Shape_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Compare shapes of molecules"
   ]
  },
  {
   "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.shape import usr, usr_similarity\n",
    "print(oddt.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd like to compare the shape of heroin with other molecules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ODDT supports three methods of molecular shape comparison: USR, USRCAT and Electroshape.<br/>\n",
    "[USR](http://dx.doi.org/10.1002/jcc.20681) looks only at the shape of molecule.<br/>\n",
    "[USR-CAT](http://dx.doi.org/10.1186/1758-2946-4-27) considers the shape and type of atoms.<br/>\n",
    "[Electroshape](http://dx.doi.org/doi:10.1007/s10822-010-9374-0) accounts for the shape and charge of atoms.<br/>\n",
    "All those methods have the same API.<br/>\n",
    "We will use USR, because it's the simplest and the fastest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "heroin = oddt.toolkit.readstring('smi', \n",
    "    'CC(=O)Oc1ccc2c3c1O[C@@H]4[C@]35CC[NH+]([C@H](C2)[C@@H]5C=C[C@@H]4OC(=O)C)C')\n",
    "\n",
    "smiles = ['CC(=O)Oc1ccc2c3c1O[C@@H]4[C@]35CC[NH+]([C@H](C2)[C@@H]5C=C[C@@H]4OC(=O)Cc6cccnc6)C',\n",
    "          'CC(=O)O[C@@H]1C=C[C@@H]2[C@H]3Cc4ccc(c5c4[C@]2([C@H]1O5)CC[NH+]3C)OC', \n",
    "          'C[N+]1(CC[C@@]23c4c5ccc(c4O[C@H]2[C@@H](C=C[C@@H]3[C@@H]1C5)O)OC)C', \n",
    "          'C[NH2+][C@@H]1Cc2ccc(c3c2[C@]4([C@@H]1CC=C([C@H]4O3)OC)C=C)OC',\n",
    "          'CCOC(=O)CNC(=O)O[C@H]1C=C[C@H]2[C@H]3Cc4ccc(c5c4[C@]2([C@H]1O5)CC[NH+]3C)OCOC',\n",
    "          'CC(=O)OC1=CC[C@H]2[C@@H]3Cc4ccc(c5c4[C@@]2([C@@H]1O5)CC[NH+]3C)OC',\n",
    "          'C[NH+]1CC[C@]23c4c5cc(c(c4O[C@H]2[C@H](C=C[C@H]3[C@H]1C5)O)O)c6cc7c8c(c6O)O[C@@H]9[C@]81CC[NH+]([C@H](C7)[C@@H]1C=C[C@@H]9O)C']\n",
    "\n",
    "molecules = [oddt.toolkit.readstring('smi', smi) for smi in smiles]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute the shape using USR we need the molecule's 3D coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "heroin.make3D()\n",
    "heroin.removeh()\n",
    "for mol in molecules:\n",
    "    mol.make3D()\n",
    "    mol.removeh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the `usr` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.12823939,   1.59761882,   0.55976415,   3.20355535,\n",
       "         2.18059039,   1.01127279,   6.30361271,   6.8250246 ,\n",
       "       -10.16452217,   5.39781094,   5.79236984,   0.93677378])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usr_heroin = usr(heroin)\n",
    "usr_heroin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "USR represents shape with 12 descriptors, which summarize the distribution of atomic distances in the molecule. For more details see [Ballester & Richards (2007)](http://dx.doi.org/10.1002/jcc.20681).<br/>\n",
    "USR-CAT and Electroshape use more descriptors, 60 and 15 respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how similar it is to a different molecule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20657099769142923"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usr_similarity(usr_heroin, usr(molecules[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The similarity function returns a number in range (0, 1], where a higher number means that the molecules are more similar and 1 means that the molecules have identical shapes.<br/>\n",
    "All methods (USR, USR-CAT and Electroshape) use the same similarity function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will find a molecule similar to oxamide. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "similar_mols = []\n",
    "for i, mol in enumerate(molecules):\n",
    "    sim = usr_similarity(usr_heroin, usr(mol))\n",
    "    similar_mols.append((i, sim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 0.48911179609705718),\n",
       " (3, 0.45645597603277854),\n",
       " (1, 0.39947994224342542),\n",
       " (5, 0.39212419145828936),\n",
       " (0, 0.20657099769142923),\n",
       " (6, 0.16292525187745338),\n",
       " (4, 0.15072869043524584)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_mols.sort(key=lambda similarity: similarity[1], reverse=True)\n",
    "similar_mols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\" 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src=\"data:image/png;base64,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\" alt=\"\">"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heroin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wMPDIz8/f+/eveQ9dH5+/vjx44cPH56eno5NgliBCktLjB8//vz583ifZp3zl7/8pba2dtKkSeRMYWHhwoUL3d3dExISWlpaNC+ira0NqLB6IFZWVhcvXty/fz9zcXp5efnKlSsdHR3DwsIeP36sSf60xuKAujpYuhQ8PcHTE0JDgY0KgCOWLl1aXFys1Od7/vz5zp07Bw0atGzZstraWvVyxpLC3lQ9Aa2shF62DCZOhP37QS6H8HBYu7adBXJ6Q79+/W7evLlp06Z//OMfTO/Xly9fZmVlffjhh+qZMkokEvwzPT1djY+TlZUGA+dDsLduoREjEHk1JhYjZ2fEwdIa1eAtPUaNGtX5j1y/fr1///66vj/K7Nixg7u/Eotw3xQWFYG3N/w6gQQsLMDdHUpKOC9XY8aNG5efnx8QEKDrQH5Hv379dB1Cp9CFKYjhDBL27t370KFDM2fODAkJiY6OVigU+J2jGgQEBCCEeDzeoUOH1HtzVVlZ2b9/f1a2jdAGnNeJN2+id975rSlsbkZOTgbRFDJRMXe+M9Qz3Dw0zMpQ4L4pHDUK+vUDoRAUCpBKISICZs+Gvn05L5dVmGsu1ODJkyftHndjtDLcsG8fFBWBhwcMGwY8Hmzdqo1C9QkqLG54+21ITYXSUigthV27wNwcAG7cuPHRRx9999132ghA1zDFpOFAq6GgM0e/7du3nz59+ty5czY2Nkwvhm4JrbG0x9KlS/l8vlwuX7x48Z07d3QVhnagwtIevr6+sbGxANDc3Dxr1qynT5/qKhItQIWlVdauXRscHAwA5eXlfn5+rEwi0E+osLTNrl27fHx8ACAvL+/zzz9HhjN22iWYHfYeIiy9WP5FVv9t2rSJiyKam5uxZYi1tbVYLOaiCBW8ePFCaaidxRX9eovuhYUQKioqIgtWDx48yGLO2NHFycmJ3FRnZ+fU1FStebYoFIrk5GRizisQCJKSkrQvbu2jF8JCCJ05cwZ7fgoEArbWKF+/fr2jFe7jxo3LyclhpRQVKHnamJmZGcrcBM3RF2EhhPbu3YtvgIODg9r7oWE68uSIiooizsfYFEQT9xEVVFVVMQMwNTUNDQ3V3CbJgNAjYSGEVqxYge/EyJEj3+js0y5vdBGqrKxUchGKiorqjP1flwJgusApBdBD0C9hyWQyPz8/fD9mzpzZJeM1hFBmZuaAAQPIHVXhe/bDDz8wd6R1dXUViUSax69kvDZ06NBTp05pnq0hol/CQr+3ioyIiOjkpzrj1Hj8+PH333//73//O34ok8vlIpGIuX3tlClT8vPz1Qu7qKjoo48+Iln16dPnjVaR3Ru9ExZC6OHDh2QL2qSkJNWJO+8tS5Tn5uaWlpaGTzY3N79ubtulPQFe320gNDS0sbGx8zl0S/RRWAihK1euEDvu8+fPt5umq27Y2dnZzPpp8uTJxMpWvT0BpFLp6wEo7TbQY9FTYSGERCIRaddKS0uVrqrn36/afPvs2bPMDQSGDBmiYgMBpd0GVCfugeivsBBCkZGR+LZ5enqSxqWkpIR08DtfuzApLS1l1k+WlpbEKKvdWrAzW55ozWfLUNBrYSkUCrJIZsaMGXiTJrb2SMrOziZPCQAwePBg0vHqqN+meYes56DXwkIIicVi7IUHAGTfLwCYOnXqTz/9pGHmuJPUlzEBf9q0aWS3uvz8fBXbymnyCNkT0HdhIYQeP36MDTwxrO9D2dDQwHysw+5ZZC1Neno6c2wMAAYMGJCens5iAN0SAxAWYnS25s+fz+IoOZOCggLihgoAdnZ2SUlJ+F11a2vr3Llz8fk5c+ZwFEA3wzCEdeTIEXxfMzMzOS3o6NGj7u7uRF5ki6iMjAx8Rpu7Bhs0Bmlj1NTUFBsbe/XqVdZznjt37r1792JjY/HbRrKNBaWr6GyVjiYIhUKhUGhtbd3Q0MC60XKvXr0iIyODgoKePHmiEw/L7oFBCguvWH/27JlMJlPPVOiN2NnZabj6uYdjkE0hRf+hwqJwAhUWhROosCicQIVF4QQqLAonUGFROIEKi8IJVFgUTqDConACFRaFE6iwKJxAhUXhBCosCidQYVE4wSCFhSf38Xg81mf5UdjCIIXl7+8vEAjmzZtHltawS1NT07p16z755JPKykou8u8JGOQM0o8//lgikRBbMxZRKBQpKSnr16+vqakBgJEjR27YsIH1UnoChiGs5uZmfID3KQUALlR1+fLlsLCw27dv418HDhxIltKTclE39XVmH10vE3oDMpls9+7d5ubmOFpLS8tvv/1WTjapY4l2bf6wjZZMJtuzZw9ZMbt582Z2i+6u6LWwsrOz8T6DSqiw6usqEolEKBSSZV7YmPTRo0f46oULF5gLdXr16vXgwQNWyu326KmwKioqAgMDyR21sLD429/+FhcXx663Z2ZmppubG8lw9OjRV65cwZeU6jALCwt2rUq7PXonLGxhJRAISBUSGBhYXV2Nryq5EWM7ZDXciG/evDlhwgQiKUdHx6SkJNzCSiQSpQC4M1fuxuiRsBQKRVpaGnMz7TFjxly7du31lLm5uR988AFJ5uTkRGTxRmpra5kWRdgou6mpiQTArMPGjBlz9epVlr9nz0BfhJWbmzt+/HhyR52dnUUikYr9I9pVoWoRtLW1CYVCplP3vHnzysrK8NW8vDy1xUp5Hd0Lq12z/062bu02Wx1tPrB8+XKiGy8vr+zsbHyemv1zgS6FJZPJYmJiLCwsiCwCAgLU6M283tNvt6P9+eefA4Cdnd3u3buxUTY1++cOXQrrwIED5I6OGjXq0qVLmuSmNDTw+p4ALS0t//vf/4iXKTX75xRdCisnJ0cgENjb2//73/9mpTeD9wQgHvEA4OPj87qjJDX71wLcC6u2Fi1Zgjw8kIcHCglBEgnzYmNjY5cMjztDY2NjREQEMSzFFrQ1NTWImv1rEe6F5eeHvvkGKRRIKkUrVqAvv+S8RIQQQoWFhUrV0ieffIJ3RcTMnDnz/v372gmmB8KxsG7dQiNGINLMicXI2RnV1HBbKIOsrKwhQ4bA7/H09Dxx4oTWYuiZcDwfq6gIvL2BzESwsAB3dygp4bZQBn5+fnfu3Nm6dSv2ZzMxMdm+fXtBQYGvr6/WYuiZaH2in9annZiamq5ateru3btffvllYWFheHg4RyaAFCY8xOmdvnULli6Fn356VWmJxeDhAfn5wPDsp3RLOK6xRo2Cfv1AKASFAqRSiIiA2bOpqnoC3DeF+/ZBURF4eMCwYcDjwdatnJdI0QM4bgopPRUdrNLJysq6cOGC9sulaBNt11g//PCDj48Pj8fLyckZM2aMNoumaBNt11jW1tYAgBDaSjtb3Rod9LEmTZp0+fJlIyOjkpIS5vwCSndCB32s8PBwAJDL5YmJidovnaIddFBjKRQKDw+PBw8eWFlZVVZWMje5pHQbdFBj8fn80NBQAGhubk5OTtZ+ABQtoJtxrJaWFldX14aGBjc3twcPHnDk7UHRIbpxmzE3Nw8KCgKA8vLykydP6iQGCqfozMYoNDTU0tKSx+ORxRSU7oQuX+lUVFQ8e/asXXcGiqFD3xVSOMEgHf0o+o9ePI5JJJK0tLSioiIzMzNbW9sFCxbY29uHhISQEdQzZ86IxeJ58+bpNk5K59ELYSUnJ7u6ugqFQj6fX1lZ2dDQYG9vr+ugKBqhe2FVVFTU1NSEhIRgMypXV1cAUCgUuo6LohG6F1ZVVZWrq+vrxtoymeyrr77Cx62trRMnTgQAsVicmJhoZGQklUrnzp3r6emp7XApnUP3wuoIY2PjmJgYfIz7WABgbm6+Zs0aPp9fXV29b98+ojyKvqH7p0JHR0dsPNSZxHw+H/sNtba2uri4cBwaRX10Lyw3N7e+fftmZGTIZDIAKCsrKygoUJG+rq5ux44du3fvHjlypLZipHQZvWgKg4KC0tLSIiMjTUxMnJycFi5cqCKxnZ1deHh4fX19XFwcHbXXWwxs5F0mk+GpEBKJJC4u7uuvv9Z1RJT20Ysaq/OUl5cfO3bMyMhIJpMtWrRI1+FQOsTAaiyKoaD7zjulW0KFReEEKiwKJ1BhUTiBCovCCVRYFE6gwqJwAhUWhROosCicQIVF4QQqLAonUGFROIEKi8IJVFgUTqDConACFRaFE6iwKJxAhUXhBCosCif8H6VBKL/S3HmIAAAAAElFTkSuQmCC\" 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       "<img src=\"data:image/png;base64,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\" alt=\"\">"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_most = similar_mols[0][0] \n",
    "molecules[idx_most]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most similar molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\" 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       "<img src=\"data:image/png;base64,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\" alt=\"\">"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_least = similar_mols[-1][0] \n",
    "molecules[idx_least]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The least similar molecule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarity between these molecules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13678393398437561"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usr_most = usr(molecules[idx_most])\n",
    "usr_least = usr(molecules[idx_least])\n",
    "usr_similarity(usr_most, usr_least)"
   ]
  }
 ],
 "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
}