notebook/Polymer_Similarity_Case2_Varying_Topologies.ipynb

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      "source": [
        "\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/olsenlabmit/Polymer-Graph-Similarity/blob/main/notebook/Polymer_Similarity_Case2_Varying_Topologies.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": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:38.033150Z",
          "start_time": "2023-04-11T18:28:38.030383Z"
        },
        "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 2: Varying Topologies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA0PTW_O3VxE"
      },
      "source": [
        "This notebook is to perform the calculations and reproduce the results of **Case 2** 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": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.197174Z",
          "start_time": "2023-04-11T18:28:38.035850Z"
        },
        "id": "psnOpmYlL_sw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1885d776-17fe-4c0d-daa9-991dac5a7878"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 MB\u001b[0m \u001b[31m26.7 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": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.216747Z",
          "start_time": "2023-04-11T18:28:41.201472Z"
        },
        "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": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.220779Z",
          "start_time": "2023-04-11T18:28:41.218100Z"
        },
        "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": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.228885Z",
          "start_time": "2023-04-11T18:28:41.222240Z"
        },
        "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": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.235340Z",
          "start_time": "2023-04-11T18:28:41.230062Z"
        },
        "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 2: Varying Topologies\n",
        "\n",
        "Case 2 computes the pairwise similarity score of polymers that have the same repeat units but different topological graph representations using examples of reversible addition-fragmentation chain transfer (RAFT) polystyrenes (C2-1: one-arm, C2-2: two-arm, C2-3: three-arm, C2-4: four-arm)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93332dHdzs9Y"
      },
      "source": [
        "# Define Polymers in Case 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIHJx_8wpS33"
      },
      "source": [
        "## Polymer C2-1\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1AyMbyIjfy2hkoL73R_5PpUYq3C8JLISd\"  width=\"50%\" height=\"50%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.338865Z",
          "start_time": "2023-04-11T18:28:41.238295Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "4gg0HbRJzsNw",
        "outputId": "c67ae1ba-62f3-4da1-da33-b8cfdf5e55e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_C2_1_top_graph_representation number of nodes:  5\n",
            "Polymer_C2_1_top_graph_representation number of edges:  6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_C2_1_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_C2_1_top_graph_representation.add_nodes_from(\n",
        "    [\"E1-arm1\", \"D1\", \"R1-arm1\", \"R1'-arm1\", \"E2\"]\n",
        ")\n",
        "Polymer_C2_1_top_graph_representation.add_edges_from(\n",
        "    [\n",
        "        (\"E1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1-arm1\"),\n",
        "        (\"R1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1'-arm1\"),\n",
        "        (\"R1'-arm1\", \"D1\"),\n",
        "        (\"D1\", \"E2\"),\n",
        "    ]\n",
        ")\n",
        "nx.draw(Polymer_C2_1_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_C2_1_top_graph_representation number of nodes: \",\n",
        "    Polymer_C2_1_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_C2_1_top_graph_representation number of edges: \",\n",
        "    Polymer_C2_1_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_C2_1 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*CC(*)c1ccccc1\"],\n",
        "    repeat_unit_smiles_weight_list=[1],\n",
        "    end_group_smiles_list=[\"*SC(=S)SCCCC\", \"*C(C)C(=O)OC\"],\n",
        "    end_group_smiles_weight_list=[1, 1],\n",
        "    graph_representation=Polymer_C2_1_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkO_5dOtpY_E"
      },
      "source": [
        "## Polymer C2-2\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1LaxtzW8LN6hIr68CojaBob3QcrA7HhpW\"  width=\"50%\" height=\"50%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.435307Z",
          "start_time": "2023-04-11T18:28:41.340102Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "FUemADZ3SN0d",
        "outputId": "9e379045-8db8-4b8c-9df4-e8dc6e475390"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_C2_2_top_graph_representation number of nodes:  9\n",
            "Polymer_C2_2_top_graph_representation number of edges:  12\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_C2_2_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_C2_2_top_graph_representation.add_nodes_from(\n",
        "    [\n",
        "        \"E1-arm1\",\n",
        "        \"D1\",\n",
        "        \"R1-arm1\",\n",
        "        \"R1'-arm1\",\n",
        "        \"E2\",\n",
        "        \"D2\",\n",
        "        \"R1-arm2\",\n",
        "        \"R1'-arm2\",\n",
        "        \"E1-arm2\",\n",
        "    ]\n",
        ")\n",
        "Polymer_C2_2_top_graph_representation.add_edges_from(\n",
        "    [\n",
        "        (\"E1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1-arm1\"),\n",
        "        (\"R1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1'-arm1\"),\n",
        "        (\"R1'-arm1\", \"D1\"),\n",
        "        (\"D1\", \"E2\"),\n",
        "        (\"E2\", \"D2\"),\n",
        "        (\"D2\", \"R1-arm2\"),\n",
        "        (\"R1-arm2\", \"D2\"),\n",
        "        (\"D2\", \"R1'-arm2\"),\n",
        "        (\"R1'-arm2\", \"D2\"),\n",
        "        (\"D2\", \"E1-arm2\"),\n",
        "    ]\n",
        ")\n",
        "nx.draw(Polymer_C2_2_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_C2_2_top_graph_representation number of nodes: \",\n",
        "    Polymer_C2_2_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_C2_2_top_graph_representation number of edges: \",\n",
        "    Polymer_C2_2_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_C2_2 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*CC(*)c1ccccc1\", \"*CC(*)c1ccccc1\"],\n",
        "    repeat_unit_smiles_weight_list=[1, 1],\n",
        "    end_group_smiles_list=[\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*C(C)C(=O)OCC(C)(C)COC(=O)C(*)C\",\n",
        "    ],\n",
        "    end_group_smiles_weight_list=[1, 1, 1],\n",
        "    graph_representation=Polymer_C2_2_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5-QuC93pa17"
      },
      "source": [
        "## Polymer C2-3\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1B4Q6MPBdB33MJ8DHwj9wMcpgCZdz6XMy\"  width=\"50%\" height=\"50%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.552680Z",
          "start_time": "2023-04-11T18:28:41.436794Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "8l7h3jW95f4B",
        "outputId": "08d0bbd8-4ffa-4262-907e-c29ac3f13d00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_C2_3_top_graph_representation number of nodes:  13\n",
            "Polymer_C2_3_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_C2_3_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_C2_3_top_graph_representation.add_nodes_from(\n",
        "    [\n",
        "        \"E1-arm1\",\n",
        "        \"D1\",\n",
        "        \"R1-arm1\",\n",
        "        \"R1'-arm1\",\n",
        "        \"E2\",\n",
        "        \"D2\",\n",
        "        \"R1-arm2\",\n",
        "        \"R1'-arm2\",\n",
        "        \"E1-arm2\",\n",
        "        \"D3\",\n",
        "        \"R1-arm3\",\n",
        "        \"R1'-arm3\",\n",
        "        \"E1-arm3\",\n",
        "    ]\n",
        ")\n",
        "Polymer_C2_3_top_graph_representation.add_edges_from(\n",
        "    [\n",
        "        (\"E1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1-arm1\"),\n",
        "        (\"R1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1'-arm1\"),\n",
        "        (\"R1'-arm1\", \"D1\"),\n",
        "        (\"D1\", \"E2\"),\n",
        "        (\"E2\", \"D2\"),\n",
        "        (\"D2\", \"R1-arm2\"),\n",
        "        (\"R1-arm2\", \"D2\"),\n",
        "        (\"D2\", \"R1'-arm2\"),\n",
        "        (\"R1'-arm2\", \"D2\"),\n",
        "        (\"D2\", \"E1-arm2\"),\n",
        "        (\"E2\", \"D3\"),\n",
        "        (\"D3\", \"R1-arm3\"),\n",
        "        (\"R1-arm3\", \"D3\"),\n",
        "        (\"D3\", \"R1'-arm3\"),\n",
        "        (\"R1'-arm3\", \"D3\"),\n",
        "        (\"D3\", \"E1-arm3\"),\n",
        "    ]\n",
        ")\n",
        "nx.draw(Polymer_C2_3_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_C2_3_top_graph_representation number of nodes: \",\n",
        "    Polymer_C2_3_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_C2_3_top_graph_representation number of edges: \",\n",
        "    Polymer_C2_3_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_C2_3 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*CC(*)c1ccccc1\", \"*CC(*)c1ccccc1\", \"*CC(*)c1ccccc1\"],\n",
        "    repeat_unit_smiles_weight_list=[1, 1, 1],\n",
        "    end_group_smiles_list=[\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*C(C)C(=O)OCC(C)(COC(=O)C(*)C)COC(=O)C(*)C\",\n",
        "    ],\n",
        "    end_group_smiles_weight_list=[1, 1, 1, 1],\n",
        "    graph_representation=Polymer_C2_3_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXenNt9Mpctm"
      },
      "source": [
        "## Polymer C2-4\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1KWlXFdkweVGMgddwZRVDENrrwNxhqew8\"  width=\"50%\" height=\"50%\" >"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:41.725299Z",
          "start_time": "2023-04-11T18:28:41.553910Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "Ys6CZFuDUtdl",
        "outputId": "d5b65a77-b132-425c-c134-1375e8687452"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_C2_4_top_graph_representation number of nodes:  17\n",
            "Polymer_C2_4_top_graph_representation number of edges:  24\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_C2_4_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_C2_4_top_graph_representation.add_nodes_from(\n",
        "    [\n",
        "        \"E1-arm1\",\n",
        "        \"D1\",\n",
        "        \"R1-arm1\",\n",
        "        \"R1'-arm1\",\n",
        "        \"E2\",\n",
        "        \"D2\",\n",
        "        \"R1-arm2\",\n",
        "        \"R1'-arm2\",\n",
        "        \"E1-arm2\",\n",
        "        \"D3\",\n",
        "        \"R1-arm3\",\n",
        "        \"R1'-arm3\",\n",
        "        \"E1-arm3\",\n",
        "        \"D4\",\n",
        "        \"R1-arm4\",\n",
        "        \"R1'-arm4\",\n",
        "        \"E1-arm4\",\n",
        "    ]\n",
        ")\n",
        "Polymer_C2_4_top_graph_representation.add_edges_from(\n",
        "    [\n",
        "        (\"E1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1-arm1\"),\n",
        "        (\"R1-arm1\", \"D1\"),\n",
        "        (\"D1\", \"R1'-arm1\"),\n",
        "        (\"R1'-arm1\", \"D1\"),\n",
        "        (\"D1\", \"E2\"),\n",
        "        (\"E2\", \"D2\"),\n",
        "        (\"D2\", \"R1-arm2\"),\n",
        "        (\"R1-arm2\", \"D2\"),\n",
        "        (\"D2\", \"R1'-arm2\"),\n",
        "        (\"R1'-arm2\", \"D2\"),\n",
        "        (\"D2\", \"E1-arm2\"),\n",
        "        (\"E2\", \"D3\"),\n",
        "        (\"D3\", \"R1-arm3\"),\n",
        "        (\"R1-arm3\", \"D3\"),\n",
        "        (\"D3\", \"R1'-arm3\"),\n",
        "        (\"R1'-arm3\", \"D3\"),\n",
        "        (\"D3\", \"E1-arm3\"),\n",
        "        (\"E2\", \"D4\"),\n",
        "        (\"D4\", \"R1-arm4\"),\n",
        "        (\"R1-arm4\", \"D4\"),\n",
        "        (\"D4\", \"R1'-arm4\"),\n",
        "        (\"R1'-arm4\", \"D4\"),\n",
        "        (\"D4\", \"E1-arm4\"),\n",
        "    ]\n",
        ")\n",
        "nx.draw(Polymer_C2_4_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_C2_4_top_graph_representation number of nodes: \",\n",
        "    Polymer_C2_4_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_C2_4_top_graph_representation number of edges: \",\n",
        "    Polymer_C2_4_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_C2_4 = Polymer(\n",
        "    repeat_unit_smiles_list=[\n",
        "        \"*CC(*)c1ccccc1\",\n",
        "        \"*CC(*)c1ccccc1\",\n",
        "        \"*CC(*)c1ccccc1\",\n",
        "        \"*CC(*)c1ccccc1\",\n",
        "    ],\n",
        "    repeat_unit_smiles_weight_list=[1, 1, 1, 1],\n",
        "    end_group_smiles_list=[\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*SC(=S)SCCCC\",\n",
        "        \"*C(C)C(=O)OCC(COC(=O)C(*)C)(COC(=O)C(*)C)COC(=O)C(*)C\",\n",
        "    ],\n",
        "    end_group_smiles_weight_list=[1, 1, 1, 1, 1],\n",
        "    graph_representation=Polymer_C2_4_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbS9tSrxzq8Q"
      },
      "source": [
        "# Calculate Similarity Scores\n",
        "## Calculate the Pairwise Similarity Score between C2-1 and C2-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:42.324489Z",
          "start_time": "2023-04-11T18:28:41.726397Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8p8pHgcmPcg7",
        "outputId": "6333ea7f-1afe-434a-ab1b-91edf2a4a2b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  1.0 , Normalized Weight for Repeat Unit =  0.475\n",
            "Topology Similarity Score =  0.2396510364417758 , Normalized Weight for Topology =  0.475\n",
            "End Group Similarity  =  0.6747058810470589 , Normalized Weight for End Group =  0.05\n",
            "Overall Pairwise Similarity Score with geometric mean function =  0.4974574024258719\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4974574024258719"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C2_1,\n",
        "    target=Polymer_C2_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 C2-1 and C2-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:42.400962Z",
          "start_time": "2023-04-11T18:28:42.326113Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UQmmuAt5e2H",
        "outputId": "dfe179c5-0ef7-4a08-e64f-91f58143fad8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  1.0 , Normalized Weight for Repeat Unit =  0.475\n",
            "Topology Similarity Score =  0.10836802322189586 , Normalized Weight for Topology =  0.475\n",
            "End Group Similarity  =  0.6420588235294118 , Normalized Weight for End Group =  0.05\n",
            "Overall Pairwise Similarity Score with geometric mean function =  0.3403743208606317\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3403743208606317"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C2_1,\n",
        "    target=Polymer_C2_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 C2-1 and C2-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:42.477181Z",
          "start_time": "2023-04-11T18:28:42.402713Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRB8mUWEdb_u",
        "outputId": "37623cb0-0b6b-4ff2-acf7-2b0a653e0236"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  1.0 , Normalized Weight for Repeat Unit =  0.475\n",
            "Topology Similarity Score =  0.06539740322986021 , Normalized Weight for Topology =  0.475\n",
            "End Group Similarity  =  0.6264705882352941 , Normalized Weight for End Group =  0.05\n",
            "Overall Pairwise Similarity Score with geometric mean function =  0.2674461027350389\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2674461027350389"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C2_1,\n",
        "    target=Polymer_C2_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": 14,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:42.480993Z",
          "start_time": "2023-04-11T18:28:42.478873Z"
        },
        "id": "riJpZumorBcY"
      },
      "outputs": [],
      "source": [
        "Polymer_C2 = [Polymer_C2_1, Polymer_C2_2, Polymer_C2_3, Polymer_C2_4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:42.485061Z",
          "start_time": "2023-04-11T18:28:42.482590Z"
        },
        "id": "hZ6mW-U3rsN6"
      },
      "outputs": [],
      "source": [
        "Polymer_C2_RU_matrix = np.ones([4, 4])\n",
        "Polymer_C2_TOP_matrix = np.ones([4, 4])\n",
        "Polymer_C2_EG_matrix = np.ones([4, 4])\n",
        "Polymer_C2_OA_matrix = np.ones([4, 4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:42.885757Z",
          "start_time": "2023-04-11T18:28:42.486231Z"
        },
        "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_C2[i],\n",
        "            target=Polymer_C2[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_C2_OA_matrix[i, j] = SOAij\n",
        "        Polymer_C2_OA_matrix[j, i] = SOAij\n",
        "\n",
        "        Polymer_C2_RU_matrix[i, j] = SRUij\n",
        "        Polymer_C2_RU_matrix[j, i] = SRUij\n",
        "\n",
        "        Polymer_C2_TOP_matrix[i, j] = STOPij\n",
        "        Polymer_C2_TOP_matrix[j, i] = STOPij\n",
        "\n",
        "        Polymer_C2_EG_matrix[i, j] = SEGij\n",
        "        Polymer_C2_EG_matrix[j, i] = SEGij"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXpuWDkUx6jc"
      },
      "source": [
        "# Visualize the Similarity Matrices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:43.453977Z",
          "start_time": "2023-04-11T18:28:42.887426Z"
        },
        "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": 18,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:43.679982Z",
          "start_time": "2023-04-11T18:28:43.455283Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "GZcQ4V1kubb_",
        "outputId": "e81de4c6-0572-4341-8932-620562ca81ac"
      },
      "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 = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "y_labels = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C2_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": 19,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:43.783583Z",
          "start_time": "2023-04-11T18:28:43.683268Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "ThI0SatduqXc",
        "outputId": "3f766016-a3d4-4c40-8b42-16237d7214ef"
      },
      "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 = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "y_labels = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C2_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": 20,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:43.892476Z",
          "start_time": "2023-04-11T18:28:43.784775Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "QyWxtuHhxq4q",
        "outputId": "13e2bdaf-41b1-409c-a56f-76160f385bc0"
      },
      "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 = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "y_labels = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C2_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": 21,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-04-11T18:28:44.050163Z",
          "start_time": "2023-04-11T18:28:43.893676Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "JbAgnupdxvOE",
        "outputId": "765d5b87-e815-4466-f77f-09710da13d4e"
      },
      "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 = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "y_labels = [\"C2-1\", \"C2-2\", \"C2-3\", \"C2-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C2_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": 21,
      "metadata": {
        "id": "i8u6EwBlxydt"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "varInspector": {
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "delete_cmd_postfix": "",
          "delete_cmd_prefix": "del ",
          "library": "var_list.py",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "delete_cmd_postfix": ") ",
          "delete_cmd_prefix": "rm(",
          "library": "var_list.r",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
        "module",
        "function",
        "builtin_function_or_method",
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