PatWalters/rescale_bonds.ipynb

## rescale_bonds.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import rdDepictor\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem.rdmolops import Get3DDistanceMatrix\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a couple of functions.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bond_lengths(mol):\n",
    "    \"\"\"\n",
    "    Return a list of bond lengths \n",
    "    :param mol: input molecule\n",
    "    :return: list of bond lengths\n",
    "    \"\"\"\n",
    "    dm = Get3DDistanceMatrix(mol)\n",
    "    bnd_list = []\n",
    "    for bnd in mol.GetBonds():\n",
    "        start = bnd.GetBeginAtomIdx()\n",
    "        end = bnd.GetEndAtomIdx()\n",
    "        bnd_list.append(dm[start,end])\n",
    "    return bnd_list\n",
    "\n",
    "def scale_molecule(mol, factor=1.5):\n",
    "    \"\"\"\n",
    "    Scale the bond lengths in a molecule\n",
    "    :param mol: input molecule\n",
    "    :param factor: scaling factor\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    mean_dist = np.mean(get_bond_lengths(mol))\n",
    "    factor = factor/mean_dist\n",
    "    matrix = np.zeros((4, 4), np.float)\n",
    "    for i in range(3):\n",
    "        matrix[i, i] = factor\n",
    "        matrix[3, 3] = 1\n",
    "    AllChem.TransformMol(mol, matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The molfile defining the template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mb = \"\"\"\n",
    "     RDKit          2D\n",
    "\n",
    "  9 10  0  0  0  0  0  0  0  0999 V2000\n",
    "    2.1845    0.2000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.4701   -0.2125    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.4701   -1.0375    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.1845   -1.4500    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.8990   -1.0375    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.8990   -0.2125    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.6836    0.0425    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.6836   -1.2924    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    4.1685   -0.6250    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "  5  6  1  0\n",
    "  7  9  1  0\n",
    "  6  7  2  0\n",
    "  8  9  1  0\n",
    "  1  6  1  0\n",
    "  1  2  2  0\n",
    "  2  3  1  0\n",
    "  3  4  2  0\n",
    "  4  5  1  0\n",
    "  5  8  2  0\n",
    "M  END\"\"\"\n",
    "tmplt = Chem.MolFromMolBlock(mb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x11545b8a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmplt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate coordinates for a structure based on this template.  Note that the template is a lot smaller than the rest of the structure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x1155e9440>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smiles = \"FC(F)(F)Oc1cccc(-n2nnc3ccc(NC4CCOCC4)nc32)c1\"\n",
    "mol = Chem.MolFromSmiles(smiles)\n",
    "rdDepictor.GenerateDepictionMatching2DStructure(mol, tmplt)\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will scale template to 1.5, this puts everything on the same scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_molecule(tmplt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x11545b8a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmplt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll generate the structure again.  Note that all of the bond lengths are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x1155e9850>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smiles = \"FC(F)(F)Oc1cccc(-n2nnc3ccc(NC4CCOCC4)nc32)c1\"\n",
    "mol = Chem.MolFromSmiles(smiles)\n",
    "rdDepictor.GenerateDepictionMatching2DStructure(mol, tmplt)\n",
    "mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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