adelenelai/Cheminformatics for Risk Assessment of Transformation Products.ipynb

## Cheminformatics for Risk Assessment of Transformation Products.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4ea67d5e",
   "metadata": {},
   "source": [
    "# Cheminformatics for Risk Assessment of Transformation Products\n",
    "\n",
    "https://adelenel.ai/tp-parent-similarity\n",
    "\n",
    "Written by Adelene Lai, 25 June 2023.\n",
    "\n",
    "\n",
    "Loeffler et al. paper that I refer to in this notebook: https://doi.org/10.1021/acs.est.2c09854"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e8d3a9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit.Chem.inchi import MolFromInchi\n",
    "from rdkit.Chem import rdFingerprintGenerator\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit import DataStructs\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c221683",
   "metadata": {},
   "source": [
    "## Transformations in PubChem\n",
    "\n",
    "Download Transformations from Zenodo:\n",
    "April 2023 version wExtraInfo.csv: https://doi.org/10.5281/zenodo.7838005\n",
    "\n",
    "Read in the data and do some basic exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a061083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predecessor</th>\n",
       "      <th>predecessorcid</th>\n",
       "      <th>transformation</th>\n",
       "      <th>successor</th>\n",
       "      <th>successorcid</th>\n",
       "      <th>evidencedoi</th>\n",
       "      <th>evidenceref</th>\n",
       "      <th>sourcecomment</th>\n",
       "      <th>sourcecommentfull</th>\n",
       "      <th>datasetref</th>\n",
       "      <th>...</th>\n",
       "      <th>biosystem</th>\n",
       "      <th>CID_to_CID</th>\n",
       "      <th>IK_to_IK</th>\n",
       "      <th>IKFB_to_IKFB</th>\n",
       "      <th>ReactionSMILES</th>\n",
       "      <th>DescReactionSMILES</th>\n",
       "      <th>predecessorEM</th>\n",
       "      <th>successorEM</th>\n",
       "      <th>MassDiff</th>\n",
       "      <th>XlogPDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-)-bisoprolol</td>\n",
       "      <td>10892731</td>\n",
       "      <td>O-deisopropylation / Human Phase I</td>\n",
       "      <td>(-)-bisoprolol, O-deisopropyl</td>\n",
       "      <td>154699479</td>\n",
       "      <td>10.1186/s13321-018-0324-5</td>\n",
       "      <td>Horikiri, Y. (1998); Pharmacokinetics and Meta...</td>\n",
       "      <td>MetXBioDB | SourceID: BIOTID01296</td>\n",
       "      <td>MetXBioDB is a biotransformation database used...</td>\n",
       "      <td>S73 | METXBIODB | Metabolite Reaction Database...</td>\n",
       "      <td>...</td>\n",
       "      <td>Human</td>\n",
       "      <td>10892731&gt;&gt;154699479</td>\n",
       "      <td>VHYCDWMUTMEGQY-KRWDZBQOSA-N&gt;&gt;AIGGWEPYIOOYLI-AW...</td>\n",
       "      <td>VHYCDWMUTMEGQY&gt;&gt;AIGGWEPYIOOYLI</td>\n",
       "      <td>CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCOC(C)C)O&gt;&gt;CC(...</td>\n",
       "      <td>CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCOC(C)C)O&gt;[O-d...</td>\n",
       "      <td>325.225308</td>\n",
       "      <td>283.178358</td>\n",
       "      <td>-42.046950</td>\n",
       "      <td>-1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(-)-Epigallocatechin</td>\n",
       "      <td>72277</td>\n",
       "      <td>Aromatic-OH glucuronidation / Human Phase II</td>\n",
       "      <td>(-)-Epigallocatechin, 3p-hydroxy-glucuronide</td>\n",
       "      <td>76970196</td>\n",
       "      <td>10.1186/s13321-018-0324-5</td>\n",
       "      <td>Wu et al. 2011; Accurate Prediction of Glucuro...</td>\n",
       "      <td>MetXBioDB | SourceID: BIOTID01529</td>\n",
       "      <td>MetXBioDB is a biotransformation database used...</td>\n",
       "      <td>S73 | METXBIODB | Metabolite Reaction Database...</td>\n",
       "      <td>...</td>\n",
       "      <td>Human</td>\n",
       "      <td>72277&gt;&gt;76970196</td>\n",
       "      <td>XMOCLSLCDHWDHP-IUODEOHRSA-N&gt;&gt;CQDATFYRGVZXLF-ZV...</td>\n",
       "      <td>XMOCLSLCDHWDHP&gt;&gt;CQDATFYRGVZXLF</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>306.073953</td>\n",
       "      <td>482.106041</td>\n",
       "      <td>176.032088</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(-)-Epigallocatechin gallate</td>\n",
       "      <td>65064</td>\n",
       "      <td>Aromatic-OH glucuronidation / Human Phase II</td>\n",
       "      <td>(-)-Epigallocatechin gallate, 3p-hydroxy-glucu...</td>\n",
       "      <td>154700057</td>\n",
       "      <td>10.1186/s13321-018-0324-5</td>\n",
       "      <td>Wu et al. 2011; Accurate Prediction of Glucuro...</td>\n",
       "      <td>MetXBioDB | SourceID: BIOTID01527</td>\n",
       "      <td>MetXBioDB is a biotransformation database used...</td>\n",
       "      <td>S73 | METXBIODB | Metabolite Reaction Database...</td>\n",
       "      <td>...</td>\n",
       "      <td>Human</td>\n",
       "      <td>65064&gt;&gt;154700057</td>\n",
       "      <td>WMBWREPUVVBILR-WIYYLYMNSA-N&gt;&gt;WNLLMSQFYPFJGS-ZC...</td>\n",
       "      <td>WMBWREPUVVBILR&gt;&gt;WNLLMSQFYPFJGS</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>458.084911</td>\n",
       "      <td>634.116999</td>\n",
       "      <td>176.032088</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(-)-Epigallocatechin gallate</td>\n",
       "      <td>65064</td>\n",
       "      <td>Aromatic-OH glucuronidation / Human Phase II</td>\n",
       "      <td>(-)-Epigallocatechin gallate, 4p-hydroxy-glucu...</td>\n",
       "      <td>154699542</td>\n",
       "      <td>10.1186/s13321-018-0324-5</td>\n",
       "      <td>Wu et al. 2011; Accurate Prediction of Glucuro...</td>\n",
       "      <td>MetXBioDB | SourceID: BIOTID01528</td>\n",
       "      <td>MetXBioDB is a biotransformation database used...</td>\n",
       "      <td>S73 | METXBIODB | Metabolite Reaction Database...</td>\n",
       "      <td>...</td>\n",
       "      <td>Human</td>\n",
       "      <td>65064&gt;&gt;154699542</td>\n",
       "      <td>WMBWREPUVVBILR-WIYYLYMNSA-N&gt;&gt;DBWSOVOTCKCPCB-XN...</td>\n",
       "      <td>WMBWREPUVVBILR&gt;&gt;DBWSOVOTCKCPCB</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...</td>\n",
       "      <td>458.084911</td>\n",
       "      <td>634.116999</td>\n",
       "      <td>176.032088</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(-)-ethosuximide</td>\n",
       "      <td>6603844</td>\n",
       "      <td>Aliphatic Hydroxylation / Human Phase I</td>\n",
       "      <td>3-(1-Hydroxyethyl)-3-methylpyrrolidine-2,5-dione</td>\n",
       "      <td>54520268</td>\n",
       "      <td>10.1186/s13321-018-0324-5</td>\n",
       "      <td>PMID:12637244</td>\n",
       "      <td>MetXBioDB | SourceID: BIOTID00032</td>\n",
       "      <td>MetXBioDB is a biotransformation database used...</td>\n",
       "      <td>S73 | METXBIODB | Metabolite Reaction Database...</td>\n",
       "      <td>...</td>\n",
       "      <td>Human</td>\n",
       "      <td>6603844&gt;&gt;54520268</td>\n",
       "      <td>HAPOVYFOVVWLRS-SSDOTTSWSA-N&gt;&gt;YPHULFCUFXXVGD-UH...</td>\n",
       "      <td>HAPOVYFOVVWLRS&gt;&gt;YPHULFCUFXXVGD</td>\n",
       "      <td>CC[C@@]1(CC(=O)NC1=O)C&gt;&gt;CC(C1(CC(=O)NC1=O)C)O</td>\n",
       "      <td>CC[C@@]1(CC(=O)NC1=O)C&gt;[Aliphatic Hydroxylatio...</td>\n",
       "      <td>141.078979</td>\n",
       "      <td>157.073893</td>\n",
       "      <td>15.994915</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    predecessor  predecessorcid  \\\n",
       "0                (-)-bisoprolol        10892731   \n",
       "1          (-)-Epigallocatechin           72277   \n",
       "2  (-)-Epigallocatechin gallate           65064   \n",
       "3  (-)-Epigallocatechin gallate           65064   \n",
       "4              (-)-ethosuximide         6603844   \n",
       "\n",
       "                                 transformation  \\\n",
       "0            O-deisopropylation / Human Phase I   \n",
       "1  Aromatic-OH glucuronidation / Human Phase II   \n",
       "2  Aromatic-OH glucuronidation / Human Phase II   \n",
       "3  Aromatic-OH glucuronidation / Human Phase II   \n",
       "4       Aliphatic Hydroxylation / Human Phase I   \n",
       "\n",
       "                                           successor  successorcid  \\\n",
       "0                      (-)-bisoprolol, O-deisopropyl     154699479   \n",
       "1       (-)-Epigallocatechin, 3p-hydroxy-glucuronide      76970196   \n",
       "2  (-)-Epigallocatechin gallate, 3p-hydroxy-glucu...     154700057   \n",
       "3  (-)-Epigallocatechin gallate, 4p-hydroxy-glucu...     154699542   \n",
       "4   3-(1-Hydroxyethyl)-3-methylpyrrolidine-2,5-dione      54520268   \n",
       "\n",
       "                 evidencedoi  \\\n",
       "0  10.1186/s13321-018-0324-5   \n",
       "1  10.1186/s13321-018-0324-5   \n",
       "2  10.1186/s13321-018-0324-5   \n",
       "3  10.1186/s13321-018-0324-5   \n",
       "4  10.1186/s13321-018-0324-5   \n",
       "\n",
       "                                         evidenceref  \\\n",
       "0  Horikiri, Y. (1998); Pharmacokinetics and Meta...   \n",
       "1  Wu et al. 2011; Accurate Prediction of Glucuro...   \n",
       "2  Wu et al. 2011; Accurate Prediction of Glucuro...   \n",
       "3  Wu et al. 2011; Accurate Prediction of Glucuro...   \n",
       "4                                      PMID:12637244   \n",
       "\n",
       "                       sourcecomment  \\\n",
       "0  MetXBioDB | SourceID: BIOTID01296   \n",
       "1  MetXBioDB | SourceID: BIOTID01529   \n",
       "2  MetXBioDB | SourceID: BIOTID01527   \n",
       "3  MetXBioDB | SourceID: BIOTID01528   \n",
       "4  MetXBioDB | SourceID: BIOTID00032   \n",
       "\n",
       "                                   sourcecommentfull  \\\n",
       "0  MetXBioDB is a biotransformation database used...   \n",
       "1  MetXBioDB is a biotransformation database used...   \n",
       "2  MetXBioDB is a biotransformation database used...   \n",
       "3  MetXBioDB is a biotransformation database used...   \n",
       "4  MetXBioDB is a biotransformation database used...   \n",
       "\n",
       "                                          datasetref  ... biosystem  \\\n",
       "0  S73 | METXBIODB | Metabolite Reaction Database...  ...     Human   \n",
       "1  S73 | METXBIODB | Metabolite Reaction Database...  ...     Human   \n",
       "2  S73 | METXBIODB | Metabolite Reaction Database...  ...     Human   \n",
       "3  S73 | METXBIODB | Metabolite Reaction Database...  ...     Human   \n",
       "4  S73 | METXBIODB | Metabolite Reaction Database...  ...     Human   \n",
       "\n",
       "            CID_to_CID                                           IK_to_IK  \\\n",
       "0  10892731>>154699479  VHYCDWMUTMEGQY-KRWDZBQOSA-N>>AIGGWEPYIOOYLI-AW...   \n",
       "1      72277>>76970196  XMOCLSLCDHWDHP-IUODEOHRSA-N>>CQDATFYRGVZXLF-ZV...   \n",
       "2     65064>>154700057  WMBWREPUVVBILR-WIYYLYMNSA-N>>WNLLMSQFYPFJGS-ZC...   \n",
       "3     65064>>154699542  WMBWREPUVVBILR-WIYYLYMNSA-N>>DBWSOVOTCKCPCB-XN...   \n",
       "4    6603844>>54520268  HAPOVYFOVVWLRS-SSDOTTSWSA-N>>YPHULFCUFXXVGD-UH...   \n",
       "\n",
       "                     IKFB_to_IKFB  \\\n",
       "0  VHYCDWMUTMEGQY>>AIGGWEPYIOOYLI   \n",
       "1  XMOCLSLCDHWDHP>>CQDATFYRGVZXLF   \n",
       "2  WMBWREPUVVBILR>>WNLLMSQFYPFJGS   \n",
       "3  WMBWREPUVVBILR>>DBWSOVOTCKCPCB   \n",
       "4  HAPOVYFOVVWLRS>>YPHULFCUFXXVGD   \n",
       "\n",
       "                                      ReactionSMILES  \\\n",
       "0  CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCOC(C)C)O>>CC(...   \n",
       "1  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...   \n",
       "2  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...   \n",
       "3  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...   \n",
       "4      CC[C@@]1(CC(=O)NC1=O)C>>CC(C1(CC(=O)NC1=O)C)O   \n",
       "\n",
       "                                  DescReactionSMILES predecessorEM  \\\n",
       "0  CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCOC(C)C)O>[O-d...    325.225308   \n",
       "1  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...    306.073953   \n",
       "2  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...    458.084911   \n",
       "3  C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=...    458.084911   \n",
       "4  CC[C@@]1(CC(=O)NC1=O)C>[Aliphatic Hydroxylatio...    141.078979   \n",
       "\n",
       "  successorEM    MassDiff  XlogPDiff  \n",
       "0  283.178358  -42.046950       -1.4  \n",
       "1  482.106041  176.032088       -0.5  \n",
       "2  634.116999  176.032088       -0.5  \n",
       "3  634.116999  176.032088       -0.5  \n",
       "4  157.073893   15.994915       -0.7  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltransformations = pd.read_csv('PubChem_all_transformations_wExtraInfo_20230417.csv')\n",
    "alltransformations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97010dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6554, 22)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltransformations.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258d7666",
   "metadata": {},
   "source": [
    "Note that in PubChem, the terms 'predecessor' and 'successor' are used to describe the parent and transformation product respectively. I will use these terms interchangeably in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d147c3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grab the SMILES of the predecessors and successors from ReactionSMILES\n",
    "rxnsmiles = list(alltransformations.loc[:,\"ReactionSMILES\"])\n",
    "pred_smiles = [i.split('>>')[0] for i in rxnsmiles]\n",
    "suc_smiles = [i.split('>>')[1] for i in rxnsmiles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fefdc4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For example, predecessor SMILES is CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCOC(C)C)O, successor SMILES is CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCO)O for reaction CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCOC(C)C)O>>CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCO)O\n"
     ]
    }
   ],
   "source": [
    "print(f'For example, predecessor SMILES is {pred_smiles[0]}, successor SMILES is {suc_smiles[0]} for reaction {rxnsmiles[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb920f0",
   "metadata": {},
   "source": [
    "## How many unique predecessors and successors are there?\n",
    "\n",
    "To find this out, it's best to use a canonical descriptor - either canonical SMILES or PubChem CID. Let's do both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a14b6d09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2375 unique predecessors, 4723 unique successors (RDKit canonical SMILES).\n"
     ]
    }
   ],
   "source": [
    "#how many predecessors are unique (canonical SMILES)?\n",
    "canon_pred_smiles = [Chem.CanonSmiles(i) for i in pred_smiles]\n",
    "canon_suc_smiles = [Chem.CanonSmiles(i) for i in suc_smiles]\n",
    "print(f'There are {len(set(canon_pred_smiles))} unique predecessors, {len(set(canon_suc_smiles))} unique successors (RDKit canonical SMILES).')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdde91d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2375 unique predecessors, 4723 unique successors(CIDs)\n"
     ]
    }
   ],
   "source": [
    "#how many predecessors are unique (PubChem CIDs)?\n",
    "print(f'There are {len(set(alltransformations.predecessorcid))} unique predecessors, {len(set(alltransformations.successorcid))} unique successors(CIDs)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fb6172",
   "metadata": {},
   "source": [
    "Both methods show the same no. of unique predecessor (parent) and successor (TP) molecules.\n",
    "\n",
    "As the dataset has 6554 rows, it shows that there are multiple possible transformations and TPs for a given parent molecule."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b3d6d8",
   "metadata": {},
   "source": [
    "## Calculate Similarity between predecessor (parents) and successor (transformation products) using ECFP4\n",
    "\n",
    "To calculate Molecular Similarity, I'll use the built-in ECFP4 a.k.a. Morgan fingerprints of RDKit. \n",
    "\n",
    "Note - this is not necessarily the best fingerprint for this purpose/for these data. I'm just showing an example of similarity calculation using RDKit here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9bb3979d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predecessor_morgan_fp: <class 'list'>, 6554, 2048\n"
     ]
    }
   ],
   "source": [
    "#Morgan fingerprints of predecessor\n",
    "mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)\n",
    "predecessor_mols = [Chem.MolFromSmiles(s) for s in pred_smiles]\n",
    "predecessor_morgan_fp = [mfpgen.GetFingerprint(i) for i in predecessor_mols]\n",
    "print(f'predecessor_morgan_fp: {type(predecessor_morgan_fp)}, {len(predecessor_morgan_fp)}, {len(predecessor_morgan_fp[0])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3f8ce69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successor_morgan_fp: <class 'list'>, 6554, 2048\n"
     ]
    }
   ],
   "source": [
    "#Morgan fingerprints of successor\n",
    "successor_mols = [Chem.MolFromSmiles(s) for s in suc_smiles]\n",
    "successor_morgan_fp = [mfpgen.GetFingerprint(i) for i in successor_mols]\n",
    "print(f'successor_morgan_fp: {type(successor_morgan_fp)}, {len(successor_morgan_fp)}, {len(successor_morgan_fp[0])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa406b21",
   "metadata": {},
   "source": [
    "### Let's explore a few examples of Parent-TP pairs and their Similarity metrics\n",
    "\n",
    "Comparing Tanimoto Similarity calculated by RDKit Morgan FPs vs. MCS (ChemMine Similarity Workbench).\n",
    "\n",
    "To calculate MCS Tanimoto, I manually copied and pasted SMILES strings into the ChemMine Similarity Workbench: https://chemminetools.ucr.edu/similarity/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e769561d",
   "metadata": {},
   "source": [
    "#### Example 1: (-)-bisoprolol and (-)-bisoprolol, O-deisopropyl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9cba93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pick random Parent and TP molecule pair\n",
    "Draw.MolsToGridImage([predecessor_mols[0],successor_mols[0]],subImgSize=(500, 300),legends=['(-)-bisoprolol','(-)-bisoprolol, O-deisopropyl'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "807a1ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCOC(C)C)O CC(C)NC[C@@H](COC1=CC=C(C=C1)COCCO)O\n"
     ]
    }
   ],
   "source": [
    "print(pred_smiles[0], suc_smiles[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f28297b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDKit MorganFP Tanimoto Similarity: 0.7727\n",
      "ChemMine MCS Tanimoto calculated for these 2 compounds is 0.8696\n"
     ]
    }
   ],
   "source": [
    "print(f\"RDKit MorganFP Tanimoto Similarity: {round(DataStructs.TanimotoSimilarity(predecessor_morgan_fp[0], successor_morgan_fp[0]),4)}\")\n",
    "print(f\"ChemMine MCS Tanimoto calculated for these 2 compounds is 0.8696\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a79c6da",
   "metadata": {},
   "source": [
    "#### Example 2: thalidomide and 5'-Hydroxythalidomide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff8c2c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pick random Parent and TP molecule pair\n",
    "Draw.MolsToGridImage([predecessor_mols[12],successor_mols[12]],subImgSize=(500, 300),legends=['thalidomide','5\\'-Hydroxythalidomide'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f1dfc8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C1CC(=O)NC(=O)[C@H]1N2C(=O)C3=CC=CC=C3C2=O C1[C@@H](C(=O)NC(=O)C1O)N2C(=O)C3=CC=CC=C3C2=O\n"
     ]
    }
   ],
   "source": [
    "print(pred_smiles[12], suc_smiles[12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30e4f8b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDKit MorganFP Tanimoto Similarity: 0.5789\n",
      "ChemMine MCS Tanimoto calculated for these 2 compounds is 0.9500\n"
     ]
    }
   ],
   "source": [
    "print(f\"RDKit MorganFP Tanimoto Similarity: {round(DataStructs.TanimotoSimilarity(predecessor_morgan_fp[12], successor_morgan_fp[12]),4)}\")\n",
    "print(f\"ChemMine MCS Tanimoto calculated for these 2 compounds is 0.9500\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c52f60f",
   "metadata": {},
   "source": [
    "#### Example 3: (4-Chloro-2-methylphenoxy)acetic acid and 4-Chloro-2-methylphenol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4345b75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pick random Parent and TP molecule pair\n",
    "Draw.MolsToGridImage([predecessor_mols[123],successor_mols[123]],subImgSize=(500, 300),legends=['(4-Chloro-2-methylphenoxy)acetic acid','4-Chloro-2-methylphenol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "674b460c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CC1=C(C=CC(=C1)Cl)O', 'CC1=C(C=CC(=C1)Cl)OCC(=O)O'}\n"
     ]
    }
   ],
   "source": [
    "print({pred_smiles[123], suc_smiles[123]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0261205",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDKit MorganFP Tanimoto Similarity: 0.4118\n",
      "MCS Tanimoto calculated for these 2 compounds is 0.6923\n"
     ]
    }
   ],
   "source": [
    "print(f\"RDKit MorganFP Tanimoto Similarity: {round(DataStructs.TanimotoSimilarity(predecessor_morgan_fp[123], successor_morgan_fp[123]),4)}\")\n",
    "print(f\"MCS Tanimoto calculated for these 2 compounds is 0.6923\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8def0753",
   "metadata": {},
   "source": [
    "#### Example 4: (s)-propafenone and (S)-4'-Hydroxypropafenone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe6cfebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pick random Parent and TP molecule pair\n",
    "Draw.MolsToGridImage([predecessor_mols[323],successor_mols[323]],subImgSize=(500, 300),legends=['(s)-propafenone','(S)-4\\'-Hydroxypropafenone'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "049185d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CCCNC[C@@H](COC1=CC=CC=C1C(=O)CCC2=CC=CC=C2)O', 'CCCNC[C@@H](COC1=CC=CC=C1C(=O)CCC2=CC=C(C=C2)O)O'}\n"
     ]
    }
   ],
   "source": [
    "print({pred_smiles[323], suc_smiles[323]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "28118f04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDKit MorganFP Tanimoto Similarity: 0.86\n",
      "MCS Tanimoto calculated for these 2 compounds is 0.9615\n"
     ]
    }
   ],
   "source": [
    "print(f\"RDKit MorganFP Tanimoto Similarity: {round(DataStructs.TanimotoSimilarity(predecessor_morgan_fp[323], successor_morgan_fp[323]),4)}\")\n",
    "print(f\"MCS Tanimoto calculated for these 2 compounds is 0.9615\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6bda3c",
   "metadata": {},
   "source": [
    "### Overall, we see very different similarity scores for the same pair of Parent-TP - the method chosen for calculating similarity is very important!!! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2004adf3",
   "metadata": {},
   "source": [
    "## Summary of MorganFP-Tanimoto Similarities for PubChem Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "553192d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_morganfp_r2_2048 = []\n",
    "for pred,suc in zip(predecessor_morgan_fp,successor_morgan_fp):\n",
    "    sim_morganfp_r2_2048.append(DataStructs.TanimotoSimilarity(pred, suc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e228cbf1",
   "metadata": {},
   "source": [
    "Löffler et al. classify a parent-TP pair as similar if their Tanimoto similarity is > 0.95. For simplicity, let's apply that same criteria here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c57854ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 similar predecessor-successor pairs: [0.9558823529411765, 0.96, 1.0, 0.96, 0.96, 0.9795918367346939, 0.9879518072289156, 0.9879518072289156, 1.0, 1.0, 0.96, 0.9583333333333334, 1.0, 1.0, 1.0, 0.9534883720930233, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9655172413793104]\n",
      "Percentage of parent-TPs that are similar = 0.34\n"
     ]
    }
   ],
   "source": [
    "similar = [i for i in sim_morganfp_r2_2048 if i > 0.95] \n",
    "print(f'{len(similar)} similar predecessor-successor pairs: {similar}')\n",
    "print(f'Percentage of parent-TPs that are similar = {round(len(similar)/len(pred_smiles)*100, 2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c0a96e",
   "metadata": {},
   "source": [
    "### Distribution of MorganFP Tanimoto Similarity of Parent and Transformation Product Molecules (PubChem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c10623b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x15fdfb4d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sim_morganfp_r2_2048, density=False, bins=10, ec='k')\n",
    "plt.ylabel('No. of Parent-TP pairs')\n",
    "plt.ylim(0,1400)\n",
    "plt.xlabel('MorganFP Tanimoto Similarity');\n",
    "plt.vlines(x = 0.95, ymin = 0, ymax = 1400,\n",
    "           colors = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c249a99",
   "metadata": {},
   "source": [
    "## Comparison with MCS Tanimoto distribution calculated by Löffler et al\n",
    "MCS Tanimoto scores taken from Table S2, column D.\n",
    "\n",
    "https://pubs.acs.org/doi/suppl/10.1021/acs.est.2c09854/suppl_file/es2c09854_si_002.xlsx\n",
    "\n",
    "Note that their dataset was a lot smaller (n=56), so we can only compare in relative terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bcd7695e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcs_tanimoto_loeffler = pd.read_csv('mcs_tanimoto_table_s2.csv', header=None)\n",
    "len(mcs_tanimoto_loeffler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fbb5556b",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_mcs_tanimoto = mcs_tanimoto_loeffler.iloc[:,0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f93f0b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 similar predecessor-successor pairs: [0.9524, 0.9811, 1.0, 1.0, 1.0, 0.9808, 0.9545, 0.9643, 1.0, 1.0]\n",
      "Percentage of parent-TPs that are similar = 17.9\n"
     ]
    }
   ],
   "source": [
    "similar_loeffler = [i for i in list_mcs_tanimoto if i > 0.95] \n",
    "print(f'{len(similar_loeffler)} similar predecessor-successor pairs: {similar_loeffler}')\n",
    "print(f'Percentage of parent-TPs that are similar = {round(len(similar_loeffler)/len(list_mcs_tanimoto)*100, 1)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2717f348",
   "metadata": {},
   "source": [
    "### Distribution of MCS Tanimoto Similarity of Parent and Transformation Product Molecules (Löffler et al)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5e387b86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x161c515d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AC7F8mGnLli1atWqVYmNjdeWVV0qSvv32WxUUFOjPf/6zbrvtNte6S5cu9VxSAACAUlguM9WrV1ffvn2LzYuLi/NYIAAAACssl5mUlBRv5AAAACgTy+fMAAAA+BPKDAAAsDXKDAAAsDXKDAAAsDVLZcYYo71792rHjh0qLCz0ViYAAAC3uV1mDhw4oJYtW6px48ZKTExUQkKCNm/e7M1sAAAAF+R2mXnyySd16tQpzZkzR4sXL1Z0dLSGDBnizWwAAAAX5PZ1ZtauXasFCxaoU6dOkqSrr75a8fHxysvLk9Pp9FpAAACA83F7z0xmZqYaN27smo6NjZXT6dSvv/7qlWAAAADucLvMOBwOBQQUXz0gIEDGGI+HAgAAcJfbh5mMMWrYsKEcDodrXk5Ojlq1alWs5Bw5csSzCQEAAM7D7TLDPZkAAIA/crvM1KtXTx06dFClSpbvTQkAAOA1bp8z06VLFw4hAQAAv+N2meFEXwAA4I8s3c7g9yf/AgAA+ANLJ8CMHTtWISEh511n0qRJ5QoEAABghaUys23bNgUFBZ1zOXtuAABARbNUZpYtW6batWt7KwsAAIBllq4ADAAA4G/K9W2mL774Qvn5+R4NBAAAYIXbZSYlJUXh4eHF5vXo0UM///yzx0MBAAC4y+1zZgYMGFBiHteeAQAAvmbpOjMAAAD+plxl5l//+pciIyM9lQUAAMAyy2Vm0KBBOnHihCTpjjvuUNWqVSVJubm5GjRokGfTAQAAXIDlW2DPmjVL//znPxUaGlpsfl5enmbPnq0ZM2Z4LBwAAGV18OBBZWVl+TqG23bu3OnrCLbldpnJzs6WMUbGGJ04cUJVqlRxLSsqKtLy5cu5oB4AwC8cPHhQjRo30am8k76OggrgdpmpXr26HA6HHA6HGjZsWGK5w+HQuHHjPBoOAICyyMrK0qm8k6p582OqXDPO13Hckrd/o46vnevrGLbkdpn5/PPPZYzR9ddfryVLlqhGjRquZUFBQYqPj1dMTIxXQgIAUBaVa8YpOCrB1zHccvpwuq8j2JbbZaZTp06SpLS0NMXFxSkggG91AwAA37N8AnB8fLyOHTumr7/+WocOHdKZM2eKLb/77rs9Fg4AAOBCLJeZ999/X3feeadyc3MVGhpa7AaUDoeDMgMAACqU5WNFjz32mOtaM8eOHdPRo0ddjyNHjngjIwAAwDlZLjM///yzHn74YYWEhHgjDwAAgCWWy0z37t21ceNGj7z4mjVr1KtXL8XExMjhcOjdd98ttnzgwIGur4OffbRr184jrw0AAC4Ols+Z6dmzp5544gnt2LFDiYmJqly5crHlt9xyi9vbys3N1ZVXXql77rlHffv2LXWdG2+8USkpKa7poKAgq5EBAMBFzHKZGTx4sCTp2WefLbHM4XCoqKjI7W316NFDPXr0OO86wcHBioqKshYSAABcMiwfZjpz5sw5H1aKjLtWr16t2rVrq2HDhho8eLAOHTp03vXz8/OVnZ1d7AEAAC5e5bry3alTpzyVo1Q9evTQvHnz9Nlnn+mll17SN998o+uvv175+fnnfM7EiRMVHh7uesTF2eMy1gAAoGwsl5mioiI999xzuvzyy1WtWjXt379fkjR27Fi99dZbHg3Xr18/9ezZU82bN1evXr300Ucfac+ePfrwww/P+ZzRo0fr+PHjrkd6OpeHBgDgYma5zIwfP14zZ85UcnJysZNxExMT9eabb3o03B9FR0crPj5ee/fuPec6wcHBCgsLK/YAAAAXL8tlZvbs2XrjjTd05513KjAw0DW/RYsW2rVrl0fD/dHhw4eVnp6u6Ohor74OAACwD8vfZvr555+VkFDyDqRnzpzR6dOnLW0rJydH+/btc02npaVp69atqlGjhmrUqKGkpCT17dtX0dHROnDggJ566ilFRESoT58+VmMDAICLlOU9M82aNdPatWtLzF+8eLFatWplaVsbN25Uq1atXM8bMWKEWrVqpaefflqBgYHatm2bevfurYYNG2rAgAFq2LChvvzyS4WGhlqNDQAALlKW98w888wz6t+/v37++WedOXNGS5cu1e7duzV79mx98MEHlrbVuXNnGWPOufzjjz+2Gg8AAFxiLO+Z6dWrlxYtWqTly5fL4XDo6aef1s6dO/X++++ra9eu3sgIAABwTpb2zBQWFmr8+PEaNGiQUlNTvZUJAADAbZb2zFSqVEkvvPCCV670CwAAUBaWDzPdcMMNWr16tReiAAAAWGf5BOAePXpo9OjR+v7779W6dWtVrVq12HIrd80GAAAoL8tl5oEHHpAkTZo0qcQyq3fNBgAAKC/LZebMmTPeyAEAAFAm5bprNgAAgK9Z3jMjSbm5uUpNTdXBgwdVUFBQbNnDDz/skWAAAADusFxmtmzZoptuukknT55Ubm6uatSooaysLIWEhKh27dqUGQAAUKEsH2Z69NFH1atXLx05ckROp1NfffWVfvzxR7Vu3VovvviiNzICAACck+Uys3XrVj322GMKDAxUYGCg8vPzFRcXp+TkZD311FPeyAgAAHBOlstM5cqV5XA4JEmRkZE6ePCgJCk8PNz1MwAAQEWxfM5Mq1attHHjRjVs2FBdunTR008/raysLM2ZM0eJiYneyAgAAHBOlvfMTJgwQdHR0ZKk5557TjVr1tQDDzygQ4cO6Y033vB4QAAAgPOxtGfGGKPw8HCFhISosLBQtWrV0vLly72VDQAA4ILc3jNz4MABtWzZUo0bN1ZiYqISEhK0efNmb2YDAAC4ILfLzJNPPqlTp05pzpw5Wrx4saKjozVkyBBvZgMAALggtw8zrV27VgsWLFCnTp0kSVdffbXi4+OVl5cnp9PptYAAAADn4/aemczMTDVu3Ng1HRsbK6fTqV9//dUrwQAAANzhdplxOBwKCCi+ekBAgIwxHg8FAADgLrcPMxlj1LBhQ9cF8yQpJydHrVq1KlZyjhw54tmEAAAA5+F2mUlJSfFmDgAAgDJxu8wMGDDAmzkAAADKxPIVgAEAAPwJZQYAANgaZQYAANiaW2UmOzvb2zkAAADKxK0yc9lll+nQoUOSpOuvv17Hjh3zZiYAAAC3uVVmqlWrpsOHD0uSVq9erdOnT3s1FAAAgLvc+mr2DTfcoC5duqhJkyaSpD59+igoKKjUdT/77DPPpQMAALgAt8rM3LlzNWvWLP3www9KTU1Vs2bNFBIS4u1sAAAAF+RWmXE6nRoyZIgkaePGjXr++edVvXp1b+YCAABwi9tXAD7r888/d/189iaTv79fEwAAQEUq03VmZs+ercTERDmdTjmdTrVo0UJz5szxdDYAAIALsrxnZtKkSRo7dqyGDRumjh07yhijL774QkOGDFFWVpYeffRRb+QEAAAoleUyM2XKFE2fPl133323a17v3r3VrFkzJSUlUWYAAECFsnyYKSMjQx06dCgxv0OHDsrIyPBIKAAAAHdZLjMJCQl6++23S8xftGiRGjRo4JFQAAAA7rJ8mGncuHHq16+f1qxZo44dO8rhcGjdunVatWpVqSUHAADAmyzvmenbt682bNigiIgIvfvuu1q6dKkiIiL09ddfq0+fPt7ICAAAcE6W98xIUuvWrTV37lxPZwEAALCsTNeZAQAA8BeUGQAAYGuUGQAAYGuUGQAAYGuUGQAAYGseKzPTpk3Ts88+66nNAQAAuMVjZWbJkiWaOXOmpzYHAADgljJdZ6Y0q1at8tSmAAAA3FauPTPGGBljyvz8NWvWqFevXoqJiZHD4dC7775bYvtJSUmKiYmR0+lU586dtX379vJEBgAAF5kylZnZs2crMTFRTqdTTqdTLVq00Jw5cyxvJzc3V1deeaWmTp1a6vLk5GRNmjRJU6dO1TfffKOoqCh17dpVJ06cKEtsAABwEbJ8mGnSpEkaO3ashg0bpo4dO8oYoy+++EJDhgxRVlaWHn30Ube31aNHD/Xo0aPUZcYYTZ48WWPGjNFtt90mSZo1a5YiIyM1f/583X///VajAwCAi5DlMjNlyhRNnz5dd999t2te79691axZMyUlJVkqM+eTlpamzMxMdevWzTUvODhYnTp10vr16ykzAABAUhnKTEZGhjp06FBifocOHZSRkeGRUJKUmZkpSYqMjCw2PzIyUj/++OM5n5efn6/8/HzXdHZ2tscyAQAA/2P5nJmEhAS9/fbbJeYvWrRIDRo08Eio33M4HMWmjTEl5v3exIkTFR4e7nrExcV5PBMAAPAflvfMjBs3Tv369dOaNWvUsWNHORwOrVu3TqtWrSq15JRVVFSUpN/20ERHR7vmHzp0qMTemt8bPXq0RowY4ZrOzs6m0AAAcBGzvGemb9++2rBhgyIiIvTuu+9q6dKlioiI0Ndff60+ffp4LFi9evUUFRWllStXuuYVFBQoNTW11MNcZwUHByssLKzYAwAAXLzKdNG81q1ba+7cueV+8ZycHO3bt881nZaWpq1bt6pGjRqqU6eOhg8frgkTJqhBgwZq0KCBJkyYoJCQEN1xxx3lfm0AAHBx8NgVgMti48aN6tKli2v67OGhAQMGaObMmRo5cqTy8vL04IMP6ujRo2rbtq0++eQThYaG+ioyAADwM26XmYCAgPOeeCv9drJuYWGh2y/euXPn815B2OFwKCkpSUlJSW5vEwAAXFrcLjPLli0757L169drypQp5bq1AQAAQFm4XWZ69+5dYt6uXbs0evRovf/++7rzzjv13HPPeTQcAADAhZTp3ky//PKLBg8erBYtWqiwsFBbt27VrFmzVKdOHU/nAwAAOC9LZeb48eN68sknlZCQoO3bt2vVqlV6//331bx5c2/lAwAAOC+3DzMlJyfr+eefV1RUlBYsWFDqYScAAICK5naZGTVqlJxOpxISEjRr1izNmjWr1PWWLl3qsXAAAAAX4naZufvuuy/41WwAAICK5naZmTlzphdjAAAAlE2Zvs0EAADgLygzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1vy6zCQlJcnhcBR7REVF+ToWAADwI5V8HeBCmjVrpk8//dQ1HRgY6MM0AADA3/h9malUqRJ7YwAAwDn59WEmSdq7d69iYmJUr1493X777dq/f/9518/Pz1d2dnaxBwAAuHj5dZlp27atZs+erY8//lj//ve/lZmZqQ4dOujw4cPnfM7EiRMVHh7uesTFxVVgYgAAUNH8usz06NFDffv2VWJiom644QZ9+OGHkqRZs2ad8zmjR4/W8ePHXY/09PSKigsAAHzA78+Z+b2qVasqMTFRe/fuPec6wcHBCg4OrsBUAADAl/x6z8wf5efna+fOnYqOjvZ1FAAA4Cf8usw8/vjjSk1NVVpamjZs2KC//OUvys7O1oABA3wdDQAA+Am/Psz0008/6W9/+5uysrJUq1YttWvXTl999ZXi4+N9HQ0AAPgJvy4zCxcu9HUEAADg5/z6MBMAAMCFUGYAAICtUWYAAICtUWYAAICt+fUJwICdHTx4UFlZWb6OYUlERITq1Knj6xgAYAllBvCCgwcPqlHjJjqVd9LXUSyp4gzR7l07KTQAbIUyA3hBVlaWTuWdVM2bH1Plmva42enpw+k6/MFLysrKoswAsBXKDOBFlWvGKTgqwdcxAOCixgnAAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1igzAADA1rgCMGzBbjdt3Llzp68jlJndsnNzzIrBZxD+jDIDv2fXmzbaTVHOUcnh0F133eXrKJZwc0zv4zMIf0eZgd+z400b8/Zv1PG1c30dw5Iz+TmSMbZ6n7k5ZsXgMwh/R5mBbdjppo2nD6f7OkKZ2el9RsWy09+GnT+DsI4TgAEAgK1RZgAAgK1RZgAAgK1RZgAAgK1RZgAAgK1RZgAAgK1RZgAAgK1RZgAAgK1x0bxystv9SiTuZQMAuLhQZsrBrvcr4V42AICLCWWmHOx4vxLuZQMAuNhQZjzATvcrAQDgYsMJwAAAwNYoMwAAwNYoMwAAwNYoMwAAwNYoMwAAwNYoMwAAwNYoMwAAwNYoMwAAwNa4aN4laufOnb6O4DY7ZQXcYbd7uvEZhL+jzFxiinKOSg6H7rrrLl9HAS5Jdr2nG+DPKDOXmDP5OZIxtrqfVN7+jTq+dq6vYwAeYcd7uvEZhL+jzFyi7HQ/qdOH030dAfA4PoOA53ACMAAAsDXKDAAAsDXKDAAAsDXKDAAAsDXKDAAAsDXKDAAAsDVblJlp06apXr16qlKlilq3bq21a9f6OhIAAPATfl9mFi1apOHDh2vMmDHasmWLrr32WvXo0UMHDx70dTQAAOAH/L7MTJo0Sffee6/uu+8+NWnSRJMnT1ZcXJymT5/u62gAAMAP+PUVgAsKCrRp0yaNGjWq2Pxu3bpp/fr1pT4nPz9f+fn5runjx49LkrKzsz2eLycn57fXzNynMwWnPL59bzh7JU8yexeZK8bpIz9JkjZt2uT6PPq73bt3S7LZ+2zHv41LLXNhvs7+K5f303blVQr2bLhzOPsZzMnJ8fi/s2e3Z4y58MrGj/38889Gkvniiy+KzR8/frxp2LBhqc955plnjCQePHjw4MGDx0XwSE9Pv2Bf8Os9M2c5HI5i08aYEvPOGj16tEaMGOGaPnPmjI4cOaKaNWue8zl2lp2drbi4OKWnpyssLMzXcbyO8V68LqWxSpfWeC+lsUqX1ni9OVZjjE6cOKGYmJgLruvXZSYiIkKBgYHKzMwsNv/QoUOKjIws9TnBwcEKDi6+e6169ereiug3wsLCLvoPze8x3ovXpTRW6dIa76U0VunSGq+3xhoeHu7Wen59AnBQUJBat26tlStXFpu/cuVKdejQwUepAACAP/HrPTOSNGLECPXv319t2rRR+/bt9cYbb+jgwYMaMmSIr6MBAAA/4Pdlpl+/fjp8+LCeffZZZWRkqHnz5lq+fLni4+N9Hc0vBAcH65lnnilxaO1ixXgvXpfSWKVLa7yX0lilS2u8/jJWhzHufOcJAADAP/n1OTMAAAAXQpkBAAC2RpkBAAC2RpkBAAC2RpmxgWnTpqlevXqqUqWKWrdurbVr155z3XXr1qljx46qWbOmnE6nGjdurJdffrkC05aflfH+3hdffKFKlSqpZcuW3g3oQVbGunr1ajkcjhKPXbt2VWDi8rH6u83Pz9eYMWMUHx+v4OBgXXHFFZoxY0YFpS0/K+MdOHBgqb/fZs2aVWDisrP6u503b56uvPJKhYSEKDo6Wvfcc48OHz5cQWnLz+p4X3vtNTVp0kROp1ONGjXS7NmzKyhp+axZs0a9evVSTEyMHA6H3n333Qs+JzU1Va1bt1aVKlVUv359vf76694PWu4bKMGrFi5caCpXrmz+/e9/mx07dphHHnnEVK1a1fz444+lrr9582Yzf/588/3335u0tDQzZ84cExISYv71r39VcPKysTres44dO2bq169vunXrZq688sqKCVtOVsf6+eefG0lm9+7dJiMjw/UoLCys4ORlU5bf7S233GLatm1rVq5cadLS0syGDRtK3KvNX1kd77Fjx4r9XtPT002NGjXMM888U7HBy8DqWNeuXWsCAgLMK6+8Yvbv32/Wrl1rmjVrZm699dYKTl42Vsc7bdo0ExoaahYuXGh++OEHs2DBAlOtWjXz3nvvVXBy65YvX27GjBljlixZYiSZZcuWnXf9/fv3m5CQEPPII4+YHTt2mH//+9+mcuXK5p133vFqTsqMn7v66qvNkCFDis1r3LixGTVqlNvb6NOnj7nrrrs8Hc0ryjrefv36mb///e/mmWeesU2ZsTrWs2Xm6NGjFZDO86yO96OPPjLh4eHm8OHDFRHP48r72V22bJlxOBzmwIED3ojnUVbH+sILL5j69esXm/fqq6+a2NhYr2X0JKvjbd++vXn88ceLzXvkkUdMx44dvZbRG9wpMyNHjjSNGzcuNu/+++837dq182IyYzjM5McKCgq0adMmdevWrdj8bt26af369W5tY8uWLVq/fr06derkjYgeVdbxpqSk6IcfftAzzzzj7YgeU57fbatWrRQdHa0///nP+vzzz70Z02PKMt733ntPbdq0UXJysi6//HI1bNhQjz/+uPLy8ioicrl44rP71ltv6YYbbvD7C4SWZawdOnTQTz/9pOXLl8sYo19//VXvvPOOevbsWRGRy6Us483Pz1eVKlWKzXM6nfr66691+vRpr2X1hS+//LLEe9O9e3dt3LjRq2OlzPixrKwsFRUVlbipZmRkZImbb/5RbGysgoOD1aZNGw0dOlT33XefN6N6RFnGu3fvXo0aNUrz5s1TpUp+f0Frl7KMNTo6Wm+88YaWLFmipUuXqlGjRvrzn/+sNWvWVETkcinLePfv369169bp+++/17JlyzR58mS98847Gjp0aEVELpfyfHYlKSMjQx999NFF+7nt0KGD5s2bp379+ikoKEhRUVGqXr26pkyZUhGRy6Us4+3evbvefPNNbdq0ScYYbdy4UTNmzNDp06eVlZVVEbErTGZmZqnvTWFhoVfHap//+l/CHA5HsWljTIl5f7R27Vrl5OToq6++0qhRo5SQkKC//e1v3ozpMe6Ot6ioSHfccYfGjRunhg0bVlQ8j7Lyu23UqJEaNWrkmm7fvr3S09P14osv6rrrrvNqTk+xMt4zZ87I4XBo3rx5rjvnTpo0SX/5y1/02muvyel0ej1veZXlsytJM2fOVPXq1XXrrbd6KZnnWRnrjh079PDDD+vpp59W9+7dlZGRoSeeeEJDhgzRW2+9VRFxy83KeMeOHavMzEy1a9dOxhhFRkZq4MCBSk5OVmBgYEXErVClvTelzfck9sz4sYiICAUGBpZo+4cOHSrRfP+oXr16SkxM1ODBg/Xoo48qKSnJi0k9w+p4T5w4oY0bN2rYsGGqVKmSKlWqpGeffVbffvutKlWqpM8++6yioltWnt/t77Vr10579+71dDyPK8t4o6Ojdfnll7uKjCQ1adJExhj99NNPXs1bXuX5/RpjNGPGDPXv319BQUHejOkRZRnrxIkT1bFjRz3xxBNq0aKFunfvrmnTpmnGjBnKyMioiNhlVpbxOp1OzZgxQydPntSBAwd08OBB1a1bV6GhoYqIiKiI2BUmKiqq1PemUqVKqlmzptdelzLjx4KCgtS6dWutXLmy2PyVK1eqQ4cObm/HGKP8/HxPx/M4q+MNCwvTtm3btHXrVtdjyJAhatSokbZu3aq2bdtWVHTLPPW73bJli6Kjoz0dz+PKMt6OHTvql19+UU5Ojmvenj17FBAQoNjYWK/mLa/y/H5TU1O1b98+3Xvvvd6M6DFlGevJkycVEFD8n5+zeyiMn98usDy/28qVKys2NlaBgYFauHChbr755hLvg921b9++xHvzySefqE2bNqpcubL3Xtirpxej3M5+BfCtt94yO3bsMMOHDzdVq1Z1fcNh1KhRpn///q71p06dat577z2zZ88es2fPHjNjxgwTFhZmxowZ46shWGJ1vH9kp28zWR3ryy+/bJYtW2b27Nljvv/+ezNq1CgjySxZssRXQ7DE6nhPnDhhYmNjzV/+8hezfft2k5qaaho0aGDuu+8+Xw3BkrL+Ld91112mbdu2FR23XKyONSUlxVSqVMlMmzbN/PDDD2bdunWmTZs25uqrr/bVECyxOt7du3ebOXPmmD179pgNGzaYfv36mRo1api0tDQfjcB9J06cMFu2bDFbtmwxksykSZPMli1bXF9D/+NYz341+9FHHzU7duwwb731Fl/Nxm9ee+01Ex8fb4KCgsxVV11lUlNTXcsGDBhgOnXq5Jp+9dVXTbNmzUxISIgJCwszrVq1MtOmTTNFRUU+SF42Vsb7R3YqM8ZYG+vzzz9vrrjiClOlShVz2WWXmWuuucZ8+OGHPkhddlZ/tzt37jQ33HCDcTqdJjY21owYMcKcPHmyglOXndXxHjt2zDidTvPGG29UcNLyszrWV1991TRt2tQ4nU4THR1t7rzzTvPTTz9VcOqyszLeHTt2mJYtWxqn02nCwsJM7969za5du3yQ2rqzl4T442PAgAHGmNJ/t6tXrzatWrUyQUFBpm7dumb69Olez+kwxs/36QEAAJzHxXWwDgAAXHIoMwAAwNYoMwAAwNYoMwAAwNYoMwAAwNYoMwAAwNYoMwAAwNYoMwC85sCBA3I4HNq6dauvo3iMp8bUuXNnDR8+3DVdt25dTZ48uVzblKSBAwfa6gaVgCdQZgAfGDhwoBwOh4YMGVJi2YMPPiiHw6GBAwcWm5+ZmamHHnpI9evXV3BwsOLi4tSrVy+tWrXKtc6WLVt08803q3bt2qpSpYrq1q2rfv36KSsrq8TrnP1H+XyP8t6gNC4uThkZGWrevHm5tnMhq1evlsPh0LFjx8q9rf379+tvf/ubYmJiVKVKFcXGxqp3797as2ePJM+NaenSpXruuefKnfePXnnlFc2cOdM1/cfSBFyMKvk6AHCpiouL08KFC/Xyyy/L6XRKkk6dOqUFCxaoTp06xdY9cOCAOnbsqOrVqys5OVktWrTQ6dOn9fHHH2vo0KHatWuXDh06pBtuuEG9evXSxx9/rOrVqystLU3vvfeeTp48Werr//4OxS+++KJWrFihTz/91DWvWrVq5RpjYGCgoqKiyrWNilRQUKCuXbuqcePGWrp0qaKjo/XTTz9p+fLlOn78uCTPjalGjRrl3sbvFRUVyeFwFLvLOHDJ8PoNEwCUMGDAANO7d2+TmJho5s6d65o/b948k5iYaHr37u2694kxxvTo0cNcfvnlJicnp8S2jh49aowxZtmyZaZSpUrm9OnTZcr0x/ta7du3z9xyyy2mdu3apmrVqqZNmzZm5cqVxZ4THx9vxo8fb+655x5TrVo1ExcXZ/71r3+5lqelpRlJZsuWLcaY/7vPy4oVK0zLli1NlSpVTJcuXcyvv/5qli9fbho3bmxCQ0PN7bffbnJzc13bOXXqlHnooYdMrVq1THBwsOnYsaP5+uuvi72GSrlvzPmeV5qzN9M7e8PA0nhqTJ06dTKPPPJIsffy5Zdfdk2/9NJLpnnz5iYkJMTExsaaBx54wJw4ccK1PCUlxYSHh5v333/fNGnSxAQGBpr9+/e7/raM+e3v7I/vzf79+80VV1xhXnjhhWLj2rZtm3E4HGbfvn3nHDvgrzjMBPjQPffco5SUFNf0jBkzNGjQoGLrHDlyRCtWrNDQoUNVtWrVEtuoXr26JCkqKkqFhYVatmyZjAduuZaTk6ObbrpJn376qbZs2aLu3burV69eOnjwYLH1XnrpJbVp00ZbtmzRgw8+qAceeEC7du0677aTkpI0depUrV+/Xunp6fqf//kfTZ48WfPnz9eHH36olStXasqUKa71R44cqSVLlmjWrFnavHmzEhIS1L17dx05ckRxcXFasmSJJGn37t3KyMjQK6+8csHnlaZWrVoKCAjQO++8o6KiIkvvl9UxXUhAQIBeffVVff/995o1a5Y+++wzjRw5stg6J0+e1MSJE/Xmm29q+/btql27drHlr7zyitq3b6/BgwcrIyNDGRkZqlOnjgYNGlTs70767W/v2muv1RVXXGFp3IBf8HWbAi5FZ//v+b///a8JDg42aWlp5sCBA6ZKlSrmv//9b7E9Mxs2bDCSzNKlSy+43aeeespUqlTJ1KhRw9x4440mOTnZZGZmupXJnTuON23a1EyZMsU1HR8fb+666y7X9JkzZ0zt2rVdd8k9116MTz/91PWciRMnGknmhx9+cM27//77Tffu3Y0xxuTk5JjKlSubefPmuZYXFBSYmJgYk5ycXGy7Z/dSufu80kydOtWEhISY0NBQ06VLF/Pss88Wy+aJMRlz4T0zf/T222+bmjVruqZTUlKMJLN169Zi6/1+z0xpr2OMMb/88osJDAw0GzZscL0vtWrVMjNnzjzn6wP+jD0zgA9FRESoZ8+emjVrllJSUtSzZ09FREQUW8f8714Wh8Nxwe2NHz9emZmZev3119W0aVO9/vrraty4sbZt22Y5W25urkaOHKmmTZuqevXqqlatmnbt2lViz0yLFi1cPzscDkVFRenQoUPn3fbvnxMZGamQkBDVr1+/2Lyz2/jhhx90+vRpdezY0bW8cuXKuvrqq7Vz585zvkZZnzd06FBlZmZq7ty5at++vRYvXqxmzZpp5cqVHhuTOz7//HN17dpVl19+uUJDQ3X33Xfr8OHDys3Nda0TFBRU7HXdFR0drZ49e2rGjBmSpA8++ECnTp3SX//6V8vbAvwBZQbwsUGDBmnmzJmaNWtWiUNMktSgQQM5HI7z/gP8ezVr1tRf//pXvfTSS9q5c6diYmL04osvWs71xBNPaMmSJRo/frzWrl2rrVu3KjExUQUFBcXWq1y5crFph8OhM2fOnHfbv3+Ow+E47zbOVeaMMecteGV9niSFhobqlltu0fjx4/Xtt9/q2muv1T/+8Q+PjelCfvzxR910001q3ry5lixZok2bNum1116TJJ0+fdq1ntPpdKvklua+++7TwoULlZeXp5SUFPXr108hISFl2hbga5QZwMduvPFGFRQUqKCgQN27dy+xvEaNGurevbtee+21Yv9Xftb5vo4cFBSkK664otTnXcjatWs1cOBA9enTR4mJiYqKitKBAwcsb6e8EhISFBQUpHXr1rnmnT59Whs3blSTJk0k/TZOScXOc3Hnee5wOBxq3Lhxmd7Dstq4caMKCwv10ksvqV27dmrYsKF++eWXMm0rKCio1PN/brrpJlWtWlXTp0/XRx99VGqRBuyCMgP4WGBgoHbu3KmdO3cqMDCw1HWmTZumoqIiXX311VqyZIn27t2rnTt36tVXX1X79u0l/Xao4K677tIHH3ygPXv2aPfu3XrxxRe1fPly9e7d23KuhIQELV26VFu3btW3336rO+64w+09C55UtWpVPfDAA3riiSe0YsUK7dixQ4MHD9bJkyd17733SpLi4+PlcDj0wQcf6L///a9ycnLcet4fbd26Vb1799Y777yjHTt2aN++fXrrrbc0Y8aMMr2HZXXFFVeosLBQU6ZM0f79+zVnzhy9/vrrZdpW3bp1tWHDBh04cEBZWVmu32FgYKAGDhyo0aNHKyEhwfV3BNgRZQbwA2FhYQoLCzvn8nr16mnz5s3q0qWLHnvsMTVv3lxdu3bVqlWrNH36dElS06ZNFRISoscee0wtW7ZUu3bt9Pbbb+vNN99U//79LWd6+eWXddlll6lDhw7q1auXunfvrquuuqrMYyyPf/7zn+rbt6/69++vq666Svv27dPHH3+syy67TJJ0+eWXa9y4cRo1apQiIyM1bNgwt573R7Gxsapbt67GjRuntm3b6qqrrtIrr7yicePGacyYMRU23pYtW2rSpEl6/vnn1bx5c82bN08TJ04s07Yef/xxBQYGqmnTpqpVq1axc57uvfdeFRQUsFcGtucwxgPf4QQA2M4XX3yhzp0766efflJkZKSv4wBlRpkBgEtMfn6+0tPT9f/+3/9TdHS05s2b5+tIQLlwmAkALjELFixQo0aNdPz4cSUnJ/s6DlBu7JkBAAC2xp4ZAABga5QZAABga5QZAABga5QZAABga5QZAABga5QZAABga5QZAABga5QZAABga5QZAABga/8fnfPxhRITyqwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mcs_tanimoto_loeffler, density=False, bins=10, ec='k')\n",
    "plt.ylabel('No. of Parent-TP pairs')\n",
    "plt.ylim(0,30)\n",
    "plt.xlabel('MCS Tanimoto Similarity');\n",
    "plt.vlines(x = 0.95, ymin = 0, ymax = 30,\n",
    "           colors = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c006574",
   "metadata": {},
   "source": [
    "## Proportionally, many more parents and TPs classified as 'similar' (Tanimoto > 0.95) using MCS Tanimoto compared to MorganFP\n",
    "\n",
    "0.34% vs. 17.9%\n",
    "\n",
    "The method chosen to calculate molecular similarity is very important."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:rdkit-latest-20230302] *",
   "language": "python",
   "name": "conda-env-rdkit-latest-20230302-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}