python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2.1-Cheminformatics-PyCaret-predicting-solubility.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQi3X7TNUl5Y",
        "colab_type": "text"
      },
      "source": [
        "# **Cheminformatics in Python [PART 2.1] Predicting Solubility of Molecules using PyCaret | End-to-End Data Science Project** \n",
        "\n",
        "Chanin Nantasenamat\n",
        "\n",
        "<i>[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor </i>\n",
        "\n",
        "In this Jupyter notebook, we will continue our journey into the world of Cheminformatics (i.e. lies at the interface of Informatics and Chemistry) by simplifying this notebook via the use of the low-code machine learning library PyCaret.\n",
        "\n",
        "\n",
        "**Information from the previous notebook:**\n",
        "\n",
        "We will be reproducing a research article (by John S. Delaney$^1$) by applying Linear Regression to predict the solubility of molecules (i.e. solubility of drugs is an important physicochemical property in Drug discovery, design and development).\n",
        "\n",
        "This idea for this notebook was inspired by the excellent blog post by Pat Walters$^2$ where he reproduced the linear regression model with similar degree of performance as that of Delaney. This example is also briefly described in the book ***Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More***.$^3$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQW_Ts66R4Ms",
        "colab_type": "text"
      },
      "source": [
        "## **1. Install conda and libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jNwdYoBR8ea",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "844c8e71-85af-48ea-99ea-1b5b01216feb"
      },
      "source": [
        "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! chmod +x Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! bash ./Miniconda3-py37_4.8.2-Linux-x86_64.sh -b -f -p /usr/local\n",
        "! conda install -c rdkit rdkit -y\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-28 02:18:57--  https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 85055499 (81M) [application/x-sh]\n",
            "Saving to: ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’\n",
            "\n",
            "\r          Miniconda   0%[                    ]       0  --.-KB/s               \r         Miniconda3  16%[==>                 ]  13.65M  68.3MB/s               \r        Miniconda3-  80%[===============>    ]  65.22M   163MB/s               \rMiniconda3-py37_4.8 100%[===================>]  81.12M   181MB/s    in 0.4s    \n",
            "\n",
            "2020-07-28 02:18:58 (181 MB/s) - ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’ saved [85055499/85055499]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - _libgcc_mutex==0.1=main\n",
            "    - asn1crypto==1.3.0=py37_0\n",
            "    - ca-certificates==2020.1.1=0\n",
            "    - certifi==2019.11.28=py37_0\n",
            "    - cffi==1.14.0=py37h2e261b9_0\n",
            "    - chardet==3.0.4=py37_1003\n",
            "    - conda-package-handling==1.6.0=py37h7b6447c_0\n",
            "    - conda==4.8.2=py37_0\n",
            "    - cryptography==2.8=py37h1ba5d50_0\n",
            "    - idna==2.8=py37_0\n",
            "    - ld_impl_linux-64==2.33.1=h53a641e_7\n",
            "    - libedit==3.1.20181209=hc058e9b_0\n",
            "    - libffi==3.2.1=hd88cf55_4\n",
            "    - libgcc-ng==9.1.0=hdf63c60_0\n",
            "    - libstdcxx-ng==9.1.0=hdf63c60_0\n",
            "    - ncurses==6.2=he6710b0_0\n",
            "    - openssl==1.1.1d=h7b6447c_4\n",
            "    - pip==20.0.2=py37_1\n",
            "    - pycosat==0.6.3=py37h7b6447c_0\n",
            "    - pycparser==2.19=py37_0\n",
            "    - pyopenssl==19.1.0=py37_0\n",
            "    - pysocks==1.7.1=py37_0\n",
            "    - python==3.7.6=h0371630_2\n",
            "    - readline==7.0=h7b6447c_5\n",
            "    - requests==2.22.0=py37_1\n",
            "    - ruamel_yaml==0.15.87=py37h7b6447c_0\n",
            "    - setuptools==45.2.0=py37_0\n",
            "    - six==1.14.0=py37_0\n",
            "    - sqlite==3.31.1=h7b6447c_0\n",
            "    - tk==8.6.8=hbc83047_0\n",
            "    - tqdm==4.42.1=py_0\n",
            "    - urllib3==1.25.8=py37_0\n",
            "    - wheel==0.34.2=py37_0\n",
            "    - xz==5.2.4=h14c3975_4\n",
            "    - yaml==0.1.7=had09818_2\n",
            "    - zlib==1.2.11=h7b6447c_3\n",
            "\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main\n",
            "  asn1crypto         pkgs/main/linux-64::asn1crypto-1.3.0-py37_0\n",
            "  ca-certificates    pkgs/main/linux-64::ca-certificates-2020.1.1-0\n",
            "  certifi            pkgs/main/linux-64::certifi-2019.11.28-py37_0\n",
            "  cffi               pkgs/main/linux-64::cffi-1.14.0-py37h2e261b9_0\n",
            "  chardet            pkgs/main/linux-64::chardet-3.0.4-py37_1003\n",
            "  conda              pkgs/main/linux-64::conda-4.8.2-py37_0\n",
            "  conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n",
            "  cryptography       pkgs/main/linux-64::cryptography-2.8-py37h1ba5d50_0\n",
            "  idna               pkgs/main/linux-64::idna-2.8-py37_0\n",
            "  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n",
            "  libedit            pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n",
            "  libffi             pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n",
            "  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n",
            "  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n",
            "  ncurses            pkgs/main/linux-64::ncurses-6.2-he6710b0_0\n",
            "  openssl            pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_4\n",
            "  pip                pkgs/main/linux-64::pip-20.0.2-py37_1\n",
            "  pycosat            pkgs/main/linux-64::pycosat-0.6.3-py37h7b6447c_0\n",
            "  pycparser          pkgs/main/linux-64::pycparser-2.19-py37_0\n",
            "  pyopenssl          pkgs/main/linux-64::pyopenssl-19.1.0-py37_0\n",
            "  pysocks            pkgs/main/linux-64::pysocks-1.7.1-py37_0\n",
            "  python             pkgs/main/linux-64::python-3.7.6-h0371630_2\n",
            "  readline           pkgs/main/linux-64::readline-7.0-h7b6447c_5\n",
            "  requests           pkgs/main/linux-64::requests-2.22.0-py37_1\n",
            "  ruamel_yaml        pkgs/main/linux-64::ruamel_yaml-0.15.87-py37h7b6447c_0\n",
            "  setuptools         pkgs/main/linux-64::setuptools-45.2.0-py37_0\n",
            "  six                pkgs/main/linux-64::six-1.14.0-py37_0\n",
            "  sqlite             pkgs/main/linux-64::sqlite-3.31.1-h7b6447c_0\n",
            "  tk                 pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n",
            "  tqdm               pkgs/main/noarch::tqdm-4.42.1-py_0\n",
            "  urllib3            pkgs/main/linux-64::urllib3-1.25.8-py37_0\n",
            "  wheel              pkgs/main/linux-64::wheel-0.34.2-py37_0\n",
            "  xz                 pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n",
            "  yaml               pkgs/main/linux-64::yaml-0.1.7-had09818_2\n",
            "  zlib               pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n",
            "\n",
            "\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /usr/local\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - rdkit\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    blas-1.0                   |              mkl           6 KB\n",
            "    bzip2-1.0.8                |       h7b6447c_0          78 KB\n",
            "    ca-certificates-2020.6.24  |                0         125 KB\n",
            "    cairo-1.14.12              |       h8948797_3         906 KB\n",
            "    certifi-2020.6.20          |           py37_0         156 KB\n",
            "    conda-4.8.3                |           py37_0         2.8 MB\n",
            "    fontconfig-2.13.0          |       h9420a91_0         227 KB\n",
            "    freetype-2.10.2            |       h5ab3b9f_0         608 KB\n",
            "    glib-2.63.1                |       h5a9c865_0         2.9 MB\n",
            "    icu-58.2                   |       he6710b0_3        10.5 MB\n",
            "    intel-openmp-2020.1        |              217         780 KB\n",
            "    jpeg-9b                    |       h024ee3a_2         214 KB\n",
            "    libboost-1.67.0            |       h46d08c1_4        13.0 MB\n",
            "    libgfortran-ng-7.3.0       |       hdf63c60_0        1006 KB\n",
            "    libpng-1.6.37              |       hbc83047_0         278 KB\n",
            "    libtiff-4.1.0              |       h2733197_0         447 KB\n",
            "    libuuid-1.0.3              |       h1bed415_2          15 KB\n",
            "    libxcb-1.14                |       h7b6447c_0         505 KB\n",
            "    libxml2-2.9.9              |       hea5a465_1         1.6 MB\n",
            "    mkl-2020.1                 |              217       129.0 MB\n",
            "    mkl-service-2.3.0          |   py37he904b0f_0         218 KB\n",
            "    mkl_fft-1.1.0              |   py37h23d657b_0         143 KB\n",
            "    mkl_random-1.1.1           |   py37h0573a6f_0         322 KB\n",
            "    numpy-1.18.5               |   py37ha1c710e_0           5 KB\n",
            "    numpy-base-1.18.5          |   py37hde5b4d6_0         4.1 MB\n",
            "    olefile-0.46               |           py37_0          50 KB\n",
            "    openssl-1.1.1g             |       h7b6447c_0         2.5 MB\n",
            "    pandas-1.0.5               |   py37h0573a6f_0         7.8 MB\n",
            "    pcre-8.44                  |       he6710b0_0         212 KB\n",
            "    pillow-7.1.2               |   py37hb39fc2d_0         603 KB\n",
            "    pixman-0.40.0              |       h7b6447c_0         370 KB\n",
            "    py-boost-1.67.0            |   py37h04863e7_4         278 KB\n",
            "    python-dateutil-2.8.1      |             py_0         215 KB\n",
            "    pytz-2020.1                |             py_0         184 KB\n",
            "    rdkit-2020.03.3.0          |   py37hc20afe1_1        24.8 MB  rdkit\n",
            "    zstd-1.3.7                 |       h0b5b093_0         401 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       207.1 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  blas               pkgs/main/linux-64::blas-1.0-mkl\n",
            "  bzip2              pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0\n",
            "  cairo              pkgs/main/linux-64::cairo-1.14.12-h8948797_3\n",
            "  fontconfig         pkgs/main/linux-64::fontconfig-2.13.0-h9420a91_0\n",
            "  freetype           pkgs/main/linux-64::freetype-2.10.2-h5ab3b9f_0\n",
            "  glib               pkgs/main/linux-64::glib-2.63.1-h5a9c865_0\n",
            "  icu                pkgs/main/linux-64::icu-58.2-he6710b0_3\n",
            "  intel-openmp       pkgs/main/linux-64::intel-openmp-2020.1-217\n",
            "  jpeg               pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n",
            "  libboost           pkgs/main/linux-64::libboost-1.67.0-h46d08c1_4\n",
            "  libgfortran-ng     pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0\n",
            "  libpng             pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n",
            "  libtiff            pkgs/main/linux-64::libtiff-4.1.0-h2733197_0\n",
            "  libuuid            pkgs/main/linux-64::libuuid-1.0.3-h1bed415_2\n",
            "  libxcb             pkgs/main/linux-64::libxcb-1.14-h7b6447c_0\n",
            "  libxml2            pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1\n",
            "  mkl                pkgs/main/linux-64::mkl-2020.1-217\n",
            "  mkl-service        pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0\n",
            "  mkl_fft            pkgs/main/linux-64::mkl_fft-1.1.0-py37h23d657b_0\n",
            "  mkl_random         pkgs/main/linux-64::mkl_random-1.1.1-py37h0573a6f_0\n",
            "  numpy              pkgs/main/linux-64::numpy-1.18.5-py37ha1c710e_0\n",
            "  numpy-base         pkgs/main/linux-64::numpy-base-1.18.5-py37hde5b4d6_0\n",
            "  olefile            pkgs/main/linux-64::olefile-0.46-py37_0\n",
            "  pandas             pkgs/main/linux-64::pandas-1.0.5-py37h0573a6f_0\n",
            "  pcre               pkgs/main/linux-64::pcre-8.44-he6710b0_0\n",
            "  pillow             pkgs/main/linux-64::pillow-7.1.2-py37hb39fc2d_0\n",
            "  pixman             pkgs/main/linux-64::pixman-0.40.0-h7b6447c_0\n",
            "  py-boost           pkgs/main/linux-64::py-boost-1.67.0-py37h04863e7_4\n",
            "  python-dateutil    pkgs/main/noarch::python-dateutil-2.8.1-py_0\n",
            "  pytz               pkgs/main/noarch::pytz-2020.1-py_0\n",
            "  rdkit              rdkit/linux-64::rdkit-2020.03.3.0-py37hc20afe1_1\n",
            "  zstd               pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                                2020.1.1-0 --> 2020.6.24-0\n",
            "  certifi                                 2019.11.28-py37_0 --> 2020.6.20-py37_0\n",
            "  conda                                        4.8.2-py37_0 --> 4.8.3-py37_0\n",
            "  openssl                                 1.1.1d-h7b6447c_4 --> 1.1.1g-h7b6447c_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "bzip2-1.0.8          | 78 KB     | : 100% 1.0/1 [00:00<00:00, 10.01it/s]\n",
            "pandas-1.0.5         | 7.8 MB    | : 100% 1.0/1 [00:00<00:00,  2.22it/s]               \n",
            "mkl-service-2.3.0    | 218 KB    | : 100% 1.0/1 [00:00<00:00, 19.17it/s]\n",
            "conda-4.8.3          | 2.8 MB    | : 100% 1.0/1 [00:00<00:00, 18.58s/it]                 \n",
            "mkl_random-1.1.1     | 322 KB    | : 100% 1.0/1 [00:00<00:00, 15.12it/s]\n",
            "pcre-8.44            | 212 KB    | : 100% 1.0/1 [00:00<00:00, 17.59it/s]\n",
            "fontconfig-2.13.0    | 227 KB    | : 100% 1.0/1 [00:00<00:00, 16.10it/s]\n",
            "libxml2-2.9.9        | 1.6 MB    | : 100% 1.0/1 [00:00<00:00,  5.63it/s]\n",
            "pillow-7.1.2         | 603 KB    | : 100% 1.0/1 [00:00<00:00, 10.25it/s]\n",
            "libgfortran-ng-7.3.0 | 1006 KB   | : 100% 1.0/1 [00:00<00:00, 11.04it/s]\n",
            "py-boost-1.67.0      | 278 KB    | : 100% 1.0/1 [00:00<00:00, 12.08it/s]\n",
            "python-dateutil-2.8. | 215 KB    | : 100% 1.0/1 [00:00<00:00, 16.41it/s]\n",
            "libpng-1.6.37        | 278 KB    | : 100% 1.0/1 [00:00<00:00, 16.63it/s]\n",
            "numpy-base-1.18.5    | 4.1 MB    | : 100% 1.0/1 [00:00<00:00,  3.82it/s]\n",
            "intel-openmp-2020.1  | 780 KB    | : 100% 1.0/1 [00:00<00:00, 13.02it/s]\n",
            "freetype-2.10.2      | 608 KB    | : 100% 1.0/1 [00:00<00:00, 11.86it/s]\n",
            "pytz-2020.1          | 184 KB    | : 100% 1.0/1 [00:00<00:00, 10.15it/s]\n",
            "certifi-2020.6.20    | 156 KB    | : 100% 1.0/1 [00:00<00:00, 17.12it/s]\n",
            "libboost-1.67.0      | 13.0 MB   | : 100% 1.0/1 [00:01<00:00,  1.08s/it]               \n",
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            "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H661uGwCNFMC",
        "colab_type": "text"
      },
      "source": [
        "## **2. Delaney's solubility dataset**\n",
        "\n",
        "The original [Delaney's dataset](https://pubs.acs.org/doi/10.1021/ci034243x) available as a [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x)$^4$. The full paper is entitled [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x).$^1$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6o9QzQnNRVx",
        "colab_type": "text"
      },
      "source": [
        "### **2.1. Download the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KKvV74LM1it",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ! wget https://pubs.acs.org/doi/suppl/10.1021/ci034243x/suppl_file/ci034243xsi20040112_053635.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJGp_xenNYKy",
        "colab_type": "text"
      },
      "source": [
        "### **2.2. Read in the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ufiOpEbNooH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLS6bwiRNtuV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "cfe35c3f-dabf-4629-fe89-0634a7e693ad"
      },
      "source": [
        "delaney_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney.csv'\n",
        "sol = pd.read_csv(delaney_url)\n",
        "sol"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>measured log(solubility:mol/L)</th>\n",
              "      <th>ESOL predicted log(solubility:mol/L)</th>\n",
              "      <th>SMILES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1,1,1,2-Tetrachloroethane</td>\n",
              "      <td>-2.180</td>\n",
              "      <td>-2.794</td>\n",
              "      <td>ClCC(Cl)(Cl)Cl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1,1,1-Trichloroethane</td>\n",
              "      <td>-2.000</td>\n",
              "      <td>-2.232</td>\n",
              "      <td>CC(Cl)(Cl)Cl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1,1,2,2-Tetrachloroethane</td>\n",
              "      <td>-1.740</td>\n",
              "      <td>-2.549</td>\n",
              "      <td>ClC(Cl)C(Cl)Cl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1,1,2-Trichloroethane</td>\n",
              "      <td>-1.480</td>\n",
              "      <td>-1.961</td>\n",
              "      <td>ClCC(Cl)Cl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1,1,2-Trichlorotrifluoroethane</td>\n",
              "      <td>-3.040</td>\n",
              "      <td>-3.077</td>\n",
              "      <td>FC(F)(Cl)C(F)(Cl)Cl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>vamidothion</td>\n",
              "      <td>1.144</td>\n",
              "      <td>-1.446</td>\n",
              "      <td>CNC(=O)C(C)SCCSP(=O)(OC)(OC)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>Vinclozolin</td>\n",
              "      <td>-4.925</td>\n",
              "      <td>-4.377</td>\n",
              "      <td>CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>Warfarin</td>\n",
              "      <td>-3.893</td>\n",
              "      <td>-3.913</td>\n",
              "      <td>CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>Xipamide</td>\n",
              "      <td>-3.790</td>\n",
              "      <td>-3.642</td>\n",
              "      <td>Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>XMC</td>\n",
              "      <td>-2.581</td>\n",
              "      <td>-2.688</td>\n",
              "      <td>CNC(=O)Oc1cc(C)cc(C)c1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1144 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Compound ID  ...                                       SMILES\n",
              "0          1,1,1,2-Tetrachloroethane  ...                               ClCC(Cl)(Cl)Cl\n",
              "1              1,1,1-Trichloroethane  ...                                 CC(Cl)(Cl)Cl\n",
              "2          1,1,2,2-Tetrachloroethane  ...                               ClC(Cl)C(Cl)Cl\n",
              "3              1,1,2-Trichloroethane  ...                                   ClCC(Cl)Cl\n",
              "4     1,1,2-Trichlorotrifluoroethane  ...                          FC(F)(Cl)C(F)(Cl)Cl\n",
              "...                              ...  ...                                          ...\n",
              "1139                     vamidothion  ...                 CNC(=O)C(C)SCCSP(=O)(OC)(OC)\n",
              "1140                     Vinclozolin  ...        CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\n",
              "1141                        Warfarin  ...       CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O \n",
              "1142                        Xipamide  ...  Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\n",
              "1143                             XMC  ...                       CNC(=O)Oc1cc(C)cc(C)c1\n",
              "\n",
              "[1144 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqQLXGKQQAvX",
        "colab_type": "text"
      },
      "source": [
        "## **3. Calculate molecular descriptors in rdkit**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iD_6apg8kYDy",
        "colab_type": "text"
      },
      "source": [
        "### **3.1. Convert list of molecules to rdkit object**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQjMv-wLOlmg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from rdkit import Chem"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaHAVM2yFm3J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mol_list = [Chem.MolFromSmiles(element) for element in sol.SMILES]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fw5BCeh7F2c9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "15c6d713-80e4-4d22-f7da-7887392def9c"
      },
      "source": [
        "len(mol_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1144"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uASSo7ZMF5iv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "4c6a6f8a-5eef-4cf7-f4a5-dd5f6b0e7729"
      },
      "source": [
        "mol_list[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<rdkit.Chem.rdchem.Mol at 0x7f01e1c995d0>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f01e1c99620>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f01e1c99670>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f01e1c99a30>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f01e1c99b70>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olyPX1TjQMvr",
        "colab_type": "text"
      },
      "source": [
        "### **3.2. Calculate molecular descriptors**\n",
        "\n",
        "To predict **LogS** (log of the aqueous solubility), the study by Delaney makes use of 4 molecular descriptors:\n",
        "1. **cLogP** *(Octanol-water partition coefficient)*\n",
        "2. **MW** *(Molecular weight)*\n",
        "3. **RB** *(Number of rotatable bonds)*\n",
        "4. **AP** *(Aromatic proportion = number of aromatic atoms / total number of heavy atoms)*\n",
        "\n",
        "Unfortunately, rdkit readily computes the first 3. As for the AP descriptor, we will calculate this by manually computing the ratio of the *number of aromatic atoms* to the *total number of heavy atoms* which rdkit can compute."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS4w5r5ocxT8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from rdkit.Chem import Descriptors"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIF7IrIlcGPD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def AromaticProportion(m):\n",
        "  aromatic_atoms = [m.GetAtomWithIdx(i).GetIsAromatic() for i in range(m.GetNumAtoms())]\n",
        "  aa_count = []\n",
        "  for i in aromatic_atoms:\n",
        "    if i==True:\n",
        "      aa_count.append(1)\n",
        "  AromaticAtom = sum(aa_count)\n",
        "  HeavyAtom = Descriptors.HeavyAtomCount(m)\n",
        "  AR = AromaticAtom/HeavyAtom\n",
        "  return AR"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkNMPVu_giw8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation\n",
        "\n",
        "def generate(smiles, verbose=False):\n",
        "\n",
        "    moldata= []\n",
        "    for elem in smiles:\n",
        "        mol=Chem.MolFromSmiles(elem) \n",
        "        moldata.append(mol)\n",
        "       \n",
        "    baseData= np.arange(1,1)\n",
        "    i=0  \n",
        "    for mol in moldata:        \n",
        "       \n",
        "        desc_MolLogP = Descriptors.MolLogP(mol)\n",
        "        desc_MolWt = Descriptors.MolWt(mol)\n",
        "        desc_NumRotatableBonds = Descriptors.NumRotatableBonds(mol)\n",
        "        desc_AromaticProportion = AromaticProportion(mol)\n",
        "           \n",
        "        row = np.array([desc_MolLogP,\n",
        "                        desc_MolWt,\n",
        "                        desc_NumRotatableBonds,\n",
        "                        desc_AromaticProportion])   \n",
        "    \n",
        "        if(i==0):\n",
        "            baseData=row\n",
        "        else:\n",
        "            baseData=np.vstack([baseData, row])\n",
        "        i=i+1      \n",
        "    \n",
        "    columnNames=[\"MolLogP\",\"MolWt\",\"NumRotatableBonds\",\"AromaticProportion\"]   \n",
        "    descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n",
        "    \n",
        "    return descriptors"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzMulCVvcf59",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = generate(sol.SMILES)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfVfpGa9wGaD",
        "colab_type": "text"
      },
      "source": [
        "## **4. Preparing the X and Y Data Matrices**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZKZKPOuCVTY",
        "colab_type": "text"
      },
      "source": [
        "### **4.1. X matrix (the computed descriptors)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VFAdZwbCg0T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "738ceff2-b929-48c8-8f42-a412e5c960b7"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.59540</td>\n",
              "      <td>167.850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.37650</td>\n",
              "      <td>133.405</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.59380</td>\n",
              "      <td>167.850</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.02890</td>\n",
              "      <td>133.405</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91890</td>\n",
              "      <td>187.375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>1.98820</td>\n",
              "      <td>287.343</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>3.42130</td>\n",
              "      <td>286.114</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>3.60960</td>\n",
              "      <td>308.333</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.695652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>2.56214</td>\n",
              "      <td>354.815</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.521739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>2.02164</td>\n",
              "      <td>179.219</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.461538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1144 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
              "0     2.59540  167.850                0.0            0.000000\n",
              "1     2.37650  133.405                0.0            0.000000\n",
              "2     2.59380  167.850                1.0            0.000000\n",
              "3     2.02890  133.405                1.0            0.000000\n",
              "4     2.91890  187.375                1.0            0.000000\n",
              "...       ...      ...                ...                 ...\n",
              "1139  1.98820  287.343                8.0            0.000000\n",
              "1140  3.42130  286.114                2.0            0.333333\n",
              "1141  3.60960  308.333                4.0            0.695652\n",
              "1142  2.56214  354.815                3.0            0.521739\n",
              "1143  2.02164  179.219                1.0            0.461538\n",
              "\n",
              "[1144 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZI9k4h6FsPF",
        "colab_type": "text"
      },
      "source": [
        "### **4.2. Y matrix**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6m4Akv3rHG3E",
        "colab_type": "text"
      },
      "source": [
        "Assigning the second column (index 1) to the Y matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcvXs7R7FrbC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "fc95e058-bfdb-412c-d3e5-383f2db9e535"
      },
      "source": [
        "Y = sol.iloc[:,1]\n",
        "Y = Y.rename(\"logS\")\n",
        "Y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      -2.180\n",
              "1      -2.000\n",
              "2      -1.740\n",
              "3      -1.480\n",
              "4      -3.040\n",
              "        ...  \n",
              "1139    1.144\n",
              "1140   -4.925\n",
              "1141   -3.893\n",
              "1142   -3.790\n",
              "1143   -2.581\n",
              "Name: logS, Length: 1144, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7Y7XBrAnGHs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "b45437e5-83eb-4302-edbe-ab171680b856"
      },
      "source": [
        "Y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      -2.180\n",
              "1      -2.000\n",
              "2      -1.740\n",
              "3      -1.480\n",
              "4      -3.040\n",
              "        ...  \n",
              "1139    1.144\n",
              "1140   -4.925\n",
              "1141   -3.893\n",
              "1142   -3.790\n",
              "1143   -2.581\n",
              "Name: logS, Length: 1144, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eabg-PCIuCoY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "f9890ad3-c5d3-4b84-a976-39a4c70aaef1"
      },
      "source": [
        "Y.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f01e1a7a208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kBlnuktQgy12SGmS5S1KDLHdJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUIMtdkhpkuUtSgyx3SWqQ5S5JDbLcJalBlrskNchyl6QGDa3ck1yY5LEkh5LsHtZ+JEnHG8p/kJ3kBODfAe8AngC+mmRfVT0yjP2NymL/UbX/ybSkURvWkfu5wKGq+mZV/TkwC1w8pH1Jkl5mKEfuwFbg8Z7bTwBvHcaOFjt6lqRXslTV2m80+Tngwqr6+93t9wNvraoP9yyzE9jZ3fxrwGMLbGoz8MyaBxy+cc0N45vd3OvL3Otrsdx/papOW2iFYR25HwG29dw+vRv7f6pqD7BnqY0kua+qptY+3nCNa24Y3+zmXl/mXl8ryT2sc+5fBc5MckaSVwOXAvuGtC9J0ssM5ci9qo4l+TDwBeAE4PqqengY+5IkHW9Yp2WoqtuB21e5mSVP22xg45obxje7udeXudfXsnMP5Q1VSdJoefkBSWrQhiz3JD+f5OEkP04y1TP+jiQHkhzs/nz7KHO+3GK5u3kf7S7F8FiSC0aVsZ8kZye5O8kDSe5Lcu6oMw0qyS8l+Z/dv8G/HnWe5UqyK0kl2TzqLINI8uvd3/eDSW5OcsqoMy1lHC+JkmRbkjuTPNI9rj8y8MpVteF+gL/O/Gff9wNTPeNvBn6ym34TcGTUWQfMfRbwNeBE4AzgG8AJo867yH34IvCubvoiYP+oMw2Yewb4MnBid/v1o860zPzbmP8AwreBzaPOM2DmdwKbuulPAp8cdaYlsp7QPe9+Cnh193w8a9S5Bsi9BXhLN/1a4H8NmntDHrlX1aNVddyXmqrqT6rqO93Nh4GTkpy4vukWt1hu5i+9MFtVL1TVt4BDzF+iYSMq4C930ycD31li2Y3kQ8A1VfUCQFU9PeI8y3Ud8E+Z//sfC1X1xao61t28m/nvs2xUY3lJlKp6sqru76a/DzzK/BUA+tqQ5T6gvwfc/+KTeYNb6HIMA/0DjcAvA7+e5HHgN4CPjjjPoN4A/M0k9yT5SpKfGXWgQSW5mPnfQr826iyr8EHg86MOsYRxeg4uKMl25s9e3DPI8kP7KGQ/Sb4M/MQCsz5eVbf0WfeNzP8a+M5hZOuz7xXn3iiWug/A+cA/qqrPJbkE2Av87fXMt5g+uTcBrwPOA34GuDHJT1X3++yo9cn+MUbwWB7EII/3JB8HjgGfXc9sryRJJoDPAb9cVf9nkHVGVu5VtaLCSHI6cDPwgar6xtqm6m+FuftejmE9LXUfknwGePFNm/8M/Id1CTWAPrk/BNzUlfm9SX7M/PU4/vd65VvKYtmT7GD+fZivJYH5x8b9Sc6tqqfWMeKC+j3ek1wB/B3g/I3yQrqIDfUcXI4kr2K+2D9bVTcNut5YnZbp3o2/DdhdVf991HmWYR9waZITk5wBnAncO+JMi/kO8Le66bcDXx9hluX4L8y/qUqSNzD/ptmGv0BUVR2sqtdX1faq2s786YK3bIRi7yfJhcy/T/B3q+qHo87Tx1heEiXzr/h7gUer6lPLWncjvtgmeS/wb4HTgO8BD1TVBUn+GfPngHsL550b5c2zxXJ38z7O/HnJY8z/arUhz08m+RvAbzL/W92fAf+wqg6MNlV/3RP2euBs4M+Bf1xVfzTaVMuX5DDzn7Ta8C9MSQ4x/wmwP+2G7q6qfzDCSEtKchHwb/j/l0S5esSR+uqej/8NOAj8uBv+WM1fAWDpdTdiuUuSVmesTstIkgZjuUtSgyx3SWqQ5S5JDbLcJalBlrskNchyl6QGWe6S1KD/C4igy8y43UwyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NKYJb_93k8s5",
        "colab_type": "text"
      },
      "source": [
        "## **4.3. Combine X and Y into same dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fozh-KEJlDe1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "0b11dc7f-ec6b-40df-e4cb-5ad0790ef2be"
      },
      "source": [
        "dataset = pd.concat([X,Y], axis=1)\n",
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
              "      <th>logS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.59540</td>\n",
              "      <td>167.850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.37650</td>\n",
              "      <td>133.405</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.59380</td>\n",
              "      <td>167.850</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.02890</td>\n",
              "      <td>133.405</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91890</td>\n",
              "      <td>187.375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>1.98820</td>\n",
              "      <td>287.343</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>3.42130</td>\n",
              "      <td>286.114</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-4.925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>3.60960</td>\n",
              "      <td>308.333</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.695652</td>\n",
              "      <td>-3.893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>2.56214</td>\n",
              "      <td>354.815</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.521739</td>\n",
              "      <td>-3.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>2.02164</td>\n",
              "      <td>179.219</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>-2.581</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1144 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion   logS\n",
              "0     2.59540  167.850                0.0            0.000000 -2.180\n",
              "1     2.37650  133.405                0.0            0.000000 -2.000\n",
              "2     2.59380  167.850                1.0            0.000000 -1.740\n",
              "3     2.02890  133.405                1.0            0.000000 -1.480\n",
              "4     2.91890  187.375                1.0            0.000000 -3.040\n",
              "...       ...      ...                ...                 ...    ...\n",
              "1139  1.98820  287.343                8.0            0.000000  1.144\n",
              "1140  3.42130  286.114                2.0            0.333333 -4.925\n",
              "1141  3.60960  308.333                4.0            0.695652 -3.893\n",
              "1142  2.56214  354.815                3.0            0.521739 -3.790\n",
              "1143  2.02164  179.219                1.0            0.461538 -2.581\n",
              "\n",
              "[1144 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ0setezmBbD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset.to_csv('delaney_solubility_with_descriptors.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARiv3f1iC565",
        "colab_type": "text"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EICKN2Hn5X_L",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwM1QHeLbxJl",
        "colab_type": "text"
      },
      "source": [
        "## **Reference**\n",
        "\n",
        "1. John S. Delaney. [ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.\n",
        "\n",
        "2. Pat Walters. [Predicting Aqueous Solubility - It's Harder Than It Looks](http://practicalcheminformatics.blogspot.com/2018/09/predicting-aqueous-solubility-its.html). ***Practical Cheminformatics Blog***\n",
        "\n",
        "3. Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. [Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More](https://learning.oreilly.com/library/view/deep-learning-for/9781492039822/), O'Reilly, 2019.\n",
        "\n",
        "4. [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x) from Delaney's ESOL:  Estimating Aqueous Solubility Directly from Molecular Structure.\n",
        "\n",
        "5. Scott M. Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions), A Unified Approach to Interpreting Model Predictions, ***Advances in Neural Information Processing Systems 30 (NIPS 2017)***, 2017."
      ]
    }
  ]
}