notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is an analysis of the errors in relative free energy calculations from the Schrödinger JACS dataset:\n",
    "> Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., ... & Romero, D. L. (2015). Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. Journal of the American Chemical Society, 137(7), 2695-2703.\n",
    "\n",
    "http://doi.org/10.1021/ja512751q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the Excel sheet\n",
    "df = pd.read_excel('ja512751q_si_003.xlsx', sheet_name='ddG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropping rows: [330]\n"
     ]
    }
   ],
   "source": [
    "# Delete rows with summary statistics\n",
    "rows_to_drop = list()\n",
    "for i, row in df.iterrows():\n",
    "    if str(df.loc[i,'Ligand1']) == 'nan':\n",
    "        rows_to_drop.append(i)\n",
    "print('dropping rows: {}'.format(rows_to_drop))\n",
    "df = df.drop(index=rows_to_drop);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate 'system' field for each entry\n",
    "system = df.loc[0,'system']\n",
    "for i, row in df.iterrows():\n",
    "    if str(df.loc[i,'system']) == 'nan':\n",
    "        df.loc[i, \"system\"] = system\n",
    "    else:\n",
    "        system = df.loc[i, \"system\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system</th>\n",
       "      <th>Ligand1</th>\n",
       "      <th>Ligand2</th>\n",
       "      <th>exp_ddG</th>\n",
       "      <th>bennett_ddG</th>\n",
       "      <th>bennett_error</th>\n",
       "      <th>ccc_ddG</th>\n",
       "      <th>ccc_error</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Error_bar</th>\n",
       "      <th>abs_error_Bar</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Error_ccc</th>\n",
       "      <th>Abs_error_ccc</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13b</td>\n",
       "      <td>CAT-17g</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.9604</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.1936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13a</td>\n",
       "      <td>CAT-17g</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.7744</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.4096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13e</td>\n",
       "      <td>CAT-17g</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.6384</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.3924</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4m</td>\n",
       "      <td>CAT-4c</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.2704</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.1024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13k</td>\n",
       "      <td>CAT-4d</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.3924</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.8225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-24</td>\n",
       "      <td>CAT-17e</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.87</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.4225</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.2916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13g</td>\n",
       "      <td>CAT-17g</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.2801</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.6569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13d</td>\n",
       "      <td>CAT-13h</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.3844</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13a</td>\n",
       "      <td>CAT-17i</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4m</td>\n",
       "      <td>CAT-13j</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.1849</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.4356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13a</td>\n",
       "      <td>CAT-13m</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.0609</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.8496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4l</td>\n",
       "      <td>CAT-13k</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.6481</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.0625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13o</td>\n",
       "      <td>CAT-17i</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0225</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4c</td>\n",
       "      <td>CAT-4o</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4j</td>\n",
       "      <td>CAT-4o</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>0.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.1369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4i</td>\n",
       "      <td>CAT-13m</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.57</td>\n",
       "      <td>6.6049</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.59</td>\n",
       "      <td>6.7081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-24</td>\n",
       "      <td>CAT-17i</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.07</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.0201</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.2544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13j</td>\n",
       "      <td>CAT-4o</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.8836</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.5041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4n</td>\n",
       "      <td>CAT-13k</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.4761</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4o</td>\n",
       "      <td>CAT-4b</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.9216</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.6900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17i</td>\n",
       "      <td>CAT-13f</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.3225</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.6384</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17c</td>\n",
       "      <td>CAT-17e</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.2916</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13k</td>\n",
       "      <td>CAT-4b</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.5129</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.7921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4m</td>\n",
       "      <td>CAT-4j</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.3364</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.1849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13n</td>\n",
       "      <td>CAT-13k</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.82</td>\n",
       "      <td>3.3124</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.47</td>\n",
       "      <td>2.1609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13d</td>\n",
       "      <td>CAT-17h</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17b</td>\n",
       "      <td>CAT-13d</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.8281</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.7396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4m</td>\n",
       "      <td>CAT-4n</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.4225</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.7056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4m</td>\n",
       "      <td>CAT-13k</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>0.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.2025</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13c</td>\n",
       "      <td>CAT-17i</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.1681</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23477</td>\n",
       "      <td>23479</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>1.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23477</td>\n",
       "      <td>23482</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>1.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.4900</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.1936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23477</td>\n",
       "      <td>23483</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.2025</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.5184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23477</td>\n",
       "      <td>23330(2qbq)</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.5041</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.3721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23480</td>\n",
       "      <td>23479</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0576</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.2116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23480</td>\n",
       "      <td>23482</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>1.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.2321</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.7921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23482</td>\n",
       "      <td>23479</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.8281</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.1764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23482</td>\n",
       "      <td>23485</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.2809</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.1449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23482</td>\n",
       "      <td>23486</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>1.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>2.57</td>\n",
       "      <td>6.6049</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.6129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23483</td>\n",
       "      <td>23479</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.5876</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23484</td>\n",
       "      <td>23479</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>2.74</td>\n",
       "      <td>7.5076</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>2.06</td>\n",
       "      <td>4.2436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23484</td>\n",
       "      <td>23482</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>2.19</td>\n",
       "      <td>4.7961</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>2.48</td>\n",
       "      <td>6.1504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23484</td>\n",
       "      <td>23485</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>3.45</td>\n",
       "      <td>11.9025</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>3.55</td>\n",
       "      <td>12.6025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23484</td>\n",
       "      <td>23486</td>\n",
       "      <td>-4.72</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>1.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>3.78</td>\n",
       "      <td>14.2884</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>3.76</td>\n",
       "      <td>14.1376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23485</td>\n",
       "      <td>23479</td>\n",
       "      <td>1.87</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.58</td>\n",
       "      <td>2.4964</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.2201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23486</td>\n",
       "      <td>23479</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.6641</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.8900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23486</td>\n",
       "      <td>23485</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.0676</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20667(2qbp)</td>\n",
       "      <td>23479</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.3225</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.0201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20667(2qbp)</td>\n",
       "      <td>23482</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.1764</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20667(2qbp)</td>\n",
       "      <td>23484</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.75</td>\n",
       "      <td>7.5625</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.07</td>\n",
       "      <td>9.4249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20667(2qbp)</td>\n",
       "      <td>23485</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.3249</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.2304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20667(2qbp)</td>\n",
       "      <td>23486</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>1.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0576</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.4761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20669(2qbr)</td>\n",
       "      <td>23466</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.5776</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.3721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20669(2qbr)</td>\n",
       "      <td>23472</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.0361</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20670(2qbs)</td>\n",
       "      <td>23466</td>\n",
       "      <td>1.24</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.89</td>\n",
       "      <td>1.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.7225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20670(2qbs)</td>\n",
       "      <td>23477</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.0961</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.3969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20670(2qbs)</td>\n",
       "      <td>23479</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.4225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20670(2qbs)</td>\n",
       "      <td>23482</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.9604</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.1449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20670(2qbs)</td>\n",
       "      <td>23483</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0225</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20670(2qbs)</td>\n",
       "      <td>23330(2qbq)</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.892653</td>\n",
       "      <td>1.22398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    system      Ligand1      Ligand2  exp_ddG  bennett_ddG  bennett_error  \\\n",
       "0     BACE      CAT-13b      CAT-17g    -0.62         0.36           0.11   \n",
       "1     BACE      CAT-13a      CAT-17g    -0.90        -0.02           0.10   \n",
       "2     BACE      CAT-13e      CAT-17g     0.22         1.50           0.11   \n",
       "3     BACE       CAT-4m       CAT-4c     1.30         0.78           0.10   \n",
       "4     BACE      CAT-13k       CAT-4d     0.59        -0.59           0.11   \n",
       "5     BACE       CAT-24      CAT-17e     1.33         1.98           0.08   \n",
       "6     BACE      CAT-13g      CAT-17g    -0.65         0.86           0.15   \n",
       "7     BACE      CAT-13d      CAT-13h     0.84         1.46           0.10   \n",
       "8     BACE      CAT-13a      CAT-17i    -0.63        -0.76           0.11   \n",
       "9     BACE       CAT-4m      CAT-13j     0.42        -0.01           0.12   \n",
       "10    BACE      CAT-13a      CAT-13m     0.08        -0.95           0.13   \n",
       "11    BACE       CAT-4l      CAT-13k    -0.36        -2.27           0.12   \n",
       "12    BACE      CAT-13o      CAT-17i    -0.93        -1.08           0.12   \n",
       "13    BACE       CAT-4c       CAT-4o    -1.53        -1.35           0.09   \n",
       "14    BACE       CAT-4j       CAT-4o    -0.36        -0.58           0.06   \n",
       "15    BACE       CAT-4i      CAT-13m    -0.50        -3.07           0.12   \n",
       "16    BACE       CAT-24      CAT-17i     1.88         2.89           0.07   \n",
       "17    BACE      CAT-13j       CAT-4o    -0.65         0.29           0.10   \n",
       "18    BACE       CAT-4n      CAT-13k    -0.61         0.08           0.10   \n",
       "19    BACE       CAT-4o       CAT-4b    -0.25        -1.21           0.11   \n",
       "20    BACE      CAT-17i      CAT-13f     0.38        -0.77           0.13   \n",
       "21    BACE      CAT-17c      CAT-17e    -0.16         0.38           0.11   \n",
       "22    BACE      CAT-13k       CAT-4b     0.07        -1.16           0.10   \n",
       "23    BACE       CAT-4m       CAT-4j     0.13         0.71           0.07   \n",
       "24    BACE      CAT-13n      CAT-13k    -1.16        -2.98           0.14   \n",
       "25    BACE      CAT-13d      CAT-17h     0.14         0.05           0.07   \n",
       "26    BACE      CAT-17b      CAT-13d    -0.45         0.46           0.09   \n",
       "27    BACE       CAT-4m       CAT-4n     0.06        -0.59           0.07   \n",
       "28    BACE       CAT-4m      CAT-13k    -0.55        -1.00           0.09   \n",
       "29    BACE      CAT-13c      CAT-17i    -0.15         0.26           0.11   \n",
       "..     ...          ...          ...      ...          ...            ...   \n",
       "300  PTP1B        23477        23479    -0.29        -0.67           0.12   \n",
       "301  PTP1B        23477        23482    -1.15        -0.45           0.11   \n",
       "302  PTP1B        23477        23483    -1.02        -1.47           0.13   \n",
       "303  PTP1B        23477  23330(2qbq)    -1.27        -1.98           0.15   \n",
       "304  PTP1B        23480        23479    -0.42        -0.18           0.07   \n",
       "305  PTP1B        23480        23482    -1.29        -0.18           0.18   \n",
       "306  PTP1B        23482        23479     0.86        -0.05           0.22   \n",
       "307  PTP1B        23482        23485    -1.01        -0.48           0.15   \n",
       "308  PTP1B        23482        23486    -2.46         0.11           0.16   \n",
       "309  PTP1B        23483        23479     0.73         1.99           0.22   \n",
       "310  PTP1B        23484        23479    -1.39         1.35           0.33   \n",
       "311  PTP1B        23484        23482    -2.25        -0.06           0.16   \n",
       "312  PTP1B        23484        23485    -3.26         0.19           0.15   \n",
       "313  PTP1B        23484        23486    -4.72        -0.94           0.15   \n",
       "314  PTP1B        23485        23479     1.87         0.29           0.33   \n",
       "315  PTP1B        23486        23479     3.33         2.04           0.30   \n",
       "316  PTP1B        23486        23485     1.46         1.72           0.16   \n",
       "317  PTP1B  20667(2qbp)        23479     2.28         1.13           0.23   \n",
       "318  PTP1B  20667(2qbp)        23482     1.42         1.00           0.10   \n",
       "319  PTP1B  20667(2qbp)        23484     3.67         0.92           0.16   \n",
       "320  PTP1B  20667(2qbp)        23485     0.41         0.98           0.17   \n",
       "321  PTP1B  20667(2qbp)        23486    -1.05        -0.81           0.19   \n",
       "322  PTP1B  20669(2qbr)        23466     0.76         1.52           0.14   \n",
       "323  PTP1B  20669(2qbr)        23472    -0.04        -0.23           0.18   \n",
       "324  PTP1B  20670(2qbs)        23466     1.24         3.24           0.13   \n",
       "325  PTP1B  20670(2qbs)        23477     0.23         0.54           0.08   \n",
       "326  PTP1B  20670(2qbs)        23479    -0.06         0.32           0.13   \n",
       "327  PTP1B  20670(2qbs)        23482    -0.92         0.06           0.11   \n",
       "328  PTP1B  20670(2qbs)        23483    -0.79        -0.64           0.13   \n",
       "329  PTP1B  20670(2qbs)  23330(2qbq)    -1.04        -0.92           0.10   \n",
       "\n",
       "     ccc_ddG  ccc_error  Unnamed: 8  Error_bar  abs_error_Bar  Unnamed: 11  \\\n",
       "0      -0.18       1.20         NaN      -0.98           0.98       0.9604   \n",
       "1      -0.26       0.92         NaN      -0.88           0.88       0.7744   \n",
       "2       1.40       0.81         NaN      -1.28           1.28       1.6384   \n",
       "3       0.98       0.46         NaN       0.52           0.52       0.2704   \n",
       "4      -0.76       0.51         NaN       1.18           1.18       1.3924   \n",
       "5       1.87       0.36         NaN      -0.65           0.65       0.4225   \n",
       "6       0.98       0.64         NaN      -1.51           1.51       2.2801   \n",
       "7       1.24       0.92         NaN      -0.62           0.62       0.3844   \n",
       "8      -0.52       0.92         NaN       0.13           0.13       0.0169   \n",
       "9      -0.24       0.59         NaN       0.43           0.43       0.1849   \n",
       "10     -1.28       0.71         NaN       1.03           1.03       1.0609   \n",
       "11     -2.11       0.59         NaN       1.91           1.91       3.6481   \n",
       "12     -1.01       0.29         NaN       0.15           0.15       0.0225   \n",
       "13     -1.15       0.46         NaN      -0.18           0.18       0.0324   \n",
       "14     -0.73       0.53         NaN       0.22           0.22       0.0484   \n",
       "15     -3.09       0.46         NaN       2.57           2.57       6.6049   \n",
       "16      3.00       0.36         NaN      -1.01           1.01       1.0201   \n",
       "17      0.06       0.59         NaN      -0.94           0.94       0.8836   \n",
       "18     -0.11       0.71         NaN      -0.69           0.69       0.4761   \n",
       "19     -1.55       0.59         NaN       0.96           0.96       0.9216   \n",
       "20     -0.90       0.77         NaN       1.15           1.15       1.3225   \n",
       "21      0.43       0.54         NaN      -0.54           0.54       0.2916   \n",
       "22     -0.82       0.59         NaN       1.23           1.23       1.5129   \n",
       "23      0.56       0.53         NaN      -0.58           0.58       0.3364   \n",
       "24     -2.63       0.71         NaN       1.82           1.82       3.3124   \n",
       "25      0.12       0.29         NaN       0.09           0.09       0.0081   \n",
       "26      0.41       0.54         NaN      -0.91           0.91       0.8281   \n",
       "27     -0.78       0.71         NaN       0.65           0.65       0.4225   \n",
       "28     -0.90       0.56         NaN       0.45           0.45       0.2025   \n",
       "29     -0.02       0.77         NaN      -0.41           0.41       0.1681   \n",
       "..       ...        ...         ...        ...            ...          ...   \n",
       "300    -0.27       1.39         NaN       0.38           0.38       0.1444   \n",
       "301    -0.71       1.39         NaN      -0.70           0.70       0.4900   \n",
       "302    -1.74       0.90         NaN       0.45           0.45       0.2025   \n",
       "303    -1.88       1.00         NaN       0.71           0.71       0.5041   \n",
       "304     0.04       1.30         NaN      -0.24           0.24       0.0576   \n",
       "305    -0.40       1.30         NaN      -1.11           1.11       1.2321   \n",
       "306     0.44       1.45         NaN       0.91           0.91       0.8281   \n",
       "307     0.06       1.38         NaN      -0.53           0.53       0.2809   \n",
       "308    -1.19       1.45         NaN      -2.57           2.57       6.6049   \n",
       "309     1.48       0.80         NaN      -1.26           1.26       1.5876   \n",
       "310     0.67       1.45         NaN      -2.74           2.74       7.5076   \n",
       "311     0.23       0.89         NaN      -2.19           2.19       4.7961   \n",
       "312     0.29       1.36         NaN      -3.45           3.45      11.9025   \n",
       "313    -0.96       1.23         NaN      -3.78           3.78      14.2884   \n",
       "314     0.38       1.38         NaN       1.58           1.58       2.4964   \n",
       "315     1.63       1.27         NaN       1.29           1.29       1.6641   \n",
       "316     1.25       1.36         NaN      -0.26           0.26       0.0676   \n",
       "317     1.27       1.17         NaN       1.15           1.15       1.3225   \n",
       "318     0.83       1.17         NaN       0.42           0.42       0.1764   \n",
       "319     0.60       1.45         NaN       2.75           2.75       7.5625   \n",
       "320     0.89       1.29         NaN      -0.57           0.57       0.3249   \n",
       "321    -0.36       1.45         NaN      -0.24           0.24       0.0576   \n",
       "322     1.37       0.61         NaN      -0.76           0.76       0.5776   \n",
       "323    -0.17       0.38         NaN       0.19           0.19       0.0361   \n",
       "324     2.89       1.16         NaN      -2.00           2.00       4.0000   \n",
       "325     0.86       1.06         NaN      -0.31           0.31       0.0961   \n",
       "326     0.59       1.20         NaN      -0.38           0.38       0.1444   \n",
       "327     0.15       1.20         NaN      -0.98           0.98       0.9604   \n",
       "328    -0.88       0.90         NaN      -0.15           0.15       0.0225   \n",
       "329    -1.02       1.00         NaN      -0.12           0.12       0.0144   \n",
       "\n",
       "     Error_ccc  Abs_error_ccc  Unnamed: 14  Unnamed: 15  Unnamed: 16  \n",
       "0        -0.44           0.44       0.1936          NaN          NaN  \n",
       "1        -0.64           0.64       0.4096          NaN          NaN  \n",
       "2        -1.18           1.18       1.3924          NaN          NaN  \n",
       "3         0.32           0.32       0.1024          NaN          NaN  \n",
       "4         1.35           1.35       1.8225          NaN          NaN  \n",
       "5        -0.54           0.54       0.2916          NaN          NaN  \n",
       "6        -1.63           1.63       2.6569          NaN          NaN  \n",
       "7        -0.40           0.40       0.1600          NaN          NaN  \n",
       "8        -0.11           0.11       0.0121          NaN          NaN  \n",
       "9         0.66           0.66       0.4356          NaN          NaN  \n",
       "10        1.36           1.36       1.8496          NaN          NaN  \n",
       "11        1.75           1.75       3.0625          NaN          NaN  \n",
       "12        0.08           0.08       0.0064          NaN          NaN  \n",
       "13       -0.38           0.38       0.1444          NaN          NaN  \n",
       "14        0.37           0.37       0.1369          NaN          NaN  \n",
       "15        2.59           2.59       6.7081          NaN          NaN  \n",
       "16       -1.12           1.12       1.2544          NaN          NaN  \n",
       "17       -0.71           0.71       0.5041          NaN          NaN  \n",
       "18       -0.50           0.50       0.2500          NaN          NaN  \n",
       "19        1.30           1.30       1.6900          NaN          NaN  \n",
       "20        1.28           1.28       1.6384          NaN          NaN  \n",
       "21       -0.59           0.59       0.3481          NaN          NaN  \n",
       "22        0.89           0.89       0.7921          NaN          NaN  \n",
       "23       -0.43           0.43       0.1849          NaN          NaN  \n",
       "24        1.47           1.47       2.1609          NaN          NaN  \n",
       "25        0.02           0.02       0.0004          NaN          NaN  \n",
       "26       -0.86           0.86       0.7396          NaN          NaN  \n",
       "27        0.84           0.84       0.7056          NaN          NaN  \n",
       "28        0.35           0.35       0.1225          NaN          NaN  \n",
       "29       -0.13           0.13       0.0169          NaN          NaN  \n",
       "..         ...            ...          ...          ...          ...  \n",
       "300      -0.02           0.02       0.0004          NaN          NaN  \n",
       "301      -0.44           0.44       0.1936          NaN          NaN  \n",
       "302       0.72           0.72       0.5184          NaN          NaN  \n",
       "303       0.61           0.61       0.3721          NaN          NaN  \n",
       "304      -0.46           0.46       0.2116          NaN          NaN  \n",
       "305      -0.89           0.89       0.7921          NaN          NaN  \n",
       "306       0.42           0.42       0.1764          NaN          NaN  \n",
       "307      -1.07           1.07       1.1449          NaN          NaN  \n",
       "308      -1.27           1.27       1.6129          NaN          NaN  \n",
       "309      -0.75           0.75       0.5625          NaN          NaN  \n",
       "310      -2.06           2.06       4.2436          NaN          NaN  \n",
       "311      -2.48           2.48       6.1504          NaN          NaN  \n",
       "312      -3.55           3.55      12.6025          NaN          NaN  \n",
       "313      -3.76           3.76      14.1376          NaN          NaN  \n",
       "314       1.49           1.49       2.2201          NaN          NaN  \n",
       "315       1.70           1.70       2.8900          NaN          NaN  \n",
       "316       0.21           0.21       0.0441          NaN          NaN  \n",
       "317       1.01           1.01       1.0201          NaN          NaN  \n",
       "318       0.59           0.59       0.3481          NaN          NaN  \n",
       "319       3.07           3.07       9.4249          NaN          NaN  \n",
       "320      -0.48           0.48       0.2304          NaN          NaN  \n",
       "321      -0.69           0.69       0.4761          NaN          NaN  \n",
       "322      -0.61           0.61       0.3721          NaN          NaN  \n",
       "323       0.13           0.13       0.0169          NaN          NaN  \n",
       "324      -1.65           1.65       2.7225          NaN          NaN  \n",
       "325      -0.63           0.63       0.3969          NaN          NaN  \n",
       "326      -0.65           0.65       0.4225          NaN          NaN  \n",
       "327      -1.07           1.07       1.1449          NaN          NaN  \n",
       "328       0.09           0.09       0.0081          NaN          NaN  \n",
       "329      -0.02           0.02       0.0004     0.892653      1.22398  \n",
       "\n",
       "[330 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_statistic(errors, ci=0.95, statistic='rmse', nbootstrap = 1000):\n",
    "    \"\"\"Compute mean and confidence intervals of specified statistic.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    errors : ndarray of shape (N,)\n",
    "        The errors to bootstrap\n",
    "    ci : float, optional, default=0.95\n",
    "        Interval for CI\n",
    "    statistic : str, optional, default='rmse'\n",
    "        Statistic to compute; one of ['rmse', 'mue']\n",
    "    nbootstrap : int, optional. default=10000\n",
    "        Number of bootstrap samples\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    mean : float\n",
    "        The mean statistic for the whole dataset\n",
    "    stderr : float\n",
    "        The standard error\n",
    "    low, high : float\n",
    "        Low and high ends of CI    \n",
    "    \"\"\"\n",
    "    \n",
    "    def compute_statistic(errors, statistic):\n",
    "        if statistic == 'rmse':\n",
    "            return np.sqrt(np.mean(errors**2))\n",
    "        elif statistic == 'mue':\n",
    "            return np.mean(np.abs(errors))\n",
    "        else:\n",
    "            raise Exception(\"unknown statistic '{}'\".format(statistic))\n",
    "            \n",
    "    sample_size = len(errors)\n",
    "    rmse_n = np.zeros([nbootstrap], np.float64)\n",
    "    for replicate in range(nbootstrap):\n",
    "        bootstrapped_sample = np.random.choice(np.arange(sample_size), size=[sample_size])\n",
    "        rmse_n[replicate] = compute_statistic(errors[bootstrapped_sample], statistic)\n",
    "        \n",
    "    rmse_n = np.sort(rmse_n)\n",
    "        \n",
    "    rmse = compute_statistic(errors, statistic)\n",
    "    drmse = np.std(rmse_n)\n",
    "    \n",
    "    low_frac = (1.0-ci)/2.0\n",
    "    high_frac = 1.0 - low_frac\n",
    "    rmse_low = rmse_n[int(np.floor(nbootstrap*low_frac))]\n",
    "    rmse_high = rmse_n[int(np.ceil(nbootstrap*high_frac))]\n",
    "\n",
    "    return rmse, drmse, rmse_low, rmse_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collect data by system\n",
    "def plot_data(rows):\n",
    "    exp_ddg = rows['exp_ddG'].values\n",
    "    ccc_ddg = rows['ccc_ddG'].values\n",
    "    ccc_error = rows['ccc_error'].values\n",
    "    error = rows['Error_ccc'].values\n",
    "    xmin = min(exp_ddg.min(), ccc_ddg.min()) - 0.5\n",
    "    xmax = min(exp_ddg.max(), ccc_ddg.max()) + 0.5\n",
    "    plt.xlabel('experiment $\\Delta \\Delta G$ (kcal/mol)');\n",
    "    plt.ylabel('cycle closure $\\Delta \\Delta G$ (kcal/mol)');\n",
    "    plt.plot([xmin, xmax], [xmin, xmax], 'b-');\n",
    "    plt.plot(exp_ddg, ccc_ddg, 'ko');\n",
    "    plt.plot([exp_ddg, exp_ddg], [ccc_ddg-2*ccc_error, ccc_ddg+2*ccc_error], 'k-', alpha=0.3);\n",
    "\n",
    "    rmse, drmse, rmse_low, rmse_high = bootstrap_statistic(error, statistic='rmse')\n",
    "    mue, dmue, mue_low, mue_high = bootstrap_statistic(error, statistic='mue')\n",
    "    title = \"\"\"{} (N = {})\n",
    "    RMSE {:.1f} [95%: {:.1f}, {:.1f}] kcal/mol\n",
    "    MUE {:.1f} [95%: {:.1f}, {:.1f}] kcal/mol\n",
    "    \"\"\".format(system, len(exp_ddg), rmse, rmse_low, rmse_high, mue, mue_low, mue_high)\n",
    "    plt.title(title);\n",
    "    \n",
    "    plt.axis('square');\n",
    "    plt.xlim(xmin, xmax);\n",
    "    plt.ylim(xmin, xmax);\n",
    "\n",
    "systems = df['system'].unique()\n",
    "nsystems = len(systems)\n",
    "nx = int(np.ceil(np.sqrt(nsystems)))\n",
    "ny = int(np.ceil(np.sqrt(nsystems)))\n",
    "fig = plt.figure(figsize=[12,12])\n",
    "for plot_index, system in enumerate(systems):\n",
    "    plt.subplot(ny, nx, plot_index+1)\n",
    "    rows = df.query(\"system == '{}'\".format(system))\n",
    "    plot_data(rows)\n",
    "    \n",
    "plt.subplot(ny, nx, nsystems+1)\n",
    "system = 'all'\n",
    "plot_data(df)\n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.savefig('jacs-analysis.pdf');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}