OpenChem_Implementation_water_solubility.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parthajitp/OpenChem-Implementation-for-Prediction-Solubility/blob/main/OpenChem_Implementation_water_solubility.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOh9kfYCNfXM",
        "outputId": "bd8d08e2-6ac6-4a08-d4ba-0d26ca5e5f62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ]
        }
      ],
      "source": [
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEyhGCdVaPQi",
        "outputId": "03d099d7-1b1c-4306-8e47-e37c20ae1a8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'OpenChem'...\n",
            "remote: Enumerating objects: 2179, done.\u001b[K\n",
            "remote: Counting objects: 100% (129/129), done.\u001b[K\n",
            "remote: Compressing objects: 100% (62/62), done.\u001b[K\n",
            "remote: Total 2179 (delta 62), reused 126 (delta 61), pack-reused 2050\u001b[K\n",
            "Receiving objects: 100% (2179/2179), 166.23 MiB | 27.85 MiB/s, done.\n",
            "Resolving deltas: 100% (1186/1186), done.\n",
            "Checking out files: 100% (259/259), done.\n",
            "/content/OpenChem\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/Mariewelt/OpenChem.git\n",
        "%cd OpenChem"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBXb4JmObQms",
        "outputId": "5089159a-b201-4fef-a5cf-1f51fae0d17c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 4.9.2\n",
            "  latest version: 4.13.0\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local/envs/OpenChem\n",
            "\n",
            "  added / updated specs:\n",
            "    - python=3.7\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _openmp_mutex-4.5          |            2_gnu          23 KB  conda-forge\n",
            "    ca-certificates-2022.5.18.1|       ha878542_0         144 KB  conda-forge\n",
            "    ld_impl_linux-64-2.36.1    |       hea4e1c9_2         667 KB  conda-forge\n",
            "    libffi-3.4.2               |       h7f98852_5          57 KB  conda-forge\n",
            "    libgcc-ng-12.1.0           |      h8d9b700_16         940 KB  conda-forge\n",
            "    libgomp-12.1.0             |      h8d9b700_16         459 KB  conda-forge\n",
            "    libnsl-2.0.0               |       h7f98852_0          31 KB  conda-forge\n",
            "    libstdcxx-ng-12.1.0        |      ha89aaad_16         4.3 MB  conda-forge\n",
            "    libzlib-1.2.12             |       h166bdaf_0          62 KB  conda-forge\n",
            "    ncurses-6.3                |       h27087fc_1        1002 KB  conda-forge\n",
            "    openssl-3.0.3              |       h166bdaf_0         2.9 MB  conda-forge\n",
            "    pip-22.1.2                 |     pyhd8ed1ab_0         1.5 MB  conda-forge\n",
            "    python-3.7.12              |hf930737_100_cpython        57.3 MB  conda-forge\n",
            "    python_abi-3.7             |          2_cp37m           4 KB  conda-forge\n",
            "    readline-8.1               |       h46c0cb4_0         295 KB  conda-forge\n",
            "    setuptools-62.3.2          |   py37h89c1867_0         1.4 MB  conda-forge\n",
            "    sqlite-3.38.5              |       h4ff8645_0         1.5 MB  conda-forge\n",
            "    tk-8.6.12                  |       h27826a3_0         3.3 MB  conda-forge\n",
            "    wheel-0.37.1               |     pyhd8ed1ab_0          31 KB  conda-forge\n",
            "    zlib-1.2.12                |       h166bdaf_0          90 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        75.9 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge\n",
            "  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_gnu\n",
            "  ca-certificates    conda-forge/linux-64::ca-certificates-2022.5.18.1-ha878542_0\n",
            "  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.36.1-hea4e1c9_2\n",
            "  libffi             conda-forge/linux-64::libffi-3.4.2-h7f98852_5\n",
            "  libgcc-ng          conda-forge/linux-64::libgcc-ng-12.1.0-h8d9b700_16\n",
            "  libgomp            conda-forge/linux-64::libgomp-12.1.0-h8d9b700_16\n",
            "  libnsl             conda-forge/linux-64::libnsl-2.0.0-h7f98852_0\n",
            "  libstdcxx-ng       conda-forge/linux-64::libstdcxx-ng-12.1.0-ha89aaad_16\n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.12-h166bdaf_0\n",
            "  ncurses            conda-forge/linux-64::ncurses-6.3-h27087fc_1\n",
            "  openssl            conda-forge/linux-64::openssl-3.0.3-h166bdaf_0\n",
            "  pip                conda-forge/noarch::pip-22.1.2-pyhd8ed1ab_0\n",
            "  python             conda-forge/linux-64::python-3.7.12-hf930737_100_cpython\n",
            "  python_abi         conda-forge/linux-64::python_abi-3.7-2_cp37m\n",
            "  readline           conda-forge/linux-64::readline-8.1-h46c0cb4_0\n",
            "  setuptools         conda-forge/linux-64::setuptools-62.3.2-py37h89c1867_0\n",
            "  sqlite             conda-forge/linux-64::sqlite-3.38.5-h4ff8645_0\n",
            "  tk                 conda-forge/linux-64::tk-8.6.12-h27826a3_0\n",
            "  wheel              conda-forge/noarch::wheel-0.37.1-pyhd8ed1ab_0\n",
            "  xz                 conda-forge/linux-64::xz-5.2.5-h516909a_1\n",
            "  zlib               conda-forge/linux-64::zlib-1.2.12-h166bdaf_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "wheel-0.37.1         | 31 KB     | : 100% 1.0/1 [00:00<00:00, 23.21it/s]\n",
            "openssl-3.0.3        | 2.9 MB    | : 100% 1.0/1 [00:00<00:00,  2.18it/s]\n",
            "libzlib-1.2.12       | 62 KB     | : 100% 1.0/1 [00:00<00:00, 34.11it/s]\n",
            "_openmp_mutex-4.5    | 23 KB     | : 100% 1.0/1 [00:00<00:00, 35.90it/s]\n",
            "libgomp-12.1.0       | 459 KB    | : 100% 1.0/1 [00:00<00:00, 11.53it/s]\n",
            "python_abi-3.7       | 4 KB      | : 100% 1.0/1 [00:00<00:00, 37.68it/s]\n",
            "libffi-3.4.2         | 57 KB     | : 100% 1.0/1 [00:00<00:00, 29.35it/s]\n",
            "ncurses-6.3          | 1002 KB   | : 100% 1.0/1 [00:00<00:00,  2.80it/s]\n",
            "readline-8.1         | 295 KB    | : 100% 1.0/1 [00:00<00:00, 14.12it/s]\n",
            "libstdcxx-ng-12.1.0  | 4.3 MB    | : 100% 1.0/1 [00:00<00:00,  1.61it/s]\n",
            "ld_impl_linux-64-2.3 | 667 KB    | : 100% 1.0/1 [00:00<00:00,  6.98it/s]\n",
            "pip-22.1.2           | 1.5 MB    | : 100% 1.0/1 [00:00<00:00,  2.93it/s]\n",
            "setuptools-62.3.2    | 1.4 MB    | : 100% 1.0/1 [00:00<00:00,  2.98it/s]\n",
            "python-3.7.12        | 57.3 MB   | : 100% 1.0/1 [00:06<00:00,  6.70s/it]               \n",
            "ca-certificates-2022 | 144 KB    | : 100% 1.0/1 [00:00<00:00, 23.66it/s]\n",
            "zlib-1.2.12          | 90 KB     | : 100% 1.0/1 [00:00<00:00, 23.29it/s]\n",
            "sqlite-3.38.5        | 1.5 MB    | : 100% 1.0/1 [00:00<00:00,  4.45it/s]\n",
            "libgcc-ng-12.1.0     | 940 KB    | : 100% 1.0/1 [00:00<00:00,  7.17it/s]\n",
            "tk-8.6.12            | 3.3 MB    | : 100% 1.0/1 [00:00<00:00,  1.89it/s]\n",
            "libnsl-2.0.0         | 31 KB     | : 100% 1.0/1 [00:00<00:00, 41.21it/s]\n",
            "Preparing transaction: | \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\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\bdone\n",
            "#\n",
            "# To activate this environment, use\n",
            "#\n",
            "#     $ conda activate OpenChem\n",
            "#\n",
            "# To deactivate an active environment, use\n",
            "#\n",
            "#     $ conda deactivate\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!conda create --name OpenChem python=3.7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "D8sRwoIibUPq"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "source activate OpenChem"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!source activate OpenChem && conda env list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trEK_HfMjJpI",
        "outputId": "2bc9e0ff-07ef-4fe5-c5fd-b6719fce5dfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# conda environments:\n",
            "#\n",
            "base                     /usr/local\n",
            "OpenChem              *  /usr/local/envs/OpenChem\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PoRXMM9bhzC",
        "outputId": "dc64ef63-4d93-4c21-fc1c-e75a22e6099e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python version\n",
            "3.7.13 (default, Apr 24 2022, 01:04:09) \n",
            "[GCC 7.5.0]\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "print(\"Python version\")\n",
        "print (sys.version)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSZO_O3kbqgA",
        "outputId": "c839f356-4e24-408f-ea4b-37662af54ccd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\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\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - ipython\n",
            "    - mkl\n",
            "    - numpy\n",
            "    - pytest\n",
            "    - pytest-cov\n",
            "    - pyyaml\n",
            "    - scikit-learn\n",
            "    - scipy\n",
            "    - six\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _openmp_mutex-4.5          |       2_kmp_llvm           6 KB  conda-forge\n",
            "    attrs-21.4.0               |     pyhd8ed1ab_0          49 KB  conda-forge\n",
            "    backcall-0.2.0             |     pyh9f0ad1d_0          13 KB  conda-forge\n",
            "    backports-1.0              |             py_2           4 KB  conda-forge\n",
            "    backports.functools_lru_cache-1.6.4|     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    certifi-2022.5.18.1        |   py37h89c1867_0         150 KB  conda-forge\n",
            "    conda-4.13.0               |   py37h89c1867_1         989 KB  conda-forge\n",
            "    coverage-6.4.1             |   py37h540881e_0         245 KB  conda-forge\n",
            "    decorator-5.1.1            |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    importlib-metadata-4.11.4  |   py37h89c1867_0          33 KB  conda-forge\n",
            "    importlib_metadata-4.11.4  |       hd8ed1ab_0           4 KB  conda-forge\n",
            "    iniconfig-1.1.1            |     pyh9f0ad1d_0           8 KB  conda-forge\n",
            "    ipython-7.33.0             |   py37h89c1867_0         1.1 MB  conda-forge\n",
            "    jedi-0.18.1                |   py37h89c1867_1        1008 KB  conda-forge\n",
            "    joblib-1.1.0               |     pyhd8ed1ab_0         210 KB  conda-forge\n",
            "    libblas-3.9.0              |14_linux64_openblas          12 KB  conda-forge\n",
            "    libcblas-3.9.0             |14_linux64_openblas          12 KB  conda-forge\n",
            "    libgfortran-ng-12.1.0      |      h69a702a_16          23 KB  conda-forge\n",
            "    libgfortran5-12.1.0        |      hdcd56e2_16         1.8 MB  conda-forge\n",
            "    liblapack-3.9.0            |14_linux64_openblas          12 KB  conda-forge\n",
            "    libopenblas-0.3.20         |pthreads_h78a6416_0        10.1 MB  conda-forge\n",
            "    llvm-openmp-14.0.4         |       he0ac6c6_0         5.8 MB  conda-forge\n",
            "    matplotlib-inline-0.1.3    |     pyhd8ed1ab_0          11 KB  conda-forge\n",
            "    mkl-2020.4                 |     h726a3e6_304       215.6 MB  conda-forge\n",
            "    numpy-1.20.3               |   py37h3e96413_2         5.7 MB  conda-forge\n",
            "    openssl-1.1.1o             |       h166bdaf_0         2.1 MB  conda-forge\n",
            "    packaging-21.3             |     pyhd8ed1ab_0          36 KB  conda-forge\n",
            "    parso-0.8.3                |     pyhd8ed1ab_0          69 KB  conda-forge\n",
            "    pexpect-4.8.0              |     pyh9f0ad1d_2          47 KB  conda-forge\n",
            "    pickleshare-0.7.5          |          py_1003           9 KB  conda-forge\n",
            "    pluggy-1.0.0               |   py37h89c1867_3          25 KB  conda-forge\n",
            "    prompt-toolkit-3.0.29      |     pyha770c72_0         252 KB  conda-forge\n",
            "    ptyprocess-0.7.0           |     pyhd3deb0d_0          16 KB  conda-forge\n",
            "    py-1.11.0                  |     pyh6c4a22f_0          74 KB  conda-forge\n",
            "    pygments-2.12.0            |     pyhd8ed1ab_0         817 KB  conda-forge\n",
            "    pyparsing-3.0.9            |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    pytest-7.1.2               |   py37h89c1867_0         458 KB  conda-forge\n",
            "    pytest-cov-3.0.0           |     pyhd8ed1ab_0          21 KB  conda-forge\n",
            "    pyyaml-6.0                 |   py37h540881e_4         178 KB  conda-forge\n",
            "    scikit-learn-0.23.2        |   py37hddcf8d6_3         6.8 MB  conda-forge\n",
            "    scipy-1.5.3                |   py37h14a347d_0        19.1 MB  conda-forge\n",
            "    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge\n",
            "    threadpoolctl-3.1.0        |     pyh8a188c0_0          18 KB  conda-forge\n",
            "    toml-0.10.2                |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    tomli-2.0.1                |     pyhd8ed1ab_0          16 KB  conda-forge\n",
            "    traitlets-5.2.2.post1      |     pyhd8ed1ab_0          85 KB  conda-forge\n",
            "    typing_extensions-4.2.0    |     pyha770c72_1          27 KB  conda-forge\n",
            "    wcwidth-0.2.5              |     pyh9f0ad1d_2          33 KB  conda-forge\n",
            "    zipp-3.8.0                 |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       273.1 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  attrs              conda-forge/noarch::attrs-21.4.0-pyhd8ed1ab_0\n",
            "  backcall           conda-forge/noarch::backcall-0.2.0-pyh9f0ad1d_0\n",
            "  backports          conda-forge/noarch::backports-1.0-py_2\n",
            "  backports.functoo~ conda-forge/noarch::backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0\n",
            "  coverage           conda-forge/linux-64::coverage-6.4.1-py37h540881e_0\n",
            "  decorator          conda-forge/noarch::decorator-5.1.1-pyhd8ed1ab_0\n",
            "  importlib-metadata conda-forge/linux-64::importlib-metadata-4.11.4-py37h89c1867_0\n",
            "  importlib_metadata conda-forge/noarch::importlib_metadata-4.11.4-hd8ed1ab_0\n",
            "  iniconfig          conda-forge/noarch::iniconfig-1.1.1-pyh9f0ad1d_0\n",
            "  ipython            conda-forge/linux-64::ipython-7.33.0-py37h89c1867_0\n",
            "  jedi               conda-forge/linux-64::jedi-0.18.1-py37h89c1867_1\n",
            "  joblib             conda-forge/noarch::joblib-1.1.0-pyhd8ed1ab_0\n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-14_linux64_openblas\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-14_linux64_openblas\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-12.1.0-h69a702a_16\n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-12.1.0-hdcd56e2_16\n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-14_linux64_openblas\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.20-pthreads_h78a6416_0\n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.12-h166bdaf_0\n",
            "  llvm-openmp        conda-forge/linux-64::llvm-openmp-14.0.4-he0ac6c6_0\n",
            "  matplotlib-inline  conda-forge/noarch::matplotlib-inline-0.1.3-pyhd8ed1ab_0\n",
            "  mkl                conda-forge/linux-64::mkl-2020.4-h726a3e6_304\n",
            "  numpy              conda-forge/linux-64::numpy-1.20.3-py37h3e96413_2\n",
            "  packaging          conda-forge/noarch::packaging-21.3-pyhd8ed1ab_0\n",
            "  parso              conda-forge/noarch::parso-0.8.3-pyhd8ed1ab_0\n",
            "  pexpect            conda-forge/noarch::pexpect-4.8.0-pyh9f0ad1d_2\n",
            "  pickleshare        conda-forge/noarch::pickleshare-0.7.5-py_1003\n",
            "  pluggy             conda-forge/linux-64::pluggy-1.0.0-py37h89c1867_3\n",
            "  prompt-toolkit     conda-forge/noarch::prompt-toolkit-3.0.29-pyha770c72_0\n",
            "  ptyprocess         conda-forge/noarch::ptyprocess-0.7.0-pyhd3deb0d_0\n",
            "  py                 conda-forge/noarch::py-1.11.0-pyh6c4a22f_0\n",
            "  pygments           conda-forge/noarch::pygments-2.12.0-pyhd8ed1ab_0\n",
            "  pyparsing          conda-forge/noarch::pyparsing-3.0.9-pyhd8ed1ab_0\n",
            "  pytest             conda-forge/linux-64::pytest-7.1.2-py37h89c1867_0\n",
            "  pytest-cov         conda-forge/noarch::pytest-cov-3.0.0-pyhd8ed1ab_0\n",
            "  pyyaml             conda-forge/linux-64::pyyaml-6.0-py37h540881e_4\n",
            "  scikit-learn       conda-forge/linux-64::scikit-learn-0.23.2-py37hddcf8d6_3\n",
            "  scipy              conda-forge/linux-64::scipy-1.5.3-py37h14a347d_0\n",
            "  threadpoolctl      conda-forge/noarch::threadpoolctl-3.1.0-pyh8a188c0_0\n",
            "  toml               conda-forge/noarch::toml-0.10.2-pyhd8ed1ab_0\n",
            "  tomli              conda-forge/noarch::tomli-2.0.1-pyhd8ed1ab_0\n",
            "  traitlets          conda-forge/noarch::traitlets-5.2.2.post1-pyhd8ed1ab_0\n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-4.2.0-pyha770c72_1\n",
            "  wcwidth            conda-forge/noarch::wcwidth-0.2.5-pyh9f0ad1d_2\n",
            "  zipp               conda-forge/noarch::zipp-3.8.0-pyhd8ed1ab_0\n",
            "\n",
            "The following packages will be REMOVED:\n",
            "\n",
            "  libgomp-9.3.0-h2828fa1_18\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2020.12.5-ha878542_0 --> 2022.5.18.1-ha878542_0\n",
            "  certifi                          2020.12.5-py37h89c1867_1 --> 2022.5.18.1-py37h89c1867_0\n",
            "  conda                                4.9.2-py37h89c1867_0 --> 4.13.0-py37h89c1867_1\n",
            "  libgcc-ng                               9.3.0-h2828fa1_18 --> 12.1.0-h8d9b700_16\n",
            "  openssl                                 1.1.1j-h7f98852_0 --> 1.1.1o-h166bdaf_0\n",
            "  python_abi                                    3.7-1_cp37m --> 3.7-2_cp37m\n",
            "  six                                   1.15.0-pyh9f0ad1d_0 --> 1.16.0-pyh6c4a22f_0\n",
            "  zlib                                 1.2.11-h516909a_1010 --> 1.2.12-h166bdaf_0\n",
            "\n",
            "The following packages will be DOWNGRADED:\n",
            "\n",
            "  _openmp_mutex                                   4.5-1_gnu --> 4.5-2_kmp_llvm\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "typing_extensions-4. | 27 KB     | : 100% 1.0/1 [00:00<00:00, 17.32it/s]\n",
            "iniconfig-1.1.1      | 8 KB      | : 100% 1.0/1 [00:00<00:00, 43.84it/s]\n",
            "tomli-2.0.1          | 16 KB     | : 100% 1.0/1 [00:00<00:00, 38.45it/s]\n",
            "wcwidth-0.2.5        | 33 KB     | : 100% 1.0/1 [00:00<00:00, 25.42it/s]\n",
            "mkl-2020.4           | 215.6 MB  | : 100% 1.0/1 [00:35<00:00, 35.04s/it]               \n",
            "numpy-1.20.3         | 5.7 MB    | : 100% 1.0/1 [00:01<00:00,  1.06s/it]\n",
            "libblas-3.9.0        | 12 KB     | : 100% 1.0/1 [00:00<00:00, 32.82it/s]\n",
            "joblib-1.1.0         | 210 KB    | : 100% 1.0/1 [00:00<00:00, 12.38it/s]\n",
            "matplotlib-inline-0. | 11 KB     | : 100% 1.0/1 [00:00<00:00, 39.18it/s]\n",
            "pytest-7.1.2         | 458 KB    | : 100% 1.0/1 [00:00<00:00,  8.54it/s]\n",
            "ipython-7.33.0       | 1.1 MB    | : 100% 1.0/1 [00:00<00:00,  3.91it/s]\n",
            "certifi-2022.5.18.1  | 150 KB    | : 100% 1.0/1 [00:00<00:00, 23.76it/s]\n",
            "ptyprocess-0.7.0     | 16 KB     | : 100% 1.0/1 [00:00<00:00, 25.70it/s]\n",
            "packaging-21.3       | 36 KB     | : 100% 1.0/1 [00:00<00:00, 33.21it/s]\n",
            "zipp-3.8.0           | 12 KB     | : 100% 1.0/1 [00:00<00:00, 28.21it/s]\n",
            "traitlets-5.2.2.post | 85 KB     | : 100% 1.0/1 [00:00<00:00, 26.23it/s]\n",
            "decorator-5.1.1      | 12 KB     | : 100% 1.0/1 [00:00<00:00, 38.23it/s]\n",
            "importlib-metadata-4 | 33 KB     | : 100% 1.0/1 [00:00<00:00, 36.35it/s]\n",
            "conda-4.13.0         | 989 KB    | : 100% 1.0/1 [00:00<00:00,  4.79it/s]\n",
            "libgfortran-ng-12.1. | 23 KB     | : 100% 1.0/1 [00:00<00:00, 36.46it/s]\n",
            "six-1.16.0           | 14 KB     | : 100% 1.0/1 [00:00<00:00, 37.62it/s]\n",
            "parso-0.8.3          | 69 KB     | : 100% 1.0/1 [00:00<00:00, 23.63it/s]\n",
            "_openmp_mutex-4.5    | 6 KB      | : 100% 1.0/1 [00:00<00:00, 44.41it/s]\n",
            "backcall-0.2.0       | 13 KB     | : 100% 1.0/1 [00:00<00:00, 41.52it/s]\n",
            "scipy-1.5.3          | 19.1 MB   | : 100% 1.0/1 [00:03<00:00,  3.01s/it]\n",
            "libopenblas-0.3.20   | 10.1 MB   | : 100% 1.0/1 [00:01<00:00,  1.57s/it]               \n",
            "scikit-learn-0.23.2  | 6.8 MB    | : 100% 1.0/1 [00:01<00:00,  1.29s/it]\n",
            "pickleshare-0.7.5    | 9 KB      | : 100% 1.0/1 [00:00<00:00, 31.29it/s]\n",
            "pexpect-4.8.0        | 47 KB     | : 100% 1.0/1 [00:00<00:00, 34.79it/s]\n",
            "backports-1.0        | 4 KB      | : 100% 1.0/1 [00:00<00:00, 39.77it/s]\n",
            "pyparsing-3.0.9      | 79 KB     | : 100% 1.0/1 [00:00<00:00, 23.92it/s]\n",
            "pluggy-1.0.0         | 25 KB     | : 100% 1.0/1 [00:00<00:00, 35.99it/s]\n",
            "py-1.11.0            | 74 KB     | : 100% 1.0/1 [00:00<00:00, 24.82it/s]\n",
            "toml-0.10.2          | 18 KB     | : 100% 1.0/1 [00:00<00:00, 38.10it/s]\n",
            "pytest-cov-3.0.0     | 21 KB     | : 100% 1.0/1 [00:00<00:00, 34.83it/s]\n",
            "libgfortran5-12.1.0  | 1.8 MB    | : 100% 1.0/1 [00:00<00:00,  3.44it/s]\n",
            "pyyaml-6.0           | 178 KB    | : 100% 1.0/1 [00:00<00:00, 19.30it/s]\n",
            "coverage-6.4.1       | 245 KB    | : 100% 1.0/1 [00:00<00:00, 14.82it/s]\n",
            "importlib_metadata-4 | 4 KB      | : 100% 1.0/1 [00:00<00:00, 45.12it/s]\n",
            "llvm-openmp-14.0.4   | 5.8 MB    | : 100% 1.0/1 [00:00<00:00,  1.25it/s]\n",
            "prompt-toolkit-3.0.2 | 252 KB    | : 100% 1.0/1 [00:00<00:00, 12.76it/s]\n",
            "liblapack-3.9.0      | 12 KB     | : 100% 1.0/1 [00:00<00:00, 37.40it/s]\n",
            "libcblas-3.9.0       | 12 KB     | : 100% 1.0/1 [00:00<00:00, 44.24it/s]\n",
            "threadpoolctl-3.1.0  | 18 KB     | : 100% 1.0/1 [00:00<00:00, 40.28it/s]\n",
            "attrs-21.4.0         | 49 KB     | : 100% 1.0/1 [00:00<00:00, 34.73it/s]\n",
            "jedi-0.18.1          | 1008 KB   | : 100% 1.0/1 [00:00<00:00,  2.58it/s]\n",
            "backports.functools_ | 9 KB      | : 100% 1.0/1 [00:00<00:00, 42.07it/s]\n",
            "pygments-2.12.0      | 817 KB    | : 100% 1.0/1 [00:00<00:00,  5.43it/s]\n",
            "openssl-1.1.1o       | 2.1 MB    | : 100% 1.0/1 [00:00<00:00,  3.25it/s]\n",
            "Preparing transaction: | \b\b/ \b\b- \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install --yes --file requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAf5BAQFdCsw",
        "outputId": "08c9587f-3f17-46e6-a8fd-026b4bd3eb1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\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\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - rdkit\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    boost-1.74.0               |   py37h796e4cb_5         342 KB  conda-forge\n",
            "    boost-cpp-1.74.0           |       hc6e9bd1_3        16.3 MB  conda-forge\n",
            "    brotli-1.0.9               |       h166bdaf_7          18 KB  conda-forge\n",
            "    brotli-bin-1.0.9           |       h166bdaf_7          19 KB  conda-forge\n",
            "    cairo-1.16.0               |    h6cf1ce9_1008         1.5 MB  conda-forge\n",
            "    cffi-1.15.0                |   py37h036bc23_0         225 KB  conda-forge\n",
            "    cycler-0.11.0              |     pyhd8ed1ab_0          10 KB  conda-forge\n",
            "    expat-2.4.8                |       h27087fc_0         187 KB  conda-forge\n",
            "    fontconfig-2.14.0          |       h8e229c2_0         305 KB  conda-forge\n",
            "    fonttools-4.33.3           |   py37h540881e_0         1.6 MB  conda-forge\n",
            "    gettext-0.19.8.1           |    h73d1719_1008         3.6 MB  conda-forge\n",
            "    greenlet-1.1.2             |   py37hd23a5d3_2          90 KB  conda-forge\n",
            "    kiwisolver-1.4.2           |   py37h7cecad7_1          73 KB  conda-forge\n",
            "    libbrotlicommon-1.0.9      |       h166bdaf_7          65 KB  conda-forge\n",
            "    libbrotlidec-1.0.9         |       h166bdaf_7          33 KB  conda-forge\n",
            "    libbrotlienc-1.0.9         |       h166bdaf_7         287 KB  conda-forge\n",
            "    libglib-2.70.2             |       h174f98d_4         3.1 MB  conda-forge\n",
            "    libuuid-2.32.1             |    h7f98852_1000          28 KB  conda-forge\n",
            "    libxcb-1.13                |    h7f98852_1004         391 KB  conda-forge\n",
            "    matplotlib-base-3.5.2      |   py37hc347a89_0         7.3 MB  conda-forge\n",
            "    munkres-1.1.4              |     pyh9f0ad1d_0          12 KB  conda-forge\n",
            "    pandas-1.3.4               |   py37he8f5f7f_1        12.7 MB  conda-forge\n",
            "    pcre-8.45                  |       h9c3ff4c_0         253 KB  conda-forge\n",
            "    pixman-0.40.0              |       h36c2ea0_0         627 KB  conda-forge\n",
            "    pthread-stubs-0.4          |    h36c2ea0_1001           5 KB  conda-forge\n",
            "    pycairo-1.21.0             |   py37h0afab05_1         116 KB  conda-forge\n",
            "    python-3.7.12              |hb7a2778_100_cpython        57.3 MB  conda-forge\n",
            "    python-dateutil-2.8.2      |     pyhd8ed1ab_0         240 KB  conda-forge\n",
            "    pytz-2022.1                |     pyhd8ed1ab_0         242 KB  conda-forge\n",
            "    rdkit-2022.03.2            |   py37hc52db9c_1        39.4 MB  conda-forge\n",
            "    reportlab-3.5.68           |   py37h69800bb_1         2.4 MB  conda-forge\n",
            "    sqlalchemy-1.4.37          |   py37h540881e_0         2.3 MB  conda-forge\n",
            "    typing-extensions-4.2.0    |       hd8ed1ab_1           8 KB  conda-forge\n",
            "    unicodedata2-14.0.0        |   py37h540881e_1         496 KB  conda-forge\n",
            "    xorg-kbproto-1.0.7         |    h7f98852_1002          27 KB  conda-forge\n",
            "    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n",
            "    xorg-libsm-1.2.3           |    hd9c2040_1000          26 KB  conda-forge\n",
            "    xorg-libx11-1.7.2          |       h7f98852_0         941 KB  conda-forge\n",
            "    xorg-libxau-1.0.9          |       h7f98852_0          13 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h7f98852_0          19 KB  conda-forge\n",
            "    xorg-libxext-1.3.4         |       h7f98852_1          54 KB  conda-forge\n",
            "    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n",
            "    xorg-renderproto-0.11.1    |    h7f98852_1002           9 KB  conda-forge\n",
            "    xorg-xextproto-7.3.0       |    h7f98852_1002          28 KB  conda-forge\n",
            "    xorg-xproto-7.0.31         |    h7f98852_1007          73 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       152.9 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  boost              conda-forge/linux-64::boost-1.74.0-py37h796e4cb_5\n",
            "  boost-cpp          conda-forge/linux-64::boost-cpp-1.74.0-hc6e9bd1_3\n",
            "  brotli             conda-forge/linux-64::brotli-1.0.9-h166bdaf_7\n",
            "  brotli-bin         conda-forge/linux-64::brotli-bin-1.0.9-h166bdaf_7\n",
            "  cairo              conda-forge/linux-64::cairo-1.16.0-h6cf1ce9_1008\n",
            "  cycler             conda-forge/noarch::cycler-0.11.0-pyhd8ed1ab_0\n",
            "  expat              conda-forge/linux-64::expat-2.4.8-h27087fc_0\n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.14.0-h8e229c2_0\n",
            "  fonttools          conda-forge/linux-64::fonttools-4.33.3-py37h540881e_0\n",
            "  gettext            conda-forge/linux-64::gettext-0.19.8.1-h73d1719_1008\n",
            "  greenlet           conda-forge/linux-64::greenlet-1.1.2-py37hd23a5d3_2\n",
            "  kiwisolver         conda-forge/linux-64::kiwisolver-1.4.2-py37h7cecad7_1\n",
            "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.0.9-h166bdaf_7\n",
            "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.0.9-h166bdaf_7\n",
            "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.0.9-h166bdaf_7\n",
            "  libglib            conda-forge/linux-64::libglib-2.70.2-h174f98d_4\n",
            "  libnsl             conda-forge/linux-64::libnsl-2.0.0-h7f98852_0\n",
            "  libuuid            conda-forge/linux-64::libuuid-2.32.1-h7f98852_1000\n",
            "  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1004\n",
            "  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.5.2-py37hc347a89_0\n",
            "  munkres            conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0\n",
            "  pandas             conda-forge/linux-64::pandas-1.3.4-py37he8f5f7f_1\n",
            "  pcre               conda-forge/linux-64::pcre-8.45-h9c3ff4c_0\n",
            "  pixman             conda-forge/linux-64::pixman-0.40.0-h36c2ea0_0\n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001\n",
            "  pycairo            conda-forge/linux-64::pycairo-1.21.0-py37h0afab05_1\n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0\n",
            "  pytz               conda-forge/noarch::pytz-2022.1-pyhd8ed1ab_0\n",
            "  rdkit              conda-forge/linux-64::rdkit-2022.03.2-py37hc52db9c_1\n",
            "  reportlab          conda-forge/linux-64::reportlab-3.5.68-py37h69800bb_1\n",
            "  sqlalchemy         conda-forge/linux-64::sqlalchemy-1.4.37-py37h540881e_0\n",
            "  typing-extensions  conda-forge/noarch::typing-extensions-4.2.0-hd8ed1ab_1\n",
            "  unicodedata2       conda-forge/linux-64::unicodedata2-14.0.0-py37h540881e_1\n",
            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h7f98852_1002\n",
            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0\n",
            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000\n",
            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.7.2-h7f98852_0\n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.9-h7f98852_0\n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0\n",
            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h7f98852_1\n",
            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003\n",
            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h7f98852_1002\n",
            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h7f98852_1002\n",
            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h7f98852_1007\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  cffi                                1.14.5-py37hc58025e_0 --> 1.15.0-py37h036bc23_0\n",
            "  libffi                                     3.3-h58526e2_2 --> 3.4.2-h7f98852_5\n",
            "  libstdcxx-ng                            9.3.0-h6de172a_18 --> 12.1.0-ha89aaad_16\n",
            "  ncurses                                    6.2-h58526e2_4 --> 6.3-h27087fc_1\n",
            "  python                        3.7.10-hffdb5ce_100_cpython --> 3.7.12-hb7a2778_100_cpython\n",
            "  readline                                   8.0-he28a2e2_2 --> 8.1-h46c0cb4_0\n",
            "  sqlite                                  3.34.0-h74cdb3f_0 --> 3.38.5-h4ff8645_0\n",
            "  tk                                      8.6.10-h21135ba_1 --> 8.6.12-h27826a3_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "xorg-libice-1.0.10   | 58 KB     | : 100% 1.0/1 [00:00<00:00, 19.32it/s]\n",
            "boost-cpp-1.74.0     | 16.3 MB   | : 100% 1.0/1 [00:04<00:00,  4.97s/it]               \n",
            "xorg-libxdmcp-1.1.3  | 19 KB     | : 100% 1.0/1 [00:00<00:00, 29.76it/s]\n",
            "libxcb-1.13          | 391 KB    | : 100% 1.0/1 [00:00<00:00,  8.26it/s]\n",
            "libbrotlidec-1.0.9   | 33 KB     | : 100% 1.0/1 [00:00<00:00, 38.79it/s]\n",
            "python-3.7.12        | 57.3 MB   | : 100% 1.0/1 [00:06<00:00,  6.72s/it]               \n",
            "unicodedata2-14.0.0  | 496 KB    | : 100% 1.0/1 [00:00<00:00, 12.06it/s]\n",
            "libbrotlicommon-1.0. | 65 KB     | : 100% 1.0/1 [00:00<00:00, 24.66it/s]\n",
            "cycler-0.11.0        | 10 KB     | : 100% 1.0/1 [00:00<00:00, 37.28it/s]\n",
            "brotli-bin-1.0.9     | 19 KB     | : 100% 1.0/1 [00:00<00:00, 26.65it/s]\n",
            "xorg-libx11-1.7.2    | 941 KB    | : 100% 1.0/1 [00:00<00:00,  6.05it/s]\n",
            "xorg-libxext-1.3.4   | 54 KB     | : 100% 1.0/1 [00:00<00:00, 27.08it/s]\n",
            "reportlab-3.5.68     | 2.4 MB    | : 100% 1.0/1 [00:00<00:00,  2.58it/s]\n",
            "xorg-renderproto-0.1 | 9 KB      | : 100% 1.0/1 [00:00<00:00, 34.18it/s]\n",
            "kiwisolver-1.4.2     | 73 KB     | : 100% 1.0/1 [00:00<00:00, 19.09it/s]\n",
            "matplotlib-base-3.5. | 7.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.13s/it]\n",
            "pcre-8.45            | 253 KB    | : 100% 1.0/1 [00:00<00:00, 16.13it/s]\n",
            "xorg-libsm-1.2.3     | 26 KB     | : 100% 1.0/1 [00:00<00:00, 35.24it/s]\n",
            "pycairo-1.21.0       | 116 KB    | : 100% 1.0/1 [00:00<00:00, 25.04it/s]\n",
            "xorg-libxau-1.0.9    | 13 KB     | : 100% 1.0/1 [00:00<00:00, 38.21it/s]\n",
            "expat-2.4.8          | 187 KB    | : 100% 1.0/1 [00:00<00:00, 17.87it/s]\n",
            "gettext-0.19.8.1     | 3.6 MB    | : 100% 1.0/1 [00:00<00:00,  1.32it/s]\n",
            "greenlet-1.1.2       | 90 KB     | : 100% 1.0/1 [00:00<00:00, 24.05it/s]\n",
            "boost-1.74.0         | 342 KB    | : 100% 1.0/1 [00:00<00:00,  9.31it/s]\n",
            "xorg-xextproto-7.3.0 | 28 KB     | : 100% 1.0/1 [00:00<00:00, 31.91it/s]\n",
            "xorg-libxrender-0.9. | 32 KB     | : 100% 1.0/1 [00:00<00:00, 34.32it/s]\n",
            "xorg-kbproto-1.0.7   | 27 KB     | : 100% 1.0/1 [00:00<00:00, 33.77it/s]\n",
            "cffi-1.15.0          | 225 KB    | : 100% 1.0/1 [00:00<00:00, 16.99it/s]\n",
            "fonttools-4.33.3     | 1.6 MB    | : 100% 1.0/1 [00:00<00:00,  2.99it/s]\n",
            "pthread-stubs-0.4    | 5 KB      | : 100% 1.0/1 [00:00<00:00, 45.77it/s]\n",
            "typing-extensions-4. | 8 KB      | : 100% 1.0/1 [00:00<00:00, 38.95it/s]\n",
            "pandas-1.3.4         | 12.7 MB   | : 100% 1.0/1 [00:02<00:00,  2.36s/it]               \n",
            "brotli-1.0.9         | 18 KB     | : 100% 1.0/1 [00:00<00:00, 36.15it/s]\n",
            "xorg-xproto-7.0.31   | 73 KB     | : 100% 1.0/1 [00:00<00:00, 27.96it/s]\n",
            "pytz-2022.1          | 242 KB    | : 100% 1.0/1 [00:00<00:00,  9.71it/s]\n",
            "sqlalchemy-1.4.37    | 2.3 MB    | : 100% 1.0/1 [00:00<00:00,  2.06it/s]\n",
            "libglib-2.70.2       | 3.1 MB    | : 100% 1.0/1 [00:00<00:00,  1.48it/s]\n",
            "python-dateutil-2.8. | 240 KB    | : 100% 1.0/1 [00:00<00:00, 19.69it/s]\n",
            "libuuid-2.32.1       | 28 KB     | : 100% 1.0/1 [00:00<00:00, 43.16it/s]\n",
            "libbrotlienc-1.0.9   | 287 KB    | : 100% 1.0/1 [00:00<00:00, 18.31it/s]\n",
            "fontconfig-2.14.0    | 305 KB    | : 100% 1.0/1 [00:00<00:00, 12.58it/s]\n",
            "rdkit-2022.03.2      | 39.4 MB   | : 100% 1.0/1 [00:06<00:00,  6.44s/it]               \n",
            "cairo-1.16.0         | 1.5 MB    | : 100% 1.0/1 [00:00<00:00,  3.46it/s]\n",
            "pixman-0.40.0        | 627 KB    | : 100% 1.0/1 [00:00<00:00,  8.77it/s]\n",
            "munkres-1.1.4        | 12 KB     | : 100% 1.0/1 [00:00<00:00, 31.78it/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\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\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\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install -c rdkit rdkit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NNH14o0dq9g",
        "outputId": "8e9c11b2-c43c-40d3-87df-6f88c32f9e32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\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\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cairo\n",
            "    - nox\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    nox-1.0                    |       h3f329b5_0           2 KB  rdkit\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:           2 KB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  nox                rdkit/linux-64::nox-1.0-h3f329b5_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "nox-1.0              | 2 KB      | : 100% 1.0/1 [00:00<00:00,  6.55it/s]\n",
            "Preparing transaction: / \b\bdone\n",
            "Verifying transaction: \\ \b\bdone\n",
            "Executing transaction: / \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install -c rdkit nox cairo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luKDzpCibtm4",
        "outputId": "c803fef7-5fc8-4125-9886-9b908a07d57f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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\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\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - pytorch\n",
            "    - torchvision\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    blas-2.106                 |              mkl          12 KB  conda-forge\n",
            "    cudatoolkit-11.1.1         |       h6406543_8        1.20 GB  conda-forge\n",
            "    ffmpeg-4.3                 |       hf484d3e_0         9.9 MB  pytorch\n",
            "    freetype-2.10.4            |       h0708190_1         890 KB  conda-forge\n",
            "    gmp-6.2.1                  |       h58526e2_0         806 KB  conda-forge\n",
            "    gnutls-3.6.13              |       h85f3911_1         2.0 MB  conda-forge\n",
            "    jpeg-9e                    |       h166bdaf_1         268 KB  conda-forge\n",
            "    lame-3.100                 |    h7f98852_1001         496 KB  conda-forge\n",
            "    libblas-3.9.0              |            6_mkl          11 KB  conda-forge\n",
            "    libcblas-3.9.0             |            6_mkl          11 KB  conda-forge\n",
            "    liblapack-3.9.0            |            6_mkl          11 KB  conda-forge\n",
            "    liblapacke-3.9.0           |            6_mkl          11 KB  conda-forge\n",
            "    libpng-1.6.37              |       h21135ba_2         306 KB  conda-forge\n",
            "    libtiff-4.0.10             |    hc3755c2_1005         602 KB  conda-forge\n",
            "    libuv-1.43.0               |       h7f98852_0         1.0 MB  conda-forge\n",
            "    nettle-3.6                 |       he412f7d_0         6.5 MB  conda-forge\n",
            "    olefile-0.46               |     pyh9f0ad1d_1          32 KB  conda-forge\n",
            "    openh264-2.1.1             |       h780b84a_0         1.5 MB  conda-forge\n",
            "    pillow-6.2.1               |   py37h6b7be26_0         637 KB  conda-forge\n",
            "    pytorch-1.11.0             |      py3.7_cpu_0        62.8 MB  pytorch\n",
            "    pytorch-mutex-1.0          |              cpu           3 KB  pytorch\n",
            "    torchvision-0.12.0         |         py37_cpu        23.4 MB  pytorch\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        1.31 GB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  blas               conda-forge/linux-64::blas-2.106-mkl\n",
            "  cudatoolkit        conda-forge/linux-64::cudatoolkit-11.1.1-h6406543_8\n",
            "  ffmpeg             pytorch/linux-64::ffmpeg-4.3-hf484d3e_0\n",
            "  freetype           conda-forge/linux-64::freetype-2.10.4-h0708190_1\n",
            "  gmp                conda-forge/linux-64::gmp-6.2.1-h58526e2_0\n",
            "  gnutls             conda-forge/linux-64::gnutls-3.6.13-h85f3911_1\n",
            "  jpeg               conda-forge/linux-64::jpeg-9e-h166bdaf_1\n",
            "  lame               conda-forge/linux-64::lame-3.100-h7f98852_1001\n",
            "  liblapacke         conda-forge/linux-64::liblapacke-3.9.0-6_mkl\n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n",
            "  libtiff            conda-forge/linux-64::libtiff-4.0.10-hc3755c2_1005\n",
            "  libuv              conda-forge/linux-64::libuv-1.43.0-h7f98852_0\n",
            "  nettle             conda-forge/linux-64::nettle-3.6-he412f7d_0\n",
            "  olefile            conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n",
            "  openh264           conda-forge/linux-64::openh264-2.1.1-h780b84a_0\n",
            "  pillow             conda-forge/linux-64::pillow-6.2.1-py37h6b7be26_0\n",
            "  pytorch            pytorch/linux-64::pytorch-1.11.0-py3.7_cpu_0\n",
            "  pytorch-mutex      pytorch/noarch::pytorch-mutex-1.0-cpu\n",
            "  torchvision        pytorch/linux-64::torchvision-0.12.0-py37_cpu\n",
            "\n",
            "The following packages will be DOWNGRADED:\n",
            "\n",
            "  libblas                         3.9.0-14_linux64_openblas --> 3.9.0-6_mkl\n",
            "  libcblas                        3.9.0-14_linux64_openblas --> 3.9.0-6_mkl\n",
            "  liblapack                       3.9.0-14_linux64_openblas --> 3.9.0-6_mkl\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "libtiff-4.0.10       | 602 KB    | : 100% 1.0/1 [00:00<00:00,  6.44it/s]\n",
            "libuv-1.43.0         | 1.0 MB    | : 100% 1.0/1 [00:00<00:00,  6.10it/s]\n",
            "ffmpeg-4.3           | 9.9 MB    | : 100% 1.0/1 [00:01<00:00,  1.42s/it]               \n",
            "openh264-2.1.1       | 1.5 MB    | : 100% 1.0/1 [00:00<00:00,  4.19it/s]\n",
            "liblapack-3.9.0      | 11 KB     | : 100% 1.0/1 [00:00<00:00, 26.09it/s]\n",
            "gmp-6.2.1            | 806 KB    | : 100% 1.0/1 [00:00<00:00,  6.97it/s]\n",
            "cudatoolkit-11.1.1   | 1.20 GB   | : 100% 1.0/1 [02:31<00:00, 151.51s/it]              \n",
            "olefile-0.46         | 32 KB     | : 100% 1.0/1 [00:00<00:00, 19.61it/s]\n",
            "freetype-2.10.4      | 890 KB    | : 100% 1.0/1 [00:00<00:00,  3.06it/s]\n",
            "libcblas-3.9.0       | 11 KB     | : 100% 1.0/1 [00:00<00:00, 33.55it/s]\n",
            "pytorch-mutex-1.0    | 3 KB      | : 100% 1.0/1 [00:00<00:00,  8.11it/s]\n",
            "liblapacke-3.9.0     | 11 KB     | : 100% 1.0/1 [00:00<00:00, 25.96it/s]\n",
            "pillow-6.2.1         | 637 KB    | : 100% 1.0/1 [00:00<00:00,  7.08it/s]\n",
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            "nettle-3.6           | 6.5 MB    | : 100% 1.0/1 [00:00<00:00,  1.10it/s]\n",
            "jpeg-9e              | 268 KB    | : 100% 1.0/1 [00:00<00:00, 14.43it/s]\n",
            "lame-3.100           | 496 KB    | : 100% 1.0/1 [00:00<00:00, 10.68it/s]\n",
            "Preparing transaction: / \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\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- By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html\n",
            "\n",
            "\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install pytorch torchvision -c pytorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X4N808j_cgaS"
      },
      "outputs": [],
      "source": [
        "from openchem.data.utils import read_smiles_property_file\n",
        "data = read_smiles_property_file('./benchmark_datasets/logp_dataset/logP_labels.csv', cols_to_read=[1, 2])\n",
        "smiles = data[0]\n",
        "labels = data[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2-wiFUGXggvp"
      },
      "outputs": [],
      "source": [
        "import rdkit as rd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H9ht4VmogvOg"
      },
      "outputs": [],
      "source": [
        "from openchem.utils.graph import Attribute\n",
        "from openchem.utils.utils import identity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5URxbUd7gjXV"
      },
      "outputs": [],
      "source": [
        "node_attributes = {}\n",
        "node_attributes['valence'] = Attribute('node', 'valence', one_hot=True, values=[1, 2, 3, 4, 5, 6])\n",
        "node_attributes['charge'] = Attribute('node', 'charge', one_hot=True, values=[-1, 0, 1, 2, 3, 4])\n",
        "node_attributes['hybridization'] = Attribute('node', 'hybridization',\n",
        "                                             one_hot=True, values=[0, 1, 2, 3, 4, 5, 6, 7])\n",
        "node_attributes['aromatic'] = Attribute('node', 'aromatic', one_hot=True,\n",
        "                                        values=[0, 1])\n",
        "node_attributes['atom_element'] = Attribute('node', 'atom_element',\n",
        "                                            one_hot=True,\n",
        "                                            values=list(range(11)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yCjPMbCUglob"
      },
      "outputs": [],
      "source": [
        "def get_atomic_attributes(atom):\n",
        "  attr_dict = {}\n",
        "\n",
        "  atomic_num = atom.GetAtomicNum()\n",
        "  atomic_mapping = {5: 0, 7: 1, 6: 2, 8: 3, 9: 4, 15: 5, 16: 6, 17: 7, 35: 8,\n",
        "                    53: 9}\n",
        "  if atomic_num in atomic_mapping.keys():\n",
        "      attr_dict['atom_element'] = atomic_mapping[atomic_num]\n",
        "  else:\n",
        "      attr_dict['atom_element'] = 10\n",
        "  attr_dict['valence'] = atom.GetTotalValence()\n",
        "  attr_dict['charge'] = atom.GetFormalCharge()\n",
        "  attr_dict['hybridization'] = atom.GetHybridization().real\n",
        "  attr_dict['aromatic'] = int(atom.GetIsAromatic())\n",
        "  return attr_dict\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WRJE2LD4hFN5"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I99MjbrJhO8R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "b1b80a1a-90ff-4d0e-f3d7-33a81088c398"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-62a1fe9cf539>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mopenchem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msave_smiles_property_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'openchem'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "from openchem.data.utils import save_smiles_property_file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dAqbuiZkhgC4"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "liIflqM0heOX"
      },
      "outputs": [],
      "source": [
        "smiles = data[0]\n",
        "labels = np.array(data[1:])\n",
        "labels = labels.T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T0JazANKg2Pp"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(smiles, labels, test_size=0.2,\n",
        "                                                    random_state=42)\n",
        "save_smiles_property_file('./benchmark_datasets/logp_dataset/train.smi', X_train, y_train)\n",
        "save_smiles_property_file('./benchmark_datasets/logp_dataset/test.smi', X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Scs_Mi1iPAh",
        "outputId": "f19833ae-8bd8-402d-c68f-63b29e03fac0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "  Downloading oauthlib-3.2.0-py3-none-any.whl (151 kB)\n",
            "\u001b[K     |████████████████████████████████| 151 kB 78.0 MB/s \n",
            "\u001b[?25hInstalling collected packages: pyasn1, rsa, pyasn1-modules, oauthlib, cachetools, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google-auth-oauthlib, absl-py, tb-nightly\n",
            "Successfully installed absl-py-1.1.0 cachetools-5.2.0 google-auth-2.6.6 google-auth-oauthlib-0.4.6 grpcio-1.46.3 markdown-3.3.7 oauthlib-3.2.0 protobuf-3.19.4 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-oauthlib-1.3.1 rsa-4.8 tb-nightly-2.10.0a20220603 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 werkzeug-2.1.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting networkx\n",
            "  Downloading networkx-2.6.3-py3-none-any.whl (1.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9 MB 30.3 MB/s \n",
            "\u001b[?25hInstalling collected packages: networkx\n",
            "Successfully installed networkx-2.6.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torchani\n",
            "  Downloading torchani-2.2-py3-none-any.whl (10.8 MB)\n",
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            "Collecting lark-parser\n",
            "  Downloading lark_parser-0.12.0-py2.py3-none-any.whl (103 kB)\n",
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            "Requirement already satisfied: typing_extensions in /usr/local/lib/python3.7/site-packages (from torch->torchani) (4.2.0)\n",
            "Installing collected packages: lark-parser, torchani\n",
            "Successfully installed lark-parser-0.12.0 torchani-2.2\n"
          ]
        }
      ],
      "source": [
        "!pip install tb-nightly\n",
        "!pip install networkx\n",
        "!pip install torchani"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd OpenChem"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0Gtn4yGAaVs",
        "outputId": "c20a6986-ae00-45ee-9844-59496be26669"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/OpenChem\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0O8TKx0nhpUs",
        "outputId": "c8063ab7-8ea5-4021-b002-51f980d50346"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/OpenChem/openchem/data/utils.py:186: UserWarning: 71/719 unsanitized smiles (9.9%)\n",
            "  warnings.warn('{:d}/{:d} unsanitized smiles ({:.1f}%)'.format(num_bad, len(smiles), 100 * invalid_rate))\n",
            "/content/OpenChem/openchem/data/utils.py:186: UserWarning: 14/179 unsanitized smiles (7.8%)\n",
            "  warnings.warn('{:d}/{:d} unsanitized smiles ({:.1f}%)'.format(num_bad, len(smiles), 100 * invalid_rate))\n",
            "Directory logs/logs_gcnn_logs created\n",
            "Directory logs/logs_gcnn_logs/checkpoint created\n",
            "2022-06-04 06:59:15,395 openchem INFO: Running on 1 GPUs\n",
            "2022-06-04 06:59:15,396 openchem INFO: Logging directory is set to logs/logs_gcnn_logs\n",
            "2022-06-04 06:59:15,396 openchem INFO: Running with config:\n",
            "batch_size:                                       100\n",
            "encoder_params/encoder_dim:                       128\n",
            "encoder_params/input_size:                        33\n",
            "encoder_params/n_layers:                          5\n",
            "logdir:                                           logs/logs_gcnn_logs\n",
            "lr_scheduler_params/gamma:                        0.8\n",
            "lr_scheduler_params/step_size:                    15\n",
            "mlp_params/input_size:                            128\n",
            "mlp_params/n_layers:                              2\n",
            "num_epochs:                                       100\n",
            "optimizer_params/lr:                              0.0005\n",
            "print_every:                                      1\n",
            "random_seed:                                      42\n",
            "save_every:                                       5\n",
            "task:                                             regression\n",
            "use_clip_grad:                                    False\n",
            "use_cuda:                                         False\n",
            "\n",
            "2022-06-04 06:59:15,403 openchem INFO: Starting training from scratch\n",
            "2022-06-04 06:59:15,403 openchem INFO: Training is set up from epoch 0\n",
            "  0% 0/100 [00:00<?, ?it/s]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:07,  1.28s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.17s/it]\u001b[A\n",
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            " 57% 4/7 [00:04<00:03,  1.13s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.05s/it]\n",
            "2022-06-04 06:59:22,726 openchem.fit INFO: TRAINING: [Time: 0m 7s, Epoch: 0, Progress: 0%, Loss: 11.1461]\n",
            "2022-06-04 06:59:23,539 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 11.7052, Metrics: -0.7091]\n",
            "2022-06-04 06:59:23,674 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  1% 1/100 [00:08<13:39,  8.28s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 06:59:30,880 openchem.fit INFO: TRAINING: [Time: 0m 15s, Epoch: 1, Progress: 1%, Loss: 8.4855]\n",
            "2022-06-04 06:59:31,681 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 8.5486, Metrics: -0.2532]\n",
            "2022-06-04 06:59:31,815 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  2% 2/100 [00:16<13:23,  8.20s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.13s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 06:59:39,096 openchem.fit INFO: TRAINING: [Time: 0m 23s, Epoch: 2, Progress: 2%, Loss: 6.3365]\n",
            "2022-06-04 06:59:39,894 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 5.8871, Metrics: 0.1294]\n",
            "2022-06-04 06:59:40,030 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  3% 3/100 [00:24<13:15,  8.20s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.14s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.15s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.14s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.13s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.05s/it]\n",
            "2022-06-04 06:59:47,386 openchem.fit INFO: TRAINING: [Time: 0m 31s, Epoch: 3, Progress: 3%, Loss: 5.2068]\n",
            "2022-06-04 06:59:48,205 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 6.1009, Metrics: 0.1111]\n",
            "2022-06-04 06:59:48,335 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  4% 4/100 [00:32<13:11,  8.24s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.13s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.13s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.13s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.14s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.16s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.15s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.06s/it]\n",
            "2022-06-04 06:59:55,766 openchem.fit INFO: TRAINING: [Time: 0m 40s, Epoch: 4, Progress: 4%, Loss: 4.0513]\n",
            "2022-06-04 06:59:56,556 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 5.1864, Metrics: 0.2425]\n",
            "2022-06-04 06:59:56,693 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  5% 5/100 [00:41<13:07,  8.29s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.14s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:00:03,945 openchem.fit INFO: TRAINING: [Time: 0m 48s, Epoch: 5, Progress: 5%, Loss: 3.4150]\n",
            "2022-06-04 07:00:04,764 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 3.8019, Metrics: 0.4378]\n",
            "2022-06-04 07:00:04,892 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  6% 6/100 [00:49<12:56,  8.26s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.13s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:00:12,120 openchem.fit INFO: TRAINING: [Time: 0m 56s, Epoch: 6, Progress: 6%, Loss: 2.7629]\n",
            "2022-06-04 07:00:12,904 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 3.6719, Metrics: 0.4573]\n",
            "2022-06-04 07:00:13,038 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  7% 7/100 [00:57<12:44,  8.22s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.14s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:00:20,272 openchem.fit INFO: TRAINING: [Time: 1m 4s, Epoch: 7, Progress: 7%, Loss: 2.2016]\n",
            "2022-06-04 07:00:21,066 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 2.0890, Metrics: 0.6845]\n",
            "2022-06-04 07:00:21,193 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  8% 8/100 [01:05<12:34,  8.20s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:00:28,383 openchem.fit INFO: TRAINING: [Time: 1m 12s, Epoch: 8, Progress: 8%, Loss: 1.7848]\n",
            "2022-06-04 07:00:29,164 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 2.4983, Metrics: 0.6238]\n",
            "2022-06-04 07:00:29,297 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            "  9% 9/100 [01:13<12:23,  8.17s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:00:36,462 openchem.fit INFO: TRAINING: [Time: 1m 21s, Epoch: 9, Progress: 9%, Loss: 1.6364]\n",
            "2022-06-04 07:00:37,241 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 2.4691, Metrics: 0.6296]\n",
            "2022-06-04 07:00:37,377 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 10% 10/100 [01:21<12:12,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:00:44,580 openchem.fit INFO: TRAINING: [Time: 1m 29s, Epoch: 10, Progress: 10%, Loss: 1.1937]\n",
            "2022-06-04 07:00:45,397 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 1.5101, Metrics: 0.7754]\n",
            "2022-06-04 07:00:45,529 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 11% 11/100 [01:30<12:05,  8.15s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:00:52,669 openchem.fit INFO: TRAINING: [Time: 1m 37s, Epoch: 11, Progress: 11%, Loss: 0.9125]\n",
            "2022-06-04 07:00:53,461 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 1.5497, Metrics: 0.7728]\n",
            "2022-06-04 07:00:53,598 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 12% 12/100 [01:38<11:54,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:08,  1.49s/it]\u001b[A\n",
            " 29% 2/7 [00:03<00:07,  1.50s/it]\u001b[A\n",
            " 43% 3/7 [00:04<00:05,  1.34s/it]\u001b[A\n",
            " 57% 4/7 [00:05<00:03,  1.24s/it]\u001b[A\n",
            " 71% 5/7 [00:06<00:02,  1.19s/it]\u001b[A\n",
            " 86% 6/7 [00:07<00:01,  1.16s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.14s/it]\n",
            "2022-06-04 07:01:01,567 openchem.fit INFO: TRAINING: [Time: 1m 46s, Epoch: 12, Progress: 12%, Loss: 0.7595]\n",
            "2022-06-04 07:01:02,356 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 1.1985, Metrics: 0.8216]\n",
            "2022-06-04 07:01:02,482 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 13% 13/100 [01:47<12:06,  8.35s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.09s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.09s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.09s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.09s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:01:09,641 openchem.fit INFO: TRAINING: [Time: 1m 54s, Epoch: 13, Progress: 13%, Loss: 0.6807]\n",
            "2022-06-04 07:01:10,428 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 1.3015, Metrics: 0.8045]\n",
            "2022-06-04 07:01:10,553 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 14% 14/100 [01:55<11:50,  8.27s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:01:17,766 openchem.fit INFO: TRAINING: [Time: 2m 2s, Epoch: 14, Progress: 14%, Loss: 0.5676]\n",
            "2022-06-04 07:01:18,557 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 1.2190, Metrics: 0.8185]\n",
            "2022-06-04 07:01:18,689 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 15% 15/100 [02:03<11:39,  8.23s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:01:25,824 openchem.fit INFO: TRAINING: [Time: 2m 10s, Epoch: 15, Progress: 15%, Loss: 0.5184]\n",
            "2022-06-04 07:01:26,616 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.9555, Metrics: 0.8603]\n",
            "2022-06-04 07:01:26,743 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 16% 16/100 [02:11<11:26,  8.18s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:01:33,897 openchem.fit INFO: TRAINING: [Time: 2m 18s, Epoch: 16, Progress: 16%, Loss: 0.6138]\n",
            "2022-06-04 07:01:34,703 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.9001, Metrics: 0.8671]\n",
            "2022-06-04 07:01:34,832 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 17% 17/100 [02:19<11:16,  8.15s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.09s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.09s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:01:41,996 openchem.fit INFO: TRAINING: [Time: 2m 26s, Epoch: 17, Progress: 17%, Loss: 0.4750]\n",
            "2022-06-04 07:01:42,790 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8616, Metrics: 0.8737]\n",
            "2022-06-04 07:01:42,923 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 18% 18/100 [02:27<11:06,  8.13s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:01:50,112 openchem.fit INFO: TRAINING: [Time: 2m 34s, Epoch: 18, Progress: 18%, Loss: 0.5818]\n",
            "2022-06-04 07:01:50,907 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8168, Metrics: 0.8793]\n",
            "2022-06-04 07:01:51,035 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 19% 19/100 [02:35<10:58,  8.13s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:01:58,196 openchem.fit INFO: TRAINING: [Time: 2m 42s, Epoch: 19, Progress: 19%, Loss: 0.3803]\n",
            "2022-06-04 07:01:58,998 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.9016, Metrics: 0.8642]\n",
            "2022-06-04 07:01:59,129 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 20% 20/100 [02:43<10:49,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:02:06,280 openchem.fit INFO: TRAINING: [Time: 2m 50s, Epoch: 20, Progress: 20%, Loss: 0.3995]\n",
            "2022-06-04 07:02:07,062 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7948, Metrics: 0.8796]\n",
            "2022-06-04 07:02:07,190 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 21% 21/100 [02:51<10:39,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:02:14,325 openchem.fit INFO: TRAINING: [Time: 2m 58s, Epoch: 21, Progress: 21%, Loss: 0.3332]\n",
            "2022-06-04 07:02:15,110 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8335, Metrics: 0.8733]\n",
            "2022-06-04 07:02:15,242 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 22% 22/100 [02:59<10:30,  8.08s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:02:22,466 openchem.fit INFO: TRAINING: [Time: 3m 7s, Epoch: 22, Progress: 22%, Loss: 0.3054]\n",
            "2022-06-04 07:02:23,248 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8291, Metrics: 0.8761]\n",
            "2022-06-04 07:02:23,376 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 23% 23/100 [03:07<10:23,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.09s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:02:30,536 openchem.fit INFO: TRAINING: [Time: 3m 15s, Epoch: 23, Progress: 23%, Loss: 0.2707]\n",
            "2022-06-04 07:02:31,325 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8860, Metrics: 0.8675]\n",
            "2022-06-04 07:02:31,457 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 24% 24/100 [03:16<10:15,  8.09s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:02:38,639 openchem.fit INFO: TRAINING: [Time: 3m 23s, Epoch: 24, Progress: 24%, Loss: 0.3161]\n",
            "2022-06-04 07:02:39,432 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7485, Metrics: 0.8881]\n",
            "2022-06-04 07:02:39,566 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 25% 25/100 [03:24<10:07,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:02:46,730 openchem.fit INFO: TRAINING: [Time: 3m 31s, Epoch: 25, Progress: 25%, Loss: 0.2445]\n",
            "2022-06-04 07:02:47,521 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7562, Metrics: 0.8870]\n",
            "2022-06-04 07:02:47,655 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 26% 26/100 [03:32<09:59,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.13s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.14s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.13s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:02:54,896 openchem.fit INFO: TRAINING: [Time: 3m 39s, Epoch: 26, Progress: 26%, Loss: 0.4137]\n",
            "2022-06-04 07:02:55,692 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7895, Metrics: 0.8808]\n",
            "2022-06-04 07:02:55,826 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 27% 27/100 [03:40<09:52,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.09s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.09s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.09s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.01s/it]\n",
            "2022-06-04 07:03:02,930 openchem.fit INFO: TRAINING: [Time: 3m 47s, Epoch: 27, Progress: 27%, Loss: 0.2707]\n",
            "2022-06-04 07:03:03,718 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7476, Metrics: 0.8886]\n",
            "2022-06-04 07:03:03,849 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 28% 28/100 [03:48<09:42,  8.09s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:10,  1.78s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:07,  1.43s/it]\u001b[A\n",
            " 43% 3/7 [00:04<00:05,  1.28s/it]\u001b[A\n",
            " 57% 4/7 [00:05<00:03,  1.22s/it]\u001b[A\n",
            " 71% 5/7 [00:06<00:02,  1.17s/it]\u001b[A\n",
            " 86% 6/7 [00:07<00:01,  1.15s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.13s/it]\n",
            "2022-06-04 07:03:11,776 openchem.fit INFO: TRAINING: [Time: 3m 56s, Epoch: 28, Progress: 28%, Loss: 0.2726]\n",
            "2022-06-04 07:03:12,572 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8184, Metrics: 0.8759]\n",
            "2022-06-04 07:03:12,704 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 29% 29/100 [03:57<09:50,  8.32s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:03:19,857 openchem.fit INFO: TRAINING: [Time: 4m 4s, Epoch: 29, Progress: 28%, Loss: 0.3141]\n",
            "2022-06-04 07:03:20,649 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7537, Metrics: 0.8866]\n",
            "2022-06-04 07:03:20,780 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 30% 30/100 [04:05<09:37,  8.25s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.14s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:03:27,939 openchem.fit INFO: TRAINING: [Time: 4m 12s, Epoch: 30, Progress: 30%, Loss: 0.2270]\n",
            "2022-06-04 07:03:28,721 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7997, Metrics: 0.8804]\n",
            "2022-06-04 07:03:28,850 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 31% 31/100 [04:13<09:25,  8.20s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:03:35,988 openchem.fit INFO: TRAINING: [Time: 4m 20s, Epoch: 31, Progress: 31%, Loss: 0.3908]\n",
            "2022-06-04 07:03:36,777 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7468, Metrics: 0.8869]\n",
            "2022-06-04 07:03:36,908 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 32% 32/100 [04:21<09:14,  8.15s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:03:44,098 openchem.fit INFO: TRAINING: [Time: 4m 28s, Epoch: 32, Progress: 32%, Loss: 0.2304]\n",
            "2022-06-04 07:03:44,894 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7821, Metrics: 0.8830]\n",
            "2022-06-04 07:03:45,022 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 33% 33/100 [04:29<09:05,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:03:52,248 openchem.fit INFO: TRAINING: [Time: 4m 36s, Epoch: 33, Progress: 33%, Loss: 0.2061]\n",
            "2022-06-04 07:03:53,043 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7620, Metrics: 0.8864]\n",
            "2022-06-04 07:03:53,172 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 34% 34/100 [04:37<08:57,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:04:00,415 openchem.fit INFO: TRAINING: [Time: 4m 45s, Epoch: 34, Progress: 34%, Loss: 0.2209]\n",
            "2022-06-04 07:04:01,202 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7785, Metrics: 0.8849]\n",
            "2022-06-04 07:04:01,329 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 35% 35/100 [04:45<08:49,  8.15s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:04:08,524 openchem.fit INFO: TRAINING: [Time: 4m 53s, Epoch: 35, Progress: 35%, Loss: 0.2950]\n",
            "2022-06-04 07:04:09,324 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8015, Metrics: 0.8815]\n",
            "2022-06-04 07:04:09,452 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 36% 36/100 [04:54<08:41,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:04:16,709 openchem.fit INFO: TRAINING: [Time: 5m 1s, Epoch: 36, Progress: 36%, Loss: 0.3048]\n",
            "2022-06-04 07:04:17,505 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7261, Metrics: 0.8915]\n",
            "2022-06-04 07:04:17,638 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 37% 37/100 [05:02<08:33,  8.15s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.13s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.13s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.13s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 07:04:24,950 openchem.fit INFO: TRAINING: [Time: 5m 9s, Epoch: 37, Progress: 37%, Loss: 0.2575]\n",
            "2022-06-04 07:04:25,762 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7455, Metrics: 0.8883]\n",
            "2022-06-04 07:04:25,891 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 38% 38/100 [05:10<08:27,  8.18s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.14s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:04:33,105 openchem.fit INFO: TRAINING: [Time: 5m 17s, Epoch: 38, Progress: 38%, Loss: 0.2537]\n",
            "2022-06-04 07:04:33,896 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7907, Metrics: 0.8836]\n",
            "2022-06-04 07:04:34,025 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 39% 39/100 [05:18<08:18,  8.17s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:04:41,193 openchem.fit INFO: TRAINING: [Time: 5m 25s, Epoch: 39, Progress: 39%, Loss: 0.2848]\n",
            "2022-06-04 07:04:41,980 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7668, Metrics: 0.8865]\n",
            "2022-06-04 07:04:42,107 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 40% 40/100 [05:26<08:08,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.13s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:04:49,339 openchem.fit INFO: TRAINING: [Time: 5m 33s, Epoch: 40, Progress: 40%, Loss: 0.2667]\n",
            "2022-06-04 07:04:50,126 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7516, Metrics: 0.8892]\n",
            "2022-06-04 07:04:50,264 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 41% 41/100 [05:34<08:00,  8.15s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.09s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.15s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.15s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.14s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 07:04:57,564 openchem.fit INFO: TRAINING: [Time: 5m 42s, Epoch: 41, Progress: 41%, Loss: 0.2641]\n",
            "2022-06-04 07:04:58,358 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8014, Metrics: 0.8796]\n",
            "2022-06-04 07:04:58,482 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 42% 42/100 [05:43<07:53,  8.17s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.09s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:05:05,630 openchem.fit INFO: TRAINING: [Time: 5m 50s, Epoch: 42, Progress: 42%, Loss: 0.2946]\n",
            "2022-06-04 07:05:06,439 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8000, Metrics: 0.8782]\n",
            "2022-06-04 07:05:06,564 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 43% 43/100 [05:51<07:44,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:05:13,738 openchem.fit INFO: TRAINING: [Time: 5m 58s, Epoch: 43, Progress: 43%, Loss: 0.2292]\n",
            "2022-06-04 07:05:14,556 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8766, Metrics: 0.8709]\n",
            "2022-06-04 07:05:14,684 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 44% 44/100 [05:59<07:35,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:05:21,874 openchem.fit INFO: TRAINING: [Time: 6m 6s, Epoch: 44, Progress: 44%, Loss: 0.2293]\n",
            "2022-06-04 07:05:22,661 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8946, Metrics: 0.8698]\n",
            "2022-06-04 07:05:22,784 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 45% 45/100 [06:07<07:26,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.09s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:05:29,939 openchem.fit INFO: TRAINING: [Time: 6m 14s, Epoch: 45, Progress: 45%, Loss: 0.2619]\n",
            "2022-06-04 07:05:30,727 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8045, Metrics: 0.8799]\n",
            "2022-06-04 07:05:30,853 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 46% 46/100 [06:15<07:17,  8.11s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:05:38,030 openchem.fit INFO: TRAINING: [Time: 6m 22s, Epoch: 46, Progress: 46%, Loss: 0.2083]\n",
            "2022-06-04 07:05:38,810 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8030, Metrics: 0.8790]\n",
            "2022-06-04 07:05:38,936 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 47% 47/100 [06:23<07:09,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:05:46,094 openchem.fit INFO: TRAINING: [Time: 6m 30s, Epoch: 47, Progress: 47%, Loss: 0.2322]\n",
            "2022-06-04 07:05:46,908 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7634, Metrics: 0.8867]\n",
            "2022-06-04 07:05:47,042 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 48% 48/100 [06:31<07:01,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:05:54,222 openchem.fit INFO: TRAINING: [Time: 6m 38s, Epoch: 48, Progress: 48%, Loss: 0.1440]\n",
            "2022-06-04 07:05:55,013 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7683, Metrics: 0.8876]\n",
            "2022-06-04 07:05:55,157 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 49% 49/100 [06:39<06:53,  8.11s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:06:02,337 openchem.fit INFO: TRAINING: [Time: 6m 46s, Epoch: 49, Progress: 49%, Loss: 0.2363]\n",
            "2022-06-04 07:06:03,115 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7416, Metrics: 0.8899]\n",
            "2022-06-04 07:06:03,240 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 50% 50/100 [06:47<06:44,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.13s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:06:10,449 openchem.fit INFO: TRAINING: [Time: 6m 55s, Epoch: 50, Progress: 50%, Loss: 0.2418]\n",
            "2022-06-04 07:06:11,258 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7526, Metrics: 0.8881]\n",
            "2022-06-04 07:06:11,385 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 51% 51/100 [06:55<06:37,  8.11s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.10s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.10s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.10s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:06:18,628 openchem.fit INFO: TRAINING: [Time: 7m 3s, Epoch: 51, Progress: 51%, Loss: 0.2912]\n",
            "2022-06-04 07:06:19,423 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7450, Metrics: 0.8882]\n",
            "2022-06-04 07:06:19,559 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 52% 52/100 [07:04<06:30,  8.13s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.14s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.17s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.16s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.15s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.13s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.15s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.07s/it]\n",
            "2022-06-04 07:06:27,032 openchem.fit INFO: TRAINING: [Time: 7m 11s, Epoch: 52, Progress: 52%, Loss: 0.1803]\n",
            "2022-06-04 07:06:27,832 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.6873, Metrics: 0.8962]\n",
            "2022-06-04 07:06:27,961 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 53% 53/100 [07:12<06:25,  8.21s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.13s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 07:06:35,241 openchem.fit INFO: TRAINING: [Time: 7m 19s, Epoch: 53, Progress: 53%, Loss: 0.2252]\n",
            "2022-06-04 07:06:36,039 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7347, Metrics: 0.8907]\n",
            "2022-06-04 07:06:36,171 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 54% 54/100 [07:20<06:17,  8.21s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.13s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.14s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.13s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.13s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.05s/it]\n",
            "2022-06-04 07:06:43,504 openchem.fit INFO: TRAINING: [Time: 7m 28s, Epoch: 54, Progress: 54%, Loss: 0.1575]\n",
            "2022-06-04 07:06:44,309 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7285, Metrics: 0.8897]\n",
            "2022-06-04 07:06:44,435 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 55% 55/100 [07:29<06:10,  8.23s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
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            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 07:06:51,711 openchem.fit INFO: TRAINING: [Time: 7m 36s, Epoch: 55, Progress: 55%, Loss: 0.1616]\n",
            "2022-06-04 07:06:52,501 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7135, Metrics: 0.8926]\n",
            "2022-06-04 07:06:52,626 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 56% 56/100 [07:37<06:01,  8.22s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.13s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 07:06:59,896 openchem.fit INFO: TRAINING: [Time: 7m 44s, Epoch: 56, Progress: 56%, Loss: 0.2067]\n",
            "2022-06-04 07:07:00,693 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7109, Metrics: 0.8942]\n",
            "2022-06-04 07:07:00,818 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 57% 57/100 [07:45<05:52,  8.21s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:06<00:02,  1.32s/it]\u001b[A\n",
            " 86% 6/7 [00:07<00:01,  1.32s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.14s/it]\n",
            "2022-06-04 07:07:08,787 openchem.fit INFO: TRAINING: [Time: 7m 53s, Epoch: 57, Progress: 56%, Loss: 0.1224]\n",
            "2022-06-04 07:07:09,596 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7363, Metrics: 0.8907]\n",
            "2022-06-04 07:07:09,722 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 58% 58/100 [07:54<05:53,  8.42s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:07:16,892 openchem.fit INFO: TRAINING: [Time: 8m 1s, Epoch: 58, Progress: 57%, Loss: 0.1844]\n",
            "2022-06-04 07:07:17,685 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7676, Metrics: 0.8867]\n",
            "2022-06-04 07:07:17,812 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 59% 59/100 [08:02<05:41,  8.32s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:07:24,997 openchem.fit INFO: TRAINING: [Time: 8m 9s, Epoch: 59, Progress: 59%, Loss: 0.1308]\n",
            "2022-06-04 07:07:25,778 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7852, Metrics: 0.8838]\n",
            "2022-06-04 07:07:25,905 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 60% 60/100 [08:10<05:30,  8.25s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:07:33,121 openchem.fit INFO: TRAINING: [Time: 8m 17s, Epoch: 60, Progress: 60%, Loss: 0.1237]\n",
            "2022-06-04 07:07:33,918 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7455, Metrics: 0.8877]\n",
            "2022-06-04 07:07:34,045 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 61% 61/100 [08:18<05:20,  8.22s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:07:41,281 openchem.fit INFO: TRAINING: [Time: 8m 25s, Epoch: 61, Progress: 61%, Loss: 0.2314]\n",
            "2022-06-04 07:07:42,080 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7466, Metrics: 0.8877]\n",
            "2022-06-04 07:07:42,207 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 62% 62/100 [08:26<05:11,  8.20s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.12s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:07:49,416 openchem.fit INFO: TRAINING: [Time: 8m 34s, Epoch: 62, Progress: 62%, Loss: 0.1770]\n",
            "2022-06-04 07:07:50,217 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7706, Metrics: 0.8846]\n",
            "2022-06-04 07:07:50,344 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 63% 63/100 [08:34<05:02,  8.18s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            "2022-06-04 07:07:57,567 openchem.fit INFO: TRAINING: [Time: 8m 42s, Epoch: 63, Progress: 63%, Loss: 0.2101]\n",
            "2022-06-04 07:07:58,372 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7903, Metrics: 0.8802]\n",
            "2022-06-04 07:07:58,499 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 64% 64/100 [08:43<04:54,  8.17s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            "2022-06-04 07:08:05,742 openchem.fit INFO: TRAINING: [Time: 8m 50s, Epoch: 64, Progress: 64%, Loss: 0.1449]\n",
            "2022-06-04 07:08:06,542 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7523, Metrics: 0.8869]\n",
            "2022-06-04 07:08:06,668 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 65% 65/100 [08:51<04:46,  8.17s/it]\n",
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            "2022-06-04 07:08:13,875 openchem.fit INFO: TRAINING: [Time: 8m 58s, Epoch: 65, Progress: 65%, Loss: 0.1219]\n",
            "2022-06-04 07:08:14,676 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8143, Metrics: 0.8784]\n",
            "2022-06-04 07:08:14,804 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 66% 66/100 [08:59<04:37,  8.16s/it]\n",
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            "2022-06-04 07:08:21,990 openchem.fit INFO: TRAINING: [Time: 9m 6s, Epoch: 66, Progress: 66%, Loss: 0.1730]\n",
            "2022-06-04 07:08:22,772 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7850, Metrics: 0.8828]\n",
            "2022-06-04 07:08:22,900 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 67% 67/100 [09:07<04:28,  8.14s/it]\n",
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            "2022-06-04 07:08:30,126 openchem.fit INFO: TRAINING: [Time: 9m 14s, Epoch: 67, Progress: 67%, Loss: 0.1135]\n",
            "2022-06-04 07:08:30,912 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8022, Metrics: 0.8767]\n",
            "2022-06-04 07:08:31,041 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 68% 68/100 [09:15<04:20,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:08:38,190 openchem.fit INFO: TRAINING: [Time: 9m 22s, Epoch: 68, Progress: 68%, Loss: 0.1606]\n",
            "2022-06-04 07:08:38,974 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7662, Metrics: 0.8866]\n",
            "2022-06-04 07:08:39,116 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 69% 69/100 [09:23<04:11,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:08:46,314 openchem.fit INFO: TRAINING: [Time: 9m 30s, Epoch: 69, Progress: 69%, Loss: 0.1673]\n",
            "2022-06-04 07:08:47,106 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7431, Metrics: 0.8880]\n",
            "2022-06-04 07:08:47,234 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 70% 70/100 [09:31<04:03,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
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            "2022-06-04 07:08:54,498 openchem.fit INFO: TRAINING: [Time: 9m 39s, Epoch: 70, Progress: 70%, Loss: 0.1390]\n",
            "2022-06-04 07:08:55,294 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8211, Metrics: 0.8802]\n",
            "2022-06-04 07:08:55,422 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 71% 71/100 [09:40<03:56,  8.14s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:09:02,655 openchem.fit INFO: TRAINING: [Time: 9m 47s, Epoch: 71, Progress: 71%, Loss: 0.2181]\n",
            "2022-06-04 07:09:03,450 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8121, Metrics: 0.8787]\n",
            "2022-06-04 07:09:03,583 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 72% 72/100 [09:48<03:48,  8.15s/it]\n",
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            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:09:10,718 openchem.fit INFO: TRAINING: [Time: 9m 55s, Epoch: 72, Progress: 72%, Loss: 0.1852]\n",
            "2022-06-04 07:09:11,513 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8116, Metrics: 0.8802]\n",
            "2022-06-04 07:09:11,643 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 73% 73/100 [09:56<03:39,  8.12s/it]\n",
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            "2022-06-04 07:09:18,838 openchem.fit INFO: TRAINING: [Time: 10m 3s, Epoch: 73, Progress: 73%, Loss: 0.1773]\n",
            "2022-06-04 07:09:19,629 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8439, Metrics: 0.8761]\n",
            "2022-06-04 07:09:19,760 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 74% 74/100 [10:04<03:31,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:09:26,931 openchem.fit INFO: TRAINING: [Time: 10m 11s, Epoch: 74, Progress: 74%, Loss: 0.1478]\n",
            "2022-06-04 07:09:27,709 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8035, Metrics: 0.8799]\n",
            "2022-06-04 07:09:27,841 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 75% 75/100 [10:12<03:22,  8.11s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.10s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:09:35,009 openchem.fit INFO: TRAINING: [Time: 10m 19s, Epoch: 75, Progress: 75%, Loss: 0.1716]\n",
            "2022-06-04 07:09:35,791 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7431, Metrics: 0.8898]\n",
            "2022-06-04 07:09:35,927 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 76% 76/100 [10:20<03:14,  8.10s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.11s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:09:43,100 openchem.fit INFO: TRAINING: [Time: 10m 27s, Epoch: 76, Progress: 76%, Loss: 0.2233]\n",
            "2022-06-04 07:09:43,913 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.6945, Metrics: 0.8954]\n",
            "2022-06-04 07:09:44,052 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 77% 77/100 [10:28<03:06,  8.11s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.12s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.12s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:09:51,279 openchem.fit INFO: TRAINING: [Time: 10m 35s, Epoch: 77, Progress: 77%, Loss: 0.2632]\n",
            "2022-06-04 07:09:52,078 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7135, Metrics: 0.8937]\n",
            "2022-06-04 07:09:52,211 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 78% 78/100 [10:36<02:58,  8.12s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:07,  1.18s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.16s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.13s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.13s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.12s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 07:09:59,526 openchem.fit INFO: TRAINING: [Time: 10m 44s, Epoch: 78, Progress: 78%, Loss: 0.1881]\n",
            "2022-06-04 07:10:00,324 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7310, Metrics: 0.8914]\n",
            "2022-06-04 07:10:00,453 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 79% 79/100 [10:45<02:51,  8.16s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:07,  1.18s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.15s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.13s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.13s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.13s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.04s/it]\n",
            "2022-06-04 07:10:07,750 openchem.fit INFO: TRAINING: [Time: 10m 52s, Epoch: 79, Progress: 79%, Loss: 0.1214]\n",
            "2022-06-04 07:10:08,539 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7546, Metrics: 0.8869]\n",
            "2022-06-04 07:10:08,662 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 80% 80/100 [10:53<02:43,  8.17s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:07,  1.52s/it]\u001b[A\n",
            " 43% 3/7 [00:04<00:05,  1.36s/it]\u001b[A\n",
            " 57% 4/7 [00:05<00:03,  1.26s/it]\u001b[A\n",
            " 71% 5/7 [00:06<00:02,  1.21s/it]\u001b[A\n",
            " 86% 6/7 [00:07<00:01,  1.18s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.14s/it]\n",
            "2022-06-04 07:10:16,634 openchem.fit INFO: TRAINING: [Time: 11m 1s, Epoch: 80, Progress: 80%, Loss: 0.1119]\n",
            "2022-06-04 07:10:17,416 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7565, Metrics: 0.8870]\n",
            "2022-06-04 07:10:17,543 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 81% 81/100 [11:02<02:39,  8.39s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
            " 14% 1/7 [00:01<00:06,  1.12s/it]\u001b[A\n",
            " 29% 2/7 [00:02<00:05,  1.11s/it]\u001b[A\n",
            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.11s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:10:24,799 openchem.fit INFO: TRAINING: [Time: 11m 9s, Epoch: 81, Progress: 81%, Loss: 0.1611]\n",
            "2022-06-04 07:10:25,592 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7672, Metrics: 0.8848]\n",
            "2022-06-04 07:10:25,715 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 82% 82/100 [11:10<02:29,  8.32s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
            " 57% 4/7 [00:04<00:03,  1.11s/it]\u001b[A\n",
            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.12s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:10:32,950 openchem.fit INFO: TRAINING: [Time: 11m 17s, Epoch: 82, Progress: 82%, Loss: 0.1593]\n",
            "2022-06-04 07:10:33,741 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7613, Metrics: 0.8861]\n",
            "2022-06-04 07:10:33,868 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 83% 83/100 [11:18<02:20,  8.27s/it]\n",
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            "2022-06-04 07:10:41,079 openchem.fit INFO: TRAINING: [Time: 11m 25s, Epoch: 83, Progress: 83%, Loss: 0.1637]\n",
            "2022-06-04 07:10:41,858 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7305, Metrics: 0.8909]\n",
            "2022-06-04 07:10:41,995 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 84% 84/100 [11:26<02:11,  8.23s/it]\n",
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            "2022-06-04 07:10:49,256 openchem.fit INFO: TRAINING: [Time: 11m 33s, Epoch: 84, Progress: 84%, Loss: 0.1153]\n",
            "2022-06-04 07:10:50,051 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7902, Metrics: 0.8807]\n",
            "2022-06-04 07:10:50,184 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 85% 85/100 [11:34<02:03,  8.22s/it]\n",
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            "2022-06-04 07:10:57,410 openchem.fit INFO: TRAINING: [Time: 11m 42s, Epoch: 85, Progress: 85%, Loss: 0.2976]\n",
            "2022-06-04 07:10:58,199 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.8058, Metrics: 0.8804]\n",
            "2022-06-04 07:10:58,332 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 86% 86/100 [11:42<01:54,  8.20s/it]\n",
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            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:11:05,557 openchem.fit INFO: TRAINING: [Time: 11m 50s, Epoch: 86, Progress: 86%, Loss: 0.1331]\n",
            "2022-06-04 07:11:06,346 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7242, Metrics: 0.8910]\n",
            "2022-06-04 07:11:06,475 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 87% 87/100 [11:51<01:46,  8.18s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 43% 3/7 [00:03<00:04,  1.11s/it]\u001b[A\n",
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            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:11:13,636 openchem.fit INFO: TRAINING: [Time: 11m 58s, Epoch: 87, Progress: 87%, Loss: 0.1405]\n",
            "2022-06-04 07:11:14,424 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7806, Metrics: 0.8836]\n",
            "2022-06-04 07:11:14,548 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 88% 88/100 [11:59<01:37,  8.15s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 71% 5/7 [00:05<00:02,  1.10s/it]\u001b[A\n",
            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:11:21,727 openchem.fit INFO: TRAINING: [Time: 12m 6s, Epoch: 88, Progress: 88%, Loss: 0.1596]\n",
            "2022-06-04 07:11:22,510 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7664, Metrics: 0.8868]\n",
            "2022-06-04 07:11:22,635 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 89% 89/100 [12:07<01:29,  8.13s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 86% 6/7 [00:06<00:01,  1.10s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:11:29,779 openchem.fit INFO: TRAINING: [Time: 12m 14s, Epoch: 89, Progress: 89%, Loss: 0.1266]\n",
            "2022-06-04 07:11:30,584 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7684, Metrics: 0.8857]\n",
            "2022-06-04 07:11:30,716 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 90% 90/100 [12:15<01:21,  8.11s/it]\n",
            "  0% 0/7 [00:00<?, ?it/s]\u001b[A\n",
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            " 86% 6/7 [00:06<00:01,  1.11s/it]\u001b[A\n",
            "100% 7/7 [00:07<00:00,  1.03s/it]\n",
            "2022-06-04 07:11:37,911 openchem.fit INFO: TRAINING: [Time: 12m 22s, Epoch: 90, Progress: 90%, Loss: 0.1611]\n",
            "2022-06-04 07:11:38,698 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7337, Metrics: 0.8896]\n",
            "2022-06-04 07:11:38,830 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
            " 91% 91/100 [12:23<01:13,  8.12s/it]\n",
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            "100% 7/7 [00:07<00:00,  1.02s/it]\n",
            "2022-06-04 07:11:45,998 openchem.fit INFO: TRAINING: [Time: 12m 30s, Epoch: 91, Progress: 91%, Loss: 0.1085]\n",
            "2022-06-04 07:11:46,780 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7159, Metrics: 0.8922]\n",
            "2022-06-04 07:11:46,903 openchem.fit WARNING: Warning: module/MLP/layers/1/bias has zero variance (i.e. constant vector)\n",
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            "2022-06-04 07:11:54,084 openchem.fit INFO: TRAINING: [Time: 12m 38s, Epoch: 92, Progress: 92%, Loss: 0.1031]\n",
            "2022-06-04 07:11:54,869 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7523, Metrics: 0.8873]\n",
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            "2022-06-04 07:12:02,195 openchem.fit INFO: TRAINING: [Time: 12m 46s, Epoch: 93, Progress: 93%, Loss: 0.1222]\n",
            "2022-06-04 07:12:02,994 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7628, Metrics: 0.8865]\n",
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          ]
        }
      ],
      "source": [
        "!python run.py --config_file=\"example_configs/logs_gcnn_config_dev.py\" --mode=\"train_eval\" --batch_size=100 --num_epochs=100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZpJbgBs8-hZ",
        "outputId": "9f5543e2-148c-482d-fdbb-9897faa17cc6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/OpenChem/openchem/data/utils.py:186: UserWarning: 71/719 unsanitized smiles (9.9%)\n",
            "  warnings.warn('{:d}/{:d} unsanitized smiles ({:.1f}%)'.format(num_bad, len(smiles), 100 * invalid_rate))\n",
            "/content/OpenChem/openchem/data/utils.py:186: UserWarning: 14/179 unsanitized smiles (7.8%)\n",
            "  warnings.warn('{:d}/{:d} unsanitized smiles ({:.1f}%)'.format(num_bad, len(smiles), 100 * invalid_rate))\n",
            "2022-06-04 07:14:23,819 openchem INFO: Running on 1 GPUs\n",
            "2022-06-04 07:14:23,819 openchem INFO: Logging directory is set to logs/logs_gcnn_logs\n",
            "2022-06-04 07:14:23,820 openchem INFO: Running with config:\n",
            "batch_size:                                       50\n",
            "encoder_params/encoder_dim:                       128\n",
            "encoder_params/input_size:                        33\n",
            "encoder_params/n_layers:                          5\n",
            "logdir:                                           logs/logs_gcnn_logs\n",
            "lr_scheduler_params/gamma:                        0.8\n",
            "lr_scheduler_params/step_size:                    15\n",
            "mlp_params/input_size:                            128\n",
            "mlp_params/n_layers:                              2\n",
            "num_epochs:                                       101\n",
            "optimizer_params/lr:                              0.0005\n",
            "print_every:                                      1\n",
            "random_seed:                                      42\n",
            "save_every:                                       5\n",
            "task:                                             regression\n",
            "use_clip_grad:                                    False\n",
            "use_cuda:                                         False\n",
            "\n",
            "2022-06-04 07:14:23,827 openchem INFO: Loading model from logs/logs_gcnn_logs/checkpoint/epoch_95\n",
            "2022-06-04 07:14:24,459 openchem.evaluate INFO: EVALUATION: [Time: 0m 0s, Loss: 0.7694, Metrics: 0.8880]\n"
          ]
        }
      ],
      "source": [
        "!python launch.py --nproc_per_node=1 run.py --config_file=\"example_configs/logs_gcnn_config_dev.py\" --mode=\"eval\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bmSRMPbamiYg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tR-kubPp9HA0"
      },
      "outputs": [],
      "source": [
        "from openchem.models.Graph2Label import Graph2Label\n",
        "from openchem.modules.encoders.gcn_encoder import GraphCNNEncoder\n",
        "from openchem.modules.mlp.openchem_mlp import OpenChemMLP\n",
        "from openchem.data.graph_data_layer import GraphDataset\n",
        "\n",
        "from openchem.utils.graph import Attribute\n",
        "from openchem.utils.utils import identity\n",
        "\n",
        "import torch.nn as nn\n",
        "from torch.optim import RMSprop, SGD, Adam\n",
        "from torch.optim.lr_scheduler import ExponentialLR, StepLR\n",
        "import torch.nn.functional as F\n",
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import copy\n",
        "import pickle\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_atomic_attributes(atom):\n",
        "    attr_dict = {}\n",
        "  \n",
        "    atomic_num = atom.GetAtomicNum()\n",
        "    atomic_mapping = {5: 0, 7: 1, 6: 2, 8: 3, 9: 4, 15: 5, 16: 6, 17: 7, 35: 8,\n",
        "                      53: 9}\n",
        "    if atomic_num in atomic_mapping.keys():\n",
        "        attr_dict['atom_element'] = atomic_mapping[atomic_num]\n",
        "    else:\n",
        "        attr_dict['atom_element'] = 10\n",
        "    attr_dict['valence'] = atom.GetTotalValence()\n",
        "    attr_dict['charge'] = atom.GetFormalCharge()\n",
        "    attr_dict['hybridization'] = atom.GetHybridization().real\n",
        "    attr_dict['aromatic'] = int(atom.GetIsAromatic())\n",
        "    return attr_dict\n",
        "\n",
        "\n",
        "node_attributes = {}\n",
        "node_attributes['valence'] = Attribute('node', 'valence', one_hot=True, values=[1, 2, 3, 4, 5, 6])\n",
        "node_attributes['charge'] = Attribute('node', 'charge', one_hot=True, values=[-1, 0, 1, 2, 3, 4])\n",
        "node_attributes['hybridization'] = Attribute('node', 'hybridization',\n",
        "                                             one_hot=True, values=[0, 1, 2, 3, 4, 5, 6, 7])\n",
        "node_attributes['aromatic'] = Attribute('node', 'aromatic', one_hot=True,\n",
        "                                        values=[0, 1])\n",
        "node_attributes['atom_element'] = Attribute('node', 'atom_element',\n",
        "                                            one_hot=True,\n",
        "                                            values=list(range(11)))\n",
        "\n",
        "\n",
        "from openchem.data.utils import read_smiles_property_file"
      ],
      "metadata": {
        "id": "QreBO28Gmn-S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = read_smiles_property_file('./benchmark_datasets/logp_dataset/ethanol_solubility_data.csv',\n",
        "                                         cols_to_read=[1,2], keep_header=False)"
      ],
      "metadata": {
        "id": "aoxBNj88mrv0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smiles = data[1]"
      ],
      "metadata": {
        "id": "tA5kWJNNm0Au"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bn3wQec2m5Mh",
        "outputId": "cb8eb8ba-efcd-45f5-cbc7-746eacbb4be9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'CCCCCCCCCCCCCCCC(=O)OC(CO)CO',\n",
              " 'c1ccc(c(c1)/C=C/C(=O)O)[N+](=O)[O-]',\n",
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              " 'CCCOC(=O)NC(=O)N',\n",
              " 'c1(c(c(c(c(c1Cl)Cl)Cl)Cl)Cl)Cl',\n",
              " 'CCCC(=O)O[C@H]1CC[C@@]2(C)C(=CC[C@H]3[C@@H]4CC[C@H]([C@H](C)CCCC(C)C)[C@@]4(C)CC[C@H]23)C1',\n",
              " 'CCCCCCCCCCCCCCCC(=O)OCc1ccccc1',\n",
              " 'C[C@@H](c1ccc2cc(ccc2c1)OC)C(=O)O',\n",
              " 'CCCCS(=O)(=O)NNC(=O)C(c1ccccc1)(c1ccccc1)O',\n",
              " 'c1ccc(cc1)CC(C(=O)O)N',\n",
              " 'c1cc2c(cc1COC(=O)NC(=O)N)OCO2',\n",
              " 'C(C1C(C2C(O1)OC(C(Cl)(Cl)Cl)O2)O)O',\n",
              " 'Cc1c(c(C(=O)C)c2c(c1O)[C@@]1(C)C(=CC(=O)C(C(=O)C)C1=O)O2)O',\n",
              " 'c1ccc2c(c1)C(=O)NS2(=O)=O',\n",
              " 'CC1=C(CCC1(C)C)C(=O)O',\n",
              " 'c1cc2cccc3CCc(c1)c23',\n",
              " 'C(CCCCC(=O)O)CCCC(=O)O',\n",
              " 'C=C[C@@H]1[C@@H]2CCNC(=O)C2=CO[C@H]1O[C@H]1[C@@H]([C@H]([C@@H]([C@@H](CO)O1)O)O)O',\n",
              " 'c1cc(ccc1Cl)[N+](=O)[O-]',\n",
              " 'c1cc2c(c3CN[C@H]4c5cc6c(cc5C[C@H]([C@H]4c13)O)OCO6)OCO2',\n",
              " 'c1ccc2c(c1)cccc2OP(=O)(Oc1cccc2ccccc12)Oc1cccc2ccccc12',\n",
              " 'CCCCCCCCCCCCCCCC',\n",
              " 'C(=C\\\\c1ccco1)/C=N/NC(=O)N',\n",
              " 'CCC(=O)NNC(=O)CC',\n",
              " 'c1ccc(cc1)c1cccc(c1)N=Cc1ccc(cc1)[N+](=O)[O-]',\n",
              " 'c1cc(cnc1)C(=O)N',\n",
              " 'c1cc(ccc1Cl)OP(=O)(Oc1ccc(cc1)Cl)Oc1ccc(cc1)Cl',\n",
              " 'CN(C(=O)C(=O)N(C)[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'C=CC(C)(C)c1c(C[C@H]2C(=O)N[C@@H](C)C(=O)N2)c2cc(CC=C(C)C)cc(CC=C(C)C)c2[nH]1',\n",
              " 'CCOC(=O)C(C)SC1=N/C(=C\\\\c2ccccc2)/C(=O)N1c1ccccc1',\n",
              " 'c1ccc(cc1)N(c1ccccc1)C(=O)N1CCCCC1',\n",
              " 'CC(c1cccc(c1)C(=O)c1ccccc1)C(=O)O',\n",
              " 'c1(c(c(Cl)nc(c1Cl)Cl)Cl)Cl',\n",
              " 'c1ccc(c(c1)N)[N+](=O)[O-]',\n",
              " 'CC1CCC(CC1)C(C)(C)OC(=O)NC(=O)N',\n",
              " 'C(#N)/C(=C(\\\\C#N)/N)/N',\n",
              " 'C(C1C(=O)NC(=O)N1)C(=O)O',\n",
              " 'CCCCCCCCCCOC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)c1cccc(c1[N+](=O)[O-])O',\n",
              " 'CC1(C)[C@@H]2CC[C@@]1(C)C(=O)C2',\n",
              " 'CCN(CC)CCOC(=O)c1ccc(cc1)NC(=O)/C=C\\\\C(=O)O',\n",
              " 'Cc1cc(C)c(c(c1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'C=C(C)C1C/C(=N/Nc2ccccc2)/C(=C(C1)/N=N/c1ccccc1)C',\n",
              " 'C([C@H]([C@H]([C@@H]([C@H](CS)O)O)O)O)O',\n",
              " 'CCCCCCCCCCP(=O)(O)O',\n",
              " 'c1c(c(Cl)nc(c1Cl)Cl)Cl',\n",
              " 'c1ccc(cc1)/N=N/c1ccc(cc1)Oc1nc(Cl)nc(Cl)n1',\n",
              " 'C=C1CCC[C@]2(C)C[C@]3(C(=C(C)C(=O)O3)C[C@@H]12)O',\n",
              " 'C([C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O)O',\n",
              " 'c1ccc(cc1)COC(=O)NCCCCCC(=O)NCCCCCC(=O)NCCCCCC(=O)O',\n",
              " 'Cc1cc(ccc1OCC(=O)Nc1ccccc1)Cl',\n",
              " '[C@@H]([C@H](C(=O)O)O)(C(=O)O)O',\n",
              " 'CN(C)CC(=O)O',\n",
              " 'c1ccc2c(c1)C=Cc1ccccc1N2C(=O)N',\n",
              " 'C1=C(C(=O)C(=CC1=O)Br)Br',\n",
              " 'c1ccc(cc1)c1c[nH]c(c2ccccc2)n1',\n",
              " 'c1ccc(cc1)P(=O)(c1ccccc1)c1ccccc1',\n",
              " 'COc1c(cc(cc1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CCCC(=O)O[C@@]1(CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3[C@H](C[C@]12C)O)C(=O)COC(=O)CC',\n",
              " 'CCCCCC(=O)O[C@H]1CC[C@@]2(C)C(=CC[C@H]3[C@@H]4CC[C@H]([C@H](C)CCCC(C)C)[C@@]4(C)CC[C@H]23)C1',\n",
              " 'c1ccc(cc1)c1ccc(cc1)Cl',\n",
              " 'C=C(C)[C@@H]1CC[C@@]2(CC[C@]3(C)[C@H](CC[C@@H]4[C@@]5(C)CC[C@@H](C(C)(C)[C@@H]5CC[C@@]34C)O)[C@@H]12)CO',\n",
              " 'CCCCC(=O)O[C@H]1CC[C@H]2[C@@H]3CCc4cc(ccc4[C@H]3CC[C@]12C)OC(=O)c1ccccc1',\n",
              " 'C1CCC2C(C1)C1CC2NC2CCCC(NC3CC(C4CCCCC34)NC3CCCC(N1)N3)N2',\n",
              " 'c1cc(cc(c1)N=NC(=O)N)C(=O)N',\n",
              " 'C1CCC(C(C1)Cl)SC1CCCCC1Cl',\n",
              " 'CC(=O)Nc1ccccc1',\n",
              " 'CC(C(C(=O)O)O)(C(=O)O)O',\n",
              " 'c1ccnc(c1)NS(=O)(=O)c1ccc(cc1)N',\n",
              " 'Cc1c(cc(c(C)c1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CN1Cc2c(ccc3c2OCO3)[C@H]2[C@H](Cc3cc4c(cc3[C@@H]12)OCO4)O',\n",
              " 'Cc1ccc2c(c1)nc(Nc1ccccc1)n2/C(=N\\\\c1ccccc1)/Nc1ccccc1',\n",
              " 'c1ccc(cc1)N(c1ccccc1)c1ccc(cc1)/N=N/c1nc2ccc(cc2s1)N(=O)=O',\n",
              " 'c1ccc(cc1)CCCOC(=O)NC(=O)N',\n",
              " 'COc1ccccc1OCC(COC(=O)N)O',\n",
              " 'COc1cc(ccc1O)c1cc(c2ccc(cc2)O)[nH]c(=O)c1C#N',\n",
              " 'CCCCCCCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'C(CCCCCCC(=O)O)CCCCCC1CCCC1',\n",
              " 'C(C(=O)O)n1cnnn1',\n",
              " 'COc1ccccc1NC(C(Cl)(Cl)Cl)Nc1ccccc1OC',\n",
              " 'c1ccc2c(c1)c1cccc3cccc2c13',\n",
              " 'c1ccc(cc1)CSSCc1ccccc1',\n",
              " 'c1ccc(cc1)[C@@H]1OC[C@@H](CO1)OC(=O)c1ccc(cc1)Br',\n",
              " 'CCCCCCCCCCCCCCC1CCC(=O)O1',\n",
              " 'c1ccc(cc1)Nc1ccc(cc1)/N=N/c1ccc(c2ccccc12)N(=O)=O',\n",
              " 'CC(=O)Nc1ccc(cc1Cl)Cl',\n",
              " 'CC[C@H](/C=C/[C@@H](C)[C@H]1CC[C@H]2[C@@H]3CC=C4C[C@H](CC[C@]4(C)[C@H]3CC[C@]12C)O)C(C)C',\n",
              " 'c1ccc(cc1)P(=S)(NN)NN',\n",
              " 'C([C@@H]([C@H]([C@@H](CO)O)O)O)O',\n",
              " 'CC(C)Cc1ccc(cc1)C(C)C(=O)O',\n",
              " 'C=C(C)C1CCC(C)CC1OC(=O)NC(=O)N',\n",
              " 'CCC(CC)(Cc1ccc(C)cc1)C(=O)NO',\n",
              " 'c1ccc(cc1)C1SC(c2ccccc2)SC(c2ccccc2)S1',\n",
              " 'COc1cc(C=CC(=O)CC(=O)C=Cc2ccc(cc2)O)ccc1O',\n",
              " 'c1cc(ccc1c1coc2c(ccc(c2[C@H]2[C@@H]([C@H]([C@@H]([C@@H](CO)O2)O)O)O)O)c1=O)O',\n",
              " 'C(c1c[nH]cn1)[C@@H](C(=O)O)N',\n",
              " 'c1ccc2c(c1)c(c1cc(ccc1n2)F)N',\n",
              " 'c1cc(cc(c1)[N+](=O)[O-])NC(=S)N',\n",
              " 'CCC(C)(C)OC(=O)NC(=O)N',\n",
              " 'CCN(CC)CCNC(=O)c1ccc(cc1)NC(=O)CCC(=O)O',\n",
              " 'C=CCNC(=O)N(c1ccccc1)c1ccccc1',\n",
              " 'CCCCCCCCCOC(=O)NC(=O)N',\n",
              " 'CCCC(CCC)(c1ccccc1)C(=O)NO',\n",
              " 'c1cc(cc(c1)S(=O)(=O)N)S(=O)(=O)N',\n",
              " 'CC(C)CCCC(C)[C@H]1CC[C@H]2[C@@H]3CC=C4C[C@H](CC[C@]4(C)[C@H]3CC[C@]12C)O',\n",
              " 'CC(C)CCC[C@@H](C)[C@H]1CC[C@H]2[C@@H]3CC=C4C[C@H](CC[C@]4(C)[C@H]3CC[C@]12C)OC(=O)CC(C)C',\n",
              " 'CC(C)CC(C)(C)OC(=O)NC(=O)N',\n",
              " 'CCCSc1ccc2c(c1)nc([nH]2)NC(=O)OC',\n",
              " 'c1cc2c(ccc(c2nc1)C(=O)O)[N+](=O)[O-]',\n",
              " 'Cc1ccc(cc1)N/C(=N/c1ccc(C)cc1)/n1c2ccc(C)cc2nc1Nc1ccc(C)cc1',\n",
              " 'c1ccc(cc1)c1ccc(c(c1)O)[N+](=O)[O-]',\n",
              " 'c1ccc(cc1)C(c1ccccc1)C(c1ccccc1)(c1ccccc1)O',\n",
              " 'Cc1coc2c3ccc4c(CCCC4(C)C)c3C(=O)C(=O)c12',\n",
              " 'CCCCCCCCC/C=C/C(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'C(CCCCCNCCCCN)CCCCC(=O)O',\n",
              " 'c1c(cc(c(c1[N+](=O)[O-])N)[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'COc1cc(ccc1O)c1cc(c2ccc(cc2)Cl)[nH]c(=O)c1C#N',\n",
              " 'COC(=O)NC(=O)N',\n",
              " 'CCOc1ccc(c(c1)n1nc2ccccc2n1)NC(=O)C',\n",
              " 'CC(=C1CC(=O)C(C)C(=O)C1)C',\n",
              " 'CCn1cc(c(=O)c2cc(c(nc12)N1CCNCC1)F)C(=O)O',\n",
              " 'COc1cc(Cc2cnc(N)nc2N)cc(c1OC)OC',\n",
              " 'C(CCCCCNCCCCCCCCN)CCCCC(=O)O',\n",
              " 'c1ccc(c(c1)C(=O)O)Cl',\n",
              " 'c1ccc(cc1)c1cc(=O)c2c(cc(c(c2O)O)O[C@H]2[C@@H]([C@H]([C@@H]([C@@H](C(=O)O)O2)O)O)O)o1',\n",
              " 'C1CN2CCN1CC2',\n",
              " 'CC(=NNc1ccc(cc1[N+](=O)[O-])[N+](=O)[O-])c1ccccc1',\n",
              " 'CC(C)(C)c1ccc(cc1)C(=O)NN(C)C',\n",
              " 'c1ccc(cc1)/C=C(\\\\c1ccccc1)/C(=O)c1ccccc1',\n",
              " 'c1ccc(c(c1)Cl)OP(=O)(O)Oc1ccccc1Cl',\n",
              " 'c1cc(c(cc1c1coc2cc(cc(c2c1=O)O)O)O)O',\n",
              " 'c1cc(ccc1Cl)N1CC(=NC1=O)N1CCOCC1',\n",
              " 'c1cc(ccc1C(=O)OCC(CO)O)Br',\n",
              " 'c1ccc(c(c1)[N+](=O)[O-])O',\n",
              " 'CCOc1c2c(c(c(c1OCC)OCC)N)C(=O)OC2C1c2c(CCN1C)cc1c(c2OC)OCO1',\n",
              " 'C/C=C/C(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'CCOC(=O)CSC1=N/C(=C\\\\c2ccccc2)/C(=O)N1c1ccccc1',\n",
              " 'c1ccc(cc1)/N=N/c1ccc(c2ccccc12)N(c1ccccc1)c1ccccc1',\n",
              " 'c1ccc2c(c1)C(=Nc1ccccc1[N+](=O)[O-])OC2=O',\n",
              " 'c1ccc(cc1)N=Cc1ccc(cc1[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CC(=O)CC(c1ccccc1)C(CC(=O)C)c1ccccc1',\n",
              " 'CCCCCCCCCCCCCCCCCNC(=O)CCCCC(=O)O',\n",
              " 'c1ccc(c(c1)[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CCCCCCCC/C=C/C(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'CCOC1(C(c2ccccc2)Br)C(=O)N(C)C(=O)N1',\n",
              " 'C#CCSC[C@@H](C(=O)O)N',\n",
              " 'C1CCC(CC1)C(=O)N(C1CCCCC1)O',\n",
              " 'CC(C(=NNC(=O)N)C)c1ccccc1',\n",
              " 'CC1(C)C2CCC1(C)C(=NO)C2=NO',\n",
              " 'c1cc(c(c(c1)[N+](=O)[O-])O)[N+](=O)[O-]',\n",
              " 'Cc1ccc(cc1)Nc1[nH]c2ccc(C)cc2n1',\n",
              " 'CC[C@H](C)[C@@H](C(=O)O)NC(=O)C',\n",
              " 'c1cc2c(Cc3cc(ccc23)N)cc1Cl',\n",
              " 'Cc1c(C)noc1N(C(=O)C)S(=O)(=O)c1ccc(cc1)N',\n",
              " 'c1ccc2c(c1)nc1cccc(c1n2)C(=O)O',\n",
              " 'c1ccc(cc1)C=NNc1ccccc1',\n",
              " 'c1cc(cc(c1)Cl)c1cc(c(C(=O)NCC(=O)O)nc1)O',\n",
              " 'Cc1cccc2cc(C(=O)O)c(=O)oc12',\n",
              " 'CCC(CC)OC(=O)NC(=O)N',\n",
              " 'COc1ccc(c(c1)OC)/N=C/c1ccc(cc1N(=O)=O)N(=O)=O',\n",
              " 'C(CCNC(=O)N)CC(=O)O',\n",
              " 'CC(C(=O)O)NC(=O)Nc1cccc2ccccc12',\n",
              " '[C@@H]1([C@@H]([C@H]([C@H]([C@@H]([C@H]1Cl)Cl)Cl)Cl)Cl)Cl',\n",
              " 'c1ccc(cc1)C(=C(c1ccccc1)c1ccccc1)c1ccccc1',\n",
              " 'C1CCN2C[C@H]3C[C@H](CN4CCC[C@@H]([C@H]34)O)[C@@H]2C1',\n",
              " 'CN(C)c1ccc(cc1)C(=N)c1ccc(cc1)N(C)C',\n",
              " 'CCCC(Cc1ccccc1)/N(N(\\\\O)/C(CCC)Cc1ccccc1)/O',\n",
              " 'c1cc(c(c(c1)[N+](=O)[O-])Cl)C(=O)O',\n",
              " 'c1ccc(cc1)C(=NNc1ccc(cc1[N+](=O)[O-])[N+](=O)[O-])c1ccccc1',\n",
              " 'c1cn([C@@H]2CS[C@H](CO)O2)c(=O)nc1N',\n",
              " 'Cc1c2ccccc2c(C)c2ccccc12',\n",
              " 'Cn1cc(c(=O)[nH]c1=O)[N+](=O)[O-]',\n",
              " 'CCCCCC(=O)NCCNC(=O)CCCCC',\n",
              " 'C=C(C)[C@H]1Cc2c(ccc3C(=O)[C@H]4c5cc(c(cc5OC[C@H]4Oc23)OC)OC)O1',\n",
              " 'C[C@@H](Cc1ccccc1)N(O)N(O)[C@H](C)Cc2ccccc2',\n",
              " 'C[C@]12CCC(=O)C=C1CC[C@H]1[C@@H]3CC[C@](C(=O)CO)([C@@]3(C)C[C@@H]([C@H]21)O)O',\n",
              " 'CCc1ccc(cc1)C(=O)c1ccccc1C(=O)O',\n",
              " 'CC(C)(C)c1cc(c(cc1O)C(C)(C)C)O',\n",
              " 'C[C@]12CCC=CC1=CC[C@@H]1[C@@H]2CC[C@]2(C)[C@H](CC[C@]12O)c1ccc(=O)oc1',\n",
              " 'CCCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'c1ccc(c(c1)Cl)OC(=O)NC(=O)N',\n",
              " 'C(CC(=O)O)C(=O)O',\n",
              " 'c1ccc2c(c1)c1NC3=CC=CC(=Nc4c5ccccc5c([nH]4)N=c4c5ccccc5c(=Nc2[nH]1)[nH]4)C3',\n",
              " 'CC(=O)Oc1ccc(cc1C(=O)O)n1nc2ccccc2n1',\n",
              " 'c1c(cc(c(c1O)O)O)[C@@H]1[C@H](C(=O)c2c(cc(cc2O1)O)O)O',\n",
              " 'CC(CO)[C@H](C(=O)N[C@@H]1C2CC[C@@](C)([C@@H]1O)C2(C)C)O',\n",
              " 'CC(=O)CC1=C(C(=O)O[C@@H]1C)C(C)(C)[C@@H]1C(=O)C(C)OC1=O',\n",
              " 'c1ccc(c(c1)C(=O)N=[N+]=[N-])[N+](=O)[O-]',\n",
              " 'C(C(=O)O)C(CC(=O)O)(C(=O)O)O',\n",
              " 'C(CCCCCNCCCCCCCN)CCCCC(=O)O',\n",
              " 'CCCCCCCCCCCCCC(C(C(=O)O)O)O',\n",
              " 'COC(=O)C(C(C(=O)OC)OC(=O)c1ccccc1)OC(=O)c1ccccc1',\n",
              " 'c1ccc2c(c1)c1=Nc3cccc(=Nc4c5ccccc5c(N=c5cccc([nH]5)N=c2[nH]1)[nH]4)[nH]3',\n",
              " 'C[N+](C)(C)CC(=O)[O-]',\n",
              " 'C[C@@H](c1cc2ccc(cc2nc1c1ccccn1)F)Nc1c2c([nH]cn2)ncn1',\n",
              " 'c1ccc(cc1)C(=O)N',\n",
              " 'CCn1cc(c(=O)c2cc3c(cc12)OCO3)C(=O)O',\n",
              " 'c1cc(ccc1c1cc(=O)c2c(cc(cc2o1)O)O)O',\n",
              " 'CC(C)[C@@H](C(=O)O)N',\n",
              " 'CC(C(=O)O)N(CCC(=O)O)C(=O)C',\n",
              " 'C1=CC[C@@H]2[C@H](C1)C(=O)N(C2=O)SC(Cl)(Cl)Cl',\n",
              " 'CCN(CC)CCOC(=O)c1ccc(cc1)NC(=O)CCC(=O)O',\n",
              " 'CC(=O)O[C@H]1CC[C@H]2[C@H]3Cc4ccc(c5c4[C@@]2(CCN3C)[C@H]1O5)OC',\n",
              " 'c1ccc(cc1)c1cc(ccc1[N+](=O)[O-])O',\n",
              " 'CC(=O)[C@@]12[C@@H](C[C@H]3[C@@H]4CCC5=CC(=O)CC[C@]5(C)[C@H]4CC[C@]13C)O2',\n",
              " 'CCOc1cc(c(cc1[N+](=O)[O-])[N+](=O)[O-])Cl',\n",
              " 'c1cc(ccc1[C@H](C(=O)O)N)O',\n",
              " 'c1ccc(cc1)Nc1ccc(cc1N)Cl',\n",
              " 'COc1ccc(cc1)N=Cc1ccccc1',\n",
              " 'Cc1cc(ccc1Nc1c(cc(C)s1)C#N)N(=O)=O',\n",
              " 'c1ccc(cc1)P(=O)(c1ccccc1)c1cc(ccc1O)O',\n",
              " 'Cc1ccc(cc1)S(=O)(=O)N',\n",
              " 'c1c(cc(c(c1O)O)O)C(=O)O',\n",
              " 'CCCC(CCC)(C1CCCCC1)C(=O)NO',\n",
              " 'C=C[C@H]1CN2CC[C@H]1C[C@H]2[C@@H](c1ccnc2ccc(cc12)OC)O',\n",
              " 'c1ccc(c(c1)C(=O)O)Nc1cccc(c1)C(F)(F)F',\n",
              " 'CC(C)c1cc(c(C)c(c1)N)N',\n",
              " 'CCCCC(C(=O)[O-])[NH3+]',\n",
              " 'Cc1ccc2ccc(C)cc2c1',\n",
              " 'c1cc(cc(c1)Nc1ccc(cc1N)Cl)Cl',\n",
              " 'CCC(=Cc1ccco1)C=NNC(=O)N',\n",
              " 'CC1(C)[C@H](CC[C@@]1(C)C(=O)O)C(=O)O',\n",
              " 'C=CC(CC)OC(=O)NC(=O)N',\n",
              " 'C(CS(=O)(=O)CCCl)Cl',\n",
              " 'CC(=C(c1ccccc1)O)C(=O)c1ccccc1',\n",
              " 'Cc1c(C#N)c(c2ccccc2)n(c2ccccc2)n1',\n",
              " 'COc1cc(ccc1O)CC1COC(=O)C1Cc1ccc(c(c1)OC)O',\n",
              " 'c1cc(cc(c1)[N+](=O)[O-])C(=O)N=[N+]=[N-]',\n",
              " 'c1c(c(cc(c1[N+](=O)[O-])O)O)[N+](=O)[O-]',\n",
              " 'Cc1ccc(C)c(c1)OP(=O)(Oc1cc(C)ccc1C)Oc1cc(C)ccc1C',\n",
              " 'CC(C)[C@@H](C)/C=C/[C@@H](C)[C@H]1CC[C@H]2/C(=C/C=C\\\\3/C[C@H](CCC3=C)O)/CCC[C@]12C',\n",
              " 'c1ccc(cc1)NN=C(C#N)C#N',\n",
              " 'CC(C)NCC(COc1ccc(cc1)CC(=O)N)O',\n",
              " 'c1ccc(cc1)C(c1ccccc1)(C(c1ccccc1)(c1ccccc1)O)O',\n",
              " 'CCn1cc(c(=O)c2ccc(C)nc12)C(=O)O',\n",
              " 'CC1(C)CC[C@@]2(CC[C@]3(C)C(=CC[C@@H]4[C@@]5(C)CC[C@@H](C(C)(C)[C@@H]5CC[C@@]34C)O)[C@@H]2C1)C(=O)O',\n",
              " 'CC(C)COC(=O)c1ccc(cc1)N=Nc1ccc(cc1N)N',\n",
              " 'C(COCn1cnc2c1[nH]c(N)nc2=O)O',\n",
              " 'c1cc(C=C2COCC(=Cc3ccco3)C2=O)oc1',\n",
              " 'CC(CCC1CCCCC1)OC(=O)NC(=O)N',\n",
              " 'Cc1ccc2cc(C(=O)O)c(=O)oc2c1',\n",
              " 'COc1ccc(cc1)S(=O)(=O)NNC(=O)C(c1ccccc1)(c1ccccc1)O',\n",
              " 'C([C@@H](C(=O)O)N)O',\n",
              " 'CCOC(=O)C(c1ccccc1)SC1=NC(=Cc2ccccc2)C(=O)N1c1ccccc1',\n",
              " 'COc1ccc(cc1)N=Cc1ccc(cc1[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'c1ccc2cc3ccccc3cc2c1',\n",
              " 'CCC(CC)CC1(CCO)C(=O)NC(=S)NC1=O',\n",
              " 'C1CC1n1cc(c(=O)c2cc(c(cc12)N1CCNCC1)F)C(=O)O',\n",
              " 'c1ccc(cc1)C(=O)C=P(c1ccccc1)(c1ccccc1)c1ccccc1',\n",
              " 'CN(C)c1ccc(cc1)C(=NC(=S)Nc1ccccc1)c1ccc(cc1)N(C)C',\n",
              " 'C[C@H]1C[C@](C)(C[C@@H]([C@@H](CC2CC(=O)NC(=O)C2)O)C1=O)O',\n",
              " 'c1cc(cc(c1)O)C(=O)N',\n",
              " 'CC1(C)COP(=O)(C(C)(C)O)OC1',\n",
              " 'c1ccc2c(c1)Cc1ccccc21',\n",
              " 'CCS(=O)(=O)C(C)(C(Cl)(Cl)Cl)S(=O)(=O)CC',\n",
              " 'CO/N=C(/c1csc(N)n1)\\\\C(=O)O',\n",
              " 'C1[C@@H]([C@H]([C@@H]([C@H]([C@@H]1N)O[C@@H]1[C@@H]([C@H]([C@@H]([C@@H](CO)O1)O)N)O)O)O[C@@H]1[C@@H](C[C@@H]([C@@H](CN)O1)O)N)N',\n",
              " 'c1ccc(cc1)SCCS(=O)CCSc1ccccc1',\n",
              " 'CN1CCN(CC1)c1cc2c(cc1F)c(=O)c(cn2c1ccc(cc1)F)C(=O)O',\n",
              " 'COCN1C(=O)c2ccccc2C1=O',\n",
              " 'c1ccc(cc1)C(=O)c1ccc(cc1)C(=O)c1ccccc1',\n",
              " 'CCC[C@H]1CC2=C(C[C@@H](CC)CC3=C1C(=O)OC3=O)C(=O)OC2=O',\n",
              " 'CCCCCCCCOC(=O)c1cc(c(c(c1)O)O)O',\n",
              " 'Cc1ccc2c(c1)C(=O)c1cc(ccc1C2=O)C(=O)O',\n",
              " 'COc1cc(c(c(c1OC)[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'c1ccc(cc1)COP(=O)(OCc1ccccc1)OCc1ccccc1',\n",
              " 'CCCC(C)CC1(CCO)C(=O)NC(=S)NC1=O',\n",
              " 'CCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'c1cc(ccc1Cl)Cl',\n",
              " 'CC(C)CCC1(CCO)C(=O)NC(=S)NC1=O',\n",
              " 'CN(C)CCn1c(nnn1)S',\n",
              " 'c1cc(ccc1C(=O)Nc1ccc(cc1N)N)N',\n",
              " 'c1ccc2c(c1)C(=O)OC2(Nc1cccc(c1)[N+](=O)[O-])Nc1cccc(c1)[N+](=O)[O-]',\n",
              " 'C[C@H]1C(=O)CC[C@@H]2[C@]1(C)CC[C@H]1[C@@]2(C)CC[C@@]2(C)[C@@H]3CC(C)(C)CC[C@]3(C)CC[C@]12C',\n",
              " 'CC1(C)COP(=O)(NCCNP2(=O)OCC(C)(C)CO2)OC1',\n",
              " 'C[C@]12C=CC(=O)C=C1CC[C@H]1[C@@H]3CC[C@](C(=O)CO)([C@@]3(C)C[C@@H]([C@H]21)O)O',\n",
              " 'CC(=O)NCC(=O)O',\n",
              " 'CC(=O)Nc1cccc2ccccc12',\n",
              " 'CCCCCCCCCCC(C)(C)C(=O)NO',\n",
              " 'CC(C(=O)c1ccccc1)C(=O)c1ccccc1',\n",
              " 'C1C[C@@H](C(=O)O)NC1',\n",
              " 'c1[nH]cnn1\\t',\n",
              " 'c1cc2c(cc1CC(=O)O)OCO2\\t',\n",
              " 'Cc1cc(C)cc(c1)C=O\\t',\n",
              " 'c1c(cc(cc1[N+](=O)[O-])[N+](=O)[O-])C(=O)O\\t',\n",
              " 'c1cc(cnc1)N\\t',\n",
              " 'c1cc(N)nnc1\\t',\n",
              " 'c1cc(c(cc1C(=O)O)[N+](=O)[O-])Cl\\t',\n",
              " 'c1cc(ccc1C=O)Cl\\t',\n",
              " 'CN(C)c1ccc(C=CC=O)cc1\\t',\n",
              " 'c1cc(ccc1C(=O)O)F\\t',\n",
              " 'C1C2CC3CC1CC(C2)(C3)N\\t',\n",
              " 'c1cc(ccc1C(=O)O)O\\t',\n",
              " 'c1cc(ccc1CC(=O)O)O\\t',\n",
              " 'Cc1ccc(cc1)C(=O)O\\t',\n",
              " 'C(CCC(=O)O)CCN\\t',\n",
              " 'c1cc(N)nnc1Cl\\t',\n",
              " 'C(CCCC(=O)O)CCCBr\\t',\n",
              " 'c1ccc(c(c1)CC(=O)OCC(=O)O)Nc1c(cccc1Cl)Cl\\t',\n",
              " 'CC(=O)Oc1ccccc1C(=O)O\\t',\n",
              " 'CC(C(=O)O)N\\t',\n",
              " 'CCC1(CC)C(=O)NC(=O)NC1=O\\t',\n",
              " 'CCCCCCCCCCCCCCCCCCN\\t',\n",
              " 'c1cc2c(cc1C(=O)O)[nH]nn2\\t',\n",
              " 'CC(C)(C)OC(=O)NCC(=O)O\\t',\n",
              " 'CCC(C)C1(CC)C(=O)NC(=O)NC1=O\\t',\n",
              " 'c1ccc2c(c1)C(=O)c1cccc(c1C2=O)Cl\\t',\n",
              " 'Cc1c(CSCCN/C(=N/C)/NC#N)nc[nH]1\\t',\n",
              " 'c1ccc(cc1)C=CC(=O)O\\t',\n",
              " 'C/C=C/C(=O)O\\t',\n",
              " 'c1ccc2c(c1)c(C=O)c1ccccc1c2Cl\\t',\n",
              " 'c1ccc(cc1)/C=C/C(=O)/C=C/c1ccccc1\\t',\n",
              " 'COC(=O)/C=C/C(=O)OC\\t',\n",
              " 'c1ccc(cc1)S(=O)(=O)c1ccccc1\\t',\n",
              " 'c1ccc(cc1)C(c1ccccc1)C(=O)O\\t',\n",
              " 'C[C@]12CCCCC1=CC[C@@H]1[C@@H]2CC[C@@]2(C)[C@H]1C[C@H](C2=O)F\\t',\n",
              " 'c1cc(CNc2cc(c(cc2C(=O)O)S(=O)(=O)N)Cl)oc1\\t',\n",
              " 'C(C(=O)O)N\\t',\n",
              " 'C[C@H]1COc2c3c(cc(c2N2CCN(C)CC2)F)c(=O)c(cn13)C(=O)O\\t',\n",
              " 'c1ccc(c(c1)C(=O)O)N=Nc1ccc(cc1)O\\t',\n",
              " 'C(=CC(=O)O)C(=O)O\\t',\n",
              " 'c1cc(cc(c1)Nc1c(cccn1)C(=O)O)C(F)(F)F\\t',\n",
              " 'Cc1ccc(cc1)S(=O)(=O)O\\t',\n",
              " 'Cc1ccc(cc1)S(=O)(=O)C[N+]#[C-]\\t',\n",
              " 'CCC1(c2ccccc2)C(=NC(=O)N=C1O)O\\t',\n",
              " 'c1ccc(cc1)CC(=O)O\\t',\n",
              " 'COc1cc(/C=C/C(=O)O)cc(c1O)OC\\t',\n",
              " 'C(CCCC(=O)O)CCC(=O)O\\t',\n",
              " 'c1ccc(cc1)C(c1ccccc1)(c1ccccc1)c1ccccc1\\t',\n",
              " 'CCCCNC(=O)C(c1cc2ccccc2c2ccccc12)N(Cc1ccco1)C(=O)Cc1ccccc1\\t',\n",
              " 'CC(C)(C)NC(=O)C(c1ccccc1)N(Cc1ccco1)C(=O)CNC(=O)OC(C)(C)C\\t',\n",
              " 'CCCCCCCCCCC(=O)O\\t',\n",
              " 'C(CC(=O)O)C(=O)C(=O)O\\t',\n",
              " 'COc1cc(ccc1O)C=O\\t',\n",
              " 'COc1ccc(cc1OC)C=O\\t',\n",
              " 'CCC(c1ccccc1)C(=O)O\\t',\n",
              " 'Cc1ccc(cc1C)C=O\\t',\n",
              " 'CC(C)COC(=O)[C@H]([C@@H](C(=O)OCC(C)C)O)O',\n",
              " 'CCC(CC)(CC(C)C)OC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)COC(=O)NC(=O)N',\n",
              " 'CCCCC(CC)CC(CC)(CC)C(=O)NO',\n",
              " 'c1ccc(cc1)N(c1ccccc1)c1ccc(cc1)/N=N/c1ccc(cc1)S(=O)(=O)C(F)(F)F',\n",
              " 'CC1CCCCC1OC(=O)NC(=O)N',\n",
              " 'CN1CC[C@]23[C@H]4C=C[C@@H]([C@@H]3Oc3c(ccc(C[C@@H]14)c23)OC)O',\n",
              " 'CCC1(CCO)C(=O)NC(=S)NC1=O',\n",
              " 'CN(C)c1ccc(cc1)C(C#N)(c1ccccc1)c1ccc(cc1)N(C)C',\n",
              " 'c1ccc2c(c1)c(=O)nc(c1ccccc1S(=O)(=O)N)[nH]2',\n",
              " 'c1ccc(cc1)C(c1ccccc1)(C(=O)NNS(=O)(=O)c1ccccc1)O',\n",
              " 'CCCCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'c1cc(ccc1C(=O)O)[N+](=O)[O-]',\n",
              " 'c1cc(ccc1C(=O)N[C@@H](CCC(=O)O)C(=O)O)NCc1cnc2c(c(=O)nc(N)[nH]2)n1',\n",
              " 'CCOC(=O)NC(C)(C(=O)O)NC(=O)OCC',\n",
              " 'Cc1c(c(c(c(c1Br)Br)Br)Br)Br',\n",
              " 'c1ccc(cc1)Nc1ccc(cc1)n1nc2ccccc2n1',\n",
              " 'COc1ccc(cc1)N=Cc1ccc(cc1)[N+](=O)[O-]',\n",
              " 'COc1cc(C=CC(=O)O)cc(c1O)OC',\n",
              " 'CCOC(=O)Nc1ccc(cc1N)NCc1ccc(cc1)F',\n",
              " 'CC[C@H](C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H]3C[C@H](CC(=O)O3)O)[C@@H]12',\n",
              " 'Cc1ccc(cc1)C(CCC(c1ccccc1)(c1ccc(C)cc1)O)(c1ccccc1)O',\n",
              " 'c1ccc2cc(ccc2c1)OP(=O)(Oc1ccc2ccccc2c1)Oc1ccc2ccccc2c1',\n",
              " 'c1ccc2c(c1)c1CCCC(=O)c1[nH]2',\n",
              " 'CC1(C)C2CC[C@@]1(C)C(=O)/C/2=C(/c1ccccc1)\\\\O',\n",
              " 'CCCCCCCCCCCCCCCCCNC(=O)CCCCCCCCC(=O)O',\n",
              " 'CN1[C@H]2CC[C@@H]1[C@H]([C@H](C2)O)C(=O)O',\n",
              " 'c1ccc2c(c1)c1=NC3=CC=CC(=Nc4c5ccccc5c(N=C5C=CC=C(C5)N=c2[nH]1)[nH]4)C3',\n",
              " 'c1ccc(cc1)/C=C/c1ccccc1',\n",
              " 'CCCn1c2c(c(=O)n(CCC)c1=O)[nH]c(C13CC4CC(CC3C4)C1)n2',\n",
              " 'c1ccc(cc1)C(=O)N=[N+]=[N-]',\n",
              " 'CCCCCCCCCCCCCCCCCCCCCCCC',\n",
              " 'c1ccc(cc1)N(c1ccccc1)c1ccc(cc1)/N=N/c1ccc(cc1C(F)(F)F)N(=O)=O',\n",
              " 'CCCCCCCCCCCC(=O)O',\n",
              " 'c1cc(ccc1C=CC(=O)CC(=O)C=Cc1ccc(cc1)O)O',\n",
              " 'CCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'CS(=O)(=O)c1ccc(cc1)C=O',\n",
              " 'CCCCCCCCCCCCCCCc1cccc(c1)O',\n",
              " 'CC(C)c1ccc(cc1)NC(=O)N(C)C',\n",
              " 'Cc1ccccc1S(=O)(=O)NNC(=O)C(c1ccccc1)(c1ccccc1)O',\n",
              " 'c1cc2c3ccc(cc3C(c2cc1Br)O)N',\n",
              " 'CC(C)CCC[C@@H](C)[C@H]1CC[C@H]2[C@@H]3CC=C4C[C@H](CC[C@]4(C)[C@H]3CC[C@]12C)OC(=O)C(C)C',\n",
              " 'CCCCCCCCCCCCCCCCOC(=O)CCCCCCCCCCCCCCC',\n",
              " 'Cc1cc(cc(C)c1Oc1c(c(N)nc(Nc2ccc(cc2)C#N)n1)Br)C#N',\n",
              " 'CC(c1ccccc1)OC(=O)NC(=O)N',\n",
              " 'c1cc(c(c(c1)Cl)Cl)c1c(N)nc(N)nn1',\n",
              " 'c1ccc(cc1)C(c1ccccc1)(C(=O)NNS(=O)(=O)c1ccc(cc1)N)O',\n",
              " 'CCC(C(=O)O)NC=O',\n",
              " 'c1c(cc(cc1O)O)O',\n",
              " 'CCCC1(CCO)C(=O)NC(=S)NC1=O',\n",
              " 'CC(=O)Nc1ccc(cc1)C(=O)O',\n",
              " 'CCCCCCCOC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)c1cccc(c1)/N=C/c1ccc(cc1N(=O)=O)N(=O)=O',\n",
              " 'CCC1(CC)C(=O)NC(=S)NC1=O',\n",
              " 'CC1=NNC(=O)N(C1)/N=C/c1cccnc1',\n",
              " 'CCCCCCCCCCCCCCCC(=O)OCC(CO)O',\n",
              " 'c1cc(ccc1O)O',\n",
              " 'CCCCCCCCCCCCCCCCCC(=O)O',\n",
              " 'c1ccc2c(c1)c(CC(C(=O)O)N)c[nH]2',\n",
              " 'COc1cc(/C=C/CO)cc(c1O[C@H]1[C@@H]([C@H]([C@@H]([C@@H](CO)O1)O)O)O)OC',\n",
              " 'CCCCCCCCCCCCCCCCCC(=O)OCCCl',\n",
              " 'CN(c1ccccc1)c1c(cc(cc1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CC(C)[C@@](CC[C@](C)(C(=O)O)O)(C(=O)O)O',\n",
              " 'c1cc(I)oc1C=O',\n",
              " 'Cc1ccnc(n1)NS(=O)(=O)c1ccc(cc1)N',\n",
              " 'c1ccc(cc1)C1C(=O)C(=O)OC1c1ccc2c(c1)OCO2',\n",
              " 'c1ccc(cc1)N=Nc1ccc(cc1)N(c1ccccc1)c1ccccc1',\n",
              " 'CC(=O)Nc1ccccc1C(=O)O',\n",
              " 'CCOC(=O)C(C(C(=O)OCC)OC(=O)C)OC(=O)C',\n",
              " 'c1cc(ccc1Cl)NC(=S)NNC(=O)c1ccncc1',\n",
              " 'CCCCCCCCCCCCC1C(=O)N=C(N)S1',\n",
              " 'CCCCCCCCCCCCCCCC(=O)OC(=O)CCCCCCCCCCCCCCC',\n",
              " 'CCCC(CCC)OC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)c1ccc(cc1)/C=N/c1cccc(c1)c1ccccc1',\n",
              " 'c1cc(ccc1C(=O)N)O',\n",
              " 'CN1C(=C(c2ccccc2S1(=O)=O)O)C(=O)Nc1ccccn1',\n",
              " 'CC(C)CCC[C@@H](C)[C@H]1CC[C@H]2/C(=C/C=C\\\\3/C[C@H](CCC3=C)O)/CCC[C@]12C',\n",
              " 'Cc1cccc(c1OCC(=O)Nc1ccccc1)Cl',\n",
              " 'c1ccc(cc1)c1c(ccc(c1[N+](=O)[O-])O)[N+](=O)[O-]',\n",
              " 'CCOC(=O)[C@H]([C@H](C(=O)N)O)O',\n",
              " 'c1ccc(cc1)Nc1cccc(c1N)Cl',\n",
              " 'CN1CC[C@]23[C@H]4CC[C@@H]([C@@H]3Oc3c(ccc(C[C@@H]14)c23)OC)O',\n",
              " 'c1cc(c(cc1c1c(c(=O)c2c(cc(cc2o1)O)O)O)O)O',\n",
              " 'CC(C)CC(C)OC(=O)NC(=O)N',\n",
              " 'CCCCCCC/C=C/C(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'CCCCCCCCCCCCCCCCCC(=O)OCCO',\n",
              " 'CC(=O)Nc1cccc(c1)C(=O)O',\n",
              " 'c1cc(ccc1C=O)O',\n",
              " 'c1ccc(cc1)Nc1ccc(cc1[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CCCCCCCCC(CC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C)O',\n",
              " 'c1ccc2c(c1)cccc2[N+](=O)[O-]',\n",
              " 'CCCCCCCC[C@@H]([C@H](CCCCCCCCCCCC(=O)O)O)N(=O)=O',\n",
              " 'c1cc2c(Cc3cc(ccc23)N)cc1Br',\n",
              " 'c1cc(c(c2c1cc(cc2[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-])Cl',\n",
              " 'Cc1ccc(cc1[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CC(C)OC(=O)NC(=O)N',\n",
              " 'CCCCOC(=O)NC(=O)N',\n",
              " 'c1ccc2c(c1)nc(CSCc1nc3ccccc3o1)o2',\n",
              " 'CC(C)CC(C(=O)O)N',\n",
              " 'c1ccc(cc1)OC(=O)Oc1ccccc1',\n",
              " 'CCCCCCCCCCCCOC(=O)c1cc(c(c(c1)O)O)O',\n",
              " 'C1CCC(CC1)n1c(CCCCOc2ccc3c(CCC(=O)N3)c2)nnn1',\n",
              " 'COc1cc2c(cc1OC)N1C(=O)C[C@H]3[C@@H]4[C@H]5C[C@H]6[C@]2(CCN6CC5=CCO3)[C@@H]14',\n",
              " 'C=C1C[C@]23C[C@H]1CC[C@H]2[C@]12CC[C@@H]([C@](C)([C@H]1[C@@H]3C(=O)O)C(=O)O2)O',\n",
              " 'C=CCc1ccc(c(c1)OC)OCCSCCOc1ccc(CC=C)cc1OC',\n",
              " 'C=CCNC(=S)NNc1ccc(cc1)[N+](=O)[O-]',\n",
              " 'c1cc(c(cc1[N+](=O)[O-])C(=O)O)N',\n",
              " 'c1ccc(cc1)C(c1ccccc1)C(c1ccccc1)c1ccccc1',\n",
              " 'Cc1cc2C(c3ccc(c(c3C(=O)c2c(c1)O)OC)O)C1[C@@H]([C@H]([C@@H]([C@@H](CO)O1)O)O)O',\n",
              " 'c1ccc(cc1)C(=O)c1ccccc1',\n",
              " 'CCCCC(=O)O[C@H]1CC[C@@]2(C)C(=CC[C@H]3[C@@H]4CC[C@H]([C@H](C)CCCC(C)C)[C@@]4(C)CC[C@H]23)C1',\n",
              " 'CC(C)C1=CC[C@H]2C(=C1)CC[C@@H]1[C@]2(C)CCC[C@@]1(C)C(=O)O',\n",
              " 'CCCCCCCCCCC1C(=O)N=C(N)S1',\n",
              " 'c1ccc2c(c1)cccc2NC(=O)NCCC(=O)O',\n",
              " 'c1ccc(cc1)COC(=O)NCCCCCC(=O)O',\n",
              " 'CC1(C)[C@@]2(C)CC[C@@]1(C(=O)OC2=O)Cl',\n",
              " 'C1[C@@H]([C@H]([C@@H]([C@@H](O1)ON(=O)=O)ON(=O)=O)ON(=O)=O)ON(=O)=O',\n",
              " 'CC(=O)Nc1ccc(cc1)O',\n",
              " 'c1ccc2c(c1)C(=O)c1ccccc1C2=O',\n",
              " 'Cc1ccc(cc1)N(/C(=N/c1ccccc1)/Nc1ccccc1)c1[nH]c2ccc(C)cc2n1',\n",
              " 'c1cc2c(cc1C=O)OCO2',\n",
              " 'CC1(C)CC(=CC(=C(C#N)C#N)C1)/C=C/c1ccc(cc1)O',\n",
              " 'COc1ccc2c(c1)c(ccn2)[C@H]([C@@H]1C[C@H]2CCN1C[C@@H]2C(=O)O)O',\n",
              " 'c1ccc(cc1)N(c1ccccc1)c1ccc(cc1)/N=N/c1ccc(cc1)N(=O)=O',\n",
              " 'c1ccnc(c1)C(=O)O',\n",
              " 'c1ccc2c(c1)c(cc(c2Cl)[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'c1ccc(c(c1)C(=O)N)O',\n",
              " 'c1ccc(c(c1)C(=O)O)[N+](=O)[O-]',\n",
              " 'C(CCC(=O)[O-])CC[NH3+]',\n",
              " 'c1cc(C(=O)O)oc1',\n",
              " 'c1ccc(cc1)/C=C/C=N/NC(=O)C(=O)N',\n",
              " 'C[C@@H]1CCC[C@H](C1)NC(=O)c1ccc2ccccc2c1',\n",
              " 'C(C(CO[N+](=O)[O-])O[N+](=O)[O-])O[N+](=O)[O-]',\n",
              " 'CCCCCCC(CC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C)O',\n",
              " 'CC(C)(C)OC(=O)NC(=O)N',\n",
              " 'CC(C)(C)S(=O)(=O)C(C)(C)C',\n",
              " 'Cc1c(cc(cc1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'C1[C@H]([C@@H]([C@H](C(O)O1)O)O)O',\n",
              " 'CCCCCCCCCCCCCCCC(=O)NN',\n",
              " 'CCCCCCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'CC(=O)OC1C(C(C(C(C1OC(=O)C)OC(=O)C)OC(=O)C)OC(=O)C)Br',\n",
              " 'c1cc(ccc1Br)Br',\n",
              " 'CCC(C(C)C)OC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)Cn1cc(CO)nn1',\n",
              " 'C(CO)CP(=O)(CCCO)CCCO',\n",
              " 'CCCCCCCCCCCCCC(=O)O',\n",
              " 'CC(C)(CO)[C@@H](C(=O)NCCC(=O)NCCSC(=O)c1cccc2ccccc12)O',\n",
              " 'C([C@@H]1[C@H]([C@@H]([C@H]([C@@H](O1)O[N+](=O)[O-])O[N+](=O)[O-])O[N+](=O)[O-])O[N+](=O)[O-])O[N+](=O)[O-]',\n",
              " 'C(=C\\\\C(=O)O)/C=C/C(=O)O',\n",
              " 'CC1(C)C2CCC1(C)C(=O)C2Cl',\n",
              " 'c1cc(ccc1N)[N+](=O)[O-]',\n",
              " 'Cc1c(c(=O)ccn1C)O',\n",
              " 'c1c(cc(cc1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CN(C)c1ccc(cc1)NC(=O)N(C=O)c1ccc(cc1)N(C)C',\n",
              " 'c1cc(c(cc1[N+](=O)[O-])[N+](=O)[O-])Oc1c(cc(cc1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CCCCCCCCCCCCCCCCC1C(=O)N=C(N)S1',\n",
              " 'c1ccc2ccccc2c1',\n",
              " 'C(C(=O)O)NC=O',\n",
              " 'CCCCCCCCCCCC(=O)OCC(=O)c1ccccc1',\n",
              " 'C(CCCCCNCCCCCCN)CCCCC(=O)O',\n",
              " 'c1cc(c(cc1[N+](=O)[O-])[N+](=O)[O-])Nc1ccc(cc1[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'Cc1c(cc2c(c1[N+](=O)[O-])C(C)(C)CC2(C)C)[N+](=O)[O-]',\n",
              " 'c1ccc(c(c1)C(=O)NO)/N=C/c1ccccn1',\n",
              " 'CCOC(=O)c1ccc(cc1C)NC(=O)C',\n",
              " 'CC(C)(CO)[C@H](C(=O)NCCC(=O)NCCSC(=O)c1ccccc1)O',\n",
              " 'CCCCC(=O)NNC(=O)CCCC',\n",
              " 'CC(C)CCCOC(=O)NC(=O)N',\n",
              " 'C(CO[N+](=O)[O-])N(c1c(cc(cc1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CCCCN(CCCC)CCOC(=O)c1ccc(cc1)/N=N/c1ccc(cc1N)N',\n",
              " 'CCC(=O)NCCNC(=O)CC',\n",
              " 'CN(C)c1ccc(cc1)C(=Nc1ccc(cc1)N)c1ccc(cc1)N(C)C',\n",
              " 'c1ccc(c(c1)Cl)Nc1ccc(cc1N)Cl',\n",
              " 'Cc1cc(c(C)c(c1C(=O)OC)O)O',\n",
              " 'CC(=O)C1=CC[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@H]3[C@H](C[C@]12C)O',\n",
              " 'c1cc2c(c(c1)N)C(=O)N(C1CCC(=O)NC1=O)C2=O',\n",
              " 'Cc1ccc(cc1)C(c1ccc(C)cc1)(C(c1ccc(C)cc1)(c1ccc(C)cc1)O)O',\n",
              " 'COc1ccc(cc1)P(c1ccc(cc1)OC)c1ccc(cc1)OC',\n",
              " 'CCOC(=O)C1C(=O)CC(C)(C(C1c1ccccc1)C(=O)OCC)O',\n",
              " 'CC1(C)CC(CC(C)(C)N1)O',\n",
              " 'Cc1cc(=O)n(c2ccccc2)n1C',\n",
              " 'c1cnccc1C(=O)N',\n",
              " 'c1cc(ccc1CCC(=O)c1c(cc(cc1O)O)O)O',\n",
              " 'CCOC(=O)/C=C/C=C/C(=O)OCC',\n",
              " 'c1cc(N)nc(c1)N',\n",
              " 'COC(=O)c1ccccc1O[C@H]1[C@@H]([C@H]([C@@H]([C@@H](CO[C@H]2[C@@H]([C@H]([C@@H](CO2)O)O)O)O1)O)O)O',\n",
              " 'C1N2CN3CN1CN(C2)C3',\n",
              " 'c1ccc(cc1)C(C(c1ccccc1)C(=O)c1ccccc1)C1C(=O)c2ccccc2C1=O',\n",
              " 'CC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'c1ccc(cc1)CCOC(=O)NC(=O)N',\n",
              " 'CC(=CC(C(Cl)(Cl)Cl)O)C',\n",
              " 'CCCCCCC(C)OC(=O)NC(=O)N',\n",
              " 'CCCCCCC(CCC)(CCC)C(=O)NO',\n",
              " 'c1cc(cc(c1)Br)C(=O)O',\n",
              " 'C1COC(=O)CO1',\n",
              " 'CC(C)(C)NCC(COc1cccc2C[C@H]([C@H](Cc12)O)O)O',\n",
              " 'CC(C)C1CCC(C)CC1OC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)Nc1ccc(cc1)N=Nc1ccccc1',\n",
              " 'COC(=O)c1ccc(cc1)N=Nc1ccc(cc1N)N',\n",
              " 'C1(C(Cl)(Cl)Cl)OC(C(Cl)(Cl)Cl)OC(C(Cl)(Cl)Cl)O1',\n",
              " 'C[C@@H]1CC[C@@]2(CC[C@]3(C)C(=CC[C@@H]4[C@@]5(C)CC[C@@H](C(C)(C)[C@@H]5CC[C@@]34C)O)[C@@H]2[C@H]1C)C(=O)O',\n",
              " 'c1cc2c3ccc(cc3C(c2cc1Cl)O)N',\n",
              " 'CC#CC(=O)N1CCC[C@H]1c1nc(c2ccc(cc2)C(=O)Nc2ccccn2)c2c(N)nccn12',\n",
              " 'c1ccc(cc1)C(=O)/C=C/c1ccc(cc1)[N+](=O)[O-]',\n",
              " 'C1CCC(CC1)N(C(=O)C1CCC1)O',\n",
              " 'CCCCc1ccc(cc1)/C(=N\\\\Nc1ccc(C)cc1)/C(=O)O',\n",
              " 'c1ccc2c(c1)[nH]c(c1cscn1)n2',\n",
              " 'c1ccc2c(c1)Nc1ccccc1S2',\n",
              " 'c1cc(c(cc1c1coc2cc(cc(c2c1=O)O)O[C@H]1[C@@H]([C@H]([C@@H]([C@@H](CO)O1)O)O)O)O)O',\n",
              " 'c1cc(Br)oc1C=O',\n",
              " 'C(CCC(=O)O)CCC(=O)O',\n",
              " 'C(CCC(=O)O)CC(=O)O',\n",
              " 'CC(=O)NCCNC(=O)C',\n",
              " 'c1cc(ccc1S(=O)(=O)N)S(=O)(=O)N',\n",
              " 'CC(C)(C)C(=O)OCOP(=O)(COCCn1cnc2c(N)ncnc12)OCOC(=O)C(C)(C)C',\n",
              " 'CCCOC(=O)c1ccc(cc1)N=Nc1ccc(cc1N)N',\n",
              " 'c1ccc(cc1)C(=O)O',\n",
              " 'C([C@@H]1[C@H]([C@@H]([C@@H](C(O)O1)O)O)O)O',\n",
              " 'c1ccc(cc1)C(CC(=O)c1ccccc1)C(C#N)c1ccc(cc1)[N+](=O)[O-]',\n",
              " 'CCCCCCCCCCCCCCCC(=O)O[C@H]1CO[C@@H](c2ccccc2)OC1',\n",
              " 'CCCCCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'c1ccc(cc1)C(=O)C(=O)c1ccccc1',\n",
              " 'C1CCC2C(C1)C1CC2NC2CCCC(NC3CC(C4CCCCC34)NC3CCCC(C3)N1)N2',\n",
              " 'C[C@H](CCC(=O)O)[C@H]1CC[C@H]2[C@H]3[C@H](CC(=O)[C@]12C)[C@@](C)(CCC(=O)O)[C@H](CC3=O)C(=O)O',\n",
              " 'C/C=C/CCCCCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'c1ccc(cc1)C(c1ccc(cc1)Cl)(C(c1ccccc1)(c1ccc(cc1)Cl)O)O',\n",
              " 'c1ccc2cc(ccc2c1)N(CCCl)CCCl',\n",
              " 'CN(C)c1ccc(cc1)C(=NC(=O)c1ccccc1)c1ccc(cc1)N(C)C',\n",
              " 'c1ccc(cc1)C(C=CC(c1ccccc1)(c1ccccc1)O)(c1ccccc1)O',\n",
              " 'c1cc(c(cc1Cl)Cl)Oc1ccc(cc1O)Cl',\n",
              " 'CCCCCCCCCCCCOC(=O)NC(=O)N',\n",
              " 'CC(C)C1CCC(C)CC1OC(=O)C(c1ccccc1)O',\n",
              " 'O=[S]1CC[S](=O)CC1',\n",
              " 'c1ccc2c(c1)OP1(=nP3(=nP4(=n1)Oc1ccccc1O4)Oc1ccccc1O3)O2',\n",
              " 'CCCCCC1(CCO)C(=O)NC(=S)NC1=O',\n",
              " 'Cc1cc(ccc1/N=N/c1ccc(cc1)N(c1ccccc1)c1ccccc1)N(=O)=O',\n",
              " 'CCCCCCCCCCCCCC(=O)NO',\n",
              " 'CC(=CC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C)C',\n",
              " 'COc1ccc(cc1)COC(=O)NC(=O)N',\n",
              " 'c1cc(ccc1C=CC(=O)O)[N+](=O)[O-]',\n",
              " 'Cc1cc(cc(c1OCC(=O)Nc1ccccc1)Cl)Cl',\n",
              " 'CC(=O)[C@@]12[C@@H](C[C@H]3[C@@H]4CCC5=CC(=O)CC[C@]5(C)[C@H]4[C@@H](C[C@]13C)O)O2',\n",
              " 'Cc1ccc(cc1)S(=O)(=O)NNC(=O)C(c1ccccc1)(c1ccccc1)O',\n",
              " 'c1cc(ccc1C(=O)O)Cl',\n",
              " 'CC(C)(O)P1(=O)OCC2(CO1)COP(=O)(C(C)(C)O)OC2',\n",
              " 'CC(C)CCC[C@@H](C)[C@H]1CC[C@H]2[C@@H]3CC=C4C[C@H](CC[C@]4(C)[C@H]3CC[C@]12C)OC(=O)C',\n",
              " 'C(CCCNC(=O)OCCCCO)CCNC(=O)OCCCCO',\n",
              " 'Cc1ccc2c(ccc(c2n1)O)[N+](=O)[O-]',\n",
              " 'CC(C)(C)c1ccc(cc1)C(=O)NN',\n",
              " 'CN1CC[C@]23[C@H]4C=CC(=O)[C@@H]3Oc3c(ccc(C[C@@H]14)c23)OC',\n",
              " 'CCCC(C)(C)OC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)C(c1ccccc1Cl)(C(c1ccccc1)(c1ccccc1Cl)O)O',\n",
              " 'c1ccc(cc1)OP(=O)(Oc1ccccc1)Oc1ccccc1',\n",
              " 'Cn1cnc2c1c(=O)[nH]c(=O)n2C',\n",
              " 'CCCC(=Cc1ccco1)C=NNC(=O)N',\n",
              " 'CC1CCC2(CCC3(C)C(=CCC4C5(C)CCC(C(C)(C)C5CCC34C)O)C2C1C)C(=O)OC',\n",
              " 'c1c(cc(c(c1[N+](=O)[O-])C(=O)N=[N+]=[N-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CC(C)CCC[C@@H](C)[C@H]1CC[C@H]2[C@@H]3CC=C4C[C@H](CC[C@]4(C)[C@H]3CC[C@]12C)OC=O',\n",
              " 'c1ccc(cc1)Nc1ccc(cc1)/N=N/c1ccc(cc1)N(=O)=O',\n",
              " 'c1ccc(c(c1)C(=O)O)O',\n",
              " 'c1ccc(c(c1)C(=O)c1ccc(cc1)O)C(=O)O',\n",
              " 'c1ccc2c(c1)c1CCCCc1n2C(=O)N',\n",
              " 'COc1ccc(cc1)N=Cc1ccc(cc1)c1ccccc1',\n",
              " 'CCOC1(C(c2ccccc2)Br)C(=O)NC(=O)N1',\n",
              " 'CCc1c(c(=O)cco1)O',\n",
              " 'CCCCCCCCCCCCCCC1C(=O)N=C(N)S1',\n",
              " 'c1ccc(cc1)CC1C(=O)N(c2ccccc2)C(=O)N1CC(=O)O',\n",
              " 'C1C=C(CC[C@@H](CC(=O)O)O)C(=O)C1',\n",
              " 'c1ccc2c(c1)ccc1ccccc21',\n",
              " 'Cc1ccc(cc1)C(c1ccccc1)(C(c1ccccc1)(c1ccc(C)cc1)O)O',\n",
              " 'CCCCC1=NC2(CCCC2)C(=O)N1Cc1ccc(cc1)c1ccccc1c1[nH]nnn1',\n",
              " 'CN[C@]1(CCCCC1=O)c1ccccc1Cl',\n",
              " 'CCCCCCCCCCCCC(=O)N(c1ccc(cc1)N(C)C)C(=O)Nc1ccc(cc1)N(C)C',\n",
              " 'CCOc1ccc(cc1OC)COC(=O)NC(=O)N',\n",
              " 'c1ccc(cc1)N(c1ccccc1)c1ccc(cc1)/N=N/c1ccc(cc1N(=O)=O)N(=O)=O',\n",
              " 'c1ccc(cc1)Nc1cc(ccc1N)Cl',\n",
              " 'c1cc(ccc1C=C1CCC(=Cc2ccc(cc2)O)C1=O)O',\n",
              " 'Cc1c(c(C)c(c(C)c1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'c1ccc2c(c1)[nH]c(=O)n2C1CCN(CCCC(c2ccc(cc2)F)c2ccc(cc2)F)CC1',\n",
              " 'c1cc(Cl)ncc1CN1CCN/C/1=N\\\\[N+](=O)[O-]',\n",
              " 'c1cc(c(cc1O)O)c1c(c(=O)c2c(cc(cc2o1)O)O)O',\n",
              " 'COc1c(cc(cc1Br)Br)Br',\n",
              " 'C1[C@H]([C@@H]2[C@@H]([C@@H]3[C@H](O2)O[C@@H](C(Cl)(Cl)Cl)O3)O)O[C@@H](C(Cl)(Cl)Cl)O1',\n",
              " 'C1C/C(=N\\\\O)/C(=N/NC(=S)N)/C(=N/O)/C1',\n",
              " 'c1cc2c(cc(c(c2nc1)O)I)S(=O)(=O)O',\n",
              " 'c1cnccc1C(=O)N/N=C/[C@H]([C@@H]1[C@@H]([C@@H](C(=O)O1)O)O)O',\n",
              " 'Cc1ccc(C)c(c1[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CCN(CC)CCNC(=O)c1ccc(cc1)NC(=O)/C=C\\\\C(=O)O',\n",
              " 'c1(=O)[nH]c(=O)[nH]c(=O)[nH]1',\n",
              " 'COc1cc(/C=C/C(=O)CC(=O)/C=C/c2ccc(c(c2)OC)O)ccc1O',\n",
              " 'CCOC(=O)c1ccc(cc1)NC(=S)N',\n",
              " 'Cc1cc(c(C)c(c1[N+](=O)[O-])[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CC(=O)NNC(=O)C',\n",
              " 'c1ccc(cc1)C(c1ccccc1)(C(=O)NNS(=O)(=O)c1ccc(cc1)[N+](=O)[O-])O',\n",
              " 'c1ccc(cc1)Nc1ccc(cc1)n1nc2ccccc2[n+]1[O-]',\n",
              " 'CCCCCCCCCCCCCCCCCC(=O)Nc1ccccc1',\n",
              " 'Cc1ccc(cc1)[N+](=O)[O-]',\n",
              " 'c1ccc(cc1)OC(=O)c1ccccc1O',\n",
              " 'CCOc1ccc(cc1/C=N/NC(=O)N)[N+](=O)[O-]',\n",
              " 'Cc1cnc(NC(=O)C2=C(c3ccccc3S(=O)(=O)N2C)O)s1',\n",
              " 'CCCCC(C)OC(=O)NC(=O)N',\n",
              " 'COc1cnc(Cl)nc1Cl',\n",
              " 'CCCOC(=O)c1cc(c(c(c1)O)O)O',\n",
              " 'c1ccc(cc1)c1ccccc1',\n",
              " 'c1cc2c(cccc2[N+](=O)[O-])c(c1)[N+](=O)[O-]',\n",
              " 'COc1ccc(cc1OC)C(=O)N1CCN(CC1)c1ccc2c(CCC(=O)N2)c1',\n",
              " 'Cc1ccc2c(c1)nc(Nc1ccccc1)[nH]2',\n",
              " 'Cc1ccccc1C(=O)O',\n",
              " 'Cc1cc(cc(c1O)[N+](=O)[O-])[N+](=O)[O-]',\n",
              " 'CN(C)c1ccc(cc1)C(=Nc1ccccc1N)c1ccc(cc1)N(C)C']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "soLtftjUm5_r"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "OpenChem Implementation V2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP2iqrqtilRzYf0Ff10LMWF",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}