notebook/Polymer_Similarity_Case1_Varying_Repeat_Units.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_Case1_Varying_Repeat_Units.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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 1: Varying Repeat Units"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA0PTW_O3VxE"
      },
      "source": [
        "This notebook is to perform the calculations and reproduce the results of **Case 1** 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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psnOpmYlL_sw",
        "outputId": "8efc5258-d5d8-40a4-c224-db6e62951a1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 MB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.0/11.0 MB\u001b[0m \u001b[31m38.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\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",
            "Unpacking coinor-libcoinutils3v5 (2.11.4+repack1-1) ...\n",
            "Selecting previously unselected package coinor-libosi1v5.\n",
            "Preparing to unpack .../1-coinor-libosi1v5_0.108.6+repack1-1_amd64.deb ...\n",
            "Unpacking coinor-libosi1v5 (0.108.6+repack1-1) ...\n",
            "Selecting previously unselected package coinor-libclp1.\n",
            "Preparing to unpack .../2-coinor-libclp1_1.17.5+repack1-1_amd64.deb ...\n",
            "Unpacking coinor-libclp1 (1.17.5+repack1-1) ...\n",
            "Selecting previously unselected package coinor-libcgl1.\n",
            "Preparing to unpack .../3-coinor-libcgl1_0.60.3+repack1-2_amd64.deb ...\n",
            "Unpacking coinor-libcgl1 (0.60.3+repack1-2) ...\n",
            "Selecting previously unselected package coinor-libcbc3.\n",
            "Preparing to unpack .../4-coinor-libcbc3_2.10.3+repack1-1build1_amd64.deb ...\n",
            "Unpacking coinor-libcbc3 (2.10.3+repack1-1build1) ...\n",
            "Selecting previously unselected package coinor-cbc.\n",
            "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": null,
      "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": null,
      "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": null,
      "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": null,
      "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 1: Varying Repeat Units\n",
        "\n",
        "Case 1 illustrates the computation of the pairwise similarity score of polymers with the same topological graph representation and end groups but different repeat units, as shown below.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1o-IJXfXqR8ns5x18NT7qeCyvRZAnxFVe\"  width=\"70%\" title=\"Case1\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1grgnaPRIqt"
      },
      "source": [
        "## Topological graph representations\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1eR35MTotX5KlCTFi8c_ZPbV0Vrwt7LoN\" weight = \"30%\" height = \"30%\" title=\"Graph Representation\">\n",
        "\n",
        "Since all four diblock copolymers in Case 1 share the same topological graph representation, we only need to define one diblock topological graph representation. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "tCJkDjZDfpf9",
        "outputId": "21ce8979-daf8-4f88-e7b8-3a0c00081bf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "diblock_top_graph_representation number of nodes:  8\n",
            "diblock_top_graph_representation number of edges:  11\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "diblock_top_graph_representation = nx.MultiDiGraph()\n",
        "diblock_top_graph_representation.add_nodes_from(\n",
        "    [\"E1\", \"D1\", \"D2\", \"E2\", \"R1\", \"R1'\", \"R2\", \"R2'\"]\n",
        ")\n",
        "diblock_top_graph_representation.add_edges_from(\n",
        "    [\n",
        "        (\"E1\", \"D1\"),\n",
        "        (\"D1\", \"R1\"),\n",
        "        (\"R1\", \"D1\"),\n",
        "        (\"D1\", \"R1'\"),\n",
        "        (\"R1'\", \"D1\"),\n",
        "        (\"D1\", \"D2\"),\n",
        "        (\"D2\", \"R2\"),\n",
        "        (\"R2\", \"D2\"),\n",
        "        (\"D2\", \"R2'\"),\n",
        "        (\"R2'\", \"D2\"),\n",
        "        (\"D2\", \"E2\"),\n",
        "    ]\n",
        ")\n",
        "nx.draw(diblock_top_graph_representation)\n",
        "print(\n",
        "    \"diblock_top_graph_representation number of nodes: \",\n",
        "    diblock_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"diblock_top_graph_representation number of edges: \",\n",
        "    diblock_top_graph_representation.number_of_edges(),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93332dHdzs9Y"
      },
      "source": [
        "# Define Polymers in Case 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIHJx_8wpS33"
      },
      "source": [
        "## Polymer C1-1\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1IZ88Fve5IQmTQ8CfA_cV-JadONiAcxMh\"  width=\"30%\" height=\"30%\" title=\"Case1-1\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4gg0HbRJzsNw"
      },
      "outputs": [],
      "source": [
        "Polymer_C1_1 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*CC(*)C\", \"*CC(*)c1ccccc1\"],\n",
        "    repeat_unit_smiles_weight_list=[1, 1],\n",
        "    end_group_smiles_list=[\"*C(C)(C)C#N\", \"*C(C)(C)C#N\"],\n",
        "    end_group_smiles_weight_list=[1, 1],\n",
        "    graph_representation=diblock_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkO_5dOtpY_E"
      },
      "source": [
        "## Polymer C1-2\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1nzreuF3ehD85KEv3xejKDwGBofqAT0ox\"  width=\"30%\" height=\"30%\" title=\"Case1-2\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FUemADZ3SN0d"
      },
      "outputs": [],
      "source": [
        "Polymer_C1_2 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*CC(*)(C)C(=O)OC\", \"*CC(*)c1ccccc1\"],\n",
        "    repeat_unit_smiles_weight_list=[1, 1],\n",
        "    end_group_smiles_list=[\"*C(C)(C)C#N\", \"*C(C)(C)C#N\"],\n",
        "    end_group_smiles_weight_list=[1, 1],\n",
        "    graph_representation=diblock_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5-QuC93pa17"
      },
      "source": [
        "## Polymer C1-3\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=12lRHhFphPDR3vJ3hu66WAdkEO-r7XEMV\"  width=\"30%\" height=\"30%\" title=\"Case1-3\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8l7h3jW95f4B"
      },
      "outputs": [],
      "source": [
        "Polymer_C1_3 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*CC(*)C\", \"*CC(*)c1ccc(C(=O)OC)cc1\"],\n",
        "    repeat_unit_smiles_weight_list=[1, 1],\n",
        "    end_group_smiles_list=[\"*C(C)(C)C#N\", \"*C(C)(C)C#N\"],\n",
        "    end_group_smiles_weight_list=[1, 1],\n",
        "    graph_representation=diblock_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXenNt9Mpctm"
      },
      "source": [
        "## Polymer C1-4\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1AI4kmDJL35XYnda4fJ1MJN1orV1BXFXP\"  width=\"30%\" height=\"30%\" title=\"Case1-1\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ys6CZFuDUtdl"
      },
      "outputs": [],
      "source": [
        "Polymer_C1_4 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*CC(*)(C)C(=O)OC\", \"*CC(*)c1ccc(C(=O)OC)cc1\"],\n",
        "    repeat_unit_smiles_weight_list=[1, 1],\n",
        "    end_group_smiles_list=[\"*C(C)(C)C#N\", \"*C(C)(C)C#N\"],\n",
        "    end_group_smiles_weight_list=[1, 1],\n",
        "    graph_representation=diblock_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbS9tSrxzq8Q"
      },
      "source": [
        "# Calculate Similarity Scores\n",
        "## Calculate the Pairwise Similarity Score between C1-1 and C1-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8p8pHgcmPcg7",
        "outputId": "a9deb47f-6b74-429d-f0f4-15cf8877b1a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.5769230769230769 , 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 =  0.7700714239676517\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7700714239676517"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C1_1,\n",
        "    target=Polymer_C1_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 C1-1 and C1-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UQmmuAt5e2H",
        "outputId": "22064c11-294f-4d45-f8cf-b8a57d3ac0d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.71875 , 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 =  0.854819621538289\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.854819621538289"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C1_1,\n",
        "    target=Polymer_C1_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 C1-1 and C1-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRB8mUWEdb_u",
        "outputId": "cf1afdde-6bd8-4f9d-bed3-cfd5d2519fa2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.29567307692307687 , 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 =  0.5605774141216268\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5605774141216268"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_C1_1,\n",
        "    target=Polymer_C1_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": null,
      "metadata": {
        "id": "riJpZumorBcY"
      },
      "outputs": [],
      "source": [
        "Polymer_C1 = [Polymer_C1_1, Polymer_C1_2, Polymer_C1_3, Polymer_C1_4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hZ6mW-U3rsN6"
      },
      "outputs": [],
      "source": [
        "Polymer_C1_RU_matrix = np.ones([4, 4])\n",
        "Polymer_C1_TOP_matrix = np.ones([4, 4])\n",
        "Polymer_C1_EG_matrix = np.ones([4, 4])\n",
        "Polymer_C1_OA_matrix = np.ones([4, 4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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_C1[i],\n",
        "            target=Polymer_C1[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_C1_OA_matrix[i, j] = SOAij\n",
        "        Polymer_C1_OA_matrix[j, i] = SOAij\n",
        "\n",
        "        Polymer_C1_RU_matrix[i, j] = SRUij\n",
        "        Polymer_C1_RU_matrix[j, i] = SRUij\n",
        "\n",
        "        Polymer_C1_TOP_matrix[i, j] = STOPij\n",
        "        Polymer_C1_TOP_matrix[j, i] = STOPij\n",
        "\n",
        "        Polymer_C1_EG_matrix[i, j] = SEGij\n",
        "        Polymer_C1_EG_matrix[j, i] = SEGij"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXpuWDkUx6jc"
      },
      "source": [
        "# Visualize the Similarity Matrices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "GZcQ4V1kubb_",
        "outputId": "73560cd7-060d-4301-f3b7-f72dda77898a"
      },
      "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 = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "y_labels = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C1_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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "ThI0SatduqXc",
        "outputId": "597d460a-fe28-45db-c067-8c1f095ebf06"
      },
      "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 = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "y_labels = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C1_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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "QyWxtuHhxq4q",
        "outputId": "ff468ff1-f6e5-466d-8e99-cd78d636db64"
      },
      "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 = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "y_labels = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C1_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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "JbAgnupdxvOE",
        "outputId": "a969bfe4-b159-4aae-9dd8-d47310995919"
      },
      "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 = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "y_labels = [\"C1-1\", \"C1-2\", \"C1-3\", \"C1-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_C1_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": null,
      "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"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}