notebook/Polymer_Similarity_SI_Tacticity.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_SI_Tacticity.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "9ExWfHqV1EXI"
      },
      "outputs": [],
      "source": [
        "# @title Copyright & License (click to expand)\n",
        "# MIT License\n",
        "\n",
        "# Copyright (c) 2022 olsenlabmit\n",
        "\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
        "# of this software and associated documentation files (the \"Software\"), to deal\n",
        "# in the Software without restriction, including without limitation the rights\n",
        "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
        "# copies of the Software, and to permit persons to whom the Software is\n",
        "# furnished to do so, subject to the following conditions:\n",
        "\n",
        "# The above copyright notice and this permission notice shall be included in all\n",
        "# copies or substantial portions of the Software.\n",
        "\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
        "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
        "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
        "# SOFTWARE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_sKD4LKzw0QJ"
      },
      "source": [
        "# SI-VII: Similarity Calculation including Tacticity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA0PTW_O3VxE"
      },
      "source": [
        "This notebook is to perform the calculations and reproduce the results of **SI-VII: Similarity Calculation including Tacticity** in the manuscript, Jiale Shi, Nathan J. Rebello, Dylan Walsh, Weizhong Zou, Michael E. Deagen, Bruno Salomao Leao, Debra J. Audus, Bradley D. Olsen, \"Quantifying Pairwise Chemical Similarity for Polymers\", submitted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9n8-Ciyiy7G"
      },
      "source": [
        "# Import and Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "psnOpmYlL_sw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6d3f72f-e4df-414b-863c-f4a85e111937"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 MB\u001b[0m \u001b[31m25.6 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[31m26.8 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[31m4.4 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": 3,
      "metadata": {
        "id": "eOlNnWbfY8Va"
      },
      "outputs": [],
      "source": [
        "def Similarity_Score_EMD_Calculation(\n",
        "    query_smiles_list=None,\n",
        "    query_smiles_weight_list=None,\n",
        "    target_smiles_list=None,\n",
        "    target_smiles_weight_list=None,\n",
        "    embedding_function=\"MorganFingerprint\",\n",
        "    similarity_score_function=\"Tanimoto\",\n",
        "):\n",
        "    # obtain the length of query smiles list and target smiles list\n",
        "    query_smiles_list_length = len(query_smiles_list)\n",
        "\n",
        "    target_smiles_list_length = len(target_smiles_list)\n",
        "\n",
        "    # check whether the query is identical to the target\n",
        "    if set(query_smiles_list) == set(target_smiles_list):\n",
        "        query_smiles_array = np.array(query_smiles_list)\n",
        "        query_smiles_level_array = np.array(query_smiles_weight_list)\n",
        "        inds_query = query_smiles_array.argsort()\n",
        "        sorted_query_smiles_array = query_smiles_array[inds_query]\n",
        "        sorted_query_smiles_level_array = query_smiles_level_array[inds_query]\n",
        "\n",
        "        target_smiles_array = np.array(target_smiles_list)\n",
        "        target_smiles_level_array = np.array(target_smiles_weight_list)\n",
        "        inds_target = target_smiles_array.argsort()\n",
        "        sorted_target_smiles_array = target_smiles_array[inds_target]\n",
        "        sorted_target_smiles_level_array = target_smiles_level_array[inds_target]\n",
        "\n",
        "        if np.array_equal(\n",
        "            sorted_query_smiles_array, sorted_target_smiles_array\n",
        "        ) and np.array_equal(\n",
        "            sorted_query_smiles_level_array, sorted_target_smiles_level_array\n",
        "        ):\n",
        "            return 1.0\n",
        "\n",
        "        query_smiles_reduced_list = list(set(query_smiles_list))\n",
        "        query_smiles_reduced_list_number = []\n",
        "\n",
        "        for i in range(0, len(query_smiles_reduced_list)):\n",
        "            query_smiles_reduced_list_number_i = 0\n",
        "            for j in range(0, len(query_smiles_list)):\n",
        "                if query_smiles_reduced_list[i] == query_smiles_list[j]:\n",
        "                    query_smiles_reduced_list_number_i = (\n",
        "                        query_smiles_reduced_list_number_i + query_smiles_weight_list[j]\n",
        "                    )\n",
        "\n",
        "            query_smiles_reduced_list_number.append(\n",
        "                query_smiles_reduced_list_number_i / sum(query_smiles_weight_list)\n",
        "            )\n",
        "\n",
        "        target_smiles_reduced_list = list(set(target_smiles_list))\n",
        "        target_smiles_reduced_list_number = []\n",
        "\n",
        "        for i in range(0, len(target_smiles_reduced_list)):\n",
        "            target_smiles_reduced_list_number_i = 0\n",
        "            for j in range(0, len(target_smiles_list)):\n",
        "                if target_smiles_reduced_list[i] == target_smiles_list[j]:\n",
        "                    target_smiles_reduced_list_number_i = (\n",
        "                        target_smiles_reduced_list_number_i\n",
        "                        + target_smiles_weight_list[j]\n",
        "                    )\n",
        "\n",
        "            target_smiles_reduced_list_number.append(\n",
        "                target_smiles_reduced_list_number_i / sum(target_smiles_weight_list)\n",
        "            )\n",
        "\n",
        "        if query_smiles_reduced_list_number == target_smiles_reduced_list_number:\n",
        "            return 1.0\n",
        "\n",
        "    # transfer SMILES to fingerprints\n",
        "    if embedding_function == \"RDKFingerprint\":\n",
        "        query_mol_list = [Chem.MolFromSmiles(x) for x in query_smiles_list]\n",
        "        query_fingerprint_list = [Chem.RDKFingerprint(x) for x in query_mol_list]\n",
        "        target_mol_list = [Chem.MolFromSmiles(x) for x in target_smiles_list]\n",
        "        target_fingerprint_list = [Chem.RDKFingerprint(x) for x in target_mol_list]\n",
        "\n",
        "    elif embedding_function == \"MorganFingerprint\":\n",
        "        query_mol_list = [Chem.MolFromSmiles(x) for x in query_smiles_list]\n",
        "        query_fingerprint_list = [\n",
        "            AllChem.GetMorganFingerprintAsBitVect(x, 2, nBits=2048, useChirality=True)\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, useChirality=True)\n",
        "            for x in target_mol_list\n",
        "        ]\n",
        "\n",
        "    elif embedding_function == \"MACCSkeys\":\n",
        "        query_mol_list = [Chem.MolFromSmiles(x) for x in query_smiles_list]\n",
        "        query_fingerprint_list = [MACCSkeys.GenMACCSKeys(x) for x in query_mol_list]\n",
        "        target_mol_list = [Chem.MolFromSmiles(x) for x in target_smiles_list]\n",
        "        target_fingerprint_list = [MACCSkeys.GenMACCSKeys(x) for x in target_mol_list]\n",
        "\n",
        "    else:\n",
        "        print(\n",
        "            embedding_function\n",
        "            + \" is not included in the current vision.\"\n",
        "            + \" Please choose an available embedding function:\"\n",
        "        )\n",
        "        print(\"MorganFingerprint, RDKFingerprint, MACCSkeys.\")\n",
        "        return False\n",
        "\n",
        "    # define the required three sets\n",
        "    Demand = {}  # query\n",
        "    Supply = {}  # target\n",
        "    T = {}  # transport flow\n",
        "\n",
        "    # define the weight for SMILES in query, w_i\n",
        "    if len(set(query_smiles_weight_list)) == 1:\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            Demand[\"P\" + str(i + 1)] = 1 / query_smiles_list_length\n",
        "\n",
        "    else:\n",
        "        query_weight_sum = sum(query_smiles_weight_list)\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            Demand[\"P\" + str(i + 1)] = query_smiles_weight_list[i] / query_weight_sum\n",
        "\n",
        "    # define weight for SMILES in target, w_j\n",
        "    if len(set(target_smiles_weight_list)) == 1:\n",
        "        for j in range(0, target_smiles_list_length):\n",
        "            Supply[\"Q\" + str(j + 1)] = 1 / target_smiles_list_length\n",
        "\n",
        "    else:\n",
        "        target_weight_sum = sum(target_smiles_weight_list)\n",
        "        for j in range(0, target_smiles_list_length):\n",
        "            Supply[\"Q\" + str(j + 1)] = target_smiles_weight_list[j] / target_weight_sum\n",
        "\n",
        "    # use similarity function to calculate d_ij\n",
        "    if similarity_score_function == \"Tanimoto\":\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            for j in range(0, target_smiles_list_length):\n",
        "                T[\n",
        "                    (\"P\" + str(i + 1), \"Q\" + str(j + 1))\n",
        "                ] = 1 - DataStructs.FingerprintSimilarity(\n",
        "                    query_fingerprint_list[i], target_fingerprint_list[j]\n",
        "                )\n",
        "\n",
        "    elif similarity_score_function == \"Dice\":\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            for j in range(0, target_smiles_list_length):\n",
        "                T[\n",
        "                    (\"P\" + str(i + 1), \"Q\" + str(j + 1))\n",
        "                ] = 1 - DataStructs.FingerprintSimilarity(\n",
        "                    query_fingerprint_list[i],\n",
        "                    target_fingerprint_list[j],\n",
        "                    metric=DataStructs.DiceSimilarity,\n",
        "                )\n",
        "\n",
        "    elif similarity_score_function == \"Cosine\":\n",
        "        for i in range(0, query_smiles_list_length):\n",
        "            for j in range(0, target_smiles_list_length):\n",
        "                T[\n",
        "                    (\"P\" + str(i + 1), \"Q\" + str(j + 1))\n",
        "                ] = 1 - DataStructs.FingerprintSimilarity(\n",
        "                    query_fingerprint_list[i],\n",
        "                    target_fingerprint_list[j],\n",
        "                    metric=DataStructs.CosineSimilarity,\n",
        "                )\n",
        "\n",
        "    else:\n",
        "        print(\n",
        "            similarity_score_function\n",
        "            + \" is not included in the current vision.\"\n",
        "            + \" Please choose an available similarity function:\"\n",
        "        )\n",
        "        print(\"Tanimoto, Dice, or Cosine\")\n",
        "        return\n",
        "\n",
        "    # linear optimization with pyomo\n",
        "    # step 0: Create an instance of the model\n",
        "    model = ConcreteModel()\n",
        "    model.dual = Suffix(direction=Suffix.IMPORT)\n",
        "\n",
        "    # step 1: define index sets\n",
        "    CUS = list(Demand.keys())\n",
        "    SRC = list(Supply.keys())\n",
        "\n",
        "    # step 2: define the decision\n",
        "    model.x = Var(CUS, SRC, domain=NonNegativeReals)\n",
        "\n",
        "    # step 3: define objective\n",
        "    model.Cost = Objective(\n",
        "        expr=sum([T[c, s] * model.x[c, s] for c in CUS for s in SRC]), sense=minimize\n",
        "    )\n",
        "\n",
        "    # step 4: define constraints\n",
        "    model.src = ConstraintList()\n",
        "    for s in SRC:\n",
        "        model.src.add(sum([model.x[c, s] for c in CUS]) == Supply[s])\n",
        "\n",
        "    model.dmd = ConstraintList()\n",
        "    for c in CUS:\n",
        "        model.dmd.add(sum([model.x[c, s] for s in SRC]) == Demand[c])\n",
        "\n",
        "    # step 5: solve the model\n",
        "    results = SolverFactory(\"cbc\").solve(model)\n",
        "\n",
        "    if \"ok\" == str(results.Solver.status):\n",
        "        SimilarityScore = 1 - model.Cost()\n",
        "        return SimilarityScore\n",
        "\n",
        "    else:\n",
        "        print(\"No Valid Solution Found\")\n",
        "        return False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNpt-pIDQLOX"
      },
      "source": [
        "## Topology Similarity Function\n",
        "\n",
        "Use graph edit distance $GED(g_1, g_2)$ to compute the topological similarity $S_{\\rm TOP}(g_1,g_2)$.\n",
        "\n",
        "$GED(g_1, g_2)$  is computed via a networkx function [graph_edit_distance](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.similarity.graph_edit_distance.html), while the topological similarity score $S_{\\rm TOP}(g_1,g_2)$ in the range of (0,1] is computed via a exponential decay function:\n",
        "\n",
        "\n",
        "$S_{\\rm TOP}(g_1, g_2) = \\exp(-\\frac{\\alpha \\cdot GED(g_1, g_2)}{(N_1 + N_2)/2})$\n",
        "\n",
        "where $N_i$ is the number of nodes in graph $g_i$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "oyUi54M_IorG"
      },
      "outputs": [],
      "source": [
        "def Topology_Similarity_Score_Calculation(Graph1=None, Graph2=None, alpha=1):\n",
        "    if Graph1 == None:\n",
        "        print(\"Missing Graph1\")\n",
        "        return\n",
        "    if Graph2 == None:\n",
        "        print(\"Missing Graph2\")\n",
        "        return\n",
        "\n",
        "    Graph1_number_nodes = Graph1.number_of_nodes()\n",
        "    Graph2_number_nodes = Graph2.number_of_nodes()\n",
        "\n",
        "    graph_edit_distance = nx.graph_edit_distance(Graph1, Graph2)\n",
        "\n",
        "    topology_similarity_score = np.exp(\n",
        "        -alpha * graph_edit_distance / ((Graph1_number_nodes + Graph2_number_nodes) / 2)\n",
        "    )\n",
        "\n",
        "    return topology_similarity_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_4bM0zPQRGC"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bdTaiEZQelj"
      },
      "source": [
        "## Weighted Mean Function for Overall Pairwise Similarity Score $S_{\\rm OA}$ \n",
        "\n",
        "From the above EMD and GED calculations, three similarity scores are obtained: $S_{\\rm RU}$  for repeat units, $S_{\\rm EG}$  for end groups, and $S_{\\rm TOP}$   for topology. To calculate the overall similarity score $S_{\\rm OA}$. Two weighted mean functions are provided. \n",
        "\n",
        "1. weighted geometric mean\n",
        "\n",
        "$S_{\\rm OA} =  S_{\\rm RU}^{W_{\\rm RU}} \\cdot S_{\\rm TOP}^{W_{\\rm TOP}} \\cdot S_{\\rm EG}^{W_{\\rm EG}} $ \n",
        "\n",
        "2. weighted arithmetic mean\n",
        "\n",
        "$S_{\\rm OA} =  S_{\\rm RU} \\cdot W_{\\rm RU} + S_{\\rm TOP} \\cdot W_{\\rm TOP} +  S_{\\rm EG} \\cdot W_{\\rm EG} $ \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "tnGFkpht12nw"
      },
      "outputs": [],
      "source": [
        "def Overall_Similarity_Score_Calculation(\n",
        "    query=None,\n",
        "    target=None,\n",
        "    embedding_function=\"RDKFingerprint\",\n",
        "    similarity_score_function=\"Tanimoto\",\n",
        "    alpha=1,\n",
        "    Repeat_Unit_Weight=0.475,\n",
        "    Topology_Weight=0.475,\n",
        "    End_Group_Weight=0.05,\n",
        "    Mean_Function=\"geometric\",\n",
        "    details_print=False,\n",
        "    return_with_components=False,\n",
        "):\n",
        "    if query == None or target == None:\n",
        "        print(\n",
        "            \"Either query polymer or target polymer is missing! Please check the input.\"\n",
        "        )\n",
        "        return\n",
        "\n",
        "    Repeat_Unit_Similarity_Score = Similarity_Score_EMD_Calculation(\n",
        "        query_smiles_list=query.repeat_unit_smiles_list,\n",
        "        query_smiles_weight_list=query.repeat_unit_smiles_weight_list,\n",
        "        target_smiles_list=target.repeat_unit_smiles_list,\n",
        "        target_smiles_weight_list=target.repeat_unit_smiles_weight_list,\n",
        "        embedding_function=embedding_function,\n",
        "        similarity_score_function=similarity_score_function,\n",
        "    )\n",
        "\n",
        "    Topology_Similarity_Score = Topology_Similarity_Score_Calculation(\n",
        "        Graph1=query.graph_representation,\n",
        "        Graph2=target.graph_representation,\n",
        "        alpha=alpha,\n",
        "    )\n",
        "\n",
        "    if len(query.end_group_smiles_list) == 0 and len(target.end_group_smiles_list) == 0:\n",
        "        End_Group_Similarity_Score = 1.0\n",
        "\n",
        "    elif (\n",
        "        len(query.end_group_smiles_list) == 0 and len(target.end_group_smiles_list) != 0\n",
        "    ) or (\n",
        "        len(query.end_group_smiles_list) != 0 and len(target.end_group_smiles_list) == 0\n",
        "    ):\n",
        "        End_Group_Similarity_Score = 1.0\n",
        "\n",
        "    else:\n",
        "        End_Group_Similarity_Score = Similarity_Score_EMD_Calculation(\n",
        "            query_smiles_list=query.end_group_smiles_list,\n",
        "            query_smiles_weight_list=query.end_group_smiles_weight_list,\n",
        "            target_smiles_list=target.end_group_smiles_list,\n",
        "            target_smiles_weight_list=target.end_group_smiles_weight_list,\n",
        "            embedding_function=embedding_function,\n",
        "            similarity_score_function=similarity_score_function,\n",
        "        )\n",
        "\n",
        "    # verify whether the weight sum is normalized.\n",
        "    if abs(Repeat_Unit_Weight + Topology_Weight + End_Group_Weight - 1) != 0.0:\n",
        "        Weight_Sum = Repeat_Unit_Weight + Topology_Weight + End_Group_Weight\n",
        "\n",
        "        Repeat_Unit_Weight = Repeat_Unit_Weight / Weight_Sum\n",
        "\n",
        "        Topology_Weight = Topology_Weight / Weight_Sum\n",
        "\n",
        "        End_Group_Weight = End_Group_Weight / Weight_Sum\n",
        "\n",
        "    if Mean_Function == \"geometric\":\n",
        "        Overall_Similarity_Score = (\n",
        "            pow(Repeat_Unit_Similarity_Score, Repeat_Unit_Weight)\n",
        "            * pow(Topology_Similarity_Score, Topology_Weight)\n",
        "            * pow(End_Group_Similarity_Score, End_Group_Weight)\n",
        "        )\n",
        "\n",
        "    elif Mean_Function == \"arithmetic\":\n",
        "        Overall_Similarity_Score = (\n",
        "            Repeat_Unit_Weight * Repeat_Unit_Similarity_Score\n",
        "            + Topology_Weight * Topology_Similarity_Score\n",
        "            + End_Group_Weight * End_Group_Similarity_Score\n",
        "        )\n",
        "\n",
        "    else:\n",
        "        print(\n",
        "            \"Your input mean function \",\n",
        "            Mean_Function,\n",
        "            \" is not implemented currently.\"\n",
        "            + \" Please choose those implemented mean functions, such as arithmetic, geometric\",\n",
        "        )\n",
        "        return\n",
        "\n",
        "    if details_print == True:\n",
        "        print(\"Details of the Similarity Score:\\n\")\n",
        "        print(\n",
        "            \"Repeat Unit Similarity Score = \",\n",
        "            Repeat_Unit_Similarity_Score,\n",
        "            \", Normalized Weight for Repeat Unit = \",\n",
        "            Repeat_Unit_Weight,\n",
        "        )\n",
        "        print(\n",
        "            \"Topology Similarity Score = \",\n",
        "            Topology_Similarity_Score,\n",
        "            \", Normalized Weight for Topology = \",\n",
        "            Topology_Weight,\n",
        "        )\n",
        "        print(\n",
        "            \"End Group Similarity  = \",\n",
        "            End_Group_Similarity_Score,\n",
        "            \", Normalized Weight for End Group = \",\n",
        "            End_Group_Weight,\n",
        "        )\n",
        "        print(\n",
        "            \"Overall Pairwise Similarity Score with \"\n",
        "            + Mean_Function\n",
        "            + \" mean function = \",\n",
        "            Overall_Similarity_Score,\n",
        "        )\n",
        "        print(\"\\n\")\n",
        "\n",
        "    if return_with_components == True:\n",
        "        return (\n",
        "            Overall_Similarity_Score,\n",
        "            Repeat_Unit_Similarity_Score,\n",
        "            Topology_Similarity_Score,\n",
        "            End_Group_Similarity_Score,\n",
        "        )\n",
        "\n",
        "    return Overall_Similarity_Score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bijKjSMqQ-gw"
      },
      "source": [
        "# Define Polymer Class for Importing Polymer data\n",
        "\n",
        "The polymer graph is decomposed into three components: repeat units, end groups and topology. \n",
        "\n",
        "(I) To compute $S_{\\rm RU}$  for repeat units, we need the polymer's (1) repeat unit SMILES list and (2) corresponding weight list.\n",
        "\n",
        "(II) To compute $S_{\\rm EG}$  for end groups, we need the polymer's (3) end group SMILES list and (4) corresponding weight list.\n",
        "\n",
        "(III) To compute $S_{\\rm TOP}$ for topology, we need the polymers' (5) topological graph representation.\n",
        "\n",
        "Therefore, in the Polymer Class, we have to define the above five elements which are required to perform polymer similarity calculation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "k-I92q5zfd_L"
      },
      "outputs": [],
      "source": [
        "class Polymer:\n",
        "    def __init__(\n",
        "        self,\n",
        "        repeat_unit_smiles_list=None,\n",
        "        repeat_unit_smiles_weight_list=None,\n",
        "        end_group_smiles_list=None,\n",
        "        end_group_smiles_weight_list=None,\n",
        "        graph_representation=None,\n",
        "    ):\n",
        "        if repeat_unit_smiles_list == None:\n",
        "            print(\n",
        "                \"Error: Repeat Unit SMILES are missing.\"\n",
        "                + \" Please define the Repeat Unit SMILES.\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            self.repeat_unit_smiles_list = repeat_unit_smiles_list\n",
        "\n",
        "        if repeat_unit_smiles_weight_list == None:\n",
        "            print(\n",
        "                \"Error: Repeat Unit weights are missing.\"\n",
        "                + \" Please re-enter the correct Repeat Unit weights.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        elif len(repeat_unit_smiles_weight_list) != len(repeat_unit_smiles_list):\n",
        "            print(\n",
        "                \"Error: Repeat Unit weights are not correct.\"\n",
        "                + \" Please re-enter the correct Repeat Unit SMILES and weights.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            self.repeat_unit_smiles_weight_list = repeat_unit_smiles_weight_list\n",
        "\n",
        "        if end_group_smiles_list == None and end_group_smiles_weight_list == None:\n",
        "            self.end_group_smiles_list = []\n",
        "            self.end_group_smiles_weight_list = []\n",
        "            print(\n",
        "                \"Notice: The Polymer has NO End Group.\\n\"\n",
        "                + \"If this is what you want, this message is NOT Error.\\n\"\n",
        "                + \"Otherwise, please enter the correct End Group SMILES and weights.\\n\"\n",
        "            )\n",
        "\n",
        "        elif (\n",
        "            end_group_smiles_list == None and end_group_smiles_weight_list != None\n",
        "        ) or (end_group_smiles_list != None and end_group_smiles_weight_list == None):\n",
        "            print(\n",
        "                \"Error: End Group SMILES and weights are not matching.\"\n",
        "                + \" Please re-enter the correct End Group SMILES and weights.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            if len(end_group_smiles_list) != len(end_group_smiles_weight_list):\n",
        "                print(\n",
        "                    \"Error: End Group SMILES and weights are not matching.\"\n",
        "                    + \" Please re-enter the correct End Group SMILES and weights.\\n\"\n",
        "                )\n",
        "                return\n",
        "\n",
        "            else:\n",
        "                if (\n",
        "                    len(end_group_smiles_list) == 0\n",
        "                    and len(end_group_smiles_weight_list) == 0\n",
        "                ):\n",
        "                    print(\n",
        "                        \"Notice: The Polymer has NO End Group.\\n\"\n",
        "                        + \"If this is what you want, this message is NOT Error.\\n\"\n",
        "                        + \"Otherwise, please enter the correct End Group SMILES and weights.\\n\"\n",
        "                    )\n",
        "\n",
        "                self.end_group_smiles_list = end_group_smiles_list\n",
        "                self.end_group_smiles_weight_list = end_group_smiles_weight_list\n",
        "\n",
        "        if graph_representation == None:\n",
        "            print(\n",
        "                \"Error: Topology graph representation is missing.\"\n",
        "                + \" Please define the topology graph representation.\\n\"\n",
        "            )\n",
        "            return\n",
        "\n",
        "        else:\n",
        "            self.graph_representation = graph_representation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vE5C5Cf0zSbF"
      },
      "source": [
        "# SI-VII: Similarity Calculation including Tacticity\n",
        "\n",
        "To elucidate the influence of tacticity on similarity calculations, four types of polypropylenes with a pure head-to-tail configuration and different tacticities are used as an example."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93332dHdzs9Y"
      },
      "source": [
        "# Define Polymers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIHJx_8wpS33"
      },
      "source": [
        "## Polymer T-1\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1L1ijqGq7vvi73DuMVH92e--3EoWxunFd\"  width=\"30%\" height=\"30%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "4gg0HbRJzsNw",
        "outputId": "09728ead-d3b6-4146-f8fb-3ffe6ab75cbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_T_1_top_graph_representation number of nodes:  2\n",
            "Polymer_T_1_top_graph_representation number of edges:  2\n",
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_T_1_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_T_1_top_graph_representation.add_nodes_from(\n",
        "    [\n",
        "        \"D1\",\n",
        "        \"R1\",\n",
        "    ]\n",
        ")\n",
        "Polymer_T_1_top_graph_representation.add_edges_from([(\"D1\", \"R1\"), (\"R1\", \"D1\")])\n",
        "nx.draw(Polymer_T_1_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_T_1_top_graph_representation number of nodes: \",\n",
        "    Polymer_T_1_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_T_1_top_graph_representation number of edges: \",\n",
        "    Polymer_T_1_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_T_1 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*C[C@H](C)*\"],\n",
        "    repeat_unit_smiles_weight_list=[1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_T_1_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkO_5dOtpY_E"
      },
      "source": [
        "## Polymer T-2\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=15bx-58E8k8na1RsxbYfE11KrED-Aio0s\"  width=\"30%\" height=\"30%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "FUemADZ3SN0d",
        "outputId": "32655e73-9f2c-4743-b1dc-fede5114f9e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_T_2_top_graph_representation number of nodes:  2\n",
            "Polymer_T_2_top_graph_representation number of edges:  2\n",
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_T_2_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_T_2_top_graph_representation.add_nodes_from(\n",
        "    [\n",
        "        \"D1\",\n",
        "        \"R1\",\n",
        "    ]\n",
        ")\n",
        "Polymer_T_2_top_graph_representation.add_edges_from([(\"D1\", \"R1\"), (\"R1\", \"D1\")])\n",
        "nx.draw(Polymer_T_2_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_T_2_top_graph_representation number of nodes: \",\n",
        "    Polymer_T_2_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_T_2_top_graph_representation number of edges: \",\n",
        "    Polymer_T_2_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_T_2 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*C[C@@H](C)*\"],\n",
        "    repeat_unit_smiles_weight_list=[1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_T_2_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5-QuC93pa17"
      },
      "source": [
        "## Polymer T-3\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=19kv63HiEuaD_S5pL1Yh9NBjTVf_EMHnk\"  width=\"30%\" height=\"30%\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "8l7h3jW95f4B",
        "outputId": "0927909b-59c7-4fa7-846e-a0e9af42ebb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_T_3_top_graph_representation number of nodes:  2\n",
            "Polymer_T_3_top_graph_representation number of edges:  2\n",
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_T_3_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_T_3_top_graph_representation.add_nodes_from(\n",
        "    [\n",
        "        \"D1\",\n",
        "        \"R1\",\n",
        "    ]\n",
        ")\n",
        "Polymer_T_3_top_graph_representation.add_edges_from([(\"D1\", \"R1\"), (\"R1\", \"D1\")])\n",
        "nx.draw(Polymer_T_3_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_T_3_top_graph_representation number of nodes: \",\n",
        "    Polymer_T_3_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_T_3_top_graph_representation number of edges: \",\n",
        "    Polymer_T_3_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_T_3 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*C[C@@H](C)C[C@H](C)*\"],\n",
        "    repeat_unit_smiles_weight_list=[1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_T_3_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXenNt9Mpctm"
      },
      "source": [
        "## Polymer T-4\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1kogUiycIbLWlYqWSEEsyCoIkyrW3qtgE\"  width=\"30%\" height=\"30%\" >"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "Ys6CZFuDUtdl",
        "outputId": "a0df8a11-377a-48c4-b733-5d74db73296b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polymer_T_4_top_graph_representation number of nodes:  3\n",
            "Polymer_T_4_top_graph_representation number of edges:  4\n",
            "Notice: The Polymer has NO End Group.\n",
            "If this is what you want, this message is NOT Error.\n",
            "Otherwise, please enter the correct End Group SMILES and weights.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Polymer_T_4_top_graph_representation = nx.MultiDiGraph()\n",
        "Polymer_T_4_top_graph_representation.add_nodes_from([\"D1\", \"R1\", \"R2\"])\n",
        "Polymer_T_4_top_graph_representation.add_edges_from(\n",
        "    [(\"D1\", \"R1\"), (\"R1\", \"D1\"), (\"D1\", \"R2\"), (\"R2\", \"D1\")]\n",
        ")\n",
        "nx.draw(Polymer_T_4_top_graph_representation)\n",
        "print(\n",
        "    \"Polymer_T_4_top_graph_representation number of nodes: \",\n",
        "    Polymer_T_4_top_graph_representation.number_of_nodes(),\n",
        ")\n",
        "print(\n",
        "    \"Polymer_T_4_top_graph_representation number of edges: \",\n",
        "    Polymer_T_4_top_graph_representation.number_of_edges(),\n",
        ")\n",
        "\n",
        "Polymer_T_4 = Polymer(\n",
        "    repeat_unit_smiles_list=[\"*C[C@H](C)*\", \"*C[C@@H](C)*\"],\n",
        "    repeat_unit_smiles_weight_list=[1, 1],\n",
        "    end_group_smiles_list=[],\n",
        "    end_group_smiles_weight_list=[],\n",
        "    graph_representation=Polymer_T_4_top_graph_representation,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbS9tSrxzq8Q"
      },
      "source": [
        "# Calculate Similarity Scores\n",
        "## Calculate the Pairwise Similarity Score between T-1 and T-2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8p8pHgcmPcg7",
        "outputId": "9a684132-181d-451d-8e24-b2660e8840ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.7272727272727273 , 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 =  0.8596194202445274\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8596194202445274"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_T_1,\n",
        "    target=Polymer_T_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 T-1 and T-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UQmmuAt5e2H",
        "outputId": "899db100-efac-4ee6-ec03-224f4b5ec60a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.5333333333333333 , 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 =  0.7418641689953074\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7418641689953074"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_T_1,\n",
        "    target=Polymer_T_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 T-1 and T-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRB8mUWEdb_u",
        "outputId": "1a564259-3938-4290-f77b-f081aa948df9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Details of the Similarity Score:\n",
            "\n",
            "Repeat Unit Similarity Score =  0.8636363636363636 , Normalized Weight for Repeat Unit =  0.475\n",
            "Topology Similarity Score =  0.30119421191220214 , 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 =  0.5274840504297654\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5274840504297654"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "Overall_Similarity_Score_Calculation(\n",
        "    query=Polymer_T_1,\n",
        "    target=Polymer_T_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": {
        "id": "riJpZumorBcY"
      },
      "outputs": [],
      "source": [
        "Polymer_T = [Polymer_T_1, Polymer_T_2, Polymer_T_3, Polymer_T_4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hZ6mW-U3rsN6"
      },
      "outputs": [],
      "source": [
        "Polymer_T_RU_matrix = np.ones([4, 4])\n",
        "Polymer_T_TOP_matrix = np.ones([4, 4])\n",
        "Polymer_T_EG_matrix = np.ones([4, 4])\n",
        "Polymer_T_OA_matrix = np.ones([4, 4])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "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_T[i],\n",
        "            target=Polymer_T[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_T_OA_matrix[i, j] = SOAij\n",
        "        Polymer_T_OA_matrix[j, i] = SOAij\n",
        "\n",
        "        Polymer_T_RU_matrix[i, j] = SRUij\n",
        "        Polymer_T_RU_matrix[j, i] = SRUij\n",
        "\n",
        "        Polymer_T_TOP_matrix[i, j] = STOPij\n",
        "        Polymer_T_TOP_matrix[j, i] = STOPij\n",
        "\n",
        "        Polymer_T_EG_matrix[i, j] = SEGij\n",
        "        Polymer_T_EG_matrix[j, i] = SEGij"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXpuWDkUx6jc"
      },
      "source": [
        "# Visualize the Similarity Matrices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "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": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "GZcQ4V1kubb_",
        "outputId": "c94e6511-644c-4678-fcef-ec7b42971a2c"
      },
      "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 = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "y_labels = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_T_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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "ThI0SatduqXc",
        "outputId": "7edc3ac9-3f22-43be-ed88-c1917afaba27"
      },
      "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 = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "y_labels = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_T_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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "QyWxtuHhxq4q",
        "outputId": "b004e360-5645-40d3-9e52-c566f7652133"
      },
      "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 = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "y_labels = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_T_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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "JbAgnupdxvOE",
        "outputId": "c84a4480-73cf-40ab-e88d-df5ee9b9cce1"
      },
      "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 = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "y_labels = [\"T-1\", \"T-2\", \"T-3\", \"T-4\"]\n",
        "\n",
        "sns.heatmap(\n",
        "    Polymer_T_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": "ifMz2-cyojfb"
      },
      "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
}