DUD-E.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>DUD-E: A Database of Useful Decoys: Enhanced</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.1-12-ga7d79bd\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function, division, unicode_literals\n",
    "\n",
    "import oddt\n",
    "from oddt.datasets import dude\n",
    "print(oddt.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd like to read files from [DUD-E](http://dude.docking.org/).<br/>\n",
    "You can download different targets and different numbers of targets, but I used only these five:\n",
    "[ampc](http://dude.docking.org/targets/ampc), \n",
    "[cxcr4](http://dude.docking.org/targets/cxcr4), \n",
    "[pur2](http://dude.docking.org/targets/pur2), \n",
    "[pygm](http://dude.docking.org/targets/pygm), \n",
    "[sahh](http://dude.docking.org/targets/sahh).<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "mkdir -p ./DUD-E_targets/\n",
    "wget -qO- http://dude.docking.org/targets/ampc/ampc.tar.gz | tar xz -C ./DUD-E_targets/\n",
    "wget -qO- http://dude.docking.org/targets/cxcr4/cxcr4.tar.gz | tar xz -C ./DUD-E_targets/\n",
    "wget -qO- http://dude.docking.org/targets/pur2/pur2.tar.gz | tar xz -C ./DUD-E_targets/\n",
    "wget -qO- http://dude.docking.org/targets/pygm/pygm.tar.gz | tar xz -C ./DUD-E_targets/\n",
    "wget -qO- http://dude.docking.org/targets/sahh/sahh.tar.gz | tar xz -C ./DUD-E_targets/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "directory = './DUD-E_targets'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the dude class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dude_database = dude(home=directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can get one target or iterate over all targets in our directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose one target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = dude_database['cxcr4']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`target` has four properties: protein, ligand, actives and decoys:<br/>\n",
    "protein - protein molecule<br/>\n",
    "ligand - ligand molecule<br/>\n",
    "actives - generator containing actives<br/>\n",
    "decoys - generator containing decoys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\" 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      ],
      "text/plain": [
       "<img 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/PTTT1r3pKf2dW/wNUM/8wxC62M9e/bs7bffbm5uPnz48DvvvKP+hYpVKwBwcHAIDAyMjY3FMCwpKUm/PSTpCuHrWF3LG8nlcrFYrOYsh8/nz549u7a2ds+ePcuXL9fo1l0L4HC53LFjx6alpdXV1dnb22vUWlfIyk0q0NubkbJ5UKZrSODq1autra0XL16cnJwsFotVtNna2jpv3rySkpJPP/1048aNeulncHCwTCa7ePGi7k2pH+3YH9HLgNrThKPb44mJiVwuF93d2tr6ww8/TE1NlclkndoUi8X+/v4AsGTJkq6fqkAikaxdu7an2RiKyHjnnXc0esCukHMs1RArLBVkZmauW7dOkWph4MCB69atS0tLQ5/K5fL3338fAGbMmKE6UUxXUAD0hg0bejph1KhRNBpNkdGPhAgIeStMS0u7deuWOmeKxeJLly4tXbrUwsICKWzUqFHffPMNUpWPjw9K36A+YrHYzc2NRqM9ffq0p3OQZ19CQoJGLXfi9OnTKrRLQoiwvLy8zMzMNEoTKhKJjh8/HhAQoJgIm5qa3rlzR9Nbo42a1atXqzgnLy8PAAIDAzVtXBkvLy86nd4p2oxEASHCOn78OABMmDBBo7kRjuPnzp1DgyOGYQBApVLDwsLUTzTa0dHB5XIZDEZlZaXqM3uKT1QTkUiE9iW1u7w/QNQCKVqK3L9/v5rnv3jxAiXgMzExiYyMbGtr0yKP7Y8//ggA69ate+WZKP+WsuO8Rty6dQsA1q5dq93l/QGihFVVVcXhcCwsLFTMdRQkJiZ2m4APJRodPnw4qJHHtq2tzdnZ2dTUtNtkpJ2orq7+9NNP6+vr1XycTuzevRsAjh07pt3l/QECt3TQdGf27NkqzqmqqkK2zczMbOfOnd1GnCJ5IedjFXlsv/vuOwDYuHGj3h6gZ1ComRa55vsPBApLLpfPnDkTAI4ePdrtp3FxcSi12uTJk1+ZtwjlsUUho0heVVVVik9FIpGDg4OFhUVtba2eH6M7Bg8ezOFw1AmG7rfLXcRuQj99+tTCwsLGxqbT3/vx48fTp08HADabHRsbq/4cXywWx8XFId8ElMcW/dl27twJAJs2bVKzHV3+3jU1NQDw9ttvE30jo4Zwt5ndu3dHRkaGhIScOnUKAKRS6f79+zdv3tzS0hIQEHDw4MFBgwZp2qZYLE5ISNi6dWt1dbWFhcWyZcvOnDnT1taWlpbGYDBEIpFAIGhubkYln0QiEaoAxWQyY2JiUJlWPp8vFOY2Nv5MpVq3tzP9/Ha98qYKLl68OH/+/K+++grVN1AGx/H09PRz585ZWlpu3rwZ3ej58+fm5uZoS7EfRR0SrVypVPrGG28AwLlz53Jzc9HPVlZWcXFxOrbc0tLy7bffql8NlUajKTJH1tfX3769Njv7l07hy5cvX05ISCgqKlJhRJGnPI/HU37G1NTUdevWKdy8PDw8FDciLZbekMlkyGagGnF5eXkff/wxhUKRy+VSqXTp0qWxsbH6Ko8rEom8vLyePn3q7e09bNgwS0tLJpPJZDJZLBaHw2GxWOhXNps9cOBAxQ4Sn8/v6HgpEMS3tj6Xy93HjduCjjs7OyP/ZktLyzfeeAOFYHQK9Zk1a9Yff/xRW1trY2Nz8+bNpKSkCxcuvHjxAgBYLNacOXOCg4MDAgLMzc2hP8dJayTDbv//qqqq/P39fXx8hgwZYm9vj77QbqHT6cr/6PoCDUmXL1/W7kH++GOcXP63fSoqKvruu+9CQkIU2+SIQYMGBQcH7969++bNm5aWlg4ODsobnRwOJywsLDExUTkHjo4Yu6nTgyNRXV0d2hlEiYoHDBigsBNWVlbo58rKyvj4+DVr1qCqNfpFi9q+tra2NTVnCgpuAYCT02QM+9t9aMSIESNGjEA/19XVZSiRlJSk8BBsbm7+8ccf7ezswsPDg4ODZ8yYgcrA9iGGZho1FlZX7zZvb++2tjbVV925cyc+Pp4ghzhUjVzTotFOToucnBapOMHe3n7u3LnoPwHH8bKysoyMjMuXL586dYrJZPJ4vDfffJNKperSc9UYtSOhxj3u6papDsioEFeJnrjGERiGubu7u7u7/+tf/6qpqUlNTR08eDChqgLNv2qDEmIvxdZpZ1QMofGuoIqsFy5c6J3bqY9BebRqJixbW1tHR0cAYDAYGo3iWkyDDKTxrgQFBfVCRIYWX7VIJEIhLTKZTO8hLZrSqxaLoNGK0Ma74uzsPH78+Hv37j1//pzoez169GjGjBmPHj165Zm2trYcDgcAGAyGmZlZe3s70X1TTS8Ji06n0+n018NiAUBwcDCO43qJyFBNcXHxzZs3T58+reb5KKTl119/PX/+fN8mD++9/AVsNtt4J++dCA4O7p34xHHjxgFARkaGmuejaVZFRUVDQ0NiYiKRXXsFvScsFov1ekzeAYDL5Y4ZM+b27dt1dXWE3mjgwIHOzs7p6el4zxskYrFYJpMpH9mwYQOGYdu2bdPCaOkr9vh1EJaJiYmZmVlvWizQa3yianx9fV++fNk1YBORn5/v5ub23nvvgdJ838PDo66urri4+MyZM0R3ryd6dShsbW2VKudQ1x/EqbYnkK9f346GqampU6ZMqa6uRj5IykRHR1OpVO2MVk+xx5rRa5tHs2fPBoCGhgYiGh86dKidnR0RLaugd+ITr1+/DgDr16/vdPz8+fNmZmZUKhXVZ+zKkiVLQGXBDkRWVtbChQvXrFmDftXXHmXvCQsVJqmoqCCi8TFjxjAYDCJaVgGKTzxy5AihdxEIBKg6hvLBffv2USgUCwuLS5cu9XRhUVERhUIZOXJkTy5AmZmZgYGBKCBqwYIF6KDxCWvlypUAUFBQQETjU6dOBQBUAlNNdP8GUXzivHnztLhWI0aOHGlqaoqeTi6XI0FbW1vfvXtX9YWLFy8GgMTExE7Hc3NzFy1ahCTl5eWVmJio95pTvbepROiiAHIuSEpKwjBMIBA0NTUhVzCRSNTc3CwQCNDPHA4nJSVFX+n8Ro8ePXz48JSUFIFAgN5MCcLPz6+oqCg/P9/b2/ujjz46dOgQl8u9evUqil9SwZYtWxITE7du3RocHIxSY+bm5m7fvv3s2bM4jnt5eW3evHnhwoVIYXpGvzpVAfo/S0lJ0XvLv//+uzoJRZlMppeXl3INc91r4GzatAkATp48qb+n6QYU7xQbG4tcLUaNGqV+EXU0Azlz5kxOTo7CSnl7exNhpZTRpwcpcjAXCoXK7qPI5VwgEFy5ciU7O3vDhg3ff/+9vu7Y2Ni4cePGhIQEDMPkcrmDg0NkZKSVlRWTyeRwOGw2W+FNamVl1elavTgwZWZmjhs3bv78+efPn9fHA3XPw4cPfXx87O3t6+rqpk2bduHCBUtLSzWvLSoqGjVqFJVKRe/jEyZM2LJlC0rjQyyaKrHbqUlaWpr640tgYODDhw91/5/g8XjIx9zT0/P+/fsrVqwAgC1btujyIJoik8nQKLxgwYITJ05oHbPfE+3t7Tweb9myZeirCw4O1jT3Tnt7O3LvYTAYsbGx+u2eCvQjrOLi4pkzZ7711ltBQUHLly9fs2ZNZGTkzp079+/ff+TIkXPnzsXHx1OpVHt7e19fXwDAMGzBggV5eXnadbq2thbF49NotMjISPRdNzU1OTs7m5iY6EW1avLTTz8BgHIik8DAwISEBB1XVVpaWpKSkpYuXaqYuiFxqE520i1osHZycsIwjEqlfvbZZ62trbr0TU20EZYWUxP0enLq1Ckcx69du4YW/TAMmzt3bnZ2tkYd6CkeH8dxHo8HAGPGjBGLxRq1qR35+flmZmYcDqe8vLygoCAqKkrh1kylUidNmhQbG1tdXa1+gy0tLcnJyWFhYehFBwDs7OzCwsKSk5MbGxtRrujz58+r3+DDhw9R2eKGhobU1NShQ4cCwJAhQ9RMMqUL+rFYqikoKKBQKJ6ensoLKteuXRs7diz8k/PjlZHQuHrx+GhzY/fu3Wo/kJa0tbWNHj1a8d+iID8/Pzo6Gn2EFDZt2rR9+/apsBMymezYsWOBgYGK6QSXy924cePdu3eVv7G8vDxTU1MrKys11wI7OjpQN3777Td0pKWlJTIykkKhYBgWERGhx9CPrvSGsJDLZVJSUqfjcrk8OTnZ29tbIa+eGlQ/Hr++vt7Ozs7MzIzoyJbw8HAAWLVqFY7jK1ascHFx6WQmS0pKduzY4ePjg7Ry4MCBnppCuWsAwN3d/YsvvsjMzOzpzL179wLAm2++2e0/VSdQxGzXyrG3b99Gbypubm43btx4ZTvaQfhyQ15eHoVCGT16dE/rv11Tyjx+/Fj5BE3j8Y8cOQIAU6dOJe51+uzZswAwcuTIlpaWhIQEAPDx8enpds+ePUtISGhqakK/8ni4tzcuFuMVFX+XNJdIJMeOHVNnximXy+fNmwcA27ZtU31mdnY2jUZzcnLqdrbXyXQJhcJX3lpTCBcW+iJemYlKOaUMktfTp08lEsnOnTvRABEQEPDKdGoKAgMDAeDgwYM6d78bKisrra2tTU1Nc3JyysrKWCyWhYWFOkM5gsfDIyLwuLj/Cksj6urqBgwYYGJioiLdoUQiQdMM1ROymzdvIgd5d3d3TVNyvhJihZWZmYlhmIr/5k6IxeKff/7ZxcUFyQvlWbCzsztx4oRG962oqGCxWGw2W30tqolEIkGVfPbv3y8Wi1Ete43SmfJ4+MmT+IoVeGGhNsLCcfzWrVtUKnXQoEE97X+jtehly5a9simRSPTJJ5+MHTtWYVD1BbHCQh4NV65c0eiqjo6OAwcOoMV0V1dX7dKj7du3T7v3c9V89dVXADBnzhy5XP7vf/8bAN577z2NWkDCys7GV63SUlj4P4sIyEO6Ezk5OXQ63cnJqW/TQhMorIyMDAzDJkyYoMW1yFcEGa38/HwtWpDJZNu2bbt9+7YW1/bE7du3qVSqs7Mzn8+/evUqhmFubm6aLooiYeE4vnKl9sKSSCQTJkwAgE4+MxKJBL0unDt3Totm9RjXT6Cw5syZAwDXrl3T4topU6YAwNatWwHA19dXnZcgomloaBg8eDCFQrl+/fqLFy8cHR1NTEz+/PPPvurPkydPLC0tTU1NlWf96BsLDQ3Vrk3jENbnn38eHh6uxYVXr14FAH9/fxzHg4KCAOD777/Xd+80Zv78+WjLSC6Xo/+ZnTt3atdUfHx8TEyM7l1C4RKjRo1Ci2SFhYUMBsPOzq6urk67BvWyMY/oPe8G9Rk/fjwA3L9/H8fxmpoaKysrc3Nz9ZNyK6Ovf0E0Y5s0aZJEItm1axcATJs2TVM7unz58h9++AHH8bFjx5qammrdGWVQpYW1a9dKJBKUewy5xGiHcVgs7UDbMsrZ/f/zn/+gP6QW61J6+aYKCgrQ1s2zZ88yMzPpdLqdnZ1yBlR1QM4FkyZNwvXq7yoSiTw8PDAMCw0NBYDFixfr0poehWVwFVZ9fX0zMzPT09PRfiLC398/JSXl8OHDyA1VfRSZGltbWzEM8/LyUnaS6ejo2LBhA5/PV9GCVCq9fPmyWCw+c+ZMcHCwh4dHWVnZpUuXAgICNOoJytUxadKkO3fujB07tqioSF/Byg8fPvTz85NKpWw2+/Hjx/rKaKcruqiyKzpKHnk1KfyvFZSXl6PkW+o7uHXqT3V1dU5OTqeOVVdXq+ntQ6PRUDuLFy/esWOHps+FE2axcBxva2tjMpkAwGKxysvL9dWsjvR9vhsFOI5HR0djGIbW95RxcXH5+uuv169fv3r1ajRWakFra2tpaalylh8nJ6fq6urGxkbVFx44cGDPnj1Lliy5fv36yZMntbs7cXz22WcikWjUqFEFBQVTpky5deuWq6trX3eKAIul9WsFesfpab1RJpNNnDgR1Ihn6qljWptSmUw2a9YsANi1a5cWt0YQZLEuX76MYdjQoUMFAgFavB08eLA61UCIxoCGQuRBW1hY2NMJRUVFpqam7777bi93DMdxxcLVvXv3tLg7Toywnj9/bmtry2AwFH5phqMtA6fIIIMAAATfSURBVBLWlClTXlkBsKioCLXf+1y5ckW7pXaE3oUlk8lQ4Y9Oi3wGoi2DW24wZDZs2KBisFaN3oUVExMDAP7+/l1XYQxBW6SwNEAsFiOffS2in+VyuZmZGSqUoruw0tPTaTSavb19T/a7z7VllMLqwxzoWjhgKcjKykJuPDoKq6mpydXVlUKh/P777ypO011bunzPfSws7bret8n10U6Aj4+PRhH9iLa2tgsXLpibm2MYduzYscbGRi06gLJ9REZGvvJMTbUlk8kePXqk8NHtj8LS116pdixduhTU8A9WIBKJEhMTQ0JC0EqmAjqdPnv27J9//ll9nzOU8srX11fNSCR1tPXs2bPDhw+HhISg8Kcff/wRHdfle+57YWnR9T4vB9Lc3Dxnzpzjx4+rPk0RzqXQ06BBgyIiIpKTk3Nzc6OiohShFopwsVduQaalpU2dOlWj0a1bbTU0NCQlJa1evVo5BNzBwSE0NFQRc9AfLZYh15nh8/m//PJLQEAAcq0GgCFDhnz++efp6eld3+CKiopiYmJQqBIAUCiUN998c+/evfp1qkbaGjhwYEJCwubNm/38/BTVD8zNzf39/ffs2ZOTk9Ope/1OWIZMeXm5okzViBEj/u///k/NiNzHjx/v2rXLz88P5e2gUqn6jc365JNPFJaJSqX6+vp++eWXN27c0DRmX02M8q3QcGhuzs7KWpyfv/bx479DZIVC4fvvvx8TE6NiC0E1lZWVsbGxoaGhz549w7sLF9OC2tpaRZT2vn37CMqrqIwBbUIbI3V1VwYNet/O7h3FESaTGR8fr0ubgwYNUrYuAODrC/HxoHWGmLq6upkzZxYXF0+YMOHevXtUKrVr7h2903vJbV9LXFxW8fk3c3KWV1QcJO4u06dDRgaIRNpcW1dXN2PGjMLCwvDw8EOHDgHAH3/8oef+dQvRJrGfcPfuFEU1Tf2iS7jYixcvPD09ASA8PBxNzAcOHMjhcHohOIW0WDpRU3OmoGBNQcEaDsdHUU2TCLy9QdNE5sq2Ki4uDr0TzJgxo6mp6eHDh4T0UhmilUvSJ3S1VYijR48CwPbt24nuACkso0EqlYaGhqqTbaAnVeE4XlNTg2HY9OnTCevm35DCMhpaW1sZDMbw4cNVn/by5Uu0svDRRx91G9fk6elJp9MJTY6Fk3MsI8LMzGzWrFklJSUFBQUqTsvNzS0uLl6zZs2+ffu6zbM9a9YssViclpZGWE8ByOUG40KRwk7FOdOnT6+qqupJVQDw1ltvAfGLDgYXV0iigqamJgcHh+HDh6OiGNrR0tJiY2Pj4eGRk5Ojx751grRYxgSHw5k2bVp+fn5JSYnioKYVBi0sLCZPnvzkyRNCbQopLCMDjYY61kk8efLkgwcPCKl08g/kXqGRERQUdODAAVTwUkFtba1AIFCOxVWNnZ0d0ZH45BzL6NFL7Ra9Qw6F/RF9FX5WATkUGj3IUR0AGAyGgZgrIIVljOhl7NN0WqYppLD6KY6OjgppEtE+KSyjhGh7ozvkW6HxYZivgZ0g3wpJCIG0WCSEQFosEkIghUVCCKSwSAiBFBYJIZDCIiEEUlgkhEAKi4QQSGGREAIpLBJCIIVFQgiksEgIgRQWCSGQwiIhBFJYJIRACouEEEhhkRACKSwSQiCFRUII/w8QxBORyKuvUAAAAABJRU5ErkJggg==\" alt=\"*****\">"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.ligand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which target has the most actives and decoys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: pygm\t\tNumber of actives: 114\t\tNumber of decoys: 4045\n",
      "Target: sahh\t\tNumber of actives: 190\t\tNumber of decoys: 3483\n",
      "Target: cxcr4\t\tNumber of actives: 122\t\tNumber of decoys: 3414\n",
      "Target: ampc\t\tNumber of actives: 62\t\tNumber of decoys: 2902\n",
      "Target: pur2\t\tNumber of actives: 201\t\tNumber of decoys: 2725\n"
     ]
    }
   ],
   "source": [
    "for target in dude_database:\n",
    "    actives = list(target.actives)\n",
    "    decoys = list(target.decoys)\n",
    "    print('Target: ' + target.dude_id, \n",
    "          'Number of actives: ' + str(len(actives)), \n",
    "          'Number of decoys: ' + str(len(decoys)), \n",
    "          sep='\\t\\t')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}