iwatobipen/cats2d_example_2.ipynb

## cats2d_example_2.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [21:54:01] Enabling RDKit 2019.09.1 jupyter extensions\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import DataStructs\n",
    "from rdkit.Chem import AllChem\n",
    "from scipy.spatial.distance import euclidean\n",
    "from cats2d.rd_cats2d import CATS2D\n",
    "import numpy as np\n",
    "\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import Draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mol1 = Chem.MolFromSmiles('CCCC1=NN(C2=C1N=C(NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)C)OCC)C')\n",
    "mol2 = Chem.MolFromSmiles('CCCC1=NC(=C2N1N=C(NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)CC)OCC)C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x7F4F5658D0F0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolsToGridImage([mol1, mol2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp1 = AllChem.GetMorganFingerprintAsBitVect(mol1, 2)\n",
    "fp2 = AllChem.GetMorganFingerprintAsBitVect(mol2, 2)\n",
    "dist = 1.0 - DataStructs.TanimotoSimilarity(fp1, fp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.41025641025641024\n"
     ]
    }
   ],
   "source": [
    "print(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cats = CATS2D()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cats1 = cats.getCATs2D(mol1)\n",
    "cats2 = cats.getCATs2D(mol2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euclidean(cats1, cats2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', ['L'], '', ['R'], ['A', 'R'], ['R'], ['R'], ['R'], ['A', 'R'], ['R'], ['D', 'R'], ['R'], ['A'], ['R'], ['R'], ['R'], ['R'], ['R'], ['R'], '', ['A'], ['A'], ['A'], '', '', ['A'], '', '', '', ['A'], '', '', '']\n"
     ]
    }
   ],
   "source": [
    "print(cats.getPcoreGroups(mol1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', ['L'], '', ['R'], ['A', 'R'], ['R'], ['R'], ['R'], ['A', 'R'], ['R'], ['D', 'R'], ['R'], ['A'], ['R'], ['R'], ['R'], ['R'], ['R'], ['R'], '', ['A'], ['A'], ['A'], '', '', ['A'], '', '', '', '', ['A'], '', '', '']\n"
     ]
    }
   ],
   "source": [
    "print(cats.getPcoreGroups(mol2))"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"RDKit WARNING: [21:54:01] Enabling RDKit 2019.09.1 jupyter extensions\n"
	]
	}
	],
	"source": [
	"from rdkit import Chem\n",
	"from rdkit.Chem import DataStructs\n",
	"from rdkit.Chem import AllChem\n",
	"from scipy.spatial.distance import euclidean\n",
	"from cats2d.rd_cats2d import CATS2D\n",
	"import numpy as np\n",
	"\n",
	"from rdkit.Chem.Draw import IPythonConsole\n",
	"from rdkit.Chem import Draw"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"mol1 = Chem.MolFromSmiles('CCCC1=NN(C2=C1N=C(NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)C)OCC)C')\n",
	"mol2 = Chem.MolFromSmiles('CCCC1=NC(=C2N1N=C(NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)CC)OCC)C')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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bKzubDRrEiJiVFTtzpu5fs7JYYCDr25dZWhZxu6FxtQvy8vIYY9u3b+eW9EyePLm0tFT7DwKgpearLBMayoiYrS1LSWGjR6fY2b0aFxdXz+u5hxbp6enckriSkhLt+7BsGSNiW7Zo35IO5OXlEZGFhYUGVQVUCgvZ2rVs9Wp28CA7eJBFR9fcWhkasqlTWUhIq6nLmp/P2rVjIhG7ebN61qxdYrH+d999p86BnTszIn7Wwn/yCSNi//kPD03pBFezsMFNOV7kwQM2bVrd66odO9jIkUwkqrmu6t6dBQTsfvZf+oULF7jqie7u7mqO8AM0nebbj/C112jgQNa7d9r580fc3L7MzDzDPfx7rvv37+fn55uZmVVXVz9+/Lh79+7cfjpaio8nItJ0OaKOcYspuSXhGjfSsSN99BGZmdVM/TcxoQ8/pK++ojt36Ngx8vam1rL4vnNn+uCD+0OGBG7aNPf//T8TpbI6ICCgsrKy/qMKC+nePWrfnp5UdNdKbCwRUSvdr7e0tDQnJ8fExKRPnz6atWBuTkeP0p49VFVVs6QyM5M++YQuXiQjI/L2prAwysiglSsXPLsCeOTIkZcvX3ZxcYmJiRk8ePCFCxe0/kAAmmu+IBSJaN26UxERDuvXv/fBBx+YmJiEhIRwK7qe1bFjxzt37pw5c4ZbSqjxUvo65PIHpqbk4sJLY4139Spt2ECrVlF+vgZHa1lVoA7V1P81a+i996hjR15abVZvv10VHf3RoUOhTk5O/fr1y8rK2rNnT/2HWFtTZiadPElaXEs8VVhYbmCguyDU7nSKjY1ljLm5uenp6WncBbGY5s9/uqRSJKL162nXLioooJAQmjatvlpODg4OV69enTJlSmFh4fjx43fv3q1xNwC01Kw71E+fPmnAgAG5ubnHjx9fsmSJUqkMCwt70YttbGw8PT0vX75MPH37FxUVRUS0F4vb9+zJtG9NEydPkr8/rVpFv/yiwdEFBS+PGbPa03O69h2pqiJ3d00Wd7YoXbt2/de//sUYCwgI+N///telS5d6vtPlclq1ikJD6fp14mWr+aqqqvPnO+rptXd11WSBJg+0O514qSqgorqu8vEhHx9Sc8WvhYXFb7/95uvrK5fLFy5cuHz5cqVSyUt/ABqlWYtvikQif3//mTNnfvLJJxEREXPmzKm9G6pcLk9NTY2Pj09PT4+Li4uPj09KSiovL7e2to6KiqqqqtKyZiZ3R+Xk5CgW83E7oD3GGnVjcuGCx/nzHlIpD+/s4ECmpiSRkIMDD63p0Icffrh9+/aQkJD//e9/6enp7dq1IyK5XJ6enp6W5pCcbJCSQqmplJJC2dk0axaZm9OTSYvaSk5OrqysdHDoaWpqzE+LzSs+XtKzp9eQITxcFMrl1L495eZqcqyent7WrVvd3NyWLVv21VdfpaWl7d2718LCQvteAaivuatQT58+3dPT8/r16zt37hw6dOiXX36ZlJSUnJyclJR053l3KJ07dy4tLT137tzEiRNDQkI6Nn4I7/Hjx9wG5apnbNp/Cg1NnEjr15NcTj4+tHAhJSfTlSvqH83jA874eFIoWuVwaB3du3dfvHjx999/v3jxYk9Pz9TU1NTU1KysLKVS6eLyMCHh6ZWTgQE9ekTjx9P27TRgAN29S/fu1dTvbpSKigojIyNV5W53d3ceP07jqE4nPz9KTqa//6b589U/OjpanJVlb2/PQ0deeokcHemf/9T8ukoikfTp02fWrFknTpwYOXJkWFhYr169eOgZgJqaf37OkSNHiOjZGR+GhoZ9+vSZOnWqVCoNDg4ODw/nNoqLjIzkprf16dMnpsHlh7XIZDJfX18rK6tr164xxpYvX05EgYGBTfXB1BEYyBwcWFoa69SJETGZTM3jCgsZETMzU7c0TP24jYR+/ZWHpnQuOjra3Nzc2PjpbZmBgUHfvn0lknRfX/b11+zUKZaayqqqnlYXio1lL7/MTE3Z4cONeCPudLK2tj5//jxjbPXq1US0VrN9xfhy/z779luWm8v09ZmhYaO2n7a2ZkSMl626NmxgREwq1badlJQUZ2dnExOTv//+m4duAahNB/sSlZSUEJGVldXAgQMdHR2dnZ2dnJwcHR179uwpFj/nmeWgQYO4OofXr18fNmzY/v2nJk8e/uzLVHJyaM8e5f79HtHRUdxvzp8/P2TIEK7wNF+TTTQUFUWpqbR3L73xBgUF0c8/k4eHOsdxt4MuLvzM8mjVs2frOHDgQFlZWd++fX19fR0cHBwcHOzs7PSfN0lj5UoiouPHSS4nZ2e6epVef53WrSN///r+X01Ppz172OHDHtHRN7nf/PXXX6NHj46Ojib+nrFp6J//pIgIsrKiCRPoxAnav5+WLVPnuLt3qbCQrKzI1paHXvA1e9bBwSEiIuLWrVua7ZAFoLlmDt7KykpuuvaePXsadWBFRcXChQu7dRvWqZNCKn3OjVF1Ndu7l40fX1NLbPjwN2xtbX19faOioi5evCiRSNq1a9euXbvt27fz9mE08PvvjIj17s1u3apZjazeMvDvv2dEbOFCHrpQWckMDJhY3IiyZC1Wfn6+ubk5aVRvISio5lSZM+c5/1dUVbHgYDZiRM2SuBEjFnfr1s3X1/fmzZvcraG5ubmRkdGOHTv4+SSa+fprRsSmTGH79zMiNmSImsf98QcjYqNH89MLZ2dGxG7e5Kc1gObX3EH43XffEZGbm1t1dbUGh3/1VemLamYqlax3b0bEjI3ZrFksPLwoMTFxzZo1PZ5sn8oNxorF4k2bNmmzJl0rCgXr0YMRsQsXlKNHPF4wsizjiDrHxcezLVvYqVM8dCEu7qGVlcLenoemdI6rw/faa69pdviJE8zCoqbqXl5e3b86OtYMRy9cyM6fL4mPj1+zZk2dZ1f6+vrbtm3T9mNo7P59ZmTE9PVZZiaztGR6eorMeiuePbFtGyNi777LQxcqKpi+PtPXZ48f89AagE40axByS+OJKDQ0VONGTp1ilpaMiPXvz+qUpPjxR/bNNywri/34Ixs5UtmtW813loODw8cff5yVlRUUFMTNsJ81a9bD+otPN501q6vHDCw5uTI/f6tMRikpDe+qsWZNTSGYlBQe3j8kJISIvL3f5KEtncrIyDA0NNTT06u/RFH9oqOZnV1NDZTIyP/zp3372J49LCuLbdvGXn756elkZ2e3du3axMREf39/7upKIpFU6qokz4wZjEixY1t56JqEcJucnFVqHldYyO7c4eH9b90qdHMrHzQIVUOhFWvWIPziiy+IaNCgQVrekMXHs759GRHr1evpyKJCwc6eZT4+zNS0przT+PGBixcvvnDhQu23O3XqFLe1woABAzSu7aRN/yseJ8lkohs3zOTy25GRRpGRenJ59oterFCw2FgWEMA2beItCP39/Ylo5cqVPLSlU/PnzyeiRYsWadlOXh4bOrTmdFLFmVzOjhxhM2YwQ8Oa02nChIAlS5b89ddftf/r79+/n5uTPHLkyILGzFXhi/zv4+kHXeLjB5aVXZDJ6Natbow1kEn8XldxC+Fb4F5dAOprviAsLy/nyoee4KPgcVER8/JiP/749F/1jBk1X1giERs9mu3cyZ7ZT61GUlISt9m9agagmqqrq8PDw729vefNmzd//nyNi18nJg6Xyaiw8Oe0tNdlMrp79zkTWbmyxX36MGNj5u/Prl9nb73FUlJYfr5m7/nUrFmziGjXrl3aNqRTMTExYrHY0NAwLS1N+9YeP2aLFrGzZ5+eThMm1JxO+vps8mS2b98LH6leuXKla9eu3MBDQiM3JMzLy/vyyy+3bdu2a9cuzbZiUCrlUVEdZTJ69OhWTIy9TEYPHpx97iurqtjx42zVKp6vq6RSKRFtUNUbBWiFmi8IN23aRERDhgzh6/kc14zqX/UXX7Bu3ZhUqtY/7/v373t5eRGRkZGROhs8RUVF+fn5cWWCicjQ0JCIbGxsNNsTo6Bgh0xGSUljSkqOyWSUmvpP1Z8UirL4+IjRo5+WLbazq3mWM2UKu36d2ds/f3KH+riJjtevX9e8iRZg2rRpRLR8+XJ+m1WdTp9/zlxdWWAgu3u34aNycnI8PDyIqEOHDqdPn27w9ar9+bgaEdx8n6lTpz7QaGPD27ffkckoJ+fD3Fx/mYwyMuruHxkby6RS1rVrzRnl5/f0uurwYXbvngbv+RS3hcWhQ4e0agVAp5opCEtKSqysrIjozLNbtmgnIKDmX3ViImvUJXV1dbX0SZmWFz3jKS4uDg4Orl3+xsXFJTAwMDExcdy4cVyO/vTTT43tc3V16Y0bpjdvdqiquldefpX75cOHssxMyc2b5levmllbK4yNmbc3CwtjtScVnTvHzMwYEfvHPzRcAVZVVcWtB392//FW5Nq1ayKRyMzMLO/ZKS7aUZ1Ojd2Soby8fObMmdz0ma1btz73NUql8uLFi2+99ZZq32NDQ8Pp06dv2rSpc+fORNSvX7/09PTG9rm8/LJMRrdu2T5+nJiSMqm4uGZ1ZGVlTl7ep6dOfcjlH1FNtK9ZwxhjU6aw3buZvj7r3bvhzUHr0bNnT9JiCwuAlqCZgnDt2rVENGrUKN5bVq2S1myc55dffuHWYo8fP76oqIj7pUKhCA8PX7BgAff4h4gsLS3rbF6an59fO0erqqoa9b7l5ZcViscFBdvv3g0sLNzJDWrJZCSTiRITR/z1V/qL7g2io2smx9rasqtXG/GO3Lju1KlTLSws7Fv5nFFur8p169bx3rI2p5NSqXzR9Jnbt28HBgY61Kq8wu3Pl/9kpDstLY1b4dqxY8dGDddzUlNn5OV9rlA8ZowpFA/v39+TnDw+MlKPO6lGjy739X1O8YaCAjZyZM3M2N9+a/TnZYyVlpaKRCITExPNJoEDtBDNEYSFhYVc8cALFy40w9s1VkREhOoZz/Hjx/39/bmLXG6txbhx43bt2lVniumePXssLCzCwsJ27NjBDZPWzlF1KBSPysou3b27uagopLq65Natrrdu2ebkSCsqGr6yLihgY8YwImZqWn3gwB8Nvj42NnbFihXcZ+QG4srLy9Xvaktz6tQpLjBa5p6u+/bt466fvLy8bt++vWvXrnHjxqnqKHGLEaOiomofcv36dS8vr7S0tKlTp3LDDDt37tTgrbnrqnv3grn8i4w0SkmZWlQUolS+cEZrRQXz8WFETE+PNarmUnZ2dlBQkKurq42NzZQpUzToLUDL0RxB+MEHHxBRS/7XkpWVNWjQICJS3QI6OzsHBgbmvmD8ccGCBUSkp6e3ZcuW2jmqzjz+hw9lWVm+UVEdb9wwqa4uevjw+pP8a8TAbmUle+cdNnr0CiLy9fV97vV4SUlJnXFdZ2dnf3//27dvq/9GLY1SqeT+S21pwdsrR0REcPPCuIsk7uJj0aJFZ8+efXZGjOoT2dvbx8TEqIYZfH191Zw+8/hxfE7OqrS017nrKoWiPCNjcUFBcHV1sTqHK5Vs40YmEjFb29vLlq2Qy+X1vPjBgwc//fTTmDFjVEWg7O3tcTsIrV1TBWF1dXVRUdHt27cvXLjAbTHfwusHPnz40Nra2sDAYNasWZcvX67/xUqlMjAwkPsumDt3bmpq6uDBg7nvu99eMMZUXn7n7t2A2FinJ0OglJDwj/z8oLt3N+flfVpVVVhc3OjpBsHBwdxX7cSJE4uLi7lfqsZ1ua0YiKh9+/YLFiwIDw/XWRmBhqjOlrS0NJlMdu3atfDw8OPHj4eEhPz888/BwcGfffZZQECAVCrl5sh07979Ucuui5OZmWlmZta+ffshQ4YEBwfX/0Q2NzeXKypmbm7ODTNwk2gmTZpUz11vVVVBfv5XCQmeqjOqoiKJu67SoMOHDrH+/YcR0ejRo59dB6JQKLjyTGZPNlgyNjb29vYOCwtr7EMBgBZIwyB88ODBf//7X4lEMnfu3OnTp48bN87T09PNza13794dOnRQXQhzbGxsXn/9dX77zbvKykpDQ0OxWKz+Qvvjx49zQ76DBg1KSUnhlrXp6enVruutmh/o4dGDe2Zz65ZNVpbvo0dRjDGlsqqk5FhlZf7NmxYymVgub/S92qVLl7ibj759+548edLf39/Ozk41rjt8+PDg4OAWPhCan5/fqE1Zvb29W/7aj5KSEpFI1K5dO7Xv6h6/8cYbqvPn4sWL3BRld3f3jIyM2q+sqKgIDQ3dsePfkZEGXP7dvGmZmSkpK7tYVHTw7t3Nd+5syMp6Lynplcb2OSoqinso0KdPH27bXsZYXFycv7+/qp6O6qRqmePSAJrRJAhlMtmIESPq/7YSi8UdOnTo0aOHo6Pjvn377t+/z3vX+cWV5La3t09OTv7yyy/VXBcRHR3du3dvIrK1tb169arqNnHOnDkRERHLli3j5soSkZGRUWTkuyUlx5XKp+NIqakzZTIqKNiRljZbJqM7dzZq0POMjIyXXnqJu0jn3qtv374bN27MysrSoLXm5+vrS0QmJiY9evTo06fPwIEDPT09x40bN2nSJG9vbx8fH4lEsmLFCqlUGhAQ8Nlnn6Wmpuq6yw27ePEiEXl6eqanp585c0ad87/2MMO//vWvxMREFxcXIrK2tv7rr7/Yk+0vuIC0sDC+edMqOXlcYeEuhaLm0i0//8u4OLeKivRbt7rIZPTw4c3GdvvOnTtDhgzh7k3ffPPN/rX2qXJyctq4cWOrHlcHeBFNgnDSpElENGbMmODg4F9++eXw4cPh4eHXrl2LiYlJS0u7f//+41ZYdvDgwYNENG3atODgYCLy8fFR88DCwsJXXnmFi7pdu3YdOXLE7P/uz+3h4fH1118/96uwsHCnTEaJiSNKS0/JZBQT48CYJqOXZWVlH3zwwcqVK998882LFy+22CHQZ2VmZhoZGYnF4jrzR1o7rqbu4sWLAwICiOj9999X88CQkBBuTHvEiBEpKSncaldDQ8Nu3brVPqOCgoJKSurWVkhPnyOT0d27n2RlvSeTUXb2fzToeUVFxfz580UiUfv27YmoQ4cO3HzpVnRSATRWo4MwIiKCiMzMzO5puRC3hdmwYQMRSaVSPz8/Igrg5tGrp7Ky8u233yaiIUOGVFdXnz17ViQSmZubv//++9HR0fUcqFCU37xpLpPRo0ex0dHdZTJ6eD9C64/SmixcuLBRlx2tBVcN/PPPP+cGPNUp2qBy7do1GxsbIho9enR1dfU777xjaWkpFou53VRu3LjxogNLSk7IZBQb61Je/rdMRlFRnZVKTR7gKZXKM2fO/PTTT0eOHKl/7gxA29DoIOSWcPn7+zdBZ3Rp9uzZRPTzzz+PHz+eiF4056UeP/zwA1d07fTp00Q0fPhwdY7KyFgsk9Hd22vLfv9U0c+Rvdnqa2GrLzExUV9f38DAgJcyaS3KqFGjiOiPP/7gRq25raHVl52dPXz4cK76z6FDh7jTqcHHjUpl1a1bXWUyevjwekLUgLxvXq6+0vDqGgB4zka49Th9+vS5c+csLS2526a2JD4+nohcXV1VPzS2hSVLlnDjV41qoavxv15a94+uY/aY9ZwqjkuhkBAqL2/sW7dSK1eurK6uXrp0KbdFZVvCPXJ2cXFJSkoSi8WNPZ26d+9+6dIlbipybGwsEY0YMeK521bXJhLpW1nN65TiLg496fTHnC7vXtUL+knTTwAgII0LwnXr1hGRVCpV1YhqG6qrq5OTk0UiUbdu3XJzc42NjbkpMJpJSEggtYPQuMtQg+T7lJFJ2dk0dCiVl9Phwxq/dSty/fr13377zdTUdPXq1bruC8/u3Llz//59KyursrIyuVzeu3fvOo+NG4ULwn7qbQDfrcin59wY4xVfi2bPIT09+u03KinR+K0BBKIRQXj8+PErV6506tSJe/7RlqSlpcnl8p49e3IzLZ2dnbltCzXTuHtKkYh8fIiIfv6ZFi2q+UEAuH2g/Pz8VCVv2oyYmBgicnd3535QM8NepFFBKHqpP730EhUW0q1bNGYMVVTQoUPavDuAEKgbhIwx7nZw9erVXLH8tkT7cdHntqbuAYsXk54eHTlCEybQyJHk7a3Nu7cK4eHhZ8+etbS0XLFiha77wj8uutzd3VU/aNyUXC5PSUnR19fnNg5Ty4IFRER79tRcYB07pvG7AwiEvpqvCw0NvXnzpq2trUQiadIO6YQqurhRTW79lmbu3btXUFBgYWFRe757A7p3pzlzqEsXMjGhCxc0fuvWgj0pwr5q1aoOHTroujv8U93D/f7776TdHWFCQkJ1dbWLi4tqkWjD3niDVq6k8HD69ls6fpwmTND43QEEQq0gVCgU69evJ6J169apqnG2Jar8O3z4MGl3R6jKVFWdZbX88gsR0dWr9O23VFFBfn7UpYvGfWjhDh06xK0QWLZsma770iRUI6KfffYZaReEjRoXrWFjQ2FhZGhYcy4NHtyGzyUAXqgVhHv37o2Pj7ezs1u8eHFTd0gnVOn16NEjMzOzgQMHat+UJgefPEkffUQPHtCOHdQWxwyJSKFQcGPs/v7+qmqobYlSqUxMTBSJRC4uLh4eHsbGxo6Ojhq3pkkQEtHkybRuXZs/lwD40nAQVlVVcYvNN2zYUKeIaBugVCrPnj3LTXZ3dHQcOnToe++9p02D2g+utm27du1KSEjo3bt3W72oCg0Nffjwoa2trZWV1YEDB7RsjZfpNgBQv4Yny/z6669paWnOzs5cjYw2Izs7e/PmzX379uWqWInF4jfeeKNE67nmWt0RTpxI69dTQADNn69lN1osAwODzp07b9y4sY1dVOXk5GzevNnJyWn27Nm2trZ5eXmbN2/WvlnNp9sI4FwC4E2DS+4rKiq2bt0aFhbWxEv7m0tZWfkvv4waNUr1DM/Ozm7RokXW1tZE5OzsnJSU1Kj2FArF6dOnVaXUuMUAdXYMgNrKysrU3JOhFSgpSd27t/bp1K1btxEjRnD/89///rcGuxQVFxdzR3FbWGD/d4Cm1hwb87YUMhmTSJi5OSN6rW9fbkM11S592dnZHh4eRMRtPa9Oe1lZWYGBgVxVFK5aZlFRERGpv/kOtFbV1Sw8nC1YwExNFZ06Gevr19mfb//+/dy0snHjxhUVFanXZDW3kaSpqemJEycYY5cuXSIiDw+Ppv0sAIInjCDcvJnZ2zMiRsREIjZiROrevQ8ePKjzqvLy8tdff52e2VOwjrKysp07d9a+Cejdu/fmzZurqqoCAwOJaPDgwU38eUB3SkuZnx/r0qXmdBKL2auvnt+//9mtd69cucINDzg4OCQkJNTTZGRkpJ+fX5cnczvFYvHGjRsZY59//jkRLVq0qAk/DgC05SC8coWtX89WrmR5eczbmxGxbt2YVMqSk+s5qM7W83W2QZfJZBKJRFVPQHVPGRcXJ5VKuW+9wYMHt51hZFBRnU45OaxrV0bEnJ2Zvz9LT6/noJycHG6YoUOHDqdPn67z19zc3KCgoNpTlF1cXPz9/VNSUrhbQyMjo0GDBv3www9N+cEAoA0H4dq1jDFWWsq2bGGRkeyPP5jaw5XHjh3jtp5/+eWX79y5w/2ysLDQyMiI+8Ly8PAIDg7Oysr65ptvPD09VV9krq6uX3zxRRN9INCl2qfTkSPs+nU1jysvL585cyYR6evrf/XVV6rfHz9+XFXGr3PnzsuXL5fJZNeuXVu2bBn3uJobmVi/fn1TfBoAqE0YQdh4t27dUm09r9pD59133129enVSUhJ3wa5aBte+ffsFCxaoHjdCG6TF6aRUKv39/bmBdIlEUllZyRgrKSmxtLScOnVqSEhIZmZmUFBQ7e3gXV1dAwMDVRdhANCk2m4QXrzI5s5lb7/N8vI0a6CwsJDbfNHY2HjXrl2MscTERH9/fzs7O9WznOHDhwcHB5eXl/PadWh5jhxhPj5s+XKNT6d9+/Zx02e8vLyKi4sZY0VFRSEhIVOnTlXdGtrY2Pj6+spkMj57DgANabtBmJTEiFivXtq0UVlZuXTpUiISiUS1N2ZydHTctGlTdnY2T32FFm/rVkbEli7Vpo2IiIjOnTtzs6umT5+uGlEwMTGZN2/eyZMnsUwCQCfULbrd+sTHExFpt4+EgYHB999/P2jQoHXr1ikUCgsLi+nTp/v4+Lz66quNKyUKrV1sLBGRdhVehg0bFhkZOX369Nzc3N9++40bUfDx8ZkzZw73TBoAdAJB2DCJRDJv3ryMjAx7e/s2WR4TGsZHEBJR9+7dz58/f/fu3RMnTrz22mu9evXioW8AoJ22G4QJCUREPNX8NDMz02ZXOWjdGKO4OCIiNzftGzM3Nzc3N9emEjcA8KsRO9S3MvzdEYLQ3b5NDx5Q167UqZOuuwIA/Gubd4RKpfLX9u1dXVw8sAsEaC0pJSXm5ZeH9OzZU9c9AYCm0DbvCDMzM33OnftnaSlZWuq6L9DqHYmM9L56NahbN113BACaRNsMQm4vJDc+nugAaLg7LgC0Em05CDXcFBDg/+J2x8VsKYC2qi0HIbaJB+1VV1cnJSWJxWJcVwG0VW0zCKOiogh3hMCHGzduyOXy3r17m5qa6rovANAk2lQQ5uXlbd26deDAgYmJiWPHjkUQgsaUSuWlS5eWLl06duxYFxcXb29vXfcIAJqKiDGm6z5oq7Ky8tixYz///PPvv/9eXV1NRF26dDl16lTtnd4A1BQTE7N79+69e/feuXOHiMRi8bRp044eParrfgFAU2ndQRgXF7dnz56dO3feu3ePiPT09MaOHSuRSGbMmGFgYKDr3kFrUlRUFBoaunv37oiICO43Tk5Oc+bMWbBggb29vW77BgBNqlUuqC8uLj548GBwcPCNGze437i6uvr4+CxevJir7g+gJrlc/ueff+7Zs+fo0aNVVVVEZGlpOWvWrAULFgwfPhyl1QGEoJXdEf7xxx/ff//9yZMnKysriahjx45vvPHGokWLMAoKjVVQULBq1arQ0NDS0lIiMjQ0nDx58sKFCydPnmxoaKjr3gFA82llQSiRSHbs2CEWi1955ZUFCxZ4e3tzm50CNFZFRYWNjU1JSQmGEwAErpUF4Y0bN86ePTt//vyuXbvqui/Q6h05csTV1dXJyUnXHQEAXWplQQgAAMCvNrWOEAAAoLEQhAAAIGgIQgAAEDQEIQAACBqCEAAABA1BCAAAgoYgBAAAQUMQAgCAoCEIAQBA0BCEAAAgaAhCAAAQNAQhAAAIGoIQAAAEDUEIAACChiAEAABBQxACAICgIQgBAEDQEIQAACBoCEIAABA0BCEAAAgaghAAAAQNQQgAAIKGIAQAAEFDEAIAgKAhCAEAQNAQhAAAIGgIQgAAEDQEIQAACBqCEAAABA1BCAAAgoYgBAAAQUMQAgCAoCEIAQBA0BCEAAAgaAhCAAAQNAQhAAAIGoIQAAAEDUEIAACChiAEAABBQxACAICgIQgBAEDQEIQAACBoCEIAABA0BCEAAAgaghAAAAQNQQgAAIKGIAQAAEFDEAIAgKAhCAEAQNAQhAAAIGgIQgAAEDQEIQAACBqCEAAABA1BCAAAgvb/AegPjx4klZx6AAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x7F4F5658D0F0>"
	]
	},
	"execution_count": 3,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"Draw.MolsToGridImage([mol1, mol2])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [],
	"source": [
	"fp1 = AllChem.GetMorganFingerprintAsBitVect(mol1, 2)\n",
	"fp2 = AllChem.GetMorganFingerprintAsBitVect(mol2, 2)\n",
	"dist = 1.0 - DataStructs.TanimotoSimilarity(fp1, fp2)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"0.41025641025641024\n"
	]
	}
	],
	"source": [
	"print(dist)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [],
	"source": [
	"cats = CATS2D()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [],
	"source": [
	"cats1 = cats.getCATs2D(mol1)\n",
	"cats2 = cats.getCATs2D(mol2)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"0.0"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"euclidean(cats1, cats2)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"['', ['L'], '', ['R'], ['A', 'R'], ['R'], ['R'], ['R'], ['A', 'R'], ['R'], ['D', 'R'], ['R'], ['A'], ['R'], ['R'], ['R'], ['R'], ['R'], ['R'], '', ['A'], ['A'], ['A'], '', '', ['A'], '', '', '', ['A'], '', '', '']\n"
	]
	}
	],
	"source": [
	"print(cats.getPcoreGroups(mol1))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"['', ['L'], '', ['R'], ['A', 'R'], ['R'], ['R'], ['R'], ['A', 'R'], ['R'], ['D', 'R'], ['R'], ['A'], ['R'], ['R'], ['R'], ['R'], ['R'], ['R'], '', ['A'], ['A'], ['A'], '', '', ['A'], '', '', '', '', ['A'], '', '', '']\n"
	]
	}
	],
	"source": [
	"print(cats.getPcoreGroups(mol2))"
	]
	}
	],
	"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": "3.7.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}