DeepChemDev.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DeepChemDev.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/seyonechithrananda/bert-loves-chemistry/blob/master/DeepChemDev.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFoC5Zsa0gph",
        "colab_type": "code",
        "outputId": "d2407098-6ee0-4dec-b13a-e47a9597af08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-19 22:55:25--  https://repo.anaconda.com/miniconda/Miniconda3-latest-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-latest-Linux-x86_64.sh’\n",
            "\n",
            "\r          Miniconda   0%[                    ]       0  --.-KB/s               \r         Miniconda3  83%[===============>    ]  67.83M   339MB/s               \rMiniconda3-latest-L 100%[===================>]  81.12M   346MB/s    in 0.2s    \n",
            "\n",
            "2020-04-19 22:55:25 (346 MB/s) - ‘Miniconda3-latest-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\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\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\bdone\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /usr/local\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\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\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \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\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \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\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\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\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\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",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 4.8.2\n",
            "  latest version: 4.8.3\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c defaults conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - deepchem-gpu=2.3.0\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _py-xgboost-mutex-2.0      |            cpu_0           8 KB  conda-forge\n",
            "    _tflow_select-2.1.0        |              gpu           2 KB\n",
            "    absl-py-0.9.0              |   py37hc8dfbb8_1         162 KB  conda-forge\n",
            "    astor-0.7.1                |             py_0          22 KB  conda-forge\n",
            "    attrs-19.3.0               |             py_0          35 KB  conda-forge\n",
            "    backcall-0.1.0             |             py_0          13 KB  conda-forge\n",
            "    bleach-3.1.4               |     pyh9f0ad1d_0         111 KB  conda-forge\n",
            "    blosc-1.18.1               |       he1b5a44_0         893 KB  conda-forge\n",
            "    bzip2-1.0.8                |       h516909a_2         396 KB  conda-forge\n",
            "    c-ares-1.15.0              |    h516909a_1001         100 KB  conda-forge\n",
            "    ca-certificates-2020.4.5.1 |       hecc5488_0         146 KB  conda-forge\n",
            "    cairo-1.16.0               |    h18b612c_1001         1.5 MB  conda-forge\n",
            "    certifi-2020.4.5.1         |   py37hc8dfbb8_0         151 KB  conda-forge\n",
            "    conda-4.8.3                |   py37hc8dfbb8_1         3.0 MB  conda-forge\n",
            "    cudatoolkit-10.1.243       |       h6bb024c_0       347.4 MB\n",
            "    cudnn-7.6.5                |       cuda10.1_0       179.9 MB\n",
            "    cupti-10.1.168             |                0         1.4 MB\n",
            "    cycler-0.10.0              |             py_2           9 KB  conda-forge\n",
            "    cython-0.29.16             |   py37h3340039_0         2.2 MB  conda-forge\n",
            "    dbus-1.13.6                |       he372182_0         602 KB  conda-forge\n",
            "    decorator-4.4.2            |             py_0          11 KB  conda-forge\n",
            "    deepchem-gpu-2.3.0         |           py37_0         2.1 MB  deepchem\n",
            "    defusedxml-0.6.0           |             py_0          22 KB  conda-forge\n",
            "    entrypoints-0.3            |py37hc8dfbb8_1001          12 KB  conda-forge\n",
            "    expat-2.2.9                |       he1b5a44_2         191 KB  conda-forge\n",
            "    fftw3f-3.3.4               |                2         1.2 MB  omnia\n",
            "    fontconfig-2.13.1          |    he4413a7_1000         327 KB  conda-forge\n",
            "    freetype-2.10.1            |       he06d7ca_0         877 KB  conda-forge\n",
            "    gast-0.3.3                 |             py_0          12 KB  conda-forge\n",
            "    gettext-0.19.8.1           |    hc5be6a0_1002         3.6 MB  conda-forge\n",
            "    glib-2.64.2                |       h6f030ca_0         3.4 MB  conda-forge\n",
            "    google-pasta-0.2.0         |     pyh8c360ce_0          42 KB  conda-forge\n",
            "    grpcio-1.23.0              |   py37hb0870dc_1         1.1 MB  conda-forge\n",
            "    gst-plugins-base-1.14.5    |       h0935bb2_2         6.8 MB  conda-forge\n",
            "    gstreamer-1.14.5           |       h36ae1b5_2         4.5 MB  conda-forge\n",
            "    h5py-2.10.0                |nompi_py37h513d04c_102         1.2 MB  conda-forge\n",
            "    hdf5-1.10.5                |nompi_h3c11f04_1104         3.1 MB  conda-forge\n",
            "    icu-58.2                   |    hf484d3e_1000        22.6 MB  conda-forge\n",
            "    importlib-metadata-1.6.0   |   py37hc8dfbb8_0          43 KB  conda-forge\n",
            "    importlib_metadata-1.6.0   |                0           3 KB  conda-forge\n",
            "    ipykernel-5.2.0            |   py37h43977f1_1         161 KB  conda-forge\n",
            "    ipython-7.13.0             |   py37hc8dfbb8_2         1.1 MB  conda-forge\n",
            "    ipython_genutils-0.2.0     |             py_1          21 KB  conda-forge\n",
            "    ipywidgets-7.5.1           |             py_0         101 KB  conda-forge\n",
            "    jedi-0.17.0                |   py37hc8dfbb8_0         807 KB  conda-forge\n",
            "    jinja2-2.11.2              |     pyh9f0ad1d_0          93 KB  conda-forge\n",
            "    joblib-0.14.1              |             py_0         198 KB  conda-forge\n",
            "    jpeg-9c                    |    h14c3975_1001         251 KB  conda-forge\n",
            "    jsonschema-3.2.0           |   py37hc8dfbb8_1          89 KB  conda-forge\n",
            "    jupyter-1.0.0              |             py_2           4 KB  conda-forge\n",
            "    jupyter_client-6.1.3       |             py_0          74 KB  conda-forge\n",
            "    jupyter_console-6.1.0      |             py_1          21 KB  conda-forge\n",
            "    jupyter_core-4.6.3         |   py37hc8dfbb8_1          71 KB  conda-forge\n",
            "    keras-applications-1.0.8   |             py_1          30 KB  conda-forge\n",
            "    keras-preprocessing-1.1.0  |             py_0          33 KB  conda-forge\n",
            "    kiwisolver-1.2.0           |   py37h99015e2_0          87 KB  conda-forge\n",
            "    libblas-3.8.0              |      14_openblas          10 KB  conda-forge\n",
            "    libboost-1.67.0            |       h46d08c1_4        13.0 MB\n",
            "    libcblas-3.8.0             |      14_openblas          10 KB  conda-forge\n",
            "    libgfortran-ng-7.3.0       |       hdf63c60_5         1.7 MB  conda-forge\n",
            "    libiconv-1.15              |    h516909a_1006         2.0 MB  conda-forge\n",
            "    liblapack-3.8.0            |      14_openblas          10 KB  conda-forge\n",
            "    libopenblas-0.3.7          |       h5ec1e0e_6         7.6 MB  conda-forge\n",
            "    libpng-1.6.37              |       hed695b0_1         308 KB  conda-forge\n",
            "    libprotobuf-3.11.4         |       h8b12597_0         4.8 MB  conda-forge\n",
            "    libsodium-1.0.17           |       h516909a_0         330 KB  conda-forge\n",
            "    libtiff-4.1.0              |       hc7e4089_6         668 KB  conda-forge\n",
            "    libuuid-2.32.1             |    h14c3975_1000          26 KB  conda-forge\n",
            "    libwebp-base-1.1.0         |       h516909a_3         845 KB  conda-forge\n",
            "    libxcb-1.13                |    h14c3975_1002         396 KB  conda-forge\n",
            "    libxgboost-1.0.2           |       he1b5a44_0         2.8 MB  conda-forge\n",
            "    libxml2-2.9.9              |       h13577e0_2         1.3 MB  conda-forge\n",
            "    lz4-c-1.8.3                |    he1b5a44_1001         187 KB  conda-forge\n",
            "    lzo-2.10                   |    h14c3975_1000         319 KB  conda-forge\n",
            "    markdown-3.2.1             |             py_0          61 KB  conda-forge\n",
            "    markupsafe-1.1.1           |   py37h8f50634_1          26 KB  conda-forge\n",
            "    matplotlib-3.1.2           |   py37h4fdacc2_0         5.0 MB\n",
            "    mdtraj-1.9.3               |   py37h1c801a5_1         1.8 MB  conda-forge\n",
            "    mistune-0.8.4              |py37h8f50634_1001          53 KB  conda-forge\n",
            "    mock-3.0.5                 |   py37hc8dfbb8_1          44 KB  conda-forge\n",
            "    nbconvert-5.6.1            |   py37hc8dfbb8_1         487 KB  conda-forge\n",
            "    nbformat-5.0.6             |             py_0          99 KB  conda-forge\n",
            "    networkx-2.4               |             py_1         1.2 MB  conda-forge\n",
            "    notebook-6.0.3             |           py37_0         6.2 MB  conda-forge\n",
            "    numexpr-2.7.1              |   py37h0da4684_1         197 KB  conda-forge\n",
            "    numpy-1.18.1               |   py37h8960a57_1         5.2 MB  conda-forge\n",
            "    olefile-0.46               |             py_0          31 KB  conda-forge\n",
            "    openmm-7.4.1               |py37_cuda101_rc_1        11.9 MB  omnia\n",
            "    openssl-1.1.1f             |       h516909a_0         2.1 MB  conda-forge\n",
            "    pandas-1.0.3               |   py37h0da4684_1        11.1 MB  conda-forge\n",
            "    pandoc-2.9.2.1             |                0        18.7 MB  conda-forge\n",
            "    pandocfilters-1.4.2        |             py_1           9 KB  conda-forge\n",
            "    parso-0.7.0                |     pyh9f0ad1d_0          67 KB  conda-forge\n",
            "    pcre-8.44                  |       he1b5a44_0         261 KB  conda-forge\n",
            "    pdbfixer-1.6               |           py37_0         190 KB  omnia\n",
            "    pexpect-4.8.0              |   py37hc8dfbb8_1          79 KB  conda-forge\n",
            "    pickleshare-0.7.5          |py37hc8dfbb8_1001          13 KB  conda-forge\n",
            "    pillow-5.3.0               |py37h00a061d_1000         595 KB  conda-forge\n",
            "    pixman-0.38.0              |    h516909a_1003         594 KB  conda-forge\n",
            "    prometheus_client-0.7.1    |             py_0          38 KB  conda-forge\n",
            "    prompt-toolkit-3.0.5       |             py_0         232 KB  conda-forge\n",
            "    prompt_toolkit-3.0.5       |                0           4 KB  conda-forge\n",
            "    protobuf-3.11.4            |   py37h3340039_1         695 KB  conda-forge\n",
            "    pthread-stubs-0.4          |    h14c3975_1001           5 KB  conda-forge\n",
            "    ptyprocess-0.6.0           |          py_1001          15 KB  conda-forge\n",
            "    py-boost-1.67.0            |   py37h04863e7_4         278 KB\n",
            "    py-xgboost-1.0.2           |   py37hc8dfbb8_0          94 KB  conda-forge\n",
            "    pygments-2.6.1             |             py_0         683 KB  conda-forge\n",
            "    pyparsing-2.4.7            |     pyh9f0ad1d_0          60 KB  conda-forge\n",
            "    pyqt-5.9.2                 |   py37hcca6a23_4         5.7 MB  conda-forge\n",
            "    pyrsistent-0.16.0          |   py37h8f50634_0          89 KB  conda-forge\n",
            "    pytables-3.6.1             |   py37h9f153d1_1         1.5 MB  conda-forge\n",
            "    python-dateutil-2.8.1      |             py_0         220 KB  conda-forge\n",
            "    python_abi-3.7             |          1_cp37m           4 KB  conda-forge\n",
            "    pytz-2019.3                |             py_0         237 KB  conda-forge\n",
            "    pyzmq-19.0.0               |   py37hac76be4_1         503 KB  conda-forge\n",
            "    qt-5.9.7                   |       h52cfd70_2        85.9 MB  conda-forge\n",
            "    qtconsole-4.7.3            |     pyh9f0ad1d_0          87 KB  conda-forge\n",
            "    qtpy-1.9.0                 |             py_0          34 KB  conda-forge\n",
            "    rdkit-2020.03.1.0          |   py37hc20afe1_1        24.5 MB  rdkit\n",
            "    scikit-learn-0.22.2.post1  |   py37hcdab131_0         7.1 MB  conda-forge\n",
            "    scipy-1.4.1                |   py37ha3d9a3c_3        18.8 MB  conda-forge\n",
            "    send2trash-1.5.0           |             py_0          12 KB  conda-forge\n",
            "    simdna-0.4.2               |             py_0         627 KB  deepchem\n",
            "    sip-4.19.8                 |py37hf484d3e_1000         290 KB  conda-forge\n",
            "    tensorboard-1.14.0         |           py37_0         3.2 MB  conda-forge\n",
            "    tensorflow-1.14.0          |gpu_py37h74c33d7_0           4 KB\n",
            "    tensorflow-base-1.14.0     |gpu_py37he45bfe2_0       146.3 MB\n",
            "    tensorflow-estimator-1.14.0|   py37h5ca1d4c_0         645 KB  conda-forge\n",
            "    tensorflow-gpu-1.14.0      |       h0d30ee6_0           3 KB\n",
            "    termcolor-1.1.0            |             py_2           6 KB  conda-forge\n",
            "    terminado-0.8.3            |   py37hc8dfbb8_1          23 KB  conda-forge\n",
            "    testpath-0.4.4             |             py_0          85 KB  conda-forge\n",
            "    tornado-6.0.4              |   py37h8f50634_1         639 KB  conda-forge\n",
            "    traitlets-4.3.3            |   py37hc8dfbb8_1         133 KB  conda-forge\n",
            "    wcwidth-0.1.9              |     pyh9f0ad1d_0          20 KB  conda-forge\n",
            "    webencodings-0.5.1         |             py_1          12 KB  conda-forge\n",
            "    werkzeug-1.0.1             |     pyh9f0ad1d_0         239 KB  conda-forge\n",
            "    widgetsnbextension-3.5.1   |           py37_0         1.8 MB  conda-forge\n",
            "    wrapt-1.12.1               |   py37h8f50634_1          46 KB  conda-forge\n",
            "    xgboost-1.0.2              |   py37h3340039_0          11 KB  conda-forge\n",
            "    xorg-kbproto-1.0.7         |    h14c3975_1002          26 KB  conda-forge\n",
            "    xorg-libice-1.0.10         |       h516909a_0          57 KB  conda-forge\n",
            "    xorg-libsm-1.2.3           |    h84519dc_1000          25 KB  conda-forge\n",
            "    xorg-libx11-1.6.9          |       h516909a_0         918 KB  conda-forge\n",
            "    xorg-libxau-1.0.9          |       h14c3975_0          13 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h516909a_0          18 KB  conda-forge\n",
            "    xorg-libxext-1.3.4         |       h516909a_0          51 KB  conda-forge\n",
            "    xorg-libxrender-0.9.10     |    h516909a_1002          31 KB  conda-forge\n",
            "    xorg-renderproto-0.11.1    |    h14c3975_1002           8 KB  conda-forge\n",
            "    xorg-xextproto-7.3.0       |    h14c3975_1002          27 KB  conda-forge\n",
            "    xorg-xproto-7.0.31         |    h14c3975_1007          72 KB  conda-forge\n",
            "    zeromq-4.3.2               |       he1b5a44_2         668 KB  conda-forge\n",
            "    zipp-3.1.0                 |             py_0          10 KB  conda-forge\n",
            "    zstd-1.4.4                 |       h3b9ef0a_2         982 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       998.0 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _py-xgboost-mutex  conda-forge/linux-64::_py-xgboost-mutex-2.0-cpu_0\n",
            "  _tflow_select      pkgs/main/linux-64::_tflow_select-2.1.0-gpu\n",
            "  absl-py            conda-forge/linux-64::absl-py-0.9.0-py37hc8dfbb8_1\n",
            "  astor              conda-forge/noarch::astor-0.7.1-py_0\n",
            "  attrs              conda-forge/noarch::attrs-19.3.0-py_0\n",
            "  backcall           conda-forge/noarch::backcall-0.1.0-py_0\n",
            "  bleach             conda-forge/noarch::bleach-3.1.4-pyh9f0ad1d_0\n",
            "  blosc              conda-forge/linux-64::blosc-1.18.1-he1b5a44_0\n",
            "  bzip2              conda-forge/linux-64::bzip2-1.0.8-h516909a_2\n",
            "  c-ares             conda-forge/linux-64::c-ares-1.15.0-h516909a_1001\n",
            "  cairo              conda-forge/linux-64::cairo-1.16.0-h18b612c_1001\n",
            "  cudatoolkit        pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0\n",
            "  cudnn              pkgs/main/linux-64::cudnn-7.6.5-cuda10.1_0\n",
            "  cupti              pkgs/main/linux-64::cupti-10.1.168-0\n",
            "  cycler             conda-forge/noarch::cycler-0.10.0-py_2\n",
            "  cython             conda-forge/linux-64::cython-0.29.16-py37h3340039_0\n",
            "  dbus               conda-forge/linux-64::dbus-1.13.6-he372182_0\n",
            "  decorator          conda-forge/noarch::decorator-4.4.2-py_0\n",
            "  deepchem-gpu       deepchem/linux-64::deepchem-gpu-2.3.0-py37_0\n",
            "  defusedxml         conda-forge/noarch::defusedxml-0.6.0-py_0\n",
            "  entrypoints        conda-forge/linux-64::entrypoints-0.3-py37hc8dfbb8_1001\n",
            "  expat              conda-forge/linux-64::expat-2.2.9-he1b5a44_2\n",
            "  fftw3f             omnia/linux-64::fftw3f-3.3.4-2\n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.13.1-he4413a7_1000\n",
            "  freetype           conda-forge/linux-64::freetype-2.10.1-he06d7ca_0\n",
            "  gast               conda-forge/noarch::gast-0.3.3-py_0\n",
            "  gettext            conda-forge/linux-64::gettext-0.19.8.1-hc5be6a0_1002\n",
            "  glib               conda-forge/linux-64::glib-2.64.2-h6f030ca_0\n",
            "  google-pasta       conda-forge/noarch::google-pasta-0.2.0-pyh8c360ce_0\n",
            "  grpcio             conda-forge/linux-64::grpcio-1.23.0-py37hb0870dc_1\n",
            "  gst-plugins-base   conda-forge/linux-64::gst-plugins-base-1.14.5-h0935bb2_2\n",
            "  gstreamer          conda-forge/linux-64::gstreamer-1.14.5-h36ae1b5_2\n",
            "  h5py               conda-forge/linux-64::h5py-2.10.0-nompi_py37h513d04c_102\n",
            "  hdf5               conda-forge/linux-64::hdf5-1.10.5-nompi_h3c11f04_1104\n",
            "  icu                conda-forge/linux-64::icu-58.2-hf484d3e_1000\n",
            "  importlib-metadata conda-forge/linux-64::importlib-metadata-1.6.0-py37hc8dfbb8_0\n",
            "  importlib_metadata conda-forge/noarch::importlib_metadata-1.6.0-0\n",
            "  ipykernel          conda-forge/linux-64::ipykernel-5.2.0-py37h43977f1_1\n",
            "  ipython            conda-forge/linux-64::ipython-7.13.0-py37hc8dfbb8_2\n",
            "  ipython_genutils   conda-forge/noarch::ipython_genutils-0.2.0-py_1\n",
            "  ipywidgets         conda-forge/noarch::ipywidgets-7.5.1-py_0\n",
            "  jedi               conda-forge/linux-64::jedi-0.17.0-py37hc8dfbb8_0\n",
            "  jinja2             conda-forge/noarch::jinja2-2.11.2-pyh9f0ad1d_0\n",
            "  joblib             conda-forge/noarch::joblib-0.14.1-py_0\n",
            "  jpeg               conda-forge/linux-64::jpeg-9c-h14c3975_1001\n",
            "  jsonschema         conda-forge/linux-64::jsonschema-3.2.0-py37hc8dfbb8_1\n",
            "  jupyter            conda-forge/noarch::jupyter-1.0.0-py_2\n",
            "  jupyter_client     conda-forge/noarch::jupyter_client-6.1.3-py_0\n",
            "  jupyter_console    conda-forge/noarch::jupyter_console-6.1.0-py_1\n",
            "  jupyter_core       conda-forge/linux-64::jupyter_core-4.6.3-py37hc8dfbb8_1\n",
            "  keras-applications conda-forge/noarch::keras-applications-1.0.8-py_1\n",
            "  keras-preprocessi~ conda-forge/noarch::keras-preprocessing-1.1.0-py_0\n",
            "  kiwisolver         conda-forge/linux-64::kiwisolver-1.2.0-py37h99015e2_0\n",
            "  libblas            conda-forge/linux-64::libblas-3.8.0-14_openblas\n",
            "  libboost           pkgs/main/linux-64::libboost-1.67.0-h46d08c1_4\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.8.0-14_openblas\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-7.3.0-hdf63c60_5\n",
            "  libiconv           conda-forge/linux-64::libiconv-1.15-h516909a_1006\n",
            "  liblapack          conda-forge/linux-64::liblapack-3.8.0-14_openblas\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.7-h5ec1e0e_6\n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-hed695b0_1\n",
            "  libprotobuf        conda-forge/linux-64::libprotobuf-3.11.4-h8b12597_0\n",
            "  libsodium          conda-forge/linux-64::libsodium-1.0.17-h516909a_0\n",
            "  libtiff            conda-forge/linux-64::libtiff-4.1.0-hc7e4089_6\n",
            "  libuuid            conda-forge/linux-64::libuuid-2.32.1-h14c3975_1000\n",
            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.1.0-h516909a_3\n",
            "  libxcb             conda-forge/linux-64::libxcb-1.13-h14c3975_1002\n",
            "  libxgboost         conda-forge/linux-64::libxgboost-1.0.2-he1b5a44_0\n",
            "  libxml2            conda-forge/linux-64::libxml2-2.9.9-h13577e0_2\n",
            "  lz4-c              conda-forge/linux-64::lz4-c-1.8.3-he1b5a44_1001\n",
            "  lzo                conda-forge/linux-64::lzo-2.10-h14c3975_1000\n",
            "  markdown           conda-forge/noarch::markdown-3.2.1-py_0\n",
            "  markupsafe         conda-forge/linux-64::markupsafe-1.1.1-py37h8f50634_1\n",
            "  matplotlib         pkgs/main/linux-64::matplotlib-3.1.2-py37h4fdacc2_0\n",
            "  mdtraj             conda-forge/linux-64::mdtraj-1.9.3-py37h1c801a5_1\n",
            "  mistune            conda-forge/linux-64::mistune-0.8.4-py37h8f50634_1001\n",
            "  mock               conda-forge/linux-64::mock-3.0.5-py37hc8dfbb8_1\n",
            "  nbconvert          conda-forge/linux-64::nbconvert-5.6.1-py37hc8dfbb8_1\n",
            "  nbformat           conda-forge/noarch::nbformat-5.0.6-py_0\n",
            "  networkx           conda-forge/noarch::networkx-2.4-py_1\n",
            "  notebook           conda-forge/linux-64::notebook-6.0.3-py37_0\n",
            "  numexpr            conda-forge/linux-64::numexpr-2.7.1-py37h0da4684_1\n",
            "  numpy              conda-forge/linux-64::numpy-1.18.1-py37h8960a57_1\n",
            "  olefile            conda-forge/noarch::olefile-0.46-py_0\n",
            "  openmm             omnia/linux-64::openmm-7.4.1-py37_cuda101_rc_1\n",
            "  pandas             conda-forge/linux-64::pandas-1.0.3-py37h0da4684_1\n",
            "  pandoc             conda-forge/linux-64::pandoc-2.9.2.1-0\n",
            "  pandocfilters      conda-forge/noarch::pandocfilters-1.4.2-py_1\n",
            "  parso              conda-forge/noarch::parso-0.7.0-pyh9f0ad1d_0\n",
            "  pcre               conda-forge/linux-64::pcre-8.44-he1b5a44_0\n",
            "  pdbfixer           omnia/linux-64::pdbfixer-1.6-py37_0\n",
            "  pexpect            conda-forge/linux-64::pexpect-4.8.0-py37hc8dfbb8_1\n",
            "  pickleshare        conda-forge/linux-64::pickleshare-0.7.5-py37hc8dfbb8_1001\n",
            "  pillow             conda-forge/linux-64::pillow-5.3.0-py37h00a061d_1000\n",
            "  pixman             conda-forge/linux-64::pixman-0.38.0-h516909a_1003\n",
            "  prometheus_client  conda-forge/noarch::prometheus_client-0.7.1-py_0\n",
            "  prompt-toolkit     conda-forge/noarch::prompt-toolkit-3.0.5-py_0\n",
            "  prompt_toolkit     conda-forge/noarch::prompt_toolkit-3.0.5-0\n",
            "  protobuf           conda-forge/linux-64::protobuf-3.11.4-py37h3340039_1\n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h14c3975_1001\n",
            "  ptyprocess         conda-forge/noarch::ptyprocess-0.6.0-py_1001\n",
            "  py-boost           pkgs/main/linux-64::py-boost-1.67.0-py37h04863e7_4\n",
            "  py-xgboost         conda-forge/linux-64::py-xgboost-1.0.2-py37hc8dfbb8_0\n",
            "  pygments           conda-forge/noarch::pygments-2.6.1-py_0\n",
            "  pyparsing          conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n",
            "  pyqt               conda-forge/linux-64::pyqt-5.9.2-py37hcca6a23_4\n",
            "  pyrsistent         conda-forge/linux-64::pyrsistent-0.16.0-py37h8f50634_0\n",
            "  pytables           conda-forge/linux-64::pytables-3.6.1-py37h9f153d1_1\n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.1-py_0\n",
            "  python_abi         conda-forge/linux-64::python_abi-3.7-1_cp37m\n",
            "  pytz               conda-forge/noarch::pytz-2019.3-py_0\n",
            "  pyzmq              conda-forge/linux-64::pyzmq-19.0.0-py37hac76be4_1\n",
            "  qt                 conda-forge/linux-64::qt-5.9.7-h52cfd70_2\n",
            "  qtconsole          conda-forge/noarch::qtconsole-4.7.3-pyh9f0ad1d_0\n",
            "  qtpy               conda-forge/noarch::qtpy-1.9.0-py_0\n",
            "  rdkit              rdkit/linux-64::rdkit-2020.03.1.0-py37hc20afe1_1\n",
            "  scikit-learn       conda-forge/linux-64::scikit-learn-0.22.2.post1-py37hcdab131_0\n",
            "  scipy              conda-forge/linux-64::scipy-1.4.1-py37ha3d9a3c_3\n",
            "  send2trash         conda-forge/noarch::send2trash-1.5.0-py_0\n",
            "  simdna             deepchem/noarch::simdna-0.4.2-py_0\n",
            "  sip                conda-forge/linux-64::sip-4.19.8-py37hf484d3e_1000\n",
            "  tensorboard        conda-forge/linux-64::tensorboard-1.14.0-py37_0\n",
            "  tensorflow         pkgs/main/linux-64::tensorflow-1.14.0-gpu_py37h74c33d7_0\n",
            "  tensorflow-base    pkgs/main/linux-64::tensorflow-base-1.14.0-gpu_py37he45bfe2_0\n",
            "  tensorflow-estima~ conda-forge/linux-64::tensorflow-estimator-1.14.0-py37h5ca1d4c_0\n",
            "  tensorflow-gpu     pkgs/main/linux-64::tensorflow-gpu-1.14.0-h0d30ee6_0\n",
            "  termcolor          conda-forge/noarch::termcolor-1.1.0-py_2\n",
            "  terminado          conda-forge/linux-64::terminado-0.8.3-py37hc8dfbb8_1\n",
            "  testpath           conda-forge/noarch::testpath-0.4.4-py_0\n",
            "  tornado            conda-forge/linux-64::tornado-6.0.4-py37h8f50634_1\n",
            "  traitlets          conda-forge/linux-64::traitlets-4.3.3-py37hc8dfbb8_1\n",
            "  wcwidth            conda-forge/noarch::wcwidth-0.1.9-pyh9f0ad1d_0\n",
            "  webencodings       conda-forge/noarch::webencodings-0.5.1-py_1\n",
            "  werkzeug           conda-forge/noarch::werkzeug-1.0.1-pyh9f0ad1d_0\n",
            "  widgetsnbextension conda-forge/linux-64::widgetsnbextension-3.5.1-py37_0\n",
            "  wrapt              conda-forge/linux-64::wrapt-1.12.1-py37h8f50634_1\n",
            "  xgboost            conda-forge/linux-64::xgboost-1.0.2-py37h3340039_0\n",
            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h14c3975_1002\n",
            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h516909a_0\n",
            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-h84519dc_1000\n",
            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.6.9-h516909a_0\n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.9-h14c3975_0\n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h516909a_0\n",
            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h516909a_0\n",
            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h516909a_1002\n",
            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h14c3975_1002\n",
            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h14c3975_1002\n",
            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h14c3975_1007\n",
            "  zeromq             conda-forge/linux-64::zeromq-4.3.2-he1b5a44_2\n",
            "  zipp               conda-forge/noarch::zipp-3.1.0-py_0\n",
            "  zstd               conda-forge/linux-64::zstd-1.4.4-h3b9ef0a_2\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates     pkgs/main::ca-certificates-2020.1.1-0 --> conda-forge::ca-certificates-2020.4.5.1-hecc5488_0\n",
            "  certifi              pkgs/main::certifi-2019.11.28-py37_0 --> conda-forge::certifi-2020.4.5.1-py37hc8dfbb8_0\n",
            "  conda                       pkgs/main::conda-4.8.2-py37_0 --> conda-forge::conda-4.8.3-py37hc8dfbb8_1\n",
            "  openssl              pkgs/main::openssl-1.1.1d-h7b6447c_4 --> conda-forge::openssl-1.1.1f-h516909a_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "tensorflow-gpu-1.14. | 3 KB      | : 100% 1.0/1 [00:00<00:00, 11.15it/s]\n",
            "cython-0.29.16       | 2.2 MB    | : 100% 1.0/1 [00:00<00:00,  1.97it/s]\n",
            "xorg-libx11-1.6.9    | 918 KB    | : 100% 1.0/1 [00:00<00:00,  5.55it/s]\n",
            "entrypoints-0.3      | 12 KB     | : 100% 1.0/1 [00:00<00:00, 33.42it/s]\n",
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            "testpath-0.4.4       | 85 KB     | : 100% 1.0/1 [00:00<00:00, 25.46it/s]\n",
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            "notebook-6.0.3       | 6.2 MB    | : 100% 1.0/1 [00:01<00:00,  1.30s/it]\n",
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            "mistune-0.8.4        | 53 KB     | : 100% 1.0/1 [00:00<00:00, 24.53it/s]\n",
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            "openmm-7.4.1         | 11.9 MB   | : 100% 1.0/1 [00:06<00:00, 38.63s/it]               \n",
            "cudnn-7.6.5          | 179.9 MB  | : 100% 1.0/1 [00:04<00:00,  4.24s/it]               \n",
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            "pickleshare-0.7.5    | 13 KB     | : 100% 1.0/1 [00:00<00:00, 40.88it/s]\n",
            "zeromq-4.3.2         | 668 KB    | : 100% 1.0/1 [00:00<00:00,  6.04it/s]\n",
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            "_tflow_select-2.1.0  | 2 KB      | : 100% 1.0/1 [00:00<00:00, 15.70it/s]\n",
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            "pandocfilters-1.4.2  | 9 KB      | : 100% 1.0/1 [00:00<00:00, 46.94it/s]\n",
            "hdf5-1.10.5          | 3.1 MB    | : 100% 1.0/1 [00:00<00:00,  2.13it/s]\n",
            "pyrsistent-0.16.0    | 89 KB     | : 100% 1.0/1 [00:00<00:00, 24.85it/s]\n",
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            "pyzmq-19.0.0         | 503 KB    | : 100% 1.0/1 [00:00<00:00,  7.87it/s]\n",
            "xorg-libxdmcp-1.1.3  | 18 KB     | : 100% 1.0/1 [00:00<00:00, 33.82it/s]\n",
            "python_abi-3.7       | 4 KB      | : 100% 1.0/1 [00:00<00:00, 44.94it/s]\n",
            "scikit-learn-0.22.2. | 7.1 MB    | : 100% 1.0/1 [00:01<00:00,  1.35s/it]\n",
            "libiconv-1.15        | 2.0 MB    | : 100% 1.0/1 [00:00<00:00,  3.78it/s]\n",
            "jpeg-9c              | 251 KB    | : 100% 1.0/1 [00:00<00:00, 10.91it/s]\n",
            "lz4-c-1.8.3          | 187 KB    | : 100% 1.0/1 [00:00<00:00, 16.33it/s]\n",
            "gstreamer-1.14.5     | 4.5 MB    | : 100% 1.0/1 [00:00<00:00,  1.19it/s]\n",
            "libprotobuf-3.11.4   | 4.8 MB    | : 100% 1.0/1 [00:00<00:00,  1.06it/s]\n",
            "ca-certificates-2020 | 146 KB    | : 100% 1.0/1 [00:00<00:00, 22.14it/s]\n",
            "bzip2-1.0.8          | 396 KB    | : 100% 1.0/1 [00:00<00:00, 10.79it/s]\n",
            "openssl-1.1.1f       | 2.1 MB    | : 100% 1.0/1 [00:00<00:00,  3.02it/s]\n",
            "expat-2.2.9          | 191 KB    | : 100% 1.0/1 [00:00<00:00, 17.83it/s]\n",
            "py-xgboost-1.0.2     | 94 KB     | : 100% 1.0/1 [00:00<00:00, 24.16it/s]\n",
            "pillow-5.3.0         | 595 KB    | : 100% 1.0/1 [00:00<00:00,  7.51it/s]\n",
            "ipykernel-5.2.0      | 161 KB    | : 100% 1.0/1 [00:00<00:00, 17.93it/s]\n",
            "cairo-1.16.0         | 1.5 MB    | : 100% 1.0/1 [00:00<00:00,  3.31it/s]\n",
            "xgboost-1.0.2        | 11 KB     | : 100% 1.0/1 [00:00<00:00, 37.70it/s]\n",
            "jinja2-2.11.2        | 93 KB     | : 100% 1.0/1 [00:00<00:00, 23.04it/s]\n",
            "zstd-1.4.4           | 982 KB    | : 100% 1.0/1 [00:00<00:00,  6.08it/s]\n",
            "prompt_toolkit-3.0.5 | 4 KB      | : 100% 1.0/1 [00:00<00:00, 45.16it/s]\n",
            "blosc-1.18.1         | 893 KB    | : 100% 1.0/1 [00:00<00:00,  6.62it/s]\n",
            "pandoc-2.9.2.1       | 18.7 MB   | : 100% 1.0/1 [00:04<00:00,  2.79s/it]               \n",
            "tensorflow-estimator | 645 KB    | : 100% 1.0/1 [00:00<00:00,  5.18it/s]\n",
            "olefile-0.46         | 31 KB     | : 100% 1.0/1 [00:00<00:00, 37.74it/s]\n",
            "libgfortran-ng-7.3.0 | 1.7 MB    | : 100% 1.0/1 [00:00<00:00,  3.37it/s]\n",
            "pdbfixer-1.6         | 190 KB    | : 100% 1.0/1 [00:02<00:00,  2.63s/it]              \n",
            "markdown-3.2.1       | 61 KB     | : 100% 1.0/1 [00:00<00:00, 21.44it/s]\n",
            "wcwidth-0.1.9        | 20 KB     | : 100% 1.0/1 [00:00<00:00, 34.68it/s]\n",
            "attrs-19.3.0         | 35 KB     | : 100% 1.0/1 [00:00<00:00, 37.20it/s]\n",
            "networkx-2.4         | 1.2 MB    | : 100% 1.0/1 [00:00<00:00,  3.50it/s]\n",
            "termcolor-1.1.0      | 6 KB      | : 100% 1.0/1 [00:00<00:00, 44.22it/s]\n",
            "gst-plugins-base-1.1 | 6.8 MB    | : 100% 1.0/1 [00:01<00:00,  1.09s/it]\n",
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            "qtconsole-4.7.3      | 87 KB     | : 100% 1.0/1 [00:00<00:00, 24.91it/s]\n",
            "pixman-0.38.0        | 594 KB    | : 100% 1.0/1 [00:00<00:00,  6.97it/s]\n",
            "cudatoolkit-10.1.243 | 347.4 MB  | : 100% 1.0/1 [00:22<00:00, 22.09s/it]               \n",
            "importlib_metadata-1 | 3 KB      | : 100% 1.0/1 [00:00<00:00, 33.43it/s]\n",
            "send2trash-1.5.0     | 12 KB     | : 100% 1.0/1 [00:00<00:00, 46.13it/s]\n",
            "pthread-stubs-0.4    | 5 KB      | : 100% 1.0/1 [00:00<00:00, 44.94it/s]\n",
            "libxml2-2.9.9        | 1.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.18s/it]\n",
            "pandas-1.0.3         | 11.1 MB   | : 100% 1.0/1 [00:05<00:00,  7.41s/it]               \n",
            "ipython_genutils-0.2 | 21 KB     | : 100% 1.0/1 [00:00<00:00, 31.83it/s]\n",
            "widgetsnbextension-3 | 1.8 MB    | : 100% 1.0/1 [00:00<00:00,  2.61it/s]\n",
            "terminado-0.8.3      | 23 KB     | : 100% 1.0/1 [00:00<00:00, 31.00it/s]\n",
            "markupsafe-1.1.1     | 26 KB     | : 100% 1.0/1 [00:00<00:00, 34.35it/s]\n",
            "prompt-toolkit-3.0.5 | 232 KB    | : 100% 1.0/1 [00:00<00:00, 12.36it/s]\n",
            "pexpect-4.8.0        | 79 KB     | : 100% 1.0/1 [00:00<00:00, 25.78it/s]\n",
            "xorg-libxau-1.0.9    | 13 KB     | : 100% 1.0/1 [00:00<00:00, 41.42it/s]\n",
            "ipywidgets-7.5.1     | 101 KB    | : 100% 1.0/1 [00:00<00:00, 24.58it/s]\n",
            "zipp-3.1.0           | 10 KB     | : 100% 1.0/1 [00:00<00:00, 42.14it/s]\n",
            "python-dateutil-2.8. | 220 KB    | : 100% 1.0/1 [00:00<00:00, 20.71it/s]\n",
            "backcall-0.1.0       | 13 KB     | : 100% 1.0/1 [00:00<00:00, 40.98it/s]\n",
            "c-ares-1.15.0        | 100 KB    | : 100% 1.0/1 [00:00<00:00, 25.55it/s]\n",
            "xorg-xextproto-7.3.0 | 27 KB     | : 100% 1.0/1 [00:00<00:00, 35.52it/s]\n",
            "scipy-1.4.1          | 18.8 MB   | : 100% 1.0/1 [00:03<00:00,  1.97s/it]               \n",
            "simdna-0.4.2         | 627 KB    | : 100% 1.0/1 [00:02<00:00,  2.40s/it]               \n",
            "mdtraj-1.9.3         | 1.8 MB    | : 100% 1.0/1 [00:00<00:00,  2.98it/s]\n",
            "jupyter_core-4.6.3   | 71 KB     | : 100% 1.0/1 [00:00<00:00, 24.76it/s]\n",
            "keras-preprocessing- | 33 KB     | : 100% 1.0/1 [00:00<00:00, 31.94it/s]\n",
            "jedi-0.17.0          | 807 KB    | : 100% 1.0/1 [00:00<00:00,  3.51it/s]\n",
            "kiwisolver-1.2.0     | 87 KB     | : 100% 1.0/1 [00:00<00:00, 25.85it/s]\n",
            "xorg-renderproto-0.1 | 8 KB      | : 100% 1.0/1 [00:00<00:00, 48.74it/s]\n",
            "jupyter_client-6.1.3 | 74 KB     | : 100% 1.0/1 [00:00<00:00, 18.75it/s]\n",
            "numpy-1.18.1         | 5.2 MB    | : 100% 1.0/1 [00:01<00:00,  1.04s/it]\n",
            "ptyprocess-0.6.0     | 15 KB     | : 100% 1.0/1 [00:00<00:00, 37.54it/s]\n",
            "libuuid-2.32.1       | 26 KB     | : 100% 1.0/1 [00:00<00:00, 33.98it/s]\n",
            "mock-3.0.5           | 44 KB     | : 100% 1.0/1 [00:00<00:00, 34.10it/s]\n",
            "qt-5.9.7             | 85.9 MB   | : 100% 1.0/1 [00:14<00:00, 14.34s/it]               \n",
            "glib-2.64.2          | 3.4 MB    | : 100% 1.0/1 [00:00<00:00,  1.32it/s]\n",
            "pyqt-5.9.2           | 5.7 MB    | : 100% 1.0/1 [00:01<00:00,  1.20s/it]\n",
            "py-boost-1.67.0      | 278 KB    | : 100% 1.0/1 [00:00<00:00, 11.78it/s]\n",
            "tensorboard-1.14.0   | 3.2 MB    | : 100% 1.0/1 [00:00<00:00,  2.19it/s]\n",
            "cupti-10.1.168       | 1.4 MB    | : 100% 1.0/1 [00:00<00:00,  8.95it/s]\n",
            "rdkit-2020.03.1.0    | 24.5 MB   | : 100% 1.0/1 [00:09<00:00,  9.17s/it]               \n",
            "_py-xgboost-mutex-2. | 8 KB      | : 100% 1.0/1 [00:00<00:00, 35.01it/s]\n",
            "tornado-6.0.4        | 639 KB    | : 100% 1.0/1 [00:00<00:00,  5.83it/s]\n",
            "parso-0.7.0          | 67 KB     | : 100% 1.0/1 [00:00<00:00, 22.94it/s]\n",
            "libtiff-4.1.0        | 668 KB    | : 100% 1.0/1 [00:00<00:00,  7.67it/s]\n",
            "prometheus_client-0. | 38 KB     | : 100% 1.0/1 [00:00<00:00, 32.79it/s]\n",
            "libxcb-1.13          | 396 KB    | : 100% 1.0/1 [00:00<00:00,  7.68it/s]\n",
            "wrapt-1.12.1         | 46 KB     | : 100% 1.0/1 [00:00<00:00, 33.60it/s]\n",
            "ipython-7.13.0       | 1.1 MB    | : 100% 1.0/1 [00:00<00:00,  3.46it/s]\n",
            "cycler-0.10.0        | 9 KB      | : 100% 1.0/1 [00:00<00:00, 34.48it/s]\n",
            "grpcio-1.23.0        | 1.1 MB    | : 100% 1.0/1 [00:00<00:00,  4.74it/s]\n",
            "pygments-2.6.1       | 683 KB    | : 100% 1.0/1 [00:00<00:00,  6.05it/s]\n",
            "xorg-libxext-1.3.4   | 51 KB     | : 100% 1.0/1 [00:00<00:00, 25.37it/s]\n",
            "bleach-3.1.4         | 111 KB    | : 100% 1.0/1 [00:00<00:00, 18.54it/s]\n",
            "jupyter_console-6.1. | 21 KB     | : 100% 1.0/1 [00:00<00:00, 43.51it/s]\n",
            "decorator-4.4.2      | 11 KB     | : 100% 1.0/1 [00:00<00:00, 40.78it/s]\n",
            "werkzeug-1.0.1       | 239 KB    | : 100% 1.0/1 [00:00<00:00, 15.69it/s]\n",
            "lzo-2.10             | 319 KB    | : 100% 1.0/1 [00:00<00:00, 12.27it/s]\n",
            "xorg-kbproto-1.0.7   | 26 KB     | : 100% 1.0/1 [00:00<00:00, 40.77it/s]\n",
            "libblas-3.8.0        | 10 KB     | : 100% 1.0/1 [00:00<00:00, 37.14it/s]\n",
            "gast-0.3.3           | 12 KB     | : 100% 1.0/1 [00:00<00:00, 47.52it/s]\n",
            "pyparsing-2.4.7      | 60 KB     | : 100% 1.0/1 [00:00<00:00, 34.45it/s]\n",
            "xorg-libice-1.0.10   | 57 KB     | : 100% 1.0/1 [00:00<00:00, 39.02it/s]\n",
            "keras-applications-1 | 30 KB     | : 100% 1.0/1 [00:00<00:00, 37.11it/s]\n",
            "sip-4.19.8           | 290 KB    | : 100% 1.0/1 [00:00<00:00, 13.74it/s]\n",
            "libcblas-3.8.0       | 10 KB     | : 100% 1.0/1 [00:00<00:00, 41.56it/s]\n",
            "fontconfig-2.13.1    | 327 KB    | : 100% 1.0/1 [00:00<00:00, 11.38it/s]\n",
            "tensorflow-base-1.14 | 146.3 MB  | : 100% 1.0/1 [00:26<00:00, 26.13s/it]               \n",
            "deepchem-gpu-2.3.0   | 2.1 MB    | : 100% 1.0/1 [00:03<00:00, 18.64s/it]               \n",
            "numexpr-2.7.1        | 197 KB    | : 100% 1.0/1 [00:00<00:00, 16.64it/s]\n",
            "gettext-0.19.8.1     | 3.6 MB    | : 100% 1.0/1 [00:00<00:00,  1.28it/s]\n",
            "dbus-1.13.6          | 602 KB    | : 100% 1.0/1 [00:00<00:00,  7.91it/s]\n",
            "certifi-2020.4.5.1   | 151 KB    | : 100% 1.0/1 [00:00<00:00, 19.28it/s]\n",
            "xorg-libsm-1.2.3     | 25 KB     | : 100% 1.0/1 [00:00<00:00, 36.31it/s]\n",
            "icu-58.2             | 22.6 MB   | : 100% 1.0/1 [00:03<00:00,  3.40s/it]               \n",
            "xorg-xproto-7.0.31   | 72 KB     | : 100% 1.0/1 [00:00<00:00, 28.46it/s]\n",
            "libsodium-1.0.17     | 330 KB    | : 100% 1.0/1 [00:00<00:00, 12.69it/s]\n",
            "freetype-2.10.1      | 877 KB    | : 100% 1.0/1 [00:00<00:00,  6.07it/s]\n",
            "nbconvert-5.6.1      | 487 KB    | : 100% 1.0/1 [00:00<00:00,  8.26it/s]\n",
            "importlib-metadata-1 | 43 KB     | : 100% 1.0/1 [00:00<00:00, 25.92it/s]\n",
            "Preparing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "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\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\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'Enabling notebook extension jupyter-js-widgets/extension...\\nPaths used for configuration of notebook: \\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.json\\nPaths used for configuration of notebook: \\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\\n      - Validating: \\x1b[32mOK\\x1b[0m\\nPaths used for configuration of notebook: \\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\\n    \\t/usr/local/etc/jupyter/nbconfig/notebook.json\\n'\n",
            "\b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\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": "code",
      "metadata": {
        "id": "g5SoI1sm06x5",
        "colab_type": "code",
        "outputId": "0e573da5-922a-4a9f-a026-96bc9e51cbe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(\"tf.__version__: %s\" % str(tf.__version__))\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if not device_name:\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.__version__: 2.2.0-rc3\n",
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yn4NG37B2HGT",
        "colab_type": "code",
        "outputId": "81e9ece3-f00b-48b6-91a5-efd2135465f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!git clone https://github.com/deepchem/deepchem.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'deepchem'...\n",
            "remote: Enumerating objects: 514, done.\u001b[K\n",
            "remote: Counting objects: 100% (514/514), done.\u001b[K\n",
            "remote: Compressing objects: 100% (336/336), done.\u001b[K\n",
            "remote: Total 30445 (delta 266), reused 349 (delta 174), pack-reused 29931\u001b[K\n",
            "Receiving objects: 100% (30445/30445), 437.76 MiB | 14.85 MiB/s, done.\n",
            "Resolving deltas: 100% (22383/22383), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF9zvLpJ2bMb",
        "colab_type": "code",
        "outputId": "71487338-93d9-4729-e372-f42fd807f2b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "%cd deepchem/\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/deepchem\n",
            "CODE_OF_CONDUCT.md  datasets  Dockerfile  LICENSE\t   README.md  setup.py\n",
            "contrib\t\t    deepchem  docs\t  MANIFEST.in\t   scripts    website\n",
            "CONTRIBUTING.md     devtools  examples\t  PUBLICATIONS.md  setup.cfg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nkXFWmg5VfO",
        "colab_type": "code",
        "outputId": "503cc964-7adf-428a-e9c9-0b48924e3dd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!python setup.py install"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |███                             | 10 kB 30.9 MB/s eta 0:00:01\r\u001b[K     |██████                          | 20 kB 36.5 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 30 kB 28.6 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 40 kB 13.2 MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 51 kB 11.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 61 kB 10.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 71 kB 10.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 81 kB 11.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 92 kB 11.8 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▊  | 102 kB 11.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 110 kB 11.3 MB/s \n",
            "\u001b[?25hrunning install\n",
            "[pbr] Writing ChangeLog\n",
            "[pbr] Generating ChangeLog\n",
            "[pbr] ChangeLog complete (0.1s)\n",
            "[pbr] Generating AUTHORS\n",
            "[pbr] AUTHORS complete (0.2s)\n",
            "running build\n",
            "running build_py\n",
            "creating build\n",
            "creating build/lib\n",
            "creating build/lib/deepchem\n",
            "creating build/lib/deepchem/utils\n",
            "creating build/lib/deepchem/utils/test\n",
            "copying deepchem/utils/test/test_seq.py -> build/lib/deepchem/utils/test\n",
            "copying deepchem/utils/test/test_generator_evaluator.py -> build/lib/deepchem/utils/test\n",
            "copying deepchem/utils/test/__init__.py -> build/lib/deepchem/utils/test\n",
            "copying deepchem/utils/test/test_rdkit_util.py -> build/lib/deepchem/utils/test\n",
            "creating build/lib/deepchem/dock\n",
            "creating build/lib/deepchem/dock/tests\n",
            "copying deepchem/dock/tests/test_pose_scoring.py -> build/lib/deepchem/dock/tests\n",
            "copying deepchem/dock/tests/test_binding_pocket.py -> build/lib/deepchem/dock/tests\n",
            "copying deepchem/dock/tests/test_pose_generation.py -> build/lib/deepchem/dock/tests\n",
            "copying deepchem/dock/tests/test_docking.py -> build/lib/deepchem/dock/tests\n",
            "copying deepchem/dock/tests/__init__.py -> build/lib/deepchem/dock/tests\n",
            "creating build/lib/deepchem/models\n",
            "copying deepchem/models/models.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/IRV.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/fcnet.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/layers.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/seqtoseq.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/callbacks.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/progressive_multitask.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/graph_models.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/cnn.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/chemnet_models.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/text_cnn.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/losses.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/multitask.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/optimizers.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/scscore.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/atomic_conv.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/__init__.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/keras_model.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/chemnet_layers.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/gan.py -> build/lib/deepchem/models\n",
            "copying deepchem/models/robust_multitask.py -> build/lib/deepchem/models\n",
            "creating build/lib/deepchem/data\n",
            "copying deepchem/data/datasets.py -> build/lib/deepchem/data\n",
            "copying deepchem/data/supports.py -> build/lib/deepchem/data\n",
            "copying deepchem/data/test_data_loader.py -> build/lib/deepchem/data\n",
            "copying deepchem/data/data_loader.py -> build/lib/deepchem/data\n",
            "copying deepchem/data/__init__.py -> build/lib/deepchem/data\n",
            "creating build/lib/deepchem/splits\n",
            "copying deepchem/splits/task_splitter.py -> build/lib/deepchem/splits\n",
            "copying deepchem/splits/test_scaffold_splitter.py -> build/lib/deepchem/splits\n",
            "copying deepchem/splits/splitters.py -> build/lib/deepchem/splits\n",
            "copying deepchem/splits/test_specified_index_splitter.py -> build/lib/deepchem/splits\n",
            "copying deepchem/splits/__init__.py -> build/lib/deepchem/splits\n",
            "creating build/lib/deepchem/trans\n",
            "creating build/lib/deepchem/trans/tests\n",
            "copying deepchem/trans/tests/test_transformers.py -> build/lib/deepchem/trans/tests\n",
            "copying deepchem/trans/tests/__init__.py -> build/lib/deepchem/trans/tests\n",
            "creating build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_load.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_drop.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_fasta_loader.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_reload.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_datasets.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_merge.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_data_loader.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_image_loader.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_image_dataset.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/__init__.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_support_generator.py -> build/lib/deepchem/data/tests\n",
            "copying deepchem/data/tests/test_shuffle.py -> build/lib/deepchem/data/tests\n",
            "creating build/lib/deepchem/feat\n",
            "creating build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_rdkit_grid_features.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_sdf_reader.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_smiles_featurizers.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_fingerprints.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_mol_graphs.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_features.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_basic.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_atomic_coordinates.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_graph_features.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_one_hot.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_coulomb_matrices.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_binding_pocket_features.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/test_convmol.py -> build/lib/deepchem/feat/tests\n",
            "copying deepchem/feat/tests/__init__.py -> build/lib/deepchem/feat/tests\n",
            "creating build/lib/deepchem/metrics\n",
            "copying deepchem/metrics/genomic_metrics.py -> build/lib/deepchem/metrics\n",
            "copying deepchem/metrics/__init__.py -> build/lib/deepchem/metrics\n",
            "copying deepchem/__init__.py -> build/lib/deepchem\n",
            "creating build/lib/deepchem/models/xgboost_models\n",
            "copying deepchem/models/xgboost_models/__init__.py -> build/lib/deepchem/models/xgboost_models\n",
            "creating build/lib/deepchem/hyper\n",
            "copying deepchem/hyper/grid_search.py -> build/lib/deepchem/hyper\n",
            "copying deepchem/hyper/__init__.py -> build/lib/deepchem/hyper\n",
            "copying deepchem/hyper/gaussian_process.py -> build/lib/deepchem/hyper\n",
            "copying deepchem/utils/genomics.py -> build/lib/deepchem/utils\n",
            "copying deepchem/utils/save.py -> build/lib/deepchem/utils\n",
            "copying deepchem/utils/rdkit_util.py -> build/lib/deepchem/utils\n",
            "copying deepchem/utils/evaluate.py -> build/lib/deepchem/utils\n",
            "copying deepchem/utils/mol_xyz_util.py -> build/lib/deepchem/utils\n",
            "copying deepchem/utils/__init__.py -> build/lib/deepchem/utils\n",
            "copying deepchem/utils/conformers.py -> build/lib/deepchem/utils\n",
            "creating build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_optimizers.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_reload.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_layers_from_config.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_singletask_to_multitask.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_multitask.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_seqtoseq.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_graph_models.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_layers.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_pretrained.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_textcnnmodel.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_predict.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/__init__.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_api.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_overfit.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_kerasmodel.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_scscore.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_chemnet_models.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_callbacks.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_generalize.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_atomic_conv.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_gan.py -> build/lib/deepchem/models/tests\n",
            "copying deepchem/models/tests/test_cnn.py -> build/lib/deepchem/models/tests\n",
            "creating build/lib/deepchem/molnet\n",
            "creating build/lib/deepchem/molnet/tests\n",
            "copying deepchem/molnet/tests/test_molnet.py -> build/lib/deepchem/molnet/tests\n",
            "copying deepchem/molnet/tests/test_dnasim.py -> build/lib/deepchem/molnet/tests\n",
            "copying deepchem/molnet/tests/__init__.py -> build/lib/deepchem/molnet/tests\n",
            "creating build/lib/deepchem/rl\n",
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            "byte-compiling /usr/local/lib/python3.7/site-packages/deepchem/metalearning/tests/test_maml.py to test_maml.cpython-37.pyc\n",
            "running install_egg_info\n",
            "Copying deepchem.egg-info to /usr/local/lib/python3.7/site-packages/deepchem-2.3.1.dev153-py3.7.egg-info\n",
            "running install_scripts\n",
            "/content/deepchem/.eggs/pbr-5.4.5-py3.7.egg/pbr/packaging.py:436: EasyInstallDeprecationWarning: Use get_header\n",
            "  header = easy_install.get_script_header(\"\", executable, is_wininst)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAqyRKzg5cTV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import deepchem as dc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYHN_QAX5d_Q",
        "colab_type": "code",
        "outputId": "39b6751b-c985-46c3-df28-79cef78e62f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "delaney_tasks, delaney_datasets, transformers = dc.molnet.load_delaney(\n",
        "    featurizer='GraphConv', split='index')                       \n",
        "train_dataset, valid_dataset, test_dataset = delaney_datasets "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.molnet.load_function.delaney_datasets:About to featurize Delaney dataset.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loading raw samples now.\n",
            "shard_size: 8192\n",
            "About to start loading CSV from /tmp/delaney-processed.csv\n",
            "Loading shard 1 of size 8192.\n",
            "Featurizing sample 0\n",
            "Featurizing sample 1000\n",
            "TIMING: featurizing shard 0 took 4.609 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.molnet.load_function.delaney_datasets:About to split dataset with index splitter.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "TIMING: dataset construction took 4.914 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 0.358 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 0.148 s\n",
            "Loading dataset from disk.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.molnet.load_function.delaney_datasets:About to transform data.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "TIMING: dataset construction took 0.153 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 0.330 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 0.042 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 0.040 s\n",
            "Loading dataset from disk.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yL5Hxzvm7CPs",
        "colab_type": "code",
        "outputId": "ee0446f2-9cfc-4c3a-b70f-bf70e2284c79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# Set up to Fit models                                                     \n",
        "metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean) \n",
        "\n",
        "n_atom_feat = 75                                                 \n",
        "batch_size = 64\n",
        "\n",
        "max_atoms_train = max([mol.get_num_atoms() for mol in train_dataset.X]) \n",
        "max_atoms_valid = max([mol.get_num_atoms() for mol in valid_dataset.X])\n",
        "max_atoms_test = max([mol.get_num_atoms() for mol in test_dataset.X]) \n",
        "max_atoms = max([max_atoms_train, max_atoms_valid, max_atoms_test])   \n",
        "\n",
        "reshard_size = 512\n",
        "transformer = dc.trans.DAGTransformer(max_atoms=max_atoms)       \n",
        "train_dataset.reshard(reshard_size)\n",
        "train_dataset = transformer.transform(train_dataset)             \n",
        "valid_dataset.reshard(reshard_size)                              \n",
        "valid_dataset = transformer.transform(valid_dataset)             "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TIMING: dataset construction took 0.397 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 7.228 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 0.064 s\n",
            "Loading dataset from disk.\n",
            "TIMING: dataset construction took 0.912 s\n",
            "Loading dataset from disk.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHJ3KzSy7RQ5",
        "colab_type": "code",
        "outputId": "7b384ab8-6ea0-4872-9831-de6a798d92cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import logging\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "\n",
        "model = dc.models.DAGModel(\n",
        "    len(delaney_tasks),\n",
        "    max_atoms=max_atoms,\n",
        "    n_atom_feat=n_atom_feat,\n",
        "    batch_size=batch_size,\n",
        "    learning_rate=1e-3,\n",
        "    use_queue=False,\n",
        "    mode='regression')\n",
        "\n",
        "# Fit trained model\n",
        "model.fit(train_dataset, nb_epoch=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "self.dropout\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.models.keras_model:Ending global_step 100: Average loss 1.23786\n",
            "INFO:deepchem.models.keras_model:Ending global_step 150: Average loss 0.896657\n",
            "INFO:deepchem.models.keras_model:TIMING: model fitting took 71.182 s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8966574859619141"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "do2GPYpe8dMg",
        "colab_type": "code",
        "outputId": "4c55eaea-ffe7-4f2c-f6f1-d7831c6f092f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(\"Evaluating model\")                                        \n",
        "train_scores = model.evaluate(train_dataset, [metric], transformers)      \n",
        "valid_scores = model.evaluate(valid_dataset, [metric], transformers)    \n",
        "\n",
        "print(\"Train scores\")                                            \n",
        "print(train_scores)\n",
        "\n",
        "print(\"Validation scores\")                                       \n",
        "print(valid_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating model\n",
            "computed_metrics: [0.31721464024384016]\n",
            "computed_metrics: [0.3231992121140615]\n",
            "Train scores\n",
            "{'mean-pearson_r2_score': 0.31721464024384016}\n",
            "Validation scores\n",
            "{'mean-pearson_r2_score': 0.3231992121140615}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fff7V3qDp-d",
        "colab_type": "code",
        "outputId": "c49f53f7-1c68-48c7-a098-799e0e16a7b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "# Fit trained model\n",
        "model.fit(train_dataset, nb_epoch=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.models.keras_model:Ending global_step 200: Average loss 0.866\n",
            "INFO:deepchem.models.keras_model:Ending global_step 300: Average loss 0.820278\n",
            "INFO:deepchem.models.keras_model:Ending global_step 400: Average loss 0.781647\n",
            "INFO:deepchem.models.keras_model:Ending global_step 500: Average loss 0.729285\n",
            "INFO:deepchem.models.keras_model:Ending global_step 600: Average loss 0.695503\n",
            "INFO:deepchem.models.keras_model:Ending global_step 700: Average loss 0.671432\n",
            "INFO:deepchem.models.keras_model:Ending global_step 800: Average loss 0.630609\n",
            "INFO:deepchem.models.keras_model:Ending global_step 900: Average loss 0.60987\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1000: Average loss 0.591561\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1100: Average loss 0.57366\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1200: Average loss 0.549305\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1300: Average loss 0.543188\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1400: Average loss 0.527566\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1500: Average loss 0.511572\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1600: Average loss 0.508754\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1650: Average loss 0.492719\n",
            "INFO:deepchem.models.keras_model:TIMING: model fitting took 341.043 s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.49271862030029295"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCbkFw2aD0pR",
        "colab_type": "code",
        "outputId": "4b0b0497-4a37-4a1f-a3e8-3cbdc2800514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(\"Evaluating model\")                                        \n",
        "train_scores = model.evaluate(train_dataset, [metric], transformers)      \n",
        "valid_scores = model.evaluate(valid_dataset, [metric], transformers)    \n",
        "\n",
        "print(\"Train scores\")                                            \n",
        "print(train_scores)\n",
        "\n",
        "print(\"Validation scores\")                                       \n",
        "print(valid_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating model\n",
            "computed_metrics: [0.5038818875008052]\n",
            "computed_metrics: [0.4880202504804988]\n",
            "Train scores\n",
            "{'mean-pearson_r2_score': 0.5038818875008052}\n",
            "Validation scores\n",
            "{'mean-pearson_r2_score': 0.4880202504804988}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dw2n0sTvFOPo",
        "colab_type": "code",
        "outputId": "89cca722-7704-41c6-f799-eb08bfa6d860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Fit trained model\n",
        "model.fit(train_dataset, nb_epoch=500)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.models.keras_model:Ending global_step 1700: Average loss 0.499869\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1800: Average loss 0.485994\n",
            "INFO:deepchem.models.keras_model:Ending global_step 1900: Average loss 0.486522\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2000: Average loss 0.469998\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2100: Average loss 0.467689\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2200: Average loss 0.468983\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2300: Average loss 0.452751\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2400: Average loss 0.450793\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2500: Average loss 0.451137\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2600: Average loss 0.441291\n",
            "INFO:deepchem.models.keras_model:Ending global_step 2700: Average loss 0.438804\n",
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            "INFO:deepchem.models.keras_model:Ending global_step 9150: Average loss 0.355185\n",
            "INFO:deepchem.models.keras_model:TIMING: model fitting took 1699.740 s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.35518482208251956"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42BYxShvFX70",
        "colab_type": "code",
        "outputId": "ef3b631a-80b1-4cda-dc88-edabd5ea3103",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(\"Evaluating model\")                                        \n",
        "train_scores = model.evaluate(train_dataset, [metric], transformers)      \n",
        "valid_scores = model.evaluate(valid_dataset, [metric], transformers)    \n",
        "\n",
        "print(\"Train scores\")                                            \n",
        "print(train_scores)\n",
        "\n",
        "print(\"Validation scores\")                                       \n",
        "print(valid_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating model\n",
            "computed_metrics: [0.6213904009812273]\n",
            "computed_metrics: [0.601818465447746]\n",
            "Train scores\n",
            "{'mean-pearson_r2_score': 0.6213904009812273}\n",
            "Validation scores\n",
            "{'mean-pearson_r2_score': 0.601818465447746}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyp1hlHwMTbj",
        "colab_type": "code",
        "outputId": "4ca6ba12-e5aa-4fcd-fc3e-9324842a445c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Fit trained model\n",
        "model.fit(train_dataset, nb_epoch=500)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.models.keras_model:Ending global_step 9200: Average loss 0.3597\n",
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            "INFO:deepchem.models.keras_model:Ending global_step 16650: Average loss 0.331564\n",
            "INFO:deepchem.models.keras_model:TIMING: model fitting took 1691.459 s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3315643310546875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCCO4IkxMZdY",
        "colab_type": "code",
        "outputId": "9855d844-d4e7-44d3-e7f2-478650d52f87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(\"Evaluating model\")                                        \n",
        "train_scores = model.evaluate(train_dataset, [metric], transformers)      \n",
        "valid_scores = model.evaluate(valid_dataset, [metric], transformers)    \n",
        "\n",
        "print(\"Train scores\")                                            \n",
        "print(train_scores)\n",
        "\n",
        "print(\"Validation scores\")                                       \n",
        "print(valid_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating model\n",
            "computed_metrics: [0.6423596529742706]\n",
            "computed_metrics: [0.6184095216778696]\n",
            "Train scores\n",
            "{'mean-pearson_r2_score': 0.6423596529742706}\n",
            "Validation scores\n",
            "{'mean-pearson_r2_score': 0.6184095216778696}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCCLFNSFZchT",
        "colab_type": "code",
        "outputId": "24373264-b8a9-4d63-cf5c-f6004c5cb899",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Fit trained model\n",
        "model.fit(train_dataset, nb_epoch=2000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:deepchem.models.keras_model:Ending global_step 16700: Average loss 0.337587\n",
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            "INFO:deepchem.models.keras_model:Ending global_step 46650: Average loss 0.309091\n",
            "INFO:deepchem.models.keras_model:TIMING: model fitting took 6756.179 s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3090912628173828"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pvdl3GunZixH",
        "colab_type": "code",
        "outputId": "ee775e53-b1a7-462d-94da-835a39cb20ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(\"Evaluating model\")                                        \n",
        "train_scores = model.evaluate(train_dataset, [metric], transformers)      \n",
        "valid_scores = model.evaluate(valid_dataset, [metric], transformers)    \n",
        "\n",
        "print(\"Train scores\")                                            \n",
        "print(train_scores)\n",
        "\n",
        "print(\"Validation scores\")                                       \n",
        "print(valid_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating model\n",
            "computed_metrics: [0.6659355251890768]\n",
            "computed_metrics: [0.6424493435009926]\n",
            "Train scores\n",
            "{'mean-pearson_r2_score': 0.6659355251890768}\n",
            "Validation scores\n",
            "{'mean-pearson_r2_score': 0.6424493435009926}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnEe180E0cSe",
        "colab_type": "code",
        "outputId": "1fb7ec66-a1be-4466-ff89-1724e85872f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Fit trained model\n",
        "model.fit(train_dataset, nb_epoch=5000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CNGVMxY0jx7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}