02_fragCounts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd41ea49-be6e-4002-a722-01f9831e38b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from ast import literal_eval\n",
    "## converting string of tuple when importing csv avoiding literal eval on nan cells\n",
    "def _converting(cell):\n",
    "    if cell.startswith('(') :\n",
    "        return literal_eval(cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55f2892d-34b5-430b-9b3c-49ee0c568b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"results/01_zincToks.csv\", converters={'tokens':_converting})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b7d6184-d165-443c-a11f-c64a7f95a353",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "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>smiles</th>\n",
       "      <th>tokens</th>\n",
       "      <th>nToks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1</td>\n",
       "      <td>(F, &lt;5&gt;, c1ccccc1, &lt;0&gt;, N, C=O, C, &lt;6&gt;, c1ccc2...</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C[C@@H]1CC(Nc2cncc(-c3nncn3C)c2)C[C@@H](C)C1</td>\n",
       "      <td>(C, &lt;0S&gt;, C1CCCCC1, &lt;2&gt;, &lt;4S&gt;, (, C, ), N, &lt;1&gt;...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N#Cc1ccc(-c2ccc(O[C@@H](C(=O)N3CCCC3)c3ccccc3)...</td>\n",
       "      <td>(C1CCNC1, &lt;3&gt;, C=O, C|R, (, &lt;0&gt;, c1ccccc1, ), ...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCOC(=O)[C@@H]1CCCN(C(=O)c2nc(-c3ccc(C)cc3)n3c...</td>\n",
       "      <td>(C, C, O, C=O, &lt;1R&gt;, C1CCNCC1, &lt;3&gt;, C=O, &lt;0&gt;, ...</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N#CC1=C(SCC(=O)Nc2cccc(Cl)c2)N=C(O)[C@H](C#N)C...</td>\n",
       "      <td>(O, &lt;4&gt;, C1=CC2(CC=N1)CCCCC2, &lt;0&gt;, &lt;1&gt;, (, C#N...</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249323</th>\n",
       "      <td>CC1(C)CC[C@H](CNC(=O)Cn2ncc3ccccc3c2=O)c2ccccc21</td>\n",
       "      <td>(C, &lt;6&gt;, c1ccc2c(c1)CCCC2, &lt;9S&gt;, &lt;6&gt;, (, C, ),...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249324</th>\n",
       "      <td>Cn1ccnc1C(=O)c1ccc(NC(=O)C2CCN(C(=O)C(C)(C)C)C...</td>\n",
       "      <td>(C, C, (, C, ), (, C, ), C=O, &lt;3&gt;, C1CCNCC1, &lt;...</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249325</th>\n",
       "      <td>Cc1ccc(NC(=O)C(=O)N(C)Cc2ccccc2)c(C)c1</td>\n",
       "      <td>(c1ccccc1, &lt;0&gt;, C, N, (, C, ), C=O, C=O, N, &lt;3...</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249326</th>\n",
       "      <td>Cc1cc(C(=O)Nc2ccc(OCC(N)=O)cc2)c(C)n1C1CC1</td>\n",
       "      <td>(C, &lt;2&gt;, c1cc[nH]c1, &lt;0&gt;, &lt;4&gt;, (, C, ), &lt;3&gt;, (...</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249327</th>\n",
       "      <td>O=C(CC(c1ccccc1)c1ccccc1)N1CCN(S(=O)(=O)c2cccc...</td>\n",
       "      <td>(O=[NH+][O-], &lt;5&gt;, c1ccccc1, &lt;0&gt;, O=[SH2]=O, &lt;...</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249328 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   smiles  \\\n",
       "0                 CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1   \n",
       "1            C[C@@H]1CC(Nc2cncc(-c3nncn3C)c2)C[C@@H](C)C1   \n",
       "2       N#Cc1ccc(-c2ccc(O[C@@H](C(=O)N3CCCC3)c3ccccc3)...   \n",
       "3       CCOC(=O)[C@@H]1CCCN(C(=O)c2nc(-c3ccc(C)cc3)n3c...   \n",
       "4       N#CC1=C(SCC(=O)Nc2cccc(Cl)c2)N=C(O)[C@H](C#N)C...   \n",
       "...                                                   ...   \n",
       "249323   CC1(C)CC[C@H](CNC(=O)Cn2ncc3ccccc3c2=O)c2ccccc21   \n",
       "249324  Cn1ccnc1C(=O)c1ccc(NC(=O)C2CCN(C(=O)C(C)(C)C)C...   \n",
       "249325             Cc1ccc(NC(=O)C(=O)N(C)Cc2ccccc2)c(C)c1   \n",
       "249326         Cc1cc(C(=O)Nc2ccc(OCC(N)=O)cc2)c(C)n1C1CC1   \n",
       "249327  O=C(CC(c1ccccc1)c1ccccc1)N1CCN(S(=O)(=O)c2cccc...   \n",
       "\n",
       "                                                   tokens  nToks  \n",
       "0       (F, <5>, c1ccccc1, <0>, N, C=O, C, <6>, c1ccc2...     18  \n",
       "1       (C, <0S>, C1CCCCC1, <2>, <4S>, (, C, ), N, <1>...     16  \n",
       "2       (C1CCNC1, <3>, C=O, C|R, (, <0>, c1ccccc1, ), ...     16  \n",
       "3       (C, C, O, C=O, <1R>, C1CCNCC1, <3>, C=O, <0>, ...     15  \n",
       "4       (O, <4>, C1=CC2(CC=N1)CCCCC2, <0>, <1>, (, C#N...     20  \n",
       "...                                                   ...    ...  \n",
       "249323  (C, <6>, c1ccc2c(c1)CCCC2, <9S>, <6>, (, C, ),...     14  \n",
       "249324  (C, C, (, C, ), (, C, ), C=O, <3>, C1CCNCC1, <...     22  \n",
       "249325  (c1ccccc1, <0>, C, N, (, C, ), C=O, C=O, N, <3...     18  \n",
       "249326  (C, <2>, c1cc[nH]c1, <0>, <4>, (, C, ), <3>, (...     22  \n",
       "249327  (O=[NH+][O-], <5>, c1ccccc1, <0>, O=[SH2]=O, <...     17  \n",
       "\n",
       "[249328 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9cfdfc35-633f-40c8-8257-9589a60eeab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "_dict=df[\"nToks\"].describe().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd56083e-1c21-4feb-82a4-a6eef4584ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr=_dict[\"75%\"]-_dict[\"25%\"]\n",
    "_dict[\"lowerWisk\"]= 3 #_dict[\"25%\"]-1.5*iqr -> rounded to 3\n",
    "_dict[\"upperWisk\"]= 30 # _dict[\"75%\"]+1.5*iqr -> rounded to 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a09c36b-253e-486b-a54c-a5494953b062",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count': 249328.0,\n",
       " 'mean': 16.712374863633446,\n",
       " 'std': 4.855034526776576,\n",
       " 'min': 3.0,\n",
       " '25%': 13.0,\n",
       " '50%': 16.0,\n",
       " '75%': 20.0,\n",
       " 'max': 51.0,\n",
       " 'lowerWisk': 3,\n",
       " 'upperWisk': 30}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35faef33-f25a-4ef0-bc51-5c23532c2322",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data for compactness employed by work of Group SELFIES\n",
    "benchM=pd.read_csv(\"data/compactnessBenchmarking.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7449bd33-d2fb-48cb-a255-2a4578e0d31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 80.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    2, figsize=(9, 6), sharex=False,\n",
    "    gridspec_kw={\"height_ratios\": (.2, .8), \"hspace\":.05}\n",
    ")\n",
    "\n",
    "################################\n",
    "# Boxplot #\n",
    "color={'color': 'black'}\n",
    "meanprops={'markerfacecolor': 'black', \"marker\":\"X\", 'markeredgecolor': 'black'}\n",
    "boxP=ax[0].boxplot(df[\"nToks\"], vert=False, showmeans=True, manage_ticks=True, medianprops=color, meanprops=meanprops)\n",
    "\n",
    "# removing borders\n",
    "ax[0].spines['right'].set_visible(False)\n",
    "ax[0].spines['left'].set_visible(False)\n",
    "ax[0].spines['bottom'].set_visible(False)\n",
    "ax[0].xaxis.set_label_position('top')\n",
    "\n",
    "values = np.array([boxP[\"whiskers\"][i].get_xdata() for i in [0,1]], dtype=int).flatten()\n",
    "values = sorted(values.tolist() + [int(df[\"nToks\"].median())])\n",
    "ax[0].set_xticks( values, labels=values )\n",
    "ax[0].xaxis.tick_top()\n",
    "\n",
    "ax[0].set_yticks([])\n",
    "ax[0].set_ylim([0.9,1.1])\n",
    "#################################\n",
    "\n",
    "# Histogram\n",
    "fixedKw=dict(fill=True, density=True, bins=np.array(range(1, 121)),\n",
    "            histtype='stepfilled', alpha=0.5, ec=\"black\")\n",
    "\n",
    "ax[1].hist(df[\"nToks\"], \n",
    "           # hatch=\"+\", \n",
    "           # facecolor=\"grey\",\n",
    "           label=\"fragSMILES\",\n",
    "           **fixedKw\n",
    "          )\n",
    "\n",
    "for bench in [\"Group SELFIES\", \"SELFIES\", \"SMILES\"]:\n",
    "    ax[1].hist(benchM.query(\"Type==@bench\").iloc[...,1], \n",
    "               label=bench,\n",
    "              **fixedKw\n",
    "              )\n",
    "\n",
    "ax[1].set_xlabel(\"# of Tokens per molecule\")\n",
    "ax[1].set_ylabel('Probability density')\n",
    "ax[1].legend()\n",
    "#################\n",
    "\n",
    "ax[1].set_xlim([0, 80])\n",
    "ax[1].set_xticks( np.array(range(0,81,10)) )\n",
    "ax[1].xaxis.set_tick_params(labelbottom=True)\n",
    "ax[0].set_xlim([0,80])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "609ed475-0c8e-4f8b-b6f7-70a3f47e85c7",
   "metadata": {},
   "source": [
    "fig.savefig(\"results/02_encodingCount.pdf\", bbox_inches='tight', dpi = 200)\n",
    "fig.savefig(\"results/02_encodingCount.svg\", bbox_inches='tight', dpi = 200)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}