{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similarity of Enantiomers with chiral Morgen FPs\n",
    "At which point the Morgang fingerprints become unable to distinuish enantiomers because the substituents of the chiral center cannot be dismabuigated within the fingerprint radius?\n",
    "\n",
    "The case study are homologues of iso-butanol\n",
    "C\\[C\\@\\@H\\]\\(O\\)CC versus C\\[C\\@H\\]\\(O\\)CC, CC\\[C\\@\\@H\\]\\(O\\)CCC versus CC\\[C\\@H\\]\\(O\\)CCC, ... C<sub>n</sub>C\\[C\\@\\@H\\]\\(O\\)CCC<sub>n</sub> versus C<sub>n</sub>C\\[C\\@H\\]\\(O\\)CCC<sub>n</sub>, with n the length of the alkyl chain on either side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import DataStructs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using rdkit version:  2019.03.3\n"
     ]
    }
   ],
   "source": [
    "print('Using rdkit version: ',rdkit.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Define the meaxmimal fingerprint radius to study\n",
    "* Define the maximal number of carbon atoms to add to either side\n",
    "* Prepare the dataframe for smiles and similarity values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "length_limit = 15\n",
    "radius_limit = 5\n",
    "rdkit_sim_df = pd.DataFrame(np.zeros((length_limit,radius_limit)),index=range(length_limit),columns=range(radius_limit))\n",
    "smiles_df = pd.DataFrame(np.nan,index=range(length_limit),columns=['smiles_a','smiles_b'])\n",
    "rdkit_sim_df.index.name = 'n'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the enantiomer pairs and calculate chiral Morgan FP Tanimozo Similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_template = 'C[C@@H](O)CC'\n",
    "b_template = 'C[C@H](O)CC'\n",
    "for  n in range(length_limit):\n",
    "    #create the smiles, and turn the into molecules\n",
    "    a_smiles = 'C'*n + a_template + 'C'*n\n",
    "    b_smiles = 'C'*n + b_template + 'C'*n\n",
    "    smiles_df.loc[n,'smiles_a'] = a_smiles\n",
    "    smiles_df.loc[n,'smiles_b'] = b_smiles\n",
    "    a_mol = Chem.MolFromSmiles(a_smiles)\n",
    "    b_mol = Chem.MolFromSmiles(b_smiles)\n",
    "    #iterae over the range of radii\n",
    "    for radius in range(radius_limit):\n",
    "        #craee the finherints for the radius and calculate similarity\n",
    "        fpa = AllChem.GetMorganFingerprintAsBitVect(a_mol,radius,useChirality=True,nBits=32000)\n",
    "        fpb = AllChem.GetMorganFingerprintAsBitVect(b_mol,radius,useChirality=True,nBits=32000)\n",
    "        sim = DataStructs.FingerprintSimilarity(fpa,fpb)\n",
    "        rdkit_sim_df.loc[n,radius] = sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdkit_sim_df.plot()\n",
    "plt.legend(loc=4,title='Morgan Radius')\n",
    "plt.title('RDKit Morgan')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write out the smiles to calculate the correponding chiral ECFP Similarities and read back in the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_df.to_csv('/home/schufan1/projects/Melloddy/chiral_test_smiles.txt',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_sim_df = pd.read_csv('/home/schufan1/projects/Melloddy/chiral_test_ECFP_PP_sim.txt',sep='\\t',index_col= 'n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp_sim_df[['0','1','2','3','4']].plot()\n",
    "plt.legend(loc=4,title='0.5 * ECFP_Diameter = Morgan Radius')\n",
    "plt.title('PipelinePilot ECFP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SciComp py3.6 PythonDS",
   "language": "python",
   "name": "py_3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
