Layout2DHydrogens

Layout2DHydrogens.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import rdCoordGen, rdDepictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_coordgen = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_coords(m, use_coordgen=False):\n",
    "    if use_coordgen:\n",
    "        rdCoordGen.AddCoords(m)\n",
    "    else:\n",
    "        rdDepictor.Compute2DCoords(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Chem.MolFromSmiles('Nc1nnc2n1CCS2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = Chem.AddHs(m, False, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the molecule has no conformers, so hydrogens have only been added to the graph and the `addCoords` parameter has no effect as there are no coordinates yet; they are generated on-the-fly only for visualization in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.GetNumConformers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SPz8IhbmXL78rkUjKy8srKiqeOdI3MDCQy+U3btzYsGHDRx991KvfBtEaVEZVzsLCYurUqYaPb/Sjp6c3depUGxubtpZBgwYp6ml+fn5iYuL3339fVFS0ZcuWLVu22Nvb+/n5BQQETJ48uae9FxcXSyQSS0tLJ6UP7VRGIIBYjFmzcOAAgoL+bH/4EAUFqKjAvXu4e7dj0bx/v4tTeXiYZmYea/ulnp6era2tjY2Nqalplx9MTU1TUlLmz5+/ceNGGxublYpH/Ql5Kh7D/rvJSPco6ml8fHzxH8+ZDxo0KCgo6PXXX3dxcenmSeLj45csWeLn55eUlKSypMo0ezb4fKSkYMECnDuHwkL0748pU2BsjCVLEBz8xC8KBLC0hIUFrK1haQlLS1hbw96+ytg408bGxtLS0sLCopsLJL799ts333xTJBIdPXr05ZdfVtrvjfRRVEY1QE5OTlxcXGJiokQiUbS4uroGBAQsWrRo2LBhT//uypUrd+zY8fnnn69bt071SZWgrYyWlMDVFaGhiIlpLaN/+xvWrGmtlTY2HT9YWKAXVz46ioyMjI6ONjIyOnv27OjR6vsELVELDNEQLS0tZ8+eXb16taWlZdsfn6ura1RU1I0bN570rREjRgDIzMxkM2pvvPwyM2dO6+ePP2YEAubiRWbyZGbuXFZjyOXykJAQALa2trdv32a1b6JpaDSqeVpaWk6dOhUXF3fo0KGHDx8C4PP5Hh4eAQEBQUFB1u12Va+pqTEzMxOJRDU1NSpalKp0baNRAI2NGD0aAwfi0SMYGyM5mdUkjY2N3t7eJ0+eHDFiRFpaWv/+6rIPIFE7XNdx8vwaGhoOHz68ePHitvtXfD7f09Nz8+bN5eXlDMP89NNPACZPnsx10h5oPxplGOb4cQZgDAzYHo0qVFZWOjs7A5gzZ05TUxMHCYgmoMVxGkxPT8/X1zcuLq68vPzw4cMBAQEikSg9PX3NmjU2NjZeXl5ff/01NGSp04MHWLQIZWUd2196CcHB7L06rwMzM7OUlBQrK6tjx47RXXvyJLTgqS/Q19f39fX19fWtrq4+fPhwYmJiamrq8ePHAfD5/IqKiqamJpFIxHXMJyouho8PrlyBVApra3RY+f7FF7h0Ce2uVbDK0dHx6NGj06dP37Vr15AhQ9577z1uchB1xvVwmKhEeXn5+++/D0Cxbn/EiBGnTp3iOlTXzp1jrKwYgBk5klHbezlHjhwRCAQ8Hi8uLo7rLETt0KS+b7KwsLh58yYAf3//F154IS8vb8aMGb6+vnfu3OE62mMOHcL06bh3D7NmIS0N9vZcB3qCefPmxcTEMAyzfPnyE+zvX0rUHNd1nKhEcXGxUCgUiUQlJSWNjY2bN29W3IYyMDCIiop69OgR1wEZhmF27GCEQgZgQkOZxkau03RDRESEmZm5h0dabi7XUYg6oTLaN4WFhQF444032lru3LmzePFixd+dw4YNS01N5TCeXM5ERTEAw+MxUVEcBumZlpaW0NDfAcbRkbl7l+s0RG1QGe2D7t+/b2BgwOPx8vLyOvzo+PHjbQ+Szps3r6SkhP14MhkTFMQAjI4OEx/Pfv+9Ul/PeHgwADNuHCOVcp2GqAcqo33Qhx9+CGD+/Pld/pTbOf79+8zkyQzAmJoy6nrT6xkqKpihQxmA8fFhmpu5TkPUAJXRvkYqlQ4YMABAWlraUw4rLS1tP8c/duwYC9muX2eGDWMAZtAgpqCAhQ5V5fp1xsKCAZgVK7iOQtQAldG+ZtOmTej2k0snTpxgbY6fmdlaekaPZkpLVdcPS86eZfT0GID58kuuozDMmTNnQkJCKioq2jeWlJSEhIT89ttvXKXSHlRG+5TGxkYHBwcAR44c6f5X2ub4/fr1i4qKkslkSg/244+ViqLj69t3LikmJDB8PsPnMz/8wHGSXbt2Abh582b7xv/85z8ADh48yFEoLULrRvuU77//vqSkxMXFZe7cud38ikgkioiIuHLlyuLFi+vr6z/66KNRo0al9ui9888iFotDQ53s7XOXL0dSEgwMlHhuLgUE4JNPIJcjJAQZGVynIdyhMtp3MAzz2WefAYiMjOzpVkIDBw6Mi4s7ceKEq6trUVHRnDlzfH19b9++3ctILS0tYWFha9asefiwJjw8+5tvoIzNpdTIu+9i1SrIZJg/H0VFXKchHKEy2nckJyfn5eXZ2dkFP+Ud8U81c+bMixcvKub4R48edXFx2bBhw6NHj57vbHV1dX5+ftu3b9fV1d27d294+PLnO4+a27wZr7yCykrMnYvycq7TEE5wfVWBKI1ip6ZNmzb1/lTt7+MPHTo0JSWlp2eQSCRubm4AzMzMfv31195HUmd1dczEiQzATJjA1NVxEEBxbXTAgAFW7Zibm4OujbKCXtvcR2RnZ7u7u5uZmZWUlHTYPu+5nTx5Mjw8vKCgAH88VK64f/VMBQUFPj4+t27dGjx48E8//aR4ZWffJpHAwwMlJVi0CHv3qrYvqVR68eLFgoKC/Pz8nJwce3t7Ly+vZcuWicVixVo3hZKSkvfff//gwYOvvvqqagMRrus4UY758+cD+PDDD5V7WsV9fCMjI3T7Pn5aWprif+aJEyfeu3dPuXnUWX4+Y2/PJCczNTXMgwcdf1pT8/xLFB48eHD8+PFPP/104cKFzs7OHS58Ozk50Z16blEZ7QsKCwv5fL6enp5EIlHF+bs/x09ISNDT0wPg5+dXX1+vijDqTPE4mJsbA3RcBeXiwixe3N3z3L3LHD9e+PHHH/v7+zs6OnYY+ujq6rq5ub311ltff/11dnZ2Q0MDlVFu9a37ptoqOjpaLpcvW7bMWjUvN1bcxw8NDV21alVBQYG3t3eXc3yxWLx27VqGYVavXr1p06aerhboA9r2b+bzEREBLy8YG3fri2VlyMlBQQHy81s/TJp0MSPjgz9Oq+Pk5DTuD25uboq/q4i64LqOk94qLS3V0dERCATXr19XdV9PmuM3NTUp9tgQCARbtmxRdQw15+bGzJ3LmJszERF/NnYYjd64wSQmMu+9x8yZw1haMsBj//Tvz/j731q7dm18fHx+fn7zsx7dp9Eot6iMarx33nkHwMKFC1nr8ffff28/xz948KBitb+BgcGhQ4dYi6G23NyYRYsYsZgRCJicnNZGRRk9dIiZOZMxMelYN83NGS8vZv165sAB5to1Ri7vWY8XL17cuHFjdXV1+8a7d+9u3LixsLBQSb8t8kR0p16zVVVVOTg41NbW5uTkjB07ls2uf/nll/Dw8KtXryp+aWVldeTIkfHjx7OZQT2NH49hw7B7N8aMgb4+MjMhEMDVFW5umDULr78OAKamcHXFuHGt/7i6gsfjOjd5XnRtVLNt3769trZ29uzZLNdQAF5eXpcvX54wYUJubq5cLndxcRk+fDjLGdSZUIht2zB9Or7+GmFhrY0vv4yjRzF2LGxsOA1HlErrbgL0JTKZLCYmBsD69es5CbBr165Lly7p6emZm5ufPn165syZDx484CSJepo6FSEh+PBD3L/f2mJtDR8fqqF9DZVRDRYbG3v37t3x48fPmDGD/d4vX768bt06ADt27Dh37tzgwYOzs7OnTZtW1nmzeS322WdgGERFcZ2DqBKVUU3V0tLy5ZdfgqOhqFQqDQwMbGhoWLlyZUhIyODBg9PS0kaMGJGXlzdlypQbN26wH0k9WVnhn//Ezp2QSLiOQlSGyqim+uGHH65fvz5kyJAFCxaw3/vKlSuvXr06cuRIRSkHYGNjc+bMmYkTJxYXF0+ZMiU3N5f9VOrp7bcxdiyqq7nOQVSGyqimUrwTb/369QKBgOWut2/fvnfvXkNDw4SEBH19/bZ2MzOz48ePz5o1SyKRTJ8+PSsri+Vg6onPx7Zt0L5nEbQJ1yuuyPP4+eefAVhZWTU0NLDc9aVLlxSlc8+ePV0eIJPJFO/CMDQ0/OWXX1iOpyYKC3u89pNoLvorUiNFR0cDWLt2LcsPBdbW1iouif71r3997bXXujxGV1c3ISEhNDRUKpXOmzcvKSmJzYTq4MEDjB6NYcPQ2Mh1FMIKKqOap7KysqCgwNjYeMWKFSx3rbgkOmrUqM8///wphwkEgl27dq1du/bRo0eBgYGxsbGsJVQHBw+isRFOTn8+Yk/6OK6Hw+QZ5HK5vNP8sKGhISsri+UkW7duBWBoaHjlypVufuVf//oXAB6P96U67J/JlpdeYgAmNpbrHIQtVEbVnZeX14QJEzo0RkZGCoVCNmNcvHhRcQFh7969Pfri1q1bFa96Wr9+vYqyqZV79xihkNHVZaqquI5C2EKTevJsikuiMpksLCxs0aJFPfruqlWrdu/eLRQKo6OjV61aJZfLVRRSTSQkoLkZ3t4wMeE6CmELlVHybKGhoUVFRaNGjVKssuqpkJCQpKQkPT29bdu2LVmypKmpSekJ1ceBAwAQFMR1DsIiKqPkGbZs2XLw4EEjI6MOq0R7xNfXNyUlxdjYeO/evf7+/g0NDcoNqSbu3EFGBvr1w7x5XEchLKI3PGmAmpqaY8eOtW8pLi5mp+vz58+/++67AHbs2NHLnemmT59+4sQJb2/vI0eOeHt7Hz582Libr4bXHAcOQC6Hry+UtKkg0RBcX5wlz+Dl5cXj8USP4/P5LNxiqqqqGjx4MIDw8HBlnbOgoMDOzg7AuHHjysvLlXVaNaHYhSkpieschF00qdcA48ePb3ycYoSoUgzDLFu2rLi42M3N7fkuiXbJxcUlLS1t6NChOTk506ZNKy0tVdaZOXfjBnJyYGyMOXO4jkLYRWWUdE0sFiclJZmYmBw4cEBXV1eJZ3ZwcDh79uyLL75YWFg4efLkoqIiJZ6cQ/v3g2GwYAGe9wIy0VRURkkXfvvtN8X797799lvFvF65rKysTp065enpWVJSMnXq1IsXLyq9C/YlJ1eD7tFrJSqjpKPq6uqgoKDGxsaIiAh/f38V9WJiYvLzzz/PmTPn3r17M2bMSEtLU1FH7CgoKMjJsZo5c4mXF9dRCOuojJLHMAyzdOnSmzdvjh8/XvECFNXp16/foUOHAgMDq6urZ8+enZKSotLuVGr//v2NjY1OTvoiEddRCOtoZ1B1V1FRwTCMpaVl+8aampra2lrFLW/l2rRp0zvvvGNiYnLhwgVHR0eln7+zlpaWt99++5tvvgkODnZ1da2trf3000/bH7Bv376kpKQffviBhTDPzdnZuaio6MSJEzNnzuQ6C2EbrRtVdxYWFp0b+/fv379/f6X39dtvv0VGRvJ4vF27drFTQwEIBIIdO3ZMmDBh8eLFCxYsuN+2/dsf8vPzf/zxR3bCPJ+cnJyioiJra+tp06ZxnYVwgMooaVVVVaW4JLp27VrFe5dZw+Pxli9fzmaPynXgwAEAgYGB7O9EQNQBXRslrc6cOVNaWjphwgTF2+1INzEMk5iYCCCIbtJrKxqNap36+nodHR2h8LE/eplMNnfu3NOnT9va2urQ24Z7IjMz89atW/b29h4eHlxnIdygMqpd7t+/b2FhsXnz5oiIiPbtzs7O06dP3717N1fB2ly4cMHw8SfSG9V7Lw7FjD44OJjH43GdhXCDyihRL4MHD/7iiy/at+zZs+fgwYNc5Xk6uVyuWEJAM3ptRmWUqBcTE5P58+e3b8nOzuYqzDOdPn26rKxsyJAh48aN4zoL4QzdYiLk+Slm9D3dEYD0MTQa1UZ5eXmHDh1q39JX36OsUi0tLYoFrYGBgVxnIVyiMqqN4uPjFcOoNlKplKswmksgEGRnZ6empo4YMYLrLIRLVEa1UXR0dIc79Q4ODlyFaW/FihWdx8U+Pj4dnoXlypIlS4qKirKystpaHB0d6+rq+vfvX1FRQQvFtBaVUaJGFixY0Llx0qRJkyZNYj9MZ3V1dbW1tR0aZTLZw4cP6d0U2oxuMRFCSK9QGSWEkF6hST0hPdDQ0HDu3Ln2LX1pOynyfKiMahdzc/N79+4ZGRl1aL9w4QLdIemOm+YjpSQAAACaSURBVDdvTpw4kesURL3QpF678Hg8S0tL/U6brpmbm3euraQzFxcX6eOioqK4DkU4RqNRQnqAx+MZGBi0bxHRtiFaj0ajhBDSK1RGCSGkV6iMEkJIr9DOoIR0V0lJiUwmc3Z2bt9YXl4ukUhGjRpFr23WWlRGCSGkV2hSTwghvUJllBBCeoXKKCGE9AqVUUII6RUqo4QQ0iv/D35MqcS3/2OGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f4ec7428bc0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Chem.MolFromSmiles('Nc1nnc2n1CCS2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f4ec7433490>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting `addCoords` to `False` make no difference as there are no conformers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = Chem.AddHs(m, False, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.GetNumConformers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f4ec74339e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I call my `add_coords` function to add a 2D conformation to the molecule:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_coords(m, use_coordgen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.GetNumConformers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f4ec7433490>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the 2D layout was created with no hydrogens, subsequent hydrogen addition does a poor job:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = Chem.AddHs(m, False, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f4ec7437940>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if I recompute a new 2D layout with hydrogens in the graph, as Fio has shown, I get good results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_coords(m2, use_coordgen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.GetNumConformers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f4ec7437940>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you switch `use_coordgen` to False at the beginning of the notebook you will see that you will get similar results, though with different 2D layouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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