{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "834aeced-c3c5-42a0-bad1-41e009dd86ee",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "86476f6e-802a-463b-a1b0-2ae228bb92af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9b2be11c-f4bb-4107-af49-abd78052afcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('pdbbind/data/index/INDEX_general_PL_data.2020',skiprows=4,sep=r'\\s+',usecols=[0,4]).drop(0)\n",
    "df = df.rename(columns={'#': 'name','release': 'affinity'})\n",
    "df_refined = pd.read_table('pdbbind/data/index/INDEX_refined_data.2020',skiprows=4,sep=r'\\s+',usecols=[0,4]).drop(0)\n",
    "df_refined = df_refined.rename(columns={'#': 'name','release': 'affinity'})\n",
    "df = pd.concat([df,df_refined])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "68983ab8-bf11-4ed6-ba06-f962dbdc077e",
   "metadata": {},
   "outputs": [],
   "source": [
    "quantities = ['ki','kd','ka','k1/2','kb','ic50','ec50']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3acbca3c-9c0b-43a1-a45e-331bf153bcfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pint import UnitRegistry\n",
    "ureg = UnitRegistry()\n",
    "\n",
    "def to_uM(affinity):\n",
    "    val = ureg(affinity)\n",
    "    try:\n",
    "        return val.m_as(ureg.uM)\n",
    "    except Exception:\n",
    "        pass\n",
    "    \n",
    "    try:\n",
    "        return 1/val.m_as(1/ureg.uM)\n",
    "    except Exception:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "58e5748b-2cea-43ff-ab51-85a5021bd50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['affinity_uM'] = df['affinity'].str.split('[=\\~><]').str[1].apply(to_uM)\n",
    "df['affinity_quantity'] = df['affinity'].str.split('[=\\~><]').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d92f0004-68c1-4487-94b9-56b4fd598de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD4CAYAAADsKpHdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQdUlEQVR4nO3df6zdd13H8efLVkYpFjfHbpp22BKL2m1B3GVWiOSSGncFYveHM8XhOrOkYZk4ZYnp+Adj0mQkoLDFjVTBdjiYdZC0OoYuhStR98MWiKUrywqb22WXlR8ydxHHOt/+cT7Vw+3p7XrOvfe03ucjOTnf8/5+Pt/v5+Z+e173+/l+z2mqCkmSfmTYA5AknRkMBEkSYCBIkhoDQZIEGAiSpGbpsAfQr/PPP7/WrFnTV9/vfe97LF++fG4HJDUeX5pvgxxjBw4c+FZVvbLXurM2ENasWcP+/fv76jsxMcHY2NjcDkhqPL403wY5xpL828nWOWUkSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1pwyEJB9NcjTJl7tq5yW5L8mj7fncrnU3JTmS5JEkl3fVL01ysK27JUla/Zwkf9XqDyZZM8c/oyTpRXgxZwg7gfEZtW3AvqpaB+xrr0myHtgMXNT63JZkSetzO7AVWNcex7d5LfDvVfVTwJ8A7+v3h5Ek9e+UgVBVnwe+M6O8CdjVlncBV3TV76qq56rqMeAIcFmSlcCKqrq/Ov8Bwx0z+hzf1t3AxuNnD5KkhdPvJ5VHqmoKoKqmklzQ6quAB7raTbba8215Zv14nyfbto4leQb4CeBbM3eaZCudswxGRkaYmJjoa/DT09N995VOxeNrcTj49WeGtu+1r1gyL8fYXH91Ra+/7GuW+mx9TixW7QB2AIyOjla/H932qwU0nzy+Fodrtt0ztH3vHF8+L8dYv3cZPd2mgWjPR1t9Eriwq91q4KlWX92j/kN9kiwFXsGJU1SSpHnWbyDsBba05S3Anq765nbn0Fo6F48fatNLzybZ0K4PXD2jz/Ft/Trw2fI/epakBXfKKaMknwDGgPOTTALvBW4Gdie5FngCuBKgqg4l2Q08DBwDrq+qF9qmrqNzx9Iy4N72APgI8LEkR+icGWyek59MknRaThkIVfX2k6zaeJL224HtPer7gYt71P+LFiiSpOHxk8qSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQMFAhJfj/JoSRfTvKJJC9Ncl6S+5I82p7P7Wp/U5IjSR5JcnlX/dIkB9u6W5JkkHFJkk5f34GQZBXwu8BoVV0MLAE2A9uAfVW1DtjXXpNkfVt/ETAO3JZkSdvc7cBWYF17jPc7LklSfwadMloKLEuyFHgZ8BSwCdjV1u8CrmjLm4C7quq5qnoMOAJclmQlsKKq7q+qAu7o6iNJWiB9B0JVfR14P/AEMAU8U1V/D4xU1VRrMwVc0LqsAp7s2sRkq61qyzPrkqQFtLTfju3awCZgLfBd4K+TvGO2Lj1qNUu91z630plaYmRkhImJidMY8f+Znp7uu690Kh5fi8ONlxwb2r7n6xjrOxCAXwYeq6pvAiT5FPAG4OkkK6tqqk0HHW3tJ4ELu/qvpjPFNNmWZ9ZPUFU7gB0Ao6OjNTY21tfAJyYm6LevdCoeX4vDNdvuGdq+d44vn5djbJBrCE8AG5K8rN0VtBE4DOwFtrQ2W4A9bXkvsDnJOUnW0rl4/FCbVno2yYa2nau7+kiSFkjfZwhV9WCSu4EvAMeAL9L56/3lwO4k19IJjStb+0NJdgMPt/bXV9ULbXPXATuBZcC97SFJWkCDTBlRVe8F3juj/Byds4Ve7bcD23vU9wMXDzIWSdJg/KSyJAkwECRJjYEgSQIMBElSM9BF5bPVwa8/M7R7iB+/+a1D2a8knYpnCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUrMobzuV5pO3Nets5RmCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJKAAQMhyY8nuTvJV5IcTvKLSc5Lcl+SR9vzuV3tb0pyJMkjSS7vql+a5GBbd0uSDDIuSdLpG/QM4UPAZ6rqZ4DXAoeBbcC+qloH7GuvSbIe2AxcBIwDtyVZ0rZzO7AVWNce4wOOS5J0mvoOhCQrgDcBHwGoqh9U1XeBTcCu1mwXcEVb3gTcVVXPVdVjwBHgsiQrgRVVdX9VFXBHVx9J0gJZOkDfVwPfBP4iyWuBA8ANwEhVTQFU1VSSC1r7VcADXf0nW+35tjyzfoIkW+mcSTAyMsLExERfAx9ZBjdecqyvvoPqd8w6e3h8LQ7D+h0DTE9Pz8vvepBAWAr8PPCuqnowyYdo00Mn0eu6QM1SP7FYtQPYATA6OlpjY2OnNeDjbr1zDx84OMiP3r/Hrxobyn61cDy+Fodrtt0ztH3vHF9Ov+9/sxnkGsIkMFlVD7bXd9MJiKfbNBDt+WhX+wu7+q8Gnmr11T3qkqQF1HcgVNU3gCeT/HQrbQQeBvYCW1ptC7CnLe8FNic5J8laOhePH2rTS88m2dDuLrq6q48kaYEMel77LuDOJC8Bvgb8Np2Q2Z3kWuAJ4EqAqjqUZDed0DgGXF9VL7TtXAfsBJYB97aHJGkBDRQIVfUlYLTHqo0nab8d2N6jvh+4eJCxSJIG4yeVJUmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpGTgQkixJ8sUkf9ten5fkviSPtudzu9relORIkkeSXN5VvzTJwbbuliQZdFySpNMzF2cINwCHu15vA/ZV1TpgX3tNkvXAZuAiYBy4LcmS1ud2YCuwrj3G52BckqTTMFAgJFkNvBX4867yJmBXW94FXNFVv6uqnquqx4AjwGVJVgIrqur+qirgjq4+kqQFsnTA/h8E/gD4sa7aSFVNAVTVVJILWn0V8EBXu8lWe74tz6yfIMlWOmcSjIyMMDEx0degR5bBjZcc66vvoPods84eHl+Lw7B+xwDT09Pz8rvuOxCSvA04WlUHkoy9mC49ajVL/cRi1Q5gB8Do6GiNjb2Y3Z7o1jv38IGDg2Zhfx6/amwo+9XC8fhaHK7Zds/Q9r1zfDn9vv/NZpCj9o3AryV5C/BSYEWSvwSeTrKynR2sBI629pPAhV39VwNPtfrqHnVJ0gLq+xpCVd1UVaurag2di8Wfrap3AHuBLa3ZFmBPW94LbE5yTpK1dC4eP9Sml55NsqHdXXR1Vx9J0gKZj/Pam4HdSa4FngCuBKiqQ0l2Aw8Dx4Drq+qF1uc6YCewDLi3PSRJC2hOAqGqJoCJtvxtYONJ2m0Htveo7wcunouxSJL64yeVJUmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSp6TsQklyY5HNJDic5lOSGVj8vyX1JHm3P53b1uSnJkSSPJLm8q35pkoNt3S1JMtiPJUk6XYOcIRwDbqyqnwU2ANcnWQ9sA/ZV1TpgX3tNW7cZuAgYB25LsqRt63ZgK7CuPcYHGJckqQ99B0JVTVXVF9rys8BhYBWwCdjVmu0CrmjLm4C7quq5qnoMOAJclmQlsKKq7q+qAu7o6iNJWiBzcg0hyRrgdcCDwEhVTUEnNIALWrNVwJNd3SZbbVVbnlmXJC2gpYNuIMnLgU8Cv1dV/zHL9H+vFTVLvde+ttKZWmJkZISJiYnTHi/AyDK48ZJjffUdVL9j1tnD42txGNbvGGB6enpeftcDBUKSH6UTBndW1ada+ekkK6tqqk0HHW31SeDCru6rgadafXWP+gmqagewA2B0dLTGxsb6Gvetd+7hAwcHzsK+PH7V2FD2q4Xj8bU4XLPtnqHte+f4cvp9/5vNIHcZBfgIcLiq/rhr1V5gS1veAuzpqm9Ock6StXQuHj/UppWeTbKhbfPqrj6SpAUyyJ8xbwR+CziY5Eut9h7gZmB3kmuBJ4ArAarqUJLdwMN07lC6vqpeaP2uA3YCy4B720OStID6DoSq+kd6z/8DbDxJn+3A9h71/cDF/Y5FkjQ4P6ksSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAs6gQEgynuSRJEeSbBv2eCRpsTkjAiHJEuBPgV8F1gNvT7J+uKOSpMXljAgE4DLgSFV9rap+ANwFbBrymCRpUVk67AE0q4Anu15PAr8ws1GSrcDW9nI6ySN97u984Ft99h1I3jeMvWqBeXxpXr35fQMdYz95shVnSiCkR61OKFTtAHYMvLNkf1WNDrodqRePL823+TrGzpQpo0ngwq7Xq4GnhjQWSVqUzpRA+BdgXZK1SV4CbAb2DnlMkrSonBFTRlV1LMnvAH8HLAE+WlWH5nGXA087SbPw+NJ8m5djLFUnTNVLkhahM2XKSJI0ZAaCJAlYRIGQZLpr+S1JHk3yqiTvTHL1MMems8OMY+g1ST7dvmrlcJLdSUaSrEny/SRfao8Pd/W5NMnB1ueWJL1ut5b+18net2ZrN4gz4qLyQkqyEbgV+JWqegL48Cm6SD8kyUuBe4B3V9XftNqbgVcC08BXq+rnenS9nc4HKx8APg2MA/cuxJh1duvxvjUvFlUgJPkl4M+At1TVV1vtD4Hpqnr/MMems8pvAvcfDwOAqvocQJI1vTokWQmsqKr72+s7gCswEHQKJ3nfWgt8nM57+Gfmal+LZsoIOAfYA1xRVV8Z9mB0VrsYODDL+rVJvpjkH9o/Zuh8PctkV5vJVpNmc7L3rQ8Bt1fV64FvzNXOFlMgPA/8M3DtsAei/9emgFdV1euAdwMfT7KCF/n1LNIMJ3vfeiPwibb8sbna2WIKhP8GfgN4fZL3DHswOqsdAi7ttaKqnquqb7flA8BXgdfQOSNY3dXUr2fRizHb+9ac/0GxmAKBqvpP4G3AVUk8U1C/Pg68Iclbjxfaf/B0SZJXtv/fgySvBtYBX6uqKeDZJBva3UVX05kKkGZ1kvetf6LzFT8AV83VvhbVRWWAqvpOknHg80mG8hXFOrtV1feTvA34YJIP0jmt/1fgBuBNwB8lOQa8ALyzqr7Tul4H7ASW0bmY7AVlvSg93rduoDMdeQPwybnaj19dIUkCFtmUkSTp5AwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSp+R+8kn6fv7jc8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['affinity_quantity'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aa358835-55f3-4551-9217-e76a15de4fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter = df[df['affinity_quantity'].str.lower().isin(quantities)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d6dda488-f709-4fe7-b372-080042cf7c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_complex = pd.read_parquet('data/pdbbind_complex.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "df7929e3-c7fd-4e1b-a165-92f8d53b9011",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = df_complex.merge(df_filter,on='name').drop('affinity',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4d105c42-0d11-49db-9012-52fafc9cd299",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_parquet('data/pdbbind.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2955b056-26dd-45fa-8d74-f17661253a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24759"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed3fe035-6035-4d39-b072-d12dc0a95857",
   "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}