RBFE_transformato_workbook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JohannesKarwou/notebooks/blob/main/RBFE_transformato_workbook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "t11snvpY52J5"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import scipy\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "_YO4AHcxadWP"
      },
      "outputs": [],
      "source": [
        "# If this notebook is opend within the zip folder, please adjust the paths accoring to the two csv files provided in the zip folder (summary_dG.csv and summary_ddG.csv)\n",
        "df_ddg = pd.read_csv(\"https://raw.githubusercontent.com/JohannesKarwou/notebooks/main/data/summary_ddG.csv\")\n",
        "df_dg = pd.read_csv(\"https://raw.githubusercontent.com/JohannesKarwou/notebooks/main/data/summary_dG.csv\")\n",
        "df_hmr = pd.read_csv(\"https://raw.githubusercontent.com/JohannesKarwou/notebooks/main/data/summary_hmr_no_hmr.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "lbwQ4hg46xUE"
      },
      "outputs": [],
      "source": [
        "def bootstrap_function(x_values, y_values):\n",
        "  # bootstrap metric\n",
        "  def bootstrap_metric(fct, x_values, y_values):\n",
        "      assert callable(fct) == True\n",
        "      bootstrapped_metric = []\n",
        "      # bootstrap metric to generate test distribution\n",
        "      for _ in range(1000):\n",
        "          indices = np.random.choice(range(0, len(x_values)), size=len(x_values), replace=True)\n",
        "          x_selection = np.take(x_values, indices)\n",
        "          y_selection = np.take(y_values, indices)\n",
        "          r = fct(x_selection, y_selection)\n",
        "          bootstrapped_metric.append(r)    \n",
        "\n",
        "      # define 90% CI\n",
        "      alpha = 10.0\n",
        "      lower_p = alpha / 2.0\n",
        "      # get value at or near percentile (take a look at the definition of percentile if \n",
        "      # you have less than 100 values to make sure you understand what is happening)\n",
        "      lower = np.percentile(bootstrapped_metric, lower_p)\n",
        "      upper_p = (100 - alpha) + (alpha / 2.0)\n",
        "      upper = np.percentile(bootstrapped_metric, upper_p)\n",
        "      # calculate true mean\n",
        "      mean = fct(x_values, y_values)\n",
        "\n",
        "      return mean, lower, upper\n",
        "\n",
        "  # bootstrap MAE\n",
        "  mean, lower, upper = bootstrap_metric(mean_absolute_error, x_values, y_values)\n",
        "  print(f'MAE:  {round(mean, 2):.2f} [{round(lower,2):.2f}, {round(upper,2):.2f}]')\n",
        "\n",
        "  # bootstrap RMSE\n",
        "  def calc_rmse(x_values, y_values):\n",
        "      from sklearn.metrics import mean_squared_error\n",
        "      return np.sqrt(mean_squared_error(x_values, y_values))\n",
        "  mean, lower, upper = bootstrap_metric(calc_rmse, x_values, y_values)\n",
        "  print(f'RMSE:  {round(mean, 2):.2f} [{round(lower,2):.2f}, {round(upper,2):.2f}]')\n",
        "  plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def bootstrap_function_spearman(x_values, y_values):\n",
        "  # bootstrap metric\n",
        "  def bootstrap_metric(x_values, y_values):\n",
        "      bootstrapped_metric = []\n",
        "      # bootstrap metric to generate test distribution\n",
        "      for _ in range(1000):\n",
        "          indices = np.random.choice(range(0, len(x_values)), size=len(x_values), replace=True)\n",
        "          x_selection = np.take(x_values, indices)\n",
        "          y_selection = np.take(y_values, indices)\n",
        "          r = scipy.stats.spearmanr(x_selection, y_selection)[0]\n",
        "          bootstrapped_metric.append(r)    \n",
        "\n",
        "      # define 90% CI\n",
        "      alpha = 10.0\n",
        "      lower_p = alpha / 2.0\n",
        "      # get value at or near percentile (take a look at the definition of percentile if \n",
        "      # you have less than 100 values to make sure you understand what is happening)\n",
        "      lower = np.percentile(bootstrapped_metric, lower_p)\n",
        "      upper_p = (100 - alpha) + (alpha / 2.0)\n",
        "      upper = np.percentile(bootstrapped_metric, upper_p)\n",
        "      # calculate true mean\n",
        "      spearman = scipy.stats.spearmanr(x_values, y_values)[0]\n",
        "\n",
        "      return spearman, lower, upper\n",
        "\n",
        "  # bootstrap MAE\n",
        "  spear, lower, upper = bootstrap_metric(x_values, y_values)\n",
        "  print(f\"Spearman's correlation:  {round(spear, 2):.2f} [{round(lower,2):.2f}, {round(upper,2):.2f}]\")\n",
        "\n",
        "def bootstrap_function_pearson(x_values, y_values):\n",
        "  # bootstrap metric\n",
        "  def bootstrap_metric(x_values, y_values):\n",
        "      bootstrapped_metric = []\n",
        "      # bootstrap metric to generate test distribution\n",
        "      for _ in range(1000):\n",
        "          indices = np.random.choice(range(0, len(x_values)), size=len(x_values), replace=True)\n",
        "          x_selection = np.take(x_values, indices)\n",
        "          y_selection = np.take(y_values, indices)\n",
        "          r = scipy.stats.pearsonr(x_selection, y_selection)[0]\n",
        "          bootstrapped_metric.append(r)    \n",
        "\n",
        "      # define 90% CI\n",
        "      alpha = 10.0\n",
        "      lower_p = alpha / 2.0\n",
        "      # get value at or near percentile (take a look at the definition of percentile if \n",
        "      # you have less than 100 values to make sure you understand what is happening)\n",
        "      lower = np.percentile(bootstrapped_metric, lower_p)\n",
        "      upper_p = (100 - alpha) + (alpha / 2.0)\n",
        "      upper = np.percentile(bootstrapped_metric, upper_p)\n",
        "      # calculate true mean\n",
        "      pearson = scipy.stats.pearsonr(x_values, y_values)[0]\n",
        "\n",
        "      return pearson, lower, upper\n",
        "\n",
        "  # bootstrap MAE\n",
        "  pearson, lower, upper = bootstrap_metric(x_values, y_values)\n",
        "  print(f\"Pearson correlation:  {round(pearson, 2):.2f} [{round(lower,2):.2f}, {round(upper,2):.2f}]\")\n"
      ],
      "metadata": {
        "id": "Bzao5mhZ8eYU"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_scipy(x,y):\n",
        "  pearson = scipy.stats.pearsonr(x_values,y_values)\n",
        "  spearman = scipy.stats.spearmanr(x_values,y_values)\n",
        "  kendall = scipy.stats.kendalltau(x_values,y_values)\n",
        "  print(f' Pearson correlation {round(pearson[0],2)}')\n",
        "  print(f' Spearmans {round(spearman[0],2)}')\n",
        "  print(f' Kendall {round(kendall[0],2)}')"
      ],
      "metadata": {
        "id": "U5uykNfX21my"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Taken from here:\n",
        "# https://stackoverflow.com/questions/33176049/how-do-you-compute-the-confidence-interval-for-pearsons-r-in-python\n",
        "\n",
        "def pearsonr_ci(x, y, ci=90, n_boots=1000):\n",
        "    x = np.asarray(x)\n",
        "    y = np.asarray(y)\n",
        "    \n",
        "   # (n_boots, n_observations) paired arrays\n",
        "    rand_ixs = np.random.randint(0, x.shape[0], size=(n_boots, x.shape[0]))\n",
        "    x_boots = x[rand_ixs]\n",
        "    y_boots = y[rand_ixs]\n",
        "    \n",
        "    # differences from mean\n",
        "    x_mdiffs = x_boots - x_boots.mean(axis=1)[:, None]\n",
        "    y_mdiffs = y_boots - y_boots.mean(axis=1)[:, None]\n",
        "    \n",
        "    # sums of squares\n",
        "    x_ss = np.einsum('ij, ij -> i', x_mdiffs, x_mdiffs)\n",
        "    y_ss = np.einsum('ij, ij -> i', y_mdiffs, y_mdiffs)\n",
        "    \n",
        "    # pearson correlations\n",
        "    r_boots = np.einsum('ij, ij -> i', x_mdiffs, y_mdiffs) / np.sqrt(x_ss * y_ss)\n",
        "    pearson = scipy.stats.pearsonr(x,y)\n",
        "    # upper and lower bounds for confidence interval\n",
        "    ci_low = np.percentile(r_boots, (100 - ci) / 2)\n",
        "    ci_high = np.percentile(r_boots, (ci + 100) / 2)\n",
        "    return round(pearson[0],2), round(ci_low,2), round(ci_high, 2)"
      ],
      "metadata": {
        "id": "Rt15YTZ9eVlZ"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## In the following cells, the calculated **dG** values are processed. The first block calculates all values (MAE, RMSE, Pearson and Spearman correlation) for the results calculated by`TRANSFORMATO` for five datasets (JNK1, 2RA0, GAL3, CDK2, TYK2). In the next block values are calculated for pmx, then for Schroedinger FEP+ and for AMBER TI. Note, not all methods from literature calculate all systems reported for `TRANSFORMATO`.:"
      ],
      "metadata": {
        "id": "KF3pBUSR56jF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmvckjqTAPvo",
        "outputId": "9f1fd366-bd07-4a07-b543-64508f1696d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Results for TRANSFORMATO #####\n",
            "for all dG values\n",
            "MAE:  0.86 [0.70, 1.02]\n",
            "RMSE:  1.17 [0.98, 1.36]\n",
            " Pearson correlation 0.73\n",
            " Spearmans 0.7\n",
            " Kendall 0.52\n",
            "Pearson correlation:  0.73 [0.63, 0.82]\n",
            "Spearman's correlation:  0.70 [0.55, 0.80]\n",
            "(0.73, 0.62, 0.82)\n",
            "#################\n",
            "dG values for Galectin\n",
            "MAE:  0.68 [0.39, 1.04]\n",
            "RMSE:  0.90 [0.48, 1.23]\n",
            " Pearson correlation 0.6\n",
            " Spearmans 0.54\n",
            " Kendall 0.4\n",
            "Pearson correlation:  0.60 [-0.06, 0.93]\n",
            "Spearman's correlation:  0.54 [-0.19, 0.94]\n",
            "(0.6, -0.07, 0.92)\n",
            "#################\n",
            "dG values for CDK2\n",
            "MAE:  0.80 [0.44, 1.18]\n",
            "RMSE:  1.12 [0.68, 1.48]\n",
            " Pearson correlation 0.61\n",
            " Spearmans 0.58\n",
            " Kendall 0.45\n",
            "Pearson correlation:  0.61 [0.24, 0.86]\n",
            "Spearman's correlation:  0.58 [-0.02, 0.91]\n",
            "(0.61, 0.25, 0.86)\n",
            "#################\n",
            "dG values for 2RA0\n",
            "MAE:  0.92 [0.67, 1.17]\n",
            "RMSE:  1.04 [0.80, 1.26]\n",
            " Pearson correlation 0.76\n",
            " Spearmans 0.65\n",
            " Kendall 0.51\n",
            "Pearson correlation:  0.76 [0.28, 0.92]\n",
            "Spearman's correlation:  0.65 [0.07, 0.96]\n",
            "(0.76, 0.31, 0.91)\n",
            "#################\n",
            "dG values for TYK2\n",
            "MAE:  1.37 [0.91, 1.84]\n",
            "RMSE:  1.74 [1.25, 2.16]\n",
            " Pearson correlation 0.42\n",
            " Spearmans 0.21\n",
            " Kendall 0.12\n",
            "Pearson correlation:  0.42 [-0.17, 0.72]\n",
            "Spearman's correlation:  0.21 [-0.36, 0.67]\n",
            "(0.42, -0.25, 0.7)\n",
            "#################\n",
            "dG values for JNK1\n",
            "MAE:  0.57 [0.38, 0.79]\n",
            "RMSE:  0.81 [0.53, 1.06]\n",
            " Pearson correlation 0.6\n",
            " Spearmans 0.62\n",
            " Kendall 0.45\n",
            "Pearson correlation:  0.60 [0.38, 0.82]\n",
            "Spearman's correlation:  0.62 [0.30, 0.82]\n",
            "(0.6, 0.37, 0.83)\n"
          ]
        }
      ],
      "source": [
        "### Here the calculated dG values are processed! ###\n",
        "############ FOR TRANSFORMATO RESULTS ##############\n",
        "\n",
        "print('#### Results for TRANSFORMATO #####')\n",
        "\n",
        "##### summary of all dG values of all systems ######\n",
        "\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][0:67])\n",
        "y_values = np.asarray_chkfinite(df_dg['TF'][0:67])\n",
        "print(f'for all dG values')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "print(pearsonr_ci(x_values,y_values))\n",
        "\n",
        "######## Values for the indiviual systems #########\n",
        "## Galectin ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][21:29])\n",
        "y_values = np.asarray_chkfinite(df_dg['TF'][21:29])\n",
        "print(f'dG values for Galectin')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "print(pearsonr_ci(x_values,y_values))\n",
        "\n",
        "## CDK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][39:52])\n",
        "y_values = np.asarray_chkfinite(df_dg['TF'][39:52])\n",
        "print(f'dG values for CDK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "print(pearsonr_ci(x_values,y_values))\n",
        "\n",
        "## 2RA0 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][29:39])\n",
        "y_values = np.asarray_chkfinite(df_dg['TF'][29:39])\n",
        "print(f'dG values for 2RA0')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "print(pearsonr_ci(x_values,y_values))\n",
        "\n",
        "## TYK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][52:67])\n",
        "y_values = np.asarray_chkfinite(df_dg['TF'][52:67])\n",
        "print(f'dG values for TYK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "print(pearsonr_ci(x_values,y_values))\n",
        "\n",
        "## JNK1 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][0:21])\n",
        "y_values = np.asarray_chkfinite(df_dg['TF'][0:21])\n",
        "print(f'dG values for JNK1')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "print(pearsonr_ci(x_values,y_values))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  ############ FOR PMX RESULTS ##############\n",
        "\n",
        "print('#### Results for PMX #####')\n",
        "\n",
        "######## Values for the indiviual systems #########\n",
        "## Galectin ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][21:29])\n",
        "y_values = np.asarray_chkfinite(df_dg['pmx'][21:29])\n",
        "print(f'dG values for Galectin')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## CDK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][39:52])\n",
        "y_values = np.asarray_chkfinite(df_dg['pmx'][39:52])\n",
        "print(f'dG values for CDK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## TYK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][52:67])\n",
        "y_values = np.asarray_chkfinite(df_dg['pmx'][52:67])\n",
        "print(f'dG values for TYK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## JNK1 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][0:21])\n",
        "y_values = np.asarray_chkfinite(df_dg['pmx'][0:21])\n",
        "print(f'dG values for JNK1')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)"
      ],
      "metadata": {
        "id": "QCwaJ0Kn4UkV",
        "outputId": "5bd5f432-59a1-4bec-e420-cf0e91c8ad0b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Results for PMX #####\n",
            "#################\n",
            "dG values for Galectin\n",
            "MAE:  0.43 [0.27, 0.58]\n",
            "RMSE:  0.50 [0.36, 0.61]\n",
            " Pearson correlation 0.9\n",
            " Spearmans 0.78\n",
            " Kendall 0.62\n",
            "Pearson correlation:  0.90 [0.61, 0.97]\n",
            "Spearman's correlation:  0.78 [0.30, 1.00]\n",
            "#################\n",
            "dG values for CDK2\n",
            "MAE:  0.89 [0.57, 1.22]\n",
            "RMSE:  1.14 [0.80, 1.42]\n",
            " Pearson correlation 0.41\n",
            " Spearmans 0.63\n",
            " Kendall 0.46\n",
            "Pearson correlation:  0.41 [0.08, 0.86]\n",
            "Spearman's correlation:  0.63 [0.16, 0.89]\n",
            "#################\n",
            "dG values for TYK2\n",
            "MAE:  1.61 [1.23, 2.05]\n",
            "RMSE:  1.87 [1.46, 2.25]\n",
            " Pearson correlation 0.54\n",
            " Spearmans 0.46\n",
            " Kendall 0.3\n",
            "Pearson correlation:  0.54 [0.12, 0.81]\n",
            "Spearman's correlation:  0.46 [0.00, 0.77]\n",
            "#################\n",
            "dG values for JNK1\n",
            "MAE:  0.57 [0.37, 0.78]\n",
            "RMSE:  0.81 [0.52, 1.05]\n",
            " Pearson correlation 0.66\n",
            " Spearmans 0.77\n",
            " Kendall 0.56\n",
            "Pearson correlation:  0.66 [0.48, 0.83]\n",
            "Spearman's correlation:  0.77 [0.58, 0.87]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "############ FOR FEP+ RESULTS ##############\n",
        "\n",
        "print('#### Results for Schroedinger FEP+ #####')\n",
        "\n",
        "######## Values for the indiviual systems #########\n",
        "## CDK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][39:52])\n",
        "y_values = np.asarray_chkfinite(df_dg['schroedinger'][39:52])\n",
        "print(f'dG values for CDK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## TYK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][52:67])\n",
        "y_values = np.asarray_chkfinite(df_dg['schroedinger'][52:67])\n",
        "print(f'dG values for TYK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## JNK1 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][0:21])\n",
        "y_values = np.asarray_chkfinite(df_dg['schroedinger'][0:21])\n",
        "print(f'dG values for JNK1')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)"
      ],
      "metadata": {
        "id": "JXfmP2F_RAHH",
        "outputId": "f62d010f-27af-471e-b94c-8e7162f26bb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Results for Schroedinger FEP+ #####\n",
            "#################\n",
            "dG values for CDK2\n",
            "MAE:  0.82 [0.60, 1.05]\n",
            "RMSE:  0.95 [0.74, 1.14]\n",
            " Pearson correlation 0.52\n",
            " Spearmans 0.58\n",
            " Kendall 0.44\n",
            "Pearson correlation:  0.52 [0.16, 0.88]\n",
            "Spearman's correlation:  0.58 [0.10, 0.87]\n",
            "#################\n",
            "dG values for TYK2\n",
            "MAE:  0.46 [0.31, 0.61]\n",
            "RMSE:  0.58 [0.38, 0.77]\n",
            " Pearson correlation 0.88\n",
            " Spearmans 0.85\n",
            " Kendall 0.68\n",
            "Pearson correlation:  0.88 [0.76, 0.96]\n",
            "Spearman's correlation:  0.85 [0.62, 0.95]\n",
            "#################\n",
            "dG values for JNK1\n",
            "MAE:  1.06 [0.89, 1.21]\n",
            "RMSE:  1.14 [0.99, 1.30]\n",
            " Pearson correlation 0.85\n",
            " Spearmans 0.9\n",
            " Kendall 0.76\n",
            "Pearson correlation:  0.85 [0.72, 0.94]\n",
            "Spearman's correlation:  0.90 [0.76, 0.97]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "############ FOR AMBER TI RESULTS ##############\n",
        "\n",
        "print('#### Results for AMBER TI #####')\n",
        "\n",
        "######## Values for the indiviual systems #########\n",
        "\n",
        "\n",
        "## CDK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][39:52])\n",
        "y_values = np.asarray_chkfinite(df_dg['AMBER TI'][39:52])\n",
        "print(f'dG values for CDK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## 2RA0 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_dg[\"literature\"][29:39])\n",
        "y_values = np.asarray_chkfinite(df_dg['AMBER TI'][29:39])\n",
        "print(f'dG values for 2RA0')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n"
      ],
      "metadata": {
        "id": "KUG6bo7U4sKV",
        "outputId": "dcb3f726-96ab-4b17-aaaf-d8972ba978d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Results for AMBER TI #####\n",
            "#################\n",
            "dG values for CDK2\n",
            "MAE:  0.72 [0.52, 0.92]\n",
            "RMSE:  0.84 [0.62, 1.03]\n",
            " Pearson correlation 0.74\n",
            " Spearmans 0.79\n",
            " Kendall 0.59\n",
            "Pearson correlation:  0.74 [0.54, 0.88]\n",
            "Spearman's correlation:  0.79 [0.44, 0.93]\n",
            "#################\n",
            "dG values for 2RA0\n",
            "MAE:  0.67 [0.35, 1.04]\n",
            "RMSE:  0.96 [0.42, 1.41]\n",
            " Pearson correlation 0.83\n",
            " Spearmans 0.81\n",
            " Kendall 0.6\n",
            "Pearson correlation:  0.83 [0.38, 0.97]\n",
            "Spearman's correlation:  0.81 [0.43, 0.95]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# As for the dG results shown previously, the same is done for the **ddG** results of the calculated mutations. First, results are reported for all systems calculated by `TRANSFORMATO`. The same is done for each system where data is available, first for pmx, followed by the Schroedinger FEP+ results and the AMBER TI results."
      ],
      "metadata": {
        "id": "BgsgDEMMHhvz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Here the calculated ddG values are processed! ###\n",
        "############ FOR TRANSFORMATO RESULTS ##############\n",
        "\n",
        "print('#### Results for TRANSFORMATO #####')\n",
        "\n",
        "##### summary of all ddG values of all systems ######\n",
        "\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][0:75])\n",
        "y_values = np.asarray_chkfinite(df_ddg['TF'][0:75])\n",
        "print(f'for all ddG values')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "######## Values for the indiviual systems #########\n",
        "## Galectin ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][0:7])\n",
        "y_values = np.asarray_chkfinite(df_ddg['TF'][0:7])\n",
        "print(f'dG values for Galectin')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## CDK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][7:20])\n",
        "y_values = np.asarray_chkfinite(df_ddg['TF'][7:20])\n",
        "print(f'dG values for CDK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## 2RA0 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][20:31])\n",
        "y_values = np.asarray_chkfinite(df_ddg['TF'][20:31])\n",
        "print(f'dG values for 2RA0')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## TYK2 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][31:46])\n",
        "y_values = np.asarray_chkfinite(df_ddg['TF'][31:46])\n",
        "print(f'dG values for TYK2')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)\n",
        "\n",
        "## JNK1 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][46:75])\n",
        "y_values = np.asarray_chkfinite(df_ddg['TF'][46:75])\n",
        "print(f'dG values for JNK1')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)"
      ],
      "metadata": {
        "id": "54hbcaQ9JPmB",
        "outputId": "84bb5bde-98be-4399-db31-3f064ce85612",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Results for TRANSFORMATO #####\n",
            "for all ddG values\n",
            "MAE:  0.87 [0.73, 1.03]\n",
            "RMSE:  1.18 [0.98, 1.36]\n",
            " Pearson correlation 0.57\n",
            " Spearmans 0.48\n",
            " Kendall 0.36\n",
            "Pearson correlation:  0.57 [0.36, 0.71]\n",
            "Spearman's correlation:  0.48 [0.29, 0.64]\n",
            "#################\n",
            "dG values for Galectin\n",
            "MAE:  0.50 [0.31, 0.69]\n",
            "RMSE:  0.58 [0.40, 0.74]\n",
            " Pearson correlation 0.76\n",
            " Spearmans 0.57\n",
            " Kendall 0.43\n",
            "Pearson correlation:  0.76 [0.08, 0.94]\n",
            "Spearman's correlation:  0.57 [-0.17, 0.96]\n",
            "#################\n",
            "dG values for CDK2\n",
            "MAE:  0.80 [0.48, 1.17]\n",
            "RMSE:  1.12 [0.72, 1.45]\n",
            " Pearson correlation 0.63\n",
            " Spearmans 0.59\n",
            " Kendall 0.46\n",
            "Pearson correlation:  0.63 [0.28, 0.88]\n",
            "Spearman's correlation:  0.59 [0.08, 0.92]\n",
            "#################\n",
            "dG values for 2RA0\n",
            "MAE:  1.01 [0.70, 1.40]\n",
            "RMSE:  1.23 [0.77, 1.69]\n",
            " Pearson correlation 0.83\n",
            " Spearmans 0.73\n",
            " Kendall 0.62\n",
            "Pearson correlation:  0.83 [0.40, 0.95]\n",
            "Spearman's correlation:  0.73 [0.22, 0.99]\n",
            "#################\n",
            "dG values for TYK2\n",
            "MAE:  1.37 [0.92, 1.85]\n",
            "RMSE:  1.74 [1.26, 2.14]\n",
            " Pearson correlation 0.42\n",
            " Spearmans 0.21\n",
            " Kendall 0.12\n",
            "Pearson correlation:  0.42 [-0.16, 0.72]\n",
            "Spearman's correlation:  0.21 [-0.38, 0.65]\n",
            "#################\n",
            "dG values for JNK1\n",
            "MAE:  0.68 [0.50, 0.87]\n",
            "RMSE:  0.91 [0.63, 1.16]\n",
            " Pearson correlation 0.34\n",
            " Spearmans 0.32\n",
            " Kendall 0.24\n",
            "Pearson correlation:  0.34 [-0.02, 0.66]\n",
            "Spearman's correlation:  0.32 [-0.02, 0.63]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Here the calculated ddG values are processed! ###\n",
        "############ FOR PMX RESULTS ##############\n",
        "\n",
        "print('#### Results for PMX #####')\n",
        "\n",
        "######## Values for the indiviual systems #########\n",
        "\n",
        "## JNK1 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][46:75])\n",
        "y_values = np.asarray_chkfinite(df_ddg['pmx'][46:75])\n",
        "print(f'ddG values for JNK1')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)"
      ],
      "metadata": {
        "id": "KZfiQlqFJ1G2",
        "outputId": "281bde48-6e11-47ef-e1e4-f8cdc9905b8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Results for PMX #####\n",
            "#################\n",
            "ddG values for JNK1\n",
            "MAE:  0.68 [0.48, 0.88]\n",
            "RMSE:  0.95 [0.67, 1.21]\n",
            " Pearson correlation 0.51\n",
            " Spearmans 0.55\n",
            " Kendall 0.4\n",
            "Pearson correlation:  0.51 [0.25, 0.71]\n",
            "Spearman's correlation:  0.55 [0.25, 0.77]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Here the calculated ddG values are processed! ###\n",
        "############ FOR Schroedinger/FEP+ RESULTS ##############\n",
        "\n",
        "print('#### Results for Schroedinger/FEP+ #####')\n",
        "\n",
        "######## Values for the indiviual systems #########\n",
        "## JNK1 ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_ddg[\"literature\"][46:75])\n",
        "y_values = np.asarray_chkfinite(df_ddg['schroedinger'][46:75])\n",
        "print(f'ddG values for JNK1')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)"
      ],
      "metadata": {
        "id": "Cq7V1YjDdd3F",
        "outputId": "bdcc99ac-5430-4bb3-989b-a07ff059a920",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Results for Schroedinger/FEP+ #####\n",
            "#################\n",
            "ddG values for JNK1\n",
            "MAE:  0.78 [0.59, 0.98]\n",
            "RMSE:  1.02 [0.75, 1.27]\n",
            " Pearson correlation 0.56\n",
            " Spearmans 0.58\n",
            " Kendall 0.44\n",
            "Pearson correlation:  0.56 [0.28, 0.78]\n",
            "Spearman's correlation:  0.58 [0.28, 0.79]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results for the HMR no-HMR comparsion"
      ],
      "metadata": {
        "id": "yT2TUoiH-bRS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_hmr"
      ],
      "metadata": {
        "id": "ALZ8kcrB-ax0",
        "outputId": "3d075d4d-ce5d-40f4-b9c0-af3a170e6286",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ligand  average   std    TF  std.1  Literature  Charmm_OpenMM  \\\n",
              "0          l4 -> l3    -0.57  0.18 -0.61   0.09       -0.47          -0.55   \n",
              "1          l6 -> l1    -1.83  0.65 -1.91   0.63       -2.66          -1.56   \n",
              "2  jmc_28 -> ejm_31    -1.64  0.27 -1.73   0.32        1.44            NaN   \n",
              "3  ejm_46 -> ejm_31    -0.51  0.07 -0.45   0.29        1.77            NaN   \n",
              "4     l51a -> l51bt    -0.61  0.30 -0.96   0.24       -1.45          -1.04   \n",
              "5      l51c -> l51d     3.87  0.56  4.03   0.66        3.36           3.40   \n",
              "6      1h1s -> 1h1q     1.32  0.43  0.60   0.60        3.07           1.64   \n",
              "7        22 -> 1h1q    -0.44  0.64 -0.29   0.55       -0.32            NaN   \n",
              "8        29 -> 1h1q     0.23  0.54 -0.22   0.56        1.70           0.80   \n",
              "\n",
              "   std.2  \n",
              "0   0.43  \n",
              "1   0.17  \n",
              "2    NaN  \n",
              "3    NaN  \n",
              "4   0.17  \n",
              "5   0.30  \n",
              "6   0.59  \n",
              "7    NaN  \n",
              "8   0.74  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8abd314-9b94-43a5-b371-36b82c33ebba\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ligand</th>\n",
              "      <th>average</th>\n",
              "      <th>std</th>\n",
              "      <th>TF</th>\n",
              "      <th>std.1</th>\n",
              "      <th>Literature</th>\n",
              "      <th>Charmm_OpenMM</th>\n",
              "      <th>std.2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>l4 -&gt; l3</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>0.18</td>\n",
              "      <td>-0.61</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-0.47</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>l6 -&gt; l1</td>\n",
              "      <td>-1.83</td>\n",
              "      <td>0.65</td>\n",
              "      <td>-1.91</td>\n",
              "      <td>0.63</td>\n",
              "      <td>-2.66</td>\n",
              "      <td>-1.56</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>jmc_28 -&gt; ejm_31</td>\n",
              "      <td>-1.64</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-1.73</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ejm_46 -&gt; ejm_31</td>\n",
              "      <td>-0.51</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-0.45</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>l51a -&gt; l51bt</td>\n",
              "      <td>-0.61</td>\n",
              "      <td>0.30</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>0.24</td>\n",
              "      <td>-1.45</td>\n",
              "      <td>-1.04</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>l51c -&gt; l51d</td>\n",
              "      <td>3.87</td>\n",
              "      <td>0.56</td>\n",
              "      <td>4.03</td>\n",
              "      <td>0.66</td>\n",
              "      <td>3.36</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1h1s -&gt; 1h1q</td>\n",
              "      <td>1.32</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>3.07</td>\n",
              "      <td>1.64</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>22 -&gt; 1h1q</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>0.64</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.55</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>29 -&gt; 1h1q</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.54</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8abd314-9b94-43a5-b371-36b82c33ebba')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8abd314-9b94-43a5-b371-36b82c33ebba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## HMR vs noHMR ###\n",
        "print('#################')\n",
        "x_values = np.asarray_chkfinite(df_hmr[\"average\"][0:9])\n",
        "y_values = np.asarray_chkfinite(df_hmr['TF'][0:9])\n",
        "print(f'ddG Values for HMR vs noHMR')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)"
      ],
      "metadata": {
        "id": "fcSW8uRCJwk_",
        "outputId": "9bacd204-ba48-460f-c08c-60c4645a1d68",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#################\n",
            "ddG Values for HMR vs noHMR\n",
            "MAE:  0.23 [0.12, 0.36]\n",
            "RMSE:  0.32 [0.17, 0.44]\n",
            " Pearson correlation 0.99\n",
            " Spearmans 1.0\n",
            " Kendall 1.0\n",
            "Pearson correlation:  0.99 [0.94, 1.00]\n",
            "Spearman's correlation:  1.00 [1.00, 1.00]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## HMR vs noHMR ###\n",
        "print('#################')\n",
        "np_x_values = np.asarray(df_hmr['Charmm_OpenMM'][0:9])\n",
        "x_values = np_x_values[np.ix_([0,1,4,5,6,8])]\n",
        "np_y_values = np.asarray(df_hmr['average'][0:9])\n",
        "y_values = np_y_values[np.ix_([0,1,4,5,6,8])]\n",
        "print(f'ddG Values for openMM vs CHARMM/OpenMM')\n",
        "bootstrap_function(x_values, y_values)\n",
        "calc_scipy(x_values,y_values)\n",
        "bootstrap_function_pearson(x_values,y_values)\n",
        "bootstrap_function_spearman(x_values,y_values)"
      ],
      "metadata": {
        "id": "HqI5RWevLECJ",
        "outputId": "900dd567-ab3f-4bd2-bbc1-79ff05cd1ec8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#################\n",
            "ddG Values for openMM vs CHARMM/OpenMM\n",
            "MAE:  0.35 [0.21, 0.46]\n",
            "RMSE:  0.39 [0.28, 0.47]\n",
            " Pearson correlation 0.98\n",
            " Spearmans 1.0\n",
            " Kendall 1.0\n",
            "Pearson correlation:  0.98 [nan, nan]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
            "  warnings.warn(PearsonRConstantInputWarning())\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Spearman's correlation:  1.00 [1.00, 1.00]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2je6-E0nCWS7"
      },
      "execution_count": 85,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Bootstrapping.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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"
    }
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  "nbformat_minor": 0
}