notebook-fraction-sign-correct.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is an analysis of predictive accuracy 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": [
    "def read_ddGs\n",
    "\n",
    "# Read the Excel sheet\n",
    "df = pd.read_excel('ja512751q_si_003.xlsx', sheet_name='dG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropping rows: [36, 53, 75, 118, 153, 165, 182]\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,'Ligand']) == '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,'Systems']\n",
    "for i, row in df.iterrows():\n",
    "    if str(df.loc[i,'Systems']) == 'nan':\n",
    "        df.loc[i, \"Systems\"] = system\n",
    "    else:\n",
    "        system = df.loc[i, \"Systems\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Systems</th>\n",
       "      <th>Ligand</th>\n",
       "      <th>Exp. dG</th>\n",
       "      <th>Pred. dG</th>\n",
       "      <th>Pred. Error</th>\n",
       "      <th>Rel. dG</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Error</th>\n",
       "      <th>Error^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4o</td>\n",
       "      <td>-9.38</td>\n",
       "      <td>-8.67</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.5041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13e</td>\n",
       "      <td>-9.96</td>\n",
       "      <td>-10.49</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.2809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17b</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>-10.91</td>\n",
       "      <td>1.18</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.7921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13k</td>\n",
       "      <td>-9.70</td>\n",
       "      <td>-9.40</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4m</td>\n",
       "      <td>-9.15</td>\n",
       "      <td>-8.50</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.4225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4b</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>-10.22</td>\n",
       "      <td>1.17</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.3481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4p</td>\n",
       "      <td>-10.08</td>\n",
       "      <td>-9.79</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17f</td>\n",
       "      <td>-9.42</td>\n",
       "      <td>-10.45</td>\n",
       "      <td>1.05</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.0609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4l</td>\n",
       "      <td>-9.34</td>\n",
       "      <td>-7.29</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.05</td>\n",
       "      <td>4.2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13g</td>\n",
       "      <td>-9.09</td>\n",
       "      <td>-10.07</td>\n",
       "      <td>1.12</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.9604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17e</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>-10.47</td>\n",
       "      <td>1.05</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13b</td>\n",
       "      <td>-9.12</td>\n",
       "      <td>-8.92</td>\n",
       "      <td>1.51</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4a</td>\n",
       "      <td>-7.93</td>\n",
       "      <td>-7.96</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4k</td>\n",
       "      <td>-7.85</td>\n",
       "      <td>-7.41</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.1936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13a</td>\n",
       "      <td>-8.84</td>\n",
       "      <td>-8.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17d</td>\n",
       "      <td>-9.42</td>\n",
       "      <td>-7.93</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.2201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13i</td>\n",
       "      <td>-9.27</td>\n",
       "      <td>-10.43</td>\n",
       "      <td>1.51</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.3456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17c</td>\n",
       "      <td>-9.86</td>\n",
       "      <td>-10.90</td>\n",
       "      <td>1.07</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.0816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-24</td>\n",
       "      <td>-11.35</td>\n",
       "      <td>-12.35</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13m</td>\n",
       "      <td>-8.76</td>\n",
       "      <td>-10.11</td>\n",
       "      <td>0.71</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.8225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4i</td>\n",
       "      <td>-8.26</td>\n",
       "      <td>-7.02</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.5376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4j</td>\n",
       "      <td>-9.02</td>\n",
       "      <td>-7.94</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.1664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13f</td>\n",
       "      <td>-9.09</td>\n",
       "      <td>-10.24</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.3225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13n</td>\n",
       "      <td>-8.54</td>\n",
       "      <td>-6.77</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3.1329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13h</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>-9.26</td>\n",
       "      <td>1.30</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-13d</td>\n",
       "      <td>-10.47</td>\n",
       "      <td>-10.50</td>\n",
       "      <td>1.05</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-4d</td>\n",
       "      <td>-9.11</td>\n",
       "      <td>-10.16</td>\n",
       "      <td>1.13</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17i</td>\n",
       "      <td>-9.47</td>\n",
       "      <td>-9.35</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.0144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17a</td>\n",
       "      <td>-10.73</td>\n",
       "      <td>-11.29</td>\n",
       "      <td>1.19</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.3136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BACE</td>\n",
       "      <td>CAT-17g</td>\n",
       "      <td>-9.74</td>\n",
       "      <td>-9.09</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.4225</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tyk2</td>\n",
       "      <td>ejm_50</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>-9.52</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.2916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Tyk2</td>\n",
       "      <td>ejm_54</td>\n",
       "      <td>-10.53</td>\n",
       "      <td>-10.74</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Tyk2</td>\n",
       "      <td>ejm_55</td>\n",
       "      <td>-9.21</td>\n",
       "      <td>-10.61</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tyk2</td>\n",
       "      <td>jmc_23</td>\n",
       "      <td>-11.70</td>\n",
       "      <td>-11.29</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.1681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Tyk2</td>\n",
       "      <td>jmc_27</td>\n",
       "      <td>-11.28</td>\n",
       "      <td>-11.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Tyk2</td>\n",
       "      <td>jmc_28</td>\n",
       "      <td>-10.98</td>\n",
       "      <td>-10.30</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.4624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Tyk2</td>\n",
       "      <td>jmc_30</td>\n",
       "      <td>-10.94</td>\n",
       "      <td>-10.78</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.0256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23466</td>\n",
       "      <td>-7.85</td>\n",
       "      <td>-7.06</td>\n",
       "      <td>0.88</td>\n",
       "      <td>2.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23467</td>\n",
       "      <td>-7.34</td>\n",
       "      <td>-6.67</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23468</td>\n",
       "      <td>-7.75</td>\n",
       "      <td>-7.11</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23469</td>\n",
       "      <td>-7.72</td>\n",
       "      <td>-7.22</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23470</td>\n",
       "      <td>-7.72</td>\n",
       "      <td>-8.06</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23471</td>\n",
       "      <td>-7.75</td>\n",
       "      <td>-7.99</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23472</td>\n",
       "      <td>-8.65</td>\n",
       "      <td>-8.61</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23473</td>\n",
       "      <td>-8.39</td>\n",
       "      <td>-8.11</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23474</td>\n",
       "      <td>-9.11</td>\n",
       "      <td>-9.06</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23475</td>\n",
       "      <td>-8.72</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23476</td>\n",
       "      <td>-9.41</td>\n",
       "      <td>-9.32</td>\n",
       "      <td>0.83</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23477</td>\n",
       "      <td>-8.85</td>\n",
       "      <td>-9.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23479</td>\n",
       "      <td>-9.14</td>\n",
       "      <td>-9.36</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23480</td>\n",
       "      <td>-8.72</td>\n",
       "      <td>-9.40</td>\n",
       "      <td>1.77</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23482</td>\n",
       "      <td>-10.01</td>\n",
       "      <td>-9.80</td>\n",
       "      <td>1.39</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23483</td>\n",
       "      <td>-9.87</td>\n",
       "      <td>-10.83</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23484</td>\n",
       "      <td>-7.75</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>1.65</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.27</td>\n",
       "      <td>5.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23485</td>\n",
       "      <td>-11.01</td>\n",
       "      <td>-9.73</td>\n",
       "      <td>1.83</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23486</td>\n",
       "      <td>-12.47</td>\n",
       "      <td>-10.99</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20667(2qbp)</td>\n",
       "      <td>-11.42</td>\n",
       "      <td>-10.62</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20669(2qbr)</td>\n",
       "      <td>-8.61</td>\n",
       "      <td>-8.43</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>20670(2qbs)</td>\n",
       "      <td>-9.08</td>\n",
       "      <td>-9.95</td>\n",
       "      <td>1.06</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.7600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>PTP1B</td>\n",
       "      <td>23330(2qbq)</td>\n",
       "      <td>-10.13</td>\n",
       "      <td>-10.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.7100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Systems       Ligand  Exp. dG  Pred. dG  Pred. Error  Rel. dG  Unnamed: 6  \\\n",
       "0      BACE       CAT-4o    -9.38     -8.67         1.14     0.16         NaN   \n",
       "1      BACE      CAT-13e    -9.96    -10.49         1.23    -1.66         NaN   \n",
       "2      BACE      CAT-17b   -10.02    -10.91         1.18    -2.08         NaN   \n",
       "3      BACE      CAT-13k    -9.70     -9.40         1.01    -0.57         NaN   \n",
       "4      BACE       CAT-4m    -9.15     -8.50         1.01     0.33         NaN   \n",
       "5      BACE       CAT-4b    -9.63    -10.22         1.17    -1.39         NaN   \n",
       "6      BACE       CAT-4p   -10.08     -9.79         1.24    -0.96         NaN   \n",
       "7      BACE      CAT-17f    -9.42    -10.45         1.05    -1.62         NaN   \n",
       "8      BACE       CAT-4l    -9.34     -7.29         1.17     1.54         NaN   \n",
       "9      BACE      CAT-13g    -9.09    -10.07         1.12    -1.24         NaN   \n",
       "10     BACE      CAT-17e   -10.02    -10.47         1.05    -1.64         NaN   \n",
       "11     BACE      CAT-13b    -9.12     -8.92         1.51    -0.09         NaN   \n",
       "12     BACE       CAT-4a    -7.93     -7.96         1.08     0.87         NaN   \n",
       "13     BACE       CAT-4k    -7.85     -7.41         1.11     1.42         NaN   \n",
       "14     BACE      CAT-13a    -8.84     -8.83         0.00     0.00         NaN   \n",
       "15     BACE      CAT-17d    -9.42     -7.93         1.22     0.90         NaN   \n",
       "16     BACE      CAT-13i    -9.27    -10.43         1.51    -1.60         NaN   \n",
       "17     BACE      CAT-17c    -9.86    -10.90         1.07    -2.07         NaN   \n",
       "18     BACE       CAT-24   -11.35    -12.35         0.99    -3.52         NaN   \n",
       "19     BACE      CAT-13m    -8.76    -10.11         0.71    -1.28         NaN   \n",
       "20     BACE       CAT-4i    -8.26     -7.02         0.85     1.81         NaN   \n",
       "21     BACE       CAT-4j    -9.02     -7.94         1.14     0.89         NaN   \n",
       "22     BACE      CAT-13f    -9.09    -10.24         1.20    -1.41         NaN   \n",
       "23     BACE      CAT-13n    -8.54     -6.77         0.71     2.06         NaN   \n",
       "24     BACE      CAT-13h    -9.63     -9.26         1.30    -0.43         NaN   \n",
       "25     BACE      CAT-13d   -10.47    -10.50         1.05    -1.67         NaN   \n",
       "26     BACE       CAT-4d    -9.11    -10.16         1.13    -1.33         NaN   \n",
       "27     BACE      CAT-17i    -9.47     -9.35         0.92    -0.52         NaN   \n",
       "28     BACE      CAT-17a   -10.73    -11.29         1.19    -2.46         NaN   \n",
       "29     BACE      CAT-17g    -9.74     -9.09         0.92    -0.26         NaN   \n",
       "..      ...          ...      ...       ...          ...      ...         ...   \n",
       "175    Tyk2       ejm_50    -8.98     -9.52         0.41     0.30         NaN   \n",
       "176    Tyk2       ejm_54   -10.53    -10.74         0.60    -0.92         NaN   \n",
       "177    Tyk2       ejm_55    -9.21    -10.61         0.42    -0.79         NaN   \n",
       "178    Tyk2       jmc_23   -11.70    -11.29         0.34    -1.47         NaN   \n",
       "179    Tyk2       jmc_27   -11.28    -11.00         0.35    -1.18         NaN   \n",
       "180    Tyk2       jmc_28   -10.98    -10.30         0.24    -0.48         NaN   \n",
       "181    Tyk2       jmc_30   -10.94    -10.78         0.30    -0.96         NaN   \n",
       "183   PTP1B        23466    -7.85     -7.06         0.88     2.03         NaN   \n",
       "184   PTP1B        23467    -7.34     -6.67         0.68     2.42         NaN   \n",
       "185   PTP1B        23468    -7.75     -7.11         0.77     1.98         NaN   \n",
       "186   PTP1B        23469    -7.72     -7.22         0.78     1.87         NaN   \n",
       "187   PTP1B        23470    -7.72     -8.06         0.89     1.03         NaN   \n",
       "188   PTP1B        23471    -7.75     -7.99         0.84     1.10         NaN   \n",
       "189   PTP1B        23472    -8.65     -8.61         0.87     0.48         NaN   \n",
       "190   PTP1B        23473    -8.39     -8.11         0.92     0.98         NaN   \n",
       "191   PTP1B        23474    -9.11     -9.06         0.91     0.03         NaN   \n",
       "192   PTP1B        23475    -8.72     -8.98         0.97     0.11         NaN   \n",
       "193   PTP1B        23476    -9.41     -9.32         0.83    -0.23         NaN   \n",
       "194   PTP1B        23477    -8.85     -9.09         0.00     0.00         NaN   \n",
       "195   PTP1B        23479    -9.14     -9.36         1.21    -0.27         NaN   \n",
       "196   PTP1B        23480    -8.72     -9.40         1.77    -0.31         NaN   \n",
       "197   PTP1B        23482   -10.01     -9.80         1.39    -0.71         NaN   \n",
       "198   PTP1B        23483    -9.87    -10.83         0.90    -1.74         NaN   \n",
       "199   PTP1B        23484    -7.75    -10.02         1.65    -0.93         NaN   \n",
       "200   PTP1B        23485   -11.01     -9.73         1.83    -0.64         NaN   \n",
       "201   PTP1B        23486   -12.47    -10.99         1.75    -1.90         NaN   \n",
       "202   PTP1B  20667(2qbp)   -11.42    -10.62         1.68    -1.53         NaN   \n",
       "203   PTP1B  20669(2qbr)    -8.61     -8.43         0.86     0.66         NaN   \n",
       "204   PTP1B  20670(2qbs)    -9.08     -9.95         1.06    -0.86         NaN   \n",
       "205   PTP1B  23330(2qbq)   -10.13    -10.97         1.00    -1.88         NaN   \n",
       "\n",
       "     Error  Error^2  \n",
       "0     0.71   0.5041  \n",
       "1     0.53   0.2809  \n",
       "2     0.89   0.7921  \n",
       "3     0.30   0.0900  \n",
       "4     0.65   0.4225  \n",
       "5     0.59   0.3481  \n",
       "6     0.29   0.0841  \n",
       "7     1.03   1.0609  \n",
       "8     2.05   4.2025  \n",
       "9     0.98   0.9604  \n",
       "10    0.45   0.2025  \n",
       "11    0.20   0.0400  \n",
       "12    0.03   0.0009  \n",
       "13    0.44   0.1936  \n",
       "14    0.01   0.0001  \n",
       "15    1.49   2.2201  \n",
       "16    1.16   1.3456  \n",
       "17    1.04   1.0816  \n",
       "18    1.00   1.0000  \n",
       "19    1.35   1.8225  \n",
       "20    1.24   1.5376  \n",
       "21    1.08   1.1664  \n",
       "22    1.15   1.3225  \n",
       "23    1.77   3.1329  \n",
       "24    0.37   0.1369  \n",
       "25    0.03   0.0009  \n",
       "26    1.05   1.1025  \n",
       "27    0.12   0.0144  \n",
       "28    0.56   0.3136  \n",
       "29    0.65   0.4225  \n",
       "..     ...      ...  \n",
       "175   0.54   0.2916  \n",
       "176   0.21   0.0441  \n",
       "177   1.40   1.9600  \n",
       "178   0.41   0.1681  \n",
       "179   0.28   0.0784  \n",
       "180   0.68   0.4624  \n",
       "181   0.16   0.0256  \n",
       "183   0.79   0.6200  \n",
       "184   0.67   0.4400  \n",
       "185   0.64   0.4000  \n",
       "186   0.50   0.2500  \n",
       "187   0.34   0.1200  \n",
       "188   0.24   0.0600  \n",
       "189   0.04   0.0000  \n",
       "190   0.28   0.0800  \n",
       "191   0.05   0.0000  \n",
       "192   0.26   0.0700  \n",
       "193   0.09   0.0100  \n",
       "194   0.24   0.0600  \n",
       "195   0.22   0.0500  \n",
       "196   0.68   0.4700  \n",
       "197   0.21   0.0400  \n",
       "198   0.96   0.9300  \n",
       "199   2.27   5.1700  \n",
       "200   1.28   1.6300  \n",
       "201   1.48   2.1800  \n",
       "202   0.80   0.6300  \n",
       "203   0.18   0.0300  \n",
       "204   0.87   0.7600  \n",
       "205   0.84   0.7100  \n",
       "\n",
       "[199 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_sign_prediction(DeltaG_predicted, DeltaG_experimental, threshold, ci=0.95, nbootstrap = 1000):\n",
    "    \"\"\"Compute mean and confidence intervals for predicting correct sign.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    DeltaG_predicted : numpy array with dimensions (Nligands,)\n",
    "        Predicted free energies (kcal/mol)\n",
    "    DeltaG_experimental : numpy array with dimensions (Nligands,)\n",
    "        Experimental free energies (kcal/mol)\n",
    "    threshold : float\n",
    "        Threshold in free energy (kcal/mol)\n",
    "    ci : float, optional, default=0.95\n",
    "        Interval for CI\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_fraction(DeltaG_predicted, DeltaG_experimental, threshold):\n",
    "        # Compute all differences\n",
    "        N = len(DeltaG_predicted)        \n",
    "        DDG_predicted = np.zeros([N*(N-1)], np.float64)\n",
    "        DDG_experimental = np.zeros([N*(N-1)], np.float64)\n",
    "        index = 0\n",
    "        for i in range(N):\n",
    "            for j in range(N):\n",
    "                if i != j:\n",
    "                    DDG_predicted[index] = (DeltaG_predicted[j] - DeltaG_predicted[i])\n",
    "                    DDG_experimental[index] = (DeltaG_experimental[j] - DeltaG_experimental[i])\n",
    "                    index += 1\n",
    "        indices = np.where(np.abs(DDG_predicted) > threshold)[0]\n",
    "        return np.sum(np.sign(DDG_predicted[indices]) == np.sign(DDG_experimental[indices])) / float(len(indices))\n",
    "            \n",
    "    N_ligands = len(DeltaG_predicted)\n",
    "    fraction_n = np.zeros([nbootstrap], np.float64)\n",
    "    for replicate in range(nbootstrap):\n",
    "        bootstrapped_sample = np.random.choice(np.arange(N_ligands), size=[N_ligands])\n",
    "        fraction_n[replicate] = compute_fraction(DeltaG_predicted[bootstrapped_sample], DeltaG_experimental[bootstrapped_sample], threshold)\n",
    "        \n",
    "    fraction_n = np.sort(fraction_n)\n",
    "        \n",
    "    fraction = compute_fraction(DeltaG_predicted, DeltaG_experimental, threshold)\n",
    "    dfraction = np.std(fraction_n)\n",
    "    \n",
    "    low_frac = (1.0-ci)/2.0\n",
    "    high_frac = 1.0 - low_frac\n",
    "    fraction_low = fraction_n[int(np.floor(nbootstrap*low_frac))]\n",
    "    fraction_high = fraction_n[int(np.ceil(nbootstrap*high_frac))]\n",
    "\n",
    "    return fraction, dfraction, fraction_low, fraction_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BACE 36\n",
      "CDK2 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/choderaj/miniconda/lib/python3.6/site-packages/ipykernel_launcher.py:40: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JNK1 21\n",
      "MCL1 42\n",
      "P38 34\n",
      "Thrombin 11\n",
      "Tyk2 16\n",
      "PTP1B 23\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "plot_data() missing 1 required positional argument: 'rows'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-fe69353a9dbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnsystems\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0msystem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'all'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mplot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: plot_data() missing 1 required positional argument: 'rows'"
     ]
    },
    {
     "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(system, rows):\n",
    "    DeltaG_experimental = rows['Exp. dG'].values\n",
    "    DeltaG_predicted = rows['Pred. dG'].values\n",
    "    plt.xlabel('threshold (kcal/mol)');\n",
    "    plt.ylabel('P(correct sign)');\n",
    "    Nligands = len(DeltaG_experimental)\n",
    "    print(system, Nligands)\n",
    "\n",
    "    [min_threshold, max_threshold] = [0, 2]\n",
    "    thresholds = np.linspace(min_threshold, max_threshold, 20)\n",
    "    fractions = thresholds * 0.0\n",
    "    dfractions = thresholds * 0.0\n",
    "    fractions_low = thresholds * 0.0\n",
    "    fractions_high = thresholds * 0.0\n",
    "    for index, threshold in enumerate(thresholds):\n",
    "        fractions[index], dfractions[index], fractions_low[index], fractions_high[index] = bootstrap_sign_prediction(DeltaG_predicted, DeltaG_experimental, threshold)\n",
    "    plt.fill_between(thresholds, fractions_low, fractions_high, alpha=0.5)\n",
    "    plt.plot(thresholds, fractions, 'ko');\n",
    "    #plt.plot(thresholds, fractions_low, 'k-')\n",
    "    #plt.plot(thresholds, fractions_high, 'k-')\n",
    "    plt.title('{} (N = {})'.format(system, Nligands));\n",
    "    \n",
    "    plt.xlim(min_threshold, max_threshold);\n",
    "    plt.ylim(0, 1);\n",
    "\n",
    "systems = df['Systems'].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(\"Systems == '{}'\".format(system))\n",
    "    plot_data(system, 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-fraction-analysis.pdf');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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