notebook/Polymer_Similarity_Case5_Segmented_Polymers.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nVg45IXBOyyO"
      },
      "source": [
        "\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/olsenlabmit/Polymer-Graph-Similarity/blob/main/notebook/Polymer_Similarity_Case5_Segmented_Polymers.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "9ExWfHqV1EXI"
      },
      "outputs": [],
      "source": [
        "# @title Copyright & License (click to expand)\n",
        "# MIT License\n",
        "\n",
        "# Copyright (c) 2022 olsenlabmit\n",
        "\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
        "# of this software and associated documentation files (the \"Software\"), to deal\n",
        "# in the Software without restriction, including without limitation the rights\n",
        "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
        "# copies of the Software, and to permit persons to whom the Software is\n",
        "# furnished to do so, subject to the following conditions:\n",
        "\n",
        "# The above copyright notice and this permission notice shall be included in all\n",
        "# copies or substantial portions of the Software.\n",
        "\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
        "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
        "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
        "# SOFTWARE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_sKD4LKzw0QJ"
      },
      "source": [
        "# Case 5: Segmented_Polymers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA0PTW_O3VxE"
      },
      "source": [
        "This notebook is to perform the calculations and reproduce the results of **Case 5** in the manuscript, Jiale Shi, Nathan J. Rebello, Dylan Walsh, Weizhong Zou, Michael E. Deagen, Bruno Salomao Leao, Debra J. Audus, Bradley D. Olsen, \"Quantifying Pairwise Chemical Similarity for Polymers\", submitted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9n8-Ciyiy7G"
      },
      "source": [
        "# Import and Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "psnOpmYlL_sw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b1bfea5-c5eb-405a-e367-9fad73452278"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 MB\u001b[0m \u001b[31m48.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25hSelecting previously unselected package coinor-libcoinutils3v5.\n",
            "(Reading database ... 122349 files and directories currently installed.)\n",
            "Preparing to unpack .../0-coinor-libcoinutils3v5_2.11.4+repack1-1_amd64.deb ...\n",
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            "Preparing to unpack .../5-coinor-cbc_2.10.3+repack1-1build1_amd64.deb ...\n",
            "Unpacking coinor-cbc (2.10.3+repack1-1build1) ...\n",
            "Setting up coinor-libcoinutils3v5 (2.11.4+repack1-1) ...\n",
            "Setting up coinor-libosi1v5 (0.108.6+repack1-1) ...\n",
            "Setting up coinor-libclp1 (1.17.5+repack1-1) ...\n",
            "Setting up coinor-libcgl1 (0.60.3+repack1-2) ...\n",
            "Setting up coinor-libcbc3 (2.10.3+repack1-1build1) ...\n",
            "Setting up coinor-cbc (2.10.3+repack1-1build1) ...\n",
            "Processing triggers for man-db (2.9.1-1) ...\n",
            "Processing triggers for libc-bin (2.31-0ubuntu9.9) ...\n"
          ]
        }
      ],
      "source": [
        "# import necessary libraries available in colab\n",
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import shutil\n",
        "import sys\n",
        "import os.path\n",
        "\n",
        "# install necessary libraries not available in colab\n",
        "if not shutil.which(\"rdkit\"):\n",
        "    !pip install -q rdkit\n",
        "\n",
        "from rdkit import Chem\n",
        "from rdkit import DataStructs\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit.Chem import MACCSkeys\n",
        "from rdkit.Chem.AtomPairs import Pairs\n",
        "\n",
        "if not shutil.which(\"pyomo\"):\n",
        "    !pip install -q pyomo\n",
        "    assert shutil.which(\"pyomo\")\n",
        "\n",
        "if not (shutil.which(\"cbc\") or os.path.isfile(\"cbc\")):\n",
        "    if \"google.colab\" in sys.modules:\n",
        "        !apt-get install -y -qq coinor-cbc\n",
        "    else:\n",
        "        try:\n",
        "            !conda install -c conda-forge coincbc\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "assert shutil.which(\"cbc\") or os.path.isfile(\"cbc\")\n",
        "\n",
        "from pyomo.environ import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2aVwhkeZcX8"
      },
      "source": [
        "# Similarity Score Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsMcsqU-Pv6Y"
      },
      "source": [
        "## Earth Mover's Distance (EMD) Similarity Function\n",
        "\n",
        "Use the earth mover's distance to compute similarity for repeating units and end groups.\n",
        "\n",
        "\n",
        "$ EMD (P,Q)= \\min_{F}\\sum_{i=1}^{m}\\sum_{j=1}^{n}f_{i,j}d_{i,j}$\n",
        "\n",
        "$ {\\rm subject\\ to\\ } f_{i,j} \\geq 0, {\\rm\\ for\\ any\\ } \\ 1 \\leq i \\leq m, 1 \\leq j \\leq n $\n",
        "\n",
        "$     \\sum_{j=1}^{n}f_{i,j} =w_{pi}, {\\rm for\\ any} \\ 1 \\leq i \\leq m $\n",
        "\n",
        "$     \\sum_{i=1}^{m}f_{i,j} =w_{qj}, {\\rm for\\ any} \\ 1 \\leq j \\leq n $\n",
        "\n",
        "$     \\sum_{i=1}^{m}\\sum_{j=1}^{n}f_{i,j} = \\sum_{i}^{m} w_{pi} = \\sum_{j}^{n} w_{qj} = 1 $ \n",
        "\n",
        "\n",
        "Molecular fragment ensemble $P = \\{(p_1,w_{p_1}) ,(p_2,w_{p_2}),...,(p_i,w_{p_i}),...,(p_m,w_{p_m})\\}$ has $m$ molecular fragments, where $p_i$ is a molecular fragment such as a repeat unit or end group and $w_{p_i}>0$ is the weight, related to the average probability (or frequency, $z$) of the molecular fragment being present in the polymer. Similarly, the second ensemble $Q = {(q_1,w_{q_1}),(q_2,w_{q_2}),...,(q_j,w_{q_j}),...,(q_n,w_{q_n})}$ has $n$ molecular fragments.  The sums of the weights for $P$ and $Q$ are both normalized and equal to one.\n",
        "The distance $d_{i,j}$ is calculated by the converting the pairwise similarity score $s_{i,j}$ between $p_{i}$ and $q_{j}$, where $d_{i,j} = 1 - s_{i,j}$.\n",
        "Transport flow $f_{i,j}$ represents the amount of weight at $p_i$ which is transported to $q_j$. \n",
        "\n",
        "The EMD linear optimization code  refers to [3.1. Transportation Networks, ND Pyomo Notebook](https://jckantor.github.io/ND-Pyomo-Cookbook/notebooks/03.01-Transportation-Networks.html)\n",
        "\n",
        "Once $EMD (P,Q)$ is calculated, the similarity score between $P$ and $Q$ is \n",
        "\n",
        "$S(P,Q) = 1- EMD (P,Q)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "eOlNnWbfY8Va"
      },
      "outputs": [],
      "source": [
        "def Similarity_Score_EMD_Calculation(\n",
        "    query_smiles_list=None,\n",
        "    query_smiles_weight_list=None,\n",
        "    target_smiles_list=None,\n",
        "    target_smiles_weight_list=None,\n",
        "    embedding_function=\"MorganFingerprint\",\n",
        "    similarity_score_function=\"Tanimoto\",\n",
        "):\n",
        "    # obtain the length of query smiles list and target smiles list\n",
        "    query_smiles_list_length = len(query_smiles_list)\n",
        "\n",
        "    target_smiles_list_length = len(target_smiles_list)\n",
        "\n",
        "    # check whether the query is identical to the target\n",
        "    if set(query_smiles_list) == set(target_smiles_list):\n",
        "        query_smiles_array = np.array(query_smiles_list)\n",
        "        query_smiles_level_array = np.array(query_smiles_weight_list)\n",
        "        inds_query = query_smiles_array.argsort()\n",
        "        sorted_query_smiles_array = query_smiles_array[inds_query]\n",
        "        sorted_query_smiles_level_array = query_smiles_level_array[inds_query]\n",
        "\n",
        "        target_smiles_array = np.array(target_smiles_list)\n",
        "        target_smiles_level_array = np.array(target_smiles_weight_list)\n",
        "        inds_target = target_smiles_array.argsort()\n",
        "        sorted_target_smiles_array = target_smiles_array[inds_target]\n",
        "        sorted_target_smiles_level_array = target_smiles_level_array[inds_target]\n",
        "\n",
        "        if np.array_equal(\n",
        "            sorted_query_smiles_array, sorted_target_smiles_array\n",
        "        ) and np.array_equal(\n",
        "            sorted_query_smiles_level_array, sorted_target_smiles_level_array\n",
        "        ):\n",
        "            return 1.0\n",
        "\n",
        "        query_smiles_reduced_list = list(set(query_smiles_list))\n",
        "        query_smiles_reduced_list_number = []\n",
        "\n",
        "        for i in range(0, len(query_smiles_reduced_list)):\n",
        "            query_smiles_reduced_list_number_i = 0\n",
        "            for j in range(0, len(query_smiles_list)):\n",
        "                if query_smiles_reduced_list[i] == query_smiles_list[j]:\n",
        "                    query_smiles_reduced_list_number_i = (\n",
        "                        query_smiles_reduced_list_number_i + query_smiles_weight_list[j]\n",
        "                    )\n",
        "\n",
        "            query_smiles_reduced_list_number.append(\n",
        "                query_smiles_reduced_list_number_i / sum(query_smiles_weight_list)\n",
        "            )\n",
        "\n",
        "        target_smiles_reduced_list = list(set(target_smiles_list))\n",
        "        target_smiles_reduced_list_number = []\n",
        "\n",
        "        for i in range(0, len(target_smiles_reduced_list)):\n",
        "            target_smiles_reduced_list_number_i = 0\n",
        "            for j in range(0, len(target_smiles_list)):\n",
        "                if target_smiles_reduced_list[i] == target_smiles_list[j]:\n",
        "                    target_smiles_reduced_list_number_i = (\n",
        "                        target_smiles_reduced_list_number_i\n",
        "                        + target_smiles_weight_list[j]\n",
        "                    )\n",
        "\n",
        "            target_smiles_reduced_list_number.append(\n",
        "                target_smiles_reduced_list_number_i / sum(target_smiles_weight_list)\n",
        "            )\n",
        "\n",
        "        if query_smiles_reduced_list_number == target_smiles_reduced_list_number:\n",
        "            return 1.0\n",
        "\n",
        "    # transfer SMILES to fingerprints\n",
        "    if embedding_function == \"RDKFingerprint\":\n",
        "        query_mol_list = [Chem.MolFromSmiles(x) for x in query_smiles_list]\n",
        "        query_fingerprint_list = [Chem.RDKFingerprint(x) for x in query_mol_list]\n",
        "        target_mol_list = [Chem.MolFromSmiles(x) for x in target_smiles_list]\n",
        "        target_fingerprint_list = [Chem.RDKFingerprint(x) for x in target_mol_list]\n",
        "\n",
        "    elif embedding_function == \"MorganFingerprint\":\n",
        "        query_mol_list = [Chem.MolFromSmiles(x) for x in query_smiles_list]\n",
        "        query_fingerprint_list = [\n",
        "            AllChem.GetMorganFingerprintAsBitVect(x, 2, nBits=2048)\n",
        "            for x in query_mol_list\n",
        "        ]\n",
        "        target_mol_list = [Chem.MolFromSmiles(x) for x in target_smiles_list]\n",
        "        target_fingerprint_list = [\n",
        "            AllChem.GetMorganFingerprintAsBitVect(x, 2, nBits=2048)\n",
        "            for x in target_mol_list\n",
        "        ]\n",
        "\n",
        "    elif embedding_function == \"MACCSkeys\":\n",
        "        query_mol_list = [Chem.MolFromSmiles(x) for x in query_smiles_list]\n",
        "        query_fingerprint_list = [MACCSkeys.GenMACCSKeys(x) for x in query_mol_list]\n",
        "        target_mol_list = [Chem.MolFromSmiles(x) for x in target_smiles_list]\n",
        "        target_fingerprint_list = [MACCSkeys.GenMACCSKeys(x) for x in target_mol_list]\n",
        "\n",
        "    else:\n",
        "        print(\n",
        "            embedding_function\n",
        "            + \" is not included in the current vision.\"\n",
        "            + \" Please choose an available embedding function:\"\n",
        "        )\n",
        "        print(\"MorganFingerprint, RDKFingerprint, MACCSkeys.\")\n",
        "        return False\n",
        "\n",
        "    # define the required three sets\n",
        "    Demand = {}  # query\n",
        "    Supply = {}  # target\n",
        "    T = {}  # transport flow\n",
        "\n",
        "    # define the weight for SMILES in query, w_i\n",
        "    if len(set(query_smiles_weight_list)) == 1:\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            Demand[\"P\" + str(i + 1)] = 1 / query_smiles_list_length\n",
        "\n",
        "    else:\n",
        "        query_weight_sum = sum(query_smiles_weight_list)\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            Demand[\"P\" + str(i + 1)] = query_smiles_weight_list[i] / query_weight_sum\n",
        "\n",
        "    # define weight for SMILES in target, w_j\n",
        "    if len(set(target_smiles_weight_list)) == 1:\n",
        "        for j in range(0, target_smiles_list_length):\n",
        "            Supply[\"Q\" + str(j + 1)] = 1 / target_smiles_list_length\n",
        "\n",
        "    else:\n",
        "        target_weight_sum = sum(target_smiles_weight_list)\n",
        "        for j in range(0, target_smiles_list_length):\n",
        "            Supply[\"Q\" + str(j + 1)] = target_smiles_weight_list[j] / target_weight_sum\n",
        "\n",
        "    # use similarity function to calculate d_ij\n",
        "    if similarity_score_function == \"Tanimoto\":\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            for j in range(0, target_smiles_list_length):\n",
        "                T[\n",
        "                    (\"P\" + str(i + 1), \"Q\" + str(j + 1))\n",
        "                ] = 1 - DataStructs.FingerprintSimilarity(\n",
        "                    query_fingerprint_list[i], target_fingerprint_list[j]\n",
        "                )\n",
        "\n",
        "    elif similarity_score_function == \"Dice\":\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            for j in range(0, target_smiles_list_length):\n",
        "                T[\n",
        "                    (\"P\" + str(i + 1), \"Q\" + str(j + 1))\n",
        "                ] = 1 - DataStructs.FingerprintSimilarity(\n",
        "                    query_fingerprint_list[i],\n",
        "                    target_fingerprint_list[j],\n",
        "                    metric=DataStructs.DiceSimilarity,\n",
        "                )\n",
        "\n",
        "    elif similarity_score_function == \"Cosine\":\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            for j in range(0, target_smiles_list_length):\n",
        "                T[\n",
        "                    (\"P\" + str(i + 1), \"Q\" + str(j + 1))\n",
        "                ] = 1 - DataStructs.FingerprintSimilarity(\n",
        "                    query_fingerprint_list[i],\n",
        "                    target_fingerprint_list[j],\n",
        "                    metric=DataStructs.CosineSimilarity,\n",
        "                )\n",
        "\n",
        "    else:\n",
        "        print(\n",
        "            similarity_score_function\n",
        "            + \" is not included in the current vision.\"\n",
        "            + \" Please choose an available similarity function:\"\n",
        "        )\n",
        "        print(\"Tanimoto, Dice, or Cosine\")\n",
        "        return\n",
        "\n",
        "    # linear optimization with pyomo\n",
        "    # step 0: Create an instance of the model\n",
        "    model = ConcreteModel()\n",
        "    model.dual = Suffix(direction=Suffix.IMPORT)\n",
        "\n",
        "    # step 1: define index sets\n",
        "    CUS = list(Demand.keys())\n",
        "    SRC = list(Supply.keys())\n",
        "\n",
        "    # step 2: define the decision\n",
        "    model.x = Var(CUS, SRC, domain=NonNegativeReals)\n",
        "\n",
        "    # step 3: define objective\n",
        "    model.Cost = Objective(\n",
        "        expr=sum([T[c, s] * model.x[c, s] for c in CUS for s in SRC]), sense=minimize\n",
        "    )\n",
        "\n",
        "    # step 4: define constraints\n",
        "    model.src = ConstraintList()\n",
        "    for s in SRC:\n",
        "        model.src.add(sum([model.x[c, s] for c in CUS]) == Supply[s])\n",
        "\n",
        "    model.dmd = ConstraintList()\n",
        "    for c in CUS:\n",
        "        model.dmd.add(sum([model.x[c, s] for s in SRC]) == Demand[c])\n",
        "\n",
        "    # step 5: solve the model\n",
        "    results = SolverFactory(\"cbc\").solve(model)\n",
        "\n",
        "    if \"ok\" == str(results.Solver.status):\n",
        "        SimilarityScore = 1 - model.Cost()\n",
        "        return SimilarityScore\n",
        "\n",
        "    else:\n",
        "        print(\"No Valid Solution Found\")\n",
        "        return False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNpt-pIDQLOX"
      },
      "source": [
        "## Topology Similarity Function\n",
        "\n",
        "Use graph edit distance $GED(g_1, g_2)$ to compute the topological similarity $S_{\\rm TOP}(g_1,g_2)$.\n",
        "\n",
        "$GED(g_1, g_2)$  is computed via a networkx function [graph_edit_distance](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.similarity.graph_edit_distance.html), while the topological similarity score $S_{\\rm TOP}(g_1,g_2)$ in the range of (0,1] is computed via a exponential decay function:\n",
        "\n",
        "\n",
        "$S_{\\rm TOP}(g_1, g_2) = \\exp(-\\frac{\\alpha \\cdot GED(g_1, g_2)}{(N_1 + N_2)/2})$\n",
        "\n",
        "where $N_i$ is the number of nodes in graph $g_i$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "oyUi54M_IorG"
      },
      "outputs": [],
      "source": [
        "def Topology_Similarity_Score_Calculation(Graph1=None, Graph2=None, alpha=1):\n",
        "    if Graph1 == None:\n",
        "        print(\"Missing Graph1\")\n",
        "        return\n",
        "    if Graph2 == None:\n",
        "        print(\"Missing Graph2\")\n",
        "        return\n",
        "\n",
        "    Graph1_number_nodes = Graph1.number_of_nodes()\n",
        "    Graph2_number_nodes = Graph2.number_of_nodes()\n",
        "\n",
        "    graph_edit_distance = nx.graph_edit_distance(Graph1, Graph2)\n",
        "\n",
        "    topology_similarity_score = np.exp(\n",
        "        -alpha * graph_edit_distance / ((Graph1_number_nodes + Graph2_number_nodes) / 2)\n",
        "    )\n",
        "\n",
        "    return topology_similarity_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_4bM0zPQRGC"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bdTaiEZQelj"
      },
      "source": [
        "## Weighted Mean Function for Overall Pairwise Similarity Score $S_{\\rm OA}$ \n",
        "\n",
        "From the above EMD and GED calculations, three similarity scores are obtained: $S_{\\rm RU}$  for repeat units, $S_{\\rm EG}$  for end groups, and $S_{\\rm TOP}$   for topology. To calculate the overall similarity score $S_{\\rm OA}$. Two weighted mean functions are provided. \n",
        "\n",
        "1. weighted geometric mean\n",
        "\n",
        "$S_{\\rm OA} =  S_{\\rm RU}^{W_{\\rm RU}} \\cdot S_{\\rm TOP}^{W_{\\rm TOP}} \\cdot S_{\\rm EG}^{W_{\\rm EG}} $ \n",
        "\n",
        "2. weighted arithmetic mean\n",
        "\n",
        "$S_{\\rm OA} =  S_{\\rm RU} \\cdot W_{\\rm RU} + S_{\\rm TOP} \\cdot W_{\\rm TOP} +  S_{\\rm EG} \\cdot W_{\\rm EG} $ \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "tnGFkpht12nw"
      },
      "outputs": [],
      "source": [
        "def Overall_Similarity_Score_Calculation(\n",
        "    query=None,\n",
        "    target=None,\n",
        "    embedding_function=\"RDKFingerprint\",\n",
        "    similarity_score_function=\"Tanimoto\",\n",
        "    alpha=1,\n",
        "    Repeat_Unit_Weight=0.475,\n",
        "    Topology_Weight=0.475,\n",
        "    End_Group_Weight=0.05,\n",
        "    Mean_Function=\"geometric\",\n",
        "    details_print=False,\n",
        "    return_with_components=False,\n",
        "):\n",
        "    if query == None or target == None:\n",
        "        print(\n",
        "            \"Either query polymer or target polymer is missing! Please check the input.\"\n",
        "        )\n",
        "        return\n",
        "\n",
        "    Repeat_Unit_Similarity_Score = Similarity_Score_EMD_Calculation(\n",
        "        query_smiles_list=query.repeat_unit_smiles_list,\n",
        "        query_smiles_weight_list=query.repeat_unit_smiles_weight_list,\n",
        "        target_smiles_list=target.repeat_unit_smiles_list,\n",
        "        target_smiles_weight_list=target.repeat_unit_smiles_weight_list,\n",
        "        embedding_function=embedding_function,\n",
        "        similarity_score_function=similarity_score_function,\n",
        "    )\n",
        "\n",
        "    Topology_Similarity_Score = Topology_Similarity_Score_Calculation(\n",
        "        Graph1=query.graph_representation,\n",
        "        Graph2=target.graph_representation,\n",
        "        alpha=alpha,\n",
        "    )\n",
        "\n",
        "    if len(query.end_group_smiles_list) == 0 and len(target.end_group_smiles_list) == 0:\n",
        "        End_Group_Similarity_Score = 1.0\n",
        "\n",
        "    elif (\n",
        "        len(query.end_group_smiles_list) == 0 and len(target.end_group_smiles_list) != 0\n",
        "    ) or (\n",
        "        len(query.end_group_smiles_list) != 0 and len(target.end_group_smiles_list) == 0\n",
        "    ):\n",
        "        End_Group_Similarity_Score = 1.0\n",
        "\n",
        "    else:\n",
        "        End_Group_Similarity_Score = Similarity_Score_EMD_Calculation(\n",
        "            query_smiles_list=query.end_group_smiles_list,\n",
        "            query_smiles_weight_list=query.end_group_smiles_weight_list,\n",
        "            target_smiles_list=target.end_group_smiles_list,\n",
        "            target_smiles_weight_list=target.end_group_smiles_weight_list,\n",
        "            embedding_function=embedding_function,\n",
        "            similarity_score_function=similarity_score_function,\n",
        "        )\n",
        "\n",
        "    # verify whether the weight sum is normalized.\n",
        "    if abs(Repeat_Unit_Weight + Topology_Weight + End_Group_Weight - 1) != 0.0:\n",
        "        Weight_Sum = Repeat_Unit_Weight + Topology_Weight + End_Group_Weight\n",
        "\n",
        "        Repeat_Unit_Weight = Repeat_Unit_Weight / Weight_Sum\n",
        "\n",
        "        Topology_Weight = Topology_Weight / Weight_Sum\n",
        "\n",
        "        End_Group_Weight = End_Group_Weight / Weight_Sum\n",
        "\n",
        "    if Mean_Function == \"geometric\":\n",
        "        Overall_Similarity_Score = (\n",
        "            pow(Repeat_Unit_Similarity_Score, Repeat_Unit_Weight)\n",
        "            * pow(Topology_Similarity_Score, Topology_Weight)\n",
        "            * pow(End_Group_Similarity_Score, End_Group_Weight)\n",
        "        )\n",
        "\n",
        "    elif Mean_Function == \"arithmetic\":\n",
        "        Overall_Similarity_Score = (\n",
        "            Repeat_Unit_Weight * Repeat_Unit_Similarity_Score\n",
        "            + Topology_Weight * Topology_Similarity_Score\n",
        "            + End_Group_Weight * End_Group_Similarity_Score\n",
        "        )\n",
        "\n",
        "    else:\n",
        "        print(\n",
        "            \"Your input mean function \",\n",
        "            Mean_Function,\n",
        "            \" is not implemented currently.\"\n",
        "            + \" Please choose those implemented mean functions, such as arithmetic, geometric\",\n",
        "        )\n",
        "        return\n",
        "\n",
        "    if details_print == True:\n",
        "        print(\"Details of the Similarity Score:\\n\")\n",
        "        print(\n",
        "            \"Repeat Unit Similarity Score = \",\n",
        "            Repeat_Unit_Similarity_Score,\n",
        "            \", Normalized Weight for Repeat Unit = \",\n",
        "            Repeat_Unit_Weight,\n",
        "        )\n",
        "        print(\n",
        "            \"Topology Similarity Score = \",\n",
        "            Topology_Similarity_Score,\n",
        "            \", Normalized Weight for Topology = \",\n",
        "            Topology_Weight,\n",
        "        )\n",
        "        print(\n",
        "            \"End Group Similarity  = \",\n",
        "            End_Group_Similarity_Score,\n",
        "            \", Normalized Weight for End Group = \",\n",
        "            End_Group_Weight,\n",
        "        )\n",
        "        print(\n",
        "            \"Overall Pairwise Similarity Score with \"\n",
        "            + Mean_Function\n",
        "            + \" mean function = \",\n",
        "            Overall_Similarity_Score,\n",
        "        )\n",
        "        print(\"\\n\")\n",
        "\n",
        "    if return_with_components == True:\n",
        "        return (\n",
        "            Overall_Similarity_Score,\n",
        "            Repeat_Unit_Similarity_Score,\n",
        "            Topology_Similarity_Score,\n",
        "            End_Group_Similarity_Score,\n",
        "        )\n",
        "\n",
        "    return Overall_Similarity_Score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bijKjSMqQ-gw"
      },
      "source": [
        "# Define Polymer Class for Importing Polymer data\n",
        "\n",
        "The polymer graph is decomposed into three components: repeat units, end groups and topology. \n",
        "\n",
        "(I) To compute $S_{\\rm RU}$  for repeat units, we need the polymer's (1) repeat unit SMILES list and (2) corresponding weight list.\n",
        "\n",
        "(II) To compute $S_{\\rm EG}$  for end groups, we need the polymer's (3) end group SMILES list and (4) corresponding weight list.\n",
        "\n",
        "(III) To compute $S_{\\rm TOP}$ for topology, we need the polymers' (5) topological graph representation.\n",
        "\n",
        "Therefore, in the Polymer Class, we have to define the above five elements which are required to perform polymer similarity calculation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "k-I92q5zfd_L"
      },
      "outputs": [],
      "source": [
        "class Polymer:\n",
        "    def __init__(\n",
        "        self,\n",
        "        repeat_unit_smiles_list=None,\n",
        "        repeat_unit_smiles_weight_list=None,\n",
        "        end_group_smiles_list=None,\n",
        "        end_group_smiles_weight_list=None,\n",
        "        graph_representation=None,\n",
        "    ):\n",
        "        if repeat_unit_smiles_list == None:\n",
        "            print(\n",
        "                \"Error: Repeat Unit SMILES are missing.\"\n",
        "                + \" Please define the Repeat Unit SMILES.\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            self.repeat_unit_smiles_list = repeat_unit_smiles_list\n",
        "\n",
        "        if repeat_unit_smiles_weight_list == None:\n",
        "            print(\n",
        "                \"Error: Repeat Unit weights are missing.\"\n",
        "                + \" Please re-enter the correct Repeat Unit weights.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        elif len(repeat_unit_smiles_weight_list) != len(repeat_unit_smiles_list):\n",
        "            print(\n",
        "                \"Error: Repeat Unit weights are not correct.\"\n",
        "                + \" Please re-enter the correct Repeat Unit SMILES and weights.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            self.repeat_unit_smiles_weight_list = repeat_unit_smiles_weight_list\n",
        "\n",
        "        if end_group_smiles_list == None and end_group_smiles_weight_list == None:\n",
        "            self.end_group_smiles_list = []\n",
        "            self.end_group_smiles_weight_list = []\n",
        "            print(\n",
        "                \"Notice: The Polymer has NO End Group.\\n\"\n",
        "                + \"If this is what you want, this message is NOT Error.\\n\"\n",
        "                + \"Otherwise, please enter the correct End Group SMILES and weights.\\n\"\n",
        "            )\n",
        "\n",
        "        elif (\n",
        "            end_group_smiles_list == None and end_group_smiles_weight_list != None\n",
        "        ) or (end_group_smiles_list != None and end_group_smiles_weight_list == None):\n",
        "            print(\n",
        "                \"Error: End Group SMILES and weights are not matching.\"\n",
        "                + \" Please re-enter the correct End Group SMILES and weights.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            if len(end_group_smiles_list) != len(end_group_smiles_weight_list):\n",
        "                print(\n",
        "                    \"Error: End Group SMILES and weights are not matching.\"\n",
        "                    + \" Please re-enter the correct End Group SMILES and weights.\\n\"\n",
        "                )\n",
        "                return\n",
        "\n",
        "            else:\n",
        "                if (\n",
        "                    len(end_group_smiles_list) == 0\n",
        "                    and len(end_group_smiles_weight_list) == 0\n",
        "                ):\n",
        "                    print(\n",
        "                        \"Notice: The Polymer has NO End Group.\\n\"\n",
        "                        + \"If this is what you want, this message is NOT Error.\\n\"\n",
        "                        + \"Otherwise, please enter the correct End Group SMILES and weights.\\n\"\n",
        "                    )\n",
        "\n",
        "                self.end_group_smiles_list = end_group_smiles_list\n",
        "                self.end_group_smiles_weight_list = end_group_smiles_weight_list\n",
        "\n",
        "        if graph_representation == None:\n",
        "            print(\n",
        "                \"Error: Topology graph representation is missing.\"\n",
        "                + \" Please define the topology graph representation.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            self.graph_representation = graph_representation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vE5C5Cf0zSbF"
      },
      "source": [
        "# Case 5: Segmented Polymers\n",
        "\n",
        "Case 5 demonstrates similarity scoring for segmented polymers. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93332dHdzs9Y"
      },
      "source": [
        "# Define Polymers in Case 5\n",
        "\n",
        "## Topological graph representations\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1blbtPi4JYAUDnptClA2AoYs06EOVPxP2\" weight = \"30%\" height = \"30%\" title=\"Graph Representation\">\n",
        "\n",
        "Since all four segmented polymers in Case 5 share the same topological graph representation, we only need to define one segmented polymer topological graph representation. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "NLUBb56IC44q",
        "outputId": "0d390498-8a0f-4664-9fa2-e03db1442b29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_C5_top_graph_representation number of nodes:  12\n",
            "Polymer_C5_top_graph_representation number of edges:  18\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_C5_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_C5_top_graph_representation.add_nodes_from(\n",
        "    [\n",
        "        \"D1\",\n",
        "        \"D2\",\n",
        "        \"D3\",\n",
        "        \"D3'\",\n",
        "        \"R1\",\n",
        "        \"R1'\",\n",
        "        \"R2\",\n",
        "        \"R2'\",\n",
        "        \"R3\",\n",
        "        \"R3'\",\n",
        "        \"R4\",\n",
        "        \"R4'\",\n",
        "    ]\n",
        ")\n",
        "Polymer_C5_top_graph_representation.add_edges_from(\n",
        "    [\n",
        "        (\"D1\", \"R1\"),\n",
        "        (\"D1\", \"R1'\"),\n",
        "        (\"R1\", \"D2\"),\n",
        "        (\"R1'\", \"D2\"),\n",
        "        (\"D2\", \"R2\"),\n",
        "        (\"D2\", \"R2'\"),\n",
        "        (\"R2\", \"D1\"),\n",
        "        (\"R2'\", \"D1\"),\n",
        "        (\"D2\", \"R4\"),\n",
        "        (\"R4\", \"D3\"),\n",
        "        (\"D3\", \"D1\"),\n",
        "        (\"D3\", \"R3\"),\n",
        "        (\"R3\", \"D3\"),\n",
        "        (\"D2\", \"D3'\"),\n",
        "        (\"D3'\", \"R4'\"),\n",
        "        (\"R4'\", \"D1\"),\n",
        "        (\"D3'\", \"R3'\"),\n",
        "        (\"R3'\", \"D3'\"),\n",
        "    ]\n",
        ")\n",
        "nx.draw(Polymer_C5_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_C5_top_graph_representation number of nodes: \",\n",
        "    Polymer_C5_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_C5_top_graph_representation number of edges: \",\n",
        "    Polymer_C5_top_graph_representation.number_of_edges(),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIHJx_8wpS33"
      },
      "source": [
        "## Polymer C5-1\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1WCapjykJfA3TWwOpn4nO1zxwXKYydGHD\"  width=\"50%\" height=\"50%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gg0HbRJzsNw",
        "outputId": "f9d86dec-ac54-44f3-b165-8c37d6cf7632"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "Polymer_C5_1 = Polymer(\n",
        "    repeat_unit_smiles_list=[\n",
        "        \"*OCCCC(C)O*\",\n",
        "        \"*C(=O)Nc1ccc(Cc2ccc(NC(*)=O)c(C)c2)cc1\",\n",
        "        \"*CCO*\",\n",
        "        \"*O*\",\n",
        "    ],\n",
        "    repeat_unit_smiles_weight_list=[1, 2, 4, 1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_C5_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkO_5dOtpY_E"
      },
      "source": [
        "## Polymer C5-2\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1EJGuVZ1Inu10cR6E31z51syHiIKP_F3q\"  width=\"50%\" height=\"50%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUemADZ3SN0d",
        "outputId": "9439555f-820e-426a-bf3f-01c95b345fb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "Polymer_C5_2 = Polymer(\n",
        "    repeat_unit_smiles_list=[\n",
        "        \"*OCCCC(C)O*\",\n",
        "        \"*C(=O)NC1CCC(CC2CCC(NC(*)=O)C(C)C2)CC1\",\n",
        "        \"*CCO*\",\n",
        "        \"*O*\",\n",
        "    ],\n",
        "    repeat_unit_smiles_weight_list=[1, 2, 4, 1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_C5_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5-QuC93pa17"
      },
      "source": [
        "## Polymer C5-3\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1TLJtXcRJgcxNWxJKB9vZQcL5L4GOa_Or\"  width=\"50%\" height=\"50%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8l7h3jW95f4B",
        "outputId": "6f752f10-1030-47a5-cae6-1f8b78d15449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "Polymer_C5_3 = Polymer(\n",
        "    repeat_unit_smiles_list=[\n",
        "        \"*OCCCC(C)O*\",\n",
        "        \"*C(=O)Nc1ccc(Cc2ccc(NC(*)=O)c(C)c2)cc1\",\n",
        "        \"*CCCCCC(=O)O*\",\n",
        "        \"*O*\",\n",
        "    ],\n",
        "    repeat_unit_smiles_weight_list=[1, 2, 4, 1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_C5_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXenNt9Mpctm"
      },
      "source": [
        "## Polymer C5-4\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=14-1yY8WGyuGEX5Z9x5Z3muFvQpT4domG\"  width=\"50%\" height=\"50%\" >"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ys6CZFuDUtdl",
        "outputId": "635329a0-102c-4a78-fcd5-ae3f4df2fa7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "Polymer_C5_4 = Polymer(\n",
        "    repeat_unit_smiles_list=[\n",
        "        \"*NCCCCCC(C)N*\",\n",
        "        \"*C(=O)Nc1ccc(Cc2ccc(NC(*)=O)c(C)c2)cc1\",\n",
        "        \"*CCO*\",\n",
        "        \"*O*\",\n",
        "    ],\n",
        "    repeat_unit_smiles_weight_list=[1, 2, 4, 1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_C5_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbS9tSrxzq8Q"
      },
      "source": [
        "# Calculate Similarity Scores\n",
        "## Calculate the Pairwise Similarity Score between C5-1 and C5-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8p8pHgcmPcg7",
        "outputId": "d44b6daa-3cbf-45bc-999e-23582336de97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.7901785714285714 , Normalized Weight for Repeat Unit =  0.475\n",
            "Topology Similarity Score =  1.0 , Normalized Weight for Topology =  0.475\n",
            "End Group Similarity  =  1.0 , Normalized Weight for End Group =  0.05\n",
            "Overall Pairwise Similarity Score with geometric mean function =  0.8941687603485811\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8941687603485811"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C5_1,\n",
        "    target=Polymer_C5_2,\n",
        "    embedding_function=\"MorganFingerprint\",\n",
        "    similarity_score_function=\"Tanimoto\",\n",
        "    alpha=1,\n",
        "    Repeat_Unit_Weight=0.475,\n",
        "    Topology_Weight=0.475,\n",
        "    End_Group_Weight=0.05,\n",
        "    Mean_Function=\"geometric\",\n",
        "    details_print=True,\n",
        "    return_with_components=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6Y5cyomz8pi"
      },
      "source": [
        "## Calculate the Pairwise Similarity Score between C5-1 and C5-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UQmmuAt5e2H",
        "outputId": "759dc61a-46bb-4f4e-bc4f-509b6cf367e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.62 , Normalized Weight for Repeat Unit =  0.475\n",
            "Topology Similarity Score =  1.0 , Normalized Weight for Topology =  0.475\n",
            "End Group Similarity  =  1.0 , Normalized Weight for End Group =  0.05\n",
            "Overall Pairwise Similarity Score with geometric mean function =  0.7968673860451013\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7968673860451013"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C5_1,\n",
        "    target=Polymer_C5_3,\n",
        "    embedding_function=\"MorganFingerprint\",\n",
        "    similarity_score_function=\"Tanimoto\",\n",
        "    alpha=1,\n",
        "    Repeat_Unit_Weight=0.475,\n",
        "    Topology_Weight=0.475,\n",
        "    End_Group_Weight=0.05,\n",
        "    Mean_Function=\"geometric\",\n",
        "    details_print=True,\n",
        "    return_with_components=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mf_Fp_gV0CUS"
      },
      "source": [
        "## Calculate the Pairwise Similarity Score between C5-1 and C5-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRB8mUWEdb_u",
        "outputId": "3e14ad57-74b0-4633-ae0f-298578c3de5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.90625 , Normalized Weight for Repeat Unit =  0.475\n",
            "Topology Similarity Score =  1.0 , Normalized Weight for Topology =  0.475\n",
            "End Group Similarity  =  1.0 , Normalized Weight for End Group =  0.05\n",
            "Overall Pairwise Similarity Score with geometric mean function =  0.9543173273562903\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9543173273562903"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C5_1,\n",
        "    target=Polymer_C5_4,\n",
        "    embedding_function=\"MorganFingerprint\",\n",
        "    similarity_score_function=\"Tanimoto\",\n",
        "    alpha=1,\n",
        "    Repeat_Unit_Weight=0.475,\n",
        "    Topology_Weight=0.475,\n",
        "    End_Group_Weight=0.05,\n",
        "    Mean_Function=\"geometric\",\n",
        "    details_print=True,\n",
        "    return_with_components=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0CvXvv8Bx2yX"
      },
      "source": [
        "## Calculate the Whole Similarity Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "riJpZumorBcY"
      },
      "outputs": [],
      "source": [
        "Polymer_C5 = [Polymer_C5_1, Polymer_C5_2, Polymer_C5_3, Polymer_C5_4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "hZ6mW-U3rsN6"
      },
      "outputs": [],
      "source": [
        "Polymer_C5_RU_matrix = np.ones([4, 4])\n",
        "Polymer_C5_TOP_matrix = np.ones([4, 4])\n",
        "Polymer_C5_EG_matrix = np.ones([4, 4])\n",
        "Polymer_C5_OA_matrix = np.ones([4, 4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "mkUp5pS1r1_s"
      },
      "outputs": [],
      "source": [
        "for i in range(0, 3):\n",
        "    for j in range(i + 1, 4):\n",
        "        SOAij, SRUij, STOPij, SEGij = Overall_Similarity_Score_Calculation(\n",
        "            query=Polymer_C5[i],\n",
        "            target=Polymer_C5[j],\n",
        "            embedding_function=\"MorganFingerprint\",\n",
        "            similarity_score_function=\"Tanimoto\",\n",
        "            alpha=1,\n",
        "            Repeat_Unit_Weight=0.475,\n",
        "            Topology_Weight=0.475,\n",
        "            End_Group_Weight=0.05,\n",
        "            Mean_Function=\"geometric\",\n",
        "            details_print=False,\n",
        "            return_with_components=True,\n",
        "        )\n",
        "\n",
        "        Polymer_C5_OA_matrix[i, j] = SOAij\n",
        "        Polymer_C5_OA_matrix[j, i] = SOAij\n",
        "\n",
        "        Polymer_C5_RU_matrix[i, j] = SRUij\n",
        "        Polymer_C5_RU_matrix[j, i] = SRUij\n",
        "\n",
        "        Polymer_C5_TOP_matrix[i, j] = STOPij\n",
        "        Polymer_C5_TOP_matrix[j, i] = STOPij\n",
        "\n",
        "        Polymer_C5_EG_matrix[i, j] = SEGij\n",
        "        Polymer_C5_EG_matrix[j, i] = SEGij"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXpuWDkUx6jc"
      },
      "source": [
        "# Visualize the Similarity Matrices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "oqnlmFD1tTn4"
      },
      "outputs": [],
      "source": [
        "# libraries for visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wY3wGH6gyALM"
      },
      "source": [
        "##  Repeat Unit Similarity,   $S_{\\rm RU}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "GZcQ4V1kubb_",
        "outputId": "9918ca05-8e6e-4935-ec28-99673ff9b06e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 6), sharey=True)\n",
        "\n",
        "x_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "y_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C5_RU_matrix,\n",
        "    linewidth=0.3,\n",
        "    annot=True,\n",
        "    fmt=\".2f\",\n",
        "    cmap=\"Blues\",\n",
        "    cbar_kws={\"label\": \"Similarity Score\"},\n",
        ")\n",
        "ax.set_title(r\"$S_{\\rm RU}$\", fontsize=20)\n",
        "ax.set_xticklabels(x_labels, rotation=0, fontsize=15, horizontalalignment=\"center\")\n",
        "ax.set_yticklabels(y_labels, rotation=360, fontsize=15, horizontalalignment=\"right\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9w3qcrcyI70"
      },
      "source": [
        "## Topology Similarity,     $S_{\\rm TOP}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "ThI0SatduqXc",
        "outputId": "24577df6-6712-4883-a58a-af559f0b1d17"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 6), sharey=True)\n",
        "\n",
        "x_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "y_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C5_TOP_matrix,\n",
        "    linewidth=0.3,\n",
        "    annot=True,\n",
        "    fmt=\".2f\",\n",
        "    cmap=\"Blues\",\n",
        "    cbar_kws={\"label\": \"Similarity Score\"},\n",
        ")\n",
        "ax.set_title(r\"$S_{\\rm TOP}$\", fontsize=20)\n",
        "ax.set_xticklabels(x_labels, rotation=0, fontsize=15, horizontalalignment=\"center\")\n",
        "ax.set_yticklabels(y_labels, rotation=360, fontsize=15, horizontalalignment=\"right\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwMscKpDyLED"
      },
      "source": [
        "## End Group Similarity,     $S_{\\rm EG}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "QyWxtuHhxq4q",
        "outputId": "5d5a67e9-5ab9-466a-9fc0-d10a90ed79e7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 6), sharey=True)\n",
        "\n",
        "x_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "y_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C5_EG_matrix,\n",
        "    linewidth=0.3,\n",
        "    annot=True,\n",
        "    fmt=\".2f\",\n",
        "    cmap=\"Blues\",\n",
        "    cbar_kws={\"label\": \"Similarity Score\"},\n",
        ")\n",
        "ax.set_title(r\"$S_{\\rm EG}$\", fontsize=20)\n",
        "ax.set_xticklabels(x_labels, rotation=0, fontsize=15, horizontalalignment=\"center\")\n",
        "ax.set_yticklabels(y_labels, rotation=360, fontsize=15, horizontalalignment=\"right\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pe1ealHoyMrq"
      },
      "source": [
        "## Overall Similarity,     $S_{\\rm OA}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "JbAgnupdxvOE",
        "outputId": "fde81569-1b39-4720-a34d-9c21e826ecf7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 6), sharey=True)\n",
        "\n",
        "x_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "y_labels = [\"C5-1\", \"C5-2\", \"C5-3\", \"C5-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C5_OA_matrix,\n",
        "    linewidth=0.3,\n",
        "    annot=True,\n",
        "    fmt=\".2f\",\n",
        "    cmap=\"Blues\",\n",
        "    cbar_kws={\"label\": \"Similarity Score\"},\n",
        ")\n",
        "ax.set_title(r\"$S_{\\rm OA}$\", fontsize=20)\n",
        "ax.set_xticklabels(x_labels, rotation=0, fontsize=15, horizontalalignment=\"center\")\n",
        "ax.set_yticklabels(y_labels, rotation=360, fontsize=15, horizontalalignment=\"right\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "ifMz2-cyojfb"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
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      "codemirror_mode": {
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "pygments_lexer": "ipython3",
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