What do we kwnow about potenial drugs of COVID-19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What do we know about therapeutics? A molecular candidate similarity analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "********************************************************************************************************************************\n",
    " Antonio Oliver Gelabert ( ORCID : http://orcid.org/0000-0001-8571-2733 )\n",
    " March, 2020\n",
    " *******************************************************************************************************************************"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proteins and targets of COVID-19:\n",
    "\n",
    "+ https://news.northwestern.edu/stories/2020/03/new-drug-target-found-for-covid-19/\n",
    "+ https://www.biorxiv.org/content/10.1101/2020.03.02.968388v1\n",
    "+ http://www.rcsb.org/pdb/results/results.do?tabtoshow=Current&qrid=F2FE26F4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drugs that are currently testing against COVID19 \n",
    "\n",
    "From source:https://www.linkedin.com/pulse/covid-19-treatments-clinical-trials-ana-gavald%25C3%25A1/?trackingId=tzMbX9MOQUK8qJMQDKuE3w%3D%3D\n",
    "\n",
    "1. REMDESIVIR - Antiviral, prefered treatment against COVID-19 (https://en.wikipedia.org/wiki/Remdesivir ), (https://www.foxnews.com/science/remdesivir-what-to-know-about-potential-coronavirus-treatment ) ,( https://www.nature.com/articles/d41573-020-00016-0 )\n",
    "2. CHLOROQUINE - Antimalarial (https://en.wikipedia.org/wiki/Chloroquine )\n",
    "3. HYDROXYCHLOROQUINE - Derivative that improves CHLOROQUINE effectiveness (https://www.nature.com/articles/s41421-020-0156-0.pdf )\n",
    "4. LOPINAVIR  - Antiviral against VIH (https://en.wikipedia.org/wiki/Lopinavir )\n",
    "5. DARUNAVIR  - Antiviral against VIH (https://en.wikipedia.org/wiki/Darunavir )\n",
    "6. FAVIPIRAVIR - Effective with Ebola and other viruses (https://www.livescience.com/flu-drug-could-treat-coronavirus.html ) (https://www.redaccionmedica.com/secciones/sanidad-hoy/coronavirus-tratamiento-china-anuncia-resultados-del-antiviral-favipiravir-4773 )\n",
    "7. THALIDOMIDE - (https://en.wikipedia.org/wiki/Thalidomide )\n",
    "8. FINGOLIMOD  - Inmunomodulator effect. Multiple sclerosis (MS) threatment (https://en.wikipedia.org/wiki/Fingolimod )\n",
    "9. GALIDESIVIR - (https://en.wikipedia.org/wiki/Galidesivir )\n",
    "\n",
    "Antiviral drug list: https://es.wikipedia.org/wiki/Anexo:C%C3%B3digo_ATC_J05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"900\"\n",
       "            src=\"http://www.rcsb.org/3d-view/6W4H/1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x297716f9e10>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(src='http://www.rcsb.org/3d-view/6W4H/1', width=1000, height=900)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two major ligands observed to interact with that protein (http://www.rcsb.org/structure/6W4H):\n",
    "+ https://en.wikipedia.org/wiki/S-Adenosyl_methionine \n",
    "+ https://pubchem.ncbi.nlm.nih.gov/compound/beta-D-Fructopyranose#section=Wikipedia "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Protein Databases:\n",
    "   1. Protein Data Bank (https://www.rcsb.org/) \n",
    "   2. Uniprot (https://www.uniprot.org/)\n",
    "   3. DUD (for testing:http://dud.docking.org/ , 40 target proteins)\n",
    "   4. DUD-E (for testing: http://dude.docking.org/, extended, 100 target proteins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Molecular Formats (https://en.wiki)pedia.org/wiki/Chemical_table_file):\n",
    "   1. One-D : \n",
    "       + SMILES           (https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system)\n",
    "       + InChI/InChI Key  (https://en.wikipedia.org/wiki/International_Chemical_Identifier)\n",
    "   2. Two-D :\n",
    "        + SDF (http://biotech.fyicenter.com/resource/sdf_format.html)\n",
    "        + MOL (https://reference.wolfram.com/language/ref/format/MOL.html)\n",
    "   3. Three-D\n",
    "        + MOL2 (http://www.csb.yale.edu/userguides/datamanip/dock/DOCK_4.0.1/html/Manual.41.html)\n",
    "        + PDB (https://en.wikipedia.org/wiki/Protein_Data_Bank_(file_format))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation of propagation  :https://www.washingtonpost.com/graphics/2020/world/corona-simulator/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real Time coronavirus case-maps:\n",
    "   + https://coronavirus.jhu.edu/map.html\n",
    "   + https://www.trackcorona.live/\n",
    "   + https://hgis.uw.edu/virus/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"https://coronavirus.jhu.edu\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x275b7790080>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(src='https://coronavirus.jhu.edu', width=900, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Pandemics : \n",
    "\n",
    "https://www.visualcapitalist.com/history-of-pandemics-deadliest/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful information, treatment and protocols described by chinese comunity: \n",
    "https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Software for molecular visualization, format conversion and structure treatment:\n",
    "\n",
    "   + OPENBABEL (http://openbabel.org/wiki/Main_Page) - command line version or: pip install openbabel\n",
    "   + PYBEL (pip install pybel)\n",
    "   + JMOL (http://jmol.sourceforge.net/download/)\n",
    "   + AVOGADRO (http://avogadro.cc/)\n",
    "   + VMD (https://www.ks.uiuc.edu/Research/vmd/)\n",
    "   + CHIMERA (https://www.cgl.ucsf.edu/chimera/)\n",
    "   + PYMOL (https://pymol.org/2/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Openbabel for molecule visuatization (example list of drugs on test with covid19, REMDESIVIR)\n",
    "\n",
    "1. Lets first obtain the SMILES code, available on wikipedia link provided : \"CCC(COC(=O)[C@@H](NP(=O)(Oc1ccccc1)OC[C@H]1O[C@@]([C@@H]([C@@H]1O)O)(C#N)c1ccc2n1ncnc2N)C)CC\" and put in a new text file. Save as REMDESIVIR.smi.\n",
    "2. To continue use openbabel to convert to 2D or 3D format to visualize molecule information and structure. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1 molecule converted\n",
      "1 molecule converted\n",
      "1 molecule converted\n"
     ]
    }
   ],
   "source": [
    "!obabel -ismi REMDESIVIR.smi -opng -O remdes.png \n",
    "!obabel -ismi REMDESIVIR.smi -osvg -O remdes.svg\n",
    "!obabel -ismi REMDESIVIR.smi -omol2 -O remdes.mol2 --gen3d --partialcharge mmff94"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To visualize 2D structure using png generated file \n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "img = cv2.imread('remdes.png') \n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg font-family=\"sans-serif\" height=\"426.592\" stroke=\"rgb(0,0,0)\" stroke-linecap=\"round\" stroke-width=\"1\" width=\"447.536\" x=\"0\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:cml=\"http://www.xml-cml.org/schema\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" y=\"0\">\n",
       "<rect fill=\"rgb(255,255,255)\" height=\"100%\" stroke-width=\"0\" width=\"100%\" x=\"0%\" y=\"0%\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"154.5\" x2=\"127.5\" y1=\"386.6\" y2=\"386.6\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"127.5\" x2=\"107.5\" y1=\"386.6\" y2=\"352.0\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"107.5\" x2=\"127.5\" y1=\"352.0\" y2=\"317.3\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"127.5\" x2=\"154.5\" y1=\"317.3\" y2=\"317.3\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"174.0\" x2=\"187.5\" y1=\"306.1\" y2=\"282.7\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"184.9\" x2=\"171.4\" y1=\"284.2\" y2=\"260.8\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"190.1\" x2=\"176.6\" y1=\"281.2\" y2=\"257.8\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"187.5\" x2=\"227.5\" y1=\"282.7\" y2=\"282.7\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"228.3\" x2=\"226.7\" y1=\"288.0\" y2=\"288.0\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"229.0\" x2=\"226.1\" y1=\"292.3\" y2=\"292.3\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"229.6\" x2=\"225.5\" y1=\"296.6\" y2=\"296.6\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"230.3\" x2=\"224.8\" y1=\"300.9\" y2=\"300.9\"/>\n",
       "<line stroke=\"rgb(0,0,0)\" stroke-width=\"1.0\" x1=\"230.9\" x2=\"224.2\" y1=\"305.2\" y2=\"305.2\"/>\n",
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       "<text fill=\"black\" font-family=\"sans-serif\" font-size=\"18.000000\" x=\"10.000000\" y=\"20.000000\"/>\n",
       "<title> - OBDepict</title>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets visualize better using SVG format \n",
    "from IPython.display import SVG, display\n",
    "display(SVG('remdes.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@<TRIPOS>MOLECULE\n",
      "*****\n",
      " 77 80 0 0 0\n",
      "SMALL\n",
      "GASTEIGER\n",
      "\n",
      "@<TRIPOS>ATOM\n",
      "      1 C           3.5063    2.0698    3.2446 C.3     1  LIG1        0.0000\n",
      "      2 C           2.6936    3.2441    2.7078 C.3     1  LIG1        0.0000\n",
      "      3 C           2.9573    3.5804    1.2244 C.3     1  LIG1        0.0000\n",
      "      4 C           2.0974    4.7705    0.7482 C.3     1  LIG1        0.2800\n",
      "      5 O           0.6951    4.4861    0.8608 O.3     1  LIG1       -0.4300\n",
      "      6 C           0.1459    3.7396   -0.1367 C.2     1  LIG1        0.6590\n",
      "      7 O           0.7652    3.1909   -1.0424 O.2     1  LIG1       -0.5700\n",
      "      8 C          -1.3576    3.5577    0.0544 C.3     1  LIG1        0.3310\n",
      "      9 H          -1.7816    3.3725   -0.9395 H       1  LIG1        0.0000\n",
      "     10 N          -1.5911    2.3940    0.9278 N.3     1  LIG1       -0.8979\n",
      "     11 P          -0.9977    0.8451    0.2818 P.3     1  LIG1        1.5103\n",
      "     12 O          -1.4864    0.1505    1.5526 O.2     1  LIG1       -0.7000\n",
      "     13 O          -2.1134    0.8706   -0.9345 O.3     1  LIG1       -0.3537\n",
      "     14 C          -3.4361    0.5363   -0.9052 C.ar    1  LIG1        0.0825\n",
      "     15 C          -4.1359    0.2381    0.2554 C.ar    1  LIG1       -0.1500\n",
      "     16 C          -5.5165    0.0627    0.2209 C.ar    1  LIG1       -0.1500\n",
      "     17 C          -6.2042    0.1613   -0.9838 C.ar    1  LIG1       -0.1500\n",
      "     18 C          -5.5038    0.4058   -2.1634 C.ar    1  LIG1       -0.1500\n",
      "     19 C          -4.1176    0.5819   -2.1265 C.ar    1  LIG1       -0.1500\n",
      "     20 O          -0.4211   -0.6603   -0.3460 O.3     1  LIG1       -0.5512\n",
      "     21 C           0.1512   -0.7375   -1.6411 C.3     1  LIG1        0.2800\n",
      "     22 C           0.5618   -2.1981   -1.8955 C.3     1  LIG1        0.2800\n",
      "     23 H           1.2209   -2.5260   -1.0814 H       1  LIG1        0.0000\n",
      "     24 O          -0.5971   -3.0765   -1.8544 O.3     1  LIG1       -0.5600\n",
      "     25 C          -0.3580   -4.1601   -2.7986 C.3     1  LIG1        0.6600\n",
      "     26 C           0.3292   -3.4287   -3.9592 C.3     1  LIG1        0.2800\n",
      "     27 H          -0.4138   -2.8732   -4.5472 H       1  LIG1        0.0000\n",
      "     28 C           1.2219   -2.4086   -3.2590 C.3     1  LIG1        0.2800\n",
      "     29 H           1.3143   -1.4904   -3.8458 H       1  LIG1        0.0000\n",
      "     30 O           2.5423   -2.9334   -3.1028 O.3     1  LIG1       -0.6800\n",
      "     31 O           1.1062   -4.2174   -4.8660 O.3     1  LIG1       -0.6800\n",
      "     32 C           0.4569   -5.2292   -2.1995 C.1     1  LIG1        0.3571\n",
      "     33 N           1.1064   -6.1035   -1.7237 N.1     1  LIG1       -0.5571\n",
      "     34 C          -1.7388   -4.7085   -3.1389 C.ar    1  LIG1       -0.3316\n",
      "     35 C          -2.9035   -4.7423   -2.3784 C.ar    1  LIG1       -0.1500\n",
      "     36 C          -3.9091   -5.3841   -3.1404 C.ar    1  LIG1       -0.1500\n",
      "     37 C          -3.3296   -5.7439   -4.3357 C.ar    1  LIG1       -0.2366\n",
      "     38 N          -2.0253   -5.3493   -4.3256 N.ar    1  LIG1        0.5052\n",
      "     39 N          -1.1750   -5.5452   -5.3532 N.ar    1  LIG1       -0.6520\n",
      "     40 C          -1.7134   -6.1383   -6.3776 C.ar    1  LIG1        0.6010\n",
      "     41 N          -3.0049   -6.5804   -6.4954 N.ar    1  LIG1       -0.6610\n",
      "     42 C          -3.8209   -6.4018   -5.5028 C.ar    1  LIG1        0.5850\n",
      "     43 N          -5.0971   -6.8662   -5.6330 N.pl3   1  LIG1       -0.8500\n",
      "     44 C          -1.9804    4.8639    0.5524 C.3     1  LIG1        0.0000\n",
      "     45 C           4.4379    3.7985    0.8595 C.3     1  LIG1        0.0000\n",
      "     46 C           5.1901    4.8457    1.6690 C.3     1  LIG1        0.0000\n",
      "     47 H           3.3194    1.9490    4.3169 H       1  LIG1        0.0000\n",
      "     48 H           4.5826    2.2153    3.1211 H       1  LIG1        0.0000\n",
      "     49 H           3.2254    1.1357    2.7470 H       1  LIG1        0.0000\n",
      "     50 H           2.8784    4.1231    3.3355 H       1  LIG1        0.0000\n",
      "     51 H           1.6326    2.9903    2.8324 H       1  LIG1        0.0000\n",
      "     52 H           2.6297    2.7028    0.6534 H       1  LIG1        0.0000\n",
      "     53 H           2.3230    5.0450   -0.2900 H       1  LIG1        0.0000\n",
      "     54 H           2.2826    5.6672    1.3514 H       1  LIG1        0.0000\n",
      "     55 H          -2.5742    2.2766    1.1398 H       1  LIG1        0.3600\n",
      "     56 H          -3.6500    0.1544    1.2184 H       1  LIG1        0.1500\n",
      "     57 H          -6.0462   -0.1279    1.1499 H       1  LIG1        0.1500\n",
      "     58 H          -7.2848    0.0522   -0.9962 H       1  LIG1        0.1500\n",
      "     59 H          -6.0424    0.4835   -3.1044 H       1  LIG1        0.1500\n",
      "     60 H          -3.5814    0.7738   -3.0529 H       1  LIG1        0.1500\n",
      "     61 H           1.0266   -0.0818   -1.6789 H       1  LIG1        0.0000\n",
      "     62 H          -0.5804   -0.4174   -2.3878 H       1  LIG1        0.0000\n",
      "     63 H           2.7106   -3.4718   -3.9069 H       1  LIG1        0.4000\n",
      "     64 H           0.4998   -4.9124   -5.2064 H       1  LIG1        0.4000\n",
      "     65 H          -3.0091   -4.3372   -1.3773 H       1  LIG1        0.1500\n",
      "     66 H          -4.9365   -5.5552   -2.8450 H       1  LIG1        0.1500\n",
      "     67 H          -1.0711   -6.3122   -7.2581 H       1  LIG1        0.0600\n",
      "     68 H          -5.1960   -7.5226   -6.3999 H       1  LIG1        0.4000\n",
      "     69 H          -5.5459   -7.1735   -4.7794 H       1  LIG1        0.4000\n",
      "     70 H          -1.6289    5.7021   -0.0661 H       1  LIG1        0.0000\n",
      "     71 H          -1.6972    5.0941    1.5852 H       1  LIG1        0.0000\n",
      "     72 H          -3.0724    4.8415    0.4933 H       1  LIG1        0.0000\n",
      "     73 H           4.4972    4.0697   -0.2022 H       1  LIG1        0.0000\n",
      "     74 H           4.9710    2.8446    0.9471 H       1  LIG1        0.0000\n",
      "     75 H           4.5901    5.7353    1.8765 H       1  LIG1        0.0000\n",
      "     76 H           6.0799    5.1645    1.1143 H       1  LIG1        0.0000\n",
      "     77 H           5.5345    4.4309    2.6216 H       1  LIG1        0.0000\n",
      "@<TRIPOS>BOND\n",
      "     1     1     2    1\n",
      "     2     2     3    1\n",
      "     3     3     4    1\n",
      "     4     4     5    1\n",
      "     5     5     6    1\n",
      "     6     6     7    2\n",
      "     7     6     8    1\n",
      "     8     8     9    1\n",
      "     9     8    10    1\n",
      "    10    10    11    1\n",
      "    11    11    12    2\n",
      "    12    11    13    1\n",
      "    13    13    14    1\n",
      "    14    14    15   ar\n",
      "    15    15    16   ar\n",
      "    16    16    17   ar\n",
      "    17    17    18   ar\n",
      "    18    18    19   ar\n",
      "    19    14    19   ar\n",
      "    20    11    20    1\n",
      "    21    20    21    1\n",
      "    22    21    22    1\n",
      "    23    22    23    1\n",
      "    24    22    24    1\n",
      "    25    24    25    1\n",
      "    26    25    26    1\n",
      "    27    26    27    1\n",
      "    28    26    28    1\n",
      "    29    28    29    1\n",
      "    30    22    28    1\n",
      "    31    28    30    1\n",
      "    32    26    31    1\n",
      "    33    32    33    3\n",
      "    34    34    35   ar\n",
      "    35    35    36   ar\n",
      "    36    36    37   ar\n",
      "    37    37    38   ar\n",
      "    38    34    38   ar\n",
      "    39    38    39   ar\n",
      "    40    39    40   ar\n",
      "    41    40    41   ar\n",
      "    42    41    42   ar\n",
      "    43    37    42   ar\n",
      "    44    42    43    1\n",
      "    45     8    44    1\n",
      "    46     3    45    1\n",
      "    47    45    46    1\n",
      "    48    25    34    1\n",
      "    49    25    32    1\n",
      "    50     1    47    1\n",
      "    51     1    48    1\n",
      "    52     1    49    1\n",
      "    53     2    50    1\n",
      "    54     2    51    1\n",
      "    55     3    52    1\n",
      "    56     4    53    1\n",
      "    57     4    54    1\n",
      "    58    10    55    1\n",
      "    59    15    56    1\n",
      "    60    16    57    1\n",
      "    61    17    58    1\n",
      "    62    18    59    1\n",
      "    63    19    60    1\n",
      "    64    21    61    1\n",
      "    65    21    62    1\n",
      "    66    30    63    1\n",
      "    67    31    64    1\n",
      "    68    35    65    1\n",
      "    69    36    66    1\n",
      "    70    40    67    1\n",
      "    71    43    68    1\n",
      "    72    43    69    1\n",
      "    73    44    70    1\n",
      "    74    44    71    1\n",
      "    75    44    72    1\n",
      "    76    45    73    1\n",
      "    77    45    74    1\n",
      "    78    46    75    1\n",
      "    79    46    76    1\n",
      "    80    46    77    1\n"
     ]
    }
   ],
   "source": [
    "# Now, lets look at the data structure into mol2 file that have spatial information of x,y,z coordinates for each atom, the atom types \n",
    "# and charges (last column):\n",
    "!cat remdes.mol2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these example, we see that this molecule has 77 atoms and 80 bonds. As well, the most electronegative atom is the 10th atom, which is Nitrogen (N). It has the spatial coordinates xyz:-1.5911    2.3940    0.9278, is type 3 N.3, i.e Nitrogen sp3 and has a charge of -0.8979. If summ of all atom charges, result will be 0 as molecule has neutral charge.\n",
    "\n",
    "Lets explore some calculated molecular properties appending them to the title : \n",
    "\n",
    "+ molecular weight (MW)\n",
    "+ logP\n",
    "+ Hydrogen Bond Donors (HBD)\n",
    "+ Hydrogen Bond Acceptors (HBA)\n",
    "+ Total Polar Surface Area (TPSA)\n",
    "\n",
    "(https://open-babel.readthedocs.io/en/latest/Descriptors/descriptors.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1 molecule converted\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCC(COC(=O)[C@@H](NP(=O)(Oc1ccccc1)OC[C@H]1O[C@@]([C@@H]([C@@H]1O)O)(C#N)c1ccc2n1ncnc2N)C)CC\t602.576 3.28428 4 14 213.36\n"
     ]
    }
   ],
   "source": [
    "!obabel -ismi REMDESIVIR.smi -osmi -O remdesivprop.smi --append \"MW\" \"logP\" \"HBD\" \"HBA2\" \"TPSA\" \n",
    "!cat remdesivprop.smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets visualize 3D structure by using available databases (LigandBox, Zinc, KEGG,...). The KEGG code is available through wikipedia provided link : D11472 (https://www.kegg.jp/entry/D11472). In compound information there are some links to other databases like pubchem where 3D structure of a conformation is available. Note that there are a lot of possible 3D structures for the same compound, so it has rotatable bonds and in fact is not a rigid structure, it moves and change conformation. Howerver, the minimal conformation energy is tipically selected as a valid structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"https://pubchem.ncbi.nlm.nih.gov/compound/121304016#section=3D-Conformer\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x275b77907f0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://pubchem.ncbi.nlm.nih.gov/compound/121304016#section=3D-Conformer', width=900, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, how we can search for posible analogues, or candidate drugs to have potentially similar effects by screening a \n",
    "large database of compounds like ZINC database that contains millions of different structures. \n",
    "\n",
    "To rapid search the first step is obtaining efficient molecular descriptors, like the properties described (Molecular Weight, \n",
    "Hydrogen Bond Donors, Hydrogen Bond acceptors,...). In fact the Lipinski Rule of Five is based in this fact (https://en.wikipedia.org/wiki/Lipinski%27s_rule_of_five)\n",
    "\n",
    "There are also structure based descriptors (like USR descriptors, https://www.ncbi.nlm.nih.gov/pubmed/17342716)\n",
    "\n",
    "And descriptors that accounts for structure and charge:\n",
    "\n",
    "+ https://www.nature.com/articles/srep43738 (PED descriptors) \n",
    "+ https://www.ncbi.nlm.nih.gov/pubmed/20614163 (ElectroShape descriptors)\n",
    "\n",
    "Threre are of course many other examples in the literature.\n",
    "\n",
    "But first of all, how this descriptors are calculated? Let's focus on PED descriptors. The recipe for calculation of PED is based on atom pair energy distribution and the search for the tails of that distribution. \n",
    "\n",
    "We will take only 12 dymension vector for description of a compound. Then:\n",
    "\n",
    "+ The Energy between pairs Eij of a molecule is searched and ranked. ($E_{i,j}=q_{i}q_{j}/r_{i,j}$)\n",
    "+ The top 6 bigger and lower (i.e. negative) are taken.\n",
    "\n",
    "Where $q_{i}$ is the charge of atom i and $q_{j}$ is the charge of 'j' atom. rij is the Euclidian distance between i and j atoms obtained by using respective coordinates (xi,yi,zi) and (xj,yj,zj) that can be obtained using mol2 file information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "file='remdes.mol2'\n",
    "fat = open(\"remdes_atoms.mol2\", 'w')\n",
    "fat.write(\"Id Atype x y z Otype Str1 Str2 Charge\\n\")\n",
    "with open(file) as f:\n",
    "    for line in f:\n",
    "        if line.rstrip() == \"@<TRIPOS>ATOM\":\n",
    "            print(\"\")\n",
    "            for line in f:\n",
    "                if line.rstrip() == \"@<TRIPOS>BOND\":\n",
    "                    break\n",
    "                fat.write(line.rstrip()+'\\n')\n",
    "fat.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('remdes_atoms.mol2', delimiter=r\"\\s+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>Id</th>\n",
       "      <th>Atype</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Otype</th>\n",
       "      <th>Str1</th>\n",
       "      <th>Str2</th>\n",
       "      <th>Charge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>3.5063</td>\n",
       "      <td>2.0698</td>\n",
       "      <td>3.2446</td>\n",
       "      <td>C.3</td>\n",
       "      <td>1</td>\n",
       "      <td>LIG1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>2.6936</td>\n",
       "      <td>3.2441</td>\n",
       "      <td>2.7078</td>\n",
       "      <td>C.3</td>\n",
       "      <td>1</td>\n",
       "      <td>LIG1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>2.9573</td>\n",
       "      <td>3.5804</td>\n",
       "      <td>1.2244</td>\n",
       "      <td>C.3</td>\n",
       "      <td>1</td>\n",
       "      <td>LIG1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0974</td>\n",
       "      <td>4.7705</td>\n",
       "      <td>0.7482</td>\n",
       "      <td>C.3</td>\n",
       "      <td>1</td>\n",
       "      <td>LIG1</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>O</td>\n",
       "      <td>0.6951</td>\n",
       "      <td>4.4861</td>\n",
       "      <td>0.8608</td>\n",
       "      <td>O.3</td>\n",
       "      <td>1</td>\n",
       "      <td>LIG1</td>\n",
       "      <td>-0.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id Atype       x       y       z Otype  Str1  Str2  Charge\n",
       "0   1     C  3.5063  2.0698  3.2446   C.3     1  LIG1    0.00\n",
       "1   2     C  2.6936  3.2441  2.7078   C.3     1  LIG1    0.00\n",
       "2   3     C  2.9573  3.5804  1.2244   C.3     1  LIG1    0.00\n",
       "3   4     C  2.0974  4.7705  0.7482   C.3     1  LIG1    0.28\n",
       "4   5     O  0.6951  4.4861  0.8608   O.3     1  LIG1   -0.43"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've read all molecule file in dataframe, lets compute descriptor pairwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "Eij=[]\n",
    "for index, row in df.iterrows():\n",
    "#    print(row['x'], row['y'],row['z'],row['Charge'])\n",
    "    for index2, row2 in df.iterrows():\n",
    "        if(index2>index):\n",
    "            dij=((row['x']-row2['x'])**2+(row['y']-row2['y'])**2+(row['z']-row2['z'])**2)**0.5\n",
    "            if(dij > 1.0):\n",
    "                Eij.append(row['Charge']*row2['Charge']/dij)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2924"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Eij)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "PED=[]\n",
    "Eij.sort(reverse = True)\n",
    "PED.extend(Eij[0:6])\n",
    "PED.extend(Eij[len(Eij)-6:len(Eij)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we have obtained the PED vector descriptor of the Remdesivir:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3169466016294657,\n",
       " 0.2696131344506417,\n",
       " 0.24632319853782667,\n",
       " 0.23682700577565688,\n",
       " 0.18206302009878567,\n",
       " 0.18012298170235472,\n",
       " -0.33521089781654956,\n",
       " -0.3359120202246985,\n",
       " -0.36447347022457766,\n",
       " -0.48120644892607634,\n",
       " -0.6916772819469656,\n",
       " -0.76183729528545]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PED"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare the similarity of this molecule with other molecule, like, for instance, Hydroxychloroquine: https://en.wikipedia.org/wiki/Hydroxychloroquine , https://www.kegg.jp/entry/D08050\n",
    "\n",
    "First of all, compute descriptors in the same way, with previous computation of structure:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1 molecule converted\n"
     ]
    }
   ],
   "source": [
    "!obabel -ismi Hydroxcl.smi -omol2 -O hydroxcl.mol2 --gen3d --partialcharge mmff94"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file='hydroxcl.mol2'\n",
    "fat = open(\"hydroxcl_atoms.mol2\", 'w')\n",
    "fat.write(\"Id Atype x y z Otype Str1 Str2 Charge\\n\")\n",
    "with open(file) as f:\n",
    "    for line in f:\n",
    "        if line.rstrip() == \"@<TRIPOS>ATOM\":\n",
    "            print(\"\")\n",
    "            for line in f:\n",
    "                if line.rstrip() == \"@<TRIPOS>BOND\":\n",
    "                    break\n",
    "                fat.write(line.rstrip()+'\\n')\n",
    "fat.close()\n",
    "\n",
    "df=pd.read_csv('hydroxcl_atoms.mol2', delimiter=r\"\\s+\")\n",
    "Eij=[]\n",
    "for index, row in df.iterrows():\n",
    "#    print(row['x'], row['y'],row['z'],row['Charge'])\n",
    "    for index2, row2 in df.iterrows():\n",
    "        if(index2>index):\n",
    "            dij=((row['x']-row2['x'])**2+(row['y']-row2['y'])**2+(row['z']-row2['z'])**2)**0.5\n",
    "            if(dij > 1.0):\n",
    "                Eij.append(row['Charge']*row2['Charge']/dij)\n",
    "PED2=[]\n",
    "Eij.sort(reverse = True)\n",
    "PED2.extend(Eij[0:6])\n",
    "PED2.extend(Eij[len(Eij)-6:len(Eij)])                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.17685097006991723,\n",
       " 0.12923546660583485,\n",
       " 0.12720712377760288,\n",
       " 0.07950186351752637,\n",
       " 0.070923767143319,\n",
       " 0.05763799801257578,\n",
       " -0.1433446930813584,\n",
       " -0.1475555715703501,\n",
       " -0.14769458640918204,\n",
       " -0.1483583272239399,\n",
       " -0.2180227423241736,\n",
       " -0.34089287657972867]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PED2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets compare both molecules Remdesivir and Hydroxychloroquine with vectors PED1 and PED2 respectively, using a Manhattan distance based similarity metrics S:\n",
    "\n",
    "$S=\\frac{1}{1+\\frac{1}{12}\\sum_{i=1}^{12}|u_i-v_i|}$\n",
    "\n",
    "Where u_i, v_i are the component vectors of molecule i and j (in the case of study,Remdesivir and Hydroxychloroquine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similarity between both molecules: 85.0  %\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "MhD=0.0\n",
    "for i in range(0,len(PED)-1):\n",
    "    MhD=MhD+1/12*np.absolute(PED[i]-PED2[i])\n",
    "S=1.0/(1.0+MhD)\n",
    "print('Similarity between both molecules:', np.round(S,2)*100,' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The comparison with this two molecules using PED method and Manhattan distance metrics for similarity analysis \n",
    "provide score of 85%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fast virtual screening (VS) can be applied by computing this descriptors for very large database and \n",
    "obtaining the first Hits, that will be the most similar ones. This descriptors were designed in order to find active electrostatic \n",
    "regions in molecules and by assessing that similar patterns of electrostatic interactions will probably have in some cases\n",
    "the same drug targets. The similarity principle is the basis for search of new compounds, together with other approaches like\n",
    "docking.\n",
    "\n",
    "Other alternatives to numerical descriptors are the binary fingerprints (http://openbabel.org/wiki/Tutorial:Fingerprints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets do the processing several MOL2 separated files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similarity between query and  CLOROQUINA.mol.mol2  :  0.84\n",
      "Similarity between query and  DARUNAVIR.mol.mol2  :  0.96\n",
      "Similarity between query and  FINGOLIMOD.mol.mol2  :  0.86\n",
      "Similarity between query and  Hydroxychloroquine.mol.mol2  :  0.84\n",
      "Similarity between query and  LOPINAVIR.mol.mol2  :  0.92\n",
      "Similarity between query and  remdes.mol2  :  0.99\n",
      "Similarity between query and  REMDESIVIR.mol.mol2  :  0.99\n"
     ]
    },
    {
     "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>u1</th>\n",
       "      <th>u2</th>\n",
       "      <th>u3</th>\n",
       "      <th>u4</th>\n",
       "      <th>u5</th>\n",
       "      <th>u6</th>\n",
       "      <th>u7</th>\n",
       "      <th>u8</th>\n",
       "      <th>u9</th>\n",
       "      <th>u10</th>\n",
       "      <th>u11</th>\n",
       "      <th>u12</th>\n",
       "      <th>label</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2713</td>\n",
       "      <td>0.2470</td>\n",
       "      <td>0.2465</td>\n",
       "      <td>0.2373</td>\n",
       "      <td>0.1888</td>\n",
       "      <td>0.1869</td>\n",
       "      <td>-0.3354</td>\n",
       "      <td>-0.3361</td>\n",
       "      <td>-0.3647</td>\n",
       "      <td>-0.4812</td>\n",
       "      <td>-0.6914</td>\n",
       "      <td>-0.7619</td>\n",
       "      <td>remdes.mol2</td>\n",
       "      <td>0.9931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2484</td>\n",
       "      <td>0.2466</td>\n",
       "      <td>0.2422</td>\n",
       "      <td>0.2367</td>\n",
       "      <td>0.1921</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>-0.3354</td>\n",
       "      <td>-0.3365</td>\n",
       "      <td>-0.3648</td>\n",
       "      <td>-0.5107</td>\n",
       "      <td>-0.7051</td>\n",
       "      <td>-0.8251</td>\n",
       "      <td>REMDESIVIR.mol.mol2</td>\n",
       "      <td>0.9872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2153</td>\n",
       "      <td>0.2151</td>\n",
       "      <td>0.1883</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.1855</td>\n",
       "      <td>0.1838</td>\n",
       "      <td>-0.3548</td>\n",
       "      <td>-0.3631</td>\n",
       "      <td>-0.4045</td>\n",
       "      <td>-0.6326</td>\n",
       "      <td>-0.6694</td>\n",
       "      <td>-0.7043</td>\n",
       "      <td>DARUNAVIR.mol.mol2</td>\n",
       "      <td>0.9576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2036</td>\n",
       "      <td>0.1855</td>\n",
       "      <td>0.1830</td>\n",
       "      <td>0.1820</td>\n",
       "      <td>0.1680</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>-0.2665</td>\n",
       "      <td>-0.3032</td>\n",
       "      <td>-0.3048</td>\n",
       "      <td>-0.3245</td>\n",
       "      <td>-0.3266</td>\n",
       "      <td>-0.3626</td>\n",
       "      <td>LOPINAVIR.mol.mol2</td>\n",
       "      <td>0.9210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2328</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.1131</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>0.0576</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>-0.1322</td>\n",
       "      <td>-0.1337</td>\n",
       "      <td>-0.1342</td>\n",
       "      <td>-0.1805</td>\n",
       "      <td>-0.3499</td>\n",
       "      <td>-0.3501</td>\n",
       "      <td>FINGOLIMOD.mol.mol2</td>\n",
       "      <td>0.8580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1453</td>\n",
       "      <td>0.1304</td>\n",
       "      <td>0.1272</td>\n",
       "      <td>0.0808</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>0.0576</td>\n",
       "      <td>-0.1434</td>\n",
       "      <td>-0.1471</td>\n",
       "      <td>-0.1476</td>\n",
       "      <td>-0.1479</td>\n",
       "      <td>-0.2170</td>\n",
       "      <td>-0.3420</td>\n",
       "      <td>Hydroxychloroquine.mol.mol2</td>\n",
       "      <td>0.8436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1334</td>\n",
       "      <td>0.1281</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>0.0586</td>\n",
       "      <td>0.0542</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>-0.1438</td>\n",
       "      <td>-0.1476</td>\n",
       "      <td>-0.1481</td>\n",
       "      <td>-0.1482</td>\n",
       "      <td>-0.2181</td>\n",
       "      <td>-0.3429</td>\n",
       "      <td>CLOROQUINA.mol.mol2</td>\n",
       "      <td>0.8364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       u1      u2      u3      u4      u5      u6      u7      u8      u9  \\\n",
       "5  0.2713  0.2470  0.2465  0.2373  0.1888  0.1869 -0.3354 -0.3361 -0.3647   \n",
       "6  0.2484  0.2466  0.2422  0.2367  0.1921  0.1853 -0.3354 -0.3365 -0.3648   \n",
       "1  0.2153  0.2151  0.1883  0.1878  0.1855  0.1838 -0.3548 -0.3631 -0.4045   \n",
       "4  0.2036  0.1855  0.1830  0.1820  0.1680  0.1630 -0.2665 -0.3032 -0.3048   \n",
       "2  0.2328  0.1840  0.1131  0.0784  0.0576  0.0575 -0.1322 -0.1337 -0.1342   \n",
       "3  0.1453  0.1304  0.1272  0.0808  0.0715  0.0576 -0.1434 -0.1471 -0.1476   \n",
       "0  0.1334  0.1281  0.0702  0.0586  0.0542  0.0431 -0.1438 -0.1476 -0.1481   \n",
       "\n",
       "      u10     u11     u12                         label       S  \n",
       "5 -0.4812 -0.6914 -0.7619                   remdes.mol2  0.9931  \n",
       "6 -0.5107 -0.7051 -0.8251           REMDESIVIR.mol.mol2  0.9872  \n",
       "1 -0.6326 -0.6694 -0.7043            DARUNAVIR.mol.mol2  0.9576  \n",
       "4 -0.3245 -0.3266 -0.3626            LOPINAVIR.mol.mol2  0.9210  \n",
       "2 -0.1805 -0.3499 -0.3501           FINGOLIMOD.mol.mol2  0.8580  \n",
       "3 -0.1479 -0.2170 -0.3420   Hydroxychloroquine.mol.mol2  0.8436  \n",
       "0 -0.1482 -0.2181 -0.3429           CLOROQUINA.mol.mol2  0.8364  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "\n",
    "directory_in_str=\".\"\n",
    "directory = os.fsencode(directory_in_str)\n",
    "\n",
    "mdesc=[]\n",
    "sim=[]\n",
    "desf = open(\"PED.shd\", 'w')\n",
    "desf.write('u1,u2,u3,u4,u5,u6,u7,u8,u9,u10,u11,u12,label,S\\n')\n",
    "for file in os.listdir(directory):\n",
    "    filename = os.fsdecode(file)\n",
    "    if filename.endswith(\".mol2\"):\n",
    "        file=filename\n",
    "        fat = open(\"atoms.txt\", 'w')\n",
    "        fat.write(\"Id Atype x y z Otype Str1 Str2 Charge\\n\")\n",
    "        with open(file) as f:\n",
    "            for line in f:\n",
    "                if line.rstrip() == \"@<TRIPOS>ATOM\":\n",
    "#                    print(\"\")\n",
    "                    for line in f:\n",
    "                        if line.rstrip() == \"@<TRIPOS>BOND\":\n",
    "                            break\n",
    "                        fat.write(line.rstrip()+'\\n')\n",
    "        fat.close()\n",
    "\n",
    "        df=pd.read_csv('atoms.txt', delimiter=r\"\\s+\")\n",
    "\n",
    "        Eij=[]\n",
    "        for index, row in df.iterrows():\n",
    "            for index2, row2 in df.iterrows():\n",
    "                if(index2>index):\n",
    "                    dij=((row['x']-row2['x'])**2+(row['y']-row2['y'])**2+(row['z']-row2['z'])**2)**0.5\n",
    "                    if(dij > 1.0):\n",
    "                        Eij.append(row['Charge']*row2['Charge']/dij)\n",
    "                \n",
    "        PED2=[]\n",
    "        Eij.sort(reverse = True)\n",
    "        PED2.extend(Eij[0:6])\n",
    "        PED2.extend(Eij[len(Eij)-6:len(Eij)])\n",
    "        \n",
    "        MhD=0.0\n",
    "        for i in range(0,len(PED2)-1):\n",
    "            MhD=MhD+1/12*np.absolute(PED[i]-PED2[i])\n",
    "        S=1.0/(1.0+MhD)\n",
    "        print('Similarity between query and ',filename,' : ', np.round(S,2))\n",
    "        !rm atoms.txt\n",
    "        fat.close()\n",
    "        sim.append(S)\n",
    "        mdesc.extend(PED)\n",
    "        desf.write(str(round(PED2[0],4))+','+str(round(PED2[1],4))+','+str(round(PED2[2],4))+','+str(round(PED2[3],4))+','+str(round(PED2[4],4))+','+str(round(PED2[5],4))+','+str(round(PED2[6],4))+','+str(round(PED2[7],4))+','+str(round(PED2[8],4))+','+str(round(PED2[9],4))+','+str(round(PED2[10],4))+','+str(round(PED2[11],4))+', '+filename+','+str(round(S,4))+'\\n')\n",
    "\n",
    "desf.close()\n",
    "df=pd.read_csv('PED.shd')\n",
    "dfs=df.sort_values(by='S', ascending=False) \n",
    "dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1334,0.1281,0.0702,0.0586,0.0542,0.0431,-0.1438,-0.1476,-0.1481,-0.1482,-0.2181,-0.3429, CLOROQUINA.mol.mol2,0.8364\n",
      "0.2153,0.2151,0.1883,0.1878,0.1855,0.1838,-0.3548,-0.3631,-0.4045,-0.6326,-0.6694,-0.7043, DARUNAVIR.mol.mol2,0.9576\n",
      "0.2328,0.184,0.1131,0.0784,0.0576,0.0575,-0.1322,-0.1337,-0.1342,-0.1805,-0.3499,-0.3501, FINGOLIMOD.mol.mol2,0.858\n",
      "0.1453,0.1304,0.1272,0.0808,0.0715,0.0576,-0.1434,-0.1471,-0.1476,-0.1479,-0.217,-0.342, Hydroxychloroquine.mol.mol2,0.8436\n",
      "0.2036,0.1855,0.183,0.182,0.168,0.163,-0.2665,-0.3032,-0.3048,-0.3245,-0.3266,-0.3626, LOPINAVIR.mol.mol2,0.921\n",
      "0.2713,0.247,0.2465,0.2373,0.1888,0.1869,-0.3354,-0.3361,-0.3647,-0.4812,-0.6914,-0.7619, remdes.mol2,0.9931\n",
      "0.2484,0.2466,0.2422,0.2367,0.1921,0.1853,-0.3354,-0.3365,-0.3648,-0.5107,-0.7051,-0.8251, REMDESIVIR.mol.mol2,0.9872\n"
     ]
    }
   ],
   "source": [
    "!cat PED.shd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8364195792331197,\n",
       " 0.9576367636434527,\n",
       " 0.8579680530592962,\n",
       " 0.8436050987129449,\n",
       " 0.9209807032665069,\n",
       " 0.9872407224365601]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's observe the similarity between REMDEVISIR and itself is not 1.0 because differerent conformations has been used. \n",
    "The file used in Virtual Screening search belongs to MOL structure on KEGG database (https://www.kegg.jp/dbget-bin/www_bget?-f+m+drug+D11472) and the used as query has been generated with OpenBabel Software. However, its by far the most cosimilar structure.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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