjANzc3BpuVlxc3KtXKUBGjHhWnqphe/bsyczMbJoQ3wy3h1KjF/iiO3fueHp6du7cWSAQBAQEqC426rXRNEo9k7GmBo0AAAyzSURBVJeXFxISYm5uzt2meHl5rV27ltuKEsDQoUMfP36s6hiqq6u5AKZOnRoREXHz5k2JRPJSG5lMNmTIkLfe8njnnYzSUlVH1MRqd3a6ffu24keJxWJjY2OBQLBy5UrVxUa9NppGKZKbmxsSEsIVB+ES6NmzZwkhH374IQCGYbiZEDMzs+3bt6s0ksTERENDQ1tuL00AgEAg4Ap2hISEREZGJiQkcOt/rKys0tLSFO950aJFzaS0x9y5cwEEBQUp2D4vL8/JyQnAuHHj1FAElnoNNI3qNJZlx4wZwy1JZBhm5MiRLz59k8lkYWFhenp6AExMTLi8Nnz4cMXL2SklPz/f2dkZgJOT06pVq8aPH+/q6vpqwQ4ej8ct7FGq89jY2KKiIlWEray4uDgA3G7JjTaurq7mVtMrVeSfUjOaRnXa5s2bBQIBwzB+fn5//fVXnW3i4+O5msE8Hk9fXx+AtbX1xo0bmzYSqVQ6ePBgAMbGxgBq716lUmltwQ4vLy8u3Ss4DfVS/8ePH1d1IWoFcXWbYmJi4uLirl+/Xl/lU5ZlufopzaEIANUAmkZ12pYtWwA4OzsvWbKkvLy8vmZVVVWff/45n8/nZpwA8Pn8pNqtSJrCzJkza+ej7ezs6nwOK5PJatfCKzVLQwh5+PCht7d3fT8VarZy5UoAgYGBAwcO5C7H1tb21Uqjq1at4n5X4uPfaMsTStVoGtVp3B8qd+Pc6D1jXFyci4tLYGAgl8suXbrUVGGEhYXVvhhgYGBw7dq1+lrOmzcPgEgkEgkEGWKxsidKS0uLi4srrWdmqqamRiqVKtvna7h//z7DMHw+39TU1MTEhMd7eU32zJkzDx06xD2+OHbsmBpCot4ETaM67bPPPuP+bvX19RVpX1FRUVJSwr0b31QT9ydPnuTz+QzDMAzD4/GioqIaaHzt2rVAV9ffO3WqsbIiixcre64vv/yyztEf95jy9u3benp63bp1mzx58tq1m0+dIqp7N2H69OmvPvYViUSWlpampqZeXl7ctN7333+vqgiopkPTqE6rXb1uZ2en+FHcrXeTzHjcunWLexjKTWSFh4c3fkznzgQgALGzU+it0ResX7++R48e3BPeFxkYGPTq1cvd3b12jVDnzpO4k1hakuHDX/PqGsY99o2IiAgODvbz83vx/QTuh41uAqot6GJQnTZy5MgTJ04AcHd3F4vFihwikUgMDAz09fUlEsmbB2Bvb5+VlWVkZFRRUTFt2rTdu3c3fsyaNVixAiYmKCtDTAx8fZU9qVwuf/To0d27d2/cuJGYmHj37t3k5GSWZa2srPLz87k2zs5fFxZ+WVkJiQSdO8PCArGx2L4dY8bA3l7ZEyoqNzf3zp07YrH45MmT3bp1W7NmTe32KlRzRis86TSuMDOUKePEHdIk69PLy8srKioYhtm4cePhw4e3bdum0GGBgfjqK0gksLBAdvZrnJd7F7Vjx461RfNKSkquXbs2d+7cqqoqPT29yspKff23awslOzmhTRts3gyWBcu+xgkVZWNj4+vr6+vru2jRIhWehmpqNI3qtNqa6oqn0SasnmdsbDxw4MDjx49XVFScOnVK0cMcHbF8Odq1Q34+JBJUV+OVm3RlmZmZ+fr6MgxTUVFRUVEBgGG6A+DzYWwMBwd06oSCAihZCpXSFbRss06rTaOKjy6btggpt6Wd0jWJV63CpUv45BN4eyMkpEkiAZCcnHz//v2oqKhVq1b17m3s4gIAJSXgdpNbsgQnTjTVqagWhY5GdRchpKioiGEYV1fXDh06KHgUl0abqujcyJEjzc3Nr1+/fvfuXTc3N0UPq66GmRnMzWFujuLiJokEAMMw3M3+6NGjuX9TVYXERHTogPJymJri119hbd1wH5QuoqNR3VVZWSkUCnk83oABAxYuXNhw47Kyso0bN3p5eXGTME01GhWJRFxxvAMHDihxmL7+sxvs6mq88tJlEzIwQK9esLQEt+PcW29BJFLd2ShtRWfqddrMmTN/+uknuVzu6uoaERHRp0+fV9sUFBRs2LBh8+bN3KZA1tbWpaWlo0ePPnjwYJPEEBsb6+Pj4+jomJ6e/uqL6PU6exYXLkAqxYwZcHJqkkgo6jVp+IUrStPi4+O7d+8OQCAQBAcHv1iY7unTpyEhIbW18oyfb2RsbW2dmJjYVAGwLMs9UoiNjW2qPilKnehNva7z8PCIi4sLDg4mhISHh/fu3fvmzZsAli1b5ujouHLlytLSUi6TlpeXW1tbh4SE3Lt3z9XVtakCYBgmICAAdPNLSmvRm3rqmStXrkydOjUlJUUgEAQFBcnl8nXr1hkbG3M7HtvY2CxcuHDevHmqeCE8OTnZ1dXV1NQ0OzubvnBOaR2aRqm/VVZWLl26dMuWLYQQMzMz7mFo+/btg4ODp02b9uoayibUs2fPwsLCzp07Hz582MjISHUnoqgmR9Mo9bJLly6NHj1aJBKxLLtkyZLZs2erNIFyOnTo8PDhQwC9evU6duyYvepWXFJUU6NplKrD0KFDY2JiTp06NWzYMDWcrqKiwtTUlM/nd+jQITU11dbW9tixY3W+NkBRzRCdYqLqUFRUBECRjdSbxN27d1mWdXV1jYuLGzx4cHZ29sCBA3/55Rf1nJ2i3hBNo1QduPojTfWOfaO44lIeHh6WlpYxMTFz5syRSCRTpkwJDQ2ld0tU80fTKFWHpl043ygujXbt2hWAQCD4z3/+s2HDBoZhVq5cGRAQUFVVpZ4wKOr10DRKvaympqa0tJTP59e+eK9qd+7cwfM0ylmwYMGhQ4dEItF///vfoUOHqicMino9tDQJ9bKioiKuhrESSzPfTEJCAl5Io3fv3t23b99PP/3EVYZu06aNesKgqNdD0yj1MjXf0WdnZ+fl5VlYWMhkstWrV//888+pqancV+7u7pMmTfL391dPJBT1emgapV6m5vkl7o6eZVknJyduQsnW1jYgICAwMJBb7E9RzRxNo9TLHjx4AEBtS4nu3bvH5/NLSkpEItHIkSMDAwOHDRv26q6ZFNVs0dfvqb8RQvbt27dw4UIjI6O8vLyDBw+OGjVKDefdu3fv06dPZ8+eXVtEiqK0CE2jLdy1a9e++eabWbNmDRkypOEpo6SkpNmzZ58/fx6Ara1tdnY2n88PDw8PCgpSV7AUpZXoC08t3Pbt248ePTps2LBOnTqFhYXl5ua+2kYikYSGhvbo0eP8+fOGhoY8Hm/EiBFr1qxhWXblytAbN4IIkak/corSFvzQ0FBNx0Cp0MCBA21tbVNTU9PT03///fdNmzaJxWILC4sOHTowDAMgNjZ25MiRhw8fZlnWwMCgqqpKIBAMHTo0NDTUzc3NxyejTZv/lpdfNDMbyeMZaPpqKKo5ojf1OoFl2TNnzmzbtu3EiRNyuRyAi4tLYGBgWlpaREQEIcTExISrK+rl5bV9+/ba3eUqK6+npY2SybL09Tu99dZZHs9YIFDTDD5FaQuaRnVLdnb23r17t23b9vDhQ4ZhGIYRCASEEJlMZmlpGR4ePn36dG6UWksmy0pLG8Xj6RkZ9RYKHUUiVzMzdZR9oihtQZ+N6hZbW9vg4OC0tLRjx45ZWlqyLLts2TKZTDZu3LikpKQZM2a8lEMBCIV2Li5/Ojkdq6kpF4k6m5oO0UjkFNVs0TSqi/h8vr+//5gxYwAYGxuLxeLIyMjWrVvX157HMxIIrBwcfmDZyidPPldjpBSlBWga1V2DBg0CEBsb6+7urkj74uKjDMNjWYmK46IoLUPXiuiuQYN8PD03VVX51tSAz2+kcXb2Sh7PzMjo7XbtNqklOorSGnSKSad17IgHD3DjBnr2bKiZXJ5/505bQO7unq6n56Cu6ChKO9Cbep3m4wMAsbGNNMvP30lItZnZSJpDKepVNI3qtEGDgMbSKMuyWVlnAVhZzVRLUBSlZehNvU5LTcW+fZgzB1lZePwYv/0GS0tYWqJ160gzM31LS8t27dqlpKS8//778+e/t3Hjafq7S1GvolNMOo0QHDmCGTNw4gQKC7F9+7N/b24+u7i4CMAHH3zArW6ytX2X5lCKqhMdjeq0lBScOoW0NLRujYQEHDoEhoFIBBubQSUlYoZhbG1tk5OT+Xz+o0ePbGxsNB0vRTVHdHyh64yMMHgwTp+GjQ3MzUEIqqrQvr13UVFRYWHh3bt327RpExAQQHMoRdWHjkZ1Wno6rl3DxImYOBHbtsHcHCyLwkIUFeUWFDwoLCwsLCyUSCQffvihUCjUdLAU1UzRNEpRFPVG6E09RVHUG6FplKIo6o3QNEpRFPVGaBqlKIp6IzSNUhRFvRGaRimKot7I/wMZzv7FLOqnKAAABTl6VFh0cmRraXRQS0wgcmRraXQgMjAyMy4wOS42AAB4nIXUC1BUVRgA4HPP3r27LCusCwKCwIUF98EbFdlV2HMX5e26vEZNs5ujdMsRh5Iwm2nIsiSdHA21nF4iCb5yGNRSk713ktDJRz7AnMZ3OT4aUSlzKLW7/y5QmLkzd/7vnsd/zv3Pme1p33Eeyb9ANPgrl59n5aeOYhAvR0rBEJMcFbTKG+V3ASKl8zbg/gGD0TfgH/D1QEqMVQg6sLyGt0ONfKnYsZ4B1MBIbwNFySNgN9RgCt9u/jODPOHJa/Sn9kU1Yj1xaPPgQp5uxZO6B9YbTPfvqgyWbWiVfP0DHzJ03Sd8IOqvpSdi33wsj+svNiRQUE/d6eMZfCuTp+zof8556IE/fchwqO9jV4SBZnrgEg6Nj+XzRxSiMI8VAlbQPK3klQzLqASsUvNqPwH7aZDGH/lrWe0wEw7Q0jggEAXqkG44Ten0SB+EgoLZ4BEmHByCQkJpHBrGho004bBwFB6BIkahUZEoMgpFRaNoFrExKCYWxRqQIQ7FxdNU/Gg02sgaTQI2mXmzRcCWBD4hkU1MQknJKDkFpaSyqWkmnJoeR6ePYceMNSnSx1kwxqkZ4zJoRcZ4ND4TpViR1UZj2wQ0YaJaYc1CCdlyHjuyE0Q4xDlYR46AcybxkyYLeHIun5sn4Lx8Pr9AwAWFfGERKiwWMDcFhTiRcypyupCzBPmV8qVlAlZSAi5j+KAAFMeibCNf7OCnmFFWIirO5TMzULBGLp+SkmumZFRqv9IyRhkYEKTXKWPYOEMsYzJbErKNTFJyijUrUTE+I5OxE26K2cLkTJqcW+xg8vILCotzQ/sU8qEM/IV0zQ6V1r76SPS83GYpaYNbLXlsbbsnntWw4KST34nb22PAqw7vEjvr/MH8srPiuYj7MHd/ZLd4wnYI/OjuVtGS1QZ+ZkWzGDXrLXC187i46I4LfOTq9+LXsy+7Pb6ypVcsbVwAznbcFrWVw+0evxnaIW7e8jF4K9ZKa8ku8KdrYqUZH84DLytLk0ZGKYnHjKtAmn+JAp+6ZJDymsrB3Se0UoneBt7W2SveGlEDvljUKbbSJeBFL/SJc0dsBb+y4rSYvr4T3BLYIja8vxt8YeFG0Ui/A24es1ScozsF3rswQfxWJYGrFrrda+rPg3PoHw/01h0BSz31dgnfANf0RBJD3xnw4uYXyZ3XfwcffqmSLNmn5zwuqvicqK6Hg0eeOUBUf6rAu3sbyb30azD+B1cb0QXtB7PzG4h92jqw7VATaa0qBdP+p8nyzRw4SviZ1B49BnWrWtpBDi7uyfa4yb6OqM7ZoP16WxXRKLrbPa5I1pGlb0WDb3Y02fdc2AxnVG67am/emQTnaM6rsBcaa8EPQrfbV7S1gI3H49wxz1WC//rpA3dFtBY8z9XlNryRB3nKHh51z7m5Htpv1K905/t3gOu2FWfdv30FPDH8S7fe+Ru4vbGEnKjNBt+d9TI5NnMlOKe6hVxxN4CH7TpD7hs+AQd+9S6ZiW7AWu9dXkYC/lBDHZZf6CLHHdXQbl6NuX1pG8AUredqncIBjyu/MHNhRZvg27PDrByylkN9Nu11cTPDvPcz9dp0rvvqNJg7vaaAa738EPzLphIuvnWGdz9pDm5uw2pwyyoLNzW1HhzxfCx3ks0E73FruC3TjeDeU2nc7NJzkGdRlx8Xn7UD1vqsOkjCUd/A/m/VjJIyG1eBXQs00tGPvPfh7Z1maePoY+Ck1/rEg78ugZwnLz4Qq6btAIf8DZ7pquo09FKmAAAHEHpUWHRNT0wgcmRraXQgMjAyMy4wOS42AAB4nH1YS44dOQ7c1ynyApUQKX6kxSzadrcxGLgKmPb0HQaYZd8fHaEsK9MNYupDv5fFJ4XIYJDyy8Gvf3/513//PPaXfnl5OY72f37mnMcfvbX28u3gi+PTr1//+XZ8/v7Lpx9PPr//5+3778ewY3Z8Bt8/+/7y/f3bjydyfD5e52nRwvvxmuccXdWPdrb1dX9W6TnOls1N+MotMrPw7McbV8o2e47jVeihYaNwNS4ap0oXj+UqbTZvhasfvx+vfobNaMb9U8bss/CMa9Gc6V0WEu/WpPDMyzOmiM4FxLFqtfugp5/dVKIvHE3wsvCcl6d2z8DuhhPZnFVEpV3bWyLmfrz2E1HKXoVU5HLtKVOxv5wZw8v9Ra/wh5gNhL+dU4d6FX3pXDVPb6MNruWRE8crPO145/7iCNU85FTtU0ukfvEkHYwSeFobqlXwZeWJKY+WK0Bj4lDlmVaiRE6sNSa4CBrOGVq5DiIVPX1oQyzhqrOP8lDzA4CaRu9HP0n98lTaGNRxDmQdTNFztuxRxVTlimnIEOWaQ6eVMVW9YqrKVQ9mn9GtPD/yhDSKjgM0FJfa04gTjDZtWB+L99RWnsg/eOrCTUFoHMit8gzi9LOZzRYHCD1Gq9dcabKzBTwcu/v0LItZVz0pNh0xDZ6tq8wynnPl87TpDjrHOd10VvHsq5yoIG7QJRQLkmRVlDpzBDJlBpZPhFNHr3jXKXp6UugMunKmoZaqGHVqXj8DlWSBFSOllzrSqXhwpCKOY54COS0Z15kfBB3qAXLMMwbkriJ8j+U4ISFIykCgRo7yMHmtOEFySB8yH3Vh9rEco/VANfuJI7Va6JkbOwNHxd/xYg4p021trSg2zRbTyaAKI9L1hjgH6tEcjg2CPCqNN2YmT+mZkFFBuXlYJbLG2oFwpeIHf2cupWxFzAzP4BrcMXXOXq7oK9f4e7K9YEmBGFWpsVj0gaZAthY13TPLNXMxUgdWbaR7h3a1cs2B3SH/c4Q7S4hMszKWlDeIau8C/UR3GSGZVeUgZ+9Ysxs6hi5RQJuRKuMuV42FdZIMizp6W6nZvgYG4ZmhVkQKApPLheuaGJDECPY0rJ85tZRXt2vVwHABsYPYpKWV8u5+ubaJ7jKv7oWCL48VK1Rs1NgVSgvplqgS5VcnOjv6f8YSejC1lwPLWKWu6VAunn8mpqwS6Vye1EI2dzv7iFq14yoklMeALMMTY5VppUhxVRJ0Kzna2DmlhVU4Qxfx1TB6cQZDmxlSymEwT9R/KKuB2GLoA+UEZqvoYnSH6nAEMfbYypNJGhwBzBbxtRnIVXkGNodgqs9cxdQwrUiVo1jFBJI26Myq0M5RsWJeMEeYKzyGLVXgfFEOa8EcCRQJ2V6q6QElqSia7fJEWcRYbB7We1Snz9WIMKugBaNccfw5pe5uqdeqAknENIbKw0hr5bCY/VoVSZy5WOI2e1QsSfsIFRpSLupBgsTKVf1yFRvZFp9HZmslVpTT/5DV4DC9RArDaN2UMq8E4Pxqi31UwCxdx6IKlgoMrihBTPVaJmBelxoVV5AKDQx7l4U32uWJREJwQG1MzO4VUYZcYy0FaiS7J/7JKqRDP6bKaN4hfJjvDX224tTYcx0OrczpRNmXN5Vhl+sEkzAwQaNNoBYV1F/fvvx0xbsufZ/e377clz5+632zQzEf/b6+GX/vKxq//b6G4c0R910LJzzyvlAZfsd9azL8zvtqhCspOPS4AMky8rjnyDIbHBqGLLPxIfl8izvJ43YivHjwPvPb13/cnv7xfMMV4qXZiNHmZJmxPnsjGR/PN3ree2j0eSsQGt2fgjLoMndwiZ9PNipIPN9yVv8JrfIUMLpjjRldl9n4lfj5ZONX4ofRHXQlchjdyJXIYfpzVpZjTcKPmVho+kbeiRym38wgcl6UduQ7McP0jbkz5jB9Y+7EDNM35k7MMP1vMe9EDtM38k7kMPYcJW2Z/SnMjLbMRm5kNM1GbiQ1zU1rIoexjdyIHMY2ciNyGNvIjchhbEfbiBnGNmYIudL4c76S9WRj5s2YxjdmqE6n8Y3ZyXP6bMxOzDB+VyMxw/jG7MQM4xuz54XQN2YnZhjOHU8GOqMNE895o9PERh5yfTY28ljRxiVurxPkCaIdG3kQOUxs5EHkMHFLydKSQPt+NHKjiY08qCgwsaMdFBWYfPZfo8mNOckQmNyYk5hhcmNOMgQmN+ak7sHkxpzEDJMbMxqVrSe3AuZ1ityYc1z1nhtzMs4w49mIricb85CPJxvzIGZh13j0D6MZG/Mg5rilfVCu8XDDG3FJluxDjR/FeC+7gtwfRTcYZN6p9zpzAR6PopsETC7sladen7oTMUkM8PEmMJvVszXx/Y//y8Trl78AD1zprAnf4u4AAAOkelRYdFNNSUxFUyByZGtpdCAyMDIzLjA5LjYAAHicTVQ9qyRHDPwrBic70NtufUu3HByMA0fvRY5sR5Oalzi45H68Sz3PYFjYobpaLZVKOi+6Hs+Lr+t6nG/n4+v78cf57dtvf8l5/z/ej/NN/nfwuFnn+X6efXI2sfnvl3xcijDn2+MyBLz82mH/Pi7z49Tjerxpn+4Av5yP8+e34+v552nnaf9FPxsE90MOEA78zuO6+Pjn+qCffjyeNdWX23jGrBSW8XrmXLFMBz5MPRKQz6gwaRb+dCNeREB8sgU1yaYokw98+CLmDbFYANJJS6v2RY0UXJTJrnHHkqDi8aQZntYXYzqp1niuWZxsuTFbuWqsaR7lsm+SFfGgySx1Jx/W9dDUlTsHWpN8Ja5lIbo3RBOHNRiVVm2EpyU3wCV532JlHzKF2e7IiaoEnFoh8Zlk4nWZCKa+82EGa3RtSGxzkD10gjhkRLdyyosHuBJMt0pGYEO3EOTeyEL1NCBbdrwXBFyeSbhlZdEcBju9gCy5C51aZg2UKbfWqByNjAAkSBpao/AISBdIhxMAo/+BE3RYofR4yXTIbGB4kNzAWhyjJsEWUALJoZkEwJOyGihBBSORQMYGVkE1xGDzzq0dId4VpnYvXjpdEh/4ryRrBmmptnJ3xZCJXRpYMBiCxiSJIHTWyVolNB/6BRrbJbQFcSelnQvX6G7fK7v5iAt1eKmjYtTBVtmkBSORIRS67eh8Y4KSBCnDI+ZQCLbhrGoSUlEI0P7Dg7kRQ4i+lirieK+NhMHo9wrOrE0ilNCG1wVTbw5K6aEwLfH7eYxMI5xBd0KkCQ7PRP9QhneF3XTSVgPpEek9M1wtafaRtz6CEZWtcRm3PmCUWLcSXyGdrxKcz91/Ns4NQYloZ1En0WIg0mqj44nK3hE82xKgoF4RgvYoKp1ik0RVs6ef9zS3/VwFHm2WQcI9aP0M7VgQWmlD7o6pBz+i2PPGVrcBigSWxA2twtiPPfTtYWTRO8UxtxgzjCbFpgmkzIZgn/YrLBVoUz9ZgWW3dcDk7OiS3jOIpGFIacQ1oeurB2LFRopWGwfN0buzcARtgFJ6i5Cu7gCWKZOxts3xgG8ERkG/dGpmOw1rxI0wtpgN3619bvNZj6AqM38ujd0BwfZb96op+KtaXSWve7e6thzYeK3kvX1IegG3e9bn+onVC60hTLGPY3z//UtbUoZgt9n3X7+0ldYgJL+GI2H78S8RwmPdpRK5PAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Active\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd5150>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd5070>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd5230>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd5310>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Active\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e601d540>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd54d0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd54d0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Active\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd54d0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Active\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd54d0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inactive\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x78f8e5cd54d0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Active\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting Available Data Options\n",
        "\n",
        "Currently, the package relies on precomputed embeddings for POI, E3 ligase, and cell line.\n",
        "Therefore, inputs to the prediction function must be among the available data.\n",
        "\n",
        "Note that this is not true for the PROTAC SMILES, which can be anything \"reasonable\" candidate.\n",
        "\n",
        "To get the list of available data to choose from, one can use the following functions:"
      ],
      "metadata": {
        "id": "LkywLbXrSJiH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Number of available E3 ligases:  {len(pdp.avail_e3_ligases()):,}')\n",
        "print(f'Number of available Uniprot IDs: {len(pdp.avail_uniprots()):,}')\n",
        "print(f'Number of available cell lines:  {len(pdp.avail_cell_lines()):,}')\n",
        "print(f'')\n",
        "print(f'Available E3 ligases:                         {pdp.avail_e3_ligases()}')\n",
        "print(f'Available Uniprot IDs (showing the first 32): {pdp.avail_uniprots()[:32]}')\n",
        "print(f'Available cell lines (showing the first 32):  {pdp.avail_cell_lines()[:32]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hd_ZTDXaTPjN",
        "outputId": "a7d23ef0-47c6-43e6-8d17-9de0bf6ba407"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of available E3 ligases:  16\n",
            "Number of available Uniprot IDs: 20,594\n",
            "Number of available cell lines:  1,138\n",
            "\n",
            "Available E3 ligases:                         ['VHL', 'CRBN', 'DCAF11', 'DCAF15', 'DCAF16', 'MDM2', 'Mdm2', 'XIAP', 'cIAP1', 'IAP', 'Iap', 'AhR', 'RNF4', 'RNF114', 'FEM1B', 'Ubr1']\n",
            "Available Uniprot IDs (showing the first 32): ['A0A024R1R8', 'A0A024RBG1', 'A0A024RCN7', 'A0A075B6H5', 'A0A075B6H7', 'A0A075B6H8', 'A0A075B6H9', 'A0A075B6I0', 'A0A075B6I1', 'A0A075B6I3', 'A0A075B6I4', 'A0A075B6I6', 'A0A075B6I7', 'A0A075B6I9', 'A0A075B6J1', 'A0A075B6J2', 'A0A075B6J6', 'A0A075B6J9', 'A0A075B6K0', 'A0A075B6K2', 'A0A075B6K4', 'A0A075B6K5', 'A0A075B6K6', 'A0A075B6L2', 'A0A075B6L6', 'A0A075B6N1', 'A0A075B6N2', 'A0A075B6N3', 'A0A075B6N4', 'A0A075B6P5', 'A0A075B6Q5', 'A0A075B6R0']\n",
            "Available cell lines (showing the first 32):  ['GM15119', 'GM17453', '84 BLCL', 'HQ00019', 'RFGd9WE', 'LG-11', 'TO 167.T', 'BayGenomics ES cell line RRA095', 'TPMab-3', 'DA02391', 'CW40101', 'CHLA-99', 'BayGenomics ES cell line CSI883', 'HG00174', 'U-410MG', 'GM09963', 'Relicell hES4', '2BBLC403.4', 'SVG-A Msh3-/-', 'GM06222', 'OA-5ca', 'SEES3-1V human ZNF559, clone2', 'DA02536', 'BOS-1', 'HCC41 BL', 'DA00900', 'BT 232', 'VPM 54', '271-20A9', 'UWB1.289+BRCA1', 'Ma-Mel-24', 'UKTS9175']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XKeFKnDbbIbD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}