atomsci/ddm/examples/tutorials/03_Explore_Data_DTC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/ATOMScience-org/AMPL/blob/master/atomsci/ddm/examples/tutorials/03_Explore_Data_DTC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0V2ybLgAH-0V"
   },
   "source": [
    "# Exploring HTR3A protein target activity data from Drug Target Commons\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ezVyoyvuitEa"
   },
   "source": [
    "# Scope of the tutorial\n",
    "*   Input data from DTC dataset for HTR3A protein target\n",
    "*   Retrieves SMILES string from PubChem (time consuming step; needs internet connection) \n",
    "*   AMPL will be used to accomplish the following steps: \n",
    "  *   Standardize SMILES string\n",
    "  *   Clean the data (look for duplicates, average the assay data, cluster the compounds etc.)\n",
    "  *   Carry out some Exploratory Data Analysis (Chemical space exploration; heat map, UMAP etc.) \n",
    "  *   Save the final dataset for modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EF6njYp-iyw9"
   },
   "source": [
    "# Time on COLAB-Pro ( ~ 6 minutes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xns-qBdRi3vZ"
   },
   "source": [
    "# Protein target (HTR3A) information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dp-RE9a8i_Xw"
   },
   "source": [
    "The Target specific data was downloaded from https://drugtargetcommons.fimm.fi/\n",
    "\n",
    "Please refer to the Drug Target Commons publication (https://pubmed.ncbi.nlm.nih.gov/29276046/) for details about the database "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mVJtFMh4jC5b"
   },
   "source": [
    "Here are some details about HTR3A gene (taken from RefSeq NCBI)\n",
    "\n",
    "*   Proteins belongs to GPCR superfamily\n",
    "*   HTR3a is a receptor for Serotonin, a biogenic hormone that functions as a neurotransmitter\n",
    "*   HTR3A (also the name of the gene) encodes the subunit of the type 3 receptor for neurotransmitter \n",
    "*   Herteromeric combination of subunit A and B (HTR3B) is needed for full function. \n",
    "*   Different alternately spliced transcript variant forms for this gene are available.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zxDMhFSoIL15"
   },
   "source": [
    "Diseases associated with HTR3A include Irritable Bowel Syndrome and Motion Sickness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jT3MKS4AjLJw"
   },
   "source": [
    "## Additional information about HTR3A gene:\n",
    "\n",
    "**Gene location:** Chromosome 11\n",
    "**Exon count:** 10\n",
    "\n",
    "mRNA and protein information for its three transcripts:\n",
    "\n",
    "*   NM_000869.6 → NP_000860.3 \n",
    "*   NM_001161772.3 → NP_001155244.1\n",
    "*   NM_213621.4 → NP_998786.3 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "od_Bn9W5gCh9"
   },
   "source": [
    "## Before you begin, make sure you close all other COLAB notebooks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2DWqm4Cxm5h_"
   },
   "source": [
    "# Change Runtime settings\n",
    "If you have access to COLAB-Pro (commercial/not-free), please change your runtime settings to use GPU and high-memory,\n",
    "\n",
    "```Runtime --> Change Runtime Type --> GPU with high-RAM```\n",
    "\n",
    "If you are not a paid COLAB-Pro customer, you can still choose GPU, with standard-RAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rlYz7j65MDcb",
    "outputId": "86e286fc-4235-4676-ca10-5884c4fbd25b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Sep 17 11:40:53 UTC 2021\n"
     ]
    }
   ],
   "source": [
    "!date # starting time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5qGO2T0cIvIm"
   },
   "source": [
    "## Install AMPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2zCWtqSWPHzI"
   },
   "outputs": [],
   "source": [
    "! pip install rdkit-pypi\n",
    "! pip install deepchem\n",
    "\n",
    "import deepchem\n",
    "# print(deepchem.__version__)\n",
    "! pip install umap\n",
    "! pip install -U --ignore-installed numba\n",
    "! pip install umap-learn\n",
    "! pip install molvs\n",
    "! pip install bravado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NeoDaO7llswd"
   },
   "outputs": [],
   "source": [
    "import deepchem as dc\n",
    "\n",
    "# get the Install AMPL_GPU_test.sh\n",
    "!wget 'https://raw.githubusercontent.com/ATOMScience-org/AMPL/master/atomsci/ddm/examples/tutorials/config/install_AMPL_GPU_test.sh'\n",
    "\n",
    "# run the script to install AMPL\n",
    "! chmod u+x install_AMPL_GPU_test.sh\n",
    "! ./install_AMPL_GPU_test.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4qtjXXtuWZLQ"
   },
   "source": [
    "## Exploring HTR3A target activity data from ExcapeDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "F-c9OaSoJHmG"
   },
   "outputs": [],
   "source": [
    "# We temporarily disable warnings for demonstration.\n",
    "# FutureWarnings and DeprecationWarnings are present from some of the AMPL \n",
    "# dependency modules.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import json\n",
    "# import numpy as np\n",
    "# import pandas as pd\n",
    "import os\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "6r_-HG0aHwsE"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Import AMPL libraries\n",
    "# \n",
    "import atomsci.ddm.utils.data_curation_functions as dcf\n",
    "import atomsci.ddm.utils.curate_data as curate_data\n",
    "import atomsci.ddm.pipeline.diversity_plots as dp\n",
    "import atomsci.ddm.pipeline.chem_diversity as cd\n",
    "# Additional python libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import getpass,os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1X7qQCYVHwsG"
   },
   "source": [
    "## Select a target to work with \n",
    "### (e.g. PDE2A, KCNH2, SCNA5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "tkweVTMSHwsG"
   },
   "outputs": [],
   "source": [
    "target_name='HTR3A'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yCA0PykcHwsH"
   },
   "source": [
    "# Define data locations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "1SVhXwHgJiZN"
   },
   "outputs": [],
   "source": [
    "ofile=target_name+'_dtc.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f1RXnxhVVcxt"
   },
   "source": [
    "## Note the file `DTC_HTR3A.csv` was downloaded from the DTC website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "bN1KN4sGJjjM"
   },
   "outputs": [],
   "source": [
    "import io\n",
    "url = 'https://raw.githubusercontent.com/ATOMScience-org/AMPL/master/atomsci/ddm/examples/tutorials/datasets/DTC_HTR3A.csv'\n",
    "download = requests.get(url).content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "XBDmluXMKD38"
   },
   "outputs": [],
   "source": [
    "# Reading the downloaded content and turning it into a pandas dataframe\n",
    "orig_df = pd.read_csv(io.StringIO(download.decode('utf-8')), sep=',', header=0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Cat88HV5c7rH",
    "outputId": "48c54e6c-2692-4e0e-a7bb-e55629cd932b"
   },
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Binding affinity to human recombinant serotoni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spiroindolones, a potent compound class for th...</td>\n",
       "      <td>Science</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5996.0</td>\n",
       "      <td>Rottmann M, McNamara C, Yeung BK, Lee MC, Zou ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>658586.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3467863</td>\n",
       "      <td>DTCC00031143</td>\n",
       "      <td>1686650</td>\n",
       "      <td>40818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P46098</td>\n",
       "      <td>TH09</td>\n",
       "      <td>GKQNHLVTVYRVPU-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22589534.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>=</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>14723619</td>\n",
       "      <td>DTCC01745268</td>\n",
       "      <td>2102728</td>\n",
       "      <td>68163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P46098</td>\n",
       "      <td>TH10</td>\n",
       "      <td>FHGYGOURWHLFKB-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22589534.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>=</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>14724448</td>\n",
       "      <td>DTCC01745401</td>\n",
       "      <td>2102876</td>\n",
       "      <td>68163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P46098</td>\n",
       "      <td>TH11</td>\n",
       "      <td>BYOFGWKOPRCCDL-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22589534.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>=</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>14726034</td>\n",
       "      <td>DTCC01745604</td>\n",
       "      <td>2103098</td>\n",
       "      <td>68163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P46098</td>\n",
       "      <td>TH12</td>\n",
       "      <td>BKOSOHWFMPLYRS-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22589534.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>=</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>14726337</td>\n",
       "      <td>DTCC01745648</td>\n",
       "      <td>2103147</td>\n",
       "      <td>68163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>1309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P46098</td>\n",
       "      <td>TH13</td>\n",
       "      <td>HOASDGCNODVMOU-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22589534.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>=</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>14724566</td>\n",
       "      <td>DTCC01745416</td>\n",
       "      <td>2102894</td>\n",
       "      <td>68163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1310 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    Compound ID  ... Record ID DTC Document ID\n",
       "0              0   CHEMBL100086  ...    917398            5847\n",
       "1              1   CHEMBL104700  ...    931177           48255\n",
       "2              2  CHEMBL1079079  ...    857661           37697\n",
       "3              3      CHEMBL108  ...    229550           34292\n",
       "4              4  CHEMBL1082723  ...   1686650           40818\n",
       "...          ...            ...  ...       ...             ...\n",
       "1305        1305            NaN  ...   2102728           68163\n",
       "1306        1306            NaN  ...   2102876           68163\n",
       "1307        1307            NaN  ...   2103098           68163\n",
       "1308        1308            NaN  ...   2103147           68163\n",
       "1309        1309            NaN  ...   2102894           68163\n",
       "\n",
       "[1310 rows x 44 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Enh3wAUAUd4u",
    "outputId": "4118bdf0-1ced-4dae-8090-00b40fafbf3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'Uniprot ID', 'Compound Name', 'Standard inchi key',\n",
       "       'Max Phase', 'Target Pref Name', 'Gene Names', 'Target Class',\n",
       "       'Wild type or mutant', 'Mutation information', 'PubMed ID',\n",
       "       'End Point Standard Type', 'End Point Standard Relation',\n",
       "       'End Point Standard Value', 'End Point Standard Units',\n",
       "       'Endpoint Mode of Action', 'Assay Format', 'Assay Type',\n",
       "       'Assay Sub Type', 'Inhibitor Type', 'Detection Technology',\n",
       "       'Compound concentration value', 'Compound concentration value units',\n",
       "       'Substrate type', 'Substrate Type Standard Relation',\n",
       "       'Substrate Type Standard Value', 'Substrate Type Standard Units',\n",
       "       'Assay cell line', 'Assay Description', 'Activity Comments', 'Title',\n",
       "       'Journal', 'Year', 'Volume', 'Issue', 'Authors', 'Annotation Comments',\n",
       "       'Assay ID', 'DTC Tid', 'DTC Activity ID', 'DTC Molregno', 'Record ID',\n",
       "       'DTC Document ID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_df.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "orig_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-FOiuGwYHwsI"
   },
   "source": [
    "### Start with a local file containing the target data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "1s8tSOedHwsI",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ofile=target_name+'_dtc.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xjFANFgZHwsJ"
   },
   "source": [
    "### Explore the dataframe and display first few lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "F5r0Gs2xHwsJ",
    "outputId": "aea93442-bc87-4b61-9a12-97c970013d79",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1310, 43)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'Uniprot ID', 'Compound Name', 'Standard inchi key',\n",
       "       'Max Phase', 'Target Pref Name', 'Gene Names', 'Target Class',\n",
       "       'Wild type or mutant', 'Mutation information', 'PubMed ID',\n",
       "       'End Point Standard Type', 'End Point Standard Relation',\n",
       "       'End Point Standard Value', 'End Point Standard Units',\n",
       "       'Endpoint Mode of Action', 'Assay Format', 'Assay Type',\n",
       "       'Assay Sub Type', 'Inhibitor Type', 'Detection Technology',\n",
       "       'Compound concentration value', 'Compound concentration value units',\n",
       "       'Substrate type', 'Substrate Type Standard Relation',\n",
       "       'Substrate Type Standard Value', 'Substrate Type Standard Units',\n",
       "       'Assay cell line', 'Assay Description', 'Activity Comments', 'Title',\n",
       "       'Journal', 'Year', 'Volume', 'Issue', 'Authors', 'Annotation Comments',\n",
       "       'Assay ID', 'DTC Tid', 'DTC Activity ID', 'DTC Molregno', 'Record ID',\n",
       "       'DTC Document ID'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show number of rows in data frame and number of columns\n",
    "print(orig_df.shape)\n",
    "\n",
    "# show column names\n",
    "display(orig_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "id": "Ve4d5QK0HwsK",
    "outputId": "66a8de2a-b801-45e0-f80f-98ba32dc0d00"
   },
   "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>Compound ID</th>\n",
       "      <th>Uniprot ID</th>\n",
       "      <th>Compound Name</th>\n",
       "      <th>Standard inchi key</th>\n",
       "      <th>Max Phase</th>\n",
       "      <th>Target Pref Name</th>\n",
       "      <th>Gene Names</th>\n",
       "      <th>Target Class</th>\n",
       "      <th>Wild type or mutant</th>\n",
       "      <th>Mutation information</th>\n",
       "      <th>PubMed ID</th>\n",
       "      <th>End Point Standard Type</th>\n",
       "      <th>End Point Standard Relation</th>\n",
       "      <th>End Point Standard Value</th>\n",
       "      <th>End Point Standard Units</th>\n",
       "      <th>Endpoint Mode of Action</th>\n",
       "      <th>Assay Format</th>\n",
       "      <th>Assay Type</th>\n",
       "      <th>Assay Sub Type</th>\n",
       "      <th>Inhibitor Type</th>\n",
       "      <th>Detection Technology</th>\n",
       "      <th>Compound concentration value</th>\n",
       "      <th>Compound concentration value units</th>\n",
       "      <th>Substrate type</th>\n",
       "      <th>Substrate Type Standard Relation</th>\n",
       "      <th>Substrate Type Standard Value</th>\n",
       "      <th>Substrate Type Standard Units</th>\n",
       "      <th>Assay cell line</th>\n",
       "      <th>Assay Description</th>\n",
       "      <th>Activity Comments</th>\n",
       "      <th>Title</th>\n",
       "      <th>Journal</th>\n",
       "      <th>Year</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Issue</th>\n",
       "      <th>Authors</th>\n",
       "      <th>Annotation Comments</th>\n",
       "      <th>Assay ID</th>\n",
       "      <th>DTC Tid</th>\n",
       "      <th>DTC Activity ID</th>\n",
       "      <th>DTC Molregno</th>\n",
       "      <th>Record ID</th>\n",
       "      <th>DTC Document ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL100086</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OSVMSQACZRYAPT-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12482441.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inhibitory concentration required against 5-HT...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Development of a presynaptic 5-HT1A antagonist.</td>\n",
       "      <td>Bioorg. Med. Chem. Lett.</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mattson RJ, Catt JD, Sloan CP, Gao Q, Carter R...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>1489397</td>\n",
       "      <td>DTCC00263953</td>\n",
       "      <td>917398</td>\n",
       "      <td>5847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL104700</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SCDKHPSUXHBJDJ-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22607684.0</td>\n",
       "      <td>KI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inhibition of 5-HT3 receptor</td>\n",
       "      <td>Not Determined</td>\n",
       "      <td>7-Azabicyclo[2.2.1]heptane as a scaffold for t...</td>\n",
       "      <td>Bioorg. Med. Chem. Lett.</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Banister SD, Rendina LM, Kassiou M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>820298.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>10478126</td>\n",
       "      <td>DTCC00272114</td>\n",
       "      <td>931177</td>\n",
       "      <td>48255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL1079079</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WGIPGQAPFNVWIX-XXFZXMJFSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19767206.0</td>\n",
       "      <td>INHIBITION</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>50.0</td>\n",
       "      <td>%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Displacement of [3H]LY278584 from 5HT3 (recept...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Synthesis and in vitro autoradiographic evalua...</td>\n",
       "      <td>Bioorg. Med. Chem. Lett.</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Donohue SR, Varnäs K, Jia Z, Gulyás B, Pike VW...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>619795.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3267809</td>\n",
       "      <td>DTCC00632772</td>\n",
       "      <td>857661</td>\n",
       "      <td>37697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL108</td>\n",
       "      <td>P46098</td>\n",
       "      <td>CARBAMAZEPINE</td>\n",
       "      <td>FFGPTBGBLSHEPO-UHFFFAOYSA-N</td>\n",
       "      <td>4</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19144451.0</td>\n",
       "      <td>INHIBITION</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>50.0</td>\n",
       "      <td>%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cell_based</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HEK293</td>\n",
       "      <td>Antagonist activity at human 5HT3A receptor ex...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Molecular properties of psychopharmacological ...</td>\n",
       "      <td>Eur. J. Med. Chem.</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Kornhuber J, Terfloth L, Bleich S, Wiltfang J,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572131.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>2849452</td>\n",
       "      <td>DTCC00144764</td>\n",
       "      <td>229550</td>\n",
       "      <td>34292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL1082723</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NITD609</td>\n",
       "      <td>CKLPLPZSUQEDRT-WPCRTTGESA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20813948.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Binding affinity to human recombinant serotoni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spiroindolones, a potent compound class for th...</td>\n",
       "      <td>Science</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5996.0</td>\n",
       "      <td>Rottmann M, McNamara C, Yeung BK, Lee MC, Zou ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>658586.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3467863</td>\n",
       "      <td>DTCC00031143</td>\n",
       "      <td>1686650</td>\n",
       "      <td>40818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Compound ID Uniprot ID  ... Record ID DTC Document ID\n",
       "0   CHEMBL100086     P46098  ...    917398            5847\n",
       "1   CHEMBL104700     P46098  ...    931177           48255\n",
       "2  CHEMBL1079079     P46098  ...    857661           37697\n",
       "3      CHEMBL108     P46098  ...    229550           34292\n",
       "4  CHEMBL1082723     P46098  ...   1686650           40818\n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XefnLXbt-yF9"
   },
   "source": [
    "## Let us use AMPL to prefilter the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qmTduoTjCZ7C",
    "outputId": "a7481ec2-efd4-4a0c-a94f-4bc73ade9a6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before replace:  Index(['Compound ID', 'Uniprot ID', 'Compound Name', 'Standard inchi key',\n",
      "       'Max Phase', 'Target Pref Name', 'Gene Names', 'Target Class',\n",
      "       'Wild type or mutant', 'Mutation information', 'PubMed ID',\n",
      "       'End Point Standard Type', 'End Point Standard Relation',\n",
      "       'End Point Standard Value', 'End Point Standard Units',\n",
      "       'Endpoint Mode of Action', 'Assay Format', 'Assay Type',\n",
      "       'Assay Sub Type', 'Inhibitor Type', 'Detection Technology',\n",
      "       'Compound concentration value', 'Compound concentration value units',\n",
      "       'Substrate type', 'Substrate Type Standard Relation',\n",
      "       'Substrate Type Standard Value', 'Substrate Type Standard Units',\n",
      "       'Assay cell line', 'Assay Description', 'Activity Comments', 'Title',\n",
      "       'Journal', 'Year', 'Volume', 'Issue', 'Authors', 'Annotation Comments',\n",
      "       'Assay ID', 'DTC Tid', 'DTC Activity ID', 'DTC Molregno', 'Record ID',\n",
      "       'DTC Document ID'],\n",
      "      dtype='object')\n",
      "After replace:  Index(['Compound_ID', 'Uniprot_ID', 'Compound_Name', 'Standard_inchi_key',\n",
      "       'Max_Phase', 'Target_Pref_Name', 'Gene_Names', 'Target_Class',\n",
      "       'Wild_type_or_mutant', 'Mutation_information', 'PubMed_ID',\n",
      "       'End_Point_Standard_Type', 'End_Point_Standard_Relation',\n",
      "       'End_Point_Standard_Value', 'End_Point_Standard_Units',\n",
      "       'Endpoint_Mode_of_Action', 'Assay_Format', 'Assay_Type',\n",
      "       'Assay_Sub_Type', 'Inhibitor_Type', 'Detection_Technology',\n",
      "       'Compound_concentration_value', 'Compound_concentration_value_units',\n",
      "       'Substrate_type', 'Substrate_Type_Standard_Relation',\n",
      "       'Substrate_Type_Standard_Value', 'Substrate_Type_Standard_Units',\n",
      "       'Assay_cell_line', 'Assay_Description', 'Activity_Comments', 'Title',\n",
      "       'Journal', 'Year', 'Volume', 'Issue', 'Authors', 'Annotation_Comments',\n",
      "       'Assay_ID', 'DTC_Tid', 'DTC_Activity_ID', 'DTC_Molregno', 'Record_ID',\n",
      "       'DTC_Document_ID'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('Before replace: ', orig_df.columns)\n",
    "# remove special character \n",
    "orig_df.columns = orig_df.columns.str.replace(' ', '_')  \n",
    "print('After replace: ', orig_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hu3eKU7sDDqY",
    "outputId": "0c4e4c47-cdb5-4c06-9572-18973c754cdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After replaceing colnames with lcase:  Index(['compound_id', 'uniprot_id', 'compound_name', 'standard_inchi_key',\n",
      "       'max_phase', 'target_pref_name', 'gene_names', 'target_class',\n",
      "       'wild_type_or_mutant', 'mutation_information', 'pubmed_id',\n",
      "       'end_point_standard_type', 'end_point_standard_relation',\n",
      "       'end_point_standard_value', 'end_point_standard_units',\n",
      "       'endpoint_mode_of_action', 'assay_format', 'assay_type',\n",
      "       'assay_sub_type', 'inhibitor_type', 'detection_technology',\n",
      "       'compound_concentration_value', 'compound_concentration_value_units',\n",
      "       'substrate_type', 'substrate_type_standard_relation',\n",
      "       'substrate_type_standard_value', 'substrate_type_standard_units',\n",
      "       'assay_cell_line', 'assay_description', 'activity_comments', 'title',\n",
      "       'journal', 'year', 'volume', 'issue', 'authors', 'annotation_comments',\n",
      "       'assay_id', 'dtc_tid', 'dtc_activity_id', 'dtc_molregno', 'record_id',\n",
      "       'dtc_document_id'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# replace uppercase to lowercase\n",
    "orig_df.columns= orig_df.columns.str.lower()\n",
    "print('After replaceing colnames with lcase: ', orig_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gjXySV81EWaA",
    "outputId": "a3a53ec9-22c5-40c8-c247-f26614eea1aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['compound_id', 'uniprot_id', 'compound_name', 'standard_inchi_key',\n",
       "       'max_phase', 'target_pref_name', 'gene_names', 'target_class',\n",
       "       'wild_type_or_mutant', 'mutation_information', 'pubmed_id',\n",
       "       'end_point_standard_type', 'end_point_standard_relation',\n",
       "       'end_point_standard_value', 'end_point_standard_units',\n",
       "       'endpoint_mode_of_action', 'assay_format', 'assay_type',\n",
       "       'assay_sub_type', 'inhibitor_type', 'detection_technology',\n",
       "       'compound_concentration_value', 'compound_concentration_value_units',\n",
       "       'substrate_type', 'substrate_type_standard_relation',\n",
       "       'substrate_type_standard_value', 'substrate_type_standard_units',\n",
       "       'assay_cell_line', 'assay_description', 'activity_comments', 'title',\n",
       "       'journal', 'year', 'volume', 'issue', 'authors', 'annotation_comments',\n",
       "       'assay_id', 'dtc_tid', 'dtc_activity_id', 'dtc_molregno', 'record_id',\n",
       "       'dtc_document_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking after rename\n",
    "orig_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dbgwzh2YGCGB",
    "outputId": "4c08fa18-e654-4cdf-914e-66c2f8ad0d80"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1310, 43)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5c5b8JqbUsfg"
   },
   "source": [
    "## The following renames the names and make it suitable for the next function call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ivfltdp0SnPj"
   },
   "outputs": [],
   "source": [
    "orig_df = orig_df.rename(columns={'end_point_standard_type': 'standard_type', \n",
    " 'end_point_standard_relation': 'standard_relation',\n",
    " 'end_point_standard_value': 'standard_value', \n",
    " 'end_point_standard_units': 'standard_units',\n",
    " 'endpoint_mode_of_action': 'mode_of_action',\n",
    " 'wild_type_or_mutant': 'wildtype_or_mutant'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kDIWlEXrPfUs"
   },
   "source": [
    "## dcf.filter_dtc_data performs the following operation\n",
    "\n",
    "```\n",
    "dset_df = orig_df[orig_df.gene_names.isin(geneNames) &\n",
    "                      ~(orig_df.standard_inchi_key.isna()) &\n",
    "                      (orig_df.standard_type == 'IC50') &\n",
    "                      (orig_df.standard_units == 'NM') &\n",
    "                      ~orig_df.standard_value.isna() &\n",
    "                      ~orig_df.compound_id.isna() &\n",
    "                      (orig_df.wildtype_or_mutant != 'mutated') ]\n",
    " ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "BK9TWNsXFtIn"
   },
   "outputs": [],
   "source": [
    "geneNames = [target_name]\n",
    "nm_df = dcf.filter_dtc_data(orig_df, geneNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YjQ45UreN5xu",
    "outputId": "1712b835-5a3b-4cd8-ac39-a7fe1913fcb2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1310, 43)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ohU14UiGFX4-",
    "outputId": "abec14b4-4b88-442a-915e-c2e661c8321b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 43)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nm_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kp9vy6KSGsxx"
   },
   "source": [
    "## Explore few columns to get an idea of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "BuPzFhEmKD-G",
    "outputId": "07fae8b3-c1ca-49f3-86c4-d272fb800863"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([nan, 'HEK293', 'CHO', 'Oocytes', 'NG108-15'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array(['Inhibitory concentration required against 5-HT3 receptor in bovine area postrema using [3H]GR-65630',\n",
       "       'Inhibition of 5-HT3 receptor',\n",
       "       'Displacement of [3H]LY278584 from 5HT3 (receptor) at 10 uM by scintillation counting',\n",
       "       'Antagonist activity at human 5HT3A receptor expressed in HEK293 cells assessed as inhibition of serotonin-induced inward Na+ current at >= 10 uM',\n",
       "       'Binding affinity to human recombinant serotonin 5HT3 receptor',\n",
       "       'Agonist activity at human 5HT3A receptor expressed in HEK293 cells by FLIPR assay',\n",
       "       'Inhibition of 5HT3 receptor',\n",
       "       'Binding affinity to human HT3A receptor',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells assessed as decrease in 100 uM 5-chloroindole-induced increase in intracellular calcium release at 3 uM relative to 5-HT',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells at 3 uM relative to 5HT',\n",
       "       'Binding affinity to human 5HT3A receptor',\n",
       "       'Displacement of [9-methyl-3H]BRL-43694 from human 5-HT3A receptor after overnight incubation by scintillation proximity assay',\n",
       "       'BindingDB_Patents: Radioligand Binding Assay. The relative affinity of the various compounds for the human 5-HT3 receptor was measured in a radioligand binding assay, using a scintillation proximity assay (SPA) format. Test compounds were dissolved to 10 mM in 100% DMSO, then serially diluted at 10x assay concentrations in 100% DMSO in 96-well polypropylene plates and further diluted to 4x assay concentrations with the assay buffer. Samples were incubated in 50 mM Tris-HCl, pH 7.5, 3 mM MgCl2, 1 mM EDTA and 10% DMSO with 10 nM [9-methyl-3H]BRL-43694 (Perkin Elmer), 3 ug of human 5-HT3 receptor membranes (Perkin Elmer) and 0.5 mg/mL SPA beads (WGA PVT, Amersham Biosciences) in a final volume of 0.2 mL. Binding reactions were set up in wells of PicoPlates-96 (Perkin Elmer) by adding consecutively 50 uL of each competing compound or buffer, SPA beads, the radioligand and 5-HT3 receptor membranes. After an overnight incubation at room temperature on a Nutator mixer, plates were centrifuged for 15 min at 1,500 rpm.',\n",
       "       'Binding affinity to serotonin 5-HT3 receptor (unknown origin) by PDSP assay',\n",
       "       nan,\n",
       "       'Activity at SER3 receptor expressed in HEK293 cells assessed as increase in calcium at 10 uM by calcium imaging assay',\n",
       "       'Activity at SER3 receptor expressed in HEK293 cells assessed as increase in calcium by calcium imaging assay',\n",
       "       'Displacement of [3H]granisetron from human recombinant 5-HT3 receptor expressed in HEK293 cells',\n",
       "       'Tested for 5-hydroxytryptamine 3 receptor agonist activity',\n",
       "       'Binding affinity to 5HT3 receptor at 1 uM by radioligand displacement assay',\n",
       "       'Displacement of [3H]GR65630 from human recombinant 5HT3 receptor expressed in HEKT cells',\n",
       "       'Binding affinity at 5HT3 receptor',\n",
       "       'Binding affinity to 5-HT3 receptor at 10 uM',\n",
       "       'Inhibition of 5-HT3 receptor (unknown origin) at 10 uM by PDSP assay relative to control',\n",
       "       'Binding affinity to 5HT3A receptor',\n",
       "       'Antagonist activity at human wild type 5-HT3A receptor expressed in HEK293 cells assessed as inhibition of 5-HT-induced response after 45 mins by FLIPR assay',\n",
       "       'Binding affinity to human wild type 5-HT3A receptor expressed in HEK293 cells after 24 hrs by liquid scintillation counting analysis',\n",
       "       'Compound was tested for its affinity towards 5-hydroxytryptamine 3 receptor',\n",
       "       'In vitro binding affinity towards 5-hydroxytryptamine 3 receptor was determined',\n",
       "       'Displacement of [3H]BRL43694 from human 5HT3 receptor expressed in CHO cells at 10 uM',\n",
       "       'Displacement of [3H]BRL 43694 from human recombinant 5HT3 receptor expressed in CHO cells',\n",
       "       'Antagonist activity at human 5HT3 receptor',\n",
       "       'Displacement of [3H]LY278584 from human 5HT3 receptor at 10 uM',\n",
       "       'Binding affinity to 5HT3 receptor',\n",
       "       'Displacement of [3H]granisetron from 5HT3A receptor expressed in HEK293 cells after 24 hrs by scintillation counting in presence of quipazine',\n",
       "       'Inhibition of human H3 receptor at 10 uM',\n",
       "       'Binding affinity to human 5-HT3 receptor by radioligand displacement assay',\n",
       "       'Agonist activity at human 5HT3A at 100 uM relative to control',\n",
       "       'Binding affinity to human 5HT3 receptor at 10 uM by radioligand binding assay',\n",
       "       'Inhibition of human 5HT3R at 10 uM', 'Inhibition of human 5HT3R',\n",
       "       'Binding affinity to human 5-HT3 receptor',\n",
       "       'Inhibition of 5-HT3 receptor at 10 uM',\n",
       "       'Displacement of [3H]GR65630 from 5HT3A receptor expressed in Xenopus oocyte',\n",
       "       'Inhibition of 5HT3A receptor expressed in Xenopus oocyte at 20 uM',\n",
       "       'Inhibition of 5HT3A receptor expressed in Xenopus oocyte at 9 uM',\n",
       "       'Displacement of [3H]BRL-43694 from human 5HT3 receptor expressed in HEK293 cells by liquid scintillation counting',\n",
       "       'Displacement of [3H]BRL-43694 from human 5HT3 receptor expressed in HEK293 cells at 100 nM by liquid scintillation counting',\n",
       "       'Displacement of [3H]BRL-43694 from human 5HT3 receptor expressed in HEK293 cells at 1000 nM by liquid scintillation counting',\n",
       "       'Displacement of [3H]BRL-43694 from human recombinant 5HT3 receptor',\n",
       "       'Binding affinity to human 5HT3 receptor',\n",
       "       'Binding activity radioligand.',\n",
       "       'Compound was evaluated for the affinity at 5-hydroxytryptamine 3 receptor',\n",
       "       'Displacement of [3H]-GR-65630 from 5-HT3 receptor at 1 to 3 uM',\n",
       "       'Inhibitory constant against 5-hydroxytryptamine 3 receptor using [3H]GR-65630 radioligand',\n",
       "       'Displacement of [3H]LY278584 from 5HT3 receptor after 1.5 hrs by scintillation counter',\n",
       "       'Displacement of [3H]BRL43694 from human recombinant 5-HT3 receptor expressed in CHO cells',\n",
       "       'Displacement of [3H]GR65630 from 5-HT3 receptor (unknown origin) expressed in mouse/rat NG108-15 cells after 30 mins by by liquid scintillation counting analysis',\n",
       "       'Inhibition of GR-65630 binding to 5-hydroxytryptamine 3 receptor',\n",
       "       'Binding affinity for 5-hydroxytryptamine 3 receptor by displacement of [3H]-BRL 43694 from NG-108-15 cell membranes',\n",
       "       'Binding affinity for 5-hydroxytryptamine 3 receptor by displacement of [3H](R)-zacopride from ondansetron-treated NG-108-15 cell membranes',\n",
       "       'Displacement of [3H]granisetron from human 5-HT3AR expressed in HEK293 cells after 24 hrs by liquid scintillation counting',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells assessed as effect on serotonin-induced response',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells assessed as decrease in 10 uM 5-chloroindole-induced increase in intracellular calcium release',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells assessed as decrease in 100 uM 5-chloroindole-induced increase in intracellular calcium release at 1 uM relative to 5-HT',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells at 1 uM relative to 5HT',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells at 10 uM relative to 5HT',\n",
       "       'Partial agonist activity at human 5HT3A receptor expressed in HEK293 cells assessed as decrease in 100 uM 5-chloroindole-induced increase in intracellular calcium release at 10 uM relative to 5-HT',\n",
       "       'BindingDB_Patents: Radioligand Binding Assay. The relative affinity of the various compounds for the human 5-HT3 receptor was measured in a radioligand binding assay, using a scintillation proximity assay (SPA) format.',\n",
       "       'Inhibition of 5HT3 receptor at 10 uM',\n",
       "       'Binding affinity to human 5HT3 receptor at 10 uM by radioligand displacement assay',\n",
       "       'Inhibition of radioligand binding to human 5HT3 at 10 uM',\n",
       "       'Binding affinity to 5HT3 receptor at 10 uM by radioligand binding assay',\n",
       "       'Inhibition of human recombinant 5HT3 receptor at 10 uM by radioligand binding assay',\n",
       "       'Antagonist activity at human recombinant 5-HT3 receptor expressed in HEK293 cells',\n",
       "       'Binding affinity to 5-HT3 receptor',\n",
       "       'Inhibition of 5-HT3 receptor (unknown origin)',\n",
       "       'Binding affinity to human 5HT3 receptor at 1 uM',\n",
       "       'In vitro binding affinity for the 5-hydroxytryptamine 3 receptor was determined with NG-108-15 mouse neuroblastoma-glioma cells',\n",
       "       'Displacement of [3H]GR65630 from 5-HT3 receptor (unknown origin)',\n",
       "       'Displacement of [3H]BRL43694 from human 5HT3 receptor at 10 uM relative to control',\n",
       "       'Displacement of [3H]NAMH from human cloned 5HT3 receptor expressed in CHO cells',\n",
       "       'Inhibition of 5-HT3 receptor at 10 uM by NIMH PDSP',\n",
       "       'Binding affinity against 5-Hydroxytryptamine 3 receptor',\n",
       "       'Inhibition of human 5-HT3 receptor',\n",
       "       'Binding affinity to 5-HT3 receptor (unknown origin)',\n",
       "       'Displacement of [3H]GR65630 from human 5HT3A receptor in HEK293 cells',\n",
       "       'Inhibition of human 5-HT3 receptor at 10 uM relative to control',\n",
       "       'Displacement of [3H]LY278584 from human cloned 5HT3 receptor',\n",
       "       'Inhibition of 5HT3 receptor at 25 uM',\n",
       "       'Inhibition of serotonin 5-HT3 receptor at 1 uM by ligand displacement assay',\n",
       "       'Inhibition of human 5HT3A receptor at 10 uM by radioligand displacement assay',\n",
       "       'Inhibition of 5HT3 at 10 uM',\n",
       "       'Inhibitory activity against 5-hydroxytryptamine 3 receptor expressed in NG 108 cells',\n",
       "       'Antagonist activity at human recombinant 5HT3 receptor assessed as inhibition of RAMH-induced [35S]GTPgammaS binding',\n",
       "       'Inhibition of human 5HT3 at 10 uM',\n",
       "       'Inhibition of 5HT3 receptor at 10 uM by radioligand displacement assay',\n",
       "       'Displacement of [3H]LY278584 from 5-HT3 at 10 uM',\n",
       "       'Binding affinity to human recombinant 5HT3 receptor',\n",
       "       'Displacement of [3H]BRL43694 from human recombinant 5HT3 receptor expressed in CHO cells at 10 uM after 60 mins by liquid scintillation counting',\n",
       "       'Inhibition of 5HT3A receptor at 10 uM',\n",
       "       'Antagonist activity at human 5HT3A receptor expressed in HEK293 cells by FMP assay',\n",
       "       'Antagonist activity at human 5HT3A receptor expressed in HEK293 cells',\n",
       "       'Antagonist activity at human 5HT3A receptor expressed in HEK293 cells assessed as inhibition of m-chlorophenylbiguanide-induced calcium influx measured every 1 sec for 1 min followed by every 3 secs for 3 mins by FLIPR assay',\n",
       "       'Inhibition of human 5HT3 receptor at 5 uM by cell based assay',\n",
       "       'Inhibition of human 5HT3 receptor',\n",
       "       'Displacement of radiolabeled MDL 72222 from human 5HT3 receptor',\n",
       "       'Antagonist activity at 5HT3A receptor expressed in Xenopus oocyte',\n",
       "       'Inhibition of 5HT3A receptor expressed in Xenopus oocyte relative to bemesetron',\n",
       "       'Displacement of [3H]BRL43694 from human 5HT3 receptor',\n",
       "       'Displacement of [3H]GR65630 from human recombinant 5HT3 receptor expressed in HEK293 cells after 60 mins',\n",
       "       'Displacement of [3H]GR65630 from human recombinant 5HT3 receptor expressed in HEK293 cells at 10 uM after 60 mins relative to control',\n",
       "       'Antagonist activity at 5HT3 receptor expressed in Xenopus oocyte',\n",
       "       'Antagonist activity at 5HT3 receptor expressed in N1E115 cells',\n",
       "       'Displacement of [3H]-BRL-43694 from human 5HT3 receptor',\n",
       "       'Inhibition of human 5HT3R at 10 nM',\n",
       "       'Displacement of [3H]LY-278584 from human recombinant 5-HT3 receptor at 10 uM after 1.5 hrs by microbeta scintillation counting analysis',\n",
       "       'Inhibition of radioligand binding to 5HT3 at 10 uM',\n",
       "       'Inhibition of human 5HT3 receptor at 10 uM',\n",
       "       'Inhibition of human 5HT3 at 100 uM',\n",
       "       'Agonist activity at human 5HT3R expressed in HEK293 cells assessed as stimulation of calcium flux by FLIPR',\n",
       "       'Binding affinity to human 5HT3 by Cerep protocol based assay',\n",
       "       'Displacement of [3H]BRL-43694 from human recombinant 5-HT3 receptor expressed in CHO cells at 10 uM after 120 mins',\n",
       "       'Displacement of [3H]granisetron from human 5HT3A receptor expressed in HEK293 cells by filter binding assay',\n",
       "       'Agonist activity at human 5HT3A receptor expressed in xenopus oocytes by electrophysiological method in presence of ondansetron',\n",
       "       'Agonist activity at human 5HT3A receptor expressed in xenopus oocytes by electrophysiological method',\n",
       "       'Agonist activity at human 5HT3A receptor expressed in xenopus oocytes by electrophysiological method relative to 5HT',\n",
       "       'Antagonist activity at human 5HT3A receptor expressed in xenopus oocytes assessed as inhibition of 5HT-induced effect by electrophysiological method',\n",
       "       'Inhibition of human 5HT3A receptor at 1 uM',\n",
       "       'Displacement of [3H]LY278584 from human recombinant 5-HT3 receptor at 10 uM after 1.5 hrs by scintillation counting analysis',\n",
       "       'Displacement of [3H]LY278584 from human recombinant 5-HT3 receptor after 1.5 hrs by scintillation counting analysis',\n",
       "       'Inhibition of human 5-HT3 receptor at 10 uM by CEREP assay',\n",
       "       'Inhibition of 5HT3 receptor at 1 uM by radioligand displacement assay',\n",
       "       'Displacement of [3H]granisetron from human 5HT3A receptor expressed in HEK293 cells by scintillation counting',\n",
       "       'Binding affinity to C-terminal-His6-tagged 5HT3A receptor (unknown origin) expressed in HEK293 cells assessed as decrease in fluorescence at 10 to 500 nM after 2 hrs by fluorimetric analysis in presence of granisetron',\n",
       "       'Binding affinity to C-terminal-His6-tagged 5HT3A receptor (unknown origin) expressed in HEK293 cells assessed as decrease in fluorescence at 10 to 500 nM after 2 hrs by fluorimetric analysis',\n",
       "       'Binding affinity to C-terminal-His6-tagged 5HT3A receptor (unknown origin) expressed in HEK293 cells after 2 hrs by fluorimetric analysis',\n",
       "       'Displacement of [3H]BRL43694 from human recombinant 5-HT3 receptor expressed in CHO cells at 1 uM',\n",
       "       'Inhibition of 5HT3 receptor (unknown origin)',\n",
       "       'Displacement of [3H]LY278584 from 5-HT3 receptor (unknown origin) at 10 uM',\n",
       "       'Inhibition of human 5HT3 expressed in COS7 cells at 10 uM',\n",
       "       'Binding affinity to human 5-HT3 receptor at 10 uM by radioligand displacement assay relative to control',\n",
       "       'Displacement of [3H]LY278584 from human recombinant 5-HT3 receptor expressed in HEK293T cells after 1 to 1.5 hrs by scintillation counting analysis',\n",
       "       'Inhibition of human 5-HT3 receptor at 1 uM relative to control',\n",
       "       'Inhibition of serotonin 5-HT3 receptor (unknown origin) at 5 uM by psychoactive drug screening programme assay',\n",
       "       'Inhibition of serotonin 5-HT3A receptor (unknown origin) at 1 uM',\n",
       "       'Binding affinity to human 5HT3 receptor by radioligand binding assay',\n",
       "       'Inhibition of serotonin 5-HT3A receptor (unknown origin) expressed in Xenopus laevis oocytes assessed as inhibition of serotonin-induced current at 3 uM by two-electrode voltage clamp method',\n",
       "       'Displacement of [3H]GR65630 from human 5-HT3 receptor expressed in African green monkey COS cells after 90 mins by scintillation counting analysis',\n",
       "       'Displacement of [3H]-GR-65630 from human recombinant 5-HT3 receptor expressed in HEK293 cells at 10 uM relative to control',\n",
       "       'Binding affinity to human serotonin 5HT3 receptor at 10 uM by radioligand binding assay',\n",
       "       'Inhibition of human 5-HT3A expressed in xenopus oocytes assessed as decrease in 5-HT-activated chloride current at 0.1 uM by two-electrode voltage clamp technique',\n",
       "       'Inhibition of human 5HT3 receptor at 1 uM',\n",
       "       'Binding affinity at 5HT3 receptor at 1 uM',\n",
       "       'Displacement of [3H]BRL43694 from 5HT3 receptor',\n",
       "       'Displacement of [3H]PIB from 5HT3 receptor at 10 uM',\n",
       "       'Binding affinity to 5HT3 at 10 uM',\n",
       "       'Activity at human 5HT3A receptor expressed in Xenopus oocytes assessed as stimulation of nicotine-induced current at 10 uM by two-electrode voltage-clamp method',\n",
       "       'Displacement of [9-methyl-3H]BRL-43694 from human 5HT3A receptor expressed in HEK293 cells',\n",
       "       'Binding affinity towards 5-hydroxytryptamine 3 receptor using [3H]GR-65630 as radioligand',\n",
       "       'Compound was evaluated for its binding affinity towards 5-hydroxytryptamine 3 receptor',\n",
       "       'Binding affinity towards 5-hydroxytryptamine 3 receptor',\n",
       "       'Displacement of [3H]granisetron from human 5HT3A expressed in HEK293 cells after 1 hr by scintillation counting',\n",
       "       'Agonist activity at human wild type 5-HT3A receptor expressed in HEK293 cells by FLIPR assay',\n",
       "       'Binding affinity towards 5-hydroxytryptamine 3 receptor by displacement of [3H]2 in Neuroblastoma-Glioma NG-108-15 cells',\n",
       "       'Binding affinity against human 5-hydroxytryptamine 3A receptor',\n",
       "       'Binding affinity to human 5HT3A expressed in COS7 cells at 100 nM after 1 hr by fluorescence microscopy',\n",
       "       'Binding affinity to human 5HT3A expressed in COS7 cells at 100 nM after 1 hr by fluorescence microscopy in presence of 10 uM ondansetron',\n",
       "       'Binding affinity to human 5HT3A receptor in HEK293 cells assessed as cell labeling by confocal microscopy in presence of quipazine',\n",
       "       'Binding affinity to human 5HT3A receptor in HEK293 cells assessed as cell labeling by confocal microscopy',\n",
       "       'Binding affinity to human 5HT3',\n",
       "       'Inhibition of 5-HT3 receptor (unknown origin) at 10 uM relative to control',\n",
       "       'The binding affinity towards 5-hydroxytryptamine 3 receptor; No affinity',\n",
       "       'Displacement of [3H]GR-65630 from human 5HT3 receptor at 10 uM',\n",
       "       'Displacement of radioligand from 5HT3 receptor by liquid scintillation spectrometry',\n",
       "       'Reversible inhibition of human 5-HT3A expressed in xenopus oocytes assessed as decrease in 5-HT-activated chloride current at 30 mM by two-electrode voltage clamp technique',\n",
       "       'Displacement of [3H]GR65630 from human 5-HT3 receptor expressed in HEK293 cells',\n",
       "       'Partial agonist activity at human 5-HT3A receptor expressed in HEK293 cells assessed as change in Ca2+ level measured as intrinsic activity at 1 uM relative to 5-HT in presence of carbachol',\n",
       "       'Agonist activity at human 5-HT3A receptor expressed in HEK293 cells in presence of carbachol',\n",
       "       'Inhibition of 5-HT3 receptor (unknown origin) at 10 uM',\n",
       "       'Binding affinity to human 5-HT3 receptor at 1 uM by radioligand displacement assay relative to control',\n",
       "       'Binding affinity to human 5-HT3 receptor at 10 uM by radioligand displacement assay',\n",
       "       'Displacement of [3H]GR65630 from human serotonin 5-HT3 receptor expressed in HEKT cells at 10 uM after 90 mins by microbeta counting analysis',\n",
       "       'Inhibition of human 5-HT3 receptor at 3 uM by radioligand binding assay',\n",
       "       'Agonist activity at human 5-HT3 receptor by FLIPR/summary (Abse5) assay',\n",
       "       'Agonist activity at human 5-HT3 receptor by FLIPR(Abse5) assay',\n",
       "       'Binding affinity to 5-HT3 (unknown origin)',\n",
       "       'Inhibition of human 5HT3 at 10 uM by [3H]RL43694 binding assay relative to control',\n",
       "       'Compound was tested for 5-hydroxytryptamine 3 receptor binding affinity',\n",
       "       'Displacement of [3H]LY278584 from 5HT3 receptor (unknown origin) at 10 uM',\n",
       "       'Binding affinity to human 5-HT3 receptor assessed as inhibition of control specific binding at 10 uM',\n",
       "       'Displacement of [3H]-LY278584 from recombinant human 5-HT3 receptor after 1.5 hrs by Microbeta scintillation counting analysis',\n",
       "       'Agonist activity at human recombinant 5HT3 receptor expressed in CHO cells assessed as cAMP level at 10 uM after 4 hrs by luciferase reporter gene assay',\n",
       "       'Antagonist activity at human recombinant 5HT3 receptor expressed in CHO cells assessed as cAMP level at 10 uM after 4 hrs by luciferase reporter gene assay',\n",
       "       'Inhibition of human 5-HT3 receptor at 10 uM',\n",
       "       'Displacement of [3H]BRL 43694 from human 5-HT3 receptor expressed in CHO cells after 120 mins by scintillation counting',\n",
       "       'Binding affinity to 5-HT3 receptor (unknown origin) at 1 to 2 uM by radioligand binding assay',\n",
       "       'Inhibition of 5HT3 receptor (unknown origin) at 10 uM',\n",
       "       'Binding affinity to recombinant 5-HT3 receptor (unknown origin) at 10 uM after 1.5 hrs by radioligand displacement assay',\n",
       "       'Displacement of [3H]LY278584 from 5-HT3 receptor (unknown origin) after 1.5 hrs by microbeta scintillation counting method',\n",
       "       \"Displacement of [3H]BRL43694 from human recombinant 5-HT3 receptor expressed in CHO cells at 10'-5 M\",\n",
       "       'Displacement of [3H]BRL 43694 from human recombinant 5-HT3 receptor expressed in CHO cells at 10 uM after 120 mins by scintillation counting analysis',\n",
       "       'Inhibition of human 5HT3 receptor expressed in CHO cells at 100 nM',\n",
       "       'Inhibition of 5HT3 receptor at 1 uM',\n",
       "       'Inhibition of human cloned 5HT3 receptor by competitive binding experiment',\n",
       "       'The compound was tested for binding affinity against 5-hydroxytryptamine 3 receptor',\n",
       "       'Displacement of [3H]granisetron from human 5HT3 receptor expressed in HEK293 cells',\n",
       "       'Antagonist activity at human 5HT3 receptor by calcium influx assay',\n",
       "       'Displacement of [3H]LY278,584 from human cloned 5HT3 receptor',\n",
       "       'Displacement of [3H]GR65630 from human recombinant 5-HT3 receptor expressed in HEKT cell membranes by radioligand binding assay',\n",
       "       'Displacement of [3H]LY278584 from human 5HT3 receptor',\n",
       "       'Antagonist activity at 5HT3 receptor in hybrid NG108-15 cells assessed as inhibition of 5HT-induced current treated 1 min prior to agonist treatment by electrophysiology',\n",
       "       'Displacement of [3H]granisetron from human recombinant 5HT3 receptor',\n",
       "       'Antagonist activity at 5HT3 receptor in hybrid NG108-15 cells assessed as inhibition of 5HT-induced current by electrophysiology',\n",
       "       'Inhibition of 5-HT3 receptor (unknown origin) at 10 uM by radioligand binding assay relative to control',\n",
       "       'Antagonist activity at serotonin 5HT3 receptor (unknown origin) by PDSP assay',\n",
       "       'Inhibition of human 5HT3A receptor',\n",
       "       'Inhibition of 5-HT3AR at 10 uM',\n",
       "       'Binding affinity to human 5HT3 receptor at 10 uM relative to control',\n",
       "       'Activity at SER3 receptor expressed in HEK293 cells assessed as increase in calcium at 10 nM by calcium imaging assay',\n",
       "       'Displacement of [3H]granisetron form 5HT3 receptor in hybrid NG180-15 cells',\n",
       "       'Compound was tested for its binding affinity for the 5-hydroxytryptamine 3 receptor',\n",
       "       'Inhibition of 5HT3R at 200 nM',\n",
       "       'Displacement of [3H]LY278584 from human 5HT3 receptor expressed in HEK293 cells',\n",
       "       'Activity at human 5HT3 receptor expressed in human HEK293 cells assessed as effect on membrane potential by FLIPR assay',\n",
       "       'Binding affinity to human 5-HT3 receptor by PDSP assay',\n",
       "       'Agonist activity at human 5HT3 receptor expressed in human skin epithelial cells assessed as stimulation of calcium flux by FLIPR assay',\n",
       "       'Displacement of [3H]BRL43694 from human recombinant 5HT3 receptor expressed in CHO cells at 10 uM after 120 mins by scintillation counting',\n",
       "       'Antagonist activity at SER3 receptor expressed in HEK293 cells assessed as inhibition of 10 nM octopamine-induced response by calcium imaging assay',\n",
       "       'Displacement of [3H]GR65630 from human 5-HT3A receptor expressed in HEK293 cells by liquid scintillation counting analysis',\n",
       "       'Inhibition of 5HT3A receptor expressed in Xenopus oocyte at 300 uM',\n",
       "       'Percent efficacy against 5-hydroxytryptamine 3A receptor',\n",
       "       'Binding affinity to serotonin 5-HT3 receptor (unknown origin)',\n",
       "       'Antagonist activity at SER3 receptor expressed in HEK293 cells assessed as inhibition of 100 nM octopamine-induced response by calcium imaging assay'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       "       12761331., 19282174., 12192085., 19208472., 18754613., 25695425.,\n",
       "       19963380., 10455251., 19146417., 24582985., 23707254., 19119012.,\n",
       "       18468904., 19338356., 19331415., 19231180., 19359175., 19576766.,\n",
       "       19515567., 19646865., 24147864., 19954972., 19646882., 20043678.,\n",
       "       20045645., 10869410.,  7582507., 14642972., 28135235., 15999145.,\n",
       "        9454810., 14709324.])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below, we are displaying the unique elements of our assaytype column in our dataframe\n",
    "# unique() is a function that is a part of the numpy library in Python, and it is used to find the unique elements of an array\n",
    "display(orig_df['assay_type'].unique())\n",
    "\n",
    "# We then use the same function on a few more columns: assay_cell_line, assay_description, pubmed_id\n",
    "display(orig_df['assay_cell_line'].unique())\n",
    "display(orig_df['assay_description'].unique())\n",
    "display(orig_df['pubmed_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "Y5krECxrHARe",
    "outputId": "2d8fae08-0970-4ecd-d51f-48ac0f6ebada"
   },
   "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>compound_id</th>\n",
       "      <th>uniprot_id</th>\n",
       "      <th>compound_name</th>\n",
       "      <th>standard_inchi_key</th>\n",
       "      <th>max_phase</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>gene_names</th>\n",
       "      <th>target_class</th>\n",
       "      <th>wildtype_or_mutant</th>\n",
       "      <th>mutation_information</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_relation</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>mode_of_action</th>\n",
       "      <th>assay_format</th>\n",
       "      <th>assay_type</th>\n",
       "      <th>assay_sub_type</th>\n",
       "      <th>inhibitor_type</th>\n",
       "      <th>detection_technology</th>\n",
       "      <th>compound_concentration_value</th>\n",
       "      <th>compound_concentration_value_units</th>\n",
       "      <th>substrate_type</th>\n",
       "      <th>substrate_type_standard_relation</th>\n",
       "      <th>substrate_type_standard_value</th>\n",
       "      <th>substrate_type_standard_units</th>\n",
       "      <th>assay_cell_line</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>activity_comments</th>\n",
       "      <th>title</th>\n",
       "      <th>journal</th>\n",
       "      <th>year</th>\n",
       "      <th>volume</th>\n",
       "      <th>issue</th>\n",
       "      <th>authors</th>\n",
       "      <th>annotation_comments</th>\n",
       "      <th>assay_id</th>\n",
       "      <th>dtc_tid</th>\n",
       "      <th>dtc_activity_id</th>\n",
       "      <th>dtc_molregno</th>\n",
       "      <th>record_id</th>\n",
       "      <th>dtc_document_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL100086</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OSVMSQACZRYAPT-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12482441.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inhibitory concentration required against 5-HT...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Development of a presynaptic 5-HT1A antagonist.</td>\n",
       "      <td>Bioorg. Med. Chem. Lett.</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mattson RJ, Catt JD, Sloan CP, Gao Q, Carter R...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>1489397</td>\n",
       "      <td>DTCC00263953</td>\n",
       "      <td>917398</td>\n",
       "      <td>5847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL104700</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SCDKHPSUXHBJDJ-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22607684.0</td>\n",
       "      <td>KI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inhibition of 5-HT3 receptor</td>\n",
       "      <td>Not Determined</td>\n",
       "      <td>7-Azabicyclo[2.2.1]heptane as a scaffold for t...</td>\n",
       "      <td>Bioorg. Med. Chem. Lett.</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Banister SD, Rendina LM, Kassiou M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>820298.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>10478126</td>\n",
       "      <td>DTCC00272114</td>\n",
       "      <td>931177</td>\n",
       "      <td>48255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL1079079</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WGIPGQAPFNVWIX-XXFZXMJFSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19767206.0</td>\n",
       "      <td>INHIBITION</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>50.0</td>\n",
       "      <td>%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Displacement of [3H]LY278584 from 5HT3 (recept...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Synthesis and in vitro autoradiographic evalua...</td>\n",
       "      <td>Bioorg. Med. Chem. Lett.</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Donohue SR, Varnäs K, Jia Z, Gulyás B, Pike VW...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>619795.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3267809</td>\n",
       "      <td>DTCC00632772</td>\n",
       "      <td>857661</td>\n",
       "      <td>37697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     compound_id uniprot_id  ... record_id dtc_document_id\n",
       "0   CHEMBL100086     P46098  ...    917398            5847\n",
       "1   CHEMBL104700     P46098  ...    931177           48255\n",
       "2  CHEMBL1079079     P46098  ...    857661           37697\n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iJio-E_ALCZ6"
   },
   "source": [
    "## Convert InChi key to SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "Qy8d87_-K7aa"
   },
   "outputs": [],
   "source": [
    "ofile = target_name+'_dtc_smiles_raw.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xxziu6wXLPml",
    "outputId": "4fcc526b-c7cf-4997-9ee3-8bfd7731d8c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTR3A_dtc_smiles_raw.csv\n"
     ]
    }
   ],
   "source": [
    "print(ofile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2SRf26EmT_iI"
   },
   "source": [
    "## Note the file HTR3A_dtc_smiles_raw.csv will be created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H5z1nEhILX4t",
    "outputId": "a9f2ba7a-8d8a-467f-aa34-eca7cfbb8345"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMILES data not found, download from PubChem  HTR3A_dtc_smiles_raw.csv\n"
     ]
    }
   ],
   "source": [
    "# import few libraries from AMPL\n",
    "import atomsci.ddm.utils.pubchem_utils as pu\n",
    "from os import path\n",
    "\n",
    "myList = orig_df['standard_inchi_key'].unique().tolist()\n",
    "\n",
    "# Retrieve SMILES strings for compounds through PUBCHEM web interface.\n",
    "\n",
    "# Let us make sure the ofile exists, if it exists then print 'File exists' and \n",
    "# if it doesnt exist, let us print \"SMILES data doesnt not exist, downloading from\n",
    "# PubCHEM\"\n",
    "\n",
    "if not path.exists(ofile) :\n",
    "    print(\"SMILES data not found, download from PubChem \",ofile)\n",
    "    save_smiles_df, fail_lst, discard_lst = pu.download_smiles(myList)\n",
    "    save_smiles_df.to_csv(ofile)\n",
    "else :\n",
    "    print(ofile, 'Exists!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-EAgFT4W4AkM"
   },
   "source": [
    "## Note the `fail_lst` and `discard_lst` will contain the failed and discarded list \n",
    "\n",
    "Check whether the file, HTR3A_dtc_smiles_raw.csv, exists. use the RHS menu option or use `ls HTR3A_dtc_smiles_raw.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p_RA6GQrI-zZ",
    "outputId": "3c6f6449-3cc9-454b-ba9a-7385fefe233b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fail_lst:  63\n",
      "discard_lst:  19\n",
      "992\n",
      "(929, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"fail_lst: \", len(fail_lst))\n",
    "print(\"discard_lst: \", len(discard_lst))\n",
    "print(len(myList))\n",
    "print(save_smiles_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "LUTnA9zN7Ti1",
    "outputId": "14d6baaf-4383-451b-b5c9-745db23c369e"
   },
   "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>CID</th>\n",
       "      <th>standard_inchi_key</th>\n",
       "      <th>smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9845261</td>\n",
       "      <td>OSVMSQACZRYAPT-UHFFFAOYSA-N</td>\n",
       "      <td>\"COC1=CC=CC(=C1)CN2CCN(CC2)C3CCC(CC3)(C4=CC5=C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10130293</td>\n",
       "      <td>SCDKHPSUXHBJDJ-UHFFFAOYSA-N</td>\n",
       "      <td>\"C1CC2CCC1N2CC3=CN=CC=C3\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44471685</td>\n",
       "      <td>WGIPGQAPFNVWIX-XXFZXMJFSA-N</td>\n",
       "      <td>\"COC1=CC=C(C=C1)C2=C(C(=NN2C3=CC=CC=C3I)C(=O)N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2554</td>\n",
       "      <td>FFGPTBGBLSHEPO-UHFFFAOYSA-N</td>\n",
       "      <td>\"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44469321</td>\n",
       "      <td>CKLPLPZSUQEDRT-WPCRTTGESA-N</td>\n",
       "      <td>\"CC1CC2=C(C3(N1)C4=C(C=CC(=C4)Cl)NC3=O)NC5=CC(...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CID  ...                                             smiles\n",
       "0   9845261  ...  \"COC1=CC=CC(=C1)CN2CCN(CC2)C3CCC(CC3)(C4=CC5=C...\n",
       "1  10130293  ...                          \"C1CC2CCC1N2CC3=CN=CC=C3\"\n",
       "2  44471685  ...  \"COC1=CC=C(C=C1)C2=C(C(=NN2C3=CC=CC=C3I)C(=O)N...\n",
       "3      2554  ...             \"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\"\n",
       "4  44469321  ...  \"CC1CC2=C(C3(N1)C4=C(C=CC(=C4)Cl)NC3=O)NC5=CC(...\n",
       "\n",
       "[5 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_smiles_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IbvY7TWY6tX1"
   },
   "source": [
    "## In this step, we reassemble the dataframe by attaching the IC50 values to create a new dataframe  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qd2IIlwAMKr-",
    "outputId": "b6eb31da-8bc1-43c4-a79d-0f4c284360ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63\n",
      "(929, 3)\n",
      "HTR3A distinct compounds = only 127\n",
      "HTR3A distinct compounds <,>,= 194\n",
      "num shared compounds 194\n",
      "Add pIC50 values.\n",
      "0       100000.0\n",
      "4        10000.0\n",
      "5        30000.0\n",
      "6        30000.0\n",
      "7        30000.0\n",
      "          ...   \n",
      "1207      3310.0\n",
      "1216        70.0\n",
      "1217        70.0\n",
      "1221         5.0\n",
      "1226       779.0\n",
      "Name: standard_value, Length: 226, dtype: float64\n",
      "100000.0\n",
      "10000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "5800.0\n",
      "30000.0\n",
      "5700.0\n",
      "7900.0\n",
      "7800.0\n",
      "30000.0\n",
      "2800.0\n",
      "100000.0\n",
      "0.83\n",
      "10000.0\n",
      "10000.0\n",
      "7943.28\n",
      "1000.0\n",
      "48.4\n",
      "50.5\n",
      "62.1\n",
      "84.8\n",
      "100.0\n",
      "87.6\n",
      "46.5\n",
      "22.1\n",
      "100.0\n",
      "24.6\n",
      "59.1\n",
      "100.0\n",
      "41.4\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "44.2\n",
      "100.0\n",
      "100.0\n",
      "69.9\n",
      "63.8\n",
      "52.6\n",
      "100.0\n",
      "100.0\n",
      "61.0\n",
      "55.2\n",
      "86.6\n",
      "64.8\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "77.6\n",
      "26.7\n",
      "100.0\n",
      "45.7\n",
      "50.6\n",
      "99.4\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "83.1\n",
      "100.0\n",
      "45.6\n",
      "15.6\n",
      "8.9\n",
      "8.93\n",
      "100.0\n",
      "48.3\n",
      "97.9\n",
      "10000.0\n",
      "9.3\n",
      "6.9\n",
      "1300.0\n",
      "10000.0\n",
      "10000.0\n",
      "10000.0\n",
      "9000.0\n",
      "100000.0\n",
      "100000.0\n",
      "12000.0\n",
      "100000.0\n",
      "1600.0\n",
      "2300.0\n",
      "1000.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "10.0\n",
      "9.4\n",
      "770.0\n",
      "11.0\n",
      "6.7\n",
      "4.9\n",
      "2.8\n",
      "32.7\n",
      "299.0\n",
      "10000.0\n",
      "10000.0\n",
      "31622.78\n",
      "31622.78\n",
      "12.0\n",
      "6.0\n",
      "2400.0\n",
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      "800.0\n",
      "2100.0\n",
      "2700.0\n",
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      "11600.0\n",
      "5700.0\n",
      "1300.0\n",
      "1900.0\n",
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      "2200.0\n",
      "1900.0\n",
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      "13600.0\n",
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      "3300.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "3981.07\n",
      "4400.0\n",
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      "30000.0\n",
      "31622.78\n",
      "10000.0\n",
      "1000.0\n",
      "10000.0\n",
      "100000.0\n",
      "100000.0\n",
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      "34.0\n",
      "57000.0\n",
      "275.0\n",
      "4800.0\n",
      "188.0\n",
      "580.0\n",
      "962.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "31622.78\n",
      "2500.0\n",
      "2511.89\n",
      "30000.0\n",
      "31622.78\n",
      "11000.0\n",
      "12589.25\n",
      "7500.0\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "100000.0\n",
      "3981.07\n",
      "4200.0\n",
      "30000.0\n",
      "31622.78\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "251188.64\n",
      "25118.86\n",
      "100000.0\n",
      "0.09\n",
      "6.92\n",
      "385.0\n",
      "10000.0\n",
      "19952.62\n",
      "50118.72\n",
      "100000.0\n",
      "1000.0\n",
      "1.41\n",
      "3160.0\n",
      "3160.0\n",
      "3100.0\n",
      "790.0\n",
      "1700.0\n",
      "1600.0\n",
      "159.0\n",
      "3500.0\n",
      "2500.0\n",
      "1400.0\n",
      "2800.0\n",
      "4900.0\n",
      "18.0\n",
      "390.0\n",
      "10000.0\n",
      "484.0\n",
      "484.0\n",
      "2480.0\n",
      "7310.0\n",
      "3310.0\n",
      "70.0\n",
      "70.0\n",
      "5.0\n",
      "779.0\n",
      "Merge in SMILES strings.\n",
      "100000.0\n",
      "10000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "5800.0\n",
      "30000.0\n",
      "5700.0\n",
      "7900.0\n",
      "7800.0\n",
      "30000.0\n",
      "2800.0\n",
      "100000.0\n",
      "0.83\n",
      "10000.0\n",
      "10000.0\n",
      "7943.28\n",
      "1000.0\n",
      "48.4\n",
      "50.5\n",
      "62.1\n",
      "84.8\n",
      "100.0\n",
      "87.6\n",
      "46.5\n",
      "22.1\n",
      "100.0\n",
      "24.6\n",
      "59.1\n",
      "100.0\n",
      "41.4\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "44.2\n",
      "100.0\n",
      "100.0\n",
      "69.9\n",
      "63.8\n",
      "52.6\n",
      "100.0\n",
      "100.0\n",
      "61.0\n",
      "55.2\n",
      "86.6\n",
      "64.8\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "77.6\n",
      "26.7\n",
      "100.0\n",
      "45.7\n",
      "50.6\n",
      "99.4\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "100.0\n",
      "83.1\n",
      "100.0\n",
      "45.6\n",
      "15.6\n",
      "8.9\n",
      "8.93\n",
      "100.0\n",
      "48.3\n",
      "97.9\n",
      "10000.0\n",
      "9.3\n",
      "6.9\n",
      "1300.0\n",
      "10000.0\n",
      "10000.0\n",
      "10000.0\n",
      "9000.0\n",
      "100000.0\n",
      "100000.0\n",
      "12000.0\n",
      "100000.0\n",
      "1600.0\n",
      "2300.0\n",
      "1000.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "30000.0\n",
      "10.0\n",
      "9.4\n",
      "770.0\n",
      "11.0\n",
      "6.7\n",
      "4.9\n",
      "2.8\n",
      "32.7\n",
      "299.0\n",
      "10000.0\n",
      "10000.0\n",
      "31622.78\n",
      "31622.78\n",
      "12.0\n",
      "6.0\n",
      "2400.0\n",
      "1400.0\n",
      "800.0\n",
      "2100.0\n",
      "2700.0\n",
      "19800.0\n",
      "2800.0\n",
      "11600.0\n",
      "5700.0\n",
      "1300.0\n",
      "1900.0\n",
      "5900.0\n",
      "15800.0\n",
      "1600.0\n",
      "2200.0\n",
      "1900.0\n",
      "2100.0\n",
      "13600.0\n",
      "2000.0\n",
      "2200.0\n",
      "3300.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "3981.07\n",
      "4400.0\n",
      "19000.0\n",
      "30000.0\n",
      "31622.78\n",
      "10000.0\n",
      "1000.0\n",
      "10000.0\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "34.0\n",
      "57000.0\n",
      "275.0\n",
      "4800.0\n",
      "188.0\n",
      "580.0\n",
      "962.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "31622.78\n",
      "2500.0\n",
      "2511.89\n",
      "30000.0\n",
      "31622.78\n",
      "11000.0\n",
      "12589.25\n",
      "7500.0\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "100000.0\n",
      "30000.0\n",
      "100000.0\n",
      "3981.07\n",
      "4200.0\n",
      "30000.0\n",
      "31622.78\n",
      "30000.0\n",
      "31622.78\n",
      "100000.0\n",
      "251188.64\n",
      "25118.86\n",
      "100000.0\n",
      "0.09\n",
      "6.92\n",
      "385.0\n",
      "10000.0\n",
      "19952.62\n",
      "50118.72\n",
      "100000.0\n",
      "1000.0\n",
      "1.41\n",
      "3160.0\n",
      "3160.0\n",
      "3100.0\n",
      "790.0\n",
      "1700.0\n",
      "1600.0\n",
      "159.0\n",
      "3500.0\n",
      "2500.0\n",
      "1400.0\n",
      "2800.0\n",
      "4900.0\n",
      "18.0\n",
      "390.0\n",
      "10000.0\n",
      "484.0\n",
      "484.0\n",
      "2480.0\n",
      "7310.0\n",
      "3310.0\n",
      "70.0\n",
      "70.0\n",
      "5.0\n",
      "779.0\n",
      "(218, 48)\n",
      "192\n"
     ]
    }
   ],
   "source": [
    "# HTR3A_dtc_smiles.csv is the ofile \n",
    "ofile = target_name+'_dtc_smiles.csv'\n",
    "if not path.exists(ofile) :\n",
    "    import atomsci.ddm.utils.data_curation_functions as dcf\n",
    "    import importlib as impl\n",
    "\n",
    "    print(len(fail_lst))\n",
    "    print(save_smiles_df.shape)\n",
    "\n",
    "# Above, we will pring the fail_list created in the earlier code block \n",
    "# We will also print the dimensions of our save_smiles_df pandas \n",
    "# dataframe using the .shape function\n",
    "\n",
    "# Notice the ifile is now HTR3A_dtc_smiles_raw.csv\n",
    "    ifile=target_name+'_dtc_smiles_raw.csv'\n",
    "\n",
    "# Here we are reading in our file (ifile) using the pandas library, and assigning its contents to the save_smiles_df from earlier\n",
    "    save_smiles_df=pd.read_csv(ifile)\n",
    "\n",
    "    ## Retrieve specific data\n",
    "    ## Will include censored data in smiles \n",
    "    ## Combine gene data with SMILES strings and call this our starting \"raw\" dataset.\n",
    "# Here we are creating a variable called targ_lst, which contains our target and is formatted as a list\n",
    "    targ_lst=[target_name]\n",
    "    ####WARNING: I had to convert this explicitly to a floating point value!!!\n",
    "# Below, using the nm_df['standard_value']=nm_df['standard_value'].astype(float) is used to convert \n",
    "# the standard_value column into a float using astype()\n",
    "# A floating point value, also called a float, represents a real number and is written with a decimal point dividing the integer and fractional point\n",
    "    nm_df['standard_value']=nm_df['standard_value'].astype(float)\n",
    "    smiles_lst, shared_inchi_keys = dcf.get_smiles_dtc_data(nm_df, targ_lst, save_smiles_df)\n",
    "    smiles_df=pd.concat(smiles_lst)\n",
    "    # ofile=target_name+'_dtc_smiles.csv'\n",
    "    smiles_df.to_csv(ofile,index=False)\n",
    "else :\n",
    "    print(\"Downloaded file previously saved\",ofile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Npvu8Wl9qFe"
   },
   "source": [
    "## Change the ofile to ifile for reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NiPDw_h2b4Uv",
    "outputId": "1c550afa-a09f-4daf-d76f-ce41c811c264"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTR3A_dtc_smiles.csv\n"
     ]
    }
   ],
   "source": [
    "ifile=target_name+'_dtc_smiles.csv'\n",
    "print(ifile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "id": "tTukf532aAPs",
    "outputId": "00ac5b3f-08d6-4da9-ae6a-bc609b2b360f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(218, 48)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound_id</th>\n",
       "      <th>uniprot_id</th>\n",
       "      <th>compound_name</th>\n",
       "      <th>standard_inchi_key</th>\n",
       "      <th>max_phase</th>\n",
       "      <th>target_pref_name</th>\n",
       "      <th>gene_names</th>\n",
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       "      <th>mutation_information</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>standard_type</th>\n",
       "      <th>standard_relation</th>\n",
       "      <th>standard_value</th>\n",
       "      <th>standard_units</th>\n",
       "      <th>mode_of_action</th>\n",
       "      <th>assay_format</th>\n",
       "      <th>assay_type</th>\n",
       "      <th>assay_sub_type</th>\n",
       "      <th>inhibitor_type</th>\n",
       "      <th>detection_technology</th>\n",
       "      <th>compound_concentration_value</th>\n",
       "      <th>compound_concentration_value_units</th>\n",
       "      <th>substrate_type</th>\n",
       "      <th>substrate_type_standard_relation</th>\n",
       "      <th>substrate_type_standard_value</th>\n",
       "      <th>substrate_type_standard_units</th>\n",
       "      <th>assay_cell_line</th>\n",
       "      <th>assay_description</th>\n",
       "      <th>activity_comments</th>\n",
       "      <th>title</th>\n",
       "      <th>journal</th>\n",
       "      <th>year</th>\n",
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       "      <th>issue</th>\n",
       "      <th>authors</th>\n",
       "      <th>annotation_comments</th>\n",
       "      <th>assay_id</th>\n",
       "      <th>dtc_tid</th>\n",
       "      <th>dtc_activity_id</th>\n",
       "      <th>dtc_molregno</th>\n",
       "      <th>record_id</th>\n",
       "      <th>dtc_document_id</th>\n",
       "      <th>PIC50</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CID</th>\n",
       "      <th>smiles</th>\n",
       "      <th>rdkit_smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL100086</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OSVMSQACZRYAPT-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12482441.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inhibitory concentration required against 5-HT...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Development of a presynaptic 5-HT1A antagonist.</td>\n",
       "      <td>Bioorg. Med. Chem. Lett.</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mattson RJ, Catt JD, Sloan CP, Gao Q, Carter R...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>1489397</td>\n",
       "      <td>DTCC00263953</td>\n",
       "      <td>917398</td>\n",
       "      <td>5847</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>9845261</td>\n",
       "      <td>COC1=CC=CC(=C1)CN2CCN(CC2)C3CCC(CC3)(C4=CC5=C(...</td>\n",
       "      <td>COc1cccc(CN2CCN(C3CCC(O)(c4ccc5c(c4)OCO5)CC3)C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL1082723</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NITD609</td>\n",
       "      <td>CKLPLPZSUQEDRT-WPCRTTGESA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20813948.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Binding affinity to human recombinant serotoni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spiroindolones, a potent compound class for th...</td>\n",
       "      <td>Science</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5996.0</td>\n",
       "      <td>Rottmann M, McNamara C, Yeung BK, Lee MC, Zou ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>658586.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3467863</td>\n",
       "      <td>DTCC00031143</td>\n",
       "      <td>1686650</td>\n",
       "      <td>40818</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>44469321</td>\n",
       "      <td>CC1CC2=C(C3(N1)C4=C(C=CC(=C4)Cl)NC3=O)NC5=CC(=...</td>\n",
       "      <td>CC1Cc2c([nH]c3cc(Cl)c(F)cc23)C2(N1)C(=O)Nc1ccc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL1083485</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YOCUJVXRRWEPDN-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20465311.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cell_based</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HEK293</td>\n",
       "      <td>Agonist activity at human 5HT3A receptor expre...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Novel alpha-7 nicotinic acetylcholine receptor...</td>\n",
       "      <td>J. Med. Chem.</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Ghiron C, Haydar SN, Aschmies S, Bothmann H, C...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>634700.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3345977</td>\n",
       "      <td>DTCC00645978</td>\n",
       "      <td>1654372</td>\n",
       "      <td>39082</td>\n",
       "      <td>4.522879</td>\n",
       "      <td>5</td>\n",
       "      <td>44611748</td>\n",
       "      <td>C1CCN(CC1)CCCCNC(=O)NC2=CN=C(C=C2)C3=CC=CC=C3F</td>\n",
       "      <td>O=C(NCCCCN1CCCCC1)Nc1ccc(-c2ccccc2F)nc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL1083787</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LMGUUVWYFJIKCD-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20465311.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cell_based</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HEK293</td>\n",
       "      <td>Agonist activity at human 5HT3A receptor expre...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Novel alpha-7 nicotinic acetylcholine receptor...</td>\n",
       "      <td>J. Med. Chem.</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Ghiron C, Haydar SN, Aschmies S, Bothmann H, C...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>634700.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3345978</td>\n",
       "      <td>DTCC00645979</td>\n",
       "      <td>1395966</td>\n",
       "      <td>39082</td>\n",
       "      <td>4.522879</td>\n",
       "      <td>6</td>\n",
       "      <td>44240743</td>\n",
       "      <td>C1CCN(CC1)CCCCNC(=O)NC2=CN=C(C=C2)C3=CC=C(C=C3)F</td>\n",
       "      <td>O=C(NCCCCN1CCCCC1)Nc1ccc(-c2ccc(F)cc2)nc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL1083788</td>\n",
       "      <td>P46098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DFBQTBNRGSIKHN-UHFFFAOYSA-N</td>\n",
       "      <td>0</td>\n",
       "      <td>SEROTONIN 3A (5-HT3A) RECEPTOR</td>\n",
       "      <td>HTR3A</td>\n",
       "      <td>Ion channel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20465311.0</td>\n",
       "      <td>IC50</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>NM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cell_based</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HEK293</td>\n",
       "      <td>Agonist activity at human 5HT3A receptor expre...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Novel alpha-7 nicotinic acetylcholine receptor...</td>\n",
       "      <td>J. Med. Chem.</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Ghiron C, Haydar SN, Aschmies S, Bothmann H, C...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>634700.0</td>\n",
       "      <td>DTCT0023221</td>\n",
       "      <td>3345979</td>\n",
       "      <td>DTCC00645980</td>\n",
       "      <td>652015</td>\n",
       "      <td>39082</td>\n",
       "      <td>4.522879</td>\n",
       "      <td>7</td>\n",
       "      <td>44611875</td>\n",
       "      <td>C1CCN(CC1)CCCCNC(=O)NC2=CN=C(C=C2)C3=CN=C(C=C3)F</td>\n",
       "      <td>O=C(NCCCCN1CCCCC1)Nc1ccc(-c2ccc(F)nc2)nc1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     compound_id  ...                                       rdkit_smiles\n",
       "0   CHEMBL100086  ...  COc1cccc(CN2CCN(C3CCC(O)(c4ccc5c(c4)OCO5)CC3)C...\n",
       "1  CHEMBL1082723  ...  CC1Cc2c([nH]c3cc(Cl)c(F)cc23)C2(N1)C(=O)Nc1ccc...\n",
       "2  CHEMBL1083485  ...            O=C(NCCCCN1CCCCC1)Nc1ccc(-c2ccccc2F)nc1\n",
       "3  CHEMBL1083787  ...          O=C(NCCCCN1CCCCC1)Nc1ccc(-c2ccc(F)cc2)nc1\n",
       "4  CHEMBL1083788  ...          O=C(NCCCCN1CCCCC1)Nc1ccc(-c2ccc(F)nc2)nc1\n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(smiles_df.shape)\n",
    "save_smiles_df = smiles_df\n",
    "save_smiles_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "miN7240L-Bc4"
   },
   "source": [
    "## Use AMPL for transforming IC50 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "id": "inaUPauW-BFE",
    "outputId": "f85c9cd4-c575-4556-e806-7af906ec45f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Post-processing dataset\n",
      "\n",
      "Total Number of results = 218\n",
      "dataset mean = 5.70481453357661 NM\n",
      "dataset stdev = 1.3171677193640752 NM\n",
      "dataset max = 10.045757490560675 NM\n",
      "dataset min = 3.6000000054478725 NM\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From our dataframe, we are working with the PIC50 column \n",
    "# !=np.inf uses the Numpy library and the != portion checks to see if the value \n",
    "# of two operands, the object of an operation, are equal. If they are not equal the \n",
    "# condition is true\n",
    "# The np.inf portion indicates the Numpy module which can be used to represent \n",
    "# positive infinite value\n",
    "data=save_smiles_df[save_smiles_df['PIC50'] != np.inf]\n",
    "\n",
    "# Here we are defining our column, which will be PIC50\n",
    "column = 'PIC50'\n",
    "\n",
    "# Here we are using the num_bins parameter to determine the number of bins our data will be divided into, creating 20 different peaks\n",
    "# These peaks will be visualized on our graph\n",
    "num_bins = 20\n",
    "\n",
    "# Here we are setting our title for the graph as our target name \n",
    "title = target_name\n",
    "\n",
    "# Here we are specifying the units that are used, which in our case we are using nanometers \n",
    "units = 'NM'\n",
    "\n",
    "# Using the filepath function, we are defining our file\n",
    "filepath = \"\"\n",
    "\n",
    "# This is the same variable we created earlier called data \n",
    "data=save_smiles_df[save_smiles_df['PIC50'] != np.inf]\n",
    "\n",
    "# Using the summarize_data submodule, we are going to compile a set of data summarization tools to calculate several descriptive features\n",
    "# These features include: column, num_bins, title, units, filepath, and data\n",
    "curate_data.summarize_data(column, num_bins, title, units, filepath, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zy41q3hXHwsL"
   },
   "source": [
    "## Let us cluster the compounds to explore the chemical space\n",
    "\n",
    "\n",
    "Project compounds into two dimensions with UMAP and Tanimoto similiarty\n",
    "\n",
    "1.   Cluster compounds by Tanimoto similarity\n",
    "2.   Repeat steps 1 and 2 with Maximum Common Substructure distance when dataset size is below # threshold (default < 300)\n",
    "\n",
    "See documentation here:\n",
    " https://ampl.readthedocs.io/en/latest/pipeline.html?highlight=diversity_plots#pipeline.diversity_plots.diversity_plots\n",
    "\n",
    " </li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2WDdVKbYHwsM"
   },
   "source": [
    "# Save output from clustering heatmap to image and upload to presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "4QproMuzHwsM"
   },
   "outputs": [],
   "source": [
    "ifile = target_name+'_dtc_smiles.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yAzI5OzjHwsM"
   },
   "source": [
    "# Plot self similarity (Tanimoto) within dataset and show distribution of distances between compounds in dataset for nearest neighbor.\n",
    "\n",
    "## Save  distribution plot as an image.\n",
    "\n",
    "We will be calling dp.diversity_function from AMPL in the following code chunk. For AMPL function explanations, \n",
    "please consult AMPL documentation here, https://ampl.readthedocs.io/en/latest/pipeline.html?highlight=diversity_plots#pipeline.diversity_plots.diversity_plots "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFC7Syc6gYBi"
   },
   "source": [
    "## Here is a brief explanation of `dp` function: \n",
    "\n",
    "### The AMPL function will calculate diversity profile for the data. \n",
    "\n",
    "### Input Args:\n",
    "\n",
    "\n",
    "*   **dset_key:** : Name of the input data variable \n",
    "*   **datastore** : \n",
    "*   **id_col**    : Ambit_InchiKey, it is a chemical identifier for the compound or drug molecules. Please check here for a detailed explanation of InChiKey, https://en.wikipedia.org/wiki/International_Chemical_Identifier#:~:text=%2B%2Fm0%2Fs1-,InChIKey,hashed%20counterpart%20of%20standard%20InChI. In this case, Excape is using InChiKey generated from Ambit\n",
    "*   **response_col**: Outcome column, in out case it is pXC50\n",
    "\n",
    "## `dp.diversity_plots` function\n",
    "\n",
    "*    Computes Fingerprints \n",
    "*    If the number of compounds are > 300, it will compute Fingerprints and use it to compute Tanimoto distance matrix. plot the distances using UMAP projection and cluster (complete cluster method will be used) the distances to create a heatmap\n",
    "*   If the number of compounds are < 100, MCS (Maximum Common Substructure) will be used for clustering in addition the above step. \n",
    "\n",
    "\n",
    "## Helpful links\n",
    "\n",
    "* Tanimoto \n",
    "    * https://en.wikipedia.org/wiki/Jaccard_index\n",
    "    * https://en.wikipedia.org/wiki/Chemical_similarity\n",
    "* UMAP\n",
    "    * https://pair-code.github.io/understanding-umap/\n",
    "* MCS\n",
    "    * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2718661/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "geRQnqPngVMo",
    "outputId": "e38a3094-810c-48b0-8cc1-f88d31ee2c55"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canonicalizing 183 molecules...\n",
      "Done\n",
      "Computing fingerprints...\n",
      "Done\n",
      "Computing Tanimoto distance matrix...\n",
      "Done\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp.diversity_plots(dset_key = ifile,\n",
    "                   datastore = False,\n",
    "                   response_col = 'PIC50',\n",
    "                   max_for_mcs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CQLUJL5_Aq2g",
    "outputId": "58476200-43cb-4301-86d1-fabb145cf742"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 48)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JWr5VyXpqpM1"
   },
   "source": [
    "## Self similarity (Tanimoto) \n",
    "\n",
    "Calculate self-similarity (using Tanimoto) for the dataset and plot the distanes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "tG_217EPHwsM",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "feat_type = 'ECFP'\n",
    "dist_metric = 'tanimoto'\n",
    "smiles_lst1 = data['rdkit_smiles'].tolist()\n",
    "calc_type = 'nearest'\n",
    "dist_sample = cd.calc_dist_smiles(feat_type, dist_metric, smiles_lst1, None, calc_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hCD85wGzx7gW",
    "outputId": "a402c01a-621e-41db-ec1b-d59e61b806e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "218\n",
      "218\n"
     ]
    }
   ],
   "source": [
    "print(len(dist_sample))\n",
    "print(len(smiles_lst1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ecuqss6kgl7I"
   },
   "source": [
    "## What does **calc_dist_smiles** function return? \n",
    "\n",
    "\n",
    "*   input is a list of SMILES strings\n",
    "*   data featurization: ECFP (Fingerprint)\n",
    "*   What distance metric to use? Tanimoto\n",
    "*   How to process distance matrix, nearest ?\n",
    "*   returns a distance matrix as a vector of distances\n",
    "\n",
    "Here is the function summary:\n",
    "\n",
    "*   rdkit is used to transform SMILES to mols\n",
    "*   mols to FP (Morgan, 1024 FP)\n",
    "*   calls calc_summary with the following options: \n",
    "         * fprints1 is the FP \n",
    "         * fprints2 is none \n",
    "         * dist_metrics will return a distance matrix\n",
    "    calc_summary(dist_metrics.tanimoto(fprints1, fprints2), calc_type=nearest, num_nearest=1, within_dset=True)\n",
    "\n",
    "* Finally, returns the distances of each atom to its closest neighbor \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H8En9iSCrHnA"
   },
   "source": [
    "\n",
    "## Explanation for the following code chunk\n",
    "\n",
    "* **scipy.stats.kde** will use kernel density function to estimate the probability density function (PDE)\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 534
    },
    "id": "vcXZQBLrHwsN",
    "outputId": "1758d890-6856-43cf-921c-6336e2cc10ef"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats.kde import gaussian_kde\n",
    "\n",
    "# import math library\n",
    "import numpy as np\n",
    "\n",
    "# for creating plots\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# current directory\n",
    "odir='./'\n",
    "\n",
    "# name for the task\n",
    "task_name='within dataset'\n",
    "\n",
    "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html\n",
    "dist_pdf = gaussian_kde(dist_sample)\n",
    "x_plt = np.linspace(min(dist_sample), max(dist_sample), 500)\n",
    "y_plt = dist_pdf(x_plt)    \n",
    "fig, ax = plt.subplots(figsize=(8.0,8.0))\n",
    "ax.plot(x_plt, y_plt, color='forestgreen')\n",
    "ax.set_xlabel('%s distance' % dist_metric)\n",
    "ax.set_ylabel('Density')\n",
    "ax.set_title(\"%s dataset\\nDistribution of %s distances between %s feature vectors\" % (\n",
    "              task_name, dist_metric, feat_type))\n",
    "fig.savefig(odir+'distance_to_background_mol.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "101z-_vAhdlp",
    "outputId": "bebf1253-f956-425b-cd9f-8c55608460b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Sep 17 11:44:55 UTC 2021\n"
     ]
    }
   ],
   "source": [
    "!date # ending time"
   ]
  }
 ],
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   "collapsed_sections": [],
   "include_colab_link": true,
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