{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "834aeced-c3c5-42a0-bad1-41e009dd86ee",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "86476f6e-802a-463b-a1b0-2ae228bb92af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cde27df-2f77-4e62-8c65-7b7a4e76b404",
   "metadata": {},
   "outputs": [],
   "source": [
    "complex = pd.read_parquet('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9b2be11c-f4bb-4107-af49-abd78052afcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('pdbbind/data/plain-text-index/index/INDEX_general_PL_data.2019',skiprows=4,sep=r'\\s+',usecols=[0,4]).drop(0)\n",
    "df = df.rename(columns={'#': 'name','release': 'affinity'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "16e0fe44-96aa-4d3a-ae42-3609e895418b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numericalunits import mL, nm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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": 137,
   "id": "58e5748b-2cea-43ff-ab51-85a5021bd50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['affinity_uM'] = df['affinity'].str.split('[=\\~><]').str[1].apply(to_uM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d92f0004-68c1-4487-94b9-56b4fd598de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>affinity</th>\n",
       "      <th>affinity_uM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3zzf</td>\n",
       "      <td>Ki=400mM</td>\n",
       "      <td>4.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3gww</td>\n",
       "      <td>IC50=355mM</td>\n",
       "      <td>3.550000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1w8l</td>\n",
       "      <td>Ki=320mM</td>\n",
       "      <td>3.200000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3fqa</td>\n",
       "      <td>IC50=320mM</td>\n",
       "      <td>3.200000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1zsb</td>\n",
       "      <td>Kd=250mM</td>\n",
       "      <td>2.500000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17675</th>\n",
       "      <td>7cpa</td>\n",
       "      <td>Ki=11fM</td>\n",
       "      <td>1.100000e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17676</th>\n",
       "      <td>2xuf</td>\n",
       "      <td>Kd=4.1fM</td>\n",
       "      <td>4.100000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17677</th>\n",
       "      <td>1avd</td>\n",
       "      <td>Kd=1fM</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17678</th>\n",
       "      <td>2xui</td>\n",
       "      <td>Kd=1.0fM</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17679</th>\n",
       "      <td>2avi</td>\n",
       "      <td>Kd=0.6fM</td>\n",
       "      <td>6.000000e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17679 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       name    affinity   affinity_uM\n",
       "1      3zzf    Ki=400mM  4.000000e+05\n",
       "2      3gww  IC50=355mM  3.550000e+05\n",
       "3      1w8l    Ki=320mM  3.200000e+05\n",
       "4      3fqa  IC50=320mM  3.200000e+05\n",
       "5      1zsb    Kd=250mM  2.500000e+05\n",
       "...     ...         ...           ...\n",
       "17675  7cpa     Ki=11fM  1.100000e-08\n",
       "17676  2xuf    Kd=4.1fM  4.100000e-09\n",
       "17677  1avd      Kd=1fM  1.000000e-09\n",
       "17678  2xui    Kd=1.0fM  1.000000e-09\n",
       "17679  2avi    Kd=0.6fM  6.000000e-10\n",
       "\n",
       "[17679 rows x 3 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d6dda488-f709-4fe7-b372-080042cf7c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_complex = pd.read_parquet('data/pdbbind_complex.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "df7929e3-c7fd-4e1b-a165-92f8d53b9011",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = df_complex.merge(df,on='name').drop('affinity',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4d105c42-0d11-49db-9012-52fafc9cd299",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_parquet('data/pdbbind.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "2955b056-26dd-45fa-8d74-f17661253a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17652"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed3fe035-6035-4d39-b072-d12dc0a95857",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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