ChEMBL_API_example_for_webinar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using a list of Schistosome parasite genes, find compounds that are active on these targets:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Aim: Find novel therapeutic targets for the parasite Schistosoma mansoni.  \n",
    "* Background: Schistosome parasites kill 250,000 people every year. Treatment of schistosomiasis relies on the drug praziquantel (only!).   \n",
    "* But what other targets could be druggable? And are there any existing marketed drugs for these targets?  \n",
    "* For further detail see comment at https://www.science.org/doi/10.1126/science.abe0710  \n",
    "* and scientific paper: https://www.science.org/doi/10.1126/science.abb7699  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 1: Use the ChEMBL python client to access the API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Available to install from Python Package Index by typing:  \n",
    "pip install chembl_webresource_client  \n",
    "* See https://github.com/chembl/chembl_webresource_client    \n",
    "  \n",
    "* Pros: simple to use, tailored for ChEMBL API endpoints\n",
    "* Cons: The keyword 'only' is limited to main fields, so it can't search within nested fields (e.g. in the molecule API, .only(['molecule_properties__alogp']) is equivalent to .only(['molecule_properties']).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################\n",
    "#This cell imports relevant python modules:\n",
    "###############################\n",
    "import pandas as pd #Use pandas python module to view and analyse data\n",
    "from chembl_webresource_client.new_client import new_client #Import ChEMBL python client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1a: Find compounds that are active on the specified targets: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(83, 7)\n"
     ]
    },
    {
     "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>molecule_chembl_id</th>\n",
       "      <th>molecule_pref_name</th>\n",
       "      <th>parent_molecule_chembl_id</th>\n",
       "      <th>pchembl_value</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL574738</td>\n",
       "      <td>AST-487</td>\n",
       "      <td>CHEMBL574738</td>\n",
       "      <td>5.92</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL522892</td>\n",
       "      <td>DOVITINIB</td>\n",
       "      <td>CHEMBL522892</td>\n",
       "      <td>5.92</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL607707</td>\n",
       "      <td>PELITINIB</td>\n",
       "      <td>CHEMBL607707</td>\n",
       "      <td>5.47</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>3400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL191003</td>\n",
       "      <td>JNJ-7706621</td>\n",
       "      <td>CHEMBL191003</td>\n",
       "      <td>6.44</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL608533</td>\n",
       "      <td>MIDOSTAURIN</td>\n",
       "      <td>CHEMBL608533</td>\n",
       "      <td>5.75</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1800.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>STAUROSPORINE</td>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>8.57</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>STAUROSPORINE</td>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>7.93</td>\n",
       "      <td>CHEMBL1075195</td>\n",
       "      <td>Serine/threonine-protein kinase TAO2</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>STAUROSPORINE</td>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>8.72</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>CHEMBL4569508</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL4569508</td>\n",
       "      <td>5.57</td>\n",
       "      <td>CHEMBL1075195</td>\n",
       "      <td>Serine/threonine-protein kinase TAO2</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CHEMBL4568087</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL4568087</td>\n",
       "      <td>6.38</td>\n",
       "      <td>CHEMBL1075195</td>\n",
       "      <td>Serine/threonine-protein kinase TAO2</td>\n",
       "      <td>420.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   molecule_chembl_id molecule_pref_name parent_molecule_chembl_id  \\\n",
       "0        CHEMBL574738            AST-487              CHEMBL574738   \n",
       "1        CHEMBL522892          DOVITINIB              CHEMBL522892   \n",
       "2        CHEMBL607707          PELITINIB              CHEMBL607707   \n",
       "3        CHEMBL191003        JNJ-7706621              CHEMBL191003   \n",
       "4        CHEMBL608533        MIDOSTAURIN              CHEMBL608533   \n",
       "..                ...                ...                       ...   \n",
       "78       CHEMBL388978      STAUROSPORINE              CHEMBL388978   \n",
       "79       CHEMBL388978      STAUROSPORINE              CHEMBL388978   \n",
       "80       CHEMBL388978      STAUROSPORINE              CHEMBL388978   \n",
       "81      CHEMBL4569508               None             CHEMBL4569508   \n",
       "82      CHEMBL4568087               None             CHEMBL4568087   \n",
       "\n",
       "   pchembl_value target_chembl_id                      target_pref_name  \\\n",
       "0           5.92       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "1           5.92       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "2           5.47       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "3           6.44       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "4           5.75       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "..           ...              ...                                   ...   \n",
       "78          8.57       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "79          7.93    CHEMBL1075195  Serine/threonine-protein kinase TAO2   \n",
       "80          8.72       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "81          5.57    CHEMBL1075195  Serine/threonine-protein kinase TAO2   \n",
       "82          6.38    CHEMBL1075195  Serine/threonine-protein kinase TAO2   \n",
       "\n",
       "     value  \n",
       "0   1200.0  \n",
       "1   1200.0  \n",
       "2   3400.0  \n",
       "3    360.0  \n",
       "4   1800.0  \n",
       "..     ...  \n",
       "78    2.71  \n",
       "79    1.17  \n",
       "80     1.9  \n",
       "81  2700.0  \n",
       "82   420.0  \n",
       "\n",
       "[83 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###############################\n",
    "#Set up the call to the ChEMBL 'activity' API:\n",
    "###############################\n",
    "activities = new_client.activity.filter(target_chembl_id__in=['CHEMBL5552','CHEMBL1075195']  ##Specify a list of example targets (#Serine/threonine-protein kinase 25 (STK25) & Serine-threonine kinases TAO2)\n",
    "                                        , pchembl_value__gte=5                               ##Specify a minimum threshold of the pChEMBL activity value. Note that pCHEMBL = -log10(IC50, XC50, AC50, Ki, Kd, potency). Greater than or equal to 5 (10um) is a typical minimum rule of thumb for binding activity between a compound and a protein target. \n",
    "                                        , assay_type='B'                                     ##Only look for Binding Assays\n",
    "                                       ).only(['target_chembl_id', 'target_pref_name', 'parent_molecule_chembl_id' ## Specify which fields (columns) to extract\n",
    "                                                               ,'molecule_chembl_id','molecule_pref_name', 'pchembl_value'])\n",
    "#Convert the list of results into a Pandas dataframe:\n",
    "act_df = pd.DataFrame(activities)\n",
    "act_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1b: Using the active compounds from the previous step, and call the 'molecule' API to find their molecular properties etc, so that the compound list can be prioritised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 55 compounds initially identified as active on the known targets. e.g.['CHEMBL4568087', 'CHEMBL1721885']...\n"
     ]
    }
   ],
   "source": [
    "###############################\n",
    "#First find the list of compounds that are within the act_df dataframe:\n",
    "###############################\n",
    "cmpd_chembl_ids = list(set(act_df['molecule_chembl_id']))\n",
    "print(\"There are {} compounds initially identified as active on the known targets. e.g.{}...\".format(len(cmpd_chembl_ids),cmpd_chembl_ids[0:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>max_phase</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>molecule_hierarchy</th>\n",
       "      <th>molecule_properties</th>\n",
       "      <th>pref_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...</td>\n",
       "      <td>{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>SUNITINIB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL296468</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL296468', 'parent...</td>\n",
       "      <td>{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>BMS-387032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL180022', 'parent...</td>\n",
       "      <td>{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...</td>\n",
       "      <td>NERATINIB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL475251</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL475251', 'parent...</td>\n",
       "      <td>{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...</td>\n",
       "      <td>R-406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_phase molecule_chembl_id  \\\n",
       "0          4          CHEMBL535   \n",
       "1          1       CHEMBL296468   \n",
       "2          4       CHEMBL180022   \n",
       "3          2       CHEMBL475251   \n",
       "\n",
       "                                  molecule_hierarchy  \\\n",
       "0  {'molecule_chembl_id': 'CHEMBL535', 'parent_ch...   \n",
       "1  {'molecule_chembl_id': 'CHEMBL296468', 'parent...   \n",
       "2  {'molecule_chembl_id': 'CHEMBL180022', 'parent...   \n",
       "3  {'molecule_chembl_id': 'CHEMBL475251', 'parent...   \n",
       "\n",
       "                                 molecule_properties   pref_name  \n",
       "0  {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...   SUNITINIB  \n",
       "1  {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...  BMS-387032  \n",
       "2  {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...   NERATINIB  \n",
       "3  {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...       R-406  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###############################\n",
    "#Set up the call to the ChEMBL 'molecule' API:\n",
    "###############################\n",
    "\n",
    "#Select a few examples of the active compounds from above (i.e. a reduced list!) : \n",
    "cmpd_chembl_ids = \",\".join(['CHEMBL296468', 'CHEMBL180022', 'CHEMBL475251','CHEMBL535']) # Amend the format of the text string so that it is suitable for the API call\n",
    "\n",
    "molecules = new_client.molecule.filter(molecule_chembl_id__in=cmpd_chembl_ids  ##Select a few examples of the active compounds (i.e. a reduced list!)\n",
    "                                       ).only([ 'molecule_chembl_id','pref_name', 'molecule_hierarchy', 'molecule_properties', 'max_phase']) ## Specify which fields (columns) to extract\n",
    "\n",
    "#Convert the list of results into a Pandas dataframe:\n",
    "mol_df = pd.DataFrame(molecules)\n",
    "mol_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 18)\n"
     ]
    },
    {
     "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>max_phase</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>molecule_hierarchy</th>\n",
       "      <th>molecule_properties</th>\n",
       "      <th>pref_name</th>\n",
       "      <th>parent_molecule_chembl_id</th>\n",
       "      <th>cx_logd</th>\n",
       "      <th>cx_logp</th>\n",
       "      <th>cx_most_apka</th>\n",
       "      <th>cx_most_bpka</th>\n",
       "      <th>alogp</th>\n",
       "      <th>hba</th>\n",
       "      <th>hbd</th>\n",
       "      <th>mw_freebase</th>\n",
       "      <th>full_mwt</th>\n",
       "      <th>num_ro5_violations</th>\n",
       "      <th>psa</th>\n",
       "      <th>heavy_atoms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...</td>\n",
       "      <td>{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>SUNITINIB</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.93</td>\n",
       "      <td>11.46</td>\n",
       "      <td>9.04</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>398.48</td>\n",
       "      <td>398.48</td>\n",
       "      <td>0</td>\n",
       "      <td>77.23</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL296468</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL296468', 'parent...</td>\n",
       "      <td>{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>BMS-387032</td>\n",
       "      <td>CHEMBL296468</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.94</td>\n",
       "      <td>7.94</td>\n",
       "      <td>10.17</td>\n",
       "      <td>3.66</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>380.54</td>\n",
       "      <td>380.54</td>\n",
       "      <td>0</td>\n",
       "      <td>80.05</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL180022', 'parent...</td>\n",
       "      <td>{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...</td>\n",
       "      <td>NERATINIB</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>3.05</td>\n",
       "      <td>4.47</td>\n",
       "      <td>12.55</td>\n",
       "      <td>8.81</td>\n",
       "      <td>5.93</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>557.05</td>\n",
       "      <td>557.05</td>\n",
       "      <td>2</td>\n",
       "      <td>112.40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL475251</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL475251', 'parent...</td>\n",
       "      <td>{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...</td>\n",
       "      <td>R-406</td>\n",
       "      <td>CHEMBL475251</td>\n",
       "      <td>3.63</td>\n",
       "      <td>3.63</td>\n",
       "      <td>10.90</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.63</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>470.46</td>\n",
       "      <td>470.46</td>\n",
       "      <td>0</td>\n",
       "      <td>128.75</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_phase molecule_chembl_id  \\\n",
       "0          4          CHEMBL535   \n",
       "1          1       CHEMBL296468   \n",
       "2          4       CHEMBL180022   \n",
       "3          2       CHEMBL475251   \n",
       "\n",
       "                                  molecule_hierarchy  \\\n",
       "0  {'molecule_chembl_id': 'CHEMBL535', 'parent_ch...   \n",
       "1  {'molecule_chembl_id': 'CHEMBL296468', 'parent...   \n",
       "2  {'molecule_chembl_id': 'CHEMBL180022', 'parent...   \n",
       "3  {'molecule_chembl_id': 'CHEMBL475251', 'parent...   \n",
       "\n",
       "                                 molecule_properties   pref_name  \\\n",
       "0  {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...   SUNITINIB   \n",
       "1  {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...  BMS-387032   \n",
       "2  {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...   NERATINIB   \n",
       "3  {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...       R-406   \n",
       "\n",
       "  parent_molecule_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp  \\\n",
       "0                 CHEMBL535    1.28    2.93        11.46         9.04  3.33   \n",
       "1              CHEMBL296468    0.33    0.94         7.94        10.17  3.66   \n",
       "2              CHEMBL180022    3.05    4.47        12.55         8.81  5.93   \n",
       "3              CHEMBL475251    3.63    3.63        10.90         3.05  3.63   \n",
       "\n",
       "   hba  hbd mw_freebase full_mwt  num_ro5_violations     psa  heavy_atoms  \n",
       "0    3    3      398.48   398.48                   0   77.23           29  \n",
       "1    7    2      380.54   380.54                   0   80.05           25  \n",
       "2    8    2      557.05   557.05                   2  112.40           40  \n",
       "3   10    3      470.46   470.46                   0  128.75           34  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###########################\n",
    "# Convert nested cells (ie those containing a dictionary) to individual columns in the dataframe so that is it easier to filter!\n",
    "###########################\n",
    "# Molecule hierarchy: \n",
    "mol_df['parent_molecule_chembl_id'] = mol_df['molecule_hierarchy'].apply(lambda x: x['parent_chembl_id'])\n",
    "\n",
    "#Physicochemical properties (only report if cells are not null)\n",
    "mol_df['cx_logd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logd'])\n",
    "mol_df['cx_logp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logp'])\n",
    "mol_df['cx_most_apka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_apka'])\n",
    "mol_df['cx_most_bpka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_bpka'])\n",
    "mol_df['alogp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['alogp'])\n",
    "mol_df['hba'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hba'])\n",
    "mol_df['hbd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hbd'])\n",
    "mol_df['mw_freebase'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['mw_freebase']) #This is the mwt of the parent compound\n",
    "mol_df['full_mwt'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['full_mwt']) #This is the mwt of the full compound including any salt\n",
    "mol_df['num_ro5_violations'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['num_ro5_violations'])\n",
    "mol_df['psa'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['psa'])\n",
    "mol_df['heavy_atoms'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['heavy_atoms'])\n",
    "\n",
    "mol_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>max_phase</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>molecule_hierarchy</th>\n",
       "      <th>molecule_properties</th>\n",
       "      <th>pref_name</th>\n",
       "      <th>parent_molecule_chembl_id</th>\n",
       "      <th>cx_logd</th>\n",
       "      <th>cx_logp</th>\n",
       "      <th>cx_most_apka</th>\n",
       "      <th>cx_most_bpka</th>\n",
       "      <th>alogp</th>\n",
       "      <th>hba</th>\n",
       "      <th>hbd</th>\n",
       "      <th>mw_freebase</th>\n",
       "      <th>full_mwt</th>\n",
       "      <th>num_ro5_violations</th>\n",
       "      <th>psa</th>\n",
       "      <th>heavy_atoms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...</td>\n",
       "      <td>{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>SUNITINIB</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.93</td>\n",
       "      <td>11.46</td>\n",
       "      <td>9.04</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>398.48</td>\n",
       "      <td>398.48</td>\n",
       "      <td>0</td>\n",
       "      <td>77.23</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL180022', 'parent...</td>\n",
       "      <td>{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...</td>\n",
       "      <td>NERATINIB</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>3.05</td>\n",
       "      <td>4.47</td>\n",
       "      <td>12.55</td>\n",
       "      <td>8.81</td>\n",
       "      <td>5.93</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>557.05</td>\n",
       "      <td>557.05</td>\n",
       "      <td>2</td>\n",
       "      <td>112.40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_phase molecule_chembl_id  \\\n",
       "0          4          CHEMBL535   \n",
       "2          4       CHEMBL180022   \n",
       "\n",
       "                                  molecule_hierarchy  \\\n",
       "0  {'molecule_chembl_id': 'CHEMBL535', 'parent_ch...   \n",
       "2  {'molecule_chembl_id': 'CHEMBL180022', 'parent...   \n",
       "\n",
       "                                 molecule_properties  pref_name  \\\n",
       "0  {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...  SUNITINIB   \n",
       "2  {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...  NERATINIB   \n",
       "\n",
       "  parent_molecule_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp  \\\n",
       "0                 CHEMBL535    1.28    2.93        11.46         9.04  3.33   \n",
       "2              CHEMBL180022    3.05    4.47        12.55         8.81  5.93   \n",
       "\n",
       "   hba  hbd mw_freebase full_mwt  num_ro5_violations     psa  heavy_atoms  \n",
       "0    3    3      398.48   398.48                   0   77.23           29  \n",
       "2    8    2      557.05   557.05                   2  112.40           40  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###########################\n",
    "#Filter the compounds based on their molecular properties (e.g. molecular weight < 400 amu), or max_phase, for example:\n",
    "###########################\n",
    "\n",
    "#Now keep only compounds with max_phase = 4 (ie approved drugs): \n",
    "res = mol_df[ mol_df['max_phase'] == 4 ]\n",
    "\n",
    "#Display results:\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 2: Use the python 'requests' module to access the API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pros: Use if don't want to install ChEMBL python client, e.g. if 'requests' module already in use as part of an existing workflow, or using a non-python coding language\n",
    "* Cons: Slightly more code required than for ChEMBL python client. Need to include code to iterate over multiple pages of records.\n",
    "* See https://docs.python-requests.org/en/latest/  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################\n",
    "#This cell imports relevant python modules:\n",
    "###############################\n",
    "import pandas as pd #Use pandas python module to view and analyse data\n",
    "import requests #This is used to access json files!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2a: Find compounds that are active on the specified targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the url string that calls the 'Activities' API with the initial query specification:\n",
      "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id__in=CHEMBL5552,CHEMBL1075195&pchembl_value__gte=5&assay_type=B&only=target_chembl_id,target_pref_name,parent_molecule_chembl_id,molecule_chembl_id,molecule_pref_name,pchembl_value&format=json\n"
     ]
    },
    {
     "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>molecule_chembl_id</th>\n",
       "      <th>molecule_pref_name</th>\n",
       "      <th>parent_molecule_chembl_id</th>\n",
       "      <th>pchembl_value</th>\n",
       "      <th>target_chembl_id</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL574738</td>\n",
       "      <td>AST-487</td>\n",
       "      <td>CHEMBL574738</td>\n",
       "      <td>5.92</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL522892</td>\n",
       "      <td>DOVITINIB</td>\n",
       "      <td>CHEMBL522892</td>\n",
       "      <td>5.92</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL607707</td>\n",
       "      <td>PELITINIB</td>\n",
       "      <td>CHEMBL607707</td>\n",
       "      <td>5.47</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>3400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL191003</td>\n",
       "      <td>JNJ-7706621</td>\n",
       "      <td>CHEMBL191003</td>\n",
       "      <td>6.44</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL608533</td>\n",
       "      <td>MIDOSTAURIN</td>\n",
       "      <td>CHEMBL608533</td>\n",
       "      <td>5.75</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1800.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>STAUROSPORINE</td>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>8.57</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>STAUROSPORINE</td>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>7.93</td>\n",
       "      <td>CHEMBL1075195</td>\n",
       "      <td>Serine/threonine-protein kinase TAO2</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>STAUROSPORINE</td>\n",
       "      <td>CHEMBL388978</td>\n",
       "      <td>8.72</td>\n",
       "      <td>CHEMBL5552</td>\n",
       "      <td>Serine/threonine-protein kinase 25</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>CHEMBL4569508</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL4569508</td>\n",
       "      <td>5.57</td>\n",
       "      <td>CHEMBL1075195</td>\n",
       "      <td>Serine/threonine-protein kinase TAO2</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CHEMBL4568087</td>\n",
       "      <td>None</td>\n",
       "      <td>CHEMBL4568087</td>\n",
       "      <td>6.38</td>\n",
       "      <td>CHEMBL1075195</td>\n",
       "      <td>Serine/threonine-protein kinase TAO2</td>\n",
       "      <td>420.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   molecule_chembl_id molecule_pref_name parent_molecule_chembl_id  \\\n",
       "0        CHEMBL574738            AST-487              CHEMBL574738   \n",
       "1        CHEMBL522892          DOVITINIB              CHEMBL522892   \n",
       "2        CHEMBL607707          PELITINIB              CHEMBL607707   \n",
       "3        CHEMBL191003        JNJ-7706621              CHEMBL191003   \n",
       "4        CHEMBL608533        MIDOSTAURIN              CHEMBL608533   \n",
       "..                ...                ...                       ...   \n",
       "78       CHEMBL388978      STAUROSPORINE              CHEMBL388978   \n",
       "79       CHEMBL388978      STAUROSPORINE              CHEMBL388978   \n",
       "80       CHEMBL388978      STAUROSPORINE              CHEMBL388978   \n",
       "81      CHEMBL4569508               None             CHEMBL4569508   \n",
       "82      CHEMBL4568087               None             CHEMBL4568087   \n",
       "\n",
       "   pchembl_value target_chembl_id                      target_pref_name  \\\n",
       "0           5.92       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "1           5.92       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "2           5.47       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "3           6.44       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "4           5.75       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "..           ...              ...                                   ...   \n",
       "78          8.57       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "79          7.93    CHEMBL1075195  Serine/threonine-protein kinase TAO2   \n",
       "80          8.72       CHEMBL5552    Serine/threonine-protein kinase 25   \n",
       "81          5.57    CHEMBL1075195  Serine/threonine-protein kinase TAO2   \n",
       "82          6.38    CHEMBL1075195  Serine/threonine-protein kinase TAO2   \n",
       "\n",
       "     value  \n",
       "0   1200.0  \n",
       "1   1200.0  \n",
       "2   3400.0  \n",
       "3    360.0  \n",
       "4   1800.0  \n",
       "..     ...  \n",
       "78    2.71  \n",
       "79    1.17  \n",
       "80     1.9  \n",
       "81  2700.0  \n",
       "82   420.0  \n",
       "\n",
       "[83 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###############################\n",
    "#Search for activity for a list of targets:\n",
    "###############################\n",
    "\n",
    "#Specify the input parameters: \n",
    "targets = ['CHEMBL5552', 'CHEMBL1075195'] # Select a few example targets: Serine/threonine-protein kinase 25 (STK25) & Serine-threonine kinases TAO2.\n",
    "targets = \",\".join(targets) #Join the targets into a suitable string to fulfil the search conditions of the API\n",
    "pchembl_value = 5 #Specify a minimum threshold of the pChEMBL activity value. Note that pCHEMBL = -log10(IC50, XC50, AC50, Ki, Kd, potency). Greater than or equal to 5 (10um) is a typical minimum rule of thumb for binding activity between a compound and a protein target. \n",
    "assay_type = 'B' #Only look for Binding Assays\n",
    "cols = \",\".join(['target_chembl_id', 'target_pref_name', 'parent_molecule_chembl_id','molecule_chembl_id','molecule_pref_name', 'pchembl_value']) # Only return the specified data fields \n",
    "\n",
    "###############################\n",
    "url_stem = \"https://www.ebi.ac.uk\" #This is the stem of the url\n",
    "url_string = url_stem + \"/chembl/api/data/activity?target_chembl_id__in={}&pchembl_value__gte={}&assay_type={}&only={}&format=json\".format(targets, pchembl_value, assay_type, cols) #This is the full url with the specified input parameters\n",
    "url = requests.get( url_string ).json() #This calls the information back from the API using the 'requests' module, and converts it to json format\n",
    "results = url['activities'] #This is a list of the results for activities\n",
    "\n",
    "#This 'while' loop iterates over several pages of records (if required), and collates the list of results\n",
    "#Remember that there is a limit to the number of records returned in any one API call (default is 20 records, maximum is 1000 records) e.g. include \"&limit=50\" in the url string\n",
    "#So need to iterate over several pages of records to gather all relevant information together!\n",
    "while url['page_meta']['next']:\n",
    "    url = requests.get(url_stem + url['page_meta']['next']).json()\n",
    "    results = results + url['activities'] #Add result (as a list) to previous list of results\n",
    "\n",
    "#Convert the list of results into a Pandas dataframe:\n",
    "act_df = pd.DataFrame(results)\n",
    "\n",
    "#Print out some useful information:\n",
    "print(\"This is the url string that calls the 'Activities' API with the initial query specification:\\n{}\".format(url_string) )\n",
    "act_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2b: Using the active compounds from the previous step, and call the 'molecule' API to find their molecular properties etc, so that the compound list can be prioritised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 55 compounds initially identified as active on the known targets. e.g.['CHEMBL4568087', 'CHEMBL1721885']...\n"
     ]
    }
   ],
   "source": [
    "###############################\n",
    "#First find the list of compounds that are within the act_df dataframe:\n",
    "###############################\n",
    "cmpd_chembl_ids = list(set(act_df['molecule_chembl_id']))\n",
    "print(\"There are {} compounds initially identified as active on the known targets. e.g.{}...\".format(len(cmpd_chembl_ids),cmpd_chembl_ids[0:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the url string that calls the 'Molecule' API with the specified query\n",
      "https://www.ebi.ac.uk/chembl/api/data/molecule?molecule_chembl_id__in=CHEMBL296468,CHEMBL180022,CHEMBL475251,CHEMBL535&only=molecule_chembl_id,pref_name,molecule_hierarchy,molecule_properties,max_phase&format=json\n"
     ]
    },
    {
     "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>max_phase</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>molecule_hierarchy</th>\n",
       "      <th>molecule_properties</th>\n",
       "      <th>pref_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...</td>\n",
       "      <td>{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>SUNITINIB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL296468</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL296468', 'parent...</td>\n",
       "      <td>{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>BMS-387032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL180022', 'parent...</td>\n",
       "      <td>{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...</td>\n",
       "      <td>NERATINIB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL475251</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL475251', 'parent...</td>\n",
       "      <td>{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...</td>\n",
       "      <td>R-406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_phase molecule_chembl_id  \\\n",
       "0          4          CHEMBL535   \n",
       "1          1       CHEMBL296468   \n",
       "2          4       CHEMBL180022   \n",
       "3          2       CHEMBL475251   \n",
       "\n",
       "                                  molecule_hierarchy  \\\n",
       "0  {'molecule_chembl_id': 'CHEMBL535', 'parent_ch...   \n",
       "1  {'molecule_chembl_id': 'CHEMBL296468', 'parent...   \n",
       "2  {'molecule_chembl_id': 'CHEMBL180022', 'parent...   \n",
       "3  {'molecule_chembl_id': 'CHEMBL475251', 'parent...   \n",
       "\n",
       "                                 molecule_properties   pref_name  \n",
       "0  {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...   SUNITINIB  \n",
       "1  {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...  BMS-387032  \n",
       "2  {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...   NERATINIB  \n",
       "3  {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...       R-406  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###############################\n",
    "#For the identified compounds, extract their molecular properties and other information from the 'molecule' ChEMBL API\n",
    "###############################\n",
    "\n",
    "#Select a few examples of the active compounds from above (i.e. a reduced list!) : \n",
    "cmpd_chembl_ids = \",\".join(['CHEMBL296468', 'CHEMBL180022', 'CHEMBL475251','CHEMBL535']) #Amend the format of the text string so that it is suitable for the API call\n",
    "cols = \",\".join([ 'molecule_chembl_id','pref_name', 'molecule_hierarchy', 'molecule_properties', 'max_phase']) # Only return the specified data fields \n",
    "\n",
    "###############################\n",
    "url_stem = \"https://www.ebi.ac.uk\" #This is the stem of the url\n",
    "url_string = url_stem + \"/chembl/api/data/molecule?molecule_chembl_id__in={}&only={}&format=json\".format(cmpd_chembl_ids,cols) #This is the full url with the specified input parameters\n",
    "url = requests.get( url_string ).json() #This calls the information back from the API using the 'requests' module, and converts it to json format\n",
    "results = url['molecules'] #This is a list of the results for activities\n",
    "\n",
    "#This 'while' loop iterates over several pages of records (if required), and collates the list of results\n",
    "#Remember that there is a limit to the number of records returned in any one API call (default is 20 records, maximum is 1000 records) e.g. include \"&limit=50\" in the url string\n",
    "#So need to iterate over several pages of records to gather all relevant information together!\n",
    "while url['page_meta']['next']:\n",
    "    url = requests.get(url_stem + url['page_meta']['next']).json()\n",
    "    results = results + url['molecules'] #Add result (as a list) to previous list of results\n",
    "\n",
    "#Convert the list of results into a Pandas dataframe:\n",
    "mol_df = pd.DataFrame(results)\n",
    "\n",
    "#Print out some useful information:\n",
    "print(\"This is the url string that calls the 'Molecule' API with the specified query\\n{}\".format(url_string) )\n",
    "mol_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 18)\n"
     ]
    },
    {
     "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>max_phase</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>molecule_hierarchy</th>\n",
       "      <th>molecule_properties</th>\n",
       "      <th>pref_name</th>\n",
       "      <th>parent_chembl_id</th>\n",
       "      <th>cx_logd</th>\n",
       "      <th>cx_logp</th>\n",
       "      <th>cx_most_apka</th>\n",
       "      <th>cx_most_bpka</th>\n",
       "      <th>alogp</th>\n",
       "      <th>hba</th>\n",
       "      <th>hbd</th>\n",
       "      <th>mw_freebase</th>\n",
       "      <th>full_mwt</th>\n",
       "      <th>num_ro5_violations</th>\n",
       "      <th>psa</th>\n",
       "      <th>heavy_atoms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...</td>\n",
       "      <td>{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>SUNITINIB</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.93</td>\n",
       "      <td>11.46</td>\n",
       "      <td>9.04</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>398.48</td>\n",
       "      <td>398.48</td>\n",
       "      <td>0</td>\n",
       "      <td>77.23</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL296468</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL296468', 'parent...</td>\n",
       "      <td>{'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>BMS-387032</td>\n",
       "      <td>CHEMBL296468</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.94</td>\n",
       "      <td>7.94</td>\n",
       "      <td>10.17</td>\n",
       "      <td>3.66</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>380.54</td>\n",
       "      <td>380.54</td>\n",
       "      <td>0</td>\n",
       "      <td>80.05</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL180022', 'parent...</td>\n",
       "      <td>{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...</td>\n",
       "      <td>NERATINIB</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>3.05</td>\n",
       "      <td>4.47</td>\n",
       "      <td>12.55</td>\n",
       "      <td>8.81</td>\n",
       "      <td>5.93</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>557.05</td>\n",
       "      <td>557.05</td>\n",
       "      <td>2</td>\n",
       "      <td>112.40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL475251</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL475251', 'parent...</td>\n",
       "      <td>{'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...</td>\n",
       "      <td>R-406</td>\n",
       "      <td>CHEMBL475251</td>\n",
       "      <td>3.63</td>\n",
       "      <td>3.63</td>\n",
       "      <td>10.90</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.63</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>470.46</td>\n",
       "      <td>470.46</td>\n",
       "      <td>0</td>\n",
       "      <td>128.75</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_phase molecule_chembl_id  \\\n",
       "0          4          CHEMBL535   \n",
       "1          1       CHEMBL296468   \n",
       "2          4       CHEMBL180022   \n",
       "3          2       CHEMBL475251   \n",
       "\n",
       "                                  molecule_hierarchy  \\\n",
       "0  {'molecule_chembl_id': 'CHEMBL535', 'parent_ch...   \n",
       "1  {'molecule_chembl_id': 'CHEMBL296468', 'parent...   \n",
       "2  {'molecule_chembl_id': 'CHEMBL180022', 'parent...   \n",
       "3  {'molecule_chembl_id': 'CHEMBL475251', 'parent...   \n",
       "\n",
       "                                 molecule_properties   pref_name  \\\n",
       "0  {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...   SUNITINIB   \n",
       "1  {'alogp': '3.66', 'aromatic_rings': 2, 'cx_log...  BMS-387032   \n",
       "2  {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...   NERATINIB   \n",
       "3  {'alogp': '3.63', 'aromatic_rings': 3, 'cx_log...       R-406   \n",
       "\n",
       "  parent_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp  hba  hbd  \\\n",
       "0        CHEMBL535    1.28    2.93        11.46         9.04  3.33    3    3   \n",
       "1     CHEMBL296468    0.33    0.94         7.94        10.17  3.66    7    2   \n",
       "2     CHEMBL180022    3.05    4.47        12.55         8.81  5.93    8    2   \n",
       "3     CHEMBL475251    3.63    3.63        10.90         3.05  3.63   10    3   \n",
       "\n",
       "  mw_freebase full_mwt  num_ro5_violations     psa  heavy_atoms  \n",
       "0      398.48   398.48                   0   77.23           29  \n",
       "1      380.54   380.54                   0   80.05           25  \n",
       "2      557.05   557.05                   2  112.40           40  \n",
       "3      470.46   470.46                   0  128.75           34  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###########################\n",
    "# Convert nested cells (ie those containing a dictionary) to individual columns in the dataframe so that is it easier to filter!\n",
    "###########################\n",
    "# Molecule hierarchy: \n",
    "mol_df['parent_chembl_id'] = mol_df['molecule_hierarchy'].apply(lambda x: x['parent_chembl_id'])\n",
    "\n",
    "#Physicochemical properties (only report if cells are not null)\n",
    "mol_df['cx_logd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logd'])\n",
    "mol_df['cx_logp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_logp'])\n",
    "mol_df['cx_most_apka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_apka'])\n",
    "mol_df['cx_most_bpka'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['cx_most_bpka'])\n",
    "mol_df['alogp'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['alogp'])\n",
    "mol_df['hba'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hba'])\n",
    "mol_df['hbd'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['hbd'])\n",
    "mol_df['mw_freebase'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['mw_freebase']) #This is the mwt of the parent compound\n",
    "mol_df['full_mwt'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['full_mwt']) #This is the mwt of the full compound including any salt\n",
    "mol_df['num_ro5_violations'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['num_ro5_violations'])\n",
    "mol_df['psa'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['psa'])\n",
    "mol_df['heavy_atoms'] = mol_df.loc[ mol_df['molecule_properties'].notnull(), 'molecule_properties'].apply(lambda x: x['heavy_atoms'])\n",
    "\n",
    "mol_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>max_phase</th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>molecule_hierarchy</th>\n",
       "      <th>molecule_properties</th>\n",
       "      <th>pref_name</th>\n",
       "      <th>parent_chembl_id</th>\n",
       "      <th>cx_logd</th>\n",
       "      <th>cx_logp</th>\n",
       "      <th>cx_most_apka</th>\n",
       "      <th>cx_most_bpka</th>\n",
       "      <th>alogp</th>\n",
       "      <th>hba</th>\n",
       "      <th>hbd</th>\n",
       "      <th>mw_freebase</th>\n",
       "      <th>full_mwt</th>\n",
       "      <th>num_ro5_violations</th>\n",
       "      <th>psa</th>\n",
       "      <th>heavy_atoms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL535', 'parent_ch...</td>\n",
       "      <td>{'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...</td>\n",
       "      <td>SUNITINIB</td>\n",
       "      <td>CHEMBL535</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.93</td>\n",
       "      <td>11.46</td>\n",
       "      <td>9.04</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>398.48</td>\n",
       "      <td>398.48</td>\n",
       "      <td>0</td>\n",
       "      <td>77.23</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>{'molecule_chembl_id': 'CHEMBL180022', 'parent...</td>\n",
       "      <td>{'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...</td>\n",
       "      <td>NERATINIB</td>\n",
       "      <td>CHEMBL180022</td>\n",
       "      <td>3.05</td>\n",
       "      <td>4.47</td>\n",
       "      <td>12.55</td>\n",
       "      <td>8.81</td>\n",
       "      <td>5.93</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>557.05</td>\n",
       "      <td>557.05</td>\n",
       "      <td>2</td>\n",
       "      <td>112.40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_phase molecule_chembl_id  \\\n",
       "0          4          CHEMBL535   \n",
       "2          4       CHEMBL180022   \n",
       "\n",
       "                                  molecule_hierarchy  \\\n",
       "0  {'molecule_chembl_id': 'CHEMBL535', 'parent_ch...   \n",
       "2  {'molecule_chembl_id': 'CHEMBL180022', 'parent...   \n",
       "\n",
       "                                 molecule_properties  pref_name  \\\n",
       "0  {'alogp': '3.33', 'aromatic_rings': 2, 'cx_log...  SUNITINIB   \n",
       "2  {'alogp': '5.93', 'aromatic_rings': 4, 'cx_log...  NERATINIB   \n",
       "\n",
       "  parent_chembl_id cx_logd cx_logp cx_most_apka cx_most_bpka alogp  hba  hbd  \\\n",
       "0        CHEMBL535    1.28    2.93        11.46         9.04  3.33    3    3   \n",
       "2     CHEMBL180022    3.05    4.47        12.55         8.81  5.93    8    2   \n",
       "\n",
       "  mw_freebase full_mwt  num_ro5_violations     psa  heavy_atoms  \n",
       "0      398.48   398.48                   0   77.23           29  \n",
       "2      557.05   557.05                   2  112.40           40  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###########################\n",
    "#Filter the compounds based on their molecular properties (e.g. molecular weight < 400 amu), or max_phase, for example:\n",
    "###########################\n",
    "\n",
    "#Now keep only compounds with max_phase = 4 (ie approved drugs), for example: \n",
    "res = mol_df[ mol_df['max_phase'] == 4 ]\n",
    "\n",
    "#Display results:\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (my-rdkit-env)",
   "language": "python",
   "name": "my-rdkit-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}