Pycon RDKit Intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Library , tahap awal untuk menggunakan fungsi dalam rdkit dan PyFingerprint\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole #Needed to show molecules\n",
    "from rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions #Only needed if modifying defaults\n",
    "\n",
    "\n",
    "from rdkit.Chem import Descriptors\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import DataStructs\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R0inWKBkFa5R0ioeYyChYo6RTPMRERsEaJZ3iRj0ZBWuUdIo1SkbBazGRTtXV1fn6+lqt1traWovFIjoO0S1xNEo65ePjExAQUF9fX1VVJToL0e2wRkm/uF1PhsAaJf1ijZIhsEZJv1ijZAisUdIvrpVHhsAaJf3iaJQMgTVK+sUaJUNgjZJ+sUbJEFijpF+sUTIE1ijpFw8xkSGwRkm/OBolQ2CNkn5xNEqGwBol/eJolAyBNUr6xRolQ2CNkn6xRskQWKOkX9w3SobAGiX94miUDIE1SvrFGiVDYI2SfrFGyRBYo6RfvFQ9GQJrlPSLl6onQ+CVQUm/NE3z9fXVNK22ttZq5a980il+a5J+WSwWWZY1TeOAlPSMNUq6xt2jpH+sUdI17h4l/WONkq5xzhPpH2uUdK2hRj/77DPRQYhuiTVKupaamtqvX79FixZNnTr11KlTouMQtYE1Sro2duzY559/PjAwsKCg4KGHHpoxY8aZM2dEhyJqgfNGyQCuX7+empq6bds2RVGsVuvTTz+9fv36wYMHi85FBLBGyUCuXr26efPm9PT0qqoqPz+/WbNmrV27duDAgaJzkdmxRslgfvrpp5SUlLfeequ2ttbf33/OnDlJSUl9+vQRnYvMizVKhnTp0qW///3vb775Zl1dnc1mi4qKeuWVV+677z7RuciMWKNkYGfOnElKStqzZ4+maXa7/cUXX1y9erXT6RSdi8yFNUqG980336SkpOTl5QHo3r37ypUrlyxZIsuy6FxkFqxR8hLHjx+Pj4//5JNPAPTs2XP58uUxMTEBAQGic5H3Y42SVykoKIiLi/viiy8A9O3bNz4+/oUXXvD19RWdi7wZa5S8UEFBQWxs7JdffglgwIABq1evjoqK8vHxEZ2LvBNrlLyTpml79uxJSEj4/vvvAYwYMSIpKWn69OkWi0V0NPI2rFHyZvX19e+9997q1at/+OEHAKNHj05ISJgxY4boXORVWKPk/Wpqat5+++3k5OTS0lIAEyZMSElJmTJliuhc5CVYo2QWN27ceOeddxITE69cuQIgLCxsw4YNkyZNEp2LDI81Subi8XjeeOONTZs2lZWVAXj00Uc3bdo0btw40bnIwFijZEZutzsjI2PDhg0ul8tisUyfPn3dunVDhw4VnYsMiTVK5nXt2rXXXntt69atqqo2rL+3YcOG0NBQ0bnIYFijZHalpaWvvvpqZmZmdXW1n5/f3Llz16xZc//994vORYbBGiUCgB9//HH9+vXN199bu3Zt7969ReciA2CNEjX57rvvEhMT8/LyNE3r06fPpUuX/P39RYcivWONErV2+vTptWvXDhs2LDk5WXQWMgDWKFHb6uvrrVZe85F+H2uUiKhD+MuWiKhDWKNERB3CGiUi6hDWKBFRh/wfN6ZRJaoVVIYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x991f582cb0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cara membaca file smiles menjai mol\n",
    "mol = Chem.MolFromSmiles(\"CONC(CC)(CC)CN=O\")\n",
    "mol2 = Chem.MolFromSmiles(\"CONC(CC)(CC)\")\n",
    "mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<rdkit.Chem.rdchem.Mol object at 0x000000991F4B02B0>\n"
     ]
    }
   ],
   "source": [
    "print(mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CCC(C)CC'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mol to SMILES\n",
    "smiles = Chem.MolToSmiles(mol)\n",
    "smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144.08899999999997"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#molecule weigth\n",
    "weight = Descriptors.MolWt(mol)\n",
    "balaban = Descriptors.BalabanJ(mol)\n",
    "radius = Descriptors.HeavyAtomMolWt(mol)\n",
    "radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=800x200 at 0x991DB10AC8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Draw group of molecule\n",
    "smiles_list = [\"CCCN\", \"C(C)(C)C(C)C(C)\", \"C=C(C)C=C\", \"C(C(=O)O)N\"]\n",
    "\n",
    "mol_list = []\n",
    "\n",
    "for x in smiles_list:\n",
    "    mol = Chem.MolFromSmiles(x)\n",
    "    mol_list.append(mol)\n",
    "\n",
    "img = Draw.MolsToGridImage(mol_list, molsPerRow=4)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x991f584940>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding Pattern of Substructure\n",
    "pattern = Chem.MolFromSmiles('CN')\n",
    "pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#Finding Pattern of Substructure\n",
    "pattern = Chem.MolFromSmiles('CN')\n",
    "\n",
    "for mol in mol_list:\n",
    "    print(mol.HasSubstructMatch(pattern))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   1,   80,  283,  294, 1032, 1057, 1381, 1544, 1719, 1773, 1785],\n",
       "       dtype=int64),)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Draw A fingerprints\n",
    "bi = {}\n",
    "fp = AllChem.GetMorganFingerprintAsBitVect(mol_list[1], 2, nBits = 2048, bitInfo = bi)\n",
    "#buat list kosong\n",
    "fp_arr = np.zeros((1),)\n",
    "\n",
    "#Masukkan index fingerprint ke dalam list kosong\n",
    "DataStructs.ConvertToNumpyArray(fp,fp_arr)\n",
    "np.nonzero(fp_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "prints = [(mol_list[1],x,bi) for x in fp.GetOnBits()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x450 at 0x991F599348>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Draw FP list location\n",
    "Draw.DrawMorganBits(prints, molsPerRow=4, legends = [str(x) for x in fp.GetOnBits()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mencari nilai kesamaan antara molekul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=800x200 at 0x991F577A48>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pilih 2 molekul\n",
    "img = Draw.MolsToGridImage([mol_list[1],mol_list[0]], molsPerRow=4)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  80,  294,  794,  981, 1046, 1057, 1171, 1840], dtype=int64),)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bi = {}\n",
    "fp2 = AllChem.GetMorganFingerprintAsBitVect(mol_list[0], 2, nBits = 2048, bitInfo = bi)\n",
    "\n",
    "#buat list kosong\n",
    "fp2_arr = np.zeros((1),)\n",
    "\n",
    "#Masukkan index fingerprint ke dalam list kosong\n",
    "DataStructs.ConvertToNumpyArray(fp2,fp2_arr)\n",
    "np.nonzero(fp2_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.  [1, 80, 283, 294, 1032, 1057, 1381, 1544, 1719, 1773, 1785]\n",
      "2.  [80, 294, 794, 981, 1046, 1057, 1171, 1840]\n"
     ]
    }
   ],
   "source": [
    "print(\"1. \", list(fp.GetOnBits()))\n",
    "print(\"2. \", list(fp2.GetOnBits()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1875\n"
     ]
    }
   ],
   "source": [
    "#compute Tanimoto Similarity Index (A∩B)/(AuB)\n",
    "print(DataStructs.TanimotoSimilarity(fp,fp2))"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}