submission_coronavirus_begone.ipynb

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)\n",
        "\n",
        "SARS-CoV-2 (2019-nCoV) coronavirus main protease, with inhibitor in turquoise.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inNW4CMePzkm",
        "colab_type": "text"
      },
      "source": [
        "# Hello\n",
        "\n",
        "Notebook is based on this great video, just wrapped into colab:\n",
        "https://www.youtube.com/watch?v=rBEKZQ22nhs\n",
        "\n",
        "It was partly released publically also @ https://github.com/vegetablejuiceftw/coronavirus-begone"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ibpOB6qqZ114",
        "colab_type": "text"
      },
      "source": [
        "# Mutation:\n",
        "\n",
        "Train the network to reconstruct the input smile.\n",
        "The input is given in the form of two augmented original smile instances and one random smile instance.\n",
        "The network should start favoring the features that are most present.\n",
        "\n",
        "But if we give the network 3 random smiles?\n",
        "- the network should try to figure out the common features of all three smiles.\n",
        "\n",
        "We can create a small population with that we inbreed for N cycles to generate diverse molecules. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ac_Dc5yiZ7sw",
        "colab_type": "text"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MbdNc32hlQuy",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# Dependencies "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKpH1h7pm2d2",
        "colab_type": "code",
        "outputId": "cdbf5205-d9ee-49d2-e2a9-9665440dda93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caxSIrK4lTQz",
        "colab_type": "text"
      },
      "source": [
        "## Install Autodock Vina and Openbabel\n",
        "\n",
        "Openbabel translates .smi, .mol2, .pdb and what else into Vina approved .pdbqt  \n",
        "(pyrx was too much of a hassle to install)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Yxhc3QMBrZz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4c736f44-44af-45f4-e511-5607934582ea"
      },
      "source": [
        "!sudo apt install autodock-vina openbabel pymol"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-430\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  apbs libglew2.0 libmaloc1 libopenbabel4v5 pymol-data python-opengl\n",
            "  python-pmw\n",
            "Suggested packages:\n",
            "  autodock autogrid glew-utils libgle3 python-pmw-doc\n",
            "The following NEW packages will be installed:\n",
            "  apbs autodock-vina libglew2.0 libmaloc1 libopenbabel4v5 openbabel pymol\n",
            "  pymol-data python-opengl python-pmw\n",
            "0 upgraded, 10 newly installed, 0 to remove and 25 not upgraded.\n",
            "Need to get 8,849 kB of archives.\n",
            "After this operation, 38.6 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libmaloc1 amd64 0.2-3.1 [48.3 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 apbs amd64 1.4-1build1 [218 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libglew2.0 amd64 2.0.0-5 [140 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopenbabel4v5 amd64 2.3.2+dfsg-3build1 [2,664 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 openbabel amd64 2.3.2+dfsg-3build1 [102 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python-pmw all 1.3.2-6build1 [79.5 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic/universe amd64 python-opengl all 3.1.0+dfsg-1 [496 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic/universe amd64 pymol-data all 1.8.4.0+dfsg-1build1 [1,193 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu bionic/universe amd64 pymol amd64 1.8.4.0+dfsg-1build1 [3,737 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu bionic/universe amd64 autodock-vina amd64 1.1.2-4 [171 kB]\n",
            "Fetched 8,849 kB in 1s (7,749 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 10.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libmaloc1.\n",
            "(Reading database ... 134443 files and directories currently installed.)\n",
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            "Setting up python-opengl (3.1.0+dfsg-1) ...\n",
            "Setting up pymol-data (1.8.4.0+dfsg-1build1) ...\n",
            "Setting up python-pmw (1.3.2-6build1) ...\n",
            "Setting up libmaloc1 (0.2-3.1) ...\n",
            "Setting up autodock-vina (1.1.2-4) ...\n",
            "Setting up libglew2.0:amd64 (2.0.0-5) ...\n",
            "Setting up libopenbabel4v5 (2.3.2+dfsg-3build1) ...\n",
            "Setting up openbabel (2.3.2+dfsg-3build1) ...\n",
            "Setting up apbs (1.4-1build1) ...\n",
            "Setting up pymol (1.8.4.0+dfsg-1build1) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCvbs_b_e0fk",
        "colab_type": "text"
      },
      "source": [
        "## Install rdkit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NdEgMPYFtSL",
        "colab_type": "code",
        "outputId": "74f1890d-cecf-4b3e-ee38-9e63164b9848",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# https://iwatobipen.wordpress.com/2018/11/01/run-rdkit-and-deep-learning-on-google-colab-rdkit/\n",
        "!wget -c https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
        "!time bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
        "!time conda install -q -y -c conda-forge rdkit\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-02 17:00:35--  https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
            "Resolving repo.continuum.io (repo.continuum.io)... 104.18.201.79, 104.18.200.79, 2606:4700::6812:c94f, ...\n",
            "Connecting to repo.continuum.io (repo.continuum.io)|104.18.201.79|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 71785000 (68M) [application/x-sh]\n",
            "Saving to: ‘Miniconda3-latest-Linux-x86_64.sh’\n",
            "\n",
            "\r          Miniconda   0%[                    ]       0  --.-KB/s               \r         Miniconda3  45%[========>           ]  31.08M   155MB/s               \rMiniconda3-latest-L 100%[===================>]  68.46M   189MB/s    in 0.4s    \n",
            "\n",
            "2020-03-02 17:00:36 (189 MB/s) - ‘Miniconda3-latest-Linux-x86_64.sh’ saved [71785000/71785000]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - _libgcc_mutex==0.1=main\n",
            "    - asn1crypto==1.2.0=py37_0\n",
            "    - ca-certificates==2019.10.16=0\n",
            "    - certifi==2019.9.11=py37_0\n",
            "    - cffi==1.13.0=py37h2e261b9_0\n",
            "    - chardet==3.0.4=py37_1003\n",
            "    - conda-package-handling==1.6.0=py37h7b6447c_0\n",
            "    - conda==4.7.12=py37_0\n",
            "    - cryptography==2.8=py37h1ba5d50_0\n",
            "    - idna==2.8=py37_0\n",
            "    - libedit==3.1.20181209=hc058e9b_0\n",
            "    - libffi==3.2.1=hd88cf55_4\n",
            "    - libgcc-ng==9.1.0=hdf63c60_0\n",
            "    - libstdcxx-ng==9.1.0=hdf63c60_0\n",
            "    - ncurses==6.1=he6710b0_1\n",
            "    - openssl==1.1.1d=h7b6447c_3\n",
            "    - pip==19.3.1=py37_0\n",
            "    - pycosat==0.6.3=py37h14c3975_0\n",
            "    - pycparser==2.19=py37_0\n",
            "    - pyopenssl==19.0.0=py37_0\n",
            "    - pysocks==1.7.1=py37_0\n",
            "    - python==3.7.4=h265db76_1\n",
            "    - readline==7.0=h7b6447c_5\n",
            "    - requests==2.22.0=py37_0\n",
            "    - ruamel_yaml==0.15.46=py37h14c3975_0\n",
            "    - setuptools==41.4.0=py37_0\n",
            "    - six==1.12.0=py37_0\n",
            "    - sqlite==3.30.0=h7b6447c_0\n",
            "    - tk==8.6.8=hbc83047_0\n",
            "    - tqdm==4.36.1=py_0\n",
            "    - urllib3==1.24.2=py37_0\n",
            "    - wheel==0.33.6=py37_0\n",
            "    - xz==5.2.4=h14c3975_4\n",
            "    - yaml==0.1.7=had09818_2\n",
            "    - zlib==1.2.11=h7b6447c_3\n",
            "\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main\n",
            "  asn1crypto         pkgs/main/linux-64::asn1crypto-1.2.0-py37_0\n",
            "  ca-certificates    pkgs/main/linux-64::ca-certificates-2019.10.16-0\n",
            "  certifi            pkgs/main/linux-64::certifi-2019.9.11-py37_0\n",
            "  cffi               pkgs/main/linux-64::cffi-1.13.0-py37h2e261b9_0\n",
            "  chardet            pkgs/main/linux-64::chardet-3.0.4-py37_1003\n",
            "  conda              pkgs/main/linux-64::conda-4.7.12-py37_0\n",
            "  conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n",
            "  cryptography       pkgs/main/linux-64::cryptography-2.8-py37h1ba5d50_0\n",
            "  idna               pkgs/main/linux-64::idna-2.8-py37_0\n",
            "  libedit            pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n",
            "  libffi             pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n",
            "  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n",
            "  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n",
            "  ncurses            pkgs/main/linux-64::ncurses-6.1-he6710b0_1\n",
            "  openssl            pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_3\n",
            "  pip                pkgs/main/linux-64::pip-19.3.1-py37_0\n",
            "  pycosat            pkgs/main/linux-64::pycosat-0.6.3-py37h14c3975_0\n",
            "  pycparser          pkgs/main/linux-64::pycparser-2.19-py37_0\n",
            "  pyopenssl          pkgs/main/linux-64::pyopenssl-19.0.0-py37_0\n",
            "  pysocks            pkgs/main/linux-64::pysocks-1.7.1-py37_0\n",
            "  python             pkgs/main/linux-64::python-3.7.4-h265db76_1\n",
            "  readline           pkgs/main/linux-64::readline-7.0-h7b6447c_5\n",
            "  requests           pkgs/main/linux-64::requests-2.22.0-py37_0\n",
            "  ruamel_yaml        pkgs/main/linux-64::ruamel_yaml-0.15.46-py37h14c3975_0\n",
            "  setuptools         pkgs/main/linux-64::setuptools-41.4.0-py37_0\n",
            "  six                pkgs/main/linux-64::six-1.12.0-py37_0\n",
            "  sqlite             pkgs/main/linux-64::sqlite-3.30.0-h7b6447c_0\n",
            "  tk                 pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n",
            "  tqdm               pkgs/main/noarch::tqdm-4.36.1-py_0\n",
            "  urllib3            pkgs/main/linux-64::urllib3-1.24.2-py37_0\n",
            "  wheel              pkgs/main/linux-64::wheel-0.33.6-py37_0\n",
            "  xz                 pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n",
            "  yaml               pkgs/main/linux-64::yaml-0.1.7-had09818_2\n",
            "  zlib               pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n",
            "\n",
            "\n",
            "Preparing transaction: | \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /usr/local\n",
            "\n",
            "real\t0m20.002s\n",
            "user\t0m10.036s\n",
            "sys\t0m4.248s\n",
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... done\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-1.72.0               |   py37h9de70de_0         316 KB  conda-forge\n",
            "    boost-cpp-1.72.0           |       h8e57a91_0        21.8 MB  conda-forge\n",
            "    bzip2-1.0.8                |       h516909a_2         396 KB  conda-forge\n",
            "    ca-certificates-2019.11.28 |       hecc5488_0         145 KB  conda-forge\n",
            "    cairo-1.16.0               |    hfb77d84_1002         1.5 MB  conda-forge\n",
            "    certifi-2019.11.28         |           py37_0         148 KB  conda-forge\n",
            "    conda-4.8.2                |           py37_0         3.0 MB  conda-forge\n",
            "    fontconfig-2.13.1          |    h86ecdb6_1001         340 KB  conda-forge\n",
            "    freetype-2.10.0            |       he983fc9_1         884 KB  conda-forge\n",
            "    gettext-0.19.8.1           |    hc5be6a0_1002         3.6 MB  conda-forge\n",
            "    glib-2.58.3                |py37h6f030ca_1002         3.3 MB  conda-forge\n",
            "    icu-64.2                   |       he1b5a44_1        12.6 MB  conda-forge\n",
            "    jpeg-9c                    |    h14c3975_1001         251 KB  conda-forge\n",
            "    libblas-3.8.0              |      14_openblas          10 KB  conda-forge\n",
            "    libcblas-3.8.0             |      14_openblas          10 KB  conda-forge\n",
            "    libgfortran-ng-7.3.0       |       hdf63c60_5         1.7 MB  conda-forge\n",
            "    libiconv-1.15              |    h516909a_1005         2.0 MB  conda-forge\n",
            "    liblapack-3.8.0            |      14_openblas          10 KB  conda-forge\n",
            "    libopenblas-0.3.7          |       h5ec1e0e_6         7.6 MB  conda-forge\n",
            "    libpng-1.6.37              |       hed695b0_0         343 KB  conda-forge\n",
            "    libtiff-4.1.0              |       hc3755c2_3         568 KB  conda-forge\n",
            "    libuuid-2.32.1             |    h14c3975_1000          26 KB  conda-forge\n",
            "    libxcb-1.13                |    h14c3975_1002         396 KB  conda-forge\n",
            "    libxml2-2.9.10             |       hee79883_0         1.3 MB  conda-forge\n",
            "    lz4-c-1.8.3                |    he1b5a44_1001         187 KB  conda-forge\n",
            "    numpy-1.18.1               |   py37h95a1406_0         5.2 MB  conda-forge\n",
            "    olefile-0.46               |             py_0          31 KB  conda-forge\n",
            "    openssl-1.1.1d             |       h516909a_0         2.1 MB  conda-forge\n",
            "    pandas-1.0.1               |   py37hb3f55d8_0        11.1 MB  conda-forge\n",
            "    pcre-8.44                  |       he1b5a44_0         261 KB  conda-forge\n",
            "    pillow-7.0.0               |   py37hb39fc2d_0         598 KB\n",
            "    pixman-0.38.0              |    h516909a_1003         594 KB  conda-forge\n",
            "    pthread-stubs-0.4          |    h14c3975_1001           5 KB  conda-forge\n",
            "    pycairo-1.19.1             |   py37h438ddbb_0          77 KB  conda-forge\n",
            "    python-dateutil-2.8.1      |             py_0         220 KB  conda-forge\n",
            "    pytz-2019.3                |             py_0         237 KB  conda-forge\n",
            "    rdkit-2019.09.3            |   py37he9dd633_1        23.8 MB  conda-forge\n",
            "    xorg-kbproto-1.0.7         |    h14c3975_1002          26 KB  conda-forge\n",
            "    xorg-libice-1.0.10         |       h516909a_0          57 KB  conda-forge\n",
            "    xorg-libsm-1.2.3           |    h84519dc_1000          25 KB  conda-forge\n",
            "    xorg-libx11-1.6.9          |       h516909a_0         918 KB  conda-forge\n",
            "    xorg-libxau-1.0.9          |       h14c3975_0          13 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h516909a_0          18 KB  conda-forge\n",
            "    xorg-libxext-1.3.4         |       h516909a_0          51 KB  conda-forge\n",
            "    xorg-libxrender-0.9.10     |    h516909a_1002          31 KB  conda-forge\n",
            "    xorg-renderproto-0.11.1    |    h14c3975_1002           8 KB  conda-forge\n",
            "    xorg-xextproto-7.3.0       |    h14c3975_1002          27 KB  conda-forge\n",
            "    xorg-xproto-7.0.31         |    h14c3975_1007          72 KB  conda-forge\n",
            "    zstd-1.4.4                 |       h3b9ef0a_1         989 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       108.8 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  boost              conda-forge/linux-64::boost-1.72.0-py37h9de70de_0\n",
            "  boost-cpp          conda-forge/linux-64::boost-cpp-1.72.0-h8e57a91_0\n",
            "  bzip2              conda-forge/linux-64::bzip2-1.0.8-h516909a_2\n",
            "  cairo              conda-forge/linux-64::cairo-1.16.0-hfb77d84_1002\n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.13.1-h86ecdb6_1001\n",
            "  freetype           conda-forge/linux-64::freetype-2.10.0-he983fc9_1\n",
            "  gettext            conda-forge/linux-64::gettext-0.19.8.1-hc5be6a0_1002\n",
            "  glib               conda-forge/linux-64::glib-2.58.3-py37h6f030ca_1002\n",
            "  icu                conda-forge/linux-64::icu-64.2-he1b5a44_1\n",
            "  jpeg               conda-forge/linux-64::jpeg-9c-h14c3975_1001\n",
            "  libblas            conda-forge/linux-64::libblas-3.8.0-14_openblas\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.8.0-14_openblas\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-7.3.0-hdf63c60_5\n",
            "  libiconv           conda-forge/linux-64::libiconv-1.15-h516909a_1005\n",
            "  liblapack          conda-forge/linux-64::liblapack-3.8.0-14_openblas\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.7-h5ec1e0e_6\n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-hed695b0_0\n",
            "  libtiff            conda-forge/linux-64::libtiff-4.1.0-hc3755c2_3\n",
            "  libuuid            conda-forge/linux-64::libuuid-2.32.1-h14c3975_1000\n",
            "  libxcb             conda-forge/linux-64::libxcb-1.13-h14c3975_1002\n",
            "  libxml2            conda-forge/linux-64::libxml2-2.9.10-hee79883_0\n",
            "  lz4-c              conda-forge/linux-64::lz4-c-1.8.3-he1b5a44_1001\n",
            "  numpy              conda-forge/linux-64::numpy-1.18.1-py37h95a1406_0\n",
            "  olefile            conda-forge/noarch::olefile-0.46-py_0\n",
            "  pandas             conda-forge/linux-64::pandas-1.0.1-py37hb3f55d8_0\n",
            "  pcre               conda-forge/linux-64::pcre-8.44-he1b5a44_0\n",
            "  pillow             pkgs/main/linux-64::pillow-7.0.0-py37hb39fc2d_0\n",
            "  pixman             conda-forge/linux-64::pixman-0.38.0-h516909a_1003\n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h14c3975_1001\n",
            "  pycairo            conda-forge/linux-64::pycairo-1.19.1-py37h438ddbb_0\n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.1-py_0\n",
            "  pytz               conda-forge/noarch::pytz-2019.3-py_0\n",
            "  rdkit              conda-forge/linux-64::rdkit-2019.09.3-py37he9dd633_1\n",
            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h14c3975_1002\n",
            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h516909a_0\n",
            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-h84519dc_1000\n",
            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.6.9-h516909a_0\n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.9-h14c3975_0\n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h516909a_0\n",
            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h516909a_0\n",
            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h516909a_1002\n",
            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h14c3975_1002\n",
            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h14c3975_1002\n",
            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h14c3975_1007\n",
            "  zstd               conda-forge/linux-64::zstd-1.4.4-h3b9ef0a_1\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates    pkgs/main::ca-certificates-2019.10.16~ --> conda-forge::ca-certificates-2019.11.28-hecc5488_0\n",
            "  certifi               pkgs/main::certifi-2019.9.11-py37_0 --> conda-forge::certifi-2019.11.28-py37_0\n",
            "  conda                      pkgs/main::conda-4.7.12-py37_0 --> conda-forge::conda-4.8.2-py37_0\n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  openssl              pkgs/main::openssl-1.1.1d-h7b6447c_3 --> conda-forge::openssl-1.1.1d-h516909a_0\n",
            "\n",
            "\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "\n",
            "real\t0m55.077s\n",
            "user\t0m46.533s\n",
            "sys\t0m5.388s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYKzFa0YenUq",
        "colab_type": "text"
      },
      "source": [
        "## Install LSTM chem\n",
        "\n",
        "[Dataset used only]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_4isvD-eqaN",
        "colab_type": "code",
        "outputId": "4f910ea3-511e-4254-e4fc-15cd40701113",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "!git clone https://github.com/topazape/LSTM_Chem.git\n",
        "!mv LSTM_Chem/* .\n",
        "!pip install bunch"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'LSTM_Chem'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects:  33% (1/3)\u001b[K\rremote: Counting objects:  66% (2/3)\u001b[K\rremote: Counting objects: 100% (3/3)\u001b[K\rremote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 567 (delta 0), reused 1 (delta 0), pack-reused 564\u001b[K\n",
            "Receiving objects: 100% (567/567), 360.56 MiB | 33.69 MiB/s, done.\n",
            "Resolving deltas: 100% (208/208), done.\n",
            "Checking out files: 100% (50/50), done.\n",
            "Collecting bunch\n",
            "  Downloading https://files.pythonhosted.org/packages/ef/bf/a4cf1779a4ffb4f610903fa08e15d1f4a8a2f4e3353a02afbe097c5bf4a8/bunch-1.0.1.tar.gz\n",
            "Building wheels for collected packages: bunch\n",
            "  Building wheel for bunch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for bunch: filename=bunch-1.0.1-cp37-none-any.whl size=7076 sha256=847a648f177275e480cfc71f4ec4b40ca1f775781dfd9f4190e84e42666ef22b\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/0f/19/fbbf81e5764e6d8b74501c4357a88c14c94466ec777c03734c\n",
            "Successfully built bunch\n",
            "Installing collected packages: bunch\n",
            "Successfully installed bunch-1.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Ce14Xh7r6S6",
        "colab_type": "text"
      },
      "source": [
        "## Misc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyDmJeykr79b",
        "colab_type": "code",
        "outputId": "e6394850-dff7-4bc3-ce0e-d1f1b1b7beff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "!pip install tinydb"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tinydb\n",
            "  Downloading https://files.pythonhosted.org/packages/22/11/c3adfc1b367d1955461f82a4a0a8ffffd37b193e98f2fe89338cdd4a8a6a/tinydb-3.15.2-py2.py3-none-any.whl\n",
            "Installing collected packages: tinydb\n",
            "Successfully installed tinydb-3.15.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GaHAshafmG7",
        "colab_type": "text"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pm6HnNE3fnLM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        },
        "outputId": "9804ca9b-7d12-447b-eff5-74d43f201857"
      },
      "source": [
        "import numpy as np\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "%matplotlib inline\n",
        "\n",
        "from lstm_chem.utils.config import process_config\n",
        "from lstm_chem.model import LSTMChem\n",
        "from lstm_chem.generator import LSTMChemGenerator\n",
        "\n",
        "from IPython.display import Image, display\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import namedtuple\n",
        "from random import sample, randint, uniform, choice, shuffle\n",
        "\n",
        "from rdkit import Chem, RDLogger\n",
        "from rdkit.Chem import Draw\n",
        "RDLogger.DisableLog('rdApp.*')\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "\n",
        "import copy\n",
        "import numpy as np\n",
        "\n",
        "from random import randint, shuffle\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "class SkipExecution(Exception):\n",
        "    def _render_traceback_(self):\n",
        "        print(\"Skip execution\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mOhc8WCehhk",
        "colab_type": "text"
      },
      "source": [
        "# Coronavirus\n",
        "\n",
        "## Step 1: download the virus & inhibitor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhNSwDslK4U0",
        "colab_type": "code",
        "outputId": "55171608-c3a2-42fb-a1bf-7083ade99e96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "!wget -nc https://files.rcsb.org/download/6LU7.pdb\n",
        "!echo \"[H][C@@]12CCCC[C@]1([H])CN(C[C@@H](O)[C@H](CSC1=CC=CC=C1)NC(=O)C1=C(C)C(O)=CC=C1)[C@@H](C2)C(=O)NC(C)(C)C Nelfinavir\" > cov_inhibitor.smi"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-02 17:02:28--  https://files.rcsb.org/download/6LU7.pdb\n",
            "Resolving files.rcsb.org (files.rcsb.org)... 128.6.244.12\n",
            "Connecting to files.rcsb.org (files.rcsb.org)|128.6.244.12|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/octet-stream]\n",
            "Saving to: ‘6LU7.pdb’\n",
            "\n",
            "\r6LU7.pdb                [<=>                 ]       0  --.-KB/s               \r6LU7.pdb                [ <=>                ] 232.00K  --.-KB/s    in 0.08s   \n",
            "\n",
            "2020-03-02 17:02:28 (2.93 MB/s) - ‘6LU7.pdb’ saved [237573]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfUzNxKKw9V9",
        "colab_type": "text"
      },
      "source": [
        "# Step 2: prep"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3TSMspZeoQ6",
        "colab_type": "code",
        "outputId": "793d97c4-5714-409c-bf79-4cf474ae6d3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        }
      },
      "source": [
        "# Source: https://github.com/sarisabban/Notes/blob/master/AutoDock.py\n",
        "'''\n",
        "Prepares the receptor by first removing all the water molecules from\n",
        "the protein's structure, then adds only the polar hydrogens, then\n",
        "it exports the resulting structure and converts it to a .pdbqt file.\n",
        "'''\n",
        "'''\n",
        "While this seems bit weird, it is as presented in the video tutorial...\n",
        "It may take quite some time.\n",
        "'''\n",
        "!grep ATOM 6LU7.pdb > R.pdb\n",
        "!pymol R.pdb -c -d \"remove resn HOH;h_add acceptors or donors;save temp.pdb\"\n",
        "!babel temp.pdb temp.pdbqt -xh\n",
        "!grep ATOM temp.pdbqt > cov_receptor.pdbqt\n",
        "!obabel cov_inhibitor.smi -opdbqt -m --gen3d best -p -xh"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " PyMOL(TM) Molecular Graphics System, Version 1.8.4.0.\n",
            " Copyright (c) Schrodinger, LLC.\n",
            " All Rights Reserved.\n",
            " \n",
            "    Created by Warren L. DeLano, Ph.D. \n",
            " \n",
            "    PyMOL is user-supported open-source software.  Although some versions\n",
            "    are freely available, PyMOL is not in the public domain.\n",
            " \n",
            "    If PyMOL is helpful in your work or study, then please volunteer \n",
            "    support for our ongoing efforts to create open and affordable scientific\n",
            "    software by purchasing a PyMOL Maintenance and/or Support subscription.\n",
            "\n",
            "    More information can be found at \"http://www.pymol.org\".\n",
            " \n",
            "    Enter \"help\" for a list of commands.\n",
            "    Enter \"help <command-name>\" for information on a specific command.\n",
            "\n",
            " Hit ESC anytime to toggle between text and graphics.\n",
            "\n",
            " Command mode. No graphics front end.\n",
            " Detected 2 CPU cores.  Enabled multithreaded rendering.\n",
            " CmdLoad: \"R.pdb\" loaded as \"R\".\n",
            "PyMOL>remove resn HOH;h_add acceptors or donors;save temp.pdb\n",
            " Save: wrote \"temp.pdb\".\n",
            " PyMOL: normal program termination.\n",
            "1 molecule converted\n",
            "56517 audit log messages \n",
            "==============================\n",
            "*** Open Babel Warning  in CheckForUnintendedBatch\n",
            "  This was a batch operation. For splitting, use non-empty base name for the output files\n",
            "WARNING: damped steplength 0.00551916 to 0.00240591\n",
            "1 molecule converted\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BXlktOetXQ3",
        "colab_type": "text"
      },
      "source": [
        "# Molecule mutation explorator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ef3KYVv3HJu9",
        "colab_type": "code",
        "outputId": "10f15e14-2d05-40c6-f1bb-e9bc3430fec7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291,
          "referenced_widgets": [
            "aa393bacaaa74be8ac114a1fb70a2fa6",
            "c3ada098a2cd47ce98d123094808d3e2",
            "b9e621e45371472ea23833840cde0605",
            "cb5b4fec1cb24256856cef33646ff924",
            "ec9296e6c01f4047b0ab33200e4b9845",
            "d3fd537ac7ce486da424dd903d81b670",
            "42db1cd9b1f64523904ede3486399ad5",
            "403745cb89d4488893f93b92dea42f2b",
            "3c3a773aa4fd45588fb756e7ba3b392d",
            "58c06c4c7bf141de9a8b726bc0099310",
            "347981cefd254030adc42f886bccd060",
            "0cb229a029e5488f8175b07f7e24e118",
            "96f7b6e4f0d748b198348a09eb91a637",
            "990646fdd3814c759947c96bba68192e",
            "fb1cddad4b49414cac083a91a6eeb495",
            "944502abd1ed4dd7b8eb47d5b4cb8776",
            "1c24cf6d42204e56ad6861c6905d294e",
            "40d57e7cd1cc4db581c45c6a1f6a74e6",
            "5af0020449384775860e0ba6ae583ff8",
            "ff8eba3f520944b096b6035b8ce08a0d",
            "b04d1fcc05c94eedb0b7cfd56a48ecd2",
            "0d3c8576d3c94bd4b310970d9fa9de96",
            "1b566530f16d40bc897beb563bce1008",
            "963ca1543cc64b0a802e3d66dcd23293"
          ]
        }
      },
      "source": [
        "# dataset from LSTM_Chem\n",
        "class SmilesTokenizer(object):\n",
        "    def __init__(self, filename):\n",
        "        atoms = [\n",
        "            'Li',\n",
        "            'Na',\n",
        "            'Al',\n",
        "            'Si',\n",
        "            'Cl',\n",
        "            'Sc',\n",
        "            'Zn',\n",
        "            'As',\n",
        "            'Se',\n",
        "            'Br',\n",
        "            'Sn',\n",
        "            'Te',\n",
        "            'Cn',\n",
        "            'H',\n",
        "            'B',\n",
        "            'C',\n",
        "            'N',\n",
        "            'O',\n",
        "            'F',\n",
        "            'P',\n",
        "            'S',\n",
        "            'K',\n",
        "            'V',\n",
        "            'I',\n",
        "        ]\n",
        "        special = [\n",
        "            '(', ')', '[', ']', '=', '#', '%', '0', '1', '2', '3', '4', '5',\n",
        "            '6', '7', '8', '9', '+', '-', 'se', 'te', 'c', 'n', 'o', 's'\n",
        "        ]\n",
        "        padding = ['G', 'A', 'E']\n",
        "\n",
        "        self.table = sorted(atoms, key=len, reverse=True) + special + padding\n",
        "        self.table_len = len(self.table)\n",
        "\n",
        "        self.one_hot_dict = {}\n",
        "        for i, symbol in enumerate(self.table):\n",
        "            vec = np.zeros(self.table_len, dtype=np.float32)\n",
        "            vec[i] = 1\n",
        "            self.one_hot_dict[symbol] = vec\n",
        "\n",
        "        self.smiles = self.load(filename)\n",
        "        self.tokenized_smiles = [self.tokenize(smi) for smi in tqdm(self.smiles)]\n",
        "        self.vectorized_smiles = [[self.one_hot_dict[symbol] for symbol in smi] for smi in tqdm(self.tokenized_smiles)]\n",
        "        self.size = len(self.smiles)\n",
        "        self.sample_size = 100\n",
        "\n",
        "    def random(self):\n",
        "        return self.vectorized_smiles[randint(0, self.size - 1)]\n",
        "\n",
        "    def sample(self, sample_size):\n",
        "        self.sample_size = sample_size \n",
        "\n",
        "    def __iter__(self):\n",
        "        return iter(sample(self.vectorized_smiles, self.sample_size))\n",
        "\n",
        "    def reverse(self, vectorized_smile, join=True):\n",
        "        index = np.argmax(vectorized_smile, axis=1)\n",
        "        tokens = [self.table[i] for i in index]\n",
        "        return \"\".join(tokens)\n",
        "\n",
        "    def tokenize(self, smiles):\n",
        "        N = len(smiles)\n",
        "        i = 0\n",
        "        token = []\n",
        "        while (i < N):\n",
        "            for j in range(self.table_len):\n",
        "                symbol = self.table[j]\n",
        "                if symbol == smiles[i:i + len(symbol)]:\n",
        "                    token.append(symbol)\n",
        "                    i += len(symbol)\n",
        "                    break\n",
        "        # token.append()\n",
        "        return token\n",
        "\n",
        "    def one_hot_encode(self, smile):\n",
        "        tokenized_smile = self.tokenize(smile) \n",
        "        result = [self.one_hot_dict[symbol] for symbol in tokenized_smile]\n",
        "        return result\n",
        "    \n",
        "    # simplef, geo , core\n",
        "    def load(self, filename, limit=50000):\n",
        "        smiles = []\n",
        "        with open(filename) as f:\n",
        "            for s in tqdm(f):\n",
        "                s = s.strip()\n",
        "                if s:\n",
        "                    smiles.append(s)\n",
        "                if len(smiles) > limit:\n",
        "                    break\n",
        "        return smiles\n",
        "\n",
        "ds = SmilesTokenizer(\"./datasets/dataset_cleansed.smi\")\n",
        "# ds = SmilesTokenizer(\"./datasets/TRPM8_inhibitors_for_fine-tune.smi\")\n",
        "print()\n",
        "print(ds.smiles[0])\n",
        "print(ds.tokenized_smiles[0])\n",
        "print(np.argmax(ds.vectorized_smiles[0], axis=1))\n",
        "print(ds.reverse(ds.vectorized_smiles[0]))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa393bacaaa74be8ac114a1fb70a2fa6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c3a773aa4fd45588fb756e7ba3b392d",
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              "HBox(children=(IntProgress(value=0, max=50001), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c24cf6d42204e56ad6861c6905d294e",
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              "HBox(children=(IntProgress(value=0, max=50001), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "O=C1Nc2cc(NC(=O)c3c[nH]cc(-c4ccc(C(F)(F)F)cc4)c3=O)ccc2C1=Cc1ccc[nH]1\n",
            "['O', '=', 'C', '1', 'N', 'c', '2', 'c', 'c', '(', 'N', 'C', '(', '=', 'O', ')', 'c', '3', 'c', '[', 'n', 'H', ']', 'c', 'c', '(', '-', 'c', '4', 'c', 'c', 'c', '(', 'C', '(', 'F', ')', '(', 'F', ')', 'F', ')', 'c', 'c', '4', ')', 'c', '3', '=', 'O', ')', 'c', 'c', 'c', '2', 'C', '1', '=', 'C', 'c', '1', 'c', 'c', 'c', '[', 'n', 'H', ']', '1']\n",
            "[17 28 15 32 16 45 33 45 45 24 16 15 24 28 17 25 45 34 45 26 46 13 27 45\n",
            " 45 24 42 45 35 45 45 45 24 15 24 18 25 24 18 25 18 25 45 45 35 25 45 34\n",
            " 28 17 25 45 45 45 33 15 32 28 15 45 32 45 45 45 26 46 13 27 32]\n",
            "O=C1Nc2cc(NC(=O)c3c[nH]cc(-c4ccc(C(F)(F)F)cc4)c3=O)ccc2C1=Cc1ccc[nH]1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10N9grf_c6xh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Attention(nn.Module):\n",
        "    r\"\"\"\n",
        "    https://github.com/IBM/pytorch-seq2seq/blob/master/seq2seq/models/attention.py\n",
        "    Applies an attention mechanism on the output features from the decoder.\n",
        "    .. math::\n",
        "            \\begin{array}{ll}\n",
        "            x = context*output \\\\\n",
        "            attn = exp(x_i) / sum_j exp(x_j) \\\\\n",
        "            output = \\tanh(w * (attn * context) + b * output)\n",
        "            \\end{array}\n",
        "    Args:\n",
        "        dim(int): The number of expected features in the output\n",
        "    Inputs: output, context\n",
        "        - **output** (batch, output_len, dimensions): tensor containing the output features from the decoder.\n",
        "        - **context** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.\n",
        "    Outputs: output, attn\n",
        "        - **output** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.\n",
        "        - **attn** (batch, output_len, input_len): tensor containing attention weights.\n",
        "    Attributes:\n",
        "        linear_out (torch.nn.Linear): applies a linear transformation to the incoming data: :math:`y = Ax + b`.\n",
        "        mask (torch.Tensor, optional): applies a :math:`-inf` to the indices specified in the `Tensor`.\n",
        "    Examples::\n",
        "         >>> attention = seq2seq.models.Attention(256)\n",
        "         >>> context = Variable(torch.randn(5, 3, 256))\n",
        "         >>> output = Variable(torch.randn(5, 5, 256))\n",
        "         >>> output, attn = attention(output, context)\n",
        "    \"\"\"\n",
        "    def __init__(self, dim):\n",
        "        super(Attention, self).__init__()\n",
        "        self.dim = dim\n",
        "        if self.dim:\n",
        "            self.linear_out = nn.Linear(dim * 2, dim)\n",
        "\n",
        "    def forward(self, output, context, mask = None):\n",
        "        # https://arxiv.org/abs/1706.03762\n",
        "        # context & mask is what we attend to\n",
        "        batch_size, hidden_size, input_size = output.size(0), output.size(2), context.size(1)\n",
        "        # (batch, out_len, dim) * (batch, in_len, dim) -> (batch, out_len, in_len)\n",
        "        # matrix by matrix product https://pytorch.org/docs/stable/torch.html#torch.bmm\n",
        "        attn = torch.bmm(output, context.transpose(1, 2))\n",
        "        # TODO: scale step missing?\n",
        "        \n",
        "        if mask is not None:\n",
        "            attn.data.masked_fill_(~mask.unsqueeze(1), -float('inf'))\n",
        "\n",
        "        attn = F.softmax(attn.view(-1, input_size), dim=1).view(batch_size, -1, input_size)\n",
        "        if not self.dim:\n",
        "            return attn\n",
        "\n",
        "        mix = torch.bmm(attn, context)  # (batch, out_len, in_len) * (batch, in_len, dim) -> (batch, out_len, dim)\n",
        "        combined = torch.cat((mix, output), dim=2)  # concat -> (batch, out_len, 2*dim)\n",
        "\n",
        "        # output -> (batch, out_len, dim)\n",
        "        output = torch.tanh(self.linear_out(combined.view(-1, 2 * hidden_size))).view(batch_size, -1, hidden_size)\n",
        "        return output, attn\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(self, embedding_size, hidden_size, output_size, num_layers=2, dropout=0.1):\n",
        "        super().__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.output_size = output_size\n",
        "        self.num_layers = num_layers\n",
        "        self.dropout = dropout\n",
        "        self.gru = nn.GRU(embedding_size, hidden_size, num_layers=num_layers, dropout=dropout, batch_first=True)\n",
        "        self.out = nn.Linear(hidden_size, output_size)\n",
        "        self.attn = Attention(hidden_size)\n",
        "        \n",
        "    def forward(self, previous, hidden_state, encoder_outputs, mask):\n",
        "        rnn_output, hidden_state = self.gru(previous, hidden_state)\n",
        "        output, attn = self.attn(rnn_output, encoder_outputs, mask)\n",
        "        output = self.out(output)\n",
        "        return output, hidden_state\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    def __init__(self, hidden_size, embedding_size, out_dim=None, num_layers=2, dropout=0.1):\n",
        "        super().__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.embedding_size = embedding_size\n",
        "        self.num_layers = num_layers\n",
        "        self.dropout = dropout\n",
        "        self.batchnorm = nn.BatchNorm1d(embedding_size)\n",
        "        # Embedding layer that will be shared with Decoder\n",
        "        self.gru = nn.GRU(embedding_size, hidden_size, num_layers=num_layers, dropout=dropout, bidirectional=True, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_size * 2, out_dim or hidden_size)   \n",
        "    \n",
        "    def forward(self, x, hidden=None):\n",
        "        x = x.permute(0, 2, 1).contiguous()\n",
        "        x = self.batchnorm(x)\n",
        "        x = x.permute(0, 2, 1).contiguous()\n",
        "        if hidden is None:\n",
        "            x, hidden = self.gru(x)\n",
        "        else:\n",
        "            x, hidden = self.gru(x, hidden)\n",
        "        # hidden = hidden[:2,:,:] + hidden[2:,:,:]\n",
        "        x = self.fc(x)\n",
        "        return x, hidden"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBimh_djlI4_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# single run\n",
        "def showTensor(aTensor):\n",
        "    print(aTensor.shape, aTensor.sum())\n",
        "    if len(aTensor.shape) > 2:\n",
        "        aTensor = aTensor[0]\n",
        "\n",
        "    plt.figure(figsize=(3, 3))\n",
        "    plt.imshow(aTensor.numpy().T)\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def load(model, path, ignore: list = None):\n",
        "    model_dict = model.state_dict()\n",
        "    pretrained_dict = torch.load(path)\n",
        "    # 1. filter out unnecessary keys\n",
        "    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and (not ignore or not any((i in k) for i in ignore))}\n",
        "    # 2. overwrite entries in the existing state dict\n",
        "    model_dict.update(pretrained_dict) \n",
        "    # 3. load the new state dict\n",
        "    model.load_state_dict(model_dict)\n",
        "\n",
        "def export_model(model, path):\n",
        "    torch.save(model.state_dict(), path)\n",
        "\n",
        "###################################\n",
        "\n",
        "def mix(target, wrong, ratio):\n",
        "    target = [(v if randint(0, ratio - 1) else wrong[i % len(wrong)]) for i, v in enumerate(target)]\n",
        "    return target\n",
        "\n",
        "def hide(target, ratio):\n",
        "    target = [(v if randint(0, ratio - 1) else v * 0) for i, v in enumerate(target)]\n",
        "    return target\n",
        "\n",
        "def hide_tensor(target, ratio):\n",
        "    target = target.clone()\n",
        "    for i in range(target.shape[1]):\n",
        "        target[:,i,:] *= (randint(0, ratio - 1) != 0) or 0.1\n",
        "    return target\n",
        "\n",
        "def mutation(s, n):\n",
        "  idx = list(range(len(s)))\n",
        "  shuffle(idx)\n",
        "  idx = idx[:n]\n",
        "  mapping = dict((idx[i], idx[i-1]) for i in range(n))\n",
        "  return [s[mapping.get(x,x)] for x in range(len(s))]\n",
        "\n",
        "def cleaned_smile(smile):\n",
        "    try:\n",
        "        mol = Chem.MolFromSmiles(smile)\n",
        "        if mol is not None:\n",
        "            return Chem.MolToSmiles(mol)\n",
        "    except:\n",
        "        pass\n",
        "\n",
        "def smile_shuffle(smile, switch=2):\n",
        "    cleaned_smile =  Chem.MolFromSmiles(smile)\n",
        "    m1.SetProp(\"_canonicalRankingNumbers\", \"True\")\n",
        "    idxs = list(range(0,m1.GetNumAtoms()))\n",
        "    idxs = mutation(idxs, switch)\n",
        "    for i,v in enumerate(idxs):\n",
        "        m1.GetAtomWithIdx(i).SetProp(\"_canonicalRankingNumber\", str(v))\n",
        "    return Chem.MolToSmiles(m1)\n",
        "\n",
        "def random_vector(ds, with_original=False):\n",
        "    index = randint(0, ds.size - 1)\n",
        "    target = ds.vectorized_smiles[index]\n",
        "    original = ds.smiles[index]\n",
        "    tensor = torch.FloatTensor(target).unsqueeze(0)\n",
        "    if with_original:\n",
        "        return tensor, original\n",
        "    return tensor\n",
        "\n",
        "def best_sub_smile(smile, first=False, draw=False):\n",
        "    new_smile = list(smile)\n",
        "\n",
        "    result = []\n",
        "    for window in range(len(smile), 16, -1):\n",
        "        for i in range(len(smile) - window):\n",
        "            snip = smile[i:i+window]\n",
        "            cleaned_snip = cleaned_smile(snip)\n",
        "            if cleaned_snip:\n",
        "                if first:\n",
        "                    return cleaned_snip\n",
        "\n",
        "                result.append(snip)\n",
        "                new_smile[i:i+window] = [\"#\"] * window\n",
        "\n",
        "    result = sorted(result, key=len, reverse=True)\n",
        "    if draw:\n",
        "        print(\"\".join(new_smile))\n",
        "        print(*result[:5], sep=\"\\n\")\n",
        "    return \"\".join(new_smile)   \n",
        " \n",
        "\n",
        "def mask(out, draw=False):\n",
        "    smile = ds.reverse(out.squeeze(0).detach().numpy())\n",
        "    if draw: print(smile)\n",
        "    smile_mask = best_sub_smile(smile, first=False)\n",
        "    if draw: print(smile_mask)\n",
        "    out = out.clone()\n",
        "    if draw: print(out.shape[1], len(smile_mask))\n",
        "    for i, v in enumerate(smile_mask[:out.shape[1]]):\n",
        "        out[:,i,:] *= (v == \"#\" ) or 0.1\n",
        "    return out\n",
        "\n",
        "def draw_smiles(smiles):\n",
        "    try:\n",
        "        mols = [Chem.MolFromSmiles(cleaned_smile(smi)) for smi in smiles if cleaned_smile(smi)]\n",
        "        display(Draw.MolsToGridImage(mols))\n",
        "    except:\n",
        "        pass\n",
        "\n",
        "def generate(model, ds, num, smile=None, draw=False):\n",
        "    model.eval()\n",
        "\n",
        "    if smile:\n",
        "        target = torch.FloatTensor(ds.one_hot_encode(smile)).unsqueeze(0) \n",
        "    else:\n",
        "        target = random_vector(ds)\n",
        "\n",
        "    size = target.shape[1]\n",
        "    \n",
        "    if draw: showTensor(target)\n",
        "\n",
        "    out_none = model(target, target, target)\n",
        "    out_none = mask(out_none, draw=draw)\n",
        "    if draw: showTensor(out_none.detach())\n",
        "\n",
        "    wrong_a = torch.cat([random_vector(ds)] * 3, dim=1)[:,:size,:]\n",
        "    wrong_b = torch.cat([random_vector(ds)] * 3, dim=1)[:,:size,:]\n",
        "\n",
        "    targets = [target, out_none, target, out_none, wrong_a, wrong_b]\n",
        "    \n",
        "    for i in range(num):\n",
        "        shuffle(targets)\n",
        "\n",
        "        genes = [hide_tensor(mol, 8) + torch.randn(mol.shape) * 0.05 for mol in targets[:3]]\n",
        "        out = model(*genes)\n",
        "        out = mask(out, draw=draw)\n",
        "        targets.append(out)\n",
        "        if draw:\n",
        "            showTensor(out.detach())\n",
        "\n",
        "    smiles = set(\n",
        "        ds.reverse(target.squeeze(0).detach().numpy()) for target in targets\n",
        "    )\n",
        "    \n",
        "    if draw: print(\n",
        "        *smiles,\n",
        "        sep='\\n',\n",
        "    )\n",
        "    smiles = set(best_sub_smile(smile, first=True, draw=draw) for smile in smiles)\n",
        "    if draw: print(smiles)\n",
        "\n",
        "    if draw:\n",
        "        draw_smiles(smiles)\n",
        "\n",
        "    return smiles \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l09bghzJseqy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LabelSmoothingLossAudio(nn.Module):\n",
        "    def __init__(self, classes, smoothing=0.0, dim=-1):\n",
        "        super().__init__()\n",
        "        self.confidence = 1.0 - smoothing\n",
        "        self.smoothing = smoothing\n",
        "        self.cls = classes\n",
        "        self.dim = dim\n",
        "\n",
        "    def forward(self, pred, target, mask):\n",
        "        # print(pred.shape, target.shape, mask.shape)\n",
        "        pred = pred.log_softmax(dim=self.dim)\n",
        "        # pred: torch.Size([32, 512, 61]) target: torch.Size([32, 512]) Mask:torch.Size([32, 512])\n",
        "        if mask is not None:\n",
        "            pred = (mask * pred.T).T\n",
        "        with torch.no_grad():\n",
        "            # true_dist = pred.data.clone()\n",
        "            true_dist = torch.zeros_like(pred)\n",
        "            true_dist.fill_(self.smoothing / (self.cls - 1))\n",
        "            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)\n",
        "        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def mutation_trainer(inp, model: nn.Module, loss_function: nn.Module, eval, data_iter=None):\n",
        "    # target, smile = random_vector(data_iter, with_original=True)\n",
        "    # targets = [[ds.one_hot_dict[symbol] for symbol in ds.tokenize(smile_shuffle(smile, switch=2))] for swaps in [5, 15, 35]]\n",
        "    # targets = [torch.FloatTensor(mol).unsqueeze(0) for mol in targets]# + [wrong]\n",
        "    \n",
        "    target = torch.FloatTensor(inp).unsqueeze(0)\n",
        "    wrong = torch.FloatTensor(data_iter.random() if eval else inp).unsqueeze(0)\n",
        "    wrong = torch.cat([wrong] * 3, dim=1)[:,:len(inp),:]\n",
        "\n",
        "    w = [v *0 for v in data_iter.random()]\n",
        "    targets = [hide(inp, 3),  hide(inp, 3),  hide(inp, 3)]#[:2]\n",
        "    targets = [torch.FloatTensor(mol).unsqueeze(0) for mol in targets] + [wrong]\n",
        "    \n",
        "    targets = [mol + torch.randn(mol.shape) * 0.05 for mol in targets][:3]\n",
        "    shuffle(targets)\n",
        "    result = model(*targets)\n",
        "    \n",
        "    batch_s, time_s, feat_s = result.shape\n",
        "    flattened_result = result.contiguous().view(batch_s * time_s, -1)\n",
        "    flattened_targets = target.contiguous().view(batch_s * time_s, -1)\n",
        "    return loss_function(flattened_result, flattened_targets)\n",
        "\n",
        "    \n",
        "def fixer_trainer(inp, model: nn.Module, loss_function: nn.Module, eval, data_iter=None):\n",
        "    target = torch.FloatTensor(mix(inp, data_iter.random(), 8)).unsqueeze(0)\n",
        "    result = model(target + torch.randn(target.shape) * 0.1)\n",
        "    \n",
        "    batch_s, time_s, feat_s = result.shape\n",
        "    # \"flatten\" all logits and targets by putting all subsequences together\n",
        "    flattened_result = result.contiguous().view(batch_s * time_s, -1)\n",
        "    flattened_targets = target.contiguous().view(batch_s * time_s, -1)\n",
        "\n",
        "    return loss_function(flattened_result, flattened_targets)\n",
        "\n",
        "def train(model, num_epochs, data_iter, eval_iter=None, loss_function=nn.MSELoss(), train_function=mutation_trainer, lr_decay=0.9, lr=0.001, weight_decay=1e-5, repreat=0):\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
        "    # optimizer = torch.optim.ASGD(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
        "    print(optimizer)\n",
        "\n",
        "    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=lr_decay)\n",
        "    eval_iter = eval_iter or data_iter\n",
        "    eval_iter = [eval_iter] if type(eval_iter) is not list else eval_iter\n",
        "\n",
        "    for epoch in range(1, num_epochs+1):\n",
        "        for e_iter in eval_iter:\n",
        "            train_acc = evaluate(model, e_iter, train_function, loss_function)  \n",
        "\n",
        "        print(\"Starting epoch %d, learning rate is %f\" % (epoch, lr_scheduler.get_lr()[0]))        \n",
        "\n",
        "        for count, batch in (enumerate(data_iter)):\n",
        "            model.zero_grad()\n",
        "            model.train()\n",
        "            optimizer.zero_grad()\n",
        "            loss = train_function(batch, model, loss_function, False, data_iter=data_iter)\n",
        "            loss.backward(retain_graph=True)\n",
        "            # torch.nn.utils.clip_grad_norm_(model.parameters(), 3)\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "        lr_scheduler.step()   \n",
        "\n",
        "    for e_iter in eval_iter:\n",
        "        train_acc = evaluate(model, e_iter, train_function, loss_function)\n",
        "\n",
        "def evaluate(model, data_iter, train_function=mutation_trainer, loss_function=nn.MSELoss()):\n",
        "    model.eval()\n",
        "    total_loss = 0\n",
        "    size = 0\n",
        "    with torch.no_grad():\n",
        "        for batch in (data_iter):\n",
        "            total_loss += abs(train_function(batch, model, loss_function, True, data_iter).item())\n",
        "            size += 1\n",
        "    print(f\"  Evaluation[{getattr(data_iter, 'prefix', '')}] - avg_loss: {total_loss/size:.4f} count:{size} Total loss:{total_loss:.4f}\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pk2C5s5u5naD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MutationNet(nn.Module):\n",
        "    def __init__(self, embedding_size, hidden_size, vocab_size, dropout=0.1):\n",
        "        super().__init__()\n",
        "        self.vocab_size = vocab_size\n",
        "\n",
        "        self.encoder = Encoder(hidden_size=hidden_size, embedding_size=embedding_size, out_dim=hidden_size, num_layers=2, dropout=dropout)\n",
        "        self.decoder = Encoder(hidden_size=hidden_size, embedding_size=hidden_size, out_dim=vocab_size, num_layers=2, dropout=dropout)\n",
        "        self.gru = nn.GRU(hidden_size, hidden_size, num_layers=2, dropout=dropout, batch_first=True)\n",
        "        \n",
        "        self.out = nn.Linear(hidden_size, self.vocab_size)   \n",
        "        # self.decoder = Decoder(embedding_size, hidden_size, hidden_size, num_layers=2, dropout=dropout, time_scale=None)\n",
        "        self.sm = nn.Softmax(dim=2)\n",
        "\n",
        "    def forward(self, *inputs):\n",
        "\n",
        "        # 1: combine the inputs\n",
        "        encoded_inputs, hidden = None, None\n",
        "        for input_sequence in inputs:\n",
        "            \n",
        "            e, hidden = self.encoder(input_sequence, hidden=hidden)\n",
        "            if encoded_inputs is None:\n",
        "                encoded_inputs = e\n",
        "            else:\n",
        "                encoded_inputs += e\n",
        "        encoded_inputs /= len(inputs)\n",
        "        \n",
        "        # 2: infere the original smile from the combined features\n",
        "        encoded_inputs, hidden = self.decoder(encoded_inputs, hidden=hidden)\n",
        "        return self.sm(encoded_inputs)\n",
        "\n",
        "        # deprecated\n",
        "        batch_size, out_seq_len, _ = encoded_inputs.shape\n",
        "        output = torch.zeros(batch_size, out_seq_len, self.vocab_size).to(device) # tensor to store decoder outputs\n",
        "\n",
        "        current = (encoded_inputs[:,0:1,:]) .clone()\n",
        "        hidden = hidden[:2,:,:] + hidden[2:,:,:]\n",
        "        for t in range(out_seq_len):\n",
        "            \n",
        "            current, hidden = self.gru(current, hidden)\n",
        "            output[:,t:(t+1),:] = self.out(current) \n",
        "\n",
        "        return self.sm(output)\n",
        "        \n",
        "\n",
        "model = MutationNet(ds.table_len, 64, ds.table_len)\n",
        "load(model, \"/content/drive/My Drive/dataset/mutation_model-3.pth\", ignore=['out'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7blYkn8VkcV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1d646724-e608-4ace-cb24-1cd7a1d99658"
      },
      "source": [
        "generate(model, ds, 10, draw=True)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 69, 52]) tensor(69.)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nThKzDCeJWYaTxCzDSWKW8T8BBKkDL4g2PwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Cc1cc(Nc2nccc(C(F)(F)F)n2)cc(-c2cccc(-(=O)(=O)Nc3cccc(C(=O)O)c3)c2)c1\n",
            "Cc1cc(###################)cc(-c2cccc(-(=O)(=O)#################)c2)c1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(39.3000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAKyUlEQVR4nO3dX4wdZRnH8e+v3f6RAkIRm9ottI0N\npDcUqFACFwpWayVgoiEQQ7ggaUwwgUiioBfeaKI3KBdGQwDlAi1YUJqGiLWWEBKtbYEgtJSW2squ\nQDFAqE1oWfp4MXPaQ3t239nzZ+bM2d8n2ez8OefMO2f22fedmXeeVxGBmY1vWtUFMOt3DhKzBAeJ\nWYKDxCzBQWKW4CAxS+goSCStlrRb0l5Jd3WrUGb9RO3eJ5E0HXgVWAWMANuAmyJiZ/eKZ1a9TmqS\ny4C9EbEvIo4C64Dru1Mss/4x1MF7FwCvN82PAJdP9IaZmhWzmdPBJs164wMOczSOqNW6ToKkEElr\ngbUAszmNy3VNrzdpNmlbY/O46zppbo0CC5vmh/NlHxMR90XEiohYMYNZHWzOrBqdBMk2YKmkxZJm\nAjcCG7pTLLP+0XZzKyLGJH0beAqYDjwYES93rWRmfaKjc5KIeBJ4sktlMetLvuNuluAgMUtwkJgl\n9Pw+SV1Mmz0bgGMffHDKOs06cek6jhzJls2YeWLZh0d7XLpJmjb9xPSxj6orx4BwTWKW4Jok16oG\naWjUHh9b1m+1RzPXHl3lmsQswUFiluAgMUtwkJglOEjMEhwkZgkOErMEB4lZgoPELCEZJJIelHRQ\n0ktNy+ZK2iRpT/777N4W06w6RWqS3wCrT1p2F7A5IpYCm/N5s4GUDJKIeAZ456TF1wMP5dMPAV/r\ncrnM+ka75yTzIuKNfPpNYF6XymPWdzo+cY8sT+q4uVIlrZW0XdL2Dzm1N61Zv2s3SN6SNB8g/31w\nvBc675bVXbtBsgG4JZ++BXiiO8Ux6z9FLgH/DvgbcIGkEUm3Aj8BVknaA3wxnzcbSMknEyPipnFW\nOamvTQm+426W4CAxS3CQmCU4SMwSHCRmCQ4Ss4RSk9Np1kyGhhcxtm9/mZs9xVP/eeH49Jc/s7zC\nkkxddToGrknMEtoex70dZ2putDuw6AP/fhaAW8+7qptFGkgaOtFAiLGxrn3uxtEdAFy74NKufWa/\n2BqbeT/eaTn6rmsSswQHiVlCbZpb1h1DSxYdn57oAkrR1/VK48S++aS+1bJucXPLrAOuSXK/OpBd\nGPjW+adeGGhcNIATFw4arx/vPZVS0z/EEo9vxyococs1iVkHXJOY0WFNImmhpC2Sdkp6WdLt+XIn\nqLMpoUhzawy4MyKWASuB2yQtwwnqbIookpzujYh4Lp8+BOwCFuAEdTZFTKqDo6RFwMXAVgomqJO0\nFlgLMJvT2i2nWWUKX92SdDrwGHBHRLzfvG6iBHXOu2V1VyhIJM0gC5CHI+LxfHHhBHVmdVbk6paA\nB4BdEXFP0yonqLMpocg5yZXAzcA/JTWelPk+WUK6R/NkdQeAG3pTRLNqFUlO9yzQ8iYLTlBnU4C7\npZglOEjMEhwkZgmlZkuZrG4/+DPZB3l6+ZAPdH//Gp/X/FmtlvW6HBMp+2GqbnBNYpbgrvInSeWD\nmux/wiL/yatStGxV7kOr4zHRMUrViuPtix+6MuuAaxIzXJOYdcRBYpbgIDFLcJCYJfT1zcRSNXI+\ntcr31CqPVT/ntmpRtjpc7t0wuu349HULPlf69sfjmsQswUFilpBsbkmaDTwDzMpfvz4ifihpMbAO\nOAfYAdwcEUd7WdiemiitZqvmVL81sZq1KFvR5lOVPQP6qYnVrEhNcgS4OiIuApYDqyWtBH4K/Cwi\nPgu8C9zau2KaVadI3q2IiP/lszPynwCuBtbny513ywZW0Wwp0/Pn2w8Cm4DXgPciojHW2AhZwjqz\ngVMoSCLio4hYDgwDlwEXFt2ApLWStkva/iFH2iymWXUmdXUrIt4DtgBXAGdJapz4DwOj47zHyems\n1ork3TpX0ln59CeAVWT5gLcA38hf5rxbNrCK3HGfDzwkaTpZUD0aERsl7QTWSfoR8DxZArvJF6CP\nH0qyqWFoySI0MnP89akPiIgXyZJkn7x8H9n5idlA8x13s4TKOzi228xKPYtepSdHnzs+vWbBJW19\nRtVDRA+S1Hc5tm8/E3UWcU1iluBn3K0jj438HYCvD6+stByNbvbt9v/yM+5mHXBN0q5+fuiqTI3v\noervYKKH5gpwTWLWAQeJWULll4Brq+rmRb/ol++hzWZWEa5JzBIcJGYJDhKzBAeJWYKDxCzBQWKW\nUDhI8mQQz0vamM8vlrRV0l5Jj0ga/6kVsxqbTE1yO9ljuw3Ou2VTQtGUQsPAV4H783nhvFs2RRSt\nSX4OfBc4ls+fg/Nu2RRRJFvKtcDBiNjRzgacd8vqrkjfrSuB6yStAWYDZwL3kufdymuTCfNuAfdB\n1lW+K6U2K1GRXMB3R8RwRCwCbgT+GhHfxHm3bIro5D7J94DvSNpLdo7SVt4ts343qa7yEfE08HQ+\nXWrerWlz5gBw7PDhsjZZqqHzFwIwduD1jj9r2hlnHJ8+duhQx5831fmOu1lCbR66GtQapKEbNUiD\na4/uck1iluAgMUtwkJglOEjMEio5cW+VwLh52cnrmjUSZbdKkt2cRLuVKhNrF92/VuO1tHpvK2Ul\n1m6VEDz13Td0+xg0ytIqMflEY99MJiG5axKzhIFIc7pxNOt7ee2CS7v+2ZPRadJmq47TnJp1wEFi\nllCbO+4TqbqZ1eBm1mByTWKW4CAxS3CQmCU4SMwSCp24S9oPHAI+AsYiYoWkucAjwCJgP3BDRLzb\nm2KaVWcyNckXImJ5RKzI5+8CNkfEUmBzPm82cDppbl1PlpQOnJzOBljRIAngz5J2SFqbL5sXEW/k\n028C81q90Xm3rO6K3ky8KiJGJX0a2CTpleaVERGSWnYCc94tq7tCNUlEjOa/DwJ/IMuS8pak+QD5\n74O9KqRZlYqkOZ0j6YzGNPAl4CVgA1lSOnByOhtgya7ykpaQ1R6QNc9+GxE/lnQO8ChwHnCA7BLw\nO4nPehs4DPy304JX7FPUex/qXn7o/j6cHxHntlpR6vMkAJK2N11GrqW670Pdyw/l7oPvuJslOEjM\nEqoIkvsq2Ga31X0f6l5+KHEfSj8nMasbN7fMEkoNEkmrJe3Oh7Xu+w6RkhZK2iJpp6SXJd2eL58r\naZOkPfnvs6su60TqPry4pLMkrZf0iqRdkq4o8xiUFiSSpgO/AL4CLANukrSsrO23aQy4MyKWASuB\n2/Iy160HdN2HF78X+FNEXAhcRLYv5R2DiCjlB7gCeKpp/m7g7rK236V9eAJYBewG5ufL5gO7qy7b\nBGUezv+IrgY2AiK7CTfU6rj02w/wSeBf5OfPTctLOwZlNrcWAM2DcNRqWGtJi4CLga0U7AHdJ+o+\nvPhi4G3g13mT8f68e1Rpx8An7gVIOh14DLgjIt5vXhfZv7K+vETY6fDifWIIuAT4ZURcTNat6WNN\nq14fgzKDZBRY2DQ/7rDW/UTSDLIAeTgiHs8X16UHdGN48f3AOrIm1/HhxfPX9PtxGAFGImJrPr+e\nLGhKOwZlBsk2YGl+ZWUm2XDXG0rc/qRJEtmowrsi4p6mVbXoAR0DMLx4RLwJvC7pgnzRNcBOyjwG\nJZ+ErQFeBV4DflD1SWGB8l5FVo2/CLyQ/6wha9dvBvYAfwHmVl3WAvvyeWBjPr0E+AewF/g9MKvq\n8iXKvhzYnh+HPwJnl3kMfMfdLMEn7mYJDhKzBAeJWYKDxCzBQWKW4CAxS3CQmCU4SMwS/g/HZ/k+\n8QtqFgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Cc(cc(Nc2nccc(C(F)(F)F)n21cc(=c2cccc(C(=O)(=OcNc3cccc(C(=O)O)c3)c2)(1\n",
            "Cc(cc(###################1cc(=c2cccc(C(=O)(=Oc#################)c2)(1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(39.3000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FiUiAgkQkQEEiEvB/f7LnGr0nj4kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Cc1cc(Nc2nccc(C(F)(F)F)n2)cc(-c2cccc(N(=O)(=O)Nc3cccc(C(=O)O)c3)c2)c1\n",
            "Cc1cc(###################)cc(-c2cccc(##########################)c2)c1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(47.4000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CC(C)(Nc2nccc(C(F)(F)F)n21cc(=c)c1cc(-(=O)(cncNc3cccc(C(=O)C)c3)cNC(1\n",
            "CC(C)(###################1cc(=c)c1cc(-(=O)(cnc#################)cNC(1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(39.3000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDhKzBAeJWcL/A0lC0R59f/n0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CCCN)(Nc2nccc(C(F)(F)F)n2NCCC)C=NCCC(N(=O)(=O)Nc3cccc(C(=O)O)c3)c4)n1\n",
            "CCCN)(###################NCCC)C=NCCC(##########################)c4)n1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(47.4000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I7wdIivEngSfC3xaSev1uYD/wU2DloNNaYF/+Ctgept8N/Bo4APwAWDLo9EXSvgHYE87D\nfwJnlXkO1OIuEqELd5EIZRKRCGUSkQhlEpEIZRKRCGUSkQhlEpEIZRKRiP8HQiAWmVQqW7sAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Cc(cc(Nc2nccc(C(F)(F)F)n21cc(=O2cccc(C(=O)(=O)Nc3cccc(C(=O)O)c3)c2)(1\n",
            "Cc(cc(###################1cc(=O2cccc(C(=O)(=O)#################)c2)(1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(39.3000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOEjMEhwkZgkOErOE/weqCqFr542jEQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CC(=O)Nc2cccc(C(F)(F)F)c2-c2c4nNcc3c((Cl)ccc23)cc1cccc(C(=O)c)c3c2cnc(\n",
            "#########################-c2c4nNcc3c((Cl)ccc23)cc1cccc(C(=O)c)c3c2cnc(\n",
            "69 70\n",
            "torch.Size([1, 69, 52]) tensor(29.4000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8P9bULLX47TIvQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Cc1cc(Nc2nccc(C(F)(F)F)n2)cc(-c2cccc(N(=O)(=O)Nc3cccc(C(=O)O)c3)c2)c1\n",
            "Cc1cc(###################)cc(-c2cccc(##########################)c2)c1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(47.4000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVZDb92melHsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CC(=O)Nc2cccc(C(F)(F)F)c2-c2c4nnnc3cc(()c)c2O)cc1cc(c(C(=O)c)(-cccnc(\n",
            "#########################-c2c4nnnc3cc(()c)c2O)cc1cc(c(C(=O)c)(-cccnc(\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(29.4000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CC(cc(Nc2cccc(C(F)(F)F)n2)cc(-c2cccc(((=O)(=O)Nc3cccc(C(=O)O)(3)c2)c1\n",
            "CC(cc(###################)cc(-c2cccc(((=O)(=O)Nc3cccc(C(=O)O)(3)c2)c1\n",
            "69 69\n",
            "torch.Size([1, 69, 52]) tensor(24.)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJGYJ/wdxxEDgLVFDowAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CC(=O)Nc2cccc(C(F)(F)F)c2-c2cnnn-c3cc(Cl)ccc23)cc1CC(=O)Nc1ccc(-c2cnc(\n",
            "#########################-c2cnnn-c3cc(Cl)ccc23)cc1CC(=O)Nc1ccc(-c2cnc(\n",
            "69 70\n",
            "torch.Size([1, 69, 52]) tensor(29.4000)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAACgCAYAAACmPUiXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAMyklEQVR4nO3dW4xdVR3H8e+vM70IXmgRa51paRsb\ntCZapCpEHhStVkTwwYjEGGM0fdEEo4mCRk2MJuqDlwdjQrzxoCLihaYhIFYIoqbSAl5oBWotMBUo\nSg1aQ2Gmfx/2PmdOZ86cteecffbZZ+b3SSaz9zp79l6np/+z1r6s/1JEYGZzWzLoCpjVnYPELMFB\nYpbgIDFLcJCYJThIzBJ6ChJJ2yXdL+mgpKvKqpRZnajb+ySSRoAHgG3ABHAXcEVE7C+vemaD10tL\n8lrgYEQciohngOuAy8qplll9jPbwt2PAIy3rE8DrOv3BMi2PFZzewyHN+uNpjvNMnFC713oJkkIk\n7QB2AKzgNF6nN/X7kGbztid2z/laL92tI8DalvXxvOwUEXFNRGyNiK1LWd7D4cwGo5cguQvYJGmD\npGXAe4Cd5VTLrD667m5FxKSkjwC3ACPAdyPivtJqZlYTPZ2TRMRNwE0l1cWslnzH3SzBQWKW4CAx\nS+j7fZJaUss9o/yxnJEXngnA1D//NWvzkxduaS4v+e0fs+1fvqlZNrX/gfSxUo//zNxObe5rdfEI\n0cjKlQBMHTs2vz8s6fiVWTKS/T45Vf6uS9+j2QKzOFuSNt+I7VqQhiV33jt7+06tR+JYhbYr6Vt7\n3i1IycevTB9akAa3JGYJDhKzBAeJWYKDxCzBQWKW4CAxS3CQmCU4SMwSHCRmCckgkfRdSUcl/aWl\nbJWkWyU9mP9e2d9qmg1OkZbk+8D2GWVXAbsjYhOwO183W5CSQRIRdwBPzii+DLg2X74WeGfJ9TKr\njW7PSVZHxKP58mPA6pLqY1Y7PZ+4R5Yndc5HRiXtkLRX0t5nOdHr4cwq122QPC5pDUD+++hcGzrv\nlg27boNkJ/D+fPn9wI3lVMesfopcAv4R8HvgHEkTkj4IfAnYJulB4M35utmClByZGBFXzPGSk/ra\nouA77mYJi3OMexnaZFyxhcktiVmCW5JuufVYNNySmCU4SMwSKu1ubXrlcW66+W5OtjzFcsnYeVVW\nAYCX75t+2wfOm0xur9Hp7WMyvf1iNHp2y6RnS7Lv3sm/PzR7w2FLn4pbErOkrudx78bztSq6nVj0\npiN3A3Dx2KvLrFIhS1asaC6fPNHmIc1+/BuWdYm5jETSRZN+F9nV6OzOy8jYGgAmH3pk1mtV2RO7\neSqebDv7rlsSswQHiVlCLe+T3PKPLIv7W18yPS/IILpZDad0sarqnpZ1nDKyrZf4nhvdrZNPP90s\nG2Q3qwi3JGYJ9WlJGieYnNqCVOWzh7ILA5/fOLvFevgzFzSX133+dwAc+sp02cZP/L7Ptete45t7\n4Jeu88+3tQUZFm5JzBKG5hKwWT/1dAlY0lpJt0naL+k+SVfm5U5QZ4tCke7WJPDxiNgMnA98WNJm\nnKDOFokiyekejYi78+X/AAeAMZygzhaJeV3dkrQeOBfYQ8EEdZJ2ADsAVnBat/U0G5jCV7ckPRf4\nKfDRiHiq9bVOCeqcd8uGXaEgkbSULEB+EBE/y4sLJ6gzG2ZFrm4J+A5wICK+2vKSE9TZolDknOT1\nwPuAP0u6Ny/7FFlCuuvzZHUPAe/uTxXNBqtIcro7gbY3WXCCOlsE/FiKWYKDxCzBQWKWMPBH5dsN\nsOr3sbav29osu/nhvXMev7H9tss/0Cxb8pt7+lnFaT2McR/duL65PHnocDn16ULbepQ4Xh5gdMPZ\n2f7bZWYpuo/xMfTY0jlfd0tillDrR+Ub3+RQ3UCs1DGvn8gGWF3+jg81y27c9X0ALh17zewdlpGt\npAS9fOOOrMwe8J46dqz3iuQtiUanv7nj2Wd632+PnC3FrAe1bklqo935Qctw40G3EtY7tyRmPXCQ\nmCUM/BLwUGjXJY2T1dfDBsItiVlCbVqSnUfuai63vZTab50u1bY7Sa/5dAF1M3LWWQBMPfFEVjBE\nc066JTFLcJCYJSS7W5JWAHcAy/Ptb4iIz0naAFwHnAnsA94XEV3fOh1IF6tVp3sdvg/Ss2Y3q6Hm\nXaxWRVqSE8BFEfEqYAuwXdL5wJeBr0XES4FjwAf7V02zwSmSdysi4r/56tL8J4CLgBvycufdsgWr\naLaUkXx8+1HgVuBvwL8jopGqfIIsYZ3ZglMoSCJiKiK2AOPAa4GXFT2ApB2S9kra+yxt5hs0q7l5\nXd2KiH8DtwEXAGdIapz4jwNH5vgbJ6ezoVYk79ZZks7Il58DbCPLB3wb8K58M+fdsgWryB33NcC1\nkkbIgur6iNglaT9wnaQvAPeQJbCrzqDv2A76+DVRxvDZKvfbjSJ5t/5EliR7ZvkhsvMTswXNd9zN\nEmrzgON87ZrY21y+ZOy8/h6sTddKy5ZNF5049apdapz86Pp1AEwefnj2MQbcdRs9ey1QfNrofnWH\nytjvfN/LXNySmCV4jHuXGlM/Q0nTP898VF9thlu3fFaj49m928mJtlfee9Z4tB3aPHe1AHmMu1kP\nhvacpHQdzgmWnH56c/nk8eNZ2crpyYY7ftMWPNfQSNaSRN6SNNazsvxvY/pp5Mkj/+i4v/kef6ap\nfz3Z8z461qdVzS+huyUxS3CQmCW4u9XQoclvdLFaFT6ZLdiVmJnqM3kxoGgXpduuTL8GmtW8a9WO\nWxKzBAeJWYKDxCzBQWKW4CAxS3CQmCUUDpI8GcQ9knbl6xsk7ZF0UNKPJS1L7cNsGM2nJbmSbNhu\ng/Nu2aJQNKXQOPB24Nv5unDeLVskirYkXwc+ATQm5TgT592yRaJItpRLgKMRsa+bAzjvlg27Is9u\nvR64VNLFwArg+cA3yPNu5a1Jx7xbwDWQDboqpdZmFSqSC/jqiBiPiPXAe4BfR8R7cd4tWyR6uU/y\nSeBjkg6SnaNUm3fLrCLzelQ+Im4Hbs+XhzbvVtvx6Z1GJp52WnP55P/+lxcWm8ddy7PUrjMzqsze\ncMbxW0bwaXRp9tKzXUz/UsaowjL20WkMfwmPz4+sfhEAU48f7XlfM/mOu1nCohx01XZAU6dBV43W\n45TCYoOSki3IXMdvWe+qBZlrv4Pax8x/r5IHX/WjBWlwS2KW4CAxS3CQmCU4SMwShu7EvZGMujUR\ndWuC6k4af6PzXtEsi333dVeRNpcw29Wtcbl5ZN14s2zy0GFgOnE2zEiePXP/ucacHafsY+P6WfXo\nlGx6dM2Lp1ees2LW6439tv3b1mMV2H6+Tnl/+Xtod8xOx2/dvrmPPHE2TP87d/y3n8EtiVmCE2Z3\nq+DNxFk30ebQuOk48pLsm37qkZZH4ZR9l/V0KdiAuadjcMJssx44SMwShu7EvTaKpgGd5535Okyk\nuZB1M+uVWxKzBAeJWYKDxCzBQWKWUOjEXdJh4D/AFDAZEVslrQJ+DKwHDgPvjohj/amm2eDMpyV5\nY0RsiYit+fpVwO6I2ATsztfNFpxeuluXkSWlAyenswWsaJAE8EtJ+yTtyMtWR8Sj+fJjwOp2f+i8\nWzbsit5MvDAijkh6EXCrpL+2vhgRIantQ2DOu2XDrlBLEhFH8t9HgZ+TZUl5XNIagPx3/wYZmw1Q\nkTSnp0t6XmMZeAvwF2AnWVI6cHI6W8CSj8pL2kjWekDWPfthRHxR0pnA9cA64CGyS8BPJvb1BHAc\n+GevFR+wFzLc72HY6w/lv4ezI+Ksdi9UOp4EQNLelsvIQ2nY38Ow1x+qfQ++426W4CAxSxhEkFwz\ngGOWbdjfw7DXHyp8D5Wfk5gNG3e3zBIqDRJJ2yXdn09rXfsHIiWtlXSbpP2S7pN0ZV6+StKtkh7M\nf68cdF07GfbpxSWdIekGSX+VdEDSBVV+BpUFiaQR4JvA24DNwBWSNld1/C5NAh+PiM3A+cCH8zoP\n2xPQwz69+DeAmyPiZcCryN5LdZ9BRFTyA1wA3NKyfjVwdVXHL+k93AhsA+4H1uRla4D7B123DnUe\nz/8TXQTsAkR2E2603edStx/gBcDfyc+fW8or+wyq7G6NAa2pKoZqWmtJ64FzgT0UfAK6JoZ9evEN\nwBPA9/Iu47fzx6Mq+wx84l6ApOcCPwU+GhFPtb4W2VdZLS8R9jq9eE2MAq8GvhUR55I91nRK16rf\nn0GVQXIEWNuyPue01nUiaSlZgPwgIn6WFw/LE9CN6cUPA9eRdbma04vn29T9c5gAJiJiT75+A1nQ\nVPYZVBkkdwGb8isry8imu95Z4fHnTZLIZhU+EBFfbXlpKJ6AjgUwvXhEPAY8IumcvOhNwH6q/Awq\nPgm7GHgA+Bvw6UGfFBao74VkzfifgHvzn4vJ+vW7gQeBXwGrBl3XAu/lDcCufHkj8AfgIPATYPmg\n65eo+xZgb/45/AJYWeVn4DvuZgk+cTdLcJCYJThIzBIcJGYJDhKzBAeJWYKDxCzBQWKW8H9RzI/z\nR4SOfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CCCN)(Nc2nccc(C(F)(F)F)n2NCCC)C=NCCC(N(=O)(=O)Nc3cccc(C(=O)O)c3)c4)n1\n",
            "Cc(cc(Nc2nccc(C(F)(F)F)n21cc(=c2cccc(C(=O)(=OcNc3cccc(C(=O)O)c3)c2)(1\n",
            "Cc1cc(Nc2nccc(C(F)(F)F)n2)cc(-c2cccc(-(=O)(=O)Nc3cccc(C(=O)O)c3)c2)c1\n",
            "CC(C)(O)CNC(=O)c1ccc(F)cc1NC(=O)c1nc(C2CC2)cnc1Nc1cncnc1CC(C)(O)CNC(=\n",
            "CC(cc(Nc2cccc(C(F)(F)F)n2)cc(-c2cccc(((=O)(=O)Nc3cccc(C(=O)O)(3)c2)c1\n",
            "Cc1cc(Nc2nccc(C(F)(F)F)n2)cc(-c2cccc(N(=O)(=O)Nc3cccc(C(=O)O)c3)c2)c1\n",
            "Cc(cc(Nc2nccc(C(F)(F)F)n21cc(=O2cccc(C(=O)(=O)Nc3cccc(C(=O)O)c3)c2)(1\n",
            "CC(=O)Nc2cccc(C(F)(F)F)c2-c2cnnn-c3cc(Cl)ccc23)cc1CC(=O)Nc1ccc(-c2cnc(\n",
            "CC(C)(Nc2nccc(C(F)(F)F)n21cc(=c)c1cc(-(=O)(cncNc3cccc(C(=O)C)c3)cNC(1\n",
            "Cc1cc(Nc2nccc(C(F)(F)F)n2)cc(-c2cccc(S(=O)(=O)Nc3cccc(C(=O)O)c3)c2)c1\n",
            "CC(=O)Nc2cccc(C(F)(F)F)c2-c2c4nnnc3cc(()c)c2O)cc1cc(c(C(=O)c)(-cccnc(\n",
            "CC(=O)Nc2cccc(C(F)(F)F)c2-c2c4nNcc3c((Cl)ccc23)cc1cccc(C(=O)c)c3c2cnc(\n",
            "CC(=O)Nc1ccc(-c2cnc(N)nc2-c2c[nH]c3cc(Br)ccc23)cc1CC(=O)Nc1ccc(-c2cnc(\n",
            "{'O=C(O)c1cccc(N[N+](=O)[O-])c1', 'CNCC(C)(O)Cc1ncncc1Nc1ncc(C2CC2)nc1C(=O)Nc1cc(F)ccc1C(=O)NCC(C)(C)O', 'CC(=O)Nc1ccc(-c2cnc(N)nc2-c2c[nH]c3cc(Br)ccc23)cc1CC(N)=O', 'CC(=O)Nc1cccc(C(F)(F)F)c1', 'O=C(O)c1cccc(NS(=O)(=O)c2ccccc2)c1', 'Nc1nccc(C(F)(F)F)n1', 'Nc1cccc(C(F)(F)F)n1'}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FxTEHB0bE+vVjMpkeeqIRwQUhj7Rq1ao9/nsgOzIycvjw4W3atOGzz9/QsmVLxlhBQQF/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\nBO3/0NggWkadkAYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x600 at 0x7F9FA79BC048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CC(=O)Nc1ccc(-c2cnc(N)nc2-c2c[nH]c3cc(Br)ccc23)cc1CC(N)=O',\n",
              " 'CC(=O)Nc1cccc(C(F)(F)F)c1',\n",
              " 'CNCC(C)(O)Cc1ncncc1Nc1ncc(C2CC2)nc1C(=O)Nc1cc(F)ccc1C(=O)NCC(C)(C)O',\n",
              " 'Nc1cccc(C(F)(F)F)n1',\n",
              " 'Nc1nccc(C(F)(F)F)n1',\n",
              " 'O=C(O)c1cccc(NS(=O)(=O)c2ccccc2)c1',\n",
              " 'O=C(O)c1cccc(N[N+](=O)[O-])c1'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASOxQK2FVyEh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1225f7ff-cd60-4ce9-df37-b94fd3de151e"
      },
      "source": [
        "initial = 'CCCCCCC(=O)Nc1c(C(O)(C(F)(F)F)C(F)(F)F)ccc2ccccc12'\n",
        "mutations = set()\n",
        "for i in range(3):\n",
        "    result = generate(model, ds, 30, smile=initial, draw=False)\n",
        "    mutations.update(result)\n",
        "print(initial)\n",
        "print(*list(sorted(mutations)), sep='\\n')\n",
        "draw_smiles(mutations)\n"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CCCCCCC(=O)Nc1c(C(O)(C(F)(F)F)C(F)(F)F)ccc2ccccc12\n",
            "C11cc(C)ccc(2ncCC(Ccc2(cc(F)F))(Cc(c(Occ3)(C)(C1CC\n",
            "C11cc(c)ccc(NncCC(Ccc2(cc(F)F))c2c(F)=c(F)(F)(C1c2\n",
            "C1c2c(c(ccc(N1CCN(OC(C(F)(F)F)C(CC(=O)ccc1ccccc11C\n",
            "C1c2c(c3ccc(N)CCC(CCcC(cc(F)F)C(C)(F))cc31)CNCC1CC\n",
            "C1c2c(c3ccc(N4)CC(C)(C(c)(F)c)C(CC(=O)c431)CN(C1CC\n",
            "C1c2ccc3ccc(NOCCC(O)(C(cc(F)c)C(CC(F))cc31)CN(C1CC\n",
            "C=C(CC)C(O)(C(F)(F)F)C(F)(F)F\n",
            "C=CCCCC(=O)CCCC(CCC)C(O)(C(F)(F)F)C(F)(F)F\n",
            "C=NCCNCCCCC(F)(F)C(C)(O)C(F)(F)F\n",
            "CC(C)(C(F)(F)F)C(F)(F)F\n",
            "CC(CCcc(-c2Nc1((C(C)(c(F)c3)F)C())(F)2)ccccccccc1Cl\n",
            "CC(O)(C(F)(F)F)C(F)(F)F\n",
            "CC(O)(C(F)F)C(F)(F)F\n",
            "CC1cccc(-c2ccc4NC(C)3c(c)(F)C(=(F)(F)2)c1Cc1cccc(-\n",
            "CC=CC(O)(C(F)(F)F)C(F)(F)F\n",
            "CCC((C(NccOcc12cc(c)(((ccccc(O)((F)F)=1C(O)c1c(ccc\n",
            "CCC(O)(C(F)(F)F)C(F)(F)F\n",
            "CCC(OCc(ccccc1c(C(O)(C(F)(F)F)CcF)(F)F)ccO)ccccc12\n",
            "CCCCCC(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCCCC1((C=CN(C)C(O)(ccF)(F)F)C(F)(F))c1-c1ccccc1C\n",
            "CCCCCC=NCN=CCCC=C=O\n",
            "CCCCCCC(=N)NCCCC(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCCCCC(=O)Cc(C(C(c)ccc2)(F(F)C(cc(c2)c1cc2ccccc12\n",
            "CCCCCCC(=O)NC1=C(C)C=CC=CC=CC=CC1C(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCCCCC(=O)NC1=C(C)C=CC=CN=CC=CC1C(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCCCCC(=O)NC1C(C)=CC=CC=CC=CC=C1C(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCCCCC(=O)NC1C=CC=CC=CC=CC=C1C(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCCCCCCCCCCCCCC(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCCCCCCCCCNNNCCN(O)C(F)(F)F\n",
            "CCCCCCCN=C)CC)CCC(O)(C(cc(F)c)C)CC(=)))CCF)CNNCCC1\n",
            "CCCCCCCNCOCNCCN)C(F)(C(F)(F)F)C(=)(F)F))))CN)=N412\n",
            "CCCCCCNNNCCCN=CCC(O)(C(F)F)C(F)(F)F\n",
            "CCCCCN1c(C=CN(C)C(O)cc(F)(F(F)2(c)(F))c1-c1ccccc1C\n",
            "CCCCNCCCCCCNNCCCC(O)(C(F)F)C(F)(F)F\n",
            "CCCCNCc1ccccc1N=C(O)C(F)(F)F\n",
            "CCCNNCc1ccccc1N(CNN)C(O)(C(F)(F)F)C(F)(F)F\n",
            "CCCcccC(-c2ccc((C(C)(C(Fc(F)C)CO)))cc2)cccc1cccc(2\n",
            "CCN(CC=O)CCc1ccc(F)cc1\n",
            "CCNC(O)(C(F)(F)F)C(F)(F)F\n",
            "CCOCCCC(=O)Cc(c(C(O)(c(F)(F)F)C(F)(F))c1cc1ccccc12\n",
            "CC[n+]1c(C=CN(C)c2ccccc2)sc(-c2ccccc2)c1-c1ccccc1\n",
            "CN(c1ccccc1)C(O)(C(F)(F)F)C(F)(F)F\n",
            "CN1cc(C)N(Nc2nC(C(cc(C(F)()(F)C(F)(F)OC(F)(F)F)cc2\n",
            "COC(C(F)(F)F)C(F)(F)F\n",
            "Cc1c(C(N)=O)Cc2nc(c((C(cc(F)F)C(F))F)c12)c1c(C(N)1\n",
            "Cc1c(C(N)=O)sc2ncnc(Nc3cccnc3OCC(F)F)c12\n",
            "Cc1ccc([SH](=O)=O)c(N)n1\n",
            "Cc1cccc(-c2ccc(OC(C)(c(F)(F)F)C(F)(F)2)c1Cc1cccc(-\n",
            "Cc1cccc(-c2ccc(OC(C)(c(cc(F)F)=(FO)cc2)c1Cc1cccc(-\n",
            "Cc1cccc(-c2ccc(OC(Cc3ccccc3)C(=O)O)cc2)c1C\n",
            "Cc1cccc(-n2Ccc(NC(Cc(c(c)c3)C)C())(F)2)c1-c1cccc(-\n",
            "NC(F)(F)C(O)C(F)(F)F\n",
            "NC1=NC(O)(C(F)(F)F)C(F)(F)C12C=C2\n",
            "O=C(O)c1ccccc1Nc1(c)(C(F)(cc())(c2(F)O=C(O)c1ccccc\n",
            "O=C(O)c1ccccc1Nc1ccnc(Nc2ccc(O)cc2)n1\n",
            "O=C1CN(CCc2ccc(F)cc2)CCN1c1ccccc1\n",
            "O=CNC1=CC=CC=CC=CC=CC1C(O)(C(F)(F)F)C(F)(F)F\n",
            "OC(C(F)(F)F)C(F)(F)F\n",
            "OC(C(F)F)C(F)(F)F\n",
            "OC(N(CNNNF)c1ccccc1)(C(F)(F)F)C(F)(F)F\n",
            "OC(Nc1ccccc1)(C(F)(F)F)C(F)(F)F\n",
            "OCCCCCc1ccccc1NC(O)(C(F)(F)F)C(F)(F)F\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xz549bW1ta98y0MbGxsvLq/lmsvTr109HR8fd3Z2Irl+/3kyv0sLabRAWFhaqVKo6n8rO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\nAEH7//P7rPSgAkcCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x2600 at 0x7F9FA81E0748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3YyM6L6tTaE",
        "colab_type": "text"
      },
      "source": [
        "# Docking\n",
        "\n",
        "Search space with the example inhibitor visualized:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3U3po5_fF9dg",
        "colab_type": "text"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ReN_L5VbatN",
        "colab_type": "text"
      },
      "source": [
        "## Create a database"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROorU_d-baFC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from tinydb import TinyDB, Query, where\n",
        "# !rm '/content/drive/My Drive/dataset/cov_db.json'\n",
        "db = TinyDB('/content/drive/My Drive/dataset/cov_db.json')\n",
        "def db_max(db, filter=True):\n",
        "    ds = db.search(where('checked') == False)\n",
        "    if not ds or not filter:\n",
        "        return min(db.all(), key=lambda x: x.get('score', 0))\n",
        "    return min(ds, key=lambda x: x.get('score', 0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpyRaQYFb0_m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "292f2b40-5640-49ac-f880-693ab3f8e06d"
      },
      "source": [
        "target_smile = cleaned_smile(\"[H][C@@]12CCCC[C@]1([H])CN(C[C@@H](O)[C@H](CSC1=CC=CC=C1)NC(=O)C1=C(C)C(O)=CC=C1)[C@@H](C2)C(=O)NC(C)(C)C\")\n",
        "draw_smiles([target_smile])\n",
        "db.upsert(dict(smile=target_smile, score=0, checked=False), where('target_smile') == target_smile)\n",
        "db_max(db)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1p59+un79elX/MAAAHYoSg/CTTz7573//a2BgEB0d7erq2vwbKysrL126xD5BTUxMlMvl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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x7F9FAC989C18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'checked': False,\n",
              " 'score': 0,\n",
              " 'smile': 'Cc1c(O)cccc1C(=O)N[C@@H](CSc1ccccc1)[C@H](O)CN1C[C@H]2CCCC[C@H]2C[C@H]1C(=O)NC(C)(C)C'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4oI6NXjce8a",
        "colab_type": "text"
      },
      "source": [
        "Docking cmd:\n",
        "\n",
        "```\n",
        "# Box(-10, 15, 70, 10, 15, 15)\n",
        "# Box(-10, 15, 70, 10, 20, 20)\n",
        "!vina --receptor cov_receptor.pdbqt --ligand cov_inhibitor.pdbqt --out cov_out.pdbqt --log ligand.log --exhaustiveness 8 --center_x -10 --center_y 15 --center_z 70 --size_x 10 --size_y 20 --size_z 20\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ob6dMqOaxMme",
        "colab_type": "code",
        "outputId": "a7d94253-fa5b-4fda-d8d1-5c69afce9ae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "while True:\n",
        "    #  fetch and mark as visited\n",
        "    current = db_max(db)\n",
        "    print(current)\n",
        "    target_smile = current['smile']\n",
        "    current['checked'] = True\n",
        "    db.upsert(current, where('smile') == target_smile)\n",
        "    \n",
        "    #  generate diverse population through mutation\n",
        "    mutations = list(sorted(set(generate(model, ds, 30, smile=initial, draw=False)), key=len, reverse=True))\n",
        "    draw_smiles(mutations)\n",
        "\n",
        "    for target_smile in mutations:\n",
        "        print(target_smile)\n",
        "\n",
        "        if db.search(where('smile') == target_smile):\n",
        "            print(f\"duplicate {target_smile}\")\n",
        "            continue\n",
        "\n",
        "        os.popen('rm inhibitor.smi').read()\n",
        "        os.popen('rm inhibitor.pdbqt').read()\n",
        "        \n",
        "        with open('inhibitor.smi', 'w') as f:\n",
        "            f.write(f\"{current['smile']} derp\")\n",
        "\n",
        "        babel = os.popen('obabel inhibitor.smi -opdbqt -m --gen3d best -p -xh').read()\n",
        "        if not os.path.isfile('inhibitor.pdbqt'):\n",
        "            print('0 molecules converted')\n",
        "            continue       \n",
        "\n",
        "        print(os.popen('vina --receptor cov_receptor.pdbqt --ligand inhibitor.pdbqt --out cov_out.pdbqt --log ligand.log --exhaustiveness 4 --center_x -10 --center_y 15 --center_z 70 --size_x 10 --size_y 20 --size_z 20').read())\n",
        "\n",
        "        score = 99\n",
        "        with open('cov_out.pdbqt', 'r') as f:\n",
        "            top = f.read(1024)\n",
        "            for line in top.split('\\n'):\n",
        "                q = 'REMARK VINA RESULT:'\n",
        "                if q in line:\n",
        "                    try:\n",
        "                        score = float(line.replace(q, '').split()[0])\n",
        "                    except:\n",
        "                        pass\n",
        "        print(\"score\", score)\n",
        "        db.upsert(dict(smile=target_smile, score=score, checked=False), where('smile') == target_smile)    "
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'smile': 'Cc1c(O)cccc1C(=O)N[C@@H](CSc1ccccc1)[C@H](O)CN1C[C@H]2CCCC[C@H]2C[C@H]1C(=O)NC(C)(C)C', 'score': 0, 'checked': True}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ffvqp5G9CX8ZFFPMvXnzxxInfk5PzrFaLxWIM6froo48cXR0KV+GDcMcOZTIp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\nYII=\n",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x1000 at 0x7F9FA8216F60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "c1cnc2ncn(-c3cc(C(Cl)(c(O)(N)F)CCC)(cnF)ncn3)c2c1c1\n",
            "#################################################################\n",
            "# If you used AutoDock Vina in your work, please cite:          #\n",
            "#                                                               #\n",
            "# O. Trott, A. J. Olson,                                        #\n",
            "# AutoDock Vina: improving the speed and accuracy of docking    #\n",
            "# with a new scoring function, efficient optimization and       #\n",
            "# multithreading, Journal of Computational Chemistry 31 (2010)  #\n",
            "# 455-461                                                       #\n",
            "#                                                               #\n",
            "# DOI 10.1002/jcc.21334                                         #\n",
            "#                                                               #\n",
            "# Please see http://vina.scripps.edu for more information.      #\n",
            "#################################################################\n",
            "\n",
            "Detected 2 CPUs\n",
            "Reading input ... done.\n",
            "Setting up the scoring function ... done.\n",
            "Analyzing the binding site ... done.\n",
            "Using random seed: 761212580\n",
            "Performing search ... \n",
            "0%   10   20   30   40   50   60   70   80   90   100%\n",
            "|----|----|----|----|----|----|----|----|----|----|\n",
            "***************************************************\n",
            "done.\n",
            "Refining results ... done.\n",
            "\n",
            "mode |   affinity | dist from best mode\n",
            "     | (kcal/mol) | rmsd l.b.| rmsd u.b.\n",
            "-----+------------+----------+----------\n",
            "   1         -3.9      0.000      0.000\n",
            "   2         -3.3      3.214      6.730\n",
            "   3         -2.8      3.918      7.689\n",
            "   4         -1.7      5.596      9.941\n",
            "   5         -1.6      5.952     11.340\n",
            "   6         -1.5      3.604      9.354\n",
            "Writing output ... done.\n",
            "\n",
            "score -3.9\n",
            "C1cnc2ncn(-c3cc(C(O)(c(F)(F)F)C(F)(F)F)nNn3)c2c1c1\n",
            "#################################################################\n",
            "# If you used AutoDock Vina in your work, please cite:          #\n",
            "#                                                               #\n",
            "# O. Trott, A. J. Olson,                                        #\n",
            "# AutoDock Vina: improving the speed and accuracy of docking    #\n",
            "# with a new scoring function, efficient optimization and       #\n",
            "# multithreading, Journal of Computational Chemistry 31 (2010)  #\n",
            "# 455-461                                                       #\n",
            "#                                                               #\n",
            "# DOI 10.1002/jcc.21334                                         #\n",
            "#                                                               #\n",
            "# Please see http://vina.scripps.edu for more information.      #\n",
            "#################################################################\n",
            "\n",
            "Detected 2 CPUs\n",
            "Reading input ... done.\n",
            "Setting up the scoring function ... done.\n",
            "Analyzing the binding site ... done.\n",
            "Using random seed: 1279592634\n",
            "Performing search ... \n",
            "0%   10   20   30   40   50   60   70   80   90   100%\n",
            "|----|----|----|----|----|----|----|----|----|----|\n",
            "***************************************************\n",
            "done.\n",
            "Refining results ... done.\n",
            "\n",
            "mode |   affinity | dist from best mode\n",
            "     | (kcal/mol) | rmsd l.b.| rmsd u.b.\n",
            "-----+------------+----------+----------\n",
            "   1         -3.9      0.000      0.000\n",
            "   2         -3.5      1.489      2.403\n",
            "   3         -2.7      3.911      9.294\n",
            "   4         -2.4      2.188      5.562\n",
            "   5         -2.1      2.570      6.147\n",
            "   6         -2.0      1.542      2.218\n",
            "   7         -1.8      2.385      4.640\n",
            "   8         -1.3      2.681      4.372\n",
            "Writing output ... done.\n",
            "\n",
            "score -3.9\n",
            "c1cnN2ncn(-O3cc(C(O)(C(F)(F)F)C(=)(F)F)ncnnnc2ccc1\n",
            "#################################################################\n",
            "# If you used AutoDock Vina in your work, please cite:          #\n",
            "#                                                               #\n",
            "# O. Trott, A. J. Olson,                                        #\n",
            "# AutoDock Vina: improving the speed and accuracy of docking    #\n",
            "# with a new scoring function, efficient optimization and       #\n",
            "# multithreading, Journal of Computational Chemistry 31 (2010)  #\n",
            "# 455-461                                                       #\n",
            "#                                                               #\n",
            "# DOI 10.1002/jcc.21334                                         #\n",
            "#                                                               #\n",
            "# Please see http://vina.scripps.edu for more information.      #\n",
            "#################################################################\n",
            "\n",
            "Detected 2 CPUs\n",
            "Reading input ... done.\n",
            "Setting up the scoring function ... done.\n",
            "Analyzing the binding site ... done.\n",
            "Using random seed: -1368376748\n",
            "Performing search ... \n",
            "0%   10   20   30   40   50   60   70   80   90   100%\n",
            "|----|----|----|----|----|----|----|----|----|----|\n",
            "***************************************************\n",
            "done.\n",
            "Refining results ... done.\n",
            "\n",
            "mode |   affinity | dist from best mode\n",
            "     | (kcal/mol) | rmsd l.b.| rmsd u.b.\n",
            "-----+------------+----------+----------\n",
            "   1         -4.5      0.000      0.000\n",
            "   2         -4.4      1.317      2.028\n",
            "   3         -2.7      5.860     10.289\n",
            "   4         -2.5      2.626      6.863\n",
            "Writing output ... done.\n",
            "\n",
            "score -4.5\n",
            "CCCCCCCCCN)NN)C=C(O)=C(F)(F)F)C(F)(F)F)))NNCCN4N11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-103-7bb62dc3d698>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m             \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'vina --receptor cov_receptor.pdbqt --ligand inhibitor.pdbqt --out cov_out.pdbqt --log ligand.log --exhaustiveness 4 --center_x -10 --center_y 15 --center_z 70 --size_x 10 --size_y 20 --size_z 20'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reRzG8CrzvY2",
        "colab_type": "text"
      },
      "source": [
        "# best result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBCRhOe8vor3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5922affa-7bd6-4874-ba42-b1d2b4316edd"
      },
      "source": [
        "best = db_max(db, filter=False)\n",
        "print(best)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'smile': 'c1cnN2ncn(-O3cc(C(O)(C(F)(F)F)C(=)(F)F)ncnnnc2ccc1', 'score': -4.5, 'checked': False}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APRJjKjkzudY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}