python/cheminformatics_predicting_solubility.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cheminformatics-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: Predicting Solubility of Molecules | 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 dive into the world of Cheminformatics which lies at the interface of Informatics and Chemistry. 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 rdkit**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jNwdYoBR8ea",
        "colab_type": "code",
        "outputId": "39acab32-ba2a-40ca-d219-58afc83974db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-11 04:08:37--  https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.130.3, 104.16.131.3, 2606:4700::6810:8203, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.130.3|: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  35%[======>             ]  28.63M   143MB/s               \r        Miniconda3-  89%[================>   ]  72.64M   181MB/s               \rMiniconda3-py37_4.8 100%[===================>]  81.12M   184MB/s    in 0.4s    \n",
            "\n",
            "2020-04-11 04:08:38 (184 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\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\bdone\n",
            "Solving environment: - \b\b\\ \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\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\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\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",
            "    cairo-1.14.12              |       h8948797_3         906 KB\n",
            "    certifi-2020.4.5.1         |           py37_0         155 KB\n",
            "    conda-4.8.3                |           py37_0         2.8 MB\n",
            "    fontconfig-2.13.0          |       h9420a91_0         227 KB\n",
            "    freetype-2.9.1             |       h8a8886c_1         550 KB\n",
            "    glib-2.63.1                |       h5a9c865_0         2.9 MB\n",
            "    icu-58.2                   |       h9c2bf20_1        10.3 MB\n",
            "    intel-openmp-2020.0        |              166         756 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-1.13                |       h1bed415_1         421 KB\n",
            "    libxml2-2.9.9              |       hea5a465_1         1.6 MB\n",
            "    mkl-2020.0                 |              166       128.9 MB\n",
            "    mkl-service-2.3.0          |   py37he904b0f_0         218 KB\n",
            "    mkl_fft-1.0.15             |   py37ha843d7b_0         154 KB\n",
            "    mkl_random-1.1.0           |   py37hd6b4f25_0         321 KB\n",
            "    numpy-1.18.1               |   py37h4f9e942_0           5 KB\n",
            "    numpy-base-1.18.1          |   py37hde5b4d6_1         4.2 MB\n",
            "    olefile-0.46               |           py37_0          50 KB\n",
            "    openssl-1.1.1f             |       h7b6447c_0         2.5 MB\n",
            "    pandas-1.0.3               |   py37h0573a6f_0         8.6 MB\n",
            "    pcre-8.43                  |       he6710b0_0         209 KB\n",
            "    pillow-7.0.0               |   py37hb39fc2d_0         598 KB\n",
            "    pixman-0.38.0              |       h7b6447c_0         364 KB\n",
            "    py-boost-1.67.0            |   py37h04863e7_4         278 KB\n",
            "    python-dateutil-2.8.1      |             py_0         224 KB\n",
            "    pytz-2019.3                |             py_0         231 KB\n",
            "    rdkit-2020.03.1.0          |   py37hc20afe1_1        24.5 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-2.9.1-h8a8886c_1\n",
            "  glib               pkgs/main/linux-64::glib-2.63.1-h5a9c865_0\n",
            "  icu                pkgs/main/linux-64::icu-58.2-h9c2bf20_1\n",
            "  intel-openmp       pkgs/main/linux-64::intel-openmp-2020.0-166\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-1.13-h1bed415_1\n",
            "  libxml2            pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1\n",
            "  mkl                pkgs/main/linux-64::mkl-2020.0-166\n",
            "  mkl-service        pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0\n",
            "  mkl_fft            pkgs/main/linux-64::mkl_fft-1.0.15-py37ha843d7b_0\n",
            "  mkl_random         pkgs/main/linux-64::mkl_random-1.1.0-py37hd6b4f25_0\n",
            "  numpy              pkgs/main/linux-64::numpy-1.18.1-py37h4f9e942_0\n",
            "  numpy-base         pkgs/main/linux-64::numpy-base-1.18.1-py37hde5b4d6_1\n",
            "  olefile            pkgs/main/linux-64::olefile-0.46-py37_0\n",
            "  pandas             pkgs/main/linux-64::pandas-1.0.3-py37h0573a6f_0\n",
            "  pcre               pkgs/main/linux-64::pcre-8.43-he6710b0_0\n",
            "  pillow             pkgs/main/linux-64::pillow-7.0.0-py37hb39fc2d_0\n",
            "  pixman             pkgs/main/linux-64::pixman-0.38.0-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-2019.3-py_0\n",
            "  rdkit              rdkit/linux-64::rdkit-2020.03.1.0-py37hc20afe1_1\n",
            "  zstd               pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  certifi                                 2019.11.28-py37_0 --> 2020.4.5.1-py37_0\n",
            "  conda                                        4.8.2-py37_0 --> 4.8.3-py37_0\n",
            "  openssl                                 1.1.1d-h7b6447c_4 --> 1.1.1f-h7b6447c_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "cairo-1.14.12        | 906 KB    | : 100% 1.0/1 [00:00<00:00,  4.97it/s]                \n",
            "glib-2.63.1          | 2.9 MB    | : 100% 1.0/1 [00:00<00:00,  5.06it/s]\n",
            "mkl_random-1.1.0     | 321 KB    | : 100% 1.0/1 [00:00<00:00, 10.64it/s]\n",
            "zstd-1.3.7           | 401 KB    | : 100% 1.0/1 [00:00<00:00, 14.89it/s]\n",
            "bzip2-1.0.8          | 78 KB     | : 100% 1.0/1 [00:00<00:00, 17.50it/s]\n",
            "libxml2-2.9.9        | 1.6 MB    | : 100% 1.0/1 [00:00<00:00,  8.02it/s]\n",
            "pandas-1.0.3         | 8.6 MB    | : 100% 1.0/1 [00:00<00:00,  1.96it/s]\n",
            "pixman-0.38.0        | 364 KB    | : 100% 1.0/1 [00:00<00:00, 13.11it/s]\n",
            "libpng-1.6.37        | 278 KB    | : 100% 1.0/1 [00:00<00:00, 15.67it/s]\n",
            "python-dateutil-2.8. | 224 KB    | : 100% 1.0/1 [00:00<00:00, 13.56it/s]\n",
            "certifi-2020.4.5.1   | 155 KB    | : 100% 1.0/1 [00:00<00:00, 18.14it/s]\n",
            "blas-1.0             | 6 KB      | : 100% 1.0/1 [00:00<00:00, 17.59it/s]\n",
            "icu-58.2             | 10.3 MB   | : 100% 1.0/1 [00:00<00:00,  1.94it/s]              \n",
            "pillow-7.0.0         | 598 KB    | : 100% 1.0/1 [00:00<00:00, 11.73it/s]\n",
            "openssl-1.1.1f       | 2.5 MB    | : 100% 1.0/1 [00:00<00:00,  7.03it/s]\n",
            "mkl-2020.0           | 128.9 MB  | : 100% 1.0/1 [00:04<00:00,  4.98s/it]               \n",
            "libgfortran-ng-7.3.0 | 1006 KB   | : 100% 1.0/1 [00:00<00:00, 11.54it/s]\n",
            "fontconfig-2.13.0    | 227 KB    | : 100% 1.0/1 [00:00<00:00, 15.37it/s]\n",
            "libxcb-1.13          | 421 KB    | : 100% 1.0/1 [00:00<00:00, 13.69it/s]\n",
            "mkl_fft-1.0.15       | 154 KB    | : 100% 1.0/1 [00:00<00:00, 15.84it/s]\n",
            "intel-openmp-2020.0  | 756 KB    | : 100% 1.0/1 [00:00<00:00, 10.37it/s]\n",
            "pytz-2019.3          | 231 KB    | : 100% 1.0/1 [00:00<00:00,  8.39it/s]\n",
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            "py-boost-1.67.0      | 278 KB    | : 100% 1.0/1 [00:00<00:00, 13.61it/s]\n",
            "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\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",
        "outputId": "be1b6a80-0196-44ef-c1d0-4709e644bca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "! wget https://pubs.acs.org/doi/suppl/10.1021/ci034243x/suppl_file/ci034243xsi20040112_053635.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-11 04:10:22--  https://pubs.acs.org/doi/suppl/10.1021/ci034243x/suppl_file/ci034243xsi20040112_053635.txt\n",
            "Resolving pubs.acs.org (pubs.acs.org)... 104.18.1.20, 104.18.0.20\n",
            "Connecting to pubs.acs.org (pubs.acs.org)|104.18.1.20|:443... connected.\n",
            "HTTP request sent, awaiting response... 503 Service Temporarily Unavailable\n",
            "2020-04-11 04:10:22 ERROR 503: Service Temporarily Unavailable.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBP_uaJZ710V",
        "colab_type": "code",
        "outputId": "c52dd5b8-d6fd-4e00-f470-7bcd3cf41061",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "! wget https://raw.githubusercontent.com/dataprofessor/data/master/delaney.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-11 04:11:51--  https://raw.githubusercontent.com/dataprofessor/data/master/delaney.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 58889 (58K) [text/plain]\n",
            "Saving to: ‘delaney.csv’\n",
            "\n",
            "\rdelaney.csv           0%[                    ]       0  --.-KB/s               \rdelaney.csv         100%[===================>]  57.51K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2020-04-11 04:11:51 (2.26 MB/s) - ‘delaney.csv’ saved [58889/58889]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLS6bwiRNtuV",
        "colab_type": "code",
        "outputId": "3f5ab0eb-b65a-444d-e967-87eec5af8a66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "sol = pd.read_csv('delaney.csv')\n",
        "sol"
      ],
      "execution_count": 0,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cUzImGPEXZRn",
        "colab_type": "text"
      },
      "source": [
        "### **2.3. Examining the SMILES data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGDLzIZmN5sJ",
        "colab_type": "text"
      },
      "source": [
        "Chemical structures are encoded by a string of text known as **SMILES** which is an acronym for **Simplified Molecular-Input Line-Entry System**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pF777dmOOZ3",
        "colab_type": "code",
        "outputId": "c279ce95-5f43-4954-8c90-b82ae0fb8adb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "sol.SMILES"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                    ClCC(Cl)(Cl)Cl\n",
              "1                                      CC(Cl)(Cl)Cl\n",
              "2                                    ClC(Cl)C(Cl)Cl\n",
              "3                                        ClCC(Cl)Cl\n",
              "4                               FC(F)(Cl)C(F)(Cl)Cl\n",
              "                           ...                     \n",
              "1139                   CNC(=O)C(C)SCCSP(=O)(OC)(OC)\n",
              "1140          CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\n",
              "1141         CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O \n",
              "1142    Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\n",
              "1143                         CNC(=O)Oc1cc(C)cc(C)c1\n",
              "Name: SMILES, Length: 1144, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHlN5973X6bJ",
        "colab_type": "text"
      },
      "source": [
        "The first element from the **SMILES** column of the **sol** dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jN0dzq9dXF5N",
        "colab_type": "code",
        "outputId": "5d00cbf5-2fa0-43c7-93fb-ad42a646a84a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sol.SMILES[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ClCC(Cl)(Cl)Cl'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NO7tSBIeXQWk",
        "colab_type": "text"
      },
      "source": [
        "### **2.4. Convert a molecule from the SMILES string to an rdkit object**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmlrBL_MOTkB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from rdkit import Chem"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0f9JLRD0XO85",
        "colab_type": "code",
        "outputId": "15a5a16d-102d-406f-fd57-a986c4632827",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Chem.MolFromSmiles(sol.SMILES[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7f66f2e3e800>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwfN-yZYEFof",
        "colab_type": "code",
        "outputId": "da010f69-bd9b-46d2-8732-92fa61255fa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Chem.MolFromSmiles('ClCC(Cl)(Cl)Cl')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7f66f2dce030>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCdX2GjXFG-A",
        "colab_type": "text"
      },
      "source": [
        "### **2.5. Working with rdkit object**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_6xQmjHFOo1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m = Chem.MolFromSmiles('ClCC(Cl)(Cl)Cl')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dn79-t4FLgT",
        "colab_type": "code",
        "outputId": "465dee18-e38a-47bb-d587-b9a478ee88f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.GetNumAtoms()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyaEoBb5FiKc",
        "colab_type": "text"
      },
      "source": [
        "#### **3.1.1. Method 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JisoMYFgka_p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mol_list= []\n",
        "for element in sol.SMILES:\n",
        "  mol = Chem.MolFromSmiles(element)\n",
        "  mol_list.append(mol)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZL7fG5Aok0Gh",
        "colab_type": "code",
        "outputId": "a6e9949d-dc38-48e8-e327-d7828c3ad6c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(mol_list)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1144"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiGYdtPUk3hV",
        "colab_type": "code",
        "outputId": "b633b242-3ef7-48b9-eef8-9c70bbce7479",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "mol_list[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<rdkit.Chem.rdchem.Mol at 0x7f66edb6d670>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66edb6d620>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66edb6d530>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66edb6d6c0>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66edb6d710>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rBxrxF8Fk5S",
        "colab_type": "text"
      },
      "source": [
        "#### **3.1.2. Method 2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaHAVM2yFm3J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mol_list2 = [Chem.MolFromSmiles(element) for element in sol.SMILES]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fw5BCeh7F2c9",
        "colab_type": "code",
        "outputId": "cb79735b-e691-4d01-ff4e-f41ed55a130e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(mol_list2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1144"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uASSo7ZMF5iv",
        "colab_type": "code",
        "outputId": "846333e8-5c46-49ee-8746-3a260b830f59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "mol_list2[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<rdkit.Chem.rdchem.Mol at 0x7f66edb6d2b0>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66edb6d3f0>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66ed9406c0>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66ed940350>,\n",
              " <rdkit.Chem.rdchem.Mol at 0x7f66ed940580>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "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": "markdown",
      "metadata": {
        "id": "k4fqhekzCB-X",
        "colab_type": "text"
      },
      "source": [
        "#### **3.2.1. LogP, MW and RB**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS4w5r5ocxT8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from rdkit.Chem import Descriptors"
      ],
      "execution_count": 0,
      "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",
        "           \n",
        "        row = np.array([desc_MolLogP,\n",
        "                        desc_MolWt,\n",
        "                        desc_NumRotatableBonds])   \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\"]   \n",
        "    descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n",
        "    \n",
        "    return descriptors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzMulCVvcf59",
        "colab_type": "code",
        "outputId": "ce540885-1126-48a1-d02f-7a1bbdda14cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df = generate(sol.SMILES)\n",
        "df"
      ],
      "execution_count": 0,
      "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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.37650</td>\n",
              "      <td>133.405</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.02890</td>\n",
              "      <td>133.405</td>\n",
              "      <td>1.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>3.42130</td>\n",
              "      <td>286.114</td>\n",
              "      <td>2.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>2.56214</td>\n",
              "      <td>354.815</td>\n",
              "      <td>3.0</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1144 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds\n",
              "0     2.59540  167.850                0.0\n",
              "1     2.37650  133.405                0.0\n",
              "2     2.59380  167.850                1.0\n",
              "3     2.02890  133.405                1.0\n",
              "4     2.91890  187.375                1.0\n",
              "...       ...      ...                ...\n",
              "1139  1.98820  287.343                8.0\n",
              "1140  3.42130  286.114                2.0\n",
              "1141  3.60960  308.333                4.0\n",
              "1142  2.56214  354.815                3.0\n",
              "1143  2.02164  179.219                1.0\n",
              "\n",
              "[1144 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XtlGQy82OiCm",
        "colab_type": "text"
      },
      "source": [
        "#### **3.2.2. Aromatic proportion**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVK5WqOYSmY8",
        "colab_type": "text"
      },
      "source": [
        "##### 3.2.1.1. Number of aromatic atoms\n",
        "\n",
        "Here, we will create a custom function to calculate the **Number of aromatic atoms**. With this descriptor we can use it to subsequently calculate the AP descriptor."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcw9oaPR4NM1",
        "colab_type": "text"
      },
      "source": [
        "Computing for a single molecule."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fJVXNHCSfLk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m = Chem.MolFromSmiles('COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FENb_2BkYyVq",
        "colab_type": "code",
        "outputId": "5d93cc1d-4761-43a1-acfd-0adb9b119d41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "aromatic_atoms = [m.GetAtomWithIdx(i).GetIsAromatic() for i in range(m.GetNumAtoms())]\n",
        "aromatic_atoms"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[False,\n",
              " False,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " False,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " True,\n",
              " False,\n",
              " False,\n",
              " True,\n",
              " True]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIF7IrIlcGPD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def AromaticAtoms(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",
        "  sum_aa_count = sum(aa_count)\n",
        "  return sum_aa_count"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PK-RJ-dpbqhn",
        "colab_type": "code",
        "outputId": "c4416942-8d37-4b65-ee3e-796a54874ad3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "AromaticAtoms(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-uywPiwe4O31",
        "colab_type": "text"
      },
      "source": [
        "Computing for molecules in the entire dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCeydvky4Rgl",
        "colab_type": "code",
        "outputId": "0c062a2d-abf8-406b-83ef-55abe6625460",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "desc_AromaticAtoms = [AromaticAtoms(element) for element in mol_list]\n",
        "desc_AromaticAtoms"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 12,\n",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXGyuCnbOplQ",
        "colab_type": "text"
      },
      "source": [
        "##### 3.2.1.2. **Number of heavy atoms**\n",
        "\n",
        "Here, we will use an existing function for calculating the Number of heavy atoms."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wgf1teHz3_pP",
        "colab_type": "text"
      },
      "source": [
        "Computing for a single molecule."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVwvt02AOvhv",
        "colab_type": "code",
        "outputId": "9da3e232-6f78-48ac-cce9-f537ace852e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m = Chem.MolFromSmiles('COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')\n",
        "Descriptors.HeavyAtomCount(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pv_BJcjA4HPz",
        "colab_type": "text"
      },
      "source": [
        "Computing for molecules in the entire dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9nsQqIkOuIU",
        "colab_type": "code",
        "outputId": "794d85da-8dcc-45f8-eadd-583e27a2efc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "desc_HeavyAtomCount = [Descriptors.HeavyAtomCount(element) for element in mol_list]\n",
        "desc_HeavyAtomCount"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 8,\n",
              " 9,\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFDGqPQPQYZb",
        "colab_type": "text"
      },
      "source": [
        "##### **3.2.1.3. Computing the Aromatic Proportion (AP) descriptor**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xVXJIre5-HC",
        "colab_type": "text"
      },
      "source": [
        "Computing for a single molecule."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZAwtZTM5WHU",
        "colab_type": "code",
        "outputId": "cc982887-defb-4a7e-a2d9-670f6264c3e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m = Chem.MolFromSmiles('COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')\n",
        "AromaticAtoms(m)/Descriptors.HeavyAtomCount(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5588235294117647"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JsHdN1ui6BBo",
        "colab_type": "text"
      },
      "source": [
        "Computing for molecules in the entire dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DogBBjDu4-mI",
        "colab_type": "code",
        "outputId": "4cf9b4ae-24d0-435a-de3d-a881e86dbb86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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      },
      "source": [
        "desc_AromaticProportion = [AromaticAtoms(element)/Descriptors.HeavyAtomCount(element) for element in mol_list]\n",
        "desc_AromaticProportion"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {
            "tags": []
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          "execution_count": 33
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      "metadata": {
        "id": "9zovyVTQOxvA",
        "colab_type": "code",
        "outputId": "9cb8eace-7c9d-4888-dd12-69d5e1b344c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
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      },
      "source": [
        "df_desc_AromaticProportion = pd.DataFrame(desc_AromaticProportion, columns=['AromaticProportion'])\n",
        "df_desc_AromaticProportion"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "data": {
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              "      AromaticProportion\n",
              "0               0.000000\n",
              "1               0.000000\n",
              "2               0.000000\n",
              "3               0.000000\n",
              "4               0.000000\n",
              "...                  ...\n",
              "1139            0.000000\n",
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              "\n",
              "[1144 rows x 1 columns]"
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          "execution_count": 34
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZKZKPOuCVTY",
        "colab_type": "text"
      },
      "source": [
        "### **3.3. X matrix (Combining all computed descriptors into 1 dataframe)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VFAdZwbCg0T",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
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      "execution_count": 0,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>MolLogP</th>\n",
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              "      <th>NumRotatableBonds</th>\n",
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              "  </thead>\n",
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              "      MolLogP    MolWt  NumRotatableBonds\n",
              "0     2.59540  167.850                0.0\n",
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              "2     2.59380  167.850                1.0\n",
              "3     2.02890  133.405                1.0\n",
              "4     2.91890  187.375                1.0\n",
              "...       ...      ...                ...\n",
              "1139  1.98820  287.343                8.0\n",
              "1140  3.42130  286.114                2.0\n",
              "1141  3.60960  308.333                4.0\n",
              "1142  2.56214  354.815                3.0\n",
              "1143  2.02164  179.219                1.0\n",
              "\n",
              "[1144 rows x 3 columns]"
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            "tags": []
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    {
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        "outputId": "a7921fd7-2041-4ac2-9166-8dff6118f5d4",
        "colab": {
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          "height": 419
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      "source": [
        "df_desc_AromaticProportion"
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.461538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1144 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      AromaticProportion\n",
              "0               0.000000\n",
              "1               0.000000\n",
              "2               0.000000\n",
              "3               0.000000\n",
              "4               0.000000\n",
              "...                  ...\n",
              "1139            0.000000\n",
              "1140            0.333333\n",
              "1141            0.695652\n",
              "1142            0.521739\n",
              "1143            0.461538\n",
              "\n",
              "[1144 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJZT1Ij7Cm7W",
        "colab_type": "text"
      },
      "source": [
        "Let's combine the 2 dataframes to produce the **X** matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lYBhrsaCqEq",
        "colab_type": "code",
        "outputId": "80b49e5d-d0f4-447e-d707-1a0676f52b0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "X = pd.concat([df,df_desc_AromaticProportion], axis=1)\n",
        "X"
      ],
      "execution_count": 0,
      "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": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZI9k4h6FsPF",
        "colab_type": "text"
      },
      "source": [
        "### **3.4. Y matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OazcNGkMHCu1",
        "colab_type": "code",
        "outputId": "5e747e4a-4519-44d1-ab04-cf4475eef243",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "sol.head()"
      ],
      "execution_count": 0,
      "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>-2.18</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>-2.00</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>-1.74</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>-1.48</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>-3.04</td>\n",
              "      <td>-3.077</td>\n",
              "      <td>FC(F)(Cl)C(F)(Cl)Cl</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "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",
        "outputId": "9e339bb1-0cd2-4df2-cbce-1fdb156e8387",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "Y = sol.iloc[:,1]\n",
        "Y"
      ],
      "execution_count": 0,
      "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: measured log(solubility:mol/L), Length: 1144, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQYE-jCRSmCn",
        "colab_type": "text"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzrfuUZNFg_X",
        "colab_type": "text"
      },
      "source": [
        "## **Data split**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMRn8EVjFlrT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOIAljc1FmXb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39nTAc3UFUMW",
        "colab_type": "text"
      },
      "source": [
        "## **Linear Regression Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0MokzGBCimk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkR1siPuFZ6X",
        "colab_type": "code",
        "outputId": "b54e1380-d6a3-4946-90d5-593c7b0f8700",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model = linear_model.LinearRegression()\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aG4DMzc5Rks9",
        "colab_type": "text"
      },
      "source": [
        "### **Predicts the X_train**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZr9CBGvRp1F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_pred_train = model.predict(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0x3saPCyRtJP",
        "colab_type": "code",
        "outputId": "0acd9a71-53dd-442c-9b7f-37bdda543863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print('Coefficients:', model.coef_)\n",
        "print('Intercept:', model.intercept_)\n",
        "print('Mean squared error (MSE): %.2f'\n",
        "      % mean_squared_error(Y_train, Y_pred_train))\n",
        "print('Coefficient of determination (R^2): %.2f'\n",
        "      % r2_score(Y_train, Y_pred_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: [-0.7282008  -0.00691046  0.01625003 -0.35627645]\n",
            "Intercept: 0.26284383753800666\n",
            "Mean squared error (MSE): 0.99\n",
            "Coefficient of determination (R^2): 0.77\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6evZTPNRecd",
        "colab_type": "text"
      },
      "source": [
        "### **Predicts the X_test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_eFbrlaHhPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_pred_test = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQnDfyl5HkUr",
        "colab_type": "code",
        "outputId": "fbbf18a7-a1e8-4d3c-bb82-b2c33046c9c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print('Coefficients:', model.coef_)\n",
        "print('Intercept:', model.intercept_)\n",
        "print('Mean squared error (MSE): %.2f'\n",
        "      % mean_squared_error(Y_test, Y_pred_test))\n",
        "print('Coefficient of determination (R^2): %.2f'\n",
        "      % r2_score(Y_test, Y_pred_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: [-0.7282008  -0.00691046  0.01625003 -0.35627645]\n",
            "Intercept: 0.26284383753800666\n",
            "Mean squared error (MSE): 1.11\n",
            "Coefficient of determination (R^2): 0.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nERFfdQBRFF5",
        "colab_type": "text"
      },
      "source": [
        "### **Linear Regression Equation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3xLiGWHFiY1",
        "colab_type": "text"
      },
      "source": [
        "The work of Delaney$^1$ provided the following linear regression equation:\n",
        "\n",
        "> LogS = 0.16 -  0.63 cLogP - 0.0062 MW + 0.066 RB - 0.74 AP\n",
        "\n",
        "The reproduction by Pat Walters$^2$ provided the following:\n",
        "\n",
        "> LogS = 0.26 -  0.74 LogP - 0.0066 MW + 0.0034 RB - 0.42 AP\n",
        "\n",
        "This notebook's reproduction gave the following equation:\n",
        "\n",
        "* Based on the Train set\n",
        "> LogS = 0.30 -0.75 LogP - .0066 MW -0.0041 RB - 0.36 AP\n",
        "\n",
        "* Based on the Full dataset\n",
        "> LogS =  0.26 -0.74 LogP - 0.0066 + MW 0.0032 RB - 0.42 AP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaWyYnMbWtYu",
        "colab_type": "text"
      },
      "source": [
        "#### **Our linear regression equation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TH6J9evHIIE",
        "colab_type": "code",
        "outputId": "62464dfb-b049-4b0c-f1a7-18f375ae74b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('LogS = %.2f %.2f LogP %.4f MW %.4f RB %.2f AP' % (model.intercept_, model.coef_[0], model.coef_[1], model.coef_[2], model.coef_[3] ) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogS = 0.26 -0.73 LogP -0.0069 MW 0.0163 RB -0.36 AP\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcJyUzsLSz2A",
        "colab_type": "text"
      },
      "source": [
        "The same equation can also be produced with the following code (which breaks up the previous one-line code into several comprehensible lines."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byUbJ9QqK5gA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yintercept = '%.2f' % model.intercept_\n",
        "LogP = '%.2f LogP' % model.coef_[0]\n",
        "MW = '%.4f MW' % model.coef_[1]\n",
        "RB = '%.4f RB' % model.coef_[2]\n",
        "AP = '%.2f AP' % model.coef_[3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QY-9rh--S-6g",
        "colab_type": "code",
        "outputId": "95fe3bb9-5ad7-47df-cf10-5e302d0ab705",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('LogS = ' + \n",
        "      ' ' + \n",
        "      yintercept + \n",
        "      ' ' + \n",
        "      LogP + \n",
        "      ' ' + \n",
        "      MW + \n",
        "      ' ' + \n",
        "      RB + \n",
        "      ' ' + \n",
        "      AP)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogS =  0.26 -0.73 LogP -0.0069 MW 0.0163 RB -0.36 AP\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3lRkSOJRm1q",
        "colab_type": "text"
      },
      "source": [
        "#### **Use entire dataset for model training (For Comparison)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUye6SsIRl9T",
        "colab_type": "code",
        "outputId": "51266238-b806-4042-e5dc-9c657b234c48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "full = linear_model.LinearRegression()\n",
        "full.fit(X, Y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tMI8n0oR1b5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "full_pred = model.predict(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZVD8Fg1R6zt",
        "colab_type": "code",
        "outputId": "a684f308-4c56-4e41-f228-0ae03dedd206",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print('Coefficients:', full.coef_)\n",
        "print('Intercept:', full.intercept_)\n",
        "print('Mean squared error (MSE): %.2f'\n",
        "      % mean_squared_error(Y, full_pred))\n",
        "print('Coefficient of determination (R^2): %.2f'\n",
        "      % r2_score(Y, full_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: [-0.74173609 -0.00659927  0.00320051 -0.42316387]\n",
            "Intercept: 0.2565006830997185\n",
            "Mean squared error (MSE): 1.01\n",
            "Coefficient of determination (R^2): 0.77\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFYYzcc1VqIo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "full_yintercept = '%.2f' % full.intercept_\n",
        "full_LogP = '%.2f LogP' % full.coef_[0]\n",
        "full_MW = '%.4f MW' % full.coef_[1]\n",
        "full_RB = '+ %.4f RB' % full.coef_[2]\n",
        "full_AP = '%.2f AP' % full.coef_[3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwU4QJhhVsKb",
        "colab_type": "code",
        "outputId": "698a51bd-c785-438e-ecaa-16040938b6ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('LogS = ' + \n",
        "      ' ' + \n",
        "      full_yintercept + \n",
        "      ' ' + \n",
        "      full_LogP + \n",
        "      ' ' + \n",
        "      full_MW + \n",
        "      ' ' + \n",
        "      full_RB + \n",
        "      ' ' + \n",
        "      full_AP)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogS =  0.26 -0.74 LogP -0.0066 MW + 0.0032 RB -0.42 AP\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qp-hjUv4IWe-",
        "colab_type": "text"
      },
      "source": [
        "## **Scatter plot of experimental vs. predicted LogS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZDdRiPWIZbX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6bP41fKEY9O",
        "colab_type": "text"
      },
      "source": [
        "### **Quick check of the variable dimensions of Train and Test sets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LA5dH5oiEUnP",
        "colab_type": "code",
        "outputId": "62c0dca1-bd6f-47d1-d043-9acd4e55ac91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Y_train.shape, Y_pred_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((915,), (915,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIu7YbbFP-7o",
        "colab_type": "code",
        "outputId": "fd9558f7-05cb-4810-dfc9-ce549d3b0642",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Y_test.shape, Y_pred_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((229,), (229,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHqv3TlYa5qF",
        "colab_type": "text"
      },
      "source": [
        "### **Vertical plot**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shQPfrHIOmRD",
        "colab_type": "code",
        "outputId": "8a5571c8-5f0b-41bf-de8b-c736e89bb5f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        }
      },
      "source": [
        "plt.figure(figsize=(5,11))\n",
        "\n",
        "# 2 row, 1 column, plot 1\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.scatter(x=Y_train, y=Y_pred_train, c=\"#7CAE00\", alpha=0.3)\n",
        "\n",
        "# Add trendline\n",
        "# https://stackoverflow.com/questions/26447191/how-to-add-trendline-in-python-matplotlib-dot-scatter-graphs\n",
        "z = np.polyfit(Y_train, Y_pred_train, 1)\n",
        "p = np.poly1d(z)\n",
        "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
        "\n",
        "plt.ylabel('Predicted LogS')\n",
        "\n",
        "\n",
        "# 2 row, 1 column, plot 2\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.scatter(x=Y_test, y=Y_pred_test, c=\"#619CFF\", alpha=0.3)\n",
        "\n",
        "z = np.polyfit(Y_test, Y_pred_test, 1)\n",
        "p = np.poly1d(z)\n",
        "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
        "\n",
        "plt.ylabel('Predicted LogS')\n",
        "plt.xlabel('Experimental LogS')\n",
        "\n",
        "plt.savefig('plot_vertical_logS.png')\n",
        "plt.savefig('plot_vertical_logS.pdf')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PswCQ7Yra_CW",
        "colab_type": "text"
      },
      "source": [
        "### **Horizontal plot**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG7NWEscT8QO",
        "colab_type": "code",
        "outputId": "d967beae-01a4-49a0-d9f9-fb37a91e50fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "plt.figure(figsize=(11,5))\n",
        "\n",
        "# 1 row, 2 column, plot 1\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.scatter(x=Y_train, y=Y_pred_train, c=\"#7CAE00\", alpha=0.3)\n",
        "\n",
        "z = np.polyfit(Y_train, Y_pred_train, 1)\n",
        "p = np.poly1d(z)\n",
        "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
        "\n",
        "plt.ylabel('Predicted LogS')\n",
        "plt.xlabel('Experimental LogS')\n",
        "\n",
        "# 1 row, 2 column, plot 2\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.scatter(x=Y_test, y=Y_pred_test, c=\"#619CFF\", alpha=0.3)\n",
        "\n",
        "z = np.polyfit(Y_test, Y_pred_test, 1)\n",
        "p = np.poly1d(z)\n",
        "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
        "\n",
        "plt.xlabel('Experimental LogS')\n",
        "\n",
        "plt.savefig('plot_horizontal_logS.png')\n",
        "plt.savefig('plot_horizontal_logS.pdf')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXn0vFThYYFT",
        "colab_type": "code",
        "outputId": "2f773866-f4d9-40ae-8215-630271755cec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "! ls -l"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 83300\n",
            "-rw-r--r-- 1 root root    58889 Apr 11 04:11 delaney.csv\n",
            "-rwxr-xr-x 1 root root 85055499 Mar 11 16:08 Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
            "-rw-r--r-- 1 root root    30020 Apr 11 04:44 plot_horizontal_logS.pdf\n",
            "-rw-r--r-- 1 root root    50234 Apr 11 04:44 plot_horizontal_logS.png\n",
            "-rw-r--r-- 1 root root    30021 Apr 11 04:40 plot_vertical_logS.pdf\n",
            "-rw-r--r-- 1 root root    51340 Apr 11 04:40 plot_vertical_logS.png\n",
            "drwxr-xr-x 1 root root     4096 Apr  3 16:24 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARiv3f1iC565",
        "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."
      ]
    }
  ]
}