Aldo/dataset_generator.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" What does this code does?\n",
        "This notebook generates 'lig', 'inter', and 'prot' files\n",
        "from TSV files retrieved from the Binding DB dataset. Those\n",
        "files are properly formatted to be used at RINDTI model\n",
        "https://rindti.readthedocs.io/en/master/index.html\n",
        "\n",
        "The files (TSV, TXT, or images) will contain information \n",
        "about:\n",
        "    Class balance: This code will downsample the dataset to easily-tune \n",
        "    a max radio of 1:3. Minimum samples per class is also easily-tune parameter.\n",
        "    results/active-inactives_info.txt file generated will provide the class balance per target.\n",
        "    results/class_balance.txt file will contain global information of dataset balance.\n",
        "    This code will balance each target active-inactives radio, NOT just whole set.\n",
        "\n",
        "    Molecular descriptors: This code will generate a image of molecular descriptor of \n",
        "    dataset distribution of all dataset after lipinski rules filtering.\n",
        "    Also complete dataset information can be generated from this code as those molecular\n",
        "    descriptors are generated from rdkit in this notebook.\n",
        "    \n",
        "    pbd2fasta: This code also contain codes that are usuful to get fasta sequences from pdbs\n",
        "    and place that information in new cells in dataframe. This code could be reused in \n",
        "    future as its own as it based on: https://github.com/kalininalab/useful_scripts/tree/main/Ilya\n",
        "\n",
        "    pdb-cleaning: This code also contains a cell code that is useful to clean pdbs from other\n",
        "    non-protein molecules, as ligands, water or metals and get only protein molecules.\n",
        "\n",
        "    pdb folder: This code also generated a folder with pdb structures and renames it as pdb id.pdb,\n",
        "    same name that would be used in prot.tsv id.\n",
        "\n",
        "    Active Threshold: This code also create the active-inactive classification based\n",
        "    on a cutoff of 10 mM. Whereas less than 10 mM indicates positive activity.\n",
        "\n",
        "    Filter by EC number: In case to be interest in specific class of enzymatic activity,\n",
        "    it could be filter by EC number. However, this highly depends on pdb files, since\n",
        "    that number is retrieved from the information contained in that pdb.ent file. If non-existent\n",
        "    will not be parse to dataframe.\n",
        "\n",
        "    Formatted dataset: The dataset is properly formatted to be used at RINDTI model\n",
        "    https://rindti.readthedocs.io/en/master/index.html \n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_22321/3427304583.py:20: DtypeWarning: Columns (9,28) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  dataset = pd.read_csv(str(pwd) + \"/\" + str(db), sep=\"\\t\",\n"
          ]
        }
      ],
      "source": [
        "## general libraries of the notebook\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "## define working directory\n",
        "pwd = os.getcwd()\n",
        "\n",
        "## possible datasets manually retrived from:\n",
        "## https://www.bindingdb.org/rwd/bind/chemsearch/marvin/Download.jsp\n",
        "#db = 'BindingDB_ChEMBL.tsv'\n",
        "#db = 'BindingDB_PubChem.tsv'\n",
        "#db = 'BindingDB_BindingDB_Articles.tsv'\n",
        "#db = 'BindingDB_Covid-19.tsv'\n",
        "db = 'BindingDB_All.tsv'\n",
        "\n",
        "# parse binding data into dataset [only columns that really matters]\n",
        "dataset = pd.read_csv(str(pwd) + \"/\" + str(db), sep=\"\\t\",\n",
        "                      usecols=['BindingDB Reactant_set_id', 'Ligand SMILES', 'Target Name', 'IC50 (nM)', 'PDB ID(s) for Ligand-Target Complex'])\n",
        "\n",
        "# create folder for results that would be generated\n",
        "new_folder = './results_' + str(db.replace('.tsv', ''))\n",
        "\n",
        "# check whether directory already exists\n",
        "if not os.path.exists(new_folder):\n",
        "    os.mkdir(new_folder)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" First step |\n",
        "This part of the notebook filters the dataset by only considering data that has IC50 values\n",
        "and targets that have PDB structures. \n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2702441, 5)"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Data processing I | Managing columns names and formatting data types\n",
        "\"\"\"\n",
        "\n",
        "# rename columns\n",
        "dataset.columns = ['Drug_ID', 'Drug', 'Target_ID', 'IC50', 'PDB_ID']\n",
        "# add prefix to drugs\n",
        "dataset['Drug_ID'] = 'drg_' + dataset['Drug_ID'].astype(str)\n",
        "# save ic50 into floats and remove str characters\n",
        "dataset[\"IC50\"] = dataset[\"IC50\"].str.replace('>', '')\n",
        "dataset[\"IC50\"] = dataset[\"IC50\"].str.replace('<', '')\n",
        "# Convert the column to numeric values, setting 'coerce' to convert non-convertible elements to NaN\n",
        "dataset['IC50'] = pd.to_numeric(dataset['IC50'], errors='coerce')\n",
        "# Drop rows with NaN values in the 'IC50' column\n",
        "dataset.dropna(subset=['IC50'], inplace=True)\n",
        "# convert data to float\n",
        "dataset = dataset.astype({\"IC50\": \"float64\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Data processing II | Creating class labels and computing logarithms\n",
        "\"\"\"\n",
        "\n",
        "# create label column in dataset\n",
        "dataset[\"label\"] = np.nan\n",
        "# label as active or inactives ## active < 10mM\n",
        "dataset.loc[dataset['IC50'] < 10000.0, 'label'] = 'active'\n",
        "dataset.loc[dataset['IC50'] >= 10000.0, 'label'] = 'inactive'\n",
        "\n",
        "# compute log of IC50 values\n",
        "# Replace zero values with a small positive value\n",
        "dataset['IC50'] = dataset['IC50'].replace(0, 1e-10)\n",
        "# Convert 'IC50' column to logarithmic values\n",
        "dataset['pIC50'] = np.log10(dataset['IC50'])\n",
        "# Drop duplicates\n",
        "dataset.drop_duplicates(subset=['Drug'], inplace=True)\n",
        "dataset.drop_duplicates(subset=['Drug_ID'], inplace=True)\n",
        "## reset index\n",
        "dataset.reset_index(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(851374, 8)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "## keep data in dataset only if pdb is available\n",
        "dataset = dataset[dataset['PDB_ID'].notna()]\n",
        "# # Extract the first pdb ID from each element in the column\n",
        "dataset['PDB_ID'] = dataset['PDB_ID'].str.split(',').str.get(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9556, 8)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of targets is 9127\n",
            " \n",
            "the number of compounds is 9556\n",
            " \n",
            "Class blance is \n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "active      7090\n",
              "inactive    2466\n",
              "Name: label, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\" CHECKPOINT | Initial information of dataset class balance\n",
        "\"\"\"\n",
        "\n",
        "# check for balance\n",
        "print('Num of targets is ' +\n",
        "      str(len(list(dataset['PDB_ID'].drop_duplicates()))))\n",
        "print(' ')\n",
        "print(\"the number of compounds is \" + str(dataset.shape[0]))\n",
        "print(' ')\n",
        "print(\"Class blance is \")\n",
        "dataset['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Second step |\n",
        "This code will add new columns with molecular descriptors values and will plot\n",
        "the molecular descriptors destribution after dataset filtering by lipinski rules\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Molecular descriptors | This cell uses RDKIT to calculate molecular descriptors,\n",
        "'Molecular formula', 'Molecular Weight', 'Number of Atoms', 'Number of Heavy Atoms', \n",
        "'Hydrogen bond donors', 'Hydrogen bond acceptors', 'Number of rotable bonds', \n",
        "'Polar surface area', 'LogP', 'aromatic rings'\n",
        "\"\"\"\n",
        "\n",
        "#libraries\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit import DataStructs\n",
        "from rdkit.Chem import Crippen\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem.Descriptors import rdMolDescriptors\n",
        "from rdkit.Chem.Draw import MolToImage\n",
        "from rdkit.Chem import MolFromSmiles\n",
        "\n",
        "## create a dataframe columns to place the information computed by RDKIT\n",
        "df = pd.DataFrame(columns=['Molecular formula', 'Molecular Weight', 'Number of Atoms', 'Number of Heavy Atoms', 'Hydrogen bond donors (Lipinski)',\n",
        "                  'Hydrogen bond acceptors (Lipinski)', 'Number of rotable bonds', 'Polar surface area', 'LogP', 'aromatic rings'])\n",
        "\n",
        "# if error skip molecule, It'll be cleaned later\n",
        "def calculate_descriptors(mol):\n",
        "    if mol is None:\n",
        "        return pd.Series({\n",
        "            'formula': None,\n",
        "            'weight': None,\n",
        "            'num_atoms': None,\n",
        "            'num_heavy_atoms': None,\n",
        "            'num_hbd': None,\n",
        "            'num_hba': None,\n",
        "            'num_rot_bonds': None,\n",
        "            'psa': None,\n",
        "            'logp': None,\n",
        "            'num_aromatic_rings': None\n",
        "        })\n",
        "    \n",
        "    mol = Chem.MolFromSmiles(mol)\n",
        "    if mol is None:\n",
        "        return pd.Series({\n",
        "            'formula': None,\n",
        "            'weight': None,\n",
        "            'num_atoms': None,\n",
        "            'num_heavy_atoms': None,\n",
        "            'num_hbd': None,\n",
        "            'num_hba': None,\n",
        "            'num_rot_bonds': None,\n",
        "            'psa': None,\n",
        "            'logp': None,\n",
        "            'num_aromatic_rings': None\n",
        "        })\n",
        "    \n",
        "    formula = rdMolDescriptors.CalcMolFormula(mol)\n",
        "    weight = rdMolDescriptors.CalcExactMolWt(mol)\n",
        "    num_atoms = rdMolDescriptors.CalcNumAtoms(mol)\n",
        "    num_heavy_atoms = rdMolDescriptors.CalcNumHeavyAtoms(mol)\n",
        "    num_hbd = rdMolDescriptors.CalcNumLipinskiHBD(mol)\n",
        "    num_hba = rdMolDescriptors.CalcNumLipinskiHBA(mol)\n",
        "    num_rot_bonds = rdMolDescriptors.CalcNumRotatableBonds(mol)\n",
        "    psa = rdMolDescriptors.CalcTPSA(mol)\n",
        "    logp = Chem.Crippen.MolLogP(mol)\n",
        "    num_aromatic_rings = rdMolDescriptors.CalcNumAromaticRings(mol)\n",
        "    \n",
        "    return pd.Series({\n",
        "        'formula': formula,\n",
        "        'weight': weight,\n",
        "        'num_atoms': num_atoms,\n",
        "        'num_heavy_atoms': num_heavy_atoms,\n",
        "        'num_hbd': num_hbd,\n",
        "        'num_hba': num_hba,\n",
        "        'num_rot_bonds': num_rot_bonds,\n",
        "        'psa': psa,\n",
        "        'logp': logp,\n",
        "        'num_aromatic_rings': num_aromatic_rings\n",
        "    })\n",
        "df = dataset.join(dataset['Drug'].apply(calculate_descriptors))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>index</th>\n",
              "      <th>Drug_ID</th>\n",
              "      <th>Drug</th>\n",
              "      <th>Target_ID</th>\n",
              "      <th>IC50</th>\n",
              "      <th>PDB_ID</th>\n",
              "      <th>label</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>formula</th>\n",
              "      <th>weight</th>\n",
              "      <th>num_atoms</th>\n",
              "      <th>num_heavy_atoms</th>\n",
              "      <th>num_hbd</th>\n",
              "      <th>num_hba</th>\n",
              "      <th>num_rot_bonds</th>\n",
              "      <th>psa</th>\n",
              "      <th>logp</th>\n",
              "      <th>num_aromatic_rings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [index, Drug_ID, Drug, Target_ID, IC50, PDB_ID, label, pIC50, formula, weight, num_atoms, num_heavy_atoms, num_hbd, num_hba, num_rot_bonds, psa, logp, num_aromatic_rings]\n",
              "Index: []"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\" Cleaning | This cell removed not-found values in previous step\n",
        "\n",
        "NOTE: output of this cell should be an empty df, since it just shows\n",
        "howmany NaN exist AFTER cleaning, it could be modified by turning-off\n",
        "the first two lines and check how many drugs you are losting by \n",
        "printing NaN values\n",
        "\"\"\"\n",
        "\n",
        "## remove not found values in previuos step\n",
        "df.dropna(subset=['weight'], inplace=True)\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "## check which values still have Nan values \n",
        "## if you want to check which will row will be eliminated\n",
        "## run this three lines before the df.dropna of previous line\n",
        "subset_df = df[df.isnull().any(axis=1)]\n",
        "subset_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Lipinski rules | This cell is used to filter and keep only the drugs \n",
        "that fulfill the statements of the Lipinski rules.\n",
        "\"\"\"\n",
        "# remove all row that contains MW | 500\n",
        "df = df[df.weight < 500]\n",
        "# remove all row that contains logP | 5\n",
        "df = df[df.logp < 5]\n",
        "# remove all row that contains logP | 5\n",
        "df = df[df.logp > 0]\n",
        "# remove all row that contains psa | 140 armgs\n",
        "df = df[df.psa < 140]\n",
        "# remove all row that contains hba | 10\n",
        "df = df[df.num_hba < 10] \n",
        "# remove all row that contains hbd | 5\n",
        "df = df[df.num_hbd < 5] \n",
        "# remove all row that contains rb | 10\n",
        "df = df[df.num_rot_bonds < 10] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Balance of the dataset | This code will downsample the dataset to easily-tune \n",
        "a max radio of 1:4. Minimum samples per class is also easily-tune parameter.\n",
        "\n",
        "This code will balance each target active-inactives radio, NOT just\n",
        "whole set\n",
        "\n",
        "UPDATE | unfortunatelly, even if this make sense to have all targets balanced\n",
        "with at least certain number of examples in custum radio of active-inactives,\n",
        "it downsample considerabely the number of examples. I'll keep it here, because\n",
        "could be useful in the future this script. \n",
        "\"\"\"\n",
        "\n",
        "# ## modules \n",
        "# from sklearn.utils import resample\n",
        "\n",
        "# ## target dataframe from original dataframe\n",
        "# targets = df['PDB_ID'].unique()\n",
        "\n",
        "# ## hyperparameters\n",
        "# min_examples = 1   # Minimum number of examples for each class\n",
        "# max_ratio = 4  # Maximum ratio between the minority and majority classes\n",
        "\n",
        "# balanced_data = []\n",
        "\n",
        "# ## loop for each target\n",
        "# for target in targets:\n",
        "#     target_data = df[df['PDB_ID'] == target]\n",
        "    \n",
        "#     # Calculate the counts of each class\n",
        "#     class_counts = target_data['label'].value_counts()\n",
        "    \n",
        "#     # only consider targets that has at least 'min_examples' examples in both classes\n",
        "#     if len(class_counts) < 2 or any(class_counts < min_examples):\n",
        "#         continue\n",
        "    \n",
        "#     # determine the minority and majority classes\n",
        "#     minority_class = class_counts.idxmin()\n",
        "#     majority_class = class_counts.idxmax()\n",
        "    \n",
        "#     # Separate the minority and majority classes\n",
        "#     minority_data = target_data[target_data['label'] == minority_class]\n",
        "#     majority_data = target_data[target_data['label'] == majority_class]\n",
        "    \n",
        "#     minority_count = minority_data.shape[0]\n",
        "#     majority_count = majority_data.shape[0]\n",
        "    \n",
        "#     if minority_count >= min_examples and minority_count * max_ratio <= majority_count:\n",
        "#         # Exclude targets with already balanced or minority-dominant class distributions\n",
        "#         continue\n",
        "    \n",
        "#     if majority_count > minority_count * max_ratio:\n",
        "#         # Undersample the majority class to achieve the desired ratio\n",
        "#         desired_majority_count = min(minority_count * max_ratio, majority_count)  # Maximum ratio\n",
        "        \n",
        "#         undersampled_majority = resample(\n",
        "#             majority_data,\n",
        "#             replace=False,\n",
        "#             n_samples=desired_majority_count,\n",
        "#             random_state=42\n",
        "#         )\n",
        "        \n",
        "#         balanced_target_data = pd.concat([minority_data, undersampled_majority])\n",
        "#     else:\n",
        "#         balanced_target_data = target_data\n",
        "    \n",
        "#     # Add the balanced data for this target to the list\n",
        "#     balanced_data.append(balanced_target_data)\n",
        "        \n",
        "# # Combine the balanced data for all targets into a single DataFrame\n",
        "# balanced_df = pd.concat(balanced_data)\n",
        "\n",
        "# # rename dataset to original df\n",
        "# df = balanced_df ## basically I reused code already named df \"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of targets is 5765\n",
            " \n",
            "the number of compounds is 6022\n",
            " \n",
            "Class blance is \n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "active      4307\n",
              "inactive    1715\n",
              "Name: label, dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\" CHECKPOINT | Initial information of dataset class balance\n",
        "\"\"\"\n",
        "\n",
        "# check for balance\n",
        "print('Num of targets is ' +\n",
        "      str(len(list(df['PDB_ID'].drop_duplicates()))))\n",
        "print(' ')\n",
        "print(\"the number of compounds is \" + str(df.shape[0]))\n",
        "print(' ')\n",
        "print(\"Class blance is \")\n",
        "df['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x1800 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\"\"\" Molecular descriptors plot | This cell uses matplot lib to show and save the \n",
        "chemical distribution image of weight, num of atoms, num of heavy atoms, hb donors, ha acceptors,\n",
        "num of rotable bonds, psa, logP\n",
        "\"\"\"\n",
        "\n",
        "#libraries\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define columns of df to plot\n",
        "columns = [\n",
        "    'weight',\n",
        "    'num_atoms',\n",
        "    'num_heavy_atoms',\n",
        "    'num_hbd',\n",
        "    'num_hba',\n",
        "    'num_rot_bonds',\n",
        "    'psa',\n",
        "    'logp',\n",
        "    'num_aromatic_rings']\n",
        "# Calculate the number of rows and columns\n",
        "n_rows = int(np.ceil(len(columns) / 2))\n",
        "n_cols = 2\n",
        "\n",
        "# Create a figure and subplots with larger size\n",
        "fig, ax = plt.subplots(n_rows, n_cols, figsize=(18, 18))\n",
        "# Make subplots\n",
        "for i, column in enumerate(columns):\n",
        "    axi = ax[i // n_cols][i % n_cols]\n",
        "    fig.suptitle(\"Analysis of mol descriptors\\n\", fontsize=17)\n",
        "    sns.histplot(data=df, x=column, ax=axi, kde=True, color='skyblue')\n",
        "    plt.savefig(str(new_folder) + \"/ChemDistributionof_\" +str(db)+ '.jpg', format='jpg')\n",
        "\n",
        "fig.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\"\"\" Y values distribution | this cell prints and save jpg images of the\n",
        "distribution of pIC50 values of the dataset\n",
        "\"\"\"\n",
        "\n",
        "plt.figure()\n",
        "df['pIC50'].plot(kind='kde')\n",
        "plt.title('pIC50 Distribution of ' + str(db))\n",
        "# Setting the x-axis range\n",
        "plt.xlim(-2, 8)\n",
        "plt.xlabel('pIC50')\n",
        "plt.ylabel('Density')\n",
        "    \n",
        "# Save the figure\n",
        "plt.savefig(str(new_folder) + \"/pIC50_dist_\" +str(db)+ '.jpg', format='jpg')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of targets is 5765\n",
            " \n",
            "the number of compounds is 6022\n",
            " \n",
            "Class blance is \n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "active      4307\n",
              "inactive    1715\n",
              "Name: label, dtype: int64"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\" CHECKPOINT | dataset class balance after filter\n",
        "\"\"\"\n",
        "print('Num of targets is ' +\n",
        "      str(len(list(df['PDB_ID'].drop_duplicates()))))\n",
        "print(' ')\n",
        "print(\"the number of compounds is \" + str(df.shape[0]))\n",
        "print(' ')\n",
        "print(\"Class blance is \")\n",
        "df['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Output saved to ./results_BindingDB_All/active-inactives_info.txt\n"
          ]
        }
      ],
      "source": [
        "\"\"\" Save active-inactive information | This save a txt files of the\n",
        "class balance information of dataset per target\n",
        "\"\"\"\n",
        "\n",
        "# Get the unique target IDs\n",
        "unique_targets = df['PDB_ID'].unique()\n",
        "\n",
        "# Create a string to store all the information\n",
        "output_string = \"\"\n",
        "\n",
        "# Print the number of unique targets\n",
        "num_targets = len(unique_targets)\n",
        "output_string += 'Num of targets is ' + str(num_targets) + '\\n\\n'\n",
        "\n",
        "# Print the information for each target\n",
        "for target in unique_targets:\n",
        "    output_string += \"PDB_ID ID: \" + str(target) + '\\n'\n",
        "    target_data = df[df['PDB_ID'] == target]\n",
        "    \n",
        "    # Print the number of compounds for the current target\n",
        "    num_compounds = target_data.shape[0]\n",
        "    output_string += \"The number of compounds is \" + str(num_compounds) + '\\n'\n",
        "    \n",
        "    # Calculate and print the class balance for the current target\n",
        "    class_balance = target_data['label'].value_counts()\n",
        "    output_string += \"Class balance is:\\n\"\n",
        "    output_string += str(class_balance) + '\\n\\n'\n",
        "\n",
        "# Save the information to a single text file\n",
        "filename = str(new_folder) + \"/active-inactives_info.txt\"\n",
        "with open(filename, 'w') as file:\n",
        "    file.write(output_string)\n",
        "\n",
        "print(\"Output saved to\", filename)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Output saved to ./results_BindingDB_All/class_balance.txt\n"
          ]
        }
      ],
      "source": [
        "\"\"\" Save class balance information | This save a txt files of the\n",
        "class balance information of whole dataset \n",
        "\"\"\"\n",
        "\n",
        "# Save the information to a text file\n",
        "filename = str(new_folder) + \"/class_balance.txt\"\n",
        "with open(filename, 'w') as file:\n",
        "    file.write('Num of targets is ' + str(len(list(df['PDB_ID'].drop_duplicates()))) + '\\n\\n')\n",
        "    file.write('The number of compounds is ' + str(df.shape[0]) + '\\n\\n')\n",
        "    file.write('Class balance is:\\n')\n",
        "    file.write(str(df['label'].value_counts()))\n",
        "\n",
        "print(\"Output saved to\", filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "' Checkpoint | This cell is designed to only to work with less complexes \\nto just be sure code runs, basically it will avoid to download huge amount of pdb files\\nhowever, second part balancing probably will requiere more samples to work properly.'"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\" Third step |\n",
        "Structural information is cleaned from non-protein molecules and placed into a results/pdbs folder. \n",
        "Additionally, fasta sequences and EC numbers are added to the dataframe.\n",
        "\"\"\"\n",
        "\n",
        "\"\"\" Checkpoint | This cell is designed to only to work with less complexes \n",
        "to just be sure code runs, basically it will avoid to download huge amount of pdb files\n",
        "however, second part balancing probably will requiere more samples to work properly.\"\"\"\n",
        "\n",
        "## turn off following line to work with entire set\n",
        "#dataset = dataset.sample(n=30, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Structure exists: './results_BindingDB_All/pdbs/pdb1c0t.ent' \n",
            "Progress: |----------------------------------------| 0.02%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1c0t.ent' \n",
            "Progress: |----------------------------------------| 0.03%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1c0u.ent' \n",
            "Progress: |----------------------------------------| 0.05%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1fkp.ent' \n",
            "Progress: |----------------------------------------| 0.07%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb2bxv.ent' \n",
            "Progress: |----------------------------------------| 0.08%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1lw2.ent' \n",
            "Progress: |----------------------------------------| 0.1%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1jlq.ent' \n",
            "Progress: |----------------------------------------| 0.12%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1rev.ent' \n",
            "Progress: |----------------------------------------| 0.13%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1klm.ent' \n",
            "Progress: |----------------------------------------| 0.15%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1rt1.ent' \n",
            "Progress: |----------------------------------------| 0.17%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1jkh.ent' \n",
            "Progress: |----------------------------------------| 0.18%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1aq1.ent' \n",
            "Progress: |----------------------------------------| 0.2%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb4eip.ent' \n",
            "Progress: |----------------------------------------| 0.22%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1xws.ent' \n",
            "Progress: |----------------------------------------| 0.23%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1uu9.ent' \n",
            "Progress: |----------------------------------------| 0.25%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb1uu7.ent' \n",
            "Progress: |----------------------------------------| 0.27%\n",
            "Structure exists: './results_BindingDB_All/pdbs/pdb2vd5.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb1vyz.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb1q3w.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb1ung.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb7zub.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb1q41.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb1uv5.ent' \n",
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            "Downloading PDB structure '6zed'...\n",
            "Desired structure doesn't exist\n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb1v0p.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb3znc.ent' \n",
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            "Desired structure doesn't exist\n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb3czr.ent' \n",
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            "Desired structure doesn't exist\n",
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            "Desired structure doesn't exist\n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb4gv2.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb3tiz.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb3w2t.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb1x70.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb6fgy.ent' \n",
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            "Desired structure doesn't exist\n",
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            "Desired structure doesn't exist\n",
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            "Desired structure doesn't exist\n",
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            "Desired structure doesn't exist\n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb4olm.ent' \n",
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            "Desired structure doesn't exist\n",
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            "Desired structure doesn't exist\n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb5tzz.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb6wxj.ent' \n",
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            "Desired structure doesn't exist\n",
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            "Desired structure doesn't exist\n",
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            "Desired structure doesn't exist\n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb7bnf.ent' \n",
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            "Desired structure doesn't exist\n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb2vtl.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb5wik.ent' \n",
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            "Structure exists: './results_BindingDB_All/pdbs/pdb5hq0.ent' \n",
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            "Downloading PDB structure '5bve'...\n"
          ]
        }
      ],
      "source": [
        "\"\"\" Download pdb files | In fact, following code will retrieve \"ent\" files\n",
        "from RCSB. pdb files will be converted later. \n",
        "\n",
        "NOTE: Modules are kept in case of repurposing this code for future as its own\n",
        "\"\"\"\n",
        "\n",
        "dataset = df ## just because I wrote this code before and is easier rename dataset\n",
        "## Libraries to download pdb data using biopython\n",
        "import os\n",
        "from Bio.PDB.PDBList import PDBList\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Get the PDB IDs from the DataFrame column\n",
        "pdb_ids = dataset['PDB_ID'].tolist()\n",
        "\n",
        "# Create a directory for saving PDB files\n",
        "os.makedirs('pdbs', exist_ok=True)\n",
        "\n",
        "# Create an instance of PDBList\n",
        "pdb_list = PDBList()\n",
        "\n",
        "# Calculate the total number of structures\n",
        "total_structures = len(pdb_ids)\n",
        "\n",
        "# Define a helper function to update the progress bar\n",
        "def update_progress(progress):\n",
        "    bar_length = 40\n",
        "    filled_length = int(round(bar_length * progress))\n",
        "    bar = '█' * filled_length + '-' * (bar_length - filled_length)\n",
        "    percent = round(progress * 100, 2)\n",
        "    print(f'Progress: |{bar}| {percent}%')\n",
        "\n",
        "# Download the PDB structures for the given IDs with progress bar\n",
        "for i, pdb_id in enumerate(pdb_ids, start=1):\n",
        "    pdb_list.retrieve_pdb_file(pdb_id, file_format='pdb', pdir=str(new_folder) + '/pdbs')\n",
        "    progress = i / total_structures\n",
        "    update_progress(progress)\n",
        "\n",
        "# Track the IDs of failed downloads\n",
        "failed_ids = []\n",
        "\n",
        "# Download the PDB structures for the given IDs with progress bar\n",
        "for i, pdb_id in enumerate(pdb_ids, start=1):\n",
        "    try:\n",
        "        pdb_list.retrieve_pdb_file(pdb_id, file_format='pdb', pdir='pdbs')\n",
        "    except:\n",
        "        failed_ids.append(pdb_id)\n",
        "    progress = i / total_structures\n",
        "    update_progress(progress)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Get EC ids | This cell is designed to get EC number from PDB.ent\n",
        "files, this will be useful to filter by specfici enzymatic activity\n",
        "\n",
        "NOTE: Modules are kept in case of repurposing this code for future as its own\n",
        "\"\"\"\n",
        "\n",
        "### libraries for add EC numbers\n",
        "import re\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "def extract_ec_number(file_path):\n",
        "    ec_number = None\n",
        "    with open(file_path, 'r') as file:\n",
        "        for line in file:\n",
        "            if line.startswith('COMPND'):\n",
        "                match = re.search(r'EC:\\s*([\\d.-]+)', line)\n",
        "                if match:\n",
        "                    ec_number = match.group(1)\n",
        "                    break\n",
        "    return ec_number\n",
        "\n",
        "# Get the PDB IDs from the DataFrame column\n",
        "pdb_ids = dataset['PDB_ID'].tolist()\n",
        "\n",
        "# List to store the EC numbers\n",
        "ec_numbers = []\n",
        "\n",
        "# Iterate over the PDB IDs\n",
        "for pdb_id in pdb_ids:\n",
        "    pdb_id_lower = pdb_id.lower()\n",
        "    file_path = f\"{new_folder}/pdbs/pdb{pdb_id_lower}.ent\"\n",
        "    ec_number = extract_ec_number(file_path)\n",
        "    ec_numbers.append(ec_number)\n",
        "\n",
        "# Assign EC numbers to the 'EC' column in the dataset\n",
        "dataset['EC number'] = ec_numbers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Remove fail-downloaded pdbs | This cell is designed to remove pdbs that \n",
        "for somereason could not be retrieved, as the model needs pdb structure. \n",
        "It is recommended to manually check how many information is lost and \n",
        "if other options exist to save the data\"\"\"\n",
        "\n",
        "# Dropping rows based on the list of IDs\n",
        "dataset = dataset[~dataset['PDB_ID'].isin(failed_ids)]\n",
        "\n",
        "# Print the IDs of the PDB files that were not downloaded\n",
        "print(\"Failed to download PDB IDs:\")\n",
        "for pdb_id in failed_ids:\n",
        "    print(pdb_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Clean pdbs | This cell is designed to clean PDB.ent files\n",
        "from non-protein components and save them in pdb format\n",
        "\n",
        "NOTE: Modules are kept in case of repurposing this code for future as its own\n",
        "\"\"\"\n",
        "\n",
        "## libraries \n",
        "import os\n",
        "from Bio.PDB import PDBParser, PDBIO\n",
        "from tqdm import tqdm\n",
        "import sys\n",
        "import warnings\n",
        "\n",
        "# Redirect warning output to a null file\n",
        "null_file = open(os.devnull, 'w')\n",
        "sys.stderr = null_file\n",
        "\n",
        "def clean_pdb_files(folder_path):\n",
        "    # Get a list of all .ent files in the folder\n",
        "    ent_files = [f for f in os.listdir(folder_path) if f.endswith('.ent')]\n",
        "\n",
        "    # Initialize the progress bar\n",
        "    progress_bar = tqdm(total=len(ent_files), unit='file(s)')\n",
        "\n",
        "    # Iterate over each .ent file\n",
        "    for ent_file in ent_files:\n",
        "        ent_path = os.path.join(folder_path, ent_file)\n",
        "\n",
        "        # Parse the PDB file\n",
        "        with warnings.catch_warnings():\n",
        "            warnings.simplefilter('ignore')\n",
        "        parser = PDBParser()\n",
        "        structure = parser.get_structure('pdb', ent_path)\n",
        "\n",
        "        # Remove ligands, metals, and water molecules\n",
        "        for model in structure:\n",
        "            for chain in model:\n",
        "                for residue in list(chain):\n",
        "                    hetfield = residue.get_id()[0]\n",
        "                    if hetfield != ' ':\n",
        "                        chain.detach_child(residue.id)\n",
        "\n",
        "        # Save the cleaned PDB file\n",
        "        pdb_id = ent_file.split('.')[0]\n",
        "        pdb_id = pdb_id.replace('pdb', '')\n",
        "        clean_pdb_file = pdb_id + '.pdb'\n",
        "        clean_pdb_path = os.path.join(folder_path, clean_pdb_file)\n",
        "\n",
        "        io = PDBIO()\n",
        "        io.set_structure(structure)\n",
        "        io.save(clean_pdb_path)\n",
        "\n",
        "        # Delete the .ent file\n",
        "        os.remove(ent_path)\n",
        "\n",
        "        progress_bar.set_description(f'Cleaning {ent_file}')\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    progress_bar.close()\n",
        "    print('Cleaned PDB files saved in', folder_path)\n",
        "\n",
        "# Reset the warning output\n",
        "sys.stderr = sys.__stderr__\n",
        "\n",
        "# usage of function\n",
        "folder_path = str(new_folder) + '/pdbs'\n",
        "clean_pdb_files(folder_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Get fasta into dataframe from  pdbs | This cell is designed to place\n",
        "sequence information from pdbs in new cells in dataframe. \n",
        "\n",
        "NOTE: Modules are kept in case of repurposing this code for future as its own\n",
        "\"\"\"\n",
        "\n",
        "### stored in previous steps\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "# Mapping of three-letter amino acid codes to one-letter codes\n",
        "threetoone = {\n",
        "    \"CYS\": \"C\", \"ASP\": \"D\", \"SER\": \"S\", \"GLN\": \"Q\", \"LYS\": \"K\", \"ILE\": \"I\", \"PRO\": \"P\", \"THR\": \"T\",\n",
        "    \"PHE\": \"F\", \"ASN\": \"N\", \"GLY\": \"G\", \"HIS\": \"H\", \"LEU\": \"L\", \"ARG\": \"R\", \"TRP\": \"W\", \"ALA\": \"A\",\n",
        "    \"VAL\": \"V\", \"GLU\": \"E\", \"TYR\": \"Y\", \"MET\": \"M\",\n",
        "}\n",
        "\n",
        "def pdb_to_sequence(pdb_filename: str, file_extension: str) -> str:\n",
        "    \"\"\"Extract sequence from PDB file and return it as a string.\"\"\"\n",
        "    sequence = \"\"\n",
        "    with open(pdb_filename, \"r\") as file:\n",
        "        for line in file.readlines():\n",
        "            if line.startswith(\"ATOM\") and line[12:16].strip() == \"CA\":\n",
        "                sequence += threetoone[line[17:20].strip()]\n",
        "    return sequence\n",
        "\n",
        "# Update the path to the PDB files\n",
        "pdb_dir = str(new_folder) + \"/pdbs/\"\n",
        "\n",
        "# Iterate over each row in the DataFrame\n",
        "for index, row in dataset.iterrows():\n",
        "    pdb_filename = row['PDB_ID']\n",
        "    fasta_sequence = \"\"\n",
        "    \n",
        "    if pd.notnull(pdb_filename):\n",
        "        pdb_filename = os.path.join(pdb_dir, pdb_filename.lower() + \".pdb\")  # Convert to lowercase\n",
        "        fasta_sequence = pdb_to_sequence(pdb_filename, \".pdb\")  \n",
        "    \n",
        "    dataset.at[index, \"fasta_sequence\"] = fasta_sequence\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" FILTER | This cell allows to focus in specific enzymatic activity (EC id)\n",
        "\n",
        "NOTE : This highly depends on pdb files, since that number is retrieved \n",
        "from the information contained in that pdb.ent file. \n",
        "If non-existent will not be parse to dataframe. \n",
        "\"\"\"\n",
        "\n",
        "## for some reason certain pdb ids didn't has EC number on ent files\n",
        "## remove NA from EC number column \n",
        "#dataset = dataset[dataset['EC number'].notna()] \n",
        "\n",
        "\n",
        "## filter by specific enzymatic activity\n",
        "#EC_id = '3.4'\n",
        "#EC_specific_dataset = dataset[dataset['EC number'].str.contains(str(EC_id))]\n",
        "\n",
        "## turn-on this line if you want to apply this filter\n",
        "#dataset = EC_specific_dataset\n",
        "#dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSD and SeqID analysis\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/home/ahr/.local/lib/python3.10/site-packages/matplotlib/pyplot.py'>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\"\"\" Heatmap of RMSD values of proteins in dataset\n",
        "NOTE: it is needed to install bassh tool TM-align\n",
        "## apt install tm-align --user\n",
        "\"\"\"\n",
        "import os\n",
        "import re\n",
        "import numpy as np\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "pdbs = str(new_folder) + \"/pdbs\"  # directory containing the PDB files\n",
        "pdb_files = sorted(os.listdir(pdbs))\n",
        "\n",
        "matrix_rmsd = [[0 for i in range(len(pdb_files))] for j in range(len(pdb_files))]\n",
        "matrix_seqid = [[0 for i in range(len(pdb_files))] for j in range(len(pdb_files))]\n",
        "\n",
        "for i in range(len(pdb_files)):\n",
        "    for j in range(i+1, len(pdb_files)):\n",
        "        pdb1 = pdb_files[i]\n",
        "        pdb2 = pdb_files[j]\n",
        "        # run TMalign command\n",
        "        cmd = f\"TMalign {os.path.join(pdbs, pdb1)} {os.path.join(pdbs, pdb2)} -a\"\n",
        "        output = os.popen(cmd).read()\n",
        "        # extract RMSD value\n",
        "        for line in output.split(\"\\n\"):\n",
        "            if \"RMSD\" in line:\n",
        "                rmsd_matches = re.findall(r\"RMSD=\\s+([\\d.]+),\", line)\n",
        "                if len(rmsd_matches) > 0:\n",
        "                    rmsd = float(rmsd_matches[0])\n",
        "                else:\n",
        "                    print(f\"Error: could not find RMSD value for {pdb1} vs. {pdb2} in files {pdb1} and {pdb2}\")\n",
        "        matrix_rmsd[i][j] = rmsd\n",
        "        matrix_rmsd[j][i] = rmsd\n",
        "\n",
        "        # extract SeqID value\n",
        "        for line in output.split(\"\\n\"):\n",
        "            if \"Seq_ID\" in line:\n",
        "                seqid_match = re.search(r\"Seq_ID=n_identical/n_aligned=\\s+([\\d.]+)\", line)\n",
        "                if seqid_match:\n",
        "                    seqid = float(seqid_match.group(1))\n",
        "                else:\n",
        "                    print(f\"Error: could not find SeqID value for {pdb1} vs. {pdb2} in files {pdb1} and {pdb2}\")\n",
        "        matrix_seqid[i][j] = seqid\n",
        "        matrix_seqid[j][i] = seqid\n",
        "\n",
        "## save matrices\n",
        "np.save(\"matrix_rmsd.npy\", matrix_rmsd)\n",
        "np.save(\"matrix_seqid.npy\", matrix_seqid)\n",
        "\n",
        "print(\"RMSD and SeqID analysis\")\n",
        "# Create DataFrames from the matrices\n",
        "heatmap_rmsd = pd.DataFrame(matrix_rmsd, columns=pdb_files, index=pdb_files)\n",
        "heatmap_seqid = pd.DataFrame(matrix_seqid, columns=pdb_files, index=pdb_files)\n",
        "\n",
        "# Create the figure and subplots\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
        "\n",
        "# Create the RMSD heatmap\n",
        "sns.heatmap(heatmap_rmsd, ax=ax1, annot=False, cmap=\"YlGnBu\", cbar=False)\n",
        "ax1.set_title('Heatmap of RMSD values')\n",
        "\n",
        "# Create the SeqID heatmap\n",
        "sns.heatmap(heatmap_seqid, ax=ax2, annot=False, cmap=\"YlGnBu\", cbar=False)\n",
        "ax2.set_title('Heatmap of SeqID values')\n",
        "\n",
        "# Remove x and y labels\n",
        "ax1.set_xlabel(\"\")\n",
        "ax1.set_ylabel(\"\")\n",
        "ax2.set_xlabel(\"\")\n",
        "ax2.set_ylabel(\"\")\n",
        "\n",
        "# Save the figure\n",
        "plt.savefig(str(new_folder) + \"/heatmap_rmsd_seqid.jpg\", format='jpg')\n",
        "# Display the plot\n",
        "plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Fourth step |\n",
        "This part generates 'lig', 'inter', and 'prot' files\n",
        "from TSV files retrieved from the Binding DB dataset. Those\n",
        "files are properly formatted to be used at RINDTI model\n",
        "https://rindti.readthedocs.io/en/master/index.html\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Delimit the dataset | creates a new df with only columns that will be useful for the formating\n",
        "\"\"\"\n",
        "# we use df and not dataset, because df is already filtered\n",
        "tsvs = df[['Drug', 'Drug_ID', 'PDB_ID', 'pIC50', 'fasta_sequence']] ## PDB ID will be used to match pdb names\n",
        "# Select and rename columns\n",
        "tsvs = tsvs.rename(columns={'PDB_ID': 'Target_ID', 'pIC50': 'Y', 'fasta_sequence': 'Target'})\n",
        "# Display the updated DataFrame\n",
        "tsvs.to_csv(str(new_folder) + \"/all_drugs.tsv\", sep=\"\\t\", index=False)\n",
        "tsvs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Delimit the dataset | creates a new df with only columns that will be useful for the formating\n",
        "\"\"\"\n",
        "\n",
        "# Read the DataFrame from your data source or use the existing DataFrame\n",
        "df = pd.read_csv(str(new_folder) + '/all_drugs.tsv', sep=\"\\t\")\n",
        "\n",
        "# Remove duplicates and keep the row with the lowest \"Y\" value\n",
        "df = df.sort_values('Y')  # Sort the DataFrame by \"Y\" column in ascending order\n",
        "df = df.drop_duplicates(subset='Drug_ID', keep='first')\n",
        "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "# Display the updated DataFrame\n",
        "df.to_csv(str(new_folder) + \"/all_drugs.tsv\", sep=\"\\t\", index=False)\n",
        "\n",
        "df.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Delimit the dataset | creates a lig.tsv file\n",
        "\"\"\"\n",
        "lig = df.loc[:, ['Drug_ID', 'Drug']]\n",
        "lig.to_csv(str(new_folder) + \"/lig.tsv\", sep=\"\\t\", index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Delimit the dataset | creates a inter.tsv file\n",
        "\"\"\"\n",
        "inter = df.loc[:, ['Drug_ID', 'Target_ID', 'Y']]\n",
        "inter.to_csv(str(new_folder) + \"/inter.tsv\", sep=\"\\t\", index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" Delimit the dataset | creates a prot.tsv file\n",
        "\"\"\"\n",
        "prot = df.loc[:, ['Target_ID', 'Target']]\n",
        "prot.to_csv(str(new_folder) + \"/inter.tsv\", sep=\"\\t\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\" prot information | creates a TXT file that contains pdb ids \n",
        "\"\"\"\n",
        "# check for how many targets\n",
        "pdb_ids = list(df['Target_ID'].drop_duplicates())\n",
        "len(pdb_ids)\n",
        "# print(pdb_ids)\n",
        "\n",
        "# write their information\n",
        "with open(str(new_folder) + \"/pdb_ids.txt\", \"w\") as output:\n",
        "        output.write(str(pdb_ids))\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}