notebook_examples/simple_examples.ipynb

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      "source": [
        "#@markdown Setup dependencies (Colab only)\n",
        "%%capture\n",
        "try:\n",
        "    import papyrus_scripts\n",
        "except:\n",
        "    !pip uninstall papyrus-scripts -y\n",
        "    !pip install rdkit-pypi\n",
        "    !pip install https://github.com/OlivierBeq/Papyrus-scripts/tarball/master --no-cache-dir\n",
        "    get_ipython().kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRvE8vDB9rQU"
      },
      "source": [
        "# Simple examples: Using Papyrus scripts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_J-i5Ea9rQY"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/simple_examples.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14He9HKN9rQZ"
      },
      "source": [
        "Herein it is assumed that the Papyrus <a href=\"https://doi.org/10.4121/16896406\">bioactivity data</a> hosted on 4TU or Google Drive was ***NOT*** dowloaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      ],
      "source": [
        "%%html\n",
        "<style>\n",
        "table {align:left;display:block}\n",
        "</style>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnTWIh6d9rQb"
      },
      "source": [
        "## Download the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3aE-Itji1iBq"
      },
      "source": [
        "\n",
        "One can easily download (part of) the Papyrus data using *papyrus* command (*download* subcommand).<br/>\n",
        "```\n",
        "papyrus download --version latest -s without -S -d mold2 -d unirep\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Edy4UaWa9rQc"
      },
      "source": [
        "It can also be carried out programatically with the following."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RUo6SlTh9rQd"
      },
      "outputs": [],
      "source": [
        "from papyrus_scripts.download import download_papyrus"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bD_NAzgX9rQe"
      },
      "source": [
        "The default behaviour is to:\n",
        "- download the curated 2D data (*nostereo* argument)\n",
        "- omit the not curated 3D data (*stereo*)\n",
        "- omit molecular structures (*structures*)\n",
        "- download all molecular/sequence descriptors (*descriptors*)\n",
        "\n",
        "Let's download the 2D curated data with molecular structures and only descriptors used below (total of 2.80 GB)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "uVwOfn2v9rQf",
        "outputId": "d8918a8c-1cb1-4e15-8bfa-433301df7b30"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Latest version: 05.5\n",
            "Number of files to be donwloaded: 9\n",
            "Total size: 2.80GB\n"
          ]
        },
        {
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            "text/plain": [
              "Donwloading version 05.5:   0%|          | 0.00/2.80G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "download_papyrus(version='latest', structures=True, descriptors=['mold2', 'unirep'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8_Lqdtu9rQf"
      },
      "source": [
        "At the time of writing (Apr. 26th, 2022) the latest version available is 05.5.\n",
        "\n",
        "A custom directory to donwload the data to can be indicated with the *outdir* argument."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1av7sy399rQg"
      },
      "source": [
        "## Reading Papyrus files"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dM2lhXdc9rQg"
      },
      "source": [
        "Functions can be found under *papyrus_scripts.reader* to facilitate the dataset being read from disk."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YXrCZZQm9rQg"
      },
      "outputs": [],
      "source": [
        "from papyrus_scripts.reader import read_papyrus, read_protein_set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-oyiKxA9rQg"
      },
      "source": [
        "### Bioactivity data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1H_7hcDJ9rQh"
      },
      "source": [
        "Let's first read the bioactivity data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Se6T4rNX9rQh"
      },
      "source": [
        "We will use the *read_papyrus* function to read the bioactivity data as a pandas dataframe. <br/>\n",
        "Let us first demonstrate the use of the function on systems with limited RAM (less than 50GB)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcwzKYa59rQh"
      },
      "source": [
        "We first ensure to read the standardized data without stereochemistry in chunks of ten thousand lines.<br/>\n",
        "Additionally we ensure the *source_path* is that given to the *download_papyrus* function above through the *outdir* argument."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G2jD8IY59rQi"
      },
      "outputs": [],
      "source": [
        "sample_data = read_papyrus(is3d=False, chunksize=10000, source_path=None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8iEgKew09rQi"
      },
      "source": [
        "The return value is an iterator of Pandas dataframes of maximum ten thousand rows each.<br/>\n",
        "Let's extract the first chunk as a pandas dataframe and have a look at few rows."
      ]
    },
    {
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              "                   Activity_ID Quality                          source  \\\n",
              "0  AAAAEENPAALFRN_on_P49654_WT    High                        ChEMBL30   \n",
              "1  AAAAEENPAALFRN_on_P56373_WT     Low                        ChEMBL30   \n",
              "2  AAAAEENPAALFRN_on_Q9UBL9_WT     Low                        ChEMBL30   \n",
              "3  AAAAKTROWFNLEP_on_P49137_WT     Low                        ChEMBL30   \n",
              "4  AAAAZQPHATYWOK_on_P00533_WT    High  Sharma2016;Sharma2016;ChEMBL30   \n",
              "\n",
              "                            CID  \\\n",
              "0                  CHEMBL492934   \n",
              "1                  CHEMBL492934   \n",
              "2                  CHEMBL492934   \n",
              "3                  CHEMBL246893   \n",
              "4  4277046;4277046;CHEMBL175513   \n",
              "\n",
              "                                              SMILES    connectivity  \\\n",
              "0      COc1cc(C(C)C)c(Oc2cnc(NCCS(C)(=O)=O)nc2N)cc1I  AAAAEENPAALFRN   \n",
              "1      COc1cc(C(C)C)c(Oc2cnc(NCCS(C)(=O)=O)nc2N)cc1I  AAAAEENPAALFRN   \n",
              "2      COc1cc(C(C)C)c(Oc2cnc(NCCS(C)(=O)=O)nc2N)cc1I  AAAAEENPAALFRN   \n",
              "3          CC1CNC(=O)c2c1c1c(ccc(C(=O)N(C)C)c1)[nH]2  AAAAKTROWFNLEP   \n",
              "4  CCOc1c(NC(=O)C=CCN(C)C)cc2c(Nc3cc(Cl)c(OCc4nc5...  AAAAZQPHATYWOK   \n",
              "\n",
              "                      InChIKey  \\\n",
              "0  AAAAEENPAALFRN-UHFFFAOYSA-N   \n",
              "1  AAAAEENPAALFRN-UHFFFAOYSA-N   \n",
              "2  AAAAEENPAALFRN-UHFFFAOYSA-N   \n",
              "3  AAAAKTROWFNLEP-UHFFFAOYSA-N   \n",
              "4  AAAAZQPHATYWOK-UHFFFAOYSA-N   \n",
              "\n",
              "                                               InChI  \\\n",
              "0  InChI=1S/C17H23IN4O4S/c1-10(2)11-7-14(25-3)12(...   \n",
              "1  InChI=1S/C17H23IN4O4S/c1-10(2)11-7-14(25-3)12(...   \n",
              "2  InChI=1S/C17H23IN4O4S/c1-10(2)11-7-14(25-3)12(...   \n",
              "3  InChI=1S/C15H17N3O2/c1-8-7-16-14(19)13-12(8)10...   \n",
              "4  InChI=1S/C32H29ClN6O3S/c1-4-41-28-16-25-22(15-...   \n",
              "\n",
              "                                       InChI_AuxInfo  target_id  ...  \\\n",
              "0  \"AuxInfo=1/1/N:7,8,1,19,16,17,4,25,12,6,5,26,9...  P49654_WT  ...   \n",
              "1  \"AuxInfo=1/1/N:7,8,1,19,16,17,4,25,12,6,5,26,9...  P56373_WT  ...   \n",
              "2  \"AuxInfo=1/1/N:7,8,1,19,16,17,4,25,12,6,5,26,9...  Q9UBL9_WT  ...   \n",
              "3  \"AuxInfo=1/1/N:1,17,18,12,11,19,3,2,13,9,10,8,...  P49137_WT  ...   \n",
              "4  \"AuxInfo=1/1/N:1,13,14,2,32,31,10,33,30,9,36,3...  P00533_WT  ...   \n",
              "\n",
              "  type_other Activity_class relation            pchembl_value  \\\n",
              "0          0            NaN        =                    7.800   \n",
              "1          0            NaN        =                    7.400   \n",
              "2          0            NaN        =                    7.400   \n",
              "3          0            NaN        <                    4.699   \n",
              "4          0            NaN        =    6.063;  6.726;  6.730   \n",
              "\n",
              "   pchembl_value_Mean pchembl_value_StdDev pchembl_value_SEM pchembl_value_N  \\\n",
              "0               7.800                0.000             0.000             1.0   \n",
              "1               7.400                0.000             0.000             1.0   \n",
              "2               7.400                0.000             0.000             1.0   \n",
              "3               4.699                0.000             0.000             1.0   \n",
              "4               6.506                0.313             0.222             3.0   \n",
              "\n",
              "  pchembl_value_Median pchembl_value_MAD  \n",
              "0                7.800             0.000  \n",
              "1                7.400             0.000  \n",
              "2                7.400             0.000  \n",
              "3                4.699             0.000  \n",
              "4                6.726             0.004  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chunk1 = next(sample_data)\n",
        "chunk1.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWtXuCpa9rQj"
      },
      "source": [
        "If you are sure your hardware can handle loading all the data, then you can drop *chunksize*.<br/>\n",
        "Then the return value is a pandas dataframe."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlWPDV_T9rQj"
      },
      "source": [
        "Below, we will show how to use:\n",
        "  - a pandas dataframe (by calling our methods on *chunk1*)\n",
        "  - an iterator of pandas dataframes (by calling our methods on *sample_data*)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHnbcLOi9rQj"
      },
      "source": [
        "### Protein target data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slf1KpA99rQj"
      },
      "source": [
        "But for now let's focus on protein data:<br/>\n",
        "Information about the protein targets is available from a different file and can be loaded as easily as was demonstrated above.<br/>\n",
        "This file being very limited in size, chunking is not needed. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wIaY8vQN9rQk",
        "outputId": "4dfa584e-a929-41a3-959f-2cb688e5465e"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>target_id</th>\n",
              "      <th>HGNC_symbol</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>P47747_WT</td>\n",
              "      <td></td>\n",
              "      <td>HRH2_CAVPO</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>Cavia porcellus (Guinea pig)</td>\n",
              "      <td>Membrane receptor-&gt;Family A G protein-coupled ...</td>\n",
              "      <td>359</td>\n",
              "      <td>MAFNGTVPSFCMDFTVYKVTISVILIILILVTVAGNVVVCLAVGLN...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B0FL73_WT</td>\n",
              "      <td></td>\n",
              "      <td>B0FL73_CAVPO</td>\n",
              "      <td>unreviewed</td>\n",
              "      <td>Cavia porcellus (Guinea pig)</td>\n",
              "      <td>Membrane receptor-&gt;Family A G protein-coupled ...</td>\n",
              "      <td>467</td>\n",
              "      <td>MGAGVLALGASEPCNLSSTAPLPDGAATAARLLVPASPPASLLPPT...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Q8K4Z4_WT</td>\n",
              "      <td></td>\n",
              "      <td>ADRB2_CAVPO</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>Cavia porcellus (Guinea pig)</td>\n",
              "      <td>Membrane receptor-&gt;Family A G protein-coupled ...</td>\n",
              "      <td>418</td>\n",
              "      <td>MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>P97266_WT</td>\n",
              "      <td></td>\n",
              "      <td>OPRM_CAVPO</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>Cavia porcellus (Guinea pig)</td>\n",
              "      <td>Membrane receptor-&gt;Family A G protein-coupled ...</td>\n",
              "      <td>98</td>\n",
              "      <td>YTKMKTATNIYIFNLALADALATSTLPFQSVNYLMGTWPFGTILCK...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>P41144_WT</td>\n",
              "      <td></td>\n",
              "      <td>OPRK_CAVPO</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>Cavia porcellus (Guinea pig)</td>\n",
              "      <td>Membrane receptor-&gt;Family A G protein-coupled ...</td>\n",
              "      <td>380</td>\n",
              "      <td>MGRRRQGPAQPASELPARNACLLPNGSAWLPGWAEPDGNGSAGPQD...</td>\n",
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              "  "
            ],
            "text/plain": [
              "   target_id HGNC_symbol     UniProtID      Status  \\\n",
              "0  P47747_WT                HRH2_CAVPO    reviewed   \n",
              "1  B0FL73_WT              B0FL73_CAVPO  unreviewed   \n",
              "2  Q8K4Z4_WT               ADRB2_CAVPO    reviewed   \n",
              "3  P97266_WT                OPRM_CAVPO    reviewed   \n",
              "4  P41144_WT                OPRK_CAVPO    reviewed   \n",
              "\n",
              "                       Organism  \\\n",
              "0  Cavia porcellus (Guinea pig)   \n",
              "1  Cavia porcellus (Guinea pig)   \n",
              "2  Cavia porcellus (Guinea pig)   \n",
              "3  Cavia porcellus (Guinea pig)   \n",
              "4  Cavia porcellus (Guinea pig)   \n",
              "\n",
              "                                      Classification  Length  \\\n",
              "0  Membrane receptor->Family A G protein-coupled ...     359   \n",
              "1  Membrane receptor->Family A G protein-coupled ...     467   \n",
              "2  Membrane receptor->Family A G protein-coupled ...     418   \n",
              "3  Membrane receptor->Family A G protein-coupled ...      98   \n",
              "4  Membrane receptor->Family A G protein-coupled ...     380   \n",
              "\n",
              "                                            Sequence  \n",
              "0  MAFNGTVPSFCMDFTVYKVTISVILIILILVTVAGNVVVCLAVGLN...  \n",
              "1  MGAGVLALGASEPCNLSSTAPLPDGAATAARLLVPASPPASLLPPT...  \n",
              "2  MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLA...  \n",
              "3  YTKMKTATNIYIFNLALADALATSTLPFQSVNYLMGTWPFGTILCK...  \n",
              "4  MGRRRQGPAQPASELPARNACLLPNGSAWLPGWAEPDGNGSAGPQD...  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "protein_data = read_protein_set(source_path=None)\n",
        "protein_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5mDv25Q9rQk"
      },
      "source": [
        "## Filtering Papyrus"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6rtoPuWB9rQk"
      },
      "source": [
        "The data contained in the dataset can be filtered very easily using functions under *papyrus_scripts.preprocess*.<br/>\n",
        "All filtering functions start with ***keep_***."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mYiV9FIX9rQk"
      },
      "outputs": [],
      "source": [
        "from papyrus_scripts.preprocess import (keep_quality, keep_source, keep_type,\n",
        "                                        keep_organism, keep_accession, keep_protein_class,\n",
        "                                        keep_match, keep_contains\n",
        "                                       )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RA_RHu_W9rQl"
      },
      "source": [
        "**The strength of the Papyrus scripts is that the data can be filtered whether chunked or not.** The only difference:\n",
        "  - when using chunked data, call *consume_chunks* once all filters are applied to reconstiture a pandas dataframe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rd4tEYkrz0W3"
      },
      "source": [
        "In addition to the 8 **keep_** functions above, one can filter compounds similar to a reference using the *keep_similar* and *keep_substructures* functions (see [advanced_querying.ipynb](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/advanced_querying.ipynb))."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8koFdnjI9rQl"
      },
      "source": [
        "### Filtering pandas dataframes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5_EoOSh9rQl"
      },
      "source": [
        "Let's first keep the data with quality 'medium' and above (namely 'high' and 'medium')."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pNH6_Ldc9rQl"
      },
      "outputs": [],
      "source": [
        "filter1 = keep_quality(data=chunk1, min_quality='medium')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AxyBFclr9rQl"
      },
      "source": [
        "<u>Using <a href=\"https://www.ebi.ac.uk/chembl/visualise/\">ChEMBL's protein target tree</a> is encouraged for this part.</u><br/>\n",
        "<br/>\n",
        "We will then filter out any protein not belonging to these two classes:\n",
        "* Ligand-gated ion channels\n",
        "* SLC superfamily of solute carriers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btjMrph69rQl"
      },
      "source": [
        "For this filter, passing protein information is required (the same applies for *keep_organism* and *keep_accession*)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yJ_Xe9Kb9rQm"
      },
      "outputs": [],
      "source": [
        "filter2 = keep_protein_class(data=filter1, protein_data=protein_data, classes=[{'l2': 'Ligand-gated ion channels'}, {'l3': 'SLC superfamily of solute carriers'}])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_e-S5wk9rQm"
      },
      "source": [
        "We now keep only K<sub>i</sub> and K<sub>D</sub> data.<br/>\n",
        "Here we will pass filter1 to the next *keep_* funtion."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jTvGYL7e9rQm"
      },
      "outputs": [],
      "source": [
        "filter3 = keep_type(data=filter2, activity_types=['Ki', 'KD'], njobs=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sopsHJtw9rQm"
      },
      "source": [
        "We finally keep only human and rat data (protein information is also required here)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZBH3pntU9rQm"
      },
      "outputs": [],
      "source": [
        "filter4 = keep_organism(data=filter3, protein_data=protein_data, organism=['Human', 'Rat'], generic_regex=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rwf9kFSt9rQn"
      },
      "source": [
        "Let us have a look at the filtered data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "jUl1wDul9rQn",
        "outputId": "e1ffa930-195c-4438-80c3-472a661bcf45"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Activity_ID</th>\n",
              "      <th>Quality</th>\n",
              "      <th>source</th>\n",
              "      <th>CID</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>connectivity</th>\n",
              "      <th>InChIKey</th>\n",
              "      <th>InChI</th>\n",
              "      <th>InChI_AuxInfo</th>\n",
              "      <th>target_id</th>\n",
              "      <th>...</th>\n",
              "      <th>relation</th>\n",
              "      <th>pchembl_value</th>\n",
              "      <th>pchembl_value_Mean</th>\n",
              "      <th>pchembl_value_StdDev</th>\n",
              "      <th>pchembl_value_SEM</th>\n",
              "      <th>pchembl_value_N</th>\n",
              "      <th>pchembl_value_Median</th>\n",
              "      <th>pchembl_value_MAD</th>\n",
              "      <th>Classification</th>\n",
              "      <th>Organism</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAEKULYONKUBOZ_on_P23975_WT</td>\n",
              "      <td>Medium</td>\n",
              "      <td>ChEMBL30</td>\n",
              "      <td>CHEMBL14144</td>\n",
              "      <td>CN1C2CCC1C(C(=O)Oc1ccccc1)C(c1ccc(Cl)cc1)C2</td>\n",
              "      <td>AAEKULYONKUBOZ</td>\n",
              "      <td>AAEKULYONKUBOZ-UHFFFAOYSA-N</td>\n",
              "      <td>InChI=1S/C21H22ClNO2/c1-23-16-11-12-19(23)20(2...</td>\n",
              "      <td>\"AuxInfo=1/0/N:1,14,13,15,12,16,19,24,20,23,4,...</td>\n",
              "      <td>P23975_WT</td>\n",
              "      <td>...</td>\n",
              "      <td>=</td>\n",
              "      <td>6.620</td>\n",
              "      <td>6.62</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.62</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Transporter-&gt;Electrochemical transporter-&gt;SLC ...</td>\n",
              "      <td>Homo sapiens (Human)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ABAOWFCFAADMKA_on_P23975_WT</td>\n",
              "      <td>Medium</td>\n",
              "      <td>ChEMBL30</td>\n",
              "      <td>CHEMBL456654</td>\n",
              "      <td>c1cnc(CC2(c3ccc4[nH]ccc4c3)CCNC2)cc1</td>\n",
              "      <td>ABAOWFCFAADMKA</td>\n",
              "      <td>ABAOWFCFAADMKA-UHFFFAOYSA-N</td>\n",
              "      <td>InChI=1S/C18H19N3/c1-2-8-20-16(3-1)12-18(7-10-...</td>\n",
              "      <td>\"AuxInfo=1/0/N:21,1,20,8,9,13,16,2,12,17,15,5,...</td>\n",
              "      <td>P23975_WT</td>\n",
              "      <td>...</td>\n",
              "      <td>=</td>\n",
              "      <td>8.100</td>\n",
              "      <td>8.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Transporter-&gt;Electrochemical transporter-&gt;SLC ...</td>\n",
              "      <td>Homo sapiens (Human)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ABARJWHHNCOEQT_on_P23975_WT</td>\n",
              "      <td>High</td>\n",
              "      <td>ChEMBL30</td>\n",
              "      <td>CHEMBL141741</td>\n",
              "      <td>CNCCC(Oc1cccc2ccccc12)c1c(C)cccc1</td>\n",
              "      <td>ABARJWHHNCOEQT</td>\n",
              "      <td>ABARJWHHNCOEQT-UHFFFAOYSA-N</td>\n",
              "      <td>InChI=1S/C21H23NO/c1-16-8-3-5-11-18(16)21(14-1...</td>\n",
              "      <td>\"AuxInfo=1/0/N:19,1,21,13,22,14,9,20,12,10,23,...</td>\n",
              "      <td>P23975_WT</td>\n",
              "      <td>...</td>\n",
              "      <td>=</td>\n",
              "      <td>6.960</td>\n",
              "      <td>6.96</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.96</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Transporter-&gt;Electrochemical transporter-&gt;SLC ...</td>\n",
              "      <td>Homo sapiens (Human)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ABXUYGWOEIXKBD_on_P23975_WT</td>\n",
              "      <td>Medium</td>\n",
              "      <td>ChEMBL30</td>\n",
              "      <td>CHEMBL275212</td>\n",
              "      <td>COC(=O)C1C(c2ccc(C=CI)cc2)CC2CCC1N2C</td>\n",
              "      <td>ABXUYGWOEIXKBD</td>\n",
              "      <td>ABXUYGWOEIXKBD-UHFFFAOYSA-N</td>\n",
              "      <td>InChI=1S/C18H22INO2/c1-20-14-7-8-16(20)17(18(2...</td>\n",
              "      <td>\"AuxInfo=1/0/N:22,1,9,14,8,15,18,19,11,12,16,1...</td>\n",
              "      <td>P23975_WT</td>\n",
              "      <td>...</td>\n",
              "      <td>=</td>\n",
              "      <td>6.350</td>\n",
              "      <td>6.35</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.35</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Transporter-&gt;Electrochemical transporter-&gt;SLC ...</td>\n",
              "      <td>Homo sapiens (Human)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAADQEFQWNJHOZ_on_P23975_WT</td>\n",
              "      <td>High</td>\n",
              "      <td>ChEMBL30</td>\n",
              "      <td>CHEMBL336100</td>\n",
              "      <td>OC1(c2ccc(Cl)cc2)c2c(C3=NCCN31)c(F)ccc2</td>\n",
              "      <td>AAADQEFQWNJHOZ</td>\n",
              "      <td>AAADQEFQWNJHOZ-UHFFFAOYSA-N</td>\n",
              "      <td>InChI=1S/C16H12ClFN2O/c17-11-6-4-10(5-7-11)16(...</td>\n",
              "      <td>\"AuxInfo=1/0/N:20,21,19,4,9,5,8,14,15,3,6,10,1...</td>\n",
              "      <td>P23975_WT</td>\n",
              "      <td>...</td>\n",
              "      <td>=</td>\n",
              "      <td>9.05</td>\n",
              "      <td>9.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Transporter-&gt;Electrochemical transporter-&gt;SLC ...</td>\n",
              "      <td>Homo sapiens (Human)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 33 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7828cd9d-1f25-45cc-838d-780b8a4c9b51')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-7828cd9d-1f25-45cc-838d-780b8a4c9b51');\n",
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              "                                                     [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",
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              "  "
            ],
            "text/plain": [
              "                   Activity_ID Quality    source           CID  \\\n",
              "0  AAEKULYONKUBOZ_on_P23975_WT  Medium  ChEMBL30   CHEMBL14144   \n",
              "1  ABAOWFCFAADMKA_on_P23975_WT  Medium  ChEMBL30  CHEMBL456654   \n",
              "2  ABARJWHHNCOEQT_on_P23975_WT    High  ChEMBL30  CHEMBL141741   \n",
              "3  ABXUYGWOEIXKBD_on_P23975_WT  Medium  ChEMBL30  CHEMBL275212   \n",
              "4  AAADQEFQWNJHOZ_on_P23975_WT    High  ChEMBL30  CHEMBL336100   \n",
              "\n",
              "                                        SMILES    connectivity  \\\n",
              "0  CN1C2CCC1C(C(=O)Oc1ccccc1)C(c1ccc(Cl)cc1)C2  AAEKULYONKUBOZ   \n",
              "1         c1cnc(CC2(c3ccc4[nH]ccc4c3)CCNC2)cc1  ABAOWFCFAADMKA   \n",
              "2            CNCCC(Oc1cccc2ccccc12)c1c(C)cccc1  ABARJWHHNCOEQT   \n",
              "3         COC(=O)C1C(c2ccc(C=CI)cc2)CC2CCC1N2C  ABXUYGWOEIXKBD   \n",
              "4      OC1(c2ccc(Cl)cc2)c2c(C3=NCCN31)c(F)ccc2  AAADQEFQWNJHOZ   \n",
              "\n",
              "                      InChIKey  \\\n",
              "0  AAEKULYONKUBOZ-UHFFFAOYSA-N   \n",
              "1  ABAOWFCFAADMKA-UHFFFAOYSA-N   \n",
              "2  ABARJWHHNCOEQT-UHFFFAOYSA-N   \n",
              "3  ABXUYGWOEIXKBD-UHFFFAOYSA-N   \n",
              "4  AAADQEFQWNJHOZ-UHFFFAOYSA-N   \n",
              "\n",
              "                                               InChI  \\\n",
              "0  InChI=1S/C21H22ClNO2/c1-23-16-11-12-19(23)20(2...   \n",
              "1  InChI=1S/C18H19N3/c1-2-8-20-16(3-1)12-18(7-10-...   \n",
              "2  InChI=1S/C21H23NO/c1-16-8-3-5-11-18(16)21(14-1...   \n",
              "3  InChI=1S/C18H22INO2/c1-20-14-7-8-16(20)17(18(2...   \n",
              "4  InChI=1S/C16H12ClFN2O/c17-11-6-4-10(5-7-11)16(...   \n",
              "\n",
              "                                       InChI_AuxInfo  target_id  ... relation  \\\n",
              "0  \"AuxInfo=1/0/N:1,14,13,15,12,16,19,24,20,23,4,...  P23975_WT  ...        =   \n",
              "1  \"AuxInfo=1/0/N:21,1,20,8,9,13,16,2,12,17,15,5,...  P23975_WT  ...        =   \n",
              "2  \"AuxInfo=1/0/N:19,1,21,13,22,14,9,20,12,10,23,...  P23975_WT  ...        =   \n",
              "3  \"AuxInfo=1/0/N:22,1,9,14,8,15,18,19,11,12,16,1...  P23975_WT  ...        =   \n",
              "4  \"AuxInfo=1/0/N:20,21,19,4,9,5,8,14,15,3,6,10,1...  P23975_WT  ...        =   \n",
              "\n",
              "  pchembl_value pchembl_value_Mean pchembl_value_StdDev  pchembl_value_SEM  \\\n",
              "0         6.620               6.62                  0.0                0.0   \n",
              "1         8.100               8.10                  0.0                0.0   \n",
              "2         6.960               6.96                  0.0                0.0   \n",
              "3         6.350               6.35                  0.0                0.0   \n",
              "4          9.05               9.05                  0.0                0.0   \n",
              "\n",
              "  pchembl_value_N pchembl_value_Median pchembl_value_MAD  \\\n",
              "0             1.0                 6.62               0.0   \n",
              "1             1.0                 8.10               0.0   \n",
              "2             1.0                 6.96               0.0   \n",
              "3             1.0                 6.35               0.0   \n",
              "4             1.0                 9.05               0.0   \n",
              "\n",
              "                                      Classification              Organism  \n",
              "0  Transporter->Electrochemical transporter->SLC ...  Homo sapiens (Human)  \n",
              "1  Transporter->Electrochemical transporter->SLC ...  Homo sapiens (Human)  \n",
              "2  Transporter->Electrochemical transporter->SLC ...  Homo sapiens (Human)  \n",
              "3  Transporter->Electrochemical transporter->SLC ...  Homo sapiens (Human)  \n",
              "4  Transporter->Electrochemical transporter->SLC ...  Homo sapiens (Human)  \n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filter4.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkqBpIPe9rQn",
        "outputId": "b6ce4b81-4980-4841-c6e6-633ea34c1c32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of activity points: 52\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of activity points: {filter4.shape[0]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hoD1lCN49rQn"
      },
      "source": [
        "Remember that this result comes from only the first chunk of the entire dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YW4zRoYt9rQn"
      },
      "source": [
        "One can now save this dataframe like any other pandas object."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mKEl6hV9rQo"
      },
      "source": [
        "### Filtering iterators of dataframes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4NgDbHy9rQo"
      },
      "source": [
        "Now that the filtering capacity of the Papyrus scripts have been demonstrated for entire dataframes, we can try with chunked iterators."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Riimv82E9rQo"
      },
      "source": [
        "Let's first reinstanciate sample data. This time we will use a chunk size of 1,000,000."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cEG5tZEj9rQo"
      },
      "outputs": [],
      "source": [
        "sample_data = read_papyrus(is3d=False, chunksize=1000000, source_path=None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwFwEH4i9rQo"
      },
      "source": [
        "For this will will go through the same filters as above but iterate over the entire dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cONhxYG99rQo"
      },
      "outputs": [],
      "source": [
        "filter1_it = keep_quality(data=sample_data, min_quality='medium')\n",
        "filter2_it = keep_protein_class(data=filter1_it, protein_data=protein_data, classes=[{'l2': 'Ligand-gated ion channels'}, {'l3': 'SLC superfamily of solute carriers'}])\n",
        "filter3_it = keep_type(data=filter2_it, activity_types=['Ki', 'KD'])\n",
        "filter4_it = keep_organism(data=filter3_it, protein_data=protein_data, organism=['Human', 'Rat'], generic_regex=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKfQ8y2N9rQo"
      },
      "source": [
        "The filters do not get applied directly on chunked iterators and one can easily check that *filter4_it* is not a pandas dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntp7f6W99rQp",
        "outputId": "456caa76-70fc-4f8a-805b-485d93a88675"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<generator object _chunked_keep_organism at 0x7f700b46ead0>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filter4_it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1yzHeiQ9rQp"
      },
      "source": [
        "To apply the filters on the entire iterator, one needs to call *consume_chunks*.<br/>\n",
        "This function can be found under *papyrus_scripts.preprocess* just like the *keep_* functions used for filtering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AEQucm6_9rQp"
      },
      "outputs": [],
      "source": [
        "from papyrus_scripts.preprocess import consume_chunks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66NS_yFS9rQp"
      },
      "source": [
        "In order to follow progress of the filtering process, one needs to pass the total number of chunks the filters will go through.<br/>\n",
        "$Total = \\displaystyle \\Bigl \\lceil\\frac{Size_{dataset}}{chunksize}\\Bigl \\rceil $<br/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WL8c41F9rQp"
      },
      "source": [
        "In version 05.5 of the Papyrus dataset the number of compound-protein activity points depends on whether stereochemistry is used or not **(remember we discourage its usage)**.<br/>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdahg3kC9rQq"
      },
      "source": [
        "| Stereochemistry | Size of dataset | \n",
        "| :--- | :---: |\n",
        "| Without | 59,775,912 |\n",
        "| With (strongly discouraged) | 61,097,228 |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MBACZ0B9rQq"
      },
      "source": [
        "In this example $Total = \\displaystyle \\Bigl \\lceil \\frac{59,775,912}{1,000,000}\\Bigl \\rceil = 60 $<br/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "uQTByLf_9rQq",
        "outputId": "699fff6d-89ed-4081-f223-5e9149c29c12"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "447f771469c64beab08f0fe95e8bbd19",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/60 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "filtered_data = consume_chunks(filter4_it, progress=True, total=60)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5i4i9uo9rQq"
      },
      "source": [
        "Although this may take up to 30 minutes to filter the entire dataset, this is the ideal way to work with this dataset on laptops."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kecbmhu99rQq",
        "outputId": "2a741e2c-d6e1-4622-8a83-94ef48737b0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of activity points: 10614\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of activity points: {filtered_data.shape[0]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSi7UAPv9rQr"
      },
      "source": [
        "We hope these simple examples demonstrated how the Papyrus data can easily be filtered.\n",
        "Let's now focus on the modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXkIKvkM9rQr"
      },
      "source": [
        "## Modelling the bioactivity data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADoGuGaU9rQr"
      },
      "source": [
        "The Papyrus scripts allow for both quantitative structure-activity relationship (QSAR) and proteochemometrics (PCM) modelling.<br/>\n",
        "All functions related to modelling can be found under *papyrus_scripts.modelling*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sCCr99cn9rQr"
      },
      "source": [
        "**Disclaimer:**<br/>\n",
        "For now, only precomputed molecular descriptors can be used, preventing the use of models outside of Papyrus.<br/>\n",
        "This major flaw will be soon fixed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlzrJb0W9rQr"
      },
      "source": [
        "### QSAR models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kGjAoITA9rQr"
      },
      "outputs": [],
      "source": [
        "from papyrus_scripts.modelling import qsar\n",
        "import xgboost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqPfFyJF9rQs"
      },
      "source": [
        "Let us first restrict the data that we just extracted from Papyrus to the human serotonin receptor (accession P31645)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zL5ntwA_9rQs"
      },
      "outputs": [],
      "source": [
        "sample_data = read_papyrus(is3d=False, chunksize=1000000, source_path=None)\n",
        "filter1_it = keep_accession(sample_data, 'P31645')\n",
        "filter2_it = keep_quality(data=filter1_it, min_quality='medium')\n",
        "filter3_it = keep_type(data=filter2_it, activity_types=['Ki', 'KD'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "507764ad5a284629b1b0c616237261c7",
            "2186b78463ef4039a488712bc27b4e80",
            "2b7fcee57ed14b91ba812204049e576b",
            "b11745b8dd084269814b71085d4ae392",
            "52db2e30fada440f8eb94e64c7b29550",
            "452da4fbcbc84b88aefee74d4f71e9bb",
            "fc960ce771444da095d914c561d9f5b1",
            "9d43cb2d21e84ebeafbb046086ab4b37",
            "dfd166e7e1cf4f3f913a047aef9a7cc5",
            "4eb5faa66ca449928c63e6d8df0dd6f7",
            "5b1102aeab1b4160a2783815023586ce"
          ]
        },
        "id": "yGVnTPHq9rQs",
        "outputId": "6e4bff0e-3989-42aa-d1bf-d4e4f336a9a1",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "507764ad5a284629b1b0c616237261c7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/60 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "SLC6A4_data = consume_chunks(filter3_it, total=60)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jl8mJtxV9rQs"
      },
      "source": [
        "We will first create a regression model predicting the average pActivity values of a compound-target pair (i.e. *pchembl_value_Mean*).<br/>\n",
        "Let's ensure the version below is that given to the *download_papyrus* function above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bNkSLe289rQs"
      },
      "outputs": [],
      "source": [
        "reg_model = xgboost.XGBRegressor(verbosity=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "6cb286f5891c48d48d2116da7648027d",
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            "72a591463c334d5c93961cca8f67b3e3",
            "9b7be43c17434e2bbdd26376a3c7b549",
            "f45a3e9f35ac4d31b412bf1472f3b2f0",
            "ef85bbba42bb46048d52d073832c7553",
            "0f26e857a5f84882bb904ef701f9b465",
            "ff1d22d970ce4997bdc8fff1ce8202b5",
            "074dd928f26b4bd288a5e2e7ffe9b1b3",
            "3b255816d4ce4394a0eaa32d6bd2f334",
            "220a73fdb76d492eb3317f5635f6d9c8",
            "15d4e6987d044254bc9a4ad26f45758a",
            "cc1c8a09813b4861a0e790a9ad6bc51d",
            "212575abbbe941a39c2a9fe0b243fab5",
            "a8844017426e4c9e9ee6f319c1c31f03",
            "d51faa9102844776bebcd855b2899fbf",
            "78464444da79473899aca77f0fee00b0",
            "34bec449325e478aa1fe4fc9f335e5f2",
            "5285614ea7574a678e9344f1a4d7c19f",
            "5fcf9dda779a459ea7943e9818473b7e",
            "07dada30f92f4079b1d239e420d7c0e5"
          ]
        },
        "id": "WXVUsDZo9rQs",
        "outputId": "be4c7944-8647-48c3-f6b5-bbc16e10fb01",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6cb286f5891c48d48d2116da7648027d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading molecular descriptors: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "220a73fdb76d492eb3317f5635f6d9c8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "reg_results, trained_reg_model = qsar(data=SLC6A4_data,\n",
        "                                      version='latest',\n",
        "                                      endpoint='pchembl_value_Mean',\n",
        "                                      num_points=30,\n",
        "                                      delta_activity=2,\n",
        "                                      descriptors='mold2',\n",
        "                                      descriptor_chunksize=50000,\n",
        "                                      activity_threshold=6.5,\n",
        "                                      model=reg_model,\n",
        "                                      folds=5,\n",
        "                                      stratify=False,\n",
        "                                      split_by='Year',\n",
        "                                      split_year=2013,\n",
        "                                      test_set_size=0.30,\n",
        "                                      cluster_method=None,\n",
        "                                      custom_groups=None,\n",
        "                                      random_state=1234,\n",
        "                                      verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "70N2mZYg9rQt",
        "outputId": "6d68368d-0a04-4784-f057-c4afc153c2f8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c81aaaa4-c23e-41ce-ab37-b17d26d3c7b8\">\n",
              "    <div 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></th>\n",
              "      <th>number</th>\n",
              "      <th>R2</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MSLE</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAE</th>\n",
              "      <th>Explained Variance</th>\n",
              "      <th>Max Error</th>\n",
              "      <th>Mean Poisson Distrib</th>\n",
              "      <th>Mean Gamma Distrib</th>\n",
              "      <th>Pearson r</th>\n",
              "      <th>Spearman r</th>\n",
              "      <th>Kendall tau</th>\n",
              "      <th>R2_0 (pred. vs. obs.)</th>\n",
              "      <th>R'2_0 (obs. vs. pred.)</th>\n",
              "      <th>k slope (pred. vs obs.)</th>\n",
              "      <th>k' slope (obs. vs pred.)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">P31645_WT</th>\n",
              "      <th>Fold 1</th>\n",
              "      <td>340.000000</td>\n",
              "      <td>0.580407</td>\n",
              "      <td>0.652239</td>\n",
              "      <td>0.807613</td>\n",
              "      <td>0.010462</td>\n",
              "      <td>0.102284</td>\n",
              "      <td>0.634414</td>\n",
              "      <td>0.583283</td>\n",
              "      <td>2.657424</td>\n",
              "      <td>0.092378</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>0.772771</td>\n",
              "      <td>0.773205</td>\n",
              "      <td>0.583661</td>\n",
              "      <td>0.582030</td>\n",
              "      <td>0.023873</td>\n",
              "      <td>0.994862</td>\n",
              "      <td>0.993210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 2</th>\n",
              "      <td>340.000000</td>\n",
              "      <td>0.557454</td>\n",
              "      <td>0.677939</td>\n",
              "      <td>0.823370</td>\n",
              "      <td>0.010450</td>\n",
              "      <td>0.102226</td>\n",
              "      <td>0.639356</td>\n",
              "      <td>0.557867</td>\n",
              "      <td>2.608378</td>\n",
              "      <td>0.094541</td>\n",
              "      <td>0.013437</td>\n",
              "      <td>0.755860</td>\n",
              "      <td>0.751179</td>\n",
              "      <td>0.565002</td>\n",
              "      <td>0.558512</td>\n",
              "      <td>-0.054101</td>\n",
              "      <td>0.982790</td>\n",
              "      <td>1.005400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 3</th>\n",
              "      <td>339.000000</td>\n",
              "      <td>0.504417</td>\n",
              "      <td>0.694161</td>\n",
              "      <td>0.833163</td>\n",
              "      <td>0.010431</td>\n",
              "      <td>0.102132</td>\n",
              "      <td>0.644731</td>\n",
              "      <td>0.505198</td>\n",
              "      <td>3.762562</td>\n",
              "      <td>0.096742</td>\n",
              "      <td>0.013692</td>\n",
              "      <td>0.711255</td>\n",
              "      <td>0.709075</td>\n",
              "      <td>0.528609</td>\n",
              "      <td>0.505358</td>\n",
              "      <td>-0.030465</td>\n",
              "      <td>0.982535</td>\n",
              "      <td>1.004953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 4</th>\n",
              "      <td>339.000000</td>\n",
              "      <td>0.513831</td>\n",
              "      <td>0.703061</td>\n",
              "      <td>0.838487</td>\n",
              "      <td>0.010790</td>\n",
              "      <td>0.103876</td>\n",
              "      <td>0.650108</td>\n",
              "      <td>0.519101</td>\n",
              "      <td>2.484164</td>\n",
              "      <td>0.098324</td>\n",
              "      <td>0.013981</td>\n",
              "      <td>0.723922</td>\n",
              "      <td>0.726824</td>\n",
              "      <td>0.541035</td>\n",
              "      <td>0.520181</td>\n",
              "      <td>-0.081575</td>\n",
              "      <td>0.975088</td>\n",
              "      <td>1.012980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 5</th>\n",
              "      <td>339.000000</td>\n",
              "      <td>0.581554</td>\n",
              "      <td>0.686914</td>\n",
              "      <td>0.828803</td>\n",
              "      <td>0.011011</td>\n",
              "      <td>0.104934</td>\n",
              "      <td>0.650517</td>\n",
              "      <td>0.588812</td>\n",
              "      <td>2.680803</td>\n",
              "      <td>0.097488</td>\n",
              "      <td>0.014097</td>\n",
              "      <td>0.782011</td>\n",
              "      <td>0.778285</td>\n",
              "      <td>0.587943</td>\n",
              "      <td>0.586172</td>\n",
              "      <td>-0.050044</td>\n",
              "      <td>0.999018</td>\n",
              "      <td>0.988066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>339.400000</td>\n",
              "      <td>0.547533</td>\n",
              "      <td>0.682863</td>\n",
              "      <td>0.826287</td>\n",
              "      <td>0.010629</td>\n",
              "      <td>0.103090</td>\n",
              "      <td>0.643825</td>\n",
              "      <td>0.550852</td>\n",
              "      <td>2.838666</td>\n",
              "      <td>0.095895</td>\n",
              "      <td>0.013708</td>\n",
              "      <td>0.749164</td>\n",
              "      <td>0.747714</td>\n",
              "      <td>0.561250</td>\n",
              "      <td>0.550450</td>\n",
              "      <td>-0.038462</td>\n",
              "      <td>0.986858</td>\n",
              "      <td>1.000922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.447214</td>\n",
              "      <td>0.029809</td>\n",
              "      <td>0.015885</td>\n",
              "      <td>0.009658</td>\n",
              "      <td>0.000213</td>\n",
              "      <td>0.001027</td>\n",
              "      <td>0.005681</td>\n",
              "      <td>0.030644</td>\n",
              "      <td>0.426235</td>\n",
              "      <td>0.001973</td>\n",
              "      <td>0.000271</td>\n",
              "      <td>0.025017</td>\n",
              "      <td>0.024241</td>\n",
              "      <td>0.021225</td>\n",
              "      <td>0.029687</td>\n",
              "      <td>0.032112</td>\n",
              "      <td>0.008018</td>\n",
              "      <td>0.008231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Test set</th>\n",
              "      <td>312.000000</td>\n",
              "      <td>0.155997</td>\n",
              "      <td>1.031592</td>\n",
              "      <td>1.015673</td>\n",
              "      <td>0.015760</td>\n",
              "      <td>0.125540</td>\n",
              "      <td>0.828603</td>\n",
              "      <td>0.171819</td>\n",
              "      <td>3.090501</td>\n",
              "      <td>0.144501</td>\n",
              "      <td>0.020467</td>\n",
              "      <td>0.415540</td>\n",
              "      <td>0.377786</td>\n",
              "      <td>0.259750</td>\n",
              "      <td>0.171248</td>\n",
              "      <td>-2.959974</td>\n",
              "      <td>0.963168</td>\n",
              "      <td>1.018996</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c81aaaa4-c23e-41ce-ab37-b17d26d3c7b8')\"\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 0 24 24\"\n",
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            "text/plain": [
              "                        number        R2       MSE      RMSE      MSLE  \\\n",
              "target                                                                   \n",
              "P31645_WT Fold 1    340.000000  0.580407  0.652239  0.807613  0.010462   \n",
              "          Fold 2    340.000000  0.557454  0.677939  0.823370  0.010450   \n",
              "          Fold 3    339.000000  0.504417  0.694161  0.833163  0.010431   \n",
              "          Fold 4    339.000000  0.513831  0.703061  0.838487  0.010790   \n",
              "          Fold 5    339.000000  0.581554  0.686914  0.828803  0.011011   \n",
              "          Mean      339.400000  0.547533  0.682863  0.826287  0.010629   \n",
              "          SD          0.447214  0.029809  0.015885  0.009658  0.000213   \n",
              "          Test set  312.000000  0.155997  1.031592  1.015673  0.015760   \n",
              "\n",
              "                       RMSLE       MAE  Explained Variance  Max Error  \\\n",
              "target                                                                  \n",
              "P31645_WT Fold 1    0.102284  0.634414            0.583283   2.657424   \n",
              "          Fold 2    0.102226  0.639356            0.557867   2.608378   \n",
              "          Fold 3    0.102132  0.644731            0.505198   3.762562   \n",
              "          Fold 4    0.103876  0.650108            0.519101   2.484164   \n",
              "          Fold 5    0.104934  0.650517            0.588812   2.680803   \n",
              "          Mean      0.103090  0.643825            0.550852   2.838666   \n",
              "          SD        0.001027  0.005681            0.030644   0.426235   \n",
              "          Test set  0.125540  0.828603            0.171819   3.090501   \n",
              "\n",
              "                    Mean Poisson Distrib  Mean Gamma Distrib  Pearson r  \\\n",
              "target                                                                    \n",
              "P31645_WT Fold 1                0.092378            0.013333   0.772771   \n",
              "          Fold 2                0.094541            0.013437   0.755860   \n",
              "          Fold 3                0.096742            0.013692   0.711255   \n",
              "          Fold 4                0.098324            0.013981   0.723922   \n",
              "          Fold 5                0.097488            0.014097   0.782011   \n",
              "          Mean                  0.095895            0.013708   0.749164   \n",
              "          SD                    0.001973            0.000271   0.025017   \n",
              "          Test set              0.144501            0.020467   0.415540   \n",
              "\n",
              "                    Spearman r  Kendall tau  R2_0 (pred. vs. obs.)  \\\n",
              "target                                                               \n",
              "P31645_WT Fold 1      0.773205     0.583661               0.582030   \n",
              "          Fold 2      0.751179     0.565002               0.558512   \n",
              "          Fold 3      0.709075     0.528609               0.505358   \n",
              "          Fold 4      0.726824     0.541035               0.520181   \n",
              "          Fold 5      0.778285     0.587943               0.586172   \n",
              "          Mean        0.747714     0.561250               0.550450   \n",
              "          SD          0.024241     0.021225               0.029687   \n",
              "          Test set    0.377786     0.259750               0.171248   \n",
              "\n",
              "                    R'2_0 (obs. vs. pred.)  k slope (pred. vs obs.)  \\\n",
              "target                                                                \n",
              "P31645_WT Fold 1                  0.023873                 0.994862   \n",
              "          Fold 2                 -0.054101                 0.982790   \n",
              "          Fold 3                 -0.030465                 0.982535   \n",
              "          Fold 4                 -0.081575                 0.975088   \n",
              "          Fold 5                 -0.050044                 0.999018   \n",
              "          Mean                   -0.038462                 0.986858   \n",
              "          SD                      0.032112                 0.008018   \n",
              "          Test set               -2.959974                 0.963168   \n",
              "\n",
              "                    k' slope (obs. vs pred.)  \n",
              "target                                        \n",
              "P31645_WT Fold 1                    0.993210  \n",
              "          Fold 2                    1.005400  \n",
              "          Fold 3                    1.004953  \n",
              "          Fold 4                    1.012980  \n",
              "          Fold 5                    0.988066  \n",
              "          Mean                      1.000922  \n",
              "          SD                        0.008231  \n",
              "          Test set                  1.018996  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reg_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDivxDxr9rQt"
      },
      "source": [
        "When looking at average R<sup>2</sup>, performance over cross-validation is correct but the model show very little capacity to predict the temporally split test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Uw0xU9L9rQt"
      },
      "source": [
        "To train a classifier, all that is needed is to change the type of model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pCAoIgId9rQt"
      },
      "outputs": [],
      "source": [
        "cls_model = xgboost.XGBClassifier(verbosity=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185,
          "referenced_widgets": [
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            "d3018e724d3f4aa7b55effd220c324c7"
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        },
        "id": "a_vNaeYt9rQt",
        "outputId": "7dde7af4-e0d1-4011-fc03-fc7a10d09994"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e4e10ec05a94316ac4a6cec14c7a4b0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading molecular descriptors: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b44056e26c764f7a8d8ca0c38c9709d5",
              "version_major": 2,
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            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "cls_results, trained_cls_model = qsar(data=SLC6A4_data,\n",
        "                                      version='latest',\n",
        "                                      endpoint='pchembl_value_Mean',\n",
        "                                      num_points=30,\n",
        "                                      delta_activity=2,\n",
        "                                      descriptors='mold2',\n",
        "                                      descriptor_chunksize=50000,\n",
        "                                      activity_threshold=6.5,\n",
        "                                      model=cls_model,\n",
        "                                      folds=5,\n",
        "                                      stratify=False,\n",
        "                                      split_by='Year',\n",
        "                                      split_year=2013,\n",
        "                                      test_set_size=0.30,\n",
        "                                      cluster_method=None,\n",
        "                                      custom_groups=None,\n",
        "                                      random_state=1234,\n",
        "                                      verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "Jt-4D5gd9rQu",
        "outputId": "6752f0ef-4027-49c5-fba3-89de0b2c144c"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>MCC</th>\n",
              "      <th>0:1</th>\n",
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              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th rowspan=\"8\" valign=\"top\">P31645_WT</th>\n",
              "      <th>Fold 1</th>\n",
              "      <td>0.534390</td>\n",
              "      <td>240:100</td>\n",
              "      <td>0.817647</td>\n",
              "      <td>0.739583</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.929167</td>\n",
              "      <td>0.763889</td>\n",
              "      <td>0.832090</td>\n",
              "      <td>0.639535</td>\n",
              "      <td>0.148417</td>\n",
              "      <td>0.851583</td>\n",
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              "      <th>Fold 2</th>\n",
              "      <td>0.475926</td>\n",
              "      <td>249:91</td>\n",
              "      <td>0.808824</td>\n",
              "      <td>0.712587</td>\n",
              "      <td>0.505495</td>\n",
              "      <td>0.919679</td>\n",
              "      <td>0.696970</td>\n",
              "      <td>0.835766</td>\n",
              "      <td>0.585987</td>\n",
              "      <td>0.132001</td>\n",
              "      <td>0.867999</td>\n",
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              "    <tr>\n",
              "      <th>Fold 3</th>\n",
              "      <td>0.492580</td>\n",
              "      <td>245:94</td>\n",
              "      <td>0.811209</td>\n",
              "      <td>0.715306</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.930612</td>\n",
              "      <td>0.734375</td>\n",
              "      <td>0.829091</td>\n",
              "      <td>0.594937</td>\n",
              "      <td>0.151585</td>\n",
              "      <td>0.848415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 4</th>\n",
              "      <td>0.484068</td>\n",
              "      <td>240:99</td>\n",
              "      <td>0.802360</td>\n",
              "      <td>0.703157</td>\n",
              "      <td>0.464646</td>\n",
              "      <td>0.941667</td>\n",
              "      <td>0.766667</td>\n",
              "      <td>0.810036</td>\n",
              "      <td>0.578616</td>\n",
              "      <td>0.181229</td>\n",
              "      <td>0.818771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 5</th>\n",
              "      <td>0.595201</td>\n",
              "      <td>242:97</td>\n",
              "      <td>0.843658</td>\n",
              "      <td>0.757689</td>\n",
              "      <td>0.556701</td>\n",
              "      <td>0.958678</td>\n",
              "      <td>0.843750</td>\n",
              "      <td>0.843636</td>\n",
              "      <td>0.670807</td>\n",
              "      <td>0.086692</td>\n",
              "      <td>0.913308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.516433</td>\n",
              "      <td>-</td>\n",
              "      <td>0.816740</td>\n",
              "      <td>0.725664</td>\n",
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              "      <td>0.613977</td>\n",
              "      <td>0.139985</td>\n",
              "      <td>0.860015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.040382</td>\n",
              "      <td>-</td>\n",
              "      <td>0.013073</td>\n",
              "      <td>0.018277</td>\n",
              "      <td>0.031129</td>\n",
              "      <td>0.012169</td>\n",
              "      <td>0.044153</td>\n",
              "      <td>0.010189</td>\n",
              "      <td>0.032350</td>\n",
              "      <td>0.028316</td>\n",
              "      <td>0.028316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Test set</th>\n",
              "      <td>0.215737</td>\n",
              "      <td>236:76</td>\n",
              "      <td>0.756410</td>\n",
              "      <td>0.580285</td>\n",
              "      <td>0.236842</td>\n",
              "      <td>0.923729</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.789855</td>\n",
              "      <td>0.321429</td>\n",
              "      <td>0.405274</td>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ce552c2-ee46-471d-bcf2-15d8b237b48a 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-2ce552c2-ee46-471d-bcf2-15d8b237b48a');\n",
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              "            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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                         MCC      0:1       ACC      BACC  Sensitivity  \\\n",
              "target                                                                   \n",
              "P31645_WT Fold 1    0.534390  240:100  0.817647  0.739583     0.550000   \n",
              "          Fold 2    0.475926   249:91  0.808824  0.712587     0.505495   \n",
              "          Fold 3    0.492580   245:94  0.811209  0.715306     0.500000   \n",
              "          Fold 4    0.484068   240:99  0.802360  0.703157     0.464646   \n",
              "          Fold 5    0.595201   242:97  0.843658  0.757689     0.556701   \n",
              "          Mean      0.516433        -  0.816740  0.725664     0.515368   \n",
              "          SD        0.040382        -  0.013073  0.018277     0.031129   \n",
              "          Test set  0.215737   236:76  0.756410  0.580285     0.236842   \n",
              "\n",
              "                    Specificity       PPV       NPV        F1     AUC 0  \\\n",
              "target                                                                    \n",
              "P31645_WT Fold 1       0.929167  0.763889  0.832090  0.639535  0.148417   \n",
              "          Fold 2       0.919679  0.696970  0.835766  0.585987  0.132001   \n",
              "          Fold 3       0.930612  0.734375  0.829091  0.594937  0.151585   \n",
              "          Fold 4       0.941667  0.766667  0.810036  0.578616  0.181229   \n",
              "          Fold 5       0.958678  0.843750  0.843636  0.670807  0.086692   \n",
              "          Mean         0.935960  0.761130  0.830124  0.613977  0.139985   \n",
              "          SD           0.012169  0.044153  0.010189  0.032350  0.028316   \n",
              "          Test set     0.923729  0.500000  0.789855  0.321429  0.405274   \n",
              "\n",
              "                       AUC 1  \n",
              "target                        \n",
              "P31645_WT Fold 1    0.851583  \n",
              "          Fold 2    0.867999  \n",
              "          Fold 3    0.848415  \n",
              "          Fold 4    0.818771  \n",
              "          Fold 5    0.913308  \n",
              "          Mean      0.860015  \n",
              "          SD        0.028316  \n",
              "          Test set  0.594726  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cls_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3H7bAh8f9rQu"
      },
      "source": [
        "Looking at the active to inactive ratio (i.e. A:N) one can clearly identify the reason of this low prediction performance over the test set.<br/>\n",
        "Oversampling and/or undersampling techniques could help the model better identify the boundary between actives and inactives in the mmecular descriptor space.<br/>\n",
        "However the use of such techniques is not the focus here."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kj6tiAD69rQu"
      },
      "source": [
        "### PCM models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zk05kzZ99rQu"
      },
      "outputs": [],
      "source": [
        "from papyrus_scripts.modelling import pcm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THMsKTiN9rQu"
      },
      "source": [
        "Let us see if including Rat data improves the quality of the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b9UwuDAW9rQu"
      },
      "outputs": [],
      "source": [
        "sample_data = read_papyrus(is3d=False, chunksize=1000000, source_path=None)\n",
        "filter1_it = keep_accession(sample_data, ['P31645', 'P31652'])\n",
        "filter2_it = keep_quality(data=filter1_it, min_quality='medium')\n",
        "filter3_it = keep_type(data=filter2_it, activity_types=['Ki', 'KD'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4120462b95534c0faef487878dbee5f4",
            "27cd9d26cec448f4ab457c893aa8f644",
            "2c2983e87b564f4a84beef9bacbaa15b",
            "ebf852f78f8c4f3e9ce1a85cc7627d1a",
            "58d1044c10e54006aca7e25393d96d91",
            "fc533e6f17754ae9adbb173b1033f719",
            "2d03114493b9438f85527931d7791aca",
            "62534e93e72744a1827f3826460ae122",
            "ff73c47a8f4b4e188f8b457661b8ba8e",
            "a5f1dac555ad46f7b04585e1f4aa6ce7",
            "365031c93a2343349ae9cc41b56e0d97"
          ]
        },
        "id": "-qL70o2-9rQu",
        "outputId": "eaeddb17-9f8f-4b10-9c4e-98a76d26172f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4120462b95534c0faef487878dbee5f4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/60 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "SLC6A4_human_rat = consume_chunks(filter3_it, total=60)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TsOIFSS_9rQv"
      },
      "source": [
        "Let's ensure the version below is that given to the *download_papyrus* function above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EJ7fNYx69rQv"
      },
      "outputs": [],
      "source": [
        "pcm_reg_model = xgboost.XGBRegressor(verbosity=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "ba006ebe770e456ba4aedd1246dcd90a",
            "5eb4771eea3942e185b06297c25c323b",
            "5e81b0afb53847248bfa842c01f34e2d",
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            "603fe12e44cb47c28711da49bca708c6",
            "b2c22b69868b4a9d8c091ce716955598",
            "e687336ad3144e9db3b1b87887a70581",
            "4cfce5de86684744b4b316fb191f0b4e",
            "35d1d92b0b0d40e3a278f9c9ffb59aa1",
            "c8b2b42ff3ee44fc80a762d24043dd8c",
            "866cd8ee21f34c8ab7f5d312e9ff41d1",
            "11b7762b47b34219a42c3f06a34d0382",
            "c9b5941e0c414c50884fe6885f33d2d0",
            "2d4b58c16cfa49928371b0eb7a500e3d",
            "35ede6f06842409d88f978038d062966",
            "814c6b3c7e5d4e01a53f719ecb254fb9",
            "3fe046ead069478ca442264c1893bc09",
            "c8402bfa7d3b44cc88ac04787ebd51e0",
            "2844ce98a7d840c8a8ff6517019035e2",
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            "c6bc149a4ef748d3b5a9711816bad77b",
            "725c3dbd03b942e7a994aff87ced85a3",
            "1d8f276030104aa6a558b7980c8846a4",
            "9945213d251445379da0d0d3530badad",
            "15f7be00251e4782aabadade04cf845e",
            "842f2a789da0423daf25c24f4d2c03a5",
            "0e61074247d1407eb9bae407f800a88d",
            "a50d097ad4274d8e930d5e6ea2cc4a68"
          ]
        },
        "id": "ogjKLxOr9rQv",
        "outputId": "b51c2ed7-dde0-47a9-9528-41432e4493a4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5beab59f1c114589b9933c61fcc5f7d9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading molecular descriptors: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e687336ad3144e9db3b1b87887a70581",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading protein descriptors: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8402bfa7d3b44cc88ac04787ebd51e0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Fitting model:   0%|          | 0/6 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pcm_reg_results, pcm_reg_trained_model = pcm(data=SLC6A4_human_rat,\n",
        "                                             version='latest',\n",
        "                                             endpoint='pchembl_value_Mean',\n",
        "                                             num_points=30,\n",
        "                                             delta_activity=2,\n",
        "                                             mol_descriptors='mold2',\n",
        "                                             mol_descriptor_chunksize=50000,\n",
        "                                             prot_descriptors='unirep',\n",
        "                                             prot_descriptor_chunksize=50000,\n",
        "                                             activity_threshold=6.5,\n",
        "                                             model=pcm_reg_model,\n",
        "                                             folds=5,\n",
        "                                             stratify=False,\n",
        "                                             split_by='Year',\n",
        "                                             split_year=2013,\n",
        "                                             test_set_size=0.30,\n",
        "                                             cluster_method=None,\n",
        "                                             custom_groups=None,\n",
        "                                             random_state=1234,\n",
        "                                             verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "-8flzUgc9rQv",
        "outputId": "1e05cf98-2078-4871-eb44-be8603323901"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a3aa37fb-3c22-41fa-97d2-bca35f5b187f\">\n",
              "    <div 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>number</th>\n",
              "      <th>R2</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MSLE</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAE</th>\n",
              "      <th>Explained Variance</th>\n",
              "      <th>Max Error</th>\n",
              "      <th>Mean Poisson Distrib</th>\n",
              "      <th>Mean Gamma Distrib</th>\n",
              "      <th>Pearson r</th>\n",
              "      <th>Spearman r</th>\n",
              "      <th>Kendall tau</th>\n",
              "      <th>R2_0 (pred. vs. obs.)</th>\n",
              "      <th>R'2_0 (obs. vs. pred.)</th>\n",
              "      <th>k slope (pred. vs obs.)</th>\n",
              "      <th>k' slope (obs. vs pred.)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fold 1</th>\n",
              "      <td>753.000000</td>\n",
              "      <td>0.606211</td>\n",
              "      <td>0.605585</td>\n",
              "      <td>0.778193</td>\n",
              "      <td>0.009173</td>\n",
              "      <td>0.095775</td>\n",
              "      <td>0.623949</td>\n",
              "      <td>0.606288</td>\n",
              "      <td>2.775585</td>\n",
              "      <td>0.083799</td>\n",
              "      <td>0.011827</td>\n",
              "      <td>0.795078</td>\n",
              "      <td>0.799634</td>\n",
              "      <td>0.599757</td>\n",
              "      <td>0.606884</td>\n",
              "      <td>0.041993</td>\n",
              "      <td>0.984664</td>\n",
              "      <td>1.004386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 2</th>\n",
              "      <td>753.000000</td>\n",
              "      <td>0.590141</td>\n",
              "      <td>0.560038</td>\n",
              "      <td>0.748357</td>\n",
              "      <td>0.008257</td>\n",
              "      <td>0.090869</td>\n",
              "      <td>0.582737</td>\n",
              "      <td>0.590141</td>\n",
              "      <td>2.891541</td>\n",
              "      <td>0.076390</td>\n",
              "      <td>0.010609</td>\n",
              "      <td>0.776346</td>\n",
              "      <td>0.769685</td>\n",
              "      <td>0.577590</td>\n",
              "      <td>0.590281</td>\n",
              "      <td>0.084164</td>\n",
              "      <td>0.988024</td>\n",
              "      <td>1.001869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 3</th>\n",
              "      <td>753.000000</td>\n",
              "      <td>0.532261</td>\n",
              "      <td>0.718617</td>\n",
              "      <td>0.847713</td>\n",
              "      <td>0.011385</td>\n",
              "      <td>0.106703</td>\n",
              "      <td>0.652612</td>\n",
              "      <td>0.533351</td>\n",
              "      <td>3.907982</td>\n",
              "      <td>0.101642</td>\n",
              "      <td>0.014687</td>\n",
              "      <td>0.735989</td>\n",
              "      <td>0.750343</td>\n",
              "      <td>0.554585</td>\n",
              "      <td>0.532784</td>\n",
              "      <td>-0.117698</td>\n",
              "      <td>0.990551</td>\n",
              "      <td>0.996132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 4</th>\n",
              "      <td>752.000000</td>\n",
              "      <td>0.573919</td>\n",
              "      <td>0.631884</td>\n",
              "      <td>0.794911</td>\n",
              "      <td>0.009590</td>\n",
              "      <td>0.097926</td>\n",
              "      <td>0.617346</td>\n",
              "      <td>0.575788</td>\n",
              "      <td>2.608555</td>\n",
              "      <td>0.087533</td>\n",
              "      <td>0.012340</td>\n",
              "      <td>0.764234</td>\n",
              "      <td>0.760647</td>\n",
              "      <td>0.568646</td>\n",
              "      <td>0.576741</td>\n",
              "      <td>0.093034</td>\n",
              "      <td>0.980021</td>\n",
              "      <td>1.008820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 5</th>\n",
              "      <td>752.000000</td>\n",
              "      <td>0.566278</td>\n",
              "      <td>0.678797</td>\n",
              "      <td>0.823892</td>\n",
              "      <td>0.010528</td>\n",
              "      <td>0.102604</td>\n",
              "      <td>0.641529</td>\n",
              "      <td>0.566558</td>\n",
              "      <td>3.013564</td>\n",
              "      <td>0.094844</td>\n",
              "      <td>0.013521</td>\n",
              "      <td>0.763478</td>\n",
              "      <td>0.766329</td>\n",
              "      <td>0.573221</td>\n",
              "      <td>0.566286</td>\n",
              "      <td>-0.061014</td>\n",
              "      <td>0.987975</td>\n",
              "      <td>0.999529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>752.600000</td>\n",
              "      <td>0.573762</td>\n",
              "      <td>0.638984</td>\n",
              "      <td>0.798613</td>\n",
              "      <td>0.009787</td>\n",
              "      <td>0.098775</td>\n",
              "      <td>0.623635</td>\n",
              "      <td>0.574425</td>\n",
              "      <td>3.039445</td>\n",
              "      <td>0.088842</td>\n",
              "      <td>0.012597</td>\n",
              "      <td>0.767025</td>\n",
              "      <td>0.769328</td>\n",
              "      <td>0.574760</td>\n",
              "      <td>0.574595</td>\n",
              "      <td>0.008096</td>\n",
              "      <td>0.986247</td>\n",
              "      <td>1.002147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.447214</td>\n",
              "      <td>0.022729</td>\n",
              "      <td>0.050539</td>\n",
              "      <td>0.031639</td>\n",
              "      <td>0.000988</td>\n",
              "      <td>0.004996</td>\n",
              "      <td>0.021879</td>\n",
              "      <td>0.022409</td>\n",
              "      <td>0.414767</td>\n",
              "      <td>0.007980</td>\n",
              "      <td>0.001280</td>\n",
              "      <td>0.017595</td>\n",
              "      <td>0.015074</td>\n",
              "      <td>0.013414</td>\n",
              "      <td>0.022768</td>\n",
              "      <td>0.076103</td>\n",
              "      <td>0.003315</td>\n",
              "      <td>0.003930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Test set</th>\n",
              "      <td>632.000000</td>\n",
              "      <td>0.305520</td>\n",
              "      <td>0.950761</td>\n",
              "      <td>0.975070</td>\n",
              "      <td>0.016073</td>\n",
              "      <td>0.126781</td>\n",
              "      <td>0.787663</td>\n",
              "      <td>0.318992</td>\n",
              "      <td>3.397805</td>\n",
              "      <td>0.139213</td>\n",
              "      <td>0.020729</td>\n",
              "      <td>0.564794</td>\n",
              "      <td>0.545691</td>\n",
              "      <td>0.384183</td>\n",
              "      <td>0.318892</td>\n",
              "      <td>-1.170678</td>\n",
              "      <td>1.000164</td>\n",
              "      <td>0.980907</td>\n",
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            "text/plain": [
              "              number        R2       MSE      RMSE      MSLE     RMSLE  \\\n",
              "Fold 1    753.000000  0.606211  0.605585  0.778193  0.009173  0.095775   \n",
              "Fold 2    753.000000  0.590141  0.560038  0.748357  0.008257  0.090869   \n",
              "Fold 3    753.000000  0.532261  0.718617  0.847713  0.011385  0.106703   \n",
              "Fold 4    752.000000  0.573919  0.631884  0.794911  0.009590  0.097926   \n",
              "Fold 5    752.000000  0.566278  0.678797  0.823892  0.010528  0.102604   \n",
              "Mean      752.600000  0.573762  0.638984  0.798613  0.009787  0.098775   \n",
              "SD          0.447214  0.022729  0.050539  0.031639  0.000988  0.004996   \n",
              "Test set  632.000000  0.305520  0.950761  0.975070  0.016073  0.126781   \n",
              "\n",
              "               MAE  Explained Variance  Max Error  Mean Poisson Distrib  \\\n",
              "Fold 1    0.623949            0.606288   2.775585              0.083799   \n",
              "Fold 2    0.582737            0.590141   2.891541              0.076390   \n",
              "Fold 3    0.652612            0.533351   3.907982              0.101642   \n",
              "Fold 4    0.617346            0.575788   2.608555              0.087533   \n",
              "Fold 5    0.641529            0.566558   3.013564              0.094844   \n",
              "Mean      0.623635            0.574425   3.039445              0.088842   \n",
              "SD        0.021879            0.022409   0.414767              0.007980   \n",
              "Test set  0.787663            0.318992   3.397805              0.139213   \n",
              "\n",
              "          Mean Gamma Distrib  Pearson r  Spearman r  Kendall tau  \\\n",
              "Fold 1              0.011827   0.795078    0.799634     0.599757   \n",
              "Fold 2              0.010609   0.776346    0.769685     0.577590   \n",
              "Fold 3              0.014687   0.735989    0.750343     0.554585   \n",
              "Fold 4              0.012340   0.764234    0.760647     0.568646   \n",
              "Fold 5              0.013521   0.763478    0.766329     0.573221   \n",
              "Mean                0.012597   0.767025    0.769328     0.574760   \n",
              "SD                  0.001280   0.017595    0.015074     0.013414   \n",
              "Test set            0.020729   0.564794    0.545691     0.384183   \n",
              "\n",
              "          R2_0 (pred. vs. obs.)  R'2_0 (obs. vs. pred.)  \\\n",
              "Fold 1                 0.606884                0.041993   \n",
              "Fold 2                 0.590281                0.084164   \n",
              "Fold 3                 0.532784               -0.117698   \n",
              "Fold 4                 0.576741                0.093034   \n",
              "Fold 5                 0.566286               -0.061014   \n",
              "Mean                   0.574595                0.008096   \n",
              "SD                     0.022768                0.076103   \n",
              "Test set               0.318892               -1.170678   \n",
              "\n",
              "          k slope (pred. vs obs.)  k' slope (obs. vs pred.)  \n",
              "Fold 1                   0.984664                  1.004386  \n",
              "Fold 2                   0.988024                  1.001869  \n",
              "Fold 3                   0.990551                  0.996132  \n",
              "Fold 4                   0.980021                  1.008820  \n",
              "Fold 5                   0.987975                  0.999529  \n",
              "Mean                     0.986247                  1.002147  \n",
              "SD                       0.003315                  0.003930  \n",
              "Test set                 1.000164                  0.980907  "
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pcm_reg_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wO3DbXNg9rQv"
      },
      "source": [
        "As with QSAR models, training a classifier is a matter of changing the underlying model to be used."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CY6oehW09rQv"
      },
      "outputs": [],
      "source": [
        "pcm_cls_model = xgboost.XGBClassifier(verbosity=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "dn8RIx_b9rQw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "ac6325444bfc440c87b3eb7264527c27",
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            "a64ebe5d77874f16948ecaaaa3a0d01a",
            "27654cdb684c4a04baff27c4378f43ab",
            "01c6ca209d80467f894efbab0aeaf73e",
            "502316df8ee14ba394f1549c3cedbab4",
            "bf48612432e14c7896a1953983b3362f",
            "84113dc7573540c98350e6f67ba405b4",
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            "1dede7521e934fc88f70779913124025",
            "080cc15e886b40f18b541b5699f07d38"
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        },
        "outputId": "84f7d82a-bcd6-49ec-a9fd-1b3ce8f0e73a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac6325444bfc440c87b3eb7264527c27",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading molecular descriptors: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading protein descriptors: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2725c367ef3f451490b7d373707992a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fitting model:   0%|          | 0/6 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "22af7d42a2d9482092e75b0a987501c5"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "pcm_cls_results, pcm_cls_trained_model = pcm(data=SLC6A4_human_rat,\n",
        "                                             version='latest',\n",
        "                                             endpoint='pchembl_value_Mean',\n",
        "                                             num_points=30,\n",
        "                                             delta_activity=2,\n",
        "                                             mol_descriptors='mold2',\n",
        "                                             mol_descriptor_chunksize=50000,\n",
        "                                             prot_descriptors='unirep',\n",
        "                                             prot_descriptor_chunksize=50000,\n",
        "                                             activity_threshold=6.5,\n",
        "                                             model=pcm_cls_model,\n",
        "                                             folds=5,\n",
        "                                             stratify=False,\n",
        "                                             split_by='Year',\n",
        "                                             split_year=2013,\n",
        "                                             test_set_size=0.30,\n",
        "                                             cluster_method=None,\n",
        "                                             custom_groups=None,\n",
        "                                             random_state=1234,\n",
        "                                             verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "-16AwbtP9rQw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "228b2724-c939-428d-d153-55e4944383bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               MCC      0:1       ACC      BACC  Sensitivity  Specificity  \\\n",
              "Fold 1    0.593308  528:225  0.837981  0.759495     0.564444     0.954545   \n",
              "Fold 2    0.600927  545:208  0.847278  0.781528     0.634615     0.928440   \n",
              "Fold 3    0.554393  530:223  0.824701  0.744297     0.547085     0.941509   \n",
              "Fold 4    0.582625  539:213  0.840426  0.750965     0.544601     0.957328   \n",
              "Fold 5    0.608263  538:214  0.848404  0.775857     0.607477     0.944238   \n",
              "Mean      0.587903        -  0.839758  0.762428     0.579645     0.945212   \n",
              "SD        0.017147        -  0.007763  0.012999     0.032444     0.009397   \n",
              "Test set  0.338444  391:241  0.702532  0.645775     0.406639     0.884910   \n",
              "\n",
              "               PPV       NPV        F1     AUC 0     AUC 1  \n",
              "Fold 1    0.841060  0.837209  0.675532  0.102689  0.897311  \n",
              "Fold 2    0.771930  0.869416  0.696570  0.111133  0.888867  \n",
              "Fold 3    0.797386  0.831667  0.648936  0.096438  0.903562  \n",
              "Fold 4    0.834532  0.841762  0.659091  0.113164  0.886836  \n",
              "Fold 5    0.812500  0.858108  0.695187  0.091148  0.908852  \n",
              "Mean      0.811481  0.847632  0.675063  0.102914  0.897086  \n",
              "SD        0.022986  0.012795  0.017347  0.007671  0.007671  \n",
              "Test set  0.685315  0.707566  0.510417  0.272187  0.727813  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8025ee67-6874-4cf6-a7d4-3900ef09778b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MCC</th>\n",
              "      <th>0:1</th>\n",
              "      <th>ACC</th>\n",
              "      <th>BACC</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "      <th>PPV</th>\n",
              "      <th>NPV</th>\n",
              "      <th>F1</th>\n",
              "      <th>AUC 0</th>\n",
              "      <th>AUC 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fold 1</th>\n",
              "      <td>0.593308</td>\n",
              "      <td>528:225</td>\n",
              "      <td>0.837981</td>\n",
              "      <td>0.759495</td>\n",
              "      <td>0.564444</td>\n",
              "      <td>0.954545</td>\n",
              "      <td>0.841060</td>\n",
              "      <td>0.837209</td>\n",
              "      <td>0.675532</td>\n",
              "      <td>0.102689</td>\n",
              "      <td>0.897311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 2</th>\n",
              "      <td>0.600927</td>\n",
              "      <td>545:208</td>\n",
              "      <td>0.847278</td>\n",
              "      <td>0.781528</td>\n",
              "      <td>0.634615</td>\n",
              "      <td>0.928440</td>\n",
              "      <td>0.771930</td>\n",
              "      <td>0.869416</td>\n",
              "      <td>0.696570</td>\n",
              "      <td>0.111133</td>\n",
              "      <td>0.888867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 3</th>\n",
              "      <td>0.554393</td>\n",
              "      <td>530:223</td>\n",
              "      <td>0.824701</td>\n",
              "      <td>0.744297</td>\n",
              "      <td>0.547085</td>\n",
              "      <td>0.941509</td>\n",
              "      <td>0.797386</td>\n",
              "      <td>0.831667</td>\n",
              "      <td>0.648936</td>\n",
              "      <td>0.096438</td>\n",
              "      <td>0.903562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 4</th>\n",
              "      <td>0.582625</td>\n",
              "      <td>539:213</td>\n",
              "      <td>0.840426</td>\n",
              "      <td>0.750965</td>\n",
              "      <td>0.544601</td>\n",
              "      <td>0.957328</td>\n",
              "      <td>0.834532</td>\n",
              "      <td>0.841762</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.113164</td>\n",
              "      <td>0.886836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold 5</th>\n",
              "      <td>0.608263</td>\n",
              "      <td>538:214</td>\n",
              "      <td>0.848404</td>\n",
              "      <td>0.775857</td>\n",
              "      <td>0.607477</td>\n",
              "      <td>0.944238</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.858108</td>\n",
              "      <td>0.695187</td>\n",
              "      <td>0.091148</td>\n",
              "      <td>0.908852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.587903</td>\n",
              "      <td>-</td>\n",
              "      <td>0.839758</td>\n",
              "      <td>0.762428</td>\n",
              "      <td>0.579645</td>\n",
              "      <td>0.945212</td>\n",
              "      <td>0.811481</td>\n",
              "      <td>0.847632</td>\n",
              "      <td>0.675063</td>\n",
              "      <td>0.102914</td>\n",
              "      <td>0.897086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.017147</td>\n",
              "      <td>-</td>\n",
              "      <td>0.007763</td>\n",
              "      <td>0.012999</td>\n",
              "      <td>0.032444</td>\n",
              "      <td>0.009397</td>\n",
              "      <td>0.022986</td>\n",
              "      <td>0.012795</td>\n",
              "      <td>0.017347</td>\n",
              "      <td>0.007671</td>\n",
              "      <td>0.007671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Test set</th>\n",
              "      <td>0.338444</td>\n",
              "      <td>391:241</td>\n",
              "      <td>0.702532</td>\n",
              "      <td>0.645775</td>\n",
              "      <td>0.406639</td>\n",
              "      <td>0.884910</td>\n",
              "      <td>0.685315</td>\n",
              "      <td>0.707566</td>\n",
              "      <td>0.510417</td>\n",
              "      <td>0.272187</td>\n",
              "      <td>0.727813</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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