{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append('~/PROTAC-Degradation-Predictor/protac_degradation_predictor')\n",
    "sys.path.append('~/PROTAC-Degradation-Predictor')\n",
    "\n",
    "import protac_degradation_predictor as pdp\n",
    "\n",
    "import os\n",
    "from collections import defaultdict\n",
    "\n",
    "from rdkit import DataStructs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Compound ID</th>\n",
       "      <th>Uniprot</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>E3 Ligase</th>\n",
       "      <th>InChI</th>\n",
       "      <th>InChI Key</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Heavy Atom Count</th>\n",
       "      <th>Ring Count</th>\n",
       "      <th>Rotatable Bond Count</th>\n",
       "      <th>...</th>\n",
       "      <th>Assay (DC50/Dmax)</th>\n",
       "      <th>Exact Mass</th>\n",
       "      <th>XLogP3</th>\n",
       "      <th>Target (Parsed)</th>\n",
       "      <th>POI Sequence</th>\n",
       "      <th>E3 Ligase Uniprot</th>\n",
       "      <th>E3 Ligase Sequence</th>\n",
       "      <th>Cell Line Identifier</th>\n",
       "      <th>Active - OR</th>\n",
       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
       "      <td>1486.282</td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
       "      <td>1500.309</td>\n",
       "      <td>102</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
       "      <td>1514.336</td>\n",
       "      <td>103</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
       "      <td>1528.363</td>\n",
       "      <td>104</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Q07817</td>\n",
       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
       "      <td>VHL</td>\n",
       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
       "      <td>1542.390</td>\n",
       "      <td>105</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
       "      <td>P40337</td>\n",
       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
       "      <td>MOLT-4</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound ID Uniprot                                             Smiles  \\\n",
       "0            1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "1            2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "2            3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "3            4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "4            5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
       "\n",
       "  E3 Ligase                                              InChI  \\\n",
       "0       VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
       "1       VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
       "2       VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
       "3       VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
       "4       VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
       "\n",
       "                     InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
       "0  SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
       "1  HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
       "2  ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
       "3  FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
       "4  PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
       "\n",
       "   Ring Count  Rotatable Bond Count  ...  Assay (DC50/Dmax)  Exact Mass  \\\n",
       "0          10                    24  ...                NaN         NaN   \n",
       "1          10                    25  ...                NaN         NaN   \n",
       "2          10                    26  ...                NaN         NaN   \n",
       "3          10                    27  ...                NaN         NaN   \n",
       "4          10                    28  ...                NaN         NaN   \n",
       "\n",
       "   XLogP3 Target (Parsed)                                       POI Sequence  \\\n",
       "0     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "1     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "2     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "3     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "4     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
       "\n",
       "   E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
       "0             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "1             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "2             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "3             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "4             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
       "\n",
       "  Cell Line Identifier Active - OR Active (Dmax 0.6, pDC50 6.0)  \n",
       "0               MOLT-4         NaN                          NaN  \n",
       "1               MOLT-4         NaN                          NaN  \n",
       "2               MOLT-4         NaN                          NaN  \n",
       "3               MOLT-4         NaN                          NaN  \n",
       "4               MOLT-4        True                         True  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the PROTAC dataset\n",
    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
    "\n",
    "# Get Dmax_threshold from the active_col\n",
    "Dmax_threshold = float(active_col.split('Dmax')[1].split(',')[0].strip('(').strip(')').strip())\n",
    "pDC50_threshold = float(active_col.split('pDC50')[1].strip('(').strip(')').strip())\n",
    "\n",
    "protac_df = pd.read_csv('~/PROTAC-Degradation-Predictor/data/PROTAC-Degradation-DB.csv')\n",
    "# Map E3 Ligase Iap to IAP\n",
    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')\n",
    "protac_df[active_col] = protac_df.apply(\n",
    "    lambda x: pdp.is_active(\n",
    "        x['DC50 (nM)'],\n",
    "        x['Dmax (%)'],\n",
    "        pDC50_threshold=pDC50_threshold,\n",
    "        Dmax_threshold=Dmax_threshold,\n",
    "    ),\n",
    "    axis=1\n",
    ")\n",
    "protac_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Precomputing fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1467/1467 [00:02<00:00, 669.56it/s]\n",
      "Computing Tanimoto similarity: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1467/1467 [00:00<00:00, 3748.38it/s]\n"
     ]
    }
   ],
   "source": [
    "def get_smiles2fp_and_avg_tanimoto(protac_df: pd.DataFrame) -> tuple:\n",
    "    \"\"\" Get the SMILES to fingerprint dictionary and the average Tanimoto similarity.\n",
    "    \n",
    "    Args:\n",
    "        protac_df (pd.DataFrame): The DataFrame containing the PROTACs.\n",
    "    \n",
    "    Returns:\n",
    "        tuple: The SMILES to fingerprint dictionary and the average Tanimoto similarity.\n",
    "    \"\"\"\n",
    "    unique_smiles = protac_df['Smiles'].unique().tolist()\n",
    "\n",
    "    smiles2fp = {}\n",
    "    for smiles in tqdm(unique_smiles, desc='Precomputing fingerprints'):\n",
    "        smiles2fp[smiles] = pdp.get_fingerprint(smiles)\n",
    "\n",
    "    # # Get the pair-wise tanimoto similarity between the PROTAC fingerprints\n",
    "    # tanimoto_matrix = defaultdict(list)\n",
    "    # for i, smiles1 in enumerate(tqdm(protac_df['Smiles'].unique(), desc='Computing Tanimoto similarity')):\n",
    "    #     fp1 = smiles2fp[smiles1]\n",
    "    #     # TODO: Use BulkTanimotoSimilarity for better performance\n",
    "    #     for j, smiles2 in enumerate(protac_df['Smiles'].unique()[i:]):\n",
    "    #         fp2 = smiles2fp[smiles2]\n",
    "    #         tanimoto_dist = 1 - DataStructs.TanimotoSimilarity(fp1, fp2)\n",
    "    #         tanimoto_matrix[smiles1].append(tanimoto_dist)\n",
    "    # avg_tanimoto = {k: np.mean(v) for k, v in tanimoto_matrix.items()}\n",
    "    # protac_df['Avg Tanimoto'] = protac_df['Smiles'].map(avg_tanimoto)\n",
    "\n",
    "\n",
    "    tanimoto_matrix = defaultdict(list)\n",
    "    fps = list(smiles2fp.values())\n",
    "\n",
    "    # Compute all-against-all Tanimoto similarity using BulkTanimotoSimilarity\n",
    "    for i, (smiles1, fp1) in enumerate(tqdm(zip(unique_smiles, fps), desc='Computing Tanimoto similarity', total=len(fps))):\n",
    "        similarities = DataStructs.BulkTanimotoSimilarity(fp1, fps[i:])  # Only compute for i to end, avoiding duplicates\n",
    "        for j, similarity in enumerate(similarities):\n",
    "            distance = 1 - similarity\n",
    "            tanimoto_matrix[smiles1].append(distance)  # Store as distance\n",
    "            if i != i + j:\n",
    "                tanimoto_matrix[unique_smiles[i + j]].append(distance)  # Symmetric filling\n",
    "\n",
    "    # Calculate average Tanimoto distance for each unique SMILES\n",
    "    avg_tanimoto = {k: np.mean(v) for k, v in tanimoto_matrix.items()}\n",
    "    protac_df['Avg Tanimoto'] = protac_df['Smiles'].map(avg_tanimoto)\n",
    "\n",
    "    smiles2fp = {s: np.array(fp) for s, fp in smiles2fp.items()}\n",
    "\n",
    "    return smiles2fp, protac_df\n",
    "\n",
    "smiles2fp, protac_df = get_smiles2fp_and_avg_tanimoto(protac_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load embedding dictionaries\n",
    "protein2embedding = pdp.load_protein2embedding('../data/uniprot2embedding.h5')\n",
    "cell2embedding = pdp.load_cell2embedding('../data/cell2embedding.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Smiles': StandardScaler(),\n",
       " 'Uniprot': StandardScaler(),\n",
       " 'E3 Ligase Uniprot': StandardScaler(),\n",
       " 'Cell Line Identifier': StandardScaler()}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_ds = pdp.protac_dataset.PROTAC_Dataset(\n",
    "    protac_df[protac_df[active_col].notna()].copy(),\n",
    "    protein2embedding,\n",
    "    cell2embedding,\n",
    "    smiles2fp,\n",
    "    active_label=active_col,\n",
    ")\n",
    "scalers = protac_ds.fit_scaling()\n",
    "# protac_ds.apply_scaling(scalers)\n",
    "protac_ds.data.head()\n",
    "scalers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(857, 256)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all, _ = protac_ds.get_numpy_arrays()\n",
    "smiles, _ = protac_ds.get_numpy_arrays('Smiles')\n",
    "smiles.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import minmax scaler\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot whisker plot of the smiles array in seaborn\n",
    "plt.figure(figsize=(30, 10))\n",
    "sns.boxplot(smiles)\n",
    "plt.title('Boxplot of the SMILES fingerprints')\n",
    "plt.xlabel('Fingerprint bit')\n",
    "plt.ylabel('Fingerprint value')\n",
    "# Rotate x-axis labels\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaled_smiles = scalers['Smiles'].transform(smiles)\n",
    "\n",
    "plt.figure(figsize=(30, 10))\n",
    "sns.boxplot(scaled_smiles)\n",
    "plt.title('Boxplot of the SMILES fingerprints')\n",
    "plt.xlabel('Fingerprint bit')\n",
    "plt.ylabel('Fingerprint value')\n",
    "# Rotate x-axis labels\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter plot the smiles features (the smiles array is (samples, features))\n",
    "num_samples = smiles.shape[0]\n",
    "\n",
    "for i in range(smiles.shape[1]):\n",
    "    plt.scatter([i] * num_samples, smiles[:, i], alpha=0.1, c=smiles[:, i], cmap='viridis')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Value')\n",
    "# Plot the colorbar in the same color map (viridis)\n",
    "plt.colorbar()\n",
    "plt.title('Scatter plot of the SMILES features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaled_smiles = scalers['Smiles'].transform(smiles)\n",
    "# scaled_smiles = MinMaxScaler().fit_transform(scaled_smiles)\n",
    "for i in range(scaled_smiles.shape[1]):\n",
    "    # Viridis palette\n",
    "    plt.scatter([i] * num_samples, scaled_smiles[:, i], alpha=0.1, c=scaled_smiles[:, i], cmap='viridis')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Value')\n",
    "plt.colorbar()\n",
    "plt.title('Scatter plot of the SMILES features')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of te smiles array\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(scalers['Smiles'].transform(smiles).flatten(), bins=100)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}