10_scaffold_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%config InlineBackend.figure_formats = ['svg']\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "from tqdm.auto import tqdm\n",
    "from pandarallel import pandarallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 8 workers.\n",
      "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n"
     ]
    }
   ],
   "source": [
    "pandarallel.initialize(progress_bar=False, nb_workers=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem \n",
    "from rdkit.Chem import Scaffolds\n",
    "from rdkit.Chem.Scaffolds.MurckoScaffold import GetScaffoldForMol, MakeScaffoldGeneric\n",
    "\n",
    "def generate_scaffolds(smiles :str) -> (str , str):\n",
    "    murcko_smi = \"\"\n",
    "    carbon_smi = \"\"\n",
    "    if smiles:\n",
    "        try:\n",
    "            mol = Chem.MolFromSmiles(smiles)\n",
    "            if mol:\n",
    "                murcko_mol = GetScaffoldForMol(mol)\n",
    "                carbon_mol = MakeScaffoldGeneric(murcko_mol)\n",
    "                murcko_smi = Chem.MolToSmiles(murcko_mol)\n",
    "                carbon_smi = Chem.MolToSmiles(carbon_mol)\n",
    "        except:\n",
    "            pass\n",
    "    return murcko_smi, carbon_smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_npz(file):\n",
    "    file = np.load(file)\n",
    "    if \"arr_0\" in file:\n",
    "        fp = file[\"arr_0\"]\n",
    "    else:\n",
    "        fp = file[\"fps\"]\n",
    "    file.close()\n",
    "    fp = np.unpackbits(fp, axis=1)\n",
    "    return fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = \"output\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#colors = \"Set1\"\n",
    "colors = sns.color_palette([\"gray\", \"royalblue\", \"limegreen\", \"fuchsia\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.read_csv(f\"{experiment}/promiscuous_processed.csv\")\n",
    "all_data[\"class_set\"] = all_data[[\"class\",\"set\"]].apply(lambda x: ' '.join(x), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>cids</th>\n",
       "      <th>NostereoAromaticSMILES</th>\n",
       "      <th>rdkit_smiles</th>\n",
       "      <th>class</th>\n",
       "      <th>set</th>\n",
       "      <th>inactive</th>\n",
       "      <th>active</th>\n",
       "      <th>encodable</th>\n",
       "      <th>NLL_0</th>\n",
       "      <th>NLL_1</th>\n",
       "      <th>...</th>\n",
       "      <th>NLL_192</th>\n",
       "      <th>NLL_193</th>\n",
       "      <th>NLL_194</th>\n",
       "      <th>NLL_195</th>\n",
       "      <th>NLL_196</th>\n",
       "      <th>NLL_197</th>\n",
       "      <th>NLL_198</th>\n",
       "      <th>NLL_199</th>\n",
       "      <th>NLL_200</th>\n",
       "      <th>class_set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1483729</td>\n",
       "      <td>C#CCN1c2ccccc2Sc3c1nccc3</td>\n",
       "      <td>C#CCN1c2ccccc2Sc2cccnc21</td>\n",
       "      <td>multitarget</td>\n",
       "      <td>training</td>\n",
       "      <td>174</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>21.387634</td>\n",
       "      <td>21.237390</td>\n",
       "      <td>...</td>\n",
       "      <td>16.529953</td>\n",
       "      <td>16.579832</td>\n",
       "      <td>16.597193</td>\n",
       "      <td>16.598688</td>\n",
       "      <td>16.543293</td>\n",
       "      <td>16.469414</td>\n",
       "      <td>16.514149</td>\n",
       "      <td>16.565979</td>\n",
       "      <td>16.504505</td>\n",
       "      <td>multitarget training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2803060</td>\n",
       "      <td>C#CCSc1c(c(sn1)SCC#C)C#N</td>\n",
       "      <td>C#CCSc1nsc(SCC#C)c1C#N</td>\n",
       "      <td>multitarget</td>\n",
       "      <td>training</td>\n",
       "      <td>244</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>25.435406</td>\n",
       "      <td>25.543407</td>\n",
       "      <td>...</td>\n",
       "      <td>16.977541</td>\n",
       "      <td>17.072138</td>\n",
       "      <td>17.067848</td>\n",
       "      <td>17.018055</td>\n",
       "      <td>16.830230</td>\n",
       "      <td>16.742233</td>\n",
       "      <td>16.677542</td>\n",
       "      <td>16.708557</td>\n",
       "      <td>16.730461</td>\n",
       "      <td>multitarget training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16195846</td>\n",
       "      <td>C#Cc1ccc(cc1)C2CC(OC(=C2)C(=O)N3CCN(CC3)Cc4ccc...</td>\n",
       "      <td>C#Cc1ccc(C2C=C(C(=O)N3CCN(Cc4ccccc4)CC3)OC(OCC...</td>\n",
       "      <td>multitarget</td>\n",
       "      <td>training</td>\n",
       "      <td>151</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>26.490920</td>\n",
       "      <td>26.334568</td>\n",
       "      <td>...</td>\n",
       "      <td>20.697884</td>\n",
       "      <td>20.671890</td>\n",
       "      <td>20.672901</td>\n",
       "      <td>20.686928</td>\n",
       "      <td>20.666473</td>\n",
       "      <td>20.573582</td>\n",
       "      <td>20.579933</td>\n",
       "      <td>20.707083</td>\n",
       "      <td>20.698412</td>\n",
       "      <td>multitarget training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15910</td>\n",
       "      <td>C(#N)c1c(c(c(c(c1Cl)Cl)Cl)C#N)Cl</td>\n",
       "      <td>N#Cc1c(Cl)c(Cl)c(Cl)c(C#N)c1Cl</td>\n",
       "      <td>multitarget</td>\n",
       "      <td>test</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>16.172943</td>\n",
       "      <td>16.166480</td>\n",
       "      <td>...</td>\n",
       "      <td>14.846571</td>\n",
       "      <td>15.024720</td>\n",
       "      <td>14.982287</td>\n",
       "      <td>14.865892</td>\n",
       "      <td>14.852013</td>\n",
       "      <td>14.901800</td>\n",
       "      <td>14.970851</td>\n",
       "      <td>15.054768</td>\n",
       "      <td>14.919376</td>\n",
       "      <td>multitarget test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>219402</td>\n",
       "      <td>C(=O)C(C1C(C(C(=O)O1)O)O)O</td>\n",
       "      <td>O=CC(O)C1OC(=O)C(O)C1O</td>\n",
       "      <td>multitarget</td>\n",
       "      <td>test</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>28.578636</td>\n",
       "      <td>28.121542</td>\n",
       "      <td>...</td>\n",
       "      <td>26.758510</td>\n",
       "      <td>26.623638</td>\n",
       "      <td>26.574932</td>\n",
       "      <td>26.662964</td>\n",
       "      <td>26.547087</td>\n",
       "      <td>26.294607</td>\n",
       "      <td>26.226974</td>\n",
       "      <td>26.233580</td>\n",
       "      <td>26.405636</td>\n",
       "      <td>multitarget test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126660</th>\n",
       "      <td>2271648</td>\n",
       "      <td>COc1ccc(cc1OC)C(=O)NCCSCc2ccco2</td>\n",
       "      <td>COc1ccc(C(=O)NCCSCc2ccco2)cc1OC</td>\n",
       "      <td>notarget</td>\n",
       "      <td>test</td>\n",
       "      <td>271</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>21.621990</td>\n",
       "      <td>21.261372</td>\n",
       "      <td>...</td>\n",
       "      <td>20.394420</td>\n",
       "      <td>20.464436</td>\n",
       "      <td>20.385246</td>\n",
       "      <td>20.422644</td>\n",
       "      <td>20.343554</td>\n",
       "      <td>20.201818</td>\n",
       "      <td>20.377756</td>\n",
       "      <td>20.376064</td>\n",
       "      <td>20.427292</td>\n",
       "      <td>notarget test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126661</th>\n",
       "      <td>16015661</td>\n",
       "      <td>COc1ccccc1OCC(=O)NCC2(CCCCC2)N3CCOCC3</td>\n",
       "      <td>COc1ccccc1OCC(=O)NCC1(N2CCOCC2)CCCCC1</td>\n",
       "      <td>notarget</td>\n",
       "      <td>test</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>20.991210</td>\n",
       "      <td>20.982899</td>\n",
       "      <td>...</td>\n",
       "      <td>24.373564</td>\n",
       "      <td>24.396523</td>\n",
       "      <td>24.438263</td>\n",
       "      <td>24.556833</td>\n",
       "      <td>24.522337</td>\n",
       "      <td>24.390020</td>\n",
       "      <td>24.422657</td>\n",
       "      <td>24.407433</td>\n",
       "      <td>24.354984</td>\n",
       "      <td>notarget test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126662</th>\n",
       "      <td>6460680</td>\n",
       "      <td>Cc1c(c(=O)n2c(n1)nc([nH]2)NC(=O)c3ccccc3F)Cl</td>\n",
       "      <td>Cc1nc2nc(NC(=O)c3ccccc3F)[nH]n2c(=O)c1Cl</td>\n",
       "      <td>notarget</td>\n",
       "      <td>test</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>40.816193</td>\n",
       "      <td>38.581573</td>\n",
       "      <td>...</td>\n",
       "      <td>34.832237</td>\n",
       "      <td>34.861350</td>\n",
       "      <td>35.050010</td>\n",
       "      <td>35.229584</td>\n",
       "      <td>35.210610</td>\n",
       "      <td>34.906185</td>\n",
       "      <td>35.122765</td>\n",
       "      <td>35.377747</td>\n",
       "      <td>35.315840</td>\n",
       "      <td>notarget test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126663</th>\n",
       "      <td>51359661</td>\n",
       "      <td>CCOC(=O)C1CSCCS(=O)(=O)N1Cc2cccc(c2)F</td>\n",
       "      <td>CCOC(=O)C1CSCCS(=O)(=O)N1Cc1cccc(F)c1</td>\n",
       "      <td>notarget</td>\n",
       "      <td>test</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>25.938965</td>\n",
       "      <td>26.273390</td>\n",
       "      <td>...</td>\n",
       "      <td>31.086374</td>\n",
       "      <td>31.321445</td>\n",
       "      <td>31.371016</td>\n",
       "      <td>31.305890</td>\n",
       "      <td>31.340025</td>\n",
       "      <td>31.437233</td>\n",
       "      <td>31.563883</td>\n",
       "      <td>31.821478</td>\n",
       "      <td>31.634583</td>\n",
       "      <td>notarget test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126664</th>\n",
       "      <td>7770013</td>\n",
       "      <td>COc1ccc(cc1OC)CC(=O)Nc2ccccc2c3ccccc3</td>\n",
       "      <td>COc1ccc(CC(=O)Nc2ccccc2-c2ccccc2)cc1OC</td>\n",
       "      <td>notarget</td>\n",
       "      <td>test</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>22.673986</td>\n",
       "      <td>22.499912</td>\n",
       "      <td>...</td>\n",
       "      <td>22.407072</td>\n",
       "      <td>22.727180</td>\n",
       "      <td>23.056517</td>\n",
       "      <td>23.267717</td>\n",
       "      <td>22.903782</td>\n",
       "      <td>22.568031</td>\n",
       "      <td>22.835087</td>\n",
       "      <td>22.813126</td>\n",
       "      <td>22.841808</td>\n",
       "      <td>notarget test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126665 rows × 210 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cids                             NostereoAromaticSMILES  \\\n",
       "0        1483729                           C#CCN1c2ccccc2Sc3c1nccc3   \n",
       "1        2803060                           C#CCSc1c(c(sn1)SCC#C)C#N   \n",
       "2       16195846  C#Cc1ccc(cc1)C2CC(OC(=C2)C(=O)N3CCN(CC3)Cc4ccc...   \n",
       "3          15910                   C(#N)c1c(c(c(c(c1Cl)Cl)Cl)C#N)Cl   \n",
       "4         219402                         C(=O)C(C1C(C(C(=O)O1)O)O)O   \n",
       "...          ...                                                ...   \n",
       "126660   2271648                    COc1ccc(cc1OC)C(=O)NCCSCc2ccco2   \n",
       "126661  16015661              COc1ccccc1OCC(=O)NCC2(CCCCC2)N3CCOCC3   \n",
       "126662   6460680       Cc1c(c(=O)n2c(n1)nc([nH]2)NC(=O)c3ccccc3F)Cl   \n",
       "126663  51359661              CCOC(=O)C1CSCCS(=O)(=O)N1Cc2cccc(c2)F   \n",
       "126664   7770013              COc1ccc(cc1OC)CC(=O)Nc2ccccc2c3ccccc3   \n",
       "\n",
       "                                             rdkit_smiles        class  \\\n",
       "0                                C#CCN1c2ccccc2Sc2cccnc21  multitarget   \n",
       "1                                  C#CCSc1nsc(SCC#C)c1C#N  multitarget   \n",
       "2       C#Cc1ccc(C2C=C(C(=O)N3CCN(Cc4ccccc4)CC3)OC(OCC...  multitarget   \n",
       "3                          N#Cc1c(Cl)c(Cl)c(Cl)c(C#N)c1Cl  multitarget   \n",
       "4                                  O=CC(O)C1OC(=O)C(O)C1O  multitarget   \n",
       "...                                                   ...          ...   \n",
       "126660                    COc1ccc(C(=O)NCCSCc2ccco2)cc1OC     notarget   \n",
       "126661              COc1ccccc1OCC(=O)NCC1(N2CCOCC2)CCCCC1     notarget   \n",
       "126662           Cc1nc2nc(NC(=O)c3ccccc3F)[nH]n2c(=O)c1Cl     notarget   \n",
       "126663              CCOC(=O)C1CSCCS(=O)(=O)N1Cc1cccc(F)c1     notarget   \n",
       "126664             COc1ccc(CC(=O)Nc2ccccc2-c2ccccc2)cc1OC     notarget   \n",
       "\n",
       "             set  inactive  active  encodable      NLL_0      NLL_1  ...  \\\n",
       "0       training       174       6       True  21.387634  21.237390  ...   \n",
       "1       training       244       6       True  25.435406  25.543407  ...   \n",
       "2       training       151       6       True  26.490920  26.334568  ...   \n",
       "3           test        24       5       True  16.172943  16.166480  ...   \n",
       "4           test       100       6       True  28.578636  28.121542  ...   \n",
       "...          ...       ...     ...        ...        ...        ...  ...   \n",
       "126660      test       271       0       True  21.621990  21.261372  ...   \n",
       "126661      test        36       0       True  20.991210  20.982899  ...   \n",
       "126662      test        35       0       True  40.816193  38.581573  ...   \n",
       "126663      test        30       0       True  25.938965  26.273390  ...   \n",
       "126664      test       188       0       True  22.673986  22.499912  ...   \n",
       "\n",
       "          NLL_192    NLL_193    NLL_194    NLL_195    NLL_196    NLL_197  \\\n",
       "0       16.529953  16.579832  16.597193  16.598688  16.543293  16.469414   \n",
       "1       16.977541  17.072138  17.067848  17.018055  16.830230  16.742233   \n",
       "2       20.697884  20.671890  20.672901  20.686928  20.666473  20.573582   \n",
       "3       14.846571  15.024720  14.982287  14.865892  14.852013  14.901800   \n",
       "4       26.758510  26.623638  26.574932  26.662964  26.547087  26.294607   \n",
       "...           ...        ...        ...        ...        ...        ...   \n",
       "126660  20.394420  20.464436  20.385246  20.422644  20.343554  20.201818   \n",
       "126661  24.373564  24.396523  24.438263  24.556833  24.522337  24.390020   \n",
       "126662  34.832237  34.861350  35.050010  35.229584  35.210610  34.906185   \n",
       "126663  31.086374  31.321445  31.371016  31.305890  31.340025  31.437233   \n",
       "126664  22.407072  22.727180  23.056517  23.267717  22.903782  22.568031   \n",
       "\n",
       "          NLL_198    NLL_199    NLL_200             class_set  \n",
       "0       16.514149  16.565979  16.504505  multitarget training  \n",
       "1       16.677542  16.708557  16.730461  multitarget training  \n",
       "2       20.579933  20.707083  20.698412  multitarget training  \n",
       "3       14.970851  15.054768  14.919376      multitarget test  \n",
       "4       26.226974  26.233580  26.405636      multitarget test  \n",
       "...           ...        ...        ...                   ...  \n",
       "126660  20.377756  20.376064  20.427292         notarget test  \n",
       "126661  24.422657  24.407433  24.354984         notarget test  \n",
       "126662  35.122765  35.377747  35.315840         notarget test  \n",
       "126663  31.563883  31.821478  31.634583         notarget test  \n",
       "126664  22.835087  22.813126  22.841808         notarget test  \n",
       "\n",
       "[126665 rows x 210 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaffolds = all_data[\"rdkit_smiles\"].parallel_map(generate_scaffolds)\n",
    "bm = [i[0] for i in scaffolds]\n",
    "carbon = [i[0] for i in scaffolds]\n",
    "all_data[\"BM scaffold\"] = bm\n",
    "all_data[\"Carbon skeleton\"] = carbon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_epoch_to_list(epoch, mylist):\n",
    "    if epoch not in mylist:\n",
    "        mylist.append(epoch)\n",
    "    return mylist \n",
    "        \n",
    "        \n",
    "epochs = list(range(26))\n",
    "steps = [100,50,25,10,5,2,1]\n",
    "for r in steps:\n",
    "    for i in range(0,201,r):\n",
    "        epochs = add_epoch_to_list(i,epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0574114fe92b489d9117a2431d76f2c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=201), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in tqdm(epochs):\n",
    "    if os.path.exists(f\"sampled/{epoch}/processed_with_nn_with_scaffold.feather\"):\n",
    "        continue\n",
    "    else:\n",
    "        data = pd.read_feather(f\"sampled/{epoch}/processed_with_nn.feather\")\n",
    "        scaffolds = data[\"normalized_smiles\"].parallel_map(generate_scaffolds)\n",
    "        bm = [i[0] for i in scaffolds]\n",
    "        carbon = [i[0] for i in scaffolds]\n",
    "        data[\"BM scaffold\"] = bm\n",
    "        data[\"Carbon skeleton\"] = carbon\n",
    "        data.to_feather(f\"sampled/{epoch}/processed_with_nn_with_scaffold.feather\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "value_var = list(map(str,list(range(0,201))))\n",
    "def renamer(x):\n",
    "    if x.startswith(\"NLL\"):\n",
    "        x = x.split(\"_\")[-1]\n",
    "    return x\n",
    "all_data_plot = all_data.copy().rename(columns=renamer)\n",
    "all_data_plot = pd.melt(all_data_plot, id_vars='class_set', value_vars=value_var, var_name=\"Epoch\", value_name='Negative Log Likelihood')\n",
    "all_data_plot.Epoch = all_data_plot.Epoch.astype(\"int\").astype('category')\n",
    "all_data_plot.class_set = all_data_plot.class_set.astype('category')\n",
    "#all_data_plot.class_set.cat.reorder_categories(['NonPromiscuous (test)', 'Promiscuous (training)', 'Promiscuous (test)'], inplace=True)\n",
    "#all_data_plot.class_set.cat.rename_categories({'NonPromiscuous (test)':\"NonPromiscuous\"}, inplace=True)\n",
    "all_data_plot[\"Negative Log Likelihood (Base 10)\"]=-1*np.log10(np.exp(-1*all_data_plot[\"Negative Log Likelihood\"]))\n",
    "all_data_plot[\"Negative Log Likelihood (Base e)\"] = all_data_plot[\"Negative Log Likelihood\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5a5286a86c04ebc971069450a31decc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=201), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "classes = all_data[\"class_set\"].unique()\n",
    "distances = [0.1, 0.3, 0.4, 0.5]\n",
    "\n",
    "\n",
    "matches = {}\n",
    "matches_without_exact_matches = {}\n",
    "matches_without_training_bm = {}\n",
    "matches_without_training_skeleton = {}\n",
    "for epoch in tqdm(range(201)):\n",
    "    data = pd.read_feather(f\"sampled/{epoch}/processed_with_nn_with_scaffold.feather\").query(\"valid == 1\").drop_duplicates(\"normalized_smiles\")\n",
    "    \n",
    "    known_compounds = set(all_data[\"rdkit_smiles\"])\n",
    "    \n",
    "    data_without_matches = data.query(\"normalized_smiles not in @known_compounds\")\n",
    "    \n",
    "    known_training_BM = set(all_data.query(\"class_set == 'multitarget training'\")[\"BM scaffold\"])\n",
    "    data_without_training_BM = data.query(\"`BM scaffold` not in @known_training_BM\")\n",
    "\n",
    "    known_training_skeleton = set(all_data.query(\"class_set == 'multitarget training'\")[\"Carbon skeleton\"])\n",
    "    data_without_training_skeleton = data.query(\"`Carbon skeleton` not in @known_training_skeleton\")\n",
    "\n",
    "    \n",
    "    for class_ in classes:\n",
    "        for type_ in [\"BM scaffold\", \"Carbon skeleton\"]:\n",
    "            name = f\"{class_} {type_}\"\n",
    "            set_smiles = all_data.query(\"class_set == @class_\")[type_].drop_duplicates()\n",
    "            \n",
    "            nb_matched = len(data.query(f\"`{type_}` in @set_smiles\").drop_duplicates(type_))\n",
    "            percent_matched = (nb_matched / len(set_smiles)) * 100\n",
    "            if name not in matches:\n",
    "                matches[name] = []\n",
    "            matches[name].append((nb_matched, percent_matched))\n",
    "\n",
    "\n",
    "            nb_matched = len(data_without_matches.query(f\"`{type_}` in @set_smiles\").drop_duplicates(type_))\n",
    "            percent_matched = (nb_matched / len(set_smiles)) * 100\n",
    "            if name not in matches_without_exact_matches:\n",
    "                matches_without_exact_matches[name] = []\n",
    "            matches_without_exact_matches[name].append((nb_matched, percent_matched))\n",
    "\n",
    "            set_smiles = all_data.query(\"class_set == @class_ and `BM scaffold` not in @known_training_BM\")[type_].drop_duplicates()\n",
    "            nb_matched = len(data_without_training_BM.query(f\"`{type_}` in @set_smiles\").drop_duplicates(type_))\n",
    "            if len(set_smiles) == 0:\n",
    "                percent_matched = 0\n",
    "            else:\n",
    "                percent_matched = (nb_matched / len(set_smiles)) * 100\n",
    "            if name not in matches_without_training_bm:\n",
    "                matches_without_training_bm[name] = []\n",
    "            matches_without_training_bm[name].append((nb_matched, percent_matched))\n",
    "\n",
    "            set_smiles = all_data.query(\"class_set == @class_ and `Carbon skeleton` not in @known_training_BM\")[type_].drop_duplicates()\n",
    "            nb_matched = len(data_without_training_skeleton.query(f\"`{type_}` in @set_smiles\").drop_duplicates(type_))\n",
    "            if len(set_smiles) == 0:\n",
    "                percent_matched = 0\n",
    "            else:\n",
    "                percent_matched = (nb_matched / len(set_smiles)) * 100\n",
    "            if name not in matches_without_training_skeleton:\n",
    "                matches_without_training_skeleton[name] = []\n",
    "            matches_without_training_skeleton[name].append((nb_matched, percent_matched))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "869"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(known_training_skeleton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multitarget training 869 0\n",
      "multitarget test 1463 1252\n",
      "singletarget test 33977 33642\n",
      "notarget test 34024 33779\n"
     ]
    }
   ],
   "source": [
    "for class_ in classes:\n",
    "    smiles = all_data.query(\"class_set == @class_\")[\"BM scaffold\"].drop_duplicates()\n",
    "    set_smiles = all_data.query(\"class_set == @class_ and `BM scaffold` not in @known_training_BM\")[\"BM scaffold\"].drop_duplicates()\n",
    "    print(class_, len(smiles), len(set_smiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 350,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.plotting_context(\"paper\", font_scale=1.4):\n",
    "    with sns.axes_style(\"whitegrid\"):\n",
    "        fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,4.5)) \n",
    "        \n",
    "        \n",
    "        axes[0].plot(np.array(matches[\"multitarget training BM scaffold\"])[:,0])\n",
    "        axes[0].plot(np.array(matches[\"multitarget test BM scaffold\"])[:,0])\n",
    "        axes[0].plot(np.array(matches[\"singletarget test BM scaffold\"])[:,0])\n",
    "        axes[0].plot(np.array(matches[\"notarget test BM scaffold\"])[:,0])\n",
    "        axes[0].set_xlim(0,200)\n",
    "        #axes[0].set_ylim(0,3000)\n",
    "        axes[0].set_xlabel(\"Epoch\")\n",
    "        axes[0].set_ylabel(\"Retrieved\")\n",
    "        axes[0].set_title(\"(a)\")\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        axes[1].plot(np.array(matches[\"multitarget training BM scaffold\"])[:,1])\n",
    "        axes[1].plot(np.array(matches[\"multitarget test BM scaffold\"])[:,1])\n",
    "        axes[1].plot(np.array(matches[\"singletarget test BM scaffold\"])[:,1])\n",
    "        axes[1].plot(np.array(matches[\"notarget test BM scaffold\"])[:,1])\n",
    "        axes[1].set_xlim(0,200)\n",
    "        #axes[1].set_ylim(20,100)\n",
    "        axes[1].set_xlabel(\"Epoch\")\n",
    "        axes[1].set_ylabel(\"Retrieved (%)\")\n",
    "        axes[1].set_title(\"(b)\")\n",
    "        \n",
    "        \n",
    "        plt.tight_layout()\n",
    "        lgd = plt.legend([\"Multi-target (training)\",\"Multi-target (test)\",\"Single-target (test)\",\"No-target (test)\" ],loc='upper center', bbox_to_anchor=(-0.1,-0.2), ncol=4)\n",
    "        \n",
    "        if not os.path.exists(\"scaffolds_retrieved.svg\"):\n",
    "            fig.savefig(\"scaffolds_retrieved.svg\", bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "        if not os.path.exists(\"scaffolds_retrieved.png\"):\n",
    "            fig.savefig(\"scaffolds_retrieved.png\", dpi=400, bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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