DeepMirror_Benchmarking.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3cE3pjrnhX19",
        "outputId": "e22c4ead-e796-4931-f69a-1efa9a993af9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.8/dist-packages (2.8.8)\n",
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            "  Downloading PyTDC-0.3.8.tar.gz (100 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2022.9.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.3 MB)\n",
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            "\u001b[?25hCollecting pysmiles\n",
            "  Downloading pysmiles-1.0.2-py2.py3-none-any.whl (22 kB)\n",
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            "Building wheels for collected packages: PyTDC\n",
            "  Building wheel for PyTDC (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyTDC: filename=PyTDC-0.3.8-py3-none-any.whl size=136139 sha256=0e13ff7054304698393e4cf419ed5035ea3077b8d0b0dfb4a3cccec059621344\n",
            "  Stored in directory: /root/.cache/pip/wheels/4a/4f/da/b494dde823e311675b3ce778ca8c22e734ef8f779dbf311805\n",
            "Successfully built PyTDC\n",
            "Installing collected packages: fuzzywuzzy, rdkit-pypi, rdkit, pbr, pysmiles, PyTDC\n",
            "Successfully installed PyTDC-0.3.8 fuzzywuzzy-0.18.0 pbr-5.11.0 pysmiles-1.0.2 rdkit-2022.9.3 rdkit-pypi-2022.9.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://data.pyg.org/whl/torch-1.13.0+cpu.html\n",
            "Collecting torch-scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcpu/torch_scatter-2.1.0%2Bpt113cpu-cp38-cp38-linux_x86_64.whl (491 kB)\n",
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            "\u001b[?25hCollecting torch-sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcpu/torch_sparse-0.6.16%2Bpt113cpu-cp38-cp38-linux_x86_64.whl (1.1 MB)\n",
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            "\u001b[?25hCollecting torch-cluster\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcpu/torch_cluster-1.6.0%2Bpt113cpu-cp38-cp38-linux_x86_64.whl (704 kB)\n",
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            "\u001b[?25hCollecting torch-spline-conv\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcpu/torch_spline_conv-1.2.1%2Bpt113cpu-cp38-cp38-linux_x86_64.whl (202 kB)\n",
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            "\u001b[?25hCollecting torch-geometric\n",
            "  Downloading torch_geometric-2.2.0.tar.gz (564 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting psutil>=5.8.0\n",
            "  Downloading psutil-5.9.4-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (280 kB)\n",
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            "Building wheels for collected packages: torch-geometric\n",
            "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-2.2.0-py3-none-any.whl size=773302 sha256=cea77a7a467127faaa1f2deb6af1a5609f9e25997717b8305438d00364aae2c8\n",
            "  Stored in directory: /root/.cache/pip/wheels/59/a3/20/198928106d3169865ae73afcbd3d3d1796cf6b429b55c65378\n",
            "Successfully built torch-geometric\n",
            "Installing collected packages: torch-spline-conv, torch-scatter, psutil, torch-sparse, torch-cluster, torch-geometric\n",
            "  Attempting uninstall: psutil\n",
            "    Found existing installation: psutil 5.4.8\n",
            "    Uninstalling psutil-5.4.8:\n",
            "      Successfully uninstalled psutil-5.4.8\n",
            "Successfully installed psutil-5.9.4 torch-cluster-1.6.0+pt113cpu torch-geometric-2.2.0 torch-scatter-2.1.0+pt113cpu torch-sparse-0.6.16+pt113cpu torch-spline-conv-1.2.1+pt113cpu\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "psutil"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install networkx PyTDC rdkit pysmiles\n",
        "!pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cpu.html"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPyfSYEziVZQ",
        "outputId": "bb8f272a-1930-4547-bb3b-099b2fd3354a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Jan  3 19:22:25 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   60C    P0    27W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdqMc2aVkGvX",
        "outputId": "38e29aa7-838c-4a1b-88d7-1440ed213f4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: torch\n",
            "Version: 1.13.0+cu116\n",
            "Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n",
            "Home-page: https://pytorch.org/\n",
            "Author: PyTorch Team\n",
            "Author-email: packages@pytorch.org\n",
            "License: BSD-3\n",
            "Location: /usr/local/lib/python3.8/dist-packages\n",
            "Requires: typing-extensions\n",
            "Required-by: torchvision, torchtext, torchaudio, fastai\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tdc.single_pred import ADME\n",
        "from pysmiles import read_smiles\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "from torch_geometric.data import Data\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import typing\n",
        "\n",
        "import torch\n",
        "from torch.utils import data \n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "\n",
        "#--------------\n",
        "import rdkit\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import PandasTools\n",
        "\n",
        "from IPython.core.display import display, HTML\n",
        "from IPython.core.debugger import set_trace  # for debugging\n",
        "import tabulate\n",
        "import altair as alt"
      ],
      "metadata": {
        "id": "FprE9UZZhdbu"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch-ignite DeepPurpose"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwUAiuozheDn",
        "outputId": "c4851f23-8ac8-4523-fd32-54e69b7f9c6e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: testpath in /usr/local/lib/python3.8/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0.0->ax-platform->DeepPurpose) (0.6.0)\n",
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            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0.0->ax-platform->DeepPurpose) (5.10.1)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0.0->ax-platform->DeepPurpose) (0.19.2)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.8/dist-packages (from bleach->nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0.0->ax-platform->DeepPurpose) (0.5.1)\n",
            "Building wheels for collected packages: DeepPurpose, wget, autograd-gamma\n",
            "  Building wheel for DeepPurpose (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for DeepPurpose: filename=DeepPurpose-0.1.5-py3-none-any.whl size=155676 sha256=c8a77b3dcbf70cba8a9eebc844d95d4ed08440afb8c3b167fb96d23229ac6833\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/7e/54/6b67734d7d19c420b501c50a81105ffd01624f4de49eb42b9f\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9674 sha256=78bed105f4eb10af704dfe9f1bec432329d8906675eb78895e90069bd86d733a\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/a8/c3/3cf2c14a1837a4e04bd98631724e81f33f462d86a1d895fae0\n",
            "  Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for autograd-gamma: filename=autograd_gamma-0.5.0-py3-none-any.whl size=4048 sha256=a009a2cb283a1153da4cda3605101fbcd129a287c8a89987f64201b06ad28001\n",
            "  Stored in directory: /root/.cache/pip/wheels/16/a2/b6/582cfdfbeeccd469504a01af3bb952fd9e7eccba40995eafea\n",
            "Successfully built DeepPurpose wget autograd-gamma\n",
            "Installing collected packages: wget, pyro-api, mock, lazy-loader, jedi, interface-meta, graphlib-backport, subword-nmt, pytorch-ignite, pyro-ppl, linear-operator, autograd-gamma, gpytorch, formulaic, dgllife, pandas-flavor, lifelines, botorch, ax-platform, DeepPurpose\n",
            "Successfully installed DeepPurpose-0.1.5 autograd-gamma-0.5.0 ax-platform-0.2.10 botorch-0.8.0 dgllife-0.3.1 formulaic-0.5.2 gpytorch-1.9.0 graphlib-backport-1.0.3 interface-meta-1.3.0 jedi-0.18.2 lazy-loader-0.1rc2 lifelines-0.27.4 linear-operator-0.2.0 mock-5.0.0 pandas-flavor-0.3.0 pyro-api-0.1.2 pyro-ppl-1.8.4 pytorch-ignite-0.4.10 subword-nmt-0.3.8 wget-3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/bp-kelley/descriptastorus\n",
        "!pip install pandas-flavor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhiOeUBWYAh2",
        "outputId": "58c71e91-8920-4525-fd92-af8b0a03e11d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/bp-kelley/descriptastorus\n",
            "  Cloning https://github.com/bp-kelley/descriptastorus to /tmp/pip-req-build-rmp7ncvq\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/bp-kelley/descriptastorus /tmp/pip-req-build-rmp7ncvq\n",
            "  Resolved https://github.com/bp-kelley/descriptastorus to commit 86eedc60546abe6f59cdbcb12025a61157ba178d\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas_flavor in /usr/local/lib/python3.8/dist-packages (from descriptastorus==2.5.0.20) (0.3.0)\n",
            "Requirement already satisfied: rdkit in /usr/local/lib/python3.8/dist-packages (from descriptastorus==2.5.0.20) (2022.9.3)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.8/dist-packages (from pandas_flavor->descriptastorus==2.5.0.20) (1.3.5)\n",
            "Requirement already satisfied: xarray in /usr/local/lib/python3.8/dist-packages (from pandas_flavor->descriptastorus==2.5.0.20) (2022.12.0)\n",
            "Requirement already satisfied: lazy-loader==0.1rc2 in /usr/local/lib/python3.8/dist-packages (from pandas_flavor->descriptastorus==2.5.0.20) (0.1rc2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.8/dist-packages (from rdkit->descriptastorus==2.5.0.20) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from rdkit->descriptastorus==2.5.0.20) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->pandas_flavor->descriptastorus==2.5.0.20) (2022.7)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->pandas_flavor->descriptastorus==2.5.0.20) (2.8.2)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.8/dist-packages (from xarray->pandas_flavor->descriptastorus==2.5.0.20) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=21.3->xarray->pandas_flavor->descriptastorus==2.5.0.20) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas_flavor->descriptastorus==2.5.0.20) (1.15.0)\n",
            "Building wheels for collected packages: descriptastorus\n",
            "  Building wheel for descriptastorus (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for descriptastorus: filename=descriptastorus-2.5.0.20-py3-none-any.whl size=1083449 sha256=acf57b2b039897eb063cd579f188a48b994c7f102ce6c01c44149a3862a4f0e1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-stsrfnzc/wheels/45/d6/37/fe624913d6e54388bccce05360ccd443eeb01defda346642c2\n",
            "Successfully built descriptastorus\n",
            "Installing collected packages: descriptastorus\n",
            "Successfully installed descriptastorus-2.5.0.20\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas-flavor in /usr/local/lib/python3.8/dist-packages (0.3.0)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.8/dist-packages (from pandas-flavor) (1.3.5)\n",
            "Requirement already satisfied: xarray in /usr/local/lib/python3.8/dist-packages (from pandas-flavor) (2022.12.0)\n",
            "Requirement already satisfied: lazy-loader==0.1rc2 in /usr/local/lib/python3.8/dist-packages (from pandas-flavor) (0.1rc2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->pandas-flavor) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->pandas-flavor) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->pandas-flavor) (2022.7)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.8/dist-packages (from xarray->pandas-flavor) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=21.3->xarray->pandas-flavor) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas-flavor) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ss_utils\n",
        "from DeepPurpose import utils, CompoundPred\n",
        "from tdc.single_pred import ADME\n",
        "import DeepPurpose.DTI as models\n",
        "from DeepPurpose.utils import *\n",
        "from DeepPurpose.dataset import *"
      ],
      "metadata": {
        "id": "xeZEG64Xlh4c"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_whole = ADME(name = 'Bioavailability_Ma')\n",
        "# data_whole = ADME(name = 'HIA_Hou')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVzTNWgxlwlC",
        "outputId": "2b05c32c-82f8-48f6-97ca-8419b105f5b8"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data = ADME(name = 'HIA_Hou').get_data(format = 'dict')\n",
        "# X, y = data['Drug'], data['Y']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWI6_U13T5Bk",
        "outputId": "cb552452-5190-4e61-ab21-56bb046c2219"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "100%|██████████| 40.1k/40.1k [00:00<00:00, 2.15MiB/s]\n",
            "Loading...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = ADME(name = 'Bioavailability_Ma').get_data(format = 'DeepPurpose')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1w24TJm9WR8I",
        "outputId": "07a3f765-25de-48ea-8a64-d2a9f3f63352"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Found local copy...\n",
            "Loading...\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug_encoding_name = 'Transformer' #'Transformer'   'MPNN'\n",
        "train, val, test = utils.data_process(X_drug = X,\n",
        "                                      y = y, \n",
        "                                      drug_encoding = drug_encoding_name,\n",
        "                                      # target_encoding = drug_encoding_name,\n",
        "                                      random_seed = 'TDC')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JoFLbSa5WC4F",
        "outputId": "5eee59ab-cb0a-4d19-c804-a19733c4ac26"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drug Property Prediction Mode...\n",
            "in total: 578 drugs\n",
            "encoding drug...\n",
            "unique drugs: 578\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2aaqUH8hVtHr",
        "outputId": "4b5ddcc2-5f9e-469c-b55b-12cbc6c6a091"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                              SMILES  Label  \\\n",
              "0                                    CC(=O)Nc1ccccc1      1   \n",
              "1                         CC(=O)Nc1nnc(S(N)(=O)=O)s1      1   \n",
              "2  C=C1/C(=C\\C=C2/CCC[C@]3(C)[C@@H]2CC[C@H]3[C@@H...      1   \n",
              "3                    Cc1c(N(C)C)c(=N)n(-c2ccccc2)n1C      1   \n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a1e48a54-bada-4520-ac04-5af74dd50294 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a1e48a54-bada-4520-ac04-5af74dd50294');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train['drug_encoding'][120][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5P9q9aInWNN",
        "outputId": "c5d5f186-4974-4976-996e-22293cfa00b5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = utils.generate_config(drug_encoding = drug_encoding_name, \n",
        "                        #  target_encoding = drug_encoding_name, \n",
        "                         cls_hidden_dims = [1024,1024,512], \n",
        "                         train_epoch = 100, \n",
        "                         LR = 0.001, \n",
        "                         batch_size = 256\n",
        "                        )"
      ],
      "metadata": {
        "id": "7_9Olg30cBIo"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model = models.model_initialize(**config)\n",
        "model = CompoundPred.model_initialize(**config)\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJLQkEfJcBOh",
        "outputId": "61d63318-124f-491b-fc61-721e9c03a6f4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<DeepPurpose.CompoundPred.Property_Prediction at 0x7f17b6296520>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.train(train, val, test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EZoZWOwOkB13",
        "outputId": "3bb8485e-2e1e-41ec-a387-c0343048da52"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Let's use 1 GPU!\n",
            "--- Data Preparation ---\n",
            "--- Go for Training ---\n",
            "Training at Epoch 1 iteration 0 with loss 0.68724. Total time 0.00083 hours\n",
            "Validation at Epoch 1 , AUROC: 0.68627 , AUPRC: 0.88238 , F1: 0.93577\n",
            "Training at Epoch 2 iteration 0 with loss 0.43694. Total time 0.00083 hours\n",
            "Validation at Epoch 2 , AUROC: 0.48179 , AUPRC: 0.86735 , F1: 0.93577\n",
            "Training at Epoch 3 iteration 0 with loss 0.42101. Total time 0.00083 hours\n",
            "Validation at Epoch 3 , AUROC: 0.52100 , AUPRC: 0.88877 , F1: 0.93577\n",
            "Training at Epoch 4 iteration 0 with loss 0.40649. Total time 0.00111 hours\n",
            "Validation at Epoch 4 , AUROC: 0.78991 , AUPRC: 0.96441 , F1: 0.93577\n",
            "Training at Epoch 5 iteration 0 with loss 0.41946. Total time 0.00111 hours\n",
            "Validation at Epoch 5 , AUROC: 0.59943 , AUPRC: 0.93182 , F1: 0.93577\n",
            "Training at Epoch 6 iteration 0 with loss 0.42961. Total time 0.00111 hours\n",
            "Validation at Epoch 6 , AUROC: 0.55182 , AUPRC: 0.91585 , F1: 0.93577\n",
            "Training at Epoch 7 iteration 0 with loss 0.42853. Total time 0.00111 hours\n",
            "Validation at Epoch 7 , AUROC: 0.62464 , AUPRC: 0.91661 , F1: 0.93577\n",
            "Training at Epoch 8 iteration 0 with loss 0.41902. Total time 0.00138 hours\n",
            "Validation at Epoch 8 , AUROC: 0.60224 , AUPRC: 0.90405 , F1: 0.93577\n",
            "Training at Epoch 9 iteration 0 with loss 0.41311. Total time 0.00138 hours\n",
            "Validation at Epoch 9 , AUROC: 0.63725 , AUPRC: 0.91573 , F1: 0.93577\n",
            "Training at Epoch 10 iteration 0 with loss 0.40947. Total time 0.00138 hours\n",
            "Validation at Epoch 10 , AUROC: 0.67226 , AUPRC: 0.92764 , F1: 0.93577\n",
            "Training at Epoch 11 iteration 0 with loss 0.43445. Total time 0.00138 hours\n",
            "Validation at Epoch 11 , AUROC: 0.70588 , AUPRC: 0.93417 , F1: 0.93577\n",
            "Training at Epoch 12 iteration 0 with loss 0.44162. Total time 0.00166 hours\n",
            "Validation at Epoch 12 , AUROC: 0.67787 , AUPRC: 0.92076 , F1: 0.93577\n",
            "Training at Epoch 13 iteration 0 with loss 0.41023. Total time 0.00166 hours\n",
            "Validation at Epoch 13 , AUROC: 0.68347 , AUPRC: 0.92177 , F1: 0.93577\n",
            "Training at Epoch 14 iteration 0 with loss 0.40464. Total time 0.00166 hours\n",
            "Validation at Epoch 14 , AUROC: 0.68907 , AUPRC: 0.93167 , F1: 0.93577\n",
            "Training at Epoch 15 iteration 0 with loss 0.39827. Total time 0.00166 hours\n",
            "Validation at Epoch 15 , AUROC: 0.69467 , AUPRC: 0.93303 , F1: 0.93577\n",
            "Training at Epoch 16 iteration 0 with loss 0.41522. Total time 0.00194 hours\n",
            "Validation at Epoch 16 , AUROC: 0.70028 , AUPRC: 0.93460 , F1: 0.93577\n",
            "Training at Epoch 17 iteration 0 with loss 0.42233. Total time 0.00194 hours\n",
            "Validation at Epoch 17 , AUROC: 0.70588 , AUPRC: 0.93943 , F1: 0.93577\n",
            "Training at Epoch 18 iteration 0 with loss 0.38650. Total time 0.00194 hours\n",
            "Validation at Epoch 18 , AUROC: 0.70868 , AUPRC: 0.93767 , F1: 0.93577\n",
            "Training at Epoch 19 iteration 0 with loss 0.39990. Total time 0.00194 hours\n",
            "Validation at Epoch 19 , AUROC: 0.71988 , AUPRC: 0.94041 , F1: 0.93577\n",
            "Training at Epoch 20 iteration 0 with loss 0.44713. Total time 0.00222 hours\n",
            "Validation at Epoch 20 , AUROC: 0.72268 , AUPRC: 0.94446 , F1: 0.93577\n",
            "Training at Epoch 21 iteration 0 with loss 0.37529. Total time 0.00222 hours\n",
            "Validation at Epoch 21 , AUROC: 0.72268 , AUPRC: 0.94559 , F1: 0.93577\n",
            "Training at Epoch 22 iteration 0 with loss 0.43333. Total time 0.00222 hours\n",
            "Validation at Epoch 22 , AUROC: 0.71148 , AUPRC: 0.94184 , F1: 0.93577\n",
            "Training at Epoch 23 iteration 0 with loss 0.38271. Total time 0.00222 hours\n",
            "Validation at Epoch 23 , AUROC: 0.71148 , AUPRC: 0.94151 , F1: 0.93577\n",
            "Training at Epoch 24 iteration 0 with loss 0.42632. Total time 0.00222 hours\n",
            "Validation at Epoch 24 , AUROC: 0.70588 , AUPRC: 0.94066 , F1: 0.93577\n",
            "Training at Epoch 25 iteration 0 with loss 0.40485. Total time 0.0025 hours\n",
            "Validation at Epoch 25 , AUROC: 0.71148 , AUPRC: 0.94407 , F1: 0.93577\n",
            "Training at Epoch 26 iteration 0 with loss 0.40901. Total time 0.0025 hours\n",
            "Validation at Epoch 26 , AUROC: 0.70588 , AUPRC: 0.94281 , F1: 0.93577\n",
            "Training at Epoch 27 iteration 0 with loss 0.41595. Total time 0.0025 hours\n",
            "Validation at Epoch 27 , AUROC: 0.70308 , AUPRC: 0.94196 , F1: 0.93577\n",
            "Training at Epoch 28 iteration 0 with loss 0.41407. Total time 0.0025 hours\n",
            "Validation at Epoch 28 , AUROC: 0.69747 , AUPRC: 0.94177 , F1: 0.93577\n",
            "Training at Epoch 29 iteration 0 with loss 0.40040. Total time 0.00277 hours\n",
            "Validation at Epoch 29 , AUROC: 0.69187 , AUPRC: 0.94103 , F1: 0.93577\n",
            "Training at Epoch 30 iteration 0 with loss 0.40550. Total time 0.00277 hours\n",
            "Validation at Epoch 30 , AUROC: 0.69747 , AUPRC: 0.94316 , F1: 0.93577\n",
            "Training at Epoch 31 iteration 0 with loss 0.40403. Total time 0.00277 hours\n",
            "Validation at Epoch 31 , AUROC: 0.70588 , AUPRC: 0.94629 , F1: 0.93577\n",
            "Training at Epoch 32 iteration 0 with loss 0.35885. Total time 0.00305 hours\n",
            "Validation at Epoch 32 , AUROC: 0.61904 , AUPRC: 0.91464 , F1: 0.93577\n",
            "Training at Epoch 33 iteration 0 with loss 0.37510. Total time 0.00305 hours\n",
            "Validation at Epoch 33 , AUROC: 0.61344 , AUPRC: 0.91150 , F1: 0.93577\n",
            "Training at Epoch 34 iteration 0 with loss 0.41302. Total time 0.00305 hours\n",
            "Validation at Epoch 34 , AUROC: 0.63305 , AUPRC: 0.92433 , F1: 0.93577\n",
            "Training at Epoch 35 iteration 0 with loss 0.41835. Total time 0.00305 hours\n",
            "Validation at Epoch 35 , AUROC: 0.64425 , AUPRC: 0.91873 , F1: 0.93577\n",
            "Training at Epoch 36 iteration 0 with loss 0.27123. Total time 0.00333 hours\n",
            "Validation at Epoch 36 , AUROC: 0.71428 , AUPRC: 0.94493 , F1: 0.93577\n",
            "Training at Epoch 37 iteration 0 with loss 0.32187. Total time 0.00333 hours\n",
            "Validation at Epoch 37 , AUROC: 0.70028 , AUPRC: 0.94265 , F1: 0.93577\n",
            "Training at Epoch 38 iteration 0 with loss 0.27912. Total time 0.00333 hours\n",
            "Validation at Epoch 38 , AUROC: 0.69047 , AUPRC: 0.94179 , F1: 0.93577\n",
            "Training at Epoch 39 iteration 0 with loss 0.45334. Total time 0.00361 hours\n",
            "Validation at Epoch 39 , AUROC: 0.64985 , AUPRC: 0.93366 , F1: 0.93577\n",
            "Training at Epoch 40 iteration 0 with loss 0.41599. Total time 0.00361 hours\n",
            "Validation at Epoch 40 , AUROC: 0.64985 , AUPRC: 0.93201 , F1: 0.93577\n",
            "Training at Epoch 41 iteration 0 with loss 0.45441. Total time 0.00388 hours\n",
            "Validation at Epoch 41 , AUROC: 0.63585 , AUPRC: 0.92742 , F1: 0.93577\n",
            "Training at Epoch 42 iteration 0 with loss 0.42169. Total time 0.00388 hours\n",
            "Validation at Epoch 42 , AUROC: 0.59943 , AUPRC: 0.89944 , F1: 0.93577\n",
            "Training at Epoch 43 iteration 0 with loss 0.39143. Total time 0.00416 hours\n",
            "Validation at Epoch 43 , AUROC: 0.51820 , AUPRC: 0.88122 , F1: 0.93577\n",
            "Training at Epoch 44 iteration 0 with loss 0.46126. Total time 0.00416 hours\n",
            "Validation at Epoch 44 , AUROC: 0.46358 , AUPRC: 0.86118 , F1: 0.93577\n",
            "Training at Epoch 45 iteration 0 with loss 0.41572. Total time 0.00444 hours\n",
            "Validation at Epoch 45 , AUROC: 0.44957 , AUPRC: 0.86462 , F1: 0.93577\n",
            "Training at Epoch 46 iteration 0 with loss 0.41683. Total time 0.00444 hours\n",
            "Validation at Epoch 46 , AUROC: 0.46078 , AUPRC: 0.86629 , F1: 0.93577\n",
            "Training at Epoch 47 iteration 0 with loss 0.42100. Total time 0.00444 hours\n",
            "Validation at Epoch 47 , AUROC: 0.56302 , AUPRC: 0.89551 , F1: 0.93577\n",
            "Training at Epoch 48 iteration 0 with loss 0.42659. Total time 0.00472 hours\n",
            "Validation at Epoch 48 , AUROC: 0.50280 , AUPRC: 0.87920 , F1: 0.93577\n",
            "Training at Epoch 49 iteration 0 with loss 0.39547. Total time 0.00472 hours\n",
            "Validation at Epoch 49 , AUROC: 0.48179 , AUPRC: 0.87727 , F1: 0.93577\n",
            "Training at Epoch 50 iteration 0 with loss 0.35556. Total time 0.00472 hours\n",
            "Validation at Epoch 50 , AUROC: 0.45098 , AUPRC: 0.87013 , F1: 0.93577\n",
            "Training at Epoch 51 iteration 0 with loss 0.43628. Total time 0.005 hours\n",
            "Validation at Epoch 51 , AUROC: 0.47058 , AUPRC: 0.86807 , F1: 0.93577\n",
            "Training at Epoch 52 iteration 0 with loss 0.44493. Total time 0.005 hours\n",
            "Validation at Epoch 52 , AUROC: 0.50280 , AUPRC: 0.87795 , F1: 0.93577\n",
            "Training at Epoch 53 iteration 0 with loss 0.42836. Total time 0.005 hours\n",
            "Validation at Epoch 53 , AUROC: 0.46778 , AUPRC: 0.87069 , F1: 0.93577\n",
            "Training at Epoch 54 iteration 0 with loss 0.39693. Total time 0.005 hours\n",
            "Validation at Epoch 54 , AUROC: 0.56162 , AUPRC: 0.89317 , F1: 0.93577\n",
            "Training at Epoch 55 iteration 0 with loss 0.38910. Total time 0.00527 hours\n",
            "Validation at Epoch 55 , AUROC: 0.48319 , AUPRC: 0.87584 , F1: 0.93577\n",
            "Training at Epoch 56 iteration 0 with loss 0.43036. Total time 0.00527 hours\n",
            "Validation at Epoch 56 , AUROC: 0.55462 , AUPRC: 0.89417 , F1: 0.93577\n",
            "Training at Epoch 57 iteration 0 with loss 0.43290. Total time 0.00527 hours\n",
            "Validation at Epoch 57 , AUROC: 0.48039 , AUPRC: 0.87618 , F1: 0.93577\n",
            "Training at Epoch 58 iteration 0 with loss 0.42317. Total time 0.00527 hours\n",
            "Validation at Epoch 58 , AUROC: 0.56022 , AUPRC: 0.89584 , F1: 0.93577\n",
            "Training at Epoch 59 iteration 0 with loss 0.41262. Total time 0.00555 hours\n",
            "Validation at Epoch 59 , AUROC: 0.54061 , AUPRC: 0.88842 , F1: 0.93577\n",
            "Training at Epoch 60 iteration 0 with loss 0.40078. Total time 0.00555 hours\n",
            "Validation at Epoch 60 , AUROC: 0.54061 , AUPRC: 0.88842 , F1: 0.93577\n",
            "Training at Epoch 61 iteration 0 with loss 0.44222. Total time 0.00555 hours\n",
            "Validation at Epoch 61 , AUROC: 0.47058 , AUPRC: 0.87304 , F1: 0.93577\n",
            "Training at Epoch 62 iteration 0 with loss 0.40599. Total time 0.00555 hours\n",
            "Validation at Epoch 62 , AUROC: 0.56582 , AUPRC: 0.89366 , F1: 0.93577\n",
            "Training at Epoch 63 iteration 0 with loss 0.44037. Total time 0.00583 hours\n",
            "Validation at Epoch 63 , AUROC: 0.47759 , AUPRC: 0.87847 , F1: 0.93577\n",
            "Training at Epoch 64 iteration 0 with loss 0.39828. Total time 0.00583 hours\n",
            "Validation at Epoch 64 , AUROC: 0.47759 , AUPRC: 0.87508 , F1: 0.93577\n",
            "Training at Epoch 65 iteration 0 with loss 0.36473. Total time 0.00583 hours\n",
            "Validation at Epoch 65 , AUROC: 0.49859 , AUPRC: 0.88121 , F1: 0.93577\n",
            "Training at Epoch 66 iteration 0 with loss 0.37470. Total time 0.00583 hours\n",
            "Validation at Epoch 66 , AUROC: 0.50560 , AUPRC: 0.88092 , F1: 0.93577\n",
            "Training at Epoch 67 iteration 0 with loss 0.38794. Total time 0.00583 hours\n",
            "Validation at Epoch 67 , AUROC: 0.43277 , AUPRC: 0.86611 , F1: 0.93577\n",
            "Training at Epoch 68 iteration 0 with loss 0.44589. Total time 0.00611 hours\n",
            "Validation at Epoch 68 , AUROC: 0.52941 , AUPRC: 0.89033 , F1: 0.93577\n",
            "Training at Epoch 69 iteration 0 with loss 0.38248. Total time 0.00611 hours\n",
            "Validation at Epoch 69 , AUROC: 0.53361 , AUPRC: 0.88653 , F1: 0.93577\n",
            "Training at Epoch 70 iteration 0 with loss 0.44359. Total time 0.00611 hours\n",
            "Validation at Epoch 70 , AUROC: 0.44117 , AUPRC: 0.86926 , F1: 0.93577\n",
            "Training at Epoch 71 iteration 0 with loss 0.40447. Total time 0.00611 hours\n",
            "Validation at Epoch 71 , AUROC: 0.57002 , AUPRC: 0.89716 , F1: 0.93577\n",
            "Training at Epoch 72 iteration 0 with loss 0.36169. Total time 0.00638 hours\n",
            "Validation at Epoch 72 , AUROC: 0.54061 , AUPRC: 0.89246 , F1: 0.93577\n",
            "Training at Epoch 73 iteration 0 with loss 0.37570. Total time 0.00638 hours\n",
            "Validation at Epoch 73 , AUROC: 0.55322 , AUPRC: 0.89612 , F1: 0.93577\n",
            "Training at Epoch 74 iteration 0 with loss 0.41451. Total time 0.00638 hours\n",
            "Validation at Epoch 74 , AUROC: 0.39915 , AUPRC: 0.86785 , F1: 0.93577\n",
            "Training at Epoch 75 iteration 0 with loss 0.39313. Total time 0.00638 hours\n",
            "Validation at Epoch 75 , AUROC: 0.40896 , AUPRC: 0.86110 , F1: 0.93577\n",
            "Training at Epoch 76 iteration 0 with loss 0.38536. Total time 0.00666 hours\n",
            "Validation at Epoch 76 , AUROC: 0.55742 , AUPRC: 0.89538 , F1: 0.93577\n",
            "Training at Epoch 77 iteration 0 with loss 0.38887. Total time 0.00666 hours\n",
            "Validation at Epoch 77 , AUROC: 0.56862 , AUPRC: 0.89670 , F1: 0.93577\n",
            "Training at Epoch 78 iteration 0 with loss 0.41841. Total time 0.00666 hours\n",
            "Validation at Epoch 78 , AUROC: 0.45378 , AUPRC: 0.87084 , F1: 0.93577\n",
            "Training at Epoch 79 iteration 0 with loss 0.41656. Total time 0.00666 hours\n",
            "Validation at Epoch 79 , AUROC: 0.44537 , AUPRC: 0.86932 , F1: 0.93577\n",
            "Training at Epoch 80 iteration 0 with loss 0.42978. Total time 0.00694 hours\n",
            "Validation at Epoch 80 , AUROC: 0.43977 , AUPRC: 0.86786 , F1: 0.93577\n",
            "Training at Epoch 81 iteration 0 with loss 0.38579. Total time 0.00694 hours\n",
            "Validation at Epoch 81 , AUROC: 0.46498 , AUPRC: 0.87423 , F1: 0.93577\n",
            "Training at Epoch 82 iteration 0 with loss 0.41336. Total time 0.00694 hours\n",
            "Validation at Epoch 82 , AUROC: 0.57002 , AUPRC: 0.89396 , F1: 0.93577\n",
            "Training at Epoch 83 iteration 0 with loss 0.39389. Total time 0.00694 hours\n",
            "Validation at Epoch 83 , AUROC: 0.44537 , AUPRC: 0.86963 , F1: 0.93577\n",
            "Training at Epoch 84 iteration 0 with loss 0.41377. Total time 0.00722 hours\n",
            "Validation at Epoch 84 , AUROC: 0.49159 , AUPRC: 0.88228 , F1: 0.93577\n",
            "Training at Epoch 85 iteration 0 with loss 0.42237. Total time 0.00722 hours\n",
            "Validation at Epoch 85 , AUROC: 0.48879 , AUPRC: 0.88096 , F1: 0.93577\n",
            "Training at Epoch 86 iteration 0 with loss 0.39241. Total time 0.00722 hours\n",
            "Validation at Epoch 86 , AUROC: 0.54061 , AUPRC: 0.88801 , F1: 0.93577\n",
            "Training at Epoch 87 iteration 0 with loss 0.41358. Total time 0.00722 hours\n",
            "Validation at Epoch 87 , AUROC: 0.51540 , AUPRC: 0.88906 , F1: 0.93577\n",
            "Training at Epoch 88 iteration 0 with loss 0.45589. Total time 0.0075 hours\n",
            "Validation at Epoch 88 , AUROC: 0.50560 , AUPRC: 0.88366 , F1: 0.93577\n",
            "Training at Epoch 89 iteration 0 with loss 0.39535. Total time 0.0075 hours\n",
            "Validation at Epoch 89 , AUROC: 0.53081 , AUPRC: 0.89212 , F1: 0.93577\n",
            "Training at Epoch 90 iteration 0 with loss 0.39943. Total time 0.0075 hours\n",
            "Validation at Epoch 90 , AUROC: 0.50560 , AUPRC: 0.88879 , F1: 0.93577\n",
            "Training at Epoch 91 iteration 0 with loss 0.47265. Total time 0.0075 hours\n",
            "Validation at Epoch 91 , AUROC: 0.50280 , AUPRC: 0.88497 , F1: 0.93577\n",
            "Training at Epoch 92 iteration 0 with loss 0.40293. Total time 0.00777 hours\n",
            "Validation at Epoch 92 , AUROC: 0.54201 , AUPRC: 0.89177 , F1: 0.93577\n",
            "Training at Epoch 93 iteration 0 with loss 0.39105. Total time 0.00777 hours\n",
            "Validation at Epoch 93 , AUROC: 0.44957 , AUPRC: 0.87009 , F1: 0.93577\n",
            "Training at Epoch 94 iteration 0 with loss 0.41707. Total time 0.00777 hours\n",
            "Validation at Epoch 94 , AUROC: 0.58543 , AUPRC: 0.90294 , F1: 0.93577\n",
            "Training at Epoch 95 iteration 0 with loss 0.40109. Total time 0.00777 hours\n",
            "Validation at Epoch 95 , AUROC: 0.49859 , AUPRC: 0.88121 , F1: 0.93577\n",
            "Training at Epoch 96 iteration 0 with loss 0.40806. Total time 0.00805 hours\n",
            "Validation at Epoch 96 , AUROC: 0.50980 , AUPRC: 0.88320 , F1: 0.93577\n",
            "Training at Epoch 97 iteration 0 with loss 0.37171. Total time 0.00805 hours\n",
            "Validation at Epoch 97 , AUROC: 0.48039 , AUPRC: 0.87881 , F1: 0.93577\n",
            "Training at Epoch 98 iteration 0 with loss 0.41020. Total time 0.00805 hours\n",
            "Validation at Epoch 98 , AUROC: 0.46218 , AUPRC: 0.87192 , F1: 0.93577\n",
            "Training at Epoch 99 iteration 0 with loss 0.40427. Total time 0.00805 hours\n",
            "Validation at Epoch 99 , AUROC: 0.42156 , AUPRC: 0.86187 , F1: 0.93577\n",
            "Training at Epoch 100 iteration 0 with loss 0.44405. Total time 0.00805 hours\n",
            "Validation at Epoch 100 , AUROC: 0.45238 , AUPRC: 0.86943 , F1: 0.93577\n",
            "--- Go for Testing ---\n",
            "Testing AUROC: 0.8006600660066007 , AUPRC: 0.9629508313751466 , F1: 0.9308755760368664\n",
            "--- Training Finished ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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v6tSpfPTRR3z++efUrl2bvn37lvgAXGlaGGlpaUWtBQhafmlpR94M2qtXLz755BMgSGjLly8HghZJSkoKderUoU6dOvTp04cFCxYUdV8dOnSI5OTkEn8/paFRRBEBghZG4YfU6NGjj1t/69atFBQUcP311/Pkk08yd+7cI+r07t2bcePGsX//fvbt28e7775L7969TzjGK664gj/84Q8EHRIwb9684x7TqFEj8vPziz7Ud+3aRdOmTalZsyZTpkxh7dqSl3+44oorGDVqFHv37gWCbqPs7Gx27dpFo0aNqF27NkuXLuWLL74o8fjnnnuO+fPnH/FTPFkADBo0iDFjxnDo0CFWr15NRkYG3bt3P6Je4WD2oUOH+NWvfsVdd90FwODBg5k+fTp5eXns37+fGTNm0KlTJyAY+0hNTS3VnFFHo4QhIgA8+OCDPPzww3Tt2rXoG/2xZGVl0bdvX84991xuvfVWRo4ceUSd8847j2HDhtG9e3d69OjBnXfeWdTFcyIeeeQRcnNz6dKlC507d+aRRx6J6rgBAwYwffp0AG655RZmz57N2WefzauvvsqZZ5551GNuvvlmevXqxdlnn80NN9zAnj17GDhwIHl5eXTq1ImHHnrosG61E9W5c2e++c1vkp6ezsCBA3nxxReLupuuuuoqNmzYAMCvf/1rOnXqRJcuXbjmmmvo168fAJ06dWLgwIF06dKF7t27c+edd3LWWWcBMGXKFL7xjbLpBrXCTF3VdOvWzbXiXtVy8a8mk7njAJ88eCktG9eOdzilsmTJkqJvfBJ7c+fO5bnnnuMf//hHvEOJueuuu46nn366qHsqUkn/Ls1sjrt3K+m91MIQkSrvvPPO49JLLyU/Pz/eocRUTk4O1157bYnJ4kRo0FskRtxdc2DF0Xe/+914hxBzSUlJRbfXFncivUtqYYjEQHJyMtu2bTuh/0lFylrhehilvXNKLQyRGGjRogWZmZls2bIl3qGIAF+vuFcaShgiMVCzZs1SrWwmUhGpS0pERKKihCEiIlFRwhARkagoYYiISFSUMEREJCpKGCIiEhUlDBERiYoShoiIREUJQ0REoqKEISIiUVHCEBGRqChhiIhIVGKaMMxsoJktM7MVZnbEwrZm1trMJpnZQjObamYtIvY9Y2ZfmdkSM3vetLCAiEhMxSxhmFki8CJwJZAO3GRm6cWqPQu86u5dgCeAkeGxFwIXAV2As4ALgEtiFLqIiBDbFkZ3YIW7r3L3HGAMMLhYnXRgcrg9JWK/A8lAElALqAlsLveIRUSkSCwTRhqwPuJ1ZlgWaQFwXbg9BKhnZinu/jlBAtkY/nzg7kuKn8DMhpvZbDObrYVqRETKVkUb9B4BXGJm8wi6nLKAfDNrD3QCWhAkmX5m1rv4we7+krt3c/duTZo0iWXcIiJVXixX3MsCWka8bhGWFXH3DYQtDDOrC1zv7jvN7HvAF+6+N9z3X6AX8EksAhcRkdi2MGYBZ5hZWzNLAoYC4yMrmFmqmRXG9DAwKtxeR9DyqGFmNQlaH0d0SYmISPmJWcJw9zzgXuADgg/7t9z9KzN7wswGhdX6AsvMbDnQDHgqLB8LrAS+JBjnWODu/xer2EVEJLZdUrj7BGBCsbJHI7bHEiSH4sflA98v9wBFROSoKtqgt4iIVFBKGCIiEhUlDBERiYoShoiIREUJQyodd5i/fiez1myPdygi1UpM75ISKQu3vPwF67cfAGD2zy4jtW6tOEckUj2ohSGVRuGE9oXJAmDvwbw4RSNS/ShhSKUx7MK2XNG5GX+65TzSGp4S73BEqh0lDKk07ri4LX/5djeuPLs5NRK1fpZIrGkMQ6SaOpibz7x1wc0Ds9ZsJ2vHAZ65oQvd2jSOd2hSQSlhiFQTB3LymbN2B1+s2saM1dtYsH4XOfkFh9WZsixbCUOOSglDpIrKyStg/vqdfLpiK5+v3Ma89TvIzfei/WaQ3rw+3ds2ZsPOA0xcrEUs5diUMESqCHcnI3sv05ZvYfqKrcxYtZ0DuflF+83grLT69Do9hR5tU7igTWMa1K4JwAuTM5Qw5LiUMEQqsV37c/lkxRamLd/CtOVb2bT74GH7z2hal4vap3JhuyBJFCYIkROhhCFSibg7SzftYfLSbKYuy2buup3kF3zdzZRatxa9z0il9xmpXNw+lab1k+MYrVQ1ShgiFVxOXgFfrNrGR0s2M2lJNlk7v35wsUaC0fP0xlzSoSmXdGhCp+b1MNMtx1I+lDBEKqC9h/KYuiyb9xdt4uNlW9hz6Osn2lPr1qLfmU3od2ZTLmqfSr1kdTNJbChhiFQQuw7k8tHizfx30UamZWwlJ+/rW17PPLUel6c3o3+nZnRJa0BCgloREntKGCJxtOdgLh8u3sy/F27kk4wtRbe9msEFbRpxRedTGZB+Kq1Sasc5UhElDJGYO5ibz9Rl2Yybt4HJy7KLWhIJBr1OT+GqLs25onMzmtbTgLVULEoYIjHg7sxas4N352Xyn4Ub2R3OsmsG3ds25ppzTmNg51NpUk9TtUvFFdOEYWYDgd8DicDf3P3pYvtbA6OAJsB24FZ3zwz3tQL+BrQEHLjK3dfELnqR0svcsZ935mTxztxM1m3fX1Te+bT6XHtuGlef05zmDar3zLu5+QUszNzFF6u2sXTTHoZd2JrzW2t6koooZgnDzBKBF4HLgUxglpmNd/fFEdWeBV5191fMrB8wEvh2uO9V4Cl3/9DM6gKHT4IjUkEcystn4lebeWv2eqav2IqHj0mcWj+ZIeelMaRrGh2a1YtvkHFUUOAs2bSbz1Zs47OVW5m5ejv7cvIPq6OEUTHFsoXRHVjh7qsAzGwMMBiITBjpwI/C7SnAuLBuOlDD3T8EcPe9sQpaJFprtu7jjZnreHtOJtv35QCQVCOBK886lRvOb8GF7VJJrKZ3N2XtPMAn4ZQln63cVvT7KdSuSR1S6tZi5urt5Bfou2BFFcuEkQasj3idCfQoVmcBcB1Bt9UQoJ6ZpQAdgJ1m9i+gLfAR8JC7H/a1xMyGA8MBWrVqVR7XIHKY/AJn8tJsXv18DZ9kbC0qP/PUetzUvRXXnptWLafjOJCTzxertvHx8i1My9jCqi37Dtt/WoPkYMqS9ilc2C6VZvWT+c/CjcxcrXXaK7KKNug9AnjBzIYB04AsIJ8gzt5AV2Ad8CYwDHg58mB3fwl4CaBbt26OSDnZtT+XMbPW8erna4uevE6umcA1XU7j5h6tOLdlw2r3xPXqrfuYsjSbKcuymbF6+2HPkdStVYNe7VLoc0YqF7VPpW1qnWr3+6kKYpkwsggGrAu1CMuKuPsGghYG4TjF9e6+08wygfkR3VnjgJ4USxgi5W311n2Mmr6asXMyi2aCbdW4Nt/p1Zobz29ZrVoTOXkFzF6znUlLs5m8NJvVWw9vRXRp0YA+ZzThko5NOLdlQ2omaoHPyi6WCWMWcIaZtSVIFEOBmyMrmFkqsN3dC4CHCe6YKjy2oZk1cfctQD9gdswil2pvztodvDRtJRMXby4axO59Riq3X9SGvh2aVpsnr3cdyGXqsmw+XLz5iClLGpxSk0s6NKFvxyb06dCE1Lq6RbiqiVnCcPc8M7sX+IDgttpR7v6VmT0BzHb38UBfYKSZOUGX1D3hsflmNgKYZEE7dg7w11jFLtWTu/Px8i38ccpKZq4J+taTEhMY0jWN717clo6nVo87nTbvPsjErzYxcfFmPl+5jbyI2XE7NKtLvzOb0b9TU7q2bEgNtSKqtJiOYbj7BGBCsbJHI7bHAmOPcuyHQJdyDVCE4LbPiYs38YfJK/hqw24A6ifX4NaerRl2YZtqMWX4+u37eX/RJv67aCNz1+0sKk8MZ8e9PP1ULu/UTFOWVDMVbdBbJG4KE8XvPspg6aY9QDAz7Pd6t+XmHq2qxaywn67YxvSM6SzI3FVUVqtGAn06NGFAejMu69SMRnWS4hihxJMShlR77s6UZdn8+oPlLNkYtChOrZ/M3Ze245vdWpJcMzHOEcbO/PVBa6J2UiL9zmzKlWc1p2/HJtSppY8KUcKQam7Gqm0888Ey5qzdAQSJ4p5L2/HNC1pSq0b1SRR9Ozbl3ws30r5pXa7u0py+HZtWq0Qp0VHCkGopY/MefvX+Uj5akg1A4zpJ3HNpe27p0apaflCeldaA93/YJ95hSAWnhCHVyta9h/jth8sZM3MdBQ51khIZ3qcdd/RuS111u4gck/4PkWohJ6+AVz5bw/OTMthzKI/EBOPbPVrxP/3P0JTiIlEqdcIwsx5Af6ApcNhN1+7+P2UUl0iZmZ6xlUfHLyqaz+iSDk145OpOtG9aPZ6jqEoKCpysnQdo3iBZz3zEQakSRvjw3DPACmADwboUhTR3k1Qom3Yd5Bf/Wcx/Fm4E4PTUOjxydTqXntk0zpFJaRzMzefzldv4cMlmJi3ZzObdh7jj4rY8cnV6vEOrdkrbwrgf+B93f6E8ghEpC/kFzj+/WMuvP1jG3kN5nFIzkfv6t+eOi9tWqzufKrPdB3OZsjSbiV9tZuqy7CPWy1i1RSscxENpE0Z9ij2pLVKRLN20m4fe+bLoeYIB6c14bFBn0hpW71XtKpNPlm/l/F98SG7+150WnZrXZ0B6M5JrJvKr95fGMbrqrbQJ4w1gIPDHcohF5ITl5hfwp6kr+cPkDHLznWb1a/HzQWcx8KxT4x2aRCm5ZjAmsedQHgnhWucDO5/K5enNaNk4mIJk8tLN8Qyx2ittwlgP/NzMLgIWArmRO939t2UVmEi0lm7azYi3F7AoK3hK++YerXjoyjOpXw2m8qhK+nRowogBHUitW4vL0ptpttsKqLQJ405gL3Bh+BPJASUMianRn63h9RnryMkvIK3hKTxzQxcuap8a77DkBNRMTODefmfEOww5hlIlDHdvW16BiJyI0Z+tAeCm7i3532+k6+E7kXJ0wv93hSviubvvO25lkTKWGC7vmVIniV9d34XL0pvFOSKRqq/UT76Y2T1mtg7YBew2s7VmdnfZhyZydHdf2p6be7Ti/R/2UbIQiZHSPrj3U4KlU58FpofFvYGnzay+uz9dxvGJlOiG81tww/kt4h2GSLVS2i6pu4Dh7v5GRNkkM8sAfgkoYYiIVFGl7ZJqCswqoXwmoH4BEZEqrLQJYzlwcwnlNwPLTj4cERGpqErbJfU48JaZ9QE+DcsuAi4BbjzewWY2EPg9kAj8rfiYh5m1BkYBTYDtwK3unhmxvz6wGBjn7veWMnYRETkJpWphuPu/gB7AJuDq8GcT0N3dxx3rWDNLBF4ErgTSgZvMrPh0k88Cr7p7F+AJYGSx/b8AppUmZhGpHvYdyqOgQJNml6dSP4fh7nOAW0/gXN2BFe6+CsDMxgCDCVoMhdKBH4XbU4CiJGRm5xOMk7wPdDuB84tIFbNrfy7/XbSR9+Zv4IvV2xh2YRseu6ZzvMOqso6bMMyssbtvL9w+Vt3CekeRRjAXVaFMgtZKpAXAdQTdVkOAemaWAuwAfkOQqC47XswiUrWt33GAu/4xh8lLs8nJLygqX755TxyjqvqiaWFsMbPm7p4NbKXkhZIsLD/ZxQZGAC+Y2TCCrqcsIB+4G5jg7pkWPuFbEjMbDgwHaNWq1UmGIiIV1YrsvazI3osZXNQ+hdNT6/KPL9bGO6wqL5qE0Y9gABrg0pM4VxbQMuJ1i7CsiLtvIGhhFE49cr277zSzXkDv8InyukCSme1194eKHf8S8BJAt27d1JkpUsW0b1KPesk1SGt4CkO6pjH43DRObZDM9IytShgxcNyE4e4fl7R9AmYBZ5hZW4JEMZRit+iaWSqw3d0LCJ4oHxWe95aIOsOAbsWThYhUfa1SarPwsQEcq6dByk+p7pIys3Qz6xjx+nIz+6eZPRzeBXVU7p4H3At8ACwB3nL3r8zsCTMbFFbrCywzs+UEA9xPlSY+Ean6lCzip7R3SY0Cfkfwod4SeA+YCtxDsHzrw8c62N0nUGyJV3d/NGJ7LDD2OO8xGhhdyrhFRE7aobx8pi3fytRl2fTp0IQrOlevFR1LmzDOBOaG2zcAM9z9KjO7FPg7x0kYIiKVTW5+AZ+t3Mb4+RuYuHgTew7mATBj9XYljONIBF+hq7cAABOKSURBVHLC7f583VpYieaSEpEqoqDAmbtuB+/N38B/vtzI9n05Rftap9Rm7bb95EXczltdlDZhLAJ+YGb/JkgYhS2KNIJbbkVEKq0V2Xt4d14W4+ZtIGvngaLydk3qMOicNK4+pzkJZlz67NT4BRlHpU0Y/4/g6esRwCvu/mVYPohgxloRkUpl695DvDd/A+/Oy2RR1u6i8uYNkhl0zmkMOvc00pvXLxpsX721+i4yWto1vaeZWROgvrvviNj1F2B/mUYmIlJODuXlM3lJNmPnZDJ1+Rbywzmo6iXX4BtnN2fwuWn0aNuYhATdkRXpROaSyieYqiOybE1ZBSQiUl4WZe3i7dnreW/BBnbuzwWgRoJxWaemXHdeC/qd2ZTkmic7YUXVFc1cUuMJphnfHW4flbsPOtZ+EZFY27U/l3Hzs3hz1noWb/y6y6lT8/rccH4LBp97Gql1a8UxwsojmhbGNr6eP2pbOcYiIlIm3J2Zq7czZtZ6Jny5kUN5wR1NDU6pybXnnsaN3VpyVlqDOEdZ+UQzNcjtJW2LiFQ0ew/l8/L01bw+Yy0rt3w9OH1x+1S+dUFLLk9vpi6nk1CqMQwzOxWoEbkKXljeAsh1981lGZyISGksWL+TBet3AtC0Xi1u7NaCb3VrRauU2nGLaffBXKYszaZ1Sh3ObdkwbnGUhdIOev8TeBP4a7HyK4BvAQPKIigRkdJoWLtm0XbvM1K5pUdr+ndqSs3EUk2XV2Zy8gqYsiybcfOymLQ0m5y8Alo1rs20B09mwu/4K23C6EYwb1RxnwC/PvlwRERKr/Np9XlzeE+aNzglbq0Jd2fe+p38a24m/164segurEL7c/LiEldZKm3CqAGUdDtB8lHKRUTKnZnR4/SUuJx7w84DvDsvi3fmZLIq4qG+M0+tx5CuaVzYLpVrXpgel9jKWmkTxgzgB+FPpHsI1rsQEakWduzP5da/zeDTlVvx8D7SJvVqce25p3HdeS3o1Lw+AFv2HIpjlGWrtAnjf4HJZtYFmByW9QO6orW2RaQa2XUgl+krtpJUI4HL05txw/kt6N0+lRpxGjeJhdJODfJFuFzqg4RLqQLzgLvdfUFZByciUtGc1jCZ9Ob1qZlo3HB+Cwadk0aDiEH3quxEpgZZANxy3IoiIlVQrRqJTLi/d7zDiItSt53MrJmZjTCzP4ZrcGNmF4VrdYuISBVV2jW9zweWEbQw7iRYlhXgcrT+tohIlVbaFsazwO/dvSsQOfT/AXBRmUUlIiIVTmkTxvnAKyWUb0RLtIqIVGmlTRgHgEYllJ8JZB/vYDMbaGbLzGyFmT1Uwv7WZjbJzBaa2dRwjirM7Fwz+9zMvgr3fauUcYuIVBgbdh7gD5MyuOy3H/PAm/PjHU7USnuX1HvAY2Z2Y/jazawN8CvgnWMdaGaJwIsE4x2ZwCwzG+/uiyOqPQu86u6vmFk/YCTwbYLV/L7j7hlmdhowx8w+cPedpYxfRCQu8guc/365kTGz1jMtY0vRw36bdx+Mb2ClUNoWxgigMbAFqA1MB1YAO4GfHefY7sAKd1/l7jnAGGBwsTrpfP1A4JTC/e6+3N0zwu0NBK2ZJqWMXUQkbnbsz+UHr83l4+VbqJmQwGWdmsY7pFIrbQsjD+gL9AHOI0g4c939oyiOTQPWR7zOBHoUq7OA4IHA3wNDgHpmluLuRQs3mVl3IAlYWfwEZjYcGA7QqlWr6K5IRKQc1U5KpEaCkVfgdGxWj29d0JIhXdNISDDO+fnEeIdXKlEnjLBLaRdwjrtP5uuWQFkaAbxgZsOAaUAWkB8RQ3PgH8Bt7l5Q/GB3fwl4CaBbt25efL+ISKzVqVWDf919IQBnpzXAzIBgapHKJuqE4e75ZraW4Nv9icgCWka8bhGWRZ5jA+GUI2ZWF7i+cJzCzOoD/wH+192/OMEYRERirkuLyr1wUqHSjmH8Ani68AnvUpoFnGFmbc0sCRgKjI+sYGapZlYY08PAqLA8CXiXYEB87AmcW0RETlJpxzBGAG2BLDPLBPZF7nT3Lkc70N3zzOxegof8EoFR7v6VmT0BzHb38QTjIyPNzAm6pAoXa/omwbhJSthdBTDM3SvP/WgiIpVcaRPGWMABO5GTufsEYEKxskcjtseG5yh+3D8JlocVEZE4iSphmFltgiVYrwVqApOA+9x9aznGJiIiFUi0Yxg/B4YRDDq/QbBY0p/KKSYREamAou2Sug64w93HAJjZa8CnZpbo7vnHPlRERKqCaFsYLYFPCl+4+0yCh/hOK4+gRESk4ok2YSQCOcXK8jiBFftERKRyivYD34B/mlnkGhjJwF/NbH9hgbsPKsvgRESk4og2YZS0BoZucxURqUaiShjufnt5ByIiIhVbaacGERGRakoJQ0REoqKEISIiUVHCEBGRqChhiIhIVJQwREQkKkoYIiISFSUMERGJihKGiEgcFRQ4b81ez6AXpvPjtxbEO5xjUsIQEYmjfTn5PDh2IQszd/H+oo3xDueYlDBEROKgVo0EaiYGq123a1InztFER9OTi4jEQXLNRP55Rw8KHM5Kq8/Zj0+Md0jHpYQhIhInPU5PAWDvobw4RxKdmHZJmdlAM1tmZivM7KES9rc2s0lmttDMpppZi4h9t5lZRvhzWyzjFhGRGCYMM0sEXgSuBNKBm8wsvVi1Z4FX3b0L8AQwMjy2MfAY0APoDjxmZo1iFbuIiMS2hdEdWOHuq9w9BxgDDC5WJx2YHG5Pidh/BfChu2939x3Ah8DAGMQsIiKhWCaMNGB9xOvMsCzSAuC6cHsIUM/MUqI8FjMbbmazzWz2li1byixwERGpeLfVjgAuMbN5wCVAFpAf7cHu/pK7d3P3bk2aNCmvGEVEqqVY3iWVBbSMeN0iLCvi7hsIWxhmVhe43t13mlkW0LfYsVPLM1gRETlcLFsYs4AzzKytmSUBQ4HxkRXMLNXMCmN6GBgVbn8ADDCzRuFg94CwTESkStm06yDPfrCMN2aui3coR4hZC8Pd88zsXoIP+kRglLt/ZWZPALPdfTxBK2KkmTkwDbgnPHa7mf2CIOkAPOHu22MVu4hILOzPzaf3M5PJzXfqJCVyU/dW8Q7pMDF9cM/dJwATipU9GrE9Fhh7lGNH8XWLQ0SkyrDwv+6Qm+8A5BV4/AI6ioo26C0iUu3UqVWDOy9uy03dW/Hv+y6OdzhHpalBREQqgJ9dHTzHfDA36htDY04tDBERiYoShoiIREUJQ0REoqKEISIiUVHCEBGRqChhiIhIVJQwREQqqC8zd3Hv63MZ+d8l8Q4F0HMYIiIV0qG8Aq55YToASYkJPHxlpzhHpBaGiEiFYvb1dq0awUe0UzGmCVELQ0SkAqlVI5GfXNGRfYfyuLVnay58evLxD4oRJQwRkQrmnkvbA5CTVxDnSA6nLikREYmKEoaIiERFCUNERKKihCEiIlFRwhARkagoYYiISFSUMEREJCpKGCIiEpWYJgwzG2hmy8xshZk9VML+VmY2xczmmdlCM7sqLK9pZq+Y2ZdmtsTMHo5l3CIiEsOEYWaJwIvAlUA6cJOZpRer9jPgLXfvCgwF/hiW3wjUcvezgfOB75tZm1jELSIigVi2MLoDK9x9lbvnAGOAwcXqOFA/3G4AbIgor2NmNYBTgBxgd/mHLCIihWKZMNKA9RGvM8OySI8Dt5pZJjABuC8sHwvsAzYC64Bn3X178ROY2XAzm21ms7ds2VLG4YuIVG8VbdD7JmC0u7cArgL+YWYJBK2TfOA0oC3wYzM7vfjB7v6Su3dz925NmjSJZdwiIlVeLBNGFtAy4nWLsCzSHcBbAO7+OZAMpAI3A++7e667ZwOfAt3KPWIRESkSy4QxCzjDzNqaWRLBoPb4YnXWAf0BzKwTQcLYEpb3C8vrAD2BpTGKW0REiGHCcPc84F7gA2AJwd1QX5nZE2Y2KKz2Y+B7ZrYAeAMY5u5OcHdVXTP7iiDx/N3dF8YqdhERifECSu4+gWAwO7Ls0YjtxcBFJRy3l+DWWhERiZOKNugtIiIl2J+Tx98+WcU7czLjFoOWaBURqeDyCpxLn53K5t2HSKqRwPXnt4hLHGphiIhUcO6wefchAHLz47fOtxKGiEgFVTPR6HxafdIansJvbjwn3uGoS0pEpKIyM8bfezEJFrQyfvz2grjGoxaGiEgFlphgmFm8wwCUMEREqpTsPQeZtrx85tJTl5SISBWwPyePv05bzV+mraRGgvHxTy6lUZ2kMj2HEoaISCWWX+C8MzeT30xcVnQn1YD0ZhzMyy/zcylhiIhUUp+t3MqT/17C4o3B8kBdWjTgp1d1oufpKeVyPiUMEZFKZu22fTz1nyVMXLwZgOYNknlwYEcGn5NGQkL5DZArYYiIVCLucPlvp5GTX0DtpER+cEk77ux9OqckJZb7uZUwREQqmZz8Aq4/rwUPDuxIs/rJMTuvEoaISCWQkGBcc85p7Nyfw4gBHTmnZcOYx6CEISJSSfzhpq5xPb8e3BMRkagoYYiISFSUMEREJCpKGCIiEhUlDBERiYoShoiIREUJQ0REoqKEISIiUTF3j3cM5cLMtgBrT+ItUoGtZRROZVHdrrm6XS/omquLk7nm1u7epKQdVTZhnCwzm+3u3eIdRyxVt2uubtcLuubqoryuWV1SIiISFSUMERGJihLG0b0U7wDioLpdc3W7XtA1Vxflcs0awxARkaiohSEiIlFRwhARkahU64RhZgPNbJmZrTCzh0rYX8vM3gz3zzCzNrGPsmxFcc0/MrPFZrbQzCaZWet4xFmWjnfNEfWuNzM3s0p/C2Y012xm3wz/1l+Z2euxjrGsRfFvu5WZTTGzeeG/76viEWdZMbNRZpZtZouOst/M7Pnw97HQzM476ZO6e7X8ARKBlcDpQBKwAEgvVudu4M/h9lDgzXjHHYNrvhSoHW7/oDpcc1ivHjAN+ALoFu+4Y/B3PgOYBzQKXzeNd9wxuOaXgB+E2+nAmnjHfZLX3Ac4D1h0lP1XAf8FDOgJzDjZc1bnFkZ3YIW7r3L3HGAMMLhYncHAK+H2WKC/mVkMYyxrx71md5/i7vvDl18ALWIcY1mL5u8M8AvgV8DBWAZXTqK55u8BL7r7DgB3z45xjGUtmmt2oH643QDYEMP4ypy7TwO2H6PKYOBVD3wBNDSz5idzzuqcMNKA9RGvM8OyEuu4ex6wC0iJSXTlI5prjnQHwTeUyuy41xw21Vu6+39iGVg5iubv3AHoYGafmtkXZjYwZtGVj2iu+XHgVjPLBCYA98UmtLgp7f/vx1XjpMKRKsvMbgW6AZfEO5byZGYJwG+BYXEOJdZqEHRL9SVoRU4zs7PdfWdcoypfNwGj3f03ZtYL+IeZneXuBfEOrLKozi2MLKBlxOsWYVmJdcysBkEzdltMoisf0VwzZnYZ8L/AIHc/FKPYysvxrrkecBYw1czWEPT1jq/kA9/R/J0zgfHunuvuq4HlBAmksormmu8A3gJw98+BZIJJ+qqqqP5/L43qnDBmAWeYWVszSyIY1B5frM544LZw+wZgsoejSZXUca/ZzLoCfyFIFpW9XxuOc83uvsvdU929jbu3IRi3GeTus+MTbpmI5t/2OILWBWaWStBFtSqWQZaxaK55HdAfwMw6ESSMLTGNMrbGA98J75bqCexy940n84bVtkvK3fPM7F7gA4I7LEa5+1dm9gQw293HAy8TNFtXEAwuDY1fxCcvymv+NVAXeDsc31/n7oPiFvRJivKaq5Qor/kDYICZLQbygZ+4e6VtPUd5zT8G/mpmDxAMgA+rzF8AzewNgqSfGo7LPAbUBHD3PxOM01wFrAD2A7ef9Dkr8e9LRERiqDp3SYmISCkoYYiISFSUMEREJCpKGCIiEhUlDBERiYoShkglYWajzezfR3stUt6UMESiEH44e/iTZ2brzOxPZtYo3rGJxIoShkj0PgKaA22AO4FrgD/GMyCRWFLCEIneIXff5O6Z7j4ReBMYULjTzG4PFyQ6aGbLzeyBcHLDwv0NwlbJxrDOEjP7VrgvxczeMLNMMzsQLmp00k/mipSlajs1iMjJMLPTgYFAbvj6e8ATBFNmzyGY0PCv4f4XwnVUJgCNCKZoWA50JJjPiPC/cwnW5NgNXAb8xczWufukGF2WyDEpYYhEb6CZ7SWYq6jwg/5H4X8fAR5097Hh69Vm9jTBqo0vECSAXkBnd18S1ima7M/dswjm8Sr0kpn1I5iSWwlDKgQlDJHoTQOGA6cQrFjXDnjezJoQTCP9FzP7U0T9GgTLYwJ0BTZGJIvDmFki8BDwLYJFbmoRLDU6tewvQ+TEKGGIRG+/u68It//HzKYQtCwKk8RdwGcn+N4jCGZTvR/4EtgL/BJoeuLhipQtJQyRE/dzgiVsXyJYH7qdu796lLrzgOZm1ukorYyLgf9z938AhGMeHYCqvAKeVDJKGCInyN2nhutJ/IxgLYI/mNlOgsHtmsB5QJq7jyQYh5gBvBOux7AcaA/Ucfdx4etvmdnFwFaCwfO2BIlGpELQbbUiJ+c3BEt/fgh8F/g2sAD4hGC8YzVAuG70lcCnwD+BJcDvCcYpAJ4EZhK0WKYB+4DXYnURItHQAkoiIhIVtTBERCQqShgiIhIVJQwREYmKEoaIiERFCUNERKKihCEiIlFRwhARkagoYYiISFT+P8TiOxBPFMfUAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RUpmL1KXV6vV"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "split_whole = data_whole.get_split()"
      ],
      "metadata": {
        "id": "40f_KcJul2Ce"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "split_whole.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNDae9d9l8I2",
        "outputId": "eb6f1fe1-dede-42a2-c804-a43a79f810ca"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['train', 'valid', 'test'])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = split_whole['train']\n",
        "valid_set = split_whole['valid']\n",
        "test_set = split_whole['test']\n",
        "len(train_set), len(valid_set), len(test_set)"
      ],
      "metadata": {
        "id": "qSMftQWel9MW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63135012-cf96-4130-94fb-320f15e40f57"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(448, 64, 128)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "pEWkL56nl_Tj",
        "outputId": "8473c534-edef-4546-f3c8-6d51e312c3b8"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Drug_ID                                               Drug  Y\n",
              "0    Dimercaprol                                           OCC(S)CS  0\n",
              "1    Edrophonium                            CC[N+](C)(C)c1cccc(O)c1  0\n",
              "2     Vidarabine   Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O  0\n",
              "3    Cefacetrile  CC(=O)OCC1=C(C(=O)O)N2C(=O)[C@@H](NC(=O)CC#N)[...  0\n",
              "4    Ticarcillin  CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](C(=O)O)c3ccsc3...  0\n",
              "5      Cefoxitin  CO[C@@]1(NC(=O)Cc2cccs2)C(=O)N2C(C(=O)O)=C(COC...  0\n",
              "6    Ceftazidime  CC(C)(O/N=C(\\C(=O)N[C@@H]1C(=O)N2C(C(=O)[O-])=...  0\n",
              "7      Arbekacin  NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]...  0\n",
              "8    Ceftriaxone  CO/N=C(\\C(=O)N[C@@H]1C(=O)N2C(C(=O)O)=C(CSc3nc...  0\n",
              "9     Epirubicin  COc1cccc2c1C(=O)c1c(O)c3c(c(O)c1C2=O)C[C@@](O)...  0\n",
              "10    Vecuronium  CC(=O)O[C@H]1C[C@@H]2CC[C@@H]3[C@H](CC[C@@]4(C...  0\n",
              "11   Pancuronium  CC(=O)O[C@H]1C[C@@H]2CC[C@@H]3[C@H](CC[C@@]4(C...  0\n",
              "12    Cefodizime  CO/N=C(\\C(=O)N[C@@H]1C(=O)N2C(C(=O)O)=C(CSc3nc...  0\n",
              "13      Amikacin  NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]...  0\n",
              "14    Alcuronium  C=CC[N@@+]12CC[C@@]34c5ccccc5N5/C=C6/[C@H]7C[C...  0\n",
              "15      Imipenem  C[C@@H](O)[C@H]1C(=O)N2C(C(=O)O)=C(SCC/N=C/N)C...  0\n",
              "16   Ibandronate                CCCCCN(C)CCC(O)(P(=O)(O)O)P(=O)(O)O  0\n",
              "17    Oxitropium  CC[N+]1(C)[C@H]2C[C@H](OC(=O)[C@H](CO)c3ccccc3...  0\n",
              "18    Alfentanil   CCC(=O)N(c1ccccc1)C1(COC)CCN(CCn2nnn(CC)c2=O)CC1  0\n",
              "19  Benzthiazide   NS(=O)(=O)c1cc2c(cc1Cl)N=C(CSCc1ccccc1)NS2(=O)=O  0"
            ],
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              "      <th>Drug_ID</th>\n",
              "      <th>Drug</th>\n",
              "      <th>Y</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dimercaprol</td>\n",
              "      <td>OCC(S)CS</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Edrophonium</td>\n",
              "      <td>CC[N+](C)(C)c1cccc(O)c1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Vidarabine</td>\n",
              "      <td>Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>Cefacetrile</td>\n",
              "      <td>CC(=O)OCC1=C(C(=O)O)N2C(=O)[C@@H](NC(=O)CC#N)[...</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>Ticarcillin</td>\n",
              "      <td>CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](C(=O)O)c3ccsc3...</td>\n",
              "      <td>0</td>\n",
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              "      <th>5</th>\n",
              "      <td>Cefoxitin</td>\n",
              "      <td>CO[C@@]1(NC(=O)Cc2cccs2)C(=O)N2C(C(=O)O)=C(COC...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ceftazidime</td>\n",
              "      <td>CC(C)(O/N=C(\\C(=O)N[C@@H]1C(=O)N2C(C(=O)[O-])=...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Arbekacin</td>\n",
              "      <td>NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ceftriaxone</td>\n",
              "      <td>CO/N=C(\\C(=O)N[C@@H]1C(=O)N2C(C(=O)O)=C(CSc3nc...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Epirubicin</td>\n",
              "      <td>COc1cccc2c1C(=O)c1c(O)c3c(c(O)c1C2=O)C[C@@](O)...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Vecuronium</td>\n",
              "      <td>CC(=O)O[C@H]1C[C@@H]2CC[C@@H]3[C@H](CC[C@@]4(C...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Pancuronium</td>\n",
              "      <td>CC(=O)O[C@H]1C[C@@H]2CC[C@@H]3[C@H](CC[C@@]4(C...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Cefodizime</td>\n",
              "      <td>CO/N=C(\\C(=O)N[C@@H]1C(=O)N2C(C(=O)O)=C(CSc3nc...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Amikacin</td>\n",
              "      <td>NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Alcuronium</td>\n",
              "      <td>C=CC[N@@+]12CC[C@@]34c5ccccc5N5/C=C6/[C@H]7C[C...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Imipenem</td>\n",
              "      <td>C[C@@H](O)[C@H]1C(=O)N2C(C(=O)O)=C(SCC/N=C/N)C...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Ibandronate</td>\n",
              "      <td>CCCCCN(C)CCC(O)(P(=O)(O)O)P(=O)(O)O</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Oxitropium</td>\n",
              "      <td>CC[N+]1(C)[C@H]2C[C@H](OC(=O)[C@H](CO)c3ccccc3...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Alfentanil</td>\n",
              "      <td>CCC(=O)N(c1ccccc1)C1(COC)CCN(CCn2nnn(CC)c2=O)CC1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Benzthiazide</td>\n",
              "      <td>NS(=O)(=O)c1cc2c(cc1Cl)N=C(CSCc1ccccc1)NS2(=O)=O</td>\n",
              "      <td>0</td>\n",
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          "metadata": {},
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    {
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        "train_set.columns"
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        },
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        "outputId": "0edce160-59d1-498f-c9f0-2dc8450edf11"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Drug_ID', 'Drug', 'Y'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "whole_df = pd.concat([train_set, valid_set, test_set], ignore_index=True)\n",
        "whole_df.reset_index()\n",
        "whole_df.head()"
      ],
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        "id": "efyABgLuFPg6",
        "outputId": "5564caf7-8250-4cea-c96a-0c665b2c5b69"
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      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "       Drug_ID                                               Drug  Y\n",
              "0  Dimercaprol                                           OCC(S)CS  0\n",
              "1  Edrophonium                            CC[N+](C)(C)c1cccc(O)c1  0\n",
              "2   Vidarabine   Nc1ncnc2c1ncn2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O  0\n",
              "3  Cefacetrile  CC(=O)OCC1=C(C(=O)O)N2C(=O)[C@@H](NC(=O)CC#N)[...  0\n",
              "4  Ticarcillin  CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](C(=O)O)c3ccsc3...  0"
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              "      <th>0</th>\n",
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              "      <td>OCC(S)CS</td>\n",
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              "      <th>1</th>\n",
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              "      <th>2</th>\n",
              "      <td>Vidarabine</td>\n",
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              "      <th>3</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ticarcillin</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "len(whole_df)"
      ],
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        {
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              "640"
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          "metadata": {},
          "execution_count": 42
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "test_1_s = train_set.iloc[80]['Drug']\n",
        "test_1_s"
      ],
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          "height": 36
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      "execution_count": 43,
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        {
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          "data": {
            "text/plain": [
              "'COc1ccc2c3c1O[C@H]1[C@@H](O)CC[C@H]4[C@@H](C2)N(C)CC[C@]314'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_1_s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnXXCM4gmGje",
        "outputId": "01897e0c-d24c-4c89-d50b-2cede030e3e8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mol = read_smiles(test_1_s)\n",
        "mol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3xmO14smHvS",
        "outputId": "24ee2d2a-1596-4f58-f3ac-e12d242b1ebd"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pysmiles.smiles_helper:Atom \"[C@H]\" contains stereochemical information that will be discarded.\n",
            "WARNING:pysmiles.smiles_helper:Atom \"[C@@H]\" contains stereochemical information that will be discarded.\n",
            "WARNING:pysmiles.smiles_helper:Atom \"[C@H]\" contains stereochemical information that will be discarded.\n",
            "WARNING:pysmiles.smiles_helper:Atom \"[C@@H]\" contains stereochemical information that will be discarded.\n",
            "WARNING:pysmiles.smiles_helper:Atom \"[C@]\" contains stereochemical information that will be discarded.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<networkx.classes.graph.Graph at 0x7f17b46ce2b0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To print Smiles\n",
        "elements = nx.get_node_attributes(mol, name = \"element\")\n",
        "nx.draw(mol, with_labels=True, labels = elements, pos=nx.spring_layout(mol))\n",
        "plt.gca().set_aspect('equal')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "7mo_dMGemI-9",
        "outputId": "2a44a219-5522-4d80-8780-7a2624e40c20"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Jp6Cg8EBiW8j05N4knoLWKDyQ2BYyPbk3iaegoPBAYlvI9OTeJJ6CgsIDiS0oPLk3iaegoPBAYgsKT+9N4gloMdMDGLc6l1xRQaVuQXX/CTh41RvhIlnca+1NcsXs9qN9bxJGIoW87yAwEhk0Fwuh+e0UgpKfb3uvkHuTeALaRyFiZlud2/1FM5a9E8NW55omLUYs229xnQIAmk5no/HY92iprTDpTSIPi2l7j49MgtzXx4giGMWIgkKkWncxlkCjs72LkWFaS/Knp0QLupDuzK+OW+1NYg/DAOMHhtI+CheiNQoR8sStzp7cm8QT0IxCZDx5q/OGg6V454fTgNTb4a9p7U0i/LYDYkczCpGxtdW5bt96dEt8CmEvbcbtczYiYFAK1GVH2t5j3OosRHq9HjtWvo4I1SnIvSR2e5MArGh6k3gCeuohIp661ZllWbzyyitoaGjAvzMXoqS6ua03CYPWzVRGcpkEepaFuvw4Nr4zAwlR4i71JxYUFCLC5VbnWff353h0nbdq1Srs27cPP//8M7y9vREX5t3WmyTrRCVKLjdCpWkx6U2S9sImHPr+ayTMn8/38LsECgoR8cStzllZWVi+fDlyc3MRGGi6dhLs72M10BYtWoTx48dj7ty5UCgU7hhql0ZrFCLiaVudc3Nz8eKLL2LHjh244447OvS1cXFxuO+++/D555+7aHSkPQoKEfGkrc5lZWV4/PHHsWnTJgwaNKhT10hPT8eHH36IGzducDw6cisKChFp3eps/kfWfqtzc2keDC0asHod1OeO41r2BpP3CmGr89WrVzFhwgQsWbIEEyZM6PR17rvvPsTExGDTpk0cjo5YQvsoRMQTtjo3NzdjzJgxGDt2LN59912nr3fo0CFMmzYNZ8+ehUxGS26uQkEhMs5sdQZrQEKYLzLmPsD5uByh1+vx5JNPws/PD5s2bQJjf7OEQ0aPHo0ZM2YgNTWVk+sRc3TrITLObHX2kgA5axchLS0NdXV1HI/Mvnnz5qG+vh6ff/45ZyEBAG+++SaWLl0Kg8HyTIs4j4JCZOLDA5GeEg2FV8f+6BReErz1yN04c3gXAGDgwIH4xz/+4ba/XKtWrcKePXuwZcsWeHs7vkXbEWPHjoVSqcSWLVs4vS65iW49RGru6kz8UOkNRuYNW3+A1k6Pnjx5EmlpadDpdFizZg3uvfdel431X//6F/74xz8iNzcXd955p0u+x44dO7B48WKcPHmS09kKaUUzChG6cOECMt+di+Up4RgfGwofmQTyW56GyGUS+MgkGD8wFJkzE8zOQwwaNAiHDx9GWloaHnnkEcyYMQM1NTWcjzUvLw+zZ8/Gjh07XBYSAPDwww8DAH788UeXfY+ujGYUIqPT6TB69Gg88cQTePXVVwHA5lZnR55uNDQ04K233sI333yDv/zlL5g5cyakUtvrICZVtTQ6KOUyswbCZWVluP/++7FhwwanHoM66rvvvsPy5cuRl5dHswqOUVCIzJIlS3DgwAHs3r0bEgm3E8JffvkFc+fOhUqlwpo1azB8+HCz9zhaVWtKfDBmPP4g5s+fj5kzZ3I6Tmv0ej1iY2PxySefYOzYsW75nl0FBYWIHDlyBI888ggKCgoQFhbmku/BsiwyMjIwf/58jB07FsuWLUNoaCiADlTVAsDqb+BeaQW+e/+PLhmnNZs2bcLGjRuRnZ3t1u/r6WiNQiSampqQmpqKNWvWuCwkAIBhGEyZMgXFxcW47bbbcNddd2H16tX48ufzjlfVAgCpN371inR7Va0pU6bgP//5Dw4fPuzW7+vpaEYhEjNmzIBOp8PGjRvd+n2Li4sx4413URk1CZCZP9YUYlWtzz77DFu3bsWuXbvc9j09HQWFCGzduhWvvfYaCgsLERDg/nMaM786jj1nroCF6QKhtQbC2orTCBrTWkqfj8K3Wq0WERER2Lp1K4YOpYK7XKCgELhLly5h8ODB2LJli8XFRVezdr7EoLmOyjVTETzxZfhFj7R5DT7Ol3z00UfIzs7G+q8y7D6dIfbRKRoBMxgMmD59OmbNmsVLSADirao1LOUp/DW/Honv74VEIrnl6cwVrNxbKoqeJ0JBi5kC9sknn6ChoQGLFy/mbQxirKq1Of8ipm0qhPTOwWgxwGz8Gp0BWp0Bu89UYfL6fEG3MRAKmlEI1K+//oolS5YgLy+P1+PTjlTVciQs3FVV62bPEwPwvzWVplN7oTq2FbprV8D4KOAblYig0VMhkfu39TwBQNW8baAZhQBptVo8++yzWLZsGSIi+G1sI6aqWkUV9Vi6s+R/IdFKdWQLruV8gaDk5xH+SiZ6PbccuoZqVGUsBqtvDS91iwFLd5bgVGW9y8coVhQUArRo0SL0798f06dP53sooqqqdWvPE4O2GfWHv0H3cbOg6DcEjFQGWWAoejz6BnQNVbj+681NWULueSIEdOvhZvbOSOzbtw+ZmZkoKioSxHmFSUPCsHJvqcV/pxz2OCT+QWjIzUTNjr+ZVNVqjwUwabDrNokBlnueaP9b3LrgOsB0IVjirYCi/1CoLxbCP/7B1jEKtOeJUFBQuIntMxKtq/DD+3bD3o/ewsaNGxEcHMzXUE2E+PtgdFQPq1W1/GOT4R+bbPXrGQZIHtDD5X/5LD2d0TdbX3CV+nfHjSumMwgh9jwRCgoKN7B3RsLYCSu7tBbeDy1AVUCkm0doW1pSBA6V1Vjsd2qPuxoIW3o6I/W1vuCqb6qDRKE0eU1oPU+EhNYoXKwjncfBSKBnpILrPO5MVa30lGi3bN+29HSmbcH1bK7J64YbaqjPF0DeJ97CdYTR80RoaEbhQpZW4Y1snZEwrsLHhQUKpvO48dGhQ6dHrVTVciVLT2ckcj90GzEFdXv+DomPL+R3xkPfWIva3WshCwiBf+wYC9fhv+eJEFFQuJCtzuOWzkioy460HaYyrsK784yEPakJfRAXFmizgTCL1jWJOUkRbg251qczV8xuP7olTIJEocS1/Z9DV38FjLcvfKMSEPjIfDAy01AQQs8ToaKzHi4i1jMSjjJW1fp21wEYZHIMuWtgh6pqcc1ezxNHCPnz5hutUbgIl2ckhMjYQPge9Sk8EXIVK5++B7Pu78/bXzLj05nOPlF219MZsaKgcBExnpHojObmZsF0E3em54m7ns6IFQWFi3ha53Fr1Go1fH19+R4GAHE8nRErCgoXEdMZCWcIaUYBtC64pqfEQOEltXsbwjCtFbjSU2LoQJgd9NTDRaytwrc/I8FIpJD3HQRGIoPmYiE0v51CUPLzbe8Vwyq8kGYURkJ+OiNWFBQuIpYzEs4S2ozCKC4sEOtSh6K2SYvNueV4/9NNmPjopA73PCGtKChcRCxnJJwlxBlFe8H+Png8Rom/Hvsan29dxvdwRIvWKFyoK6zCC3VG0V5DQwMCA+n2whkUFC7UFVbhhT6jAID6+np069aN72GIGt16uJjQz0g4SywzCgoK51BQuMGtq/B6vR469uazOzGvwothRkG3Hs6joHCT9qvwc1dsxn+bWETGxot+FV4MMwq69XAeBYWbBfv7IORqIRIiIvCnqffyPRyn6PV66HQ6eHubtxoUErr1cB4tZvKgoqICd9xxB9/DcJparYZCoRBEbU9bKCicR0HBg99++w3h4eF8D8NpYlifAGiNggsUFDzwlBmFGNYnAFqj4AIFhZs1NzejsbERPXr04HsoTjPeeggd3Xo4j4LCzSoqKhAeHi74+3pHNDc3061HF0FB4WaectsB0IyiK6GgcDNPWcgExDOjoDUK51FQuNlvv/1GMwo3oxmF8ygo3My4RuEJxDCj0Ol0UKvV8Pf353sookZB4WY0o3AvlUqFgIAASCT0q+4M+vTczJMWM8Uwo6DbDm7QWQ83YlnWIxYza5q0yCqoxPfVgVD534eXM08iuqcSTw4R3sE2CgpuUKcwN6qpqUFUVBTq6ur4HkqnFFXUY01OOQ6UXgUAk8LBxqPySQN6YM7oCMSH87tvwRhmB4rKUHj6LMaPuV+wYSYGFBRudPLkSUyfPh2FhYV8D6XDWruyC7/4jpjCTEzo1sONxHrb0RoSxRa7st+KZQF1ix5LdxYDgFvDwl6YGUv27z5ThYOlNaKqJMY3Cgo3EuNCZlFFPZbuLDELieunc6A6tg0ttZWQeCvgFdoP3RKfauvGrm4xYOnOEsSFBbqlYpdYwkysKCjcSIwzijU55dDoTNsfqo5uRUN+FoLHp0HedzAYqQzq8wVQlx1pCwoA0Oj0WJtTjnWpQ106RrGEmZhRULiYcVGt5IoKOZp+6INQrDtwThSLajVNWhwovWoyjTdorqP+0NcInvgyfAcMb3vdN3IYfCOHmXw9ywLZZ6+itknr0p9VDGEmdhQULmJxUc0vHIUNQMneUqzcWyr4RbWsgkqz17SXSsDqbsA3KtGhazAAsk5UYtb9/TkeXSuxhJnYUVC4gKcsqpVcUZn1TtWrVZD4KsFIHGtspNEZkH3iLCJ1/4FUKoVMJoNMJuv0P0ulUpMj+mIIM09AQcExT1pUU2l0Zq9JFUoYmlVgDXqHw+KXs+fw7vat0Ol0bQV5O/vPer0eEomkLTS6PfRH+AwYZfL9OhNmJZcbHXpvV0VBwSFPW1RTys1/PXx6R4OReaG5NA9+0SMdus6DSaOw8tOXOBkTy7JtgaHT6fBixikcLDfdwNaZMFNpWjgZn6eisx4csraoVrdvPbolPoWwlzbj9jkbETAoBeqyIybvMy6qCUl0TyV8ZKa/IhK5HwJHPou63evQXJoHQ4sGrF4H9bnjuJa9wewacpkE0b0COBsTwzCQyWTw8fGBn58fuvvLzd7TPswcpZR7cTZGT0QzCo544qLapCFhWLm31Ox15bDHIfEPQkNuJmp2/A2MtwI+PSOgTHza7L0sgEmDw1w2xtYwu2KyltI+zBiJFPK+g8BIZNBcLITmt1MISn7e5Bpch5knoqDgiCcuqoX4+yC2O4OCKwYwtxzT9o9Nhn9sss2vZ5jWNomuDD4xhJknoKDgCFdPCISyqKbT6fDOO+8gf8se+P/fm7hhf23WjFwmxZykCO4H106Ivw9GR/XAnuIqsydMQgkzT0BrFByx94TA8evwv6hWUVGB5ORk5OXloWDPFvz5d7FQeHXsV0XhJUF6SrRbFmfTkiIglzkWxrdyR5h5AgoKjth7QuD4dfhdVNu2bRuGDh2KiRMn4qeffkKvXr2QmtAHT0bKwOq0sNdkgGEAhZcU6SkxbnvcGx8eiPSUaEGHmdjRrQdHxL6optFo8Nprr+HHH3/Etm3bkJh4c11Fp9Nh21/n4U+vvoVSWSiyz14Fg5sbx4CbR7iTB/TAnKQIt//lM4aSQ0fhAbC6GxjXWye4vStCRfUoOFLTpMWIZfvN1ikAoOl0NhqPfY+W2gqTRTV5WIzJ+3xkEuS+Psbt98slJSV4+umnMWDAAHz22WdmzXI+/vhjbN26Ffv27QPDMKht0iLrRCVKLjdCpWmBUu6F6F4BmDSY//MrpyrrsTan3G6YjbsdmP3UBBw8eBAxMTFWr0daUVBw5Oeff8a0z3PRcls0wHTijo41gK0owvzEQPzhD39wS9FalmXxxRdfYMGCBVi6dClmzJhh1sGsuroasbGxyMnJQWxsrJUrCY8jYbZhwwYsX74cR48ehZ+fH88jFjYKCiddunQJr7/+OnJycvDS23/Dhv8ooXFg+/atFF5SLB7uj4y1H+LYsWN49dVXMXv2bAQEuOZWRKVSYfbs2Th16hQyMjJw1113WXzfCy+8gICAAKxcudIl4+Db9OnTodfr8eWXX3pEm0dXocXMTtJqtVi2bBni4uIQHh6O4uJiLPjD03gzJabTi2rPPDQS27dvx08//YSCggL069cP77zzDq5du8bp2I8dO4bBgwdDqVTi6NGjVkPi6NGj+PHHH/H2229z+v2FZM2aNTh58iT+8Y9/8D0UQaOg6IRdu3bh7rvvxuHDh5Gfn4/33nuvrcFMakIfpKfEQOElhb3/QFl7QhAXF4eMjAwcPnwYFy5cQEREBN544w1UV1c7NW6DwYDly5dj4sSJeP/997Fu3Tqr5fYNBgPmzp2LDz74wKOrWPv6+iIrKwuLFvQtCUYAABBbSURBVC3CyZMn+R6OYNGtRweUl5fjlVdewdmzZ7Fq1SqkpKRYfa+ji2qOPCG4ePEiPvzwQ2RkZOC5557D/PnzERbWsZ2E1dXVmDp1Kurr6/Htt9+iT58+Nt+/YcMGrF+/Hj///HOXaJ6TmZmJ9PR0FBQUeHQwdhpL7GpsbGQXLlzIBgcHs8uWLWM1Go3DX1vTqGHXHShnX844yT7/xVH25YyT7LoD5WxNo+PXMLp06RI7b948NigoiJ0xYwZbXl7u0Nft3buX7d27N7tw4UL2xo0bdt9/7do1NjQ0lD1+/HiHxyhmaWlp7GOPPcYaDAa+hyI4Hj+jaF+KTqXRQSmXOdzfgWVZZGRkYMGCBUhKSsKyZcvQu3dvN43cnPFnKfrPVZwqKcfF0jOI7OGL92f8HxIH3232/paWFrz99tv44osv8OWXX2Ls2LFm17L0ubyTvgAajQZ///vf3fnj8U6r1WLUqFGYMmUKXnnlFb6HIygeGxTO9ncoKirCSy+9hKamJnz88ccYMWKEu4ZuPhYbP4sUBuh0OgSpL2Hho0Pw9NgEAK23K8888wyUSiU2bdqE2267ze615DIJ9AYD1OcL8HX67zH6rj5u+gmF4+LFixg2bBi2bt2K4cOH2/+CLsIjg8KZZjV1dXVYvHgxsrKy8M477+CFF16AVNq5cwRccPRnAViwuhvodTkPv4sJxOrVq7FgwQK8+uqrbWsMHbmWwksm2BJ9rvbDDz9gzpw5OHHiBEJCQvgejiB43CrVzVJ09v4ymJai25R7AevWrUNMTAwYhkFxcTFmzZolgJBw7GcBGDAyH1T1HoEPt+QhPDwc99xzT9vegI5ey/i5bM6/6PwPIjIPP/wwnnnmGaSmpsJg6MSxWQ/kUTOKoop6TF6fD3VL62nNyrXPg9VpcfvszyHxbq2E1Fj0E67/mo2ez35g+sW6G+h5OgOffZCOuLg4dw/dzK0/i5G9snpA6y3EtPBr+HLlEiiVSjz3pzfxSYnMbCOYI9dSeEmROTOhyx2c0ul0eOCBBzB27FgsXryY7+HwzqMOhVkqRQeDAY3Ht6Pb8Kdsfi0j80L8M/MFERKAc70qtHoDLvpG4fTp09iyZQsW7ixBS49ooN1jTup7YZtMJkNGRgaGDBmC4cOH44EHHuB7SLzymFsPS6XogNZKR6qjW2DQNNn8ehYMcv5Xio5vtsrqdX/wRfgOGA6JtxyMVAbfyGEIGmN6CtVYVu9acwuSHvodJLffZRISnbmWED4Xd+vVqxc2b96M5557DpcuXeJ7OLzymKCwVIoOALx7RcLnjrvRcHSr3WsYS9HxjcuyelxeqysaM2YM5syZg8mTJ0OnMy9O1FV4TFBYKkVnFDjqWTQW7IC+ucHmNYRSio7LsnqeVqKPD4sWLYKvry/efPNNk9drmrRYd+AcXs48iee/PIaXM09i3YFzHjn7ctsahTMbnxxhqRSdkXePPlD0vxcNed/BK8R2k2AhlKLjqvGOStMCSyvV1PeiYyQSCTZv3ozBgwdj5MiRCI8faWMvyhVRtIvsKJcHhe0NPtx9qJZK0bUXOOpZXN74Jyjve8zOdfjv78BV4x2l3Aushajo7LW6spCQEGRmZuKJ11chYLQEN/SsqNtFdpRLg8LVPTgNBgMKCwuxc+dO7P+lDuwdI8HILM9OvIJ6wy9mFBqP74BXjzstvkco/R36B8shgwG6dneGnS2rx7IQdYk+ITnH9ILviFRodfZ3FAi9XWRHuSwoXNWDs76+Hnv27MHOnTuxa9cudOvWDSkpKfjzcw9h0REDblhZpwCAbiOmoOnXbOvjAL/9HS5fvow1a9bgs03fwm/KCpMnFUDnelWwAPW94ICxXaTulmU9sbaL7CiXbLiytlkIsP/B3rrBh2VZnDp1qi0YCgsLMXLkSKSkpGDChAno3/9ms5yZXx232N/BEQyA8bGhdvcLuGKtpaioCCtXrsT333+PZ555Bi+//DL+mt/Q+Z+FAcYPvPmzOPW53HKtrsrSZ2htL4q24rTJY2ZP+AxdMqOwuPEJjm3y0ej0WL2nBBO7XWoLB4VCgZSUFCxcuBBJSUlW60mmJUXgUFmNxYCyx6DTQpWfhfqHI8yKywLcr7UYDAb8+9//xooVK1BcXIy5c+fi3Llz6N69e+vPIq/v9M9ya68KZz4XKWvAi6OF0bmML57YLrKjOH88am3jk6ObfFgW2HvmMtZu2IS7774bOTk5KC8vx0cffYQJEybYLDrrTH+HRRNioNTVY+DAgfj222/RfqK1Of8iJq/Px57iKmh1BrPHjZr/vbb7TBUmr8+3eT5CrVbjs88+Q2xsLNLT0zF16lRcuHABCxcubAsJZ3+WW3tVdPZaPjIG0l+2Y+27b6Clpes+9aC9KC6YUVjb+NSRD1Yul+PJBcs71YOzQ/0dbj09OmYdpk6dilmzZuGLL77A2rVrkXdVyslaS1VVFdauXYt169bhvvvuw9q1a5GUlGSzoKtTPwtH13pk/khMmTIFKSkp+O677yzOtjwd7UVxQVBY2/jUkQ9W6+SHmprQB3FhgZ0qRZeYmIiCggKsXr0aiRMnQ/nYYuhgPmZbay3tF7Ak9ZVYuXIltmzZgsmTJ+PgwYMYMGCAW34Wrq71/fffY968eUhMTMQPP/xgsi7UFXC5r0WsOA8KaxufOvrBOvuhxoUFYl3q0E41q/Hy8sJrr72GooD7cOhCA27to+fQWkuLHs+8+yVqv/8Ac+fORVlZWadrGzjzs3BxLZlMhtWrV2PNmjUYMWIEsrKyMHKkY/svPAGX+1rEivOgsLbxqaMfLFcfarC/T6duYWqatDhaed2smY+ji1gsAE33/jhxuhS9unOzB6GzPwtX10pLS0P//v3x+OOPY8WKFUhNTeVkLEIn9naRXOB8MbP1QzW/bPsPtrk0D4YWDVi9Dupzx3Ete4PJe4XwoXKx1iKTSrH9V+dK7AvNQw89hP3792Px4sX485//DA8qZ2LVpCGW95Aohz2OoAf+gIbcTFR+9Cwq105D44kfoIg0/90wQNx7UTifUUwaEmZxgw/g+CYfIWzw4WKtRewLWNbcddddyM/Px6OPPorS0lJs3LjRLS0Q+RLi74PRUT0s7kXxj02Gf2yy7QuwBmjOn8C3X5zDCy+8ALlc7rrBugjnQWHrQwXsf7AM07qYxvfzZqGstQhVaGgo9u/fj+nTp2PMmDHYtm0bQkND+R6Wy6QlReBAabVD27dvpfD2wlvTxuKbj9/HBx98gAULFmDGjBkdDldXH6y0xSXHzNOSIiCXda7W5K2bhfjiyFqLY9cR7wKWPQqFAt988w3GjRuHhIQE/Prrr3wPyWV6+dyA7ug/IWM6VkPTuK9l8rhEbN++Hdu3b8f+/fsRERGBVatWQa1W271GUUU9Zn51HCOW7cfKvaXYVngJ+0uqsa3wElbtLcXwZfsxa/NxFFXUd/bHs0v6tgsaS/bsJkegQoa887XQGRxP4NYPNQbjBvbkekgddrG2Gccu1kF/y/gZmTcYqRfqD34Fr6BekCp7AAA0F06gqejfUPQd1PZeuUyC38X3xtA7u8NTMQyD5ORkhISEYMqUKYiLi0NEBP9BzyWVSoVx48bhieR78cTDDyHvfB30dtZmrLWL7NWrFyZPnowxY8Zg06ZNWLBgASQSCeLj4+HlZf4flc35F/GnzEKUVjdCZ2DNfh+Nr52vuY5thZcQqJC55EyJS4vrOlM2n281TVqMWLbfajGcptPZaDz2PVpqK0zWWuRhMW3v8ZFJkPv6GN5vo9zl8OHDmDRpEhYvXoy0tDSzf8/n1LmztFotUlJSEBkZiU8//RQMw3DaLrKwsBBLlixBbm4uXnvtNcyePRt+fn4AOnaw0sj4H1uu/x65vAo3lx+qu9Fhqo47d+4cHn74YYwbNw4rVqyATCZzuhkTX/R6PSZPngyWZZGZmWnWuoGLfS1Gp06dwpIlS3Do0CHMmzcPox5NxfSviszO5zSd2gvVsa3QXbsCxkcB36hEBI2eConcv+09rqic7rZy/Vx+qO5i6xSsPV21zD3QWgpg0qRJ8Pb2xmMLVmLF/guim1WyLIs5c+bg7Nmz2LVrF3x83PM7+ssvv2DJkiXIk90F6Z2D0X63n+rIFjQc2YKQh1+B/M546BtrUbt7LQzNKvR87kMw0tZbF1f8R8qj+nq4gpCmf2LS0tKCR+b9FcU+MYDM2+Tf2dr+LpTP7u2338b27duRk5MDpVLp1u9d06RF4vt70f5XzqBtRuUnv0dwyp/gFzPq5us31Pjvp39AUNI0+Mc/2PY617e9HlNc11VSE/ogPSUGCi8pbJzfAmB9AasrOnPlOi4EDjILCdXRrajbtx7dEp9C2EubcfucjQgYlAJ12REANwu9nKp03Qq+PWvXrsXXX3+NXbt2uT0kgNbNfpJbihZp/1vcutFvgGk/VIm3Aor+Q6G+WGjyOtenVT2qAZCrcHkwq6tYk1NuthDs6PZ3VzUdcmQx9Z///Cfee+89HDp0iLd9IRZPqzZb3+gn9e+OG1fKTV7jerMfBYWDuDyY5ems1SRxdPs714VeHC06NNS3Dulz52Lv3r3o27ev09+3syyeVvW1vtFP31QHicJ85sPlZj8Kig7i8mCWp7J2TqYj29+NU2dnP2uHCzyfrsK/W7SYs+Ib3ttK2jytejbXbI1Cfb4AgaN/b+E63G32ozUKwjlr52Tab3+3h4upc4c62wNgvHyQWdrCewd3SwcrJXI/dBsxBXV7/g71+QKweh109VW4uu0DyAJC4B87xuT9XB+spBkF4Zy1czIdLTXwy9lyHDyoQmxsLIKDgzs0BmPV7FufVomhara1g5XdEiZBolDi2v7Poau/AsbbF75RCQh8ZD4YmensgeuDlRQUhHPWzsl0tIZD07WreOONFTh9+jTkcjliY2MRGxuLgQMHtv2ztQBxphs83x3cbR2sDIh/EAHtHoNa4oqDlRQUhHOWCr0YOVpqQC6TYPpjD2LWyhfBsiwuXbqE06dP48yZMzhx4gS++uornDlzBj4+Pmbh0bNPpOirZjtTOd0VBytpwxXhnL1zMo5wZMOQMUDOnDmD06dPtwVJuVcfyO+dBKbdHg71+QJUf/cX3DF/q0OLqXKZBK+Mi+J14VpIm/1oRkE4Z68miT2OTp0ZhsHtt9+O22+/HePGjWt7/eXMk9hWeMnkvWKsms1lFXZn0VMP4hJ81iSxVzXb8evwX3QoNaEPMmcmYPzAUPjIJJDf8jRELpPARybB+IGhyJyZ4LIdwTSjIC5hbDrUualztFNPHDytarYQNvtRUBCX4Wvq7KlVs/nc7EeLmcTl3F2TxNZiqiMFh4CuV3TIHgoK4jbunDpT0SFuUVAQj0RFh7hFTz2IR+KyGzyhxUziwYS0D0Hs6NaDeDwxF3gWCgoK0mVQ0aHOo6AghNhFi5mEELsoKAghdlFQEELsoqAghNhFQUEIsYuCghBiFwUFIcQuCgpCiF0UFIQQuygoCCF2UVAQQuyioCCE2EVBQQixi4KCEGIXBQUhxC4KCkKIXRQUhBC7KCgIIXZRUBBC7KKgIITYRUFBCLHr/wEPEsH96S0HswAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Just for visualisation\n",
        "test_mol = Chem.MolFromSmiles(test_1_s)\n",
        "Draw.MolToImage(test_mol)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "vG1kkU8CmKrS",
        "outputId": "b171aa83-f429-4736-d27c-4bbfd20860c9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300 at 0x7F17B41D25E0>"
            ],
            "image/png": 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xEUVHo7lzEZ/f6kZXV/Tee+j//b8r4eHheFV0zzZZ7Jrbt2+/884706dPd3Fx6Tp+HDlyJJ7ySk3YbWpra/FPqeZFPDZlmEWueuJvf/sbAHyiz42sr169CgCDBg0y5HzrJ0+QRILGjEEAaPhwOQD4+vpGREQw23+EhoYWFhbqcgu5XI73pxw9erTm8suu6devH57gQccJu42dnZ2NjU1jY2NdXR1zkZvjbN3CAI9w/PhxAAgLC2M2IzUAbm6wZQvcugVXrsCrr17r27fv3bt39+/fX1BQMHbsWEtLy8TERB8fnw8//FCpVHar5tLS0p07d86bN2/UqFFLly6Njo6+c+cO/o3WhqqqqpSUlO4/UMeYVnYUAFxdXbOzs4uKipjlP25ubrdv3y4qKho/fjxZbT1G30OF6Om8nDBCJ5gFBkJg4PQPPihJSko6fPhwXFzcrVu3AMDCwkImk3300Uc//vjjnj17Zs+e3XU9d+7ckUqlqampBQUFePF01+XNzc3t7Ozwb7eNjU2/fv1sbW09PDzGjx+P++GsYKImLC4uZtaz05bwuaSmpj58+HDgwIHMnnFEsLCweOmll1566aWioqLjx4//97//xVYEgKysrLlz5y5fvvyrr77CKzMYVCrVb7/9dvjw4Tt37uTn51dXV7epVigU2tjYWFtb29ra9u/f39bW1szMzMPDY9SoUT4+Pu7u7h4eHp0NwH7++ecKhaLNRW9v78TERLxFmDaYnAnbNxocmXGiC/o2YWxsLBg8Fu0CNze3LVu2bNmy5ffff5dKpT/88ANeBnX8+PFTp05t2LDhq6++qq2tPXnyZGJiYmZmZmFhoUKhsLa27tu374ABA2xtbW1sbJycnGxtbYcOHerv7z906NDBgwd3mAjtmg5/lfr27cvsNKcNJmfC9t/XXtASurm58Xi84uJiPU2aOXnyJAAsX76c9Zp1JCAgICAgQCKRJCUl7d2799y5c83NzYcOHbp7925eXp6Dg8OwYcNmzJjh6+s7atSoIUOGDBo0iCPTbjShJuwNJrSwsHBwcKiqqqqsrNTcaYYV0tLS8vLy3N3dAwIC2K2ZLTTDVH9//6KionXr1q1Zs4a0Lm0xrewo9FITgj6fAseiy5cvN+TE1J5hZWVVUVFhZmbG7L5jFHD9Y2WdXtknBH0+Bc6LcjAWbc/JkyeVSuWsWbOYIz2MApMzYYctIbMKgZwuXdFTS3j9+vXc3FwXFxdm5TuXYRpt0kK6h8mZsH2LgVchtLS0VFRUkNOlK3pqCZkxeu7HojU1NefPnxcKhczu6cYC1z9Z1rG3t7e2tm5oaKjXWFjaCyJSPbWERhSLxsXFKZXKkJAQ1lNT+sbkTAgAeHpuL8vN6OMRbty4kZOT079/f2Z3cC5jpLEomKYJe2VuRh+PgGPR4cOHMwdoc5aamppffvlFIBAsxgv1jQpTNGGvHKXQxyNgE+K9yTlOfHy8UqmcOXOmvvcW0AemaML2jUYvMCF+KLwgi5UKb968mZOTw0pVBoBJIJEW0hNM0YS9siW0tLTs27evUqmsqqpipULcxQKAyspKVirUH7W1tTgWXbJkCWktPYGaEKBX9AmB7aeIiYnB/1NVVSWXy1mpU08kJCQoFIrp06cbYywK1ISdXTFGWHyK27dvZ2dn4/+vqKhg/p+bGHUsCqZpwg6zo8wqBHK6dIXFlpCJRQFAJpNdu3ZN9zr1RH19fXJyMp/PN8a8KMYUTdi+xcCrEJqbm7nf/+kCFlvCY8eOaf6Zmpqqe516Ij4+vqmpafr06fjxjRFTNKGDg4OVlVVdXV1DQwNzsRdEpGw9QkZGxoMHDzSvlJSU6Fin/jDeMXoGUzQh9NLcDFuPgLtYmnB2Vm19fX1SUhKfzzfSvCiGmrDTK0YHW4/wP//zP21Cu4qKCm72lk+fPi2Xy6dNm4Z/gIwUEzVhLx6v170ldHFxabOIvrq6Gm9VzjWYzW9IC9EJEzVhL2gJDx8+/N5772leYR5B90kzGzZssLKyYv6sqqr6448/dKyTdRoaGs6dO8fn85cuXUpai05QE7ZiRH3C2traVatWrVmz5ssvv9QcPOjTp4+9vb1CoWi/q193mTdvnpeXF/MnQujKlSs61sk6iYmJcrk8ODjYqGNRMFkTGm84mpqais/rtbKykkgkbbbcY+sp+Hy+n5+f5pWCggId62QdYx+jZzBRExpjS6hSqXbu3Dl16tTc3Fx80vCWLVs0C2RmZlZWVrq6uorF4vY70naXjRs3WltbM39yLUHa2Nj4888/8/n8ZcuWkdaiM7ocpmG8ZGRkAIDmUR6NjY0AYG5ubsgDT7Tn4cOH+HBvHo8nEokUCoXmq2q1WiKR4LNj8dq/CRMm4EM8e4xarWZOBWzzWXGBH3/8EQCmTp1KWggLmKgJ8cwYe3t7zYv4bMeKigpCojolNjYWbx/m7Oz8008/tXm1rKyM2eEvPDz84sWLw4YNAwBbW9vDhw/rct/XXnuNMaGrq2s5PrOTG+DReYlEoqf6r169Gh8f3+aiTCaLiYnJyspi914makKEEM7+NTQ0MFdeeeWVRYsWPXnyhKCqNjQ2NopEImyDJUuWtP+BSE5OZs4AZ740dXV1q1atYmxZX1/fs7tfunTJ1tYW1yMUCn/99VddnoVFZDKZtbU1j8d7+PChnm6xYsUKR0fHNhfz8vIA4Msvv2T3XqZrQg8PDwDIyckhLaRT0tLSRowYAQCWlpbtf/KbmpoiIyPxJmghISH4jCFNpFIp7tR5e3v3+CRtzVNNPv30055Vwjp4mVVQUJD+bmFIE5poYga4nYlBCO3atSs4OPjBgwd+fn6pqaltcjD3798PCAjYuXMnn88Xi8XJycnu7u5tKlmzZk16evrYsWOzsrICAgJ27dqFuj9+qNktzMzM7NnjsE4vmC/6DOx62oh4+eWXAeDYsWOkhbSlpKRk/vz58DQH0/5wdqaJ8/T0vHLlSte1yeVyJqBdvHhxZWVlt8SkpaUx59fOmTOne0+iH2QymY2NDY/HKygoYLfmR48eyZ6e603DUb1TXl4+YsQIR0dHT09PqVTa3NxMWlErcXFx/fv3BwAnJ6fExMQ2r5aXlzOr5rrV2YuLi8OpnUGDBl24cKFbkpizU8ePH9+tN+oJ3AwGBASwUptarU5LS3vrrbemTZvm7u6+Y8cOfH3FihU2NjbfPsvOnTupCdkhKSkJJzOYfcQ8PDwkEkmPExisgJssfADgnDlzioqK2hRISUnBMae9vX10dHR363/48CHePlQoFIrF4paWFi3f+MYbb+BPafDgwUxDQZBXXnkFAP75z3/qUgk+qn7lypVjxozRPKr+5ZdfxgVWrFjB4/EcngWXpCbUCaVSKRaLcTJjxowZubm5UqnU19cX/wPY2dmJRKLCwkLDC8vIyBgzZgwAWFpaRkVFqVQqzVebm5sZ2QEBAbm5uT27i2Y9M2fO1DIPfPPmTQcHBwCwtra+efNmz27NFnK53NbWtsexaEVFxddffx0aGjp8+HAzM7P2vTOmgaXhqF64f//+hAkT2jcFarU6OTmZGWozMzMLCwtLTU01jCq1Wr1v3z48XuLj49M+jZmXlxcYGNiDFqwzUlJScCAwYMCAM2fOaPOWSZMm4Q/nm2++0fHuOoKPK508eXK33pWbm/v+++/Pnj3bw8Oj68OGPTw8GhsbETWhPpBKpTj49PDwuHTpUodl/vjjj/DwcObM5ODg4ISEBL1OoCktLWXOVQ4PD9cctGwje8iQIRcvXmTrvmVlZQsWLIBO5t+0Z+PGjVjk+vXr2dLQM/D8gS+++OK5JVtaWlJSUtauXTthwgQ8DUMbLCws8OdMTcgmNTU1r776Kv6Iw8LCqquruy6fn58fGRnJ/LONGTNm3759crmcdWFM11RznF1T9sqVKxnZVVVV7N4dz3QzNzcHgEmTJnU9Xnrv3j2cLnrxxRfZldEt5HK5nZ0dAHQRkFdXV+/fv/+ll17y9fXt06ePlt7TZNu2bYiakEV+/fXXgQMHAoCdnV235nDV1tZKJJJBgwbhfxhnZ2exWNzd/H5nPHec/erVq3glka2t7aFDh1i5aYekpqbiG9nZ2R09erSLkpMnTwaAKVOm6E/Mc4mLiwMAf3//9i/l5uZu3759zpw5Hh4e2pziJhQK7e3t3dzcfHx8goOD58+f//LLL7/77rvfffddSUkJ4qYJb968OWLECIlEoo82QR/gJASezTxlypSezYxRKpUxMTH+/v74X87GxiYiIkLHqYN3797F01BwH6/DHAyWPXny5OzsbF3upQ21tbVMpNBhSIzZvHkzAAwdOlT3TmmPwXPxdu7cif9UqVSpqakbN24MCAjobNtfa2trd3f3ESNGTJw4cfr06WFhYRs3bvzyyy+TkpJyc3O7GJpKT08/d+5cm4symSw+Pp71WVbamvDNN9/ET+Xm5vb555+zHh2xS35+Pk7HCwSCyMhIpVKpY4UXL14MDQ3FfXo+nx8aGvrcUfIO6XqcvaCgYOrUqfgWIpFId9ndEoaDNx8fn1u3brUvkJub6+Tk1L9//x7nZnWkoaEBx6IZGRnR0dHLli3z8/PDmm1sbFxdXb28vMaNGzd79uwlS5a89tprO3bsOHfuXEZGBtmRJ23Q1oS4TcAxCVttgp5Ii4nB046HDBnSWQ6mZ9y+fTsiIsLS0hJ/CMHBwTExMVq2DJWVlcw4+/r169s3ODExMXgkYNCgQb/99huLsrUkMzMTT1LrcKoqQiggIIDH48XGxhpM0pMnTxISEsRi8ezZs62srPr162dubj548GAfH59FixatW7dux44d8fHxmZmZdXV1BlPFOt3uE7LVJuiF2lq0erXSzGyyn9/y5cv11FyXlJSIxWI8AQVHaBKJ5Lmj2NXV1YMHD7a3t2/f9aqrq4uIiMC1LVu2jK2eZw/QXLSxbNmyNh/g1q1bAeB///d/9SegsLDwxIkT27ZtmzVrluYYOgb/tkZEROhPABF6mJjJysoSiUQ9axP0xeXLyMMDASBb29ojR/R9t/r6+n379uFVDgAwYMCAyMjI9tNcNElNTW2/9CY1NRUv/8PbVehTsrbExsbi5PCQIUMuX77MXC8sLHRxcVm6dCmL96qpqbl48aJEIgkLC8MnKGvi6uoaGhoqFosTEhLKyspSU1NxMP/RRx+xqIE4OmVHe9YmsE9LC4qKQmZmCABNmoR0W1HeLVQqVUJCAh5MBwALC4vw8PC7d+9q8148SIDnbUycOJFTsX1+fj7e9bBN9igoKGjatGm61FxfX49dFx4e7uvr22bo3M7OLjg4WCQSSaXSDjufiYmJQqGQx+MdPHhQFxmcgoUhivr6+l27dnl6ejJtwocfflhRVqZ7zVrx8CF64QUEgHg8JBKh540764mLFy+GhYXhrCaO0pOTk7soX1hYOH36dNB6uNzwaM5xmz17Nt5J8YMPPvDw8OhWPUqlMiMjQyqVRkRE+Pr6thk/sLGxYVyXkZGhzdSI/fv345TbqVOnevhsHIO1cULcJgQFBeEPV+bnh8LDUWYmW/V3TGws6tcPASAXF9QuoWx4cnJyRCIRM0Y8YcKEDpdonDhxgtmu4uzZs0SkaklSUhKOEp2cnM6dO1dUVGRra1vW5S9sS0sLdp1IJAoODsY73zAIhUJfX9/w8HCJRJKent6zLsw//vEPAOjTpw+H8hE6wP5g/YULF/b/9a+Iz0cAiM9Hixejbq6d0YrGRiQSIQAEgJYsQVzaGKasrCwqKorZSd7T0zMqKqqmpgY9m/mYN28ebl44TklJydy5c5lG28vL6/z5823K4DRmZGRkcHBwm3kqAoGAcd3FixdZGWdWq9V4bYejoyOnwvieobcZMzk5SCRCffq0+mTCBCSVIraW7aWloeHDEQCyskLcSGa0p7Gxce/evUzmxsHBYd26dUwO5uuvvyYtsBuoVCqm++ru7r5161Zm8CA0NBTPaOssofLceYI9o6WlBQ/5eHl54Tkuxouep62VlaGoKOTm1mpFDw8UFYVqanpeoVqNJBJkbo4AkJ8fun2bPa16AUfps2fPBgBzc3Mej+fn59fhaDj3+e233/AcQCYrzjBw4MAlS5bs2LEjKSnJYBM5ZDIZTolNmjSJ+yPyXWCQuaNNTUgqRb6+rVa0s0MiEXr0qKu3NDai9h9rSQmaP//PHEy7fR+4zNWrV/FYvFF3Y8rLyx0dHe3t7TUTKnl5eQT14FhjwYIF3NkeobsYcAK3SoWSk1FoaKsVzc1ReHjbpqyuDv3tb2jQoNYyjo5owwbErD3dupU7OZgesGnTJgD44IMPSAvpOY8fP+bz+X369Olsiqnhyc3NdXZ2BoDVq1dzc+Pm50JiFcW1a2jFCiQQtLZp8+YhPEuroQFNnIgsLNDbb6MzZ9DPP6OPPkL29mjgQISXUctkaNMmVFpKQDMb/PLLLwAwYsQI0kJ6jkQiAYDly5eTFvIMaWlpeNWlWCwmraUnkFvKlJ+PIiNR374IAL3/PkIIvfce4vHQ6dPPFLt5E1laotBQIhrZpaWlxdHREQDu3btHWksPwdPiObhF3ZkzZ/Bq7D179pDW0m1IryesqkKff46KipBKhfr1QzNndlBm40bE46F2i+6Mkddffx0AmC29jIvi4mI+n29lZcXNLMjBgwfxiMjJkydJa+kepDf/dXCAbdvA1RWys6GqCp7uZfIMkycDQpCWZnBx7IOPs8SLU42O48ePq9XqBQsWMLvUcYr169d/+OGHKpVq1apVHDxNsQtIm5ChvBwA4OlK9mcYMgQAoKzMoHr0w7x582xtba9fv15YWEhaS7fh/nmAYrF406ZNcrl80aJFWVlZpOVoC2dMiPefk8s7eKmxEQDA3NygevSDhYXF/Pnz0dOdGoyIkpKSK1euWFpaLly4kLSWrti1a9eSJUsqKysXLFhQUlJCWo5WcMaEuA189KiDl3CjMXiwQfXoDSONSGNjY1Uq1YIFC/Dyds4iEAiOHj0aFBSUn58fGhra0NBAWpEWkO6UauDtjby90bN7riCE0IsvIhubDsbujZO6ujpLS0uBQFBqVGMteM1HD3b+JkJ5ebm3tzcAhISEcHCFShu4ZMKDBxEA2r79mYtHjiAA9N57hDTpBRzRHThwgLQQbSkpKREIBBYWFrW1taS1aEteXh5e/7Fy5UqOD+JzyYRqNfrLXxAAmjIFbd+OPv64dZLanDmosZG0ODY5cOAAACxcuJC0EG35z3/+AwCLFy8mLaR7pKen40Qux2cpccmEmMREtGIFGj0a+fqi0FB06BAiu2uGHigvLxcKhUbUsMyYMQMAjuh/0xDWSUlJwRsc7969m7SWTuGeCU0D3MXqer9djlBWVoZ/Mmp0Wf5CjiNHjvB4PD6ff+LECdJaOoYz2VETw4hypCdOnGhpaZk7d2777c+MglWrVn388cdqtXr16tWXLl0iLacDqAnJsHTp0uCBA1+vrYWmJtJangP3x+ify9///vfNmzfL5fIlS5bcv3+ftJy28FD3zzGnsIO/P6SnQ0ICvPQSaSmdUlFR4erqyufzS0pK8HpII0WlUq1YseLkyZMDBw68evUqXp3MEWhLSI6lSwEAuB2RMrGoUTsQAAQCwZEjR4KDgx8/frxw4cKamhrSiv6EmpAcy5cDACQkQEsLaSmdgg+IX46lGjlWVlbx8fEjR468c+fOsmXLFAoFaUWt0HCUKD4+cP8+pKRASAhpKR2AY1Eej1dSUsJs8WzsFBQUBAUFFRcXv/rqq9HR0dqco6ZvyCswaZYtA+BuRBoXF9fS0jJnzpxe40AA8PDw+Pnnn/v27fvjjz/i44qJQ01IFKZbyMl4BMeiRp0X7ZDRo0cfPXqUx+MlJSXhMw/JQsNRoiAEHh5QWAi//w5TppBW8wyVlZV4/+LeFItqsnr16ujoaCcnp9LSUrJKaEtIFB6PsznSU6dONTc3z5o1q1c6EADUajUA0D4h5WlEevIkaR1t6QVj9F2Tn58PACtXriQthIajxFGpwM0NysogIwP8/EiraaW6utrFxUWtVhcXF+Md4noZnJqEQFtC0ggErTNmuNQYxsXFKZXKWbNm9UoHAscmIVATcgDudQt70xh9h3Aq2KbhKAdQKMDJCerqIDcXvLxIq4GamhpnZ2e1Wl1UVDRgwADSctiHmYRQWlpKW0IKAABYWADewuzUKcJKAADg1KlTSqVy5syZvdKBoDEJgQsOBGpCrsCliJTGogaGhqPcoL4epkyB0FDYuRN4PIJCampqXFxcWlpaioqKnJycCCrRE5WVlS4uLpyaECskLYACAAC2tnD3LmkRAAAJCQkKhWLWrFm90oHwNBZdsGABRxwINBzlEL/+CkuWgJsb2NuDpye88QaQ2Mida6Ea63Aw2KbhKDeQSGDrVhg/HlauBBcXyMmBAweguhoSEmDWLIOpqK+vd3Jyam5u7q2xKJ6EgBAqLi7u378/aTlPIbfHFOUpN28ioRAtW/bM5o7l5WjECOTsjAx4Ju7hw4cBICQkxGB3NDDffvstAMybN4+0kGeg4SgH+OYbQAh27waB4M+Ljo7w2WdQWgoxMQYTgmNRToVq7MLBWBRoOMoJxowBmQxyc9teLy8HJydYvx4OHoSamtY1hzY2rSdYsQ2ORZVK5ePHj/Eipl4GZych0OwoBygp6XjqtqMj9OkD+HyvESNaj3DUBktLsLJaMXr0L3fudFbE2tra/NnT5hobG5uamsaPH98rHQhPJyHMmTOHUw4EakJOwOeDStXxS2o14AVv9vat+0E1NEBz83MqbGqCpqb6xsbq6urOinT4kpWVVVZW1h9//DFhwgQttRsRnN0ogIajHGD8eKipgfz8ttdLSsDVFTZuhL17u1dhUxPI5Q0AXZhVJpMplUrNKxEREUql8uLFiwMGDLh8+fLw4cO7d1Nuw+lJCKQzQxSENm9GfD7Kz297/ehRBIAMcl7FrVu37OzsZDLZvHnzAGDo0KHGdXzic5FKpQAwa9Ys0kI6gJqQA2RmIjMzFBqKlMo/L5aWoqFD0eDBqKnJABKmTZuGTz6rq6vDsai/v3+DAUdH9E1oaCgAfPPNN6SFdAA1ITf4+mvE46ExY9AXX6DDh9H27cjVFdnZoUuXDHDz6OhoW1vbxMRE/GdZWdmwYcMA4MUXX2xubjaAAH3D8dORqQk5w4ULaOlS5OaGbG2RlxfasAHl5BjgtgqFYuzYsT4+Ppp+y87Oxh2nDRs2GECDvuH4JARqQlPn/fffFwgEYWFhba5fu3bN2toaAD7++GMiwlhk0aJFAPD111+TFtIx1IQmDY48rayskpOT2796+vRpoVDI4/G+/fZbw2tjCxyL8vn8oqIi0lo6hprQpMETuPz8/FQqVYcF9u/fDwACgeDUqVMG1sYWR44cAYAZM2aQFtIpdO6o6XLr1q2LFy8CwNixYzvbA3fDhg1///vfVSrVypUrr169aliB7MDN+aLPQPpXgEKMadOmAYCNjc2FCxe6KKZWq9944w0AcHR0zMrKMpg8Vqivr7eysuLz+U+ePCGtpVNoS2iiHDp06MaNGwDg5eU1derULkryeLz9+/cvXry4oqJiwYIFxE9u6BanT5+Wy+VTp051c3MjraVTqAlNEaVS+c9//rOhoQEAxo8fz3verjYCgeDo0aMBAQF5eXmhoaH4jUaBEcSiQMNRk2Tbtm0CgQAA7OzsUlNTtXxXeXn5iBEjAGDhwoVGMYgvk8msra35fP7jx49Ja+kK2hKaHGVlZcePH1epVADg5eXl7++v5RsdHR3Pnj3r7Ox89uzZjRs36lMjO5w+fVomkwUFBbm7u5PW0hXUhCbHpk2bcp8uIJ40aVK33jt06NDExEQbG5tvv/32ww8/ZF8cqxjNplWkm2KKQUlLS2MW8vTt2/fWrVs9qOTMmTNCoRAA9uzZw7pCtsCxKI/HKywsJK3lOVATmhaaidCJEyf2uJ6DBw8CgEAgiIuLY08dm8TExABAUFAQaSHPh4ajJsR3332HhyUwU3Q4oHv9+vVisRgP4l+5coUNdSxjNLEo0HDUZFAoFGPGjGH+3fv165eZmaljnZs2bQIAR0fH+/fvsyKSLZhY9OHDh6S1PB/aEpoK77//fmZmJvPn8OHDfX19daxz165dS5YswYP4JXhDKm6wc+dOmUw2bty4wYMHk9byfKgJTYKysrJTp06pNLaTCgwM1L1aPIgfGBiYn5/PkUF8hUJx7NixXbt2AcDAgQNJy9EO0k0xxRAsxUevPcXR0TE7O5utysvLy729vQEgJCREoVCwVa32PHnyJCEhITIyMjg4uE+fPvgZeTze5cuXDS+mB1AT9n5SU1OdnZ01TRgYGMjuLXJzc/EtVq1apVar2a28PQUFBbGxsZGRkSEhIfb29pqPxuPxrK2thwwZcuTIEX3LYAu672jv5+23324z6/qFF15g9xZeXl5nzpyZMWNGdHS0p6fnJ598wm79NTU1GRkZly9fvnTpUlpaWpvHcXV1nfiUgIAAru3t+1zovqO9nIMHD27ZsqWxsZG54uzsnJqaqo+MRUpKysKFC5VK5e7duzdv3qxLVfX19bdu3br+lHv37ml+Ue3t7UeNGoVdN23aNE9PT521k4SasDfT1NTk7++fkZGheXHq1Kl4La8+iI6ODg8P5/P5MTExy5Yt0/6Nzc3NDx48wG3d9evX79+/r1armVdtbGzGjh3LNHe+vr7PXflhRNBwtDfzySef3G13APCMGTP0d8dVq1bl5eVt37599erVycnJwcHBnZVsaWnJyspi2rr09HSFQsG8amZmNnLkSGy5qVOnjhs3TqB5ZFXvgraEvZnKyspPPvnk/Pnzd+/exeMT7u7u6enpLi4uer3v5s2b9+zZ079//9jYWE3PFxUVMW3djRs3NINkgUDg7e3NtHWTJk2ytLTUq0juQE3Y+2lqatq1a9fx48czMzOnTJny22+/6fuOKpVq2bJlCQkJQqHw9ddfLy0tbWhouH79el1dHVOGx+MNHz580qRJ/v7+/v7+48ePZ0YXTA1qQlNBrVYfO3bM2toab8KpbyorKz09Pevr6zUvaqYxAwMDHR0dDaCE+1AT9iquXLni4uLi5eWlebGoqCg/P3/SpEkWFhaGFPPgwYMtW7Zcv37d19f3nXfe8ff313cYbKRQE/YqBAJBRETE3mePUvviiy/+7//+r6CgYMiQIaSEUbqAzh2l6Iv79+9rJjwpnUFNSNEXp06d6k2jefqDjhNS9MW2bdtISzAOqAl7G48ePTp37pzmlaysLMPLUKlUvXh4nV1oYqZXIRAINGd7aWLIxEx5ebmvr+9PP/3U3d3cTBPaJ+xtbNiwQf4sn332mYE1DBgw4ObNm6NGjTLwfY0UGo72NgQCQZsJX3h7QgPD8f12OQVtCSks8/3330dGRhLpiBop1IQUlpkyZYpQKMzJySEtxGig4SiFZXx8fHbs2EFahTFBTdir2Lt3r4+PT5uLc+fOtbW17d+/vwEESKXSkJCQQYMGGeBevQY6REFhjebm5jfffDM+Pn716tX/+te/SMsxGqgJKSzT0tJSXl7u6upKWojRQE1IYYnqahAKwdaWtA7jg2ZHKSxx7hwMHAhLlsAPP5CWYmRQE1JY4rXXoLAQXn4Z7twhLcXIoOEohUIY2hJS2GDGDPjrX+GXX6ClhbQU44O2hBQ2ePgQYmPh+HHIz4cHD+DZ8yEoXUNNSGGVkhKguzl1ExqOUnQjMxO2b4fbt1v/pA7sPtSEFN2wtga5HBYvhpEj4e9/hydPSAsyPmg4SmEDhCA9HWJj4S9/geHDSasxMqgJKTpw9Sp4etIQVEdoOErRgfh48PWF6dPhq6+gqIi0GmOFmpCiA1FRUFwM774L6ekwejR88w1pQUYJDUcpPQIhkMtB8xwlpRKamsDOjpwmY4W2hJQece8euLnBihUQEwMyGQCAuTl1YM+gJqT0CF9fyM2FuXPhu+/A3R2WL4ezZ0lrMlZoOErRmaoqOHUKAOCNNwgrMU6oCSk9QqUCuss9S9BwlNIdSkthyxYYMgSEQhAKwc8PPv8cmpr+LDBsGGze3PZd//0vODtDdrYhlRoRdLc1itY8egTTpkF1Nbz1FkyZAgoF/Pwz/OMfcPYs/PIL4GOAy8qgtrbtGxsboayMrnLqDGpCita8+SaUlcHvv8OYMa1XwsLghRdg7VqIigKxmKg4I4aGoxTtePQIzpyB9ev/dCBmzRrw94d9+4AmF3oKNSFFO65dA4Rg8uQOXgoMhOJiePjQ4Jp6CTQcpWhHWRkAwODBHbyEjz0sLQUPDwCAo0chNvaZArQ32CXUhBTtwAMSCkUHL+GLZmatfwYGwsaNzxT45Rf4/nt9ijNuqAkp2jFwIADAo0cdvIQDUVwAADw9YdWqZwrU1FATdgHtE1K0IzAQLCzgl1/aXlepICUF/PzAyYmErN4ANSFFO/r1g7VrISYGzpx55vonn0BODrzzDiFZvQEajlK05osv4PZtWLQIli2DoCBQKuHsWbhwAdatg9dfJy3OiKEmpGiNnR389hscOABxcSCRgIUF+PjAiROwdCnweK1lQkJg1Ki2bxw0CGbPBhsbA+s1FugEbgqFMLRPSKEQhpqQQiEMNSGFQhhqQgqFMNSEFAphqAkpFMJQE1IohKEmpFAIQ01IoRCGmpBCIQw1IYVCGGpCCoUw1IQUCmGoCSkUwlATUiiEoSakUAhDTUihEIaakEIhDDUhhUIYakIKhTDUhBQKYagJKRTCUBNSKIShJqRQCENNSKEQhpqQQiEMNSGFQhhqQgqFMNSEFAphqAkpFMJQE1IohKEmpFAIQ01IoRDm/wO+mW6WvYXtFQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Chem.MolToSmiles(test_mol, allHsExplicit=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17vUbxK2mO26",
        "outputId": "181b2103-f922-4522-85ed-ad62385f48e1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CH3][O][c]1[cH][cH][c]2[c]3[c]1[O][C@H]1[C@@H]([OH])[CH2][CH2][C@H]4[C@@H]([CH2]2)[N]([CH3])[CH2][CH2][C@@]341\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adjacency matrix\n",
        "am_test1 = nx.to_numpy_matrix(mol, weight='order')"
      ],
      "metadata": {
        "id": "q0e8KGTvmTf5"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "am_test1"
      ],
      "metadata": {
        "id": "ca-Hv16UmU5b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check what all molecules are present in all the smiles? C, N, O only?\n",
        "\n",
        "ele_dict = {}\n",
        "for i in range(train_set.shape[0]):\n",
        "    temp_ = train_set.iloc[i]['Drug']\n",
        "    for c in temp_:\n",
        "        if c not in [\"@\", \"[\", \"]\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"\\\\\", \"/\", \"#\", \"-\", \"+\", \"=\", \"(\", \")\", \".\"]:\n",
        "            c = c.upper()\n",
        "            if c in ele_dict:\n",
        "                ele_dict[c] += 1\n",
        "            else:\n",
        "                ele_dict[c] = 1\n",
        "                \n",
        "ele_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jf0bZU8AmV9s",
        "outputId": "8af5acc5-4cd5-434b-a2d8-0e3ce1496e5a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'O': 1518,\n",
              " 'C': 8077,\n",
              " 'S': 132,\n",
              " 'N': 1055,\n",
              " 'H': 805,\n",
              " 'P': 7,\n",
              " 'L': 92,\n",
              " 'F': 82,\n",
              " 'B': 5,\n",
              " 'R': 5,\n",
              " 'A': 1,\n",
              " 'U': 1,\n",
              " 'I': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.iloc[3]['Drug']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "AI3rfQvpmXrZ",
        "outputId": "20c67376-9cf9-4a9e-83e2-fc2b75627bce"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'CC(=O)OCC1=C(C(=O)O)N2C(=O)[C@@H](NC(=O)CC#N)[C@H]2SC1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class SymbolFeaturizer:\n",
        "    \"\"\"\n",
        "    Symbol featurizer takes in a symbol and returns an array representing its\n",
        "    one-hot encoding.\n",
        "    \"\"\"\n",
        "    def __init__(self, symbols, feature_size=None):\n",
        "        self.atm2indx = {k:i for i, k in enumerate(symbols)}\n",
        "        self.indx2atm = {v:k for k,v in self.atm2indx.items()}\n",
        "        self.feature_size = feature_size if feature_size is not None else len(symbols)\n",
        "    \n",
        "    def __call__(self, atom_symbol):\n",
        "        out = np.zeros(self.feature_size)\n",
        "        out[self.atm2indx[atom_symbol]] = 1.\n",
        "        return out\n",
        "\n",
        "    \n",
        "atm_featurizer = SymbolFeaturizer(['C', 'N', 'O'])\n",
        "node_features = [atm_featurizer(atm.GetSymbol()) for atm in test_mol.GetAtoms()]\n",
        "node_features = np.array(node_features)\n",
        "print(f\"Node features:\\n {node_features}\")\n",
        "\n",
        "\n",
        "# Compute the adjacency matrix for paracetemol from rdkit \n",
        "num_nodes = test_mol.GetNumAtoms()\n",
        "adj_mat = np.zeros((num_nodes, num_nodes))\n",
        "\n",
        "for bnd in test_mol.GetBonds():\n",
        "    adj_mat[bnd.GetBeginAtomIdx(), bnd.GetEndAtomIdx()] = bnd.GetBondTypeAsDouble()  \n",
        "    adj_mat[bnd.GetEndAtomIdx(), bnd.GetBeginAtomIdx()] = bnd.GetBondTypeAsDouble()\n",
        "\n",
        "print(f\"\\n\\nAdjacency matrix:\\n {adj_mat}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOjfw6EtmZI3",
        "outputId": "9e1129ef-8541-4c8e-b3a2-b555e11bdf08"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node features:\n",
            " [[1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]]\n",
            "\n",
            "\n",
            "Adjacency matrix:\n",
            " [[0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [1.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  1.  0.  1.5 0.  0.  0.  1.5 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  1.5 0.  1.5 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  1.5 0.  1.5 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  1.5 0.  1.5 0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  1.5 0.  1.5 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  1. ]\n",
            " [0.  0.  1.5 0.  0.  0.  1.5 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  1. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  1.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  0.\n",
            "  0.  0.  0.  1. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  1.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.\n",
            "  1.  1.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.\n",
            "  0.  0.  0.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.\n",
            "  0.  0.  1.  0. ]\n",
            " [0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
            "  0.  1.  0.  1. ]\n",
            " [0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.  0.  0.  1.  0.  0.  0.\n",
            "  0.  0.  1.  0. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mol_to_edge_list_graph(mol: Chem.Mol, atm_featurizer: SymbolFeaturizer) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
        "    \"\"\"\n",
        "    Function that takes in a RDKit molecule (of N atoms, E bonds) and returns three numpy arrays:\n",
        "    * the node features array (of dtype np.float32, shape [N, d]), which is a one hot encoding \n",
        "    of the element of each atom type.\n",
        "    * the edge list (of dtype np.int32, shape [2E, 2]) that represents the start and end index\n",
        "    of each edge.\n",
        "    * the edge feature list (of dtype np.float32, shape [2E, 1]) which describes the feature type\n",
        "    associated with each edge.\n",
        "    \"\"\"\n",
        "    # Node features\n",
        "    node_features = [atm_featurizer(atm.GetSymbol()) for atm in mol.GetAtoms()]\n",
        "    node_features = np.array(node_features, dtype=np.float32)\n",
        "    \n",
        "    # Edge list and edge feature list\n",
        "    edge_list = []\n",
        "    edge_feature_list = []\n",
        "    for bnd in mol.GetBonds():\n",
        "        bnd_indices = [bnd.GetBeginAtomIdx(), bnd.GetEndAtomIdx()]\n",
        "        bnd_type = bnd.GetBondTypeAsDouble()\n",
        "        edge_list.extend([bnd_indices,  bnd_indices[::-1]])\n",
        "        edge_feature_list.extend([bnd_type, bnd_type])\n",
        "    edge_list = np.array(edge_list, dtype=np.int32)\n",
        "    edge_feature_list = np.array(edge_feature_list, dtype=np.float32)\n",
        "    \n",
        "    return node_features, edge_list, edge_feature_list\n",
        "    \n",
        "\n",
        "node_features, edge_list, edge_feature_list = mol_to_edge_list_graph(test_mol, atm_featurizer)\n",
        "print(f\"Node feature list:\\n{node_features}\")\n",
        "print(f\"\\nEdge list:\\n{edge_list}\")\n",
        "print(f\"\\nEdge feature list:\\n{edge_feature_list}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rFgcc-Uzmbda",
        "outputId": "879e95b5-2611-4bf3-ae4e-d2718ba1d85e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node feature list:\n",
            "[[1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]\n",
            " [1. 0. 0.]]\n",
            "\n",
            "Edge list:\n",
            "[[ 0  1]\n",
            " [ 1  0]\n",
            " [ 1  2]\n",
            " [ 2  1]\n",
            " [ 2  3]\n",
            " [ 3  2]\n",
            " [ 3  4]\n",
            " [ 4  3]\n",
            " [ 4  5]\n",
            " [ 5  4]\n",
            " [ 5  6]\n",
            " [ 6  5]\n",
            " [ 6  7]\n",
            " [ 7  6]\n",
            " [ 7  8]\n",
            " [ 8  7]\n",
            " [ 8  9]\n",
            " [ 9  8]\n",
            " [ 9 10]\n",
            " [10  9]\n",
            " [10 11]\n",
            " [11 10]\n",
            " [10 12]\n",
            " [12 10]\n",
            " [12 13]\n",
            " [13 12]\n",
            " [13 14]\n",
            " [14 13]\n",
            " [14 15]\n",
            " [15 14]\n",
            " [15 16]\n",
            " [16 15]\n",
            " [15 17]\n",
            " [17 15]\n",
            " [17 18]\n",
            " [18 17]\n",
            " [17 19]\n",
            " [19 17]\n",
            " [19 20]\n",
            " [20 19]\n",
            " [20 21]\n",
            " [21 20]\n",
            " [ 7  2]\n",
            " [ 2  7]\n",
            " [21  9]\n",
            " [ 9 21]\n",
            " [16  5]\n",
            " [ 5 16]\n",
            " [21  6]\n",
            " [ 6 21]\n",
            " [21 14]\n",
            " [14 21]]\n",
            "\n",
            "Edge feature list:\n",
            "[1.  1.  1.  1.  1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.  1.  1.  1.\n",
            " 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
            " 1.  1.  1.  1.  1.  1.  1.5 1.5 1.  1.  1.  1.  1.  1.  1.  1. ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Graphs:\n",
        "    ATOM_FEATURIZER = SymbolFeaturizer(['Ag', 'Al', 'Ar', 'As', 'Au', 'B', 'Ba', 'Be', 'Bi', 'Br', 'C',\n",
        "                    'Ca', 'Cd', 'Ce', 'Cl', 'Co', 'Cr', 'Cs', 'Cu', 'Dy', 'Eu', 'F',\n",
        "                    'Fe', 'Ga', 'Ge', 'H', 'He', 'Hf', 'Hg', 'I', 'In', 'Ir', 'K', 'La',\n",
        "                    'Li', 'Mg', 'Mn', 'Mo', 'N', 'Na', 'Nd', 'Ni', 'O', 'Os', 'P', 'Pb',\n",
        "                    'Pd', 'Pr', 'Pt', 'Rb', 'Re', 'Rh', 'Ru', 'S', 'Sb', 'Sc', 'Se',\n",
        "                    'Si', 'Sm', 'Sn', 'Sr', 'Ta', 'Te', 'Ti', 'Tl', 'V', 'W', 'Xe', 'Y',\n",
        "                    'Yb', 'Zn', 'Zr'])\n",
        "    # ^ you can change the number of symbols here to play with the dimensionality,\n",
        "    # we only need to have the symbols: ['Br', 'C', 'Cl', 'F', 'I', 'N', 'O', 'P', 'S']\n",
        "\n",
        "    BOND_FEATURIZER = SymbolFeaturizer([1., 1.5, 2., 3.])  \n",
        "    # ^ single, aromatic, double and triple bonds (see earlier how RDKit represents these as doubles.)\n",
        "    \n",
        "    def __init__(self, node_features: torch.Tensor, \n",
        "                 edge_list: torch.Tensor, edge_features: torch.Tensor, node_to_graph_id: torch.Tensor):\n",
        "        self.node_features = node_features\n",
        "        self.edge_list = edge_list\n",
        "        self.edge_features = edge_features\n",
        "        self.node_to_graph_id = node_to_graph_id\n",
        "        \n",
        "    def to(self, *args, **kwargs):\n",
        "        new_graph = type(self)(self.node_features.to(*args, **kwargs),\n",
        "                               self.edge_list.to(*args, **kwargs),\n",
        "                               self.edge_features.to(*args, **kwargs),\n",
        "                               self.node_to_graph_id.to(*args, **kwargs)\n",
        "                              )\n",
        "        return new_graph\n",
        "    \n",
        "    @classmethod\n",
        "    def from_smiles_string(cls, smiles_str: str):\n",
        "        # Convert to form we need using previous code:\n",
        "        mol = Chem.MolFromSmiles(smiles_str)\n",
        "        node_features, edge_list, edge_features = mol_to_edge_list_graph(mol, cls.ATOM_FEATURIZER)\n",
        "        edge_features = [cls.BOND_FEATURIZER(elem) for elem in edge_features]\n",
        "        # ^ nb here we're converting the edge feature list into one-hot form\n",
        "        \n",
        "        # Convert to tensors:\n",
        "        node_features = torch.tensor(node_features, dtype=torch.float32)\n",
        "        edge_list = torch.tensor(edge_list, dtype=torch.int64)\n",
        "        edge_features = torch.tensor(edge_features, dtype=torch.float32)\n",
        "        node_to_graph_id = torch.zeros(node_features.shape[0], dtype=torch.int64)\n",
        "        # ^we only (currently) have one molecule per SMILES so all the nodes can be assigned \n",
        "        # the same id\n",
        "        \n",
        "        return cls(node_features, edge_list, edge_features, node_to_graph_id)\n",
        "    \n",
        "    @property\n",
        "    def num_graphs(self):\n",
        "        return torch.unique(self.node_to_graph_id).shape[0]\n",
        "    \n",
        "    @classmethod\n",
        "    def concatenate(cls, list_of_graphs):\n",
        "        # node features and edge_features simply get concatenated\n",
        "        new_node_features = torch.cat([e.node_features for e in list_of_graphs], dim=0)\n",
        "        new_edge_features = torch.cat([e.edge_features for e in list_of_graphs], dim=0)\n",
        "        \n",
        "        # edge_list and node\n",
        "        new_edge_lists = []\n",
        "        new_node_ids = []\n",
        "        num_nodes_seen_so_far = 0\n",
        "        num_graphs_so_far = 0\n",
        "        for graph in list_of_graphs:\n",
        "            new_edge_lists.append(graph.edge_list + num_nodes_seen_so_far)\n",
        "            # ^ shift up the edges to reflect the nodes new indices\n",
        "            \n",
        "            new_node_ids.append(graph.node_to_graph_id + num_graphs_so_far)\n",
        "            # ^shift up the node to graph id to reflect the number of graphs before\n",
        "            \n",
        "            num_nodes_seen_so_far += graph.node_features.shape[0]\n",
        "            num_graphs_so_far +=  torch.unique(graph.node_to_graph_id).shape[0]\n",
        "        \n",
        "        new_edge_lists = torch.cat(new_edge_lists, dim=0)\n",
        "        new_new_node_ids = torch.cat(new_node_ids, dim=0)\n",
        "        \n",
        "        new_concatenated_graph = cls(node_features=new_node_features,\n",
        "                                     edge_list=new_edge_lists,\n",
        "                                     edge_features=new_edge_features,\n",
        "                                     node_to_graph_id=new_new_node_ids)\n",
        "        return new_concatenated_graph\n",
        "    \n",
        "    \n",
        "# We're now going to create an instance of this class and test the concatenate function\n",
        "# (you could also write a reverse function if you wanted to be sure your code worked correctly)\n",
        "# acetone_g = Graphs.from_smiles_string('CC(=O)C')\n",
        "# ethane_g = Graphs.from_smiles_string('CC')        \n",
        "    \n",
        "# graph_of_both = Graphs.concatenate([acetone_g, ethane_g])\n",
        "# print(f\"graph_of_both.node_features:\\n{graph_of_both.node_features}\\n\\n\")\n",
        "# print(f\"graph_of_both.edge_list:\\n{graph_of_both.edge_list}\\n\\n\")\n",
        "# print(f\"graph_of_both.edge_features:\\n{graph_of_both.edge_features}\\n\\n\")\n",
        "# print(f\"graph_of_both.node_to_graph_id:\\n{graph_of_both.node_to_graph_id}\\n\\n\")"
      ],
      "metadata": {
        "id": "6W5vWV9nmdEB"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GNN(nn.Module):\n",
        "    def __init__(self, node_feature_dimension, num_propagation_steps:int =4):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.num_propagation_steps = num_propagation_steps\n",
        "        # called T above.\n",
        "        \n",
        "        # Our sub modules:\n",
        "        self.message_projection = nn.Linear(node_feature_dimension, node_feature_dimension, bias=False)\n",
        "        self.update_gru = nn.GRUCell(input_size=node_feature_dimension,\n",
        "                                     hidden_size=node_feature_dimension, bias=True)\n",
        "        self.attn_net = nn.Linear(node_feature_dimension, 1)\n",
        "        self.proj_net = nn.Linear(node_feature_dimension, node_feature_dimension)\n",
        "        self.final_lin = nn.Linear(node_feature_dimension, 1)\n",
        "        \n",
        "    def forward(self, graphs_in: Graphs):\n",
        "        \"\"\"\n",
        "        Produces a column vector of predictions, with each element in this vector a prediction\n",
        "        for each marked graph in `graphs_in`.\n",
        "        \n",
        "        In the comments below N is the number of nodes in graph_in (across all graphs), \n",
        "        d the feature dimension, and G is the number of individual molecular graphs.        \n",
        "        \"\"\"\n",
        "        # 1. Message passing and updating\n",
        "        m = graphs_in.node_features  # shape: [N, d]\n",
        "        \n",
        "        for t in range(self.num_propagation_steps):\n",
        "            projs = self.message_projection(m)  # [N, d]\n",
        "            \n",
        "            # Update the node embeddings (eqn 1 above)\n",
        "            # 1a. compute the sum for each node\n",
        "            msgs = torch.zeros_like(m)  # [N, d]\n",
        "            msgs.index_add_(0, graphs_in.edge_list[:, 0], projs.index_select(0, graphs_in.edge_list[:, 1]))\n",
        "            \n",
        "            # 1b. update the embeddings via GRU cell\n",
        "            m = self.update_gru(msgs, m)  # [N, d]\n",
        "            \n",
        "            \n",
        "        # 2. Aggregation (eqn 2 above)\n",
        "        # a compute weighted embeddings\n",
        "        attn_coeffs = torch.sigmoid(self.attn_net(m))  # [N, 1]\n",
        "        proj_embeddings = self.proj_net(m)  # [N, d']\n",
        "        weighted_embeddings = attn_coeffs * proj_embeddings\n",
        "        \n",
        "        # perform the sum\n",
        "        graph_embedding = torch.zeros(graphs_in.num_graphs, weighted_embeddings.shape[1],\n",
        "                                 device=m.device, dtype=m.dtype)  \n",
        "        graph_embedding.index_add_(0, graphs_in.node_to_graph_id, weighted_embeddings)  # [G, d']\n",
        "        \n",
        "        # 3. Final linear projection.\n",
        "        final_prediction = self.final_lin(graph_embedding)  # [G, 1]\n",
        "        return final_prediction\n",
        "    \n",
        "    \n",
        "gnn = GNN(len(Graphs.ATOM_FEATURIZER.indx2atm))"
      ],
      "metadata": {
        "id": "KUcBh0G4meyQ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def collate_for_graphs(batch):\n",
        "    \"\"\"\n",
        "    This is a custom collate function for use minibatches of graphs along with their value.\n",
        "    It ensures that we concatenate graphs correctly.\n",
        "    \"\"\"\n",
        "    # Split up the graphs and the y values\n",
        "    list_of_graphs, list_of_targets = zip(*batch)\n",
        "    list_of_graphs = list(list_of_graphs)\n",
        "    list_of_targets = list(list_of_targets)\n",
        "    \n",
        "    # The graphs need to be concatenated (i.e. collated) using the function you wrote\n",
        "    graphs = Graphs.concatenate(list_of_graphs)\n",
        "    \n",
        "    # The y values can use the default collate function as before.\n",
        "    targets = data.dataloader.default_collate(list_of_targets)\n",
        "    \n",
        "    return graphs, targets"
      ],
      "metadata": {
        "id": "x5lmvvFfmmqU"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Then we train and evaluate\n",
        "out = ss_utils.train_neural_network(train_set, valid_set, \"Drug\", \"Y\",\n",
        "                                    transform=Graphs.from_smiles_string, neural_network=gnn,\n",
        "                                   collate_func=collate_for_graphs)\n",
        "\n",
        "# And then we print out as a table some of the results.\n",
        "display(HTML(tabulate.tabulate(out['out_table'], tablefmt=\"html\"))) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset is of size 448 and valid of size 64\n",
            "Epoch - 0\n",
            "Training Results - Epoch: 0  Avg loss: 0.96\n",
            "Validation Results - Epoch: 0 Avg loss: 0.96\n"
          ]
        },
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          "data": {
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              "[1/7]  14%|#4         [00:00<?]"
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              "model_id": "b0fca3325a3049dfaa81886111271db0"
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "Epoch - 1\n",
            "Training Results - Epoch: 1  Avg loss: 0.35\n",
            "Validation Results - Epoch: 1 Avg loss: 0.34\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1/7]  14%|#4         [00:00<?]"
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              "model_id": "8097a7e16c3b4a07b1bc88cc1033c12b"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch - 2\n",
            "Training Results - Epoch: 2  Avg loss: 0.30\n",
            "Validation Results - Epoch: 2 Avg loss: 0.22\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1/7]  14%|#4         [00:00<?]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "00a6615f373148b3bcc78bc1d388f30f"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch - 3\n",
            "Training Results - Epoch: 3  Avg loss: 0.28\n",
            "Validation Results - Epoch: 3 Avg loss: 0.25\n"
          ]
        },
        {
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          "data": {
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              "[1/7]  14%|#4         [00:00<?]"
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              "model_id": "51614f45e5f549cc97c97d88d8871aa7"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch - 4\n",
            "Training Results - Epoch: 4  Avg loss: 0.28\n",
            "Validation Results - Epoch: 4 Avg loss: 0.21\n"
          ]
        },
        {
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          "data": {
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              "[1/7]  14%|#4         [00:00<?]"
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              "model_id": "18f31f037faa449eabfb581fc9b366dd"
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          "metadata": {}
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          "name": "stdout",
          "text": [
            "Epoch - 5\n",
            "Training Results - Epoch: 5  Avg loss: 0.28\n",
            "Validation Results - Epoch: 5 Avg loss: 0.26\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1/7]  14%|#4         [00:00<?]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "943d71591ea1467ab89dd7c86f3269f0"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch - 6\n",
            "Training Results - Epoch: 6  Avg loss: 0.28\n",
            "Validation Results - Epoch: 6 Avg loss: 0.21\n"
          ]
        },
        {
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          "data": {
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              "[1/7]  14%|#4         [00:00<?]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch - 7\n",
            "Training Results - Epoch: 7  Avg loss: 0.27\n",
            "Validation Results - Epoch: 7 Avg loss: 0.24\n"
          ]
        },
        {
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          "data": {
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              "[1/7]  14%|#4         [00:00<?]"
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          "metadata": {}
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        {
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          "name": "stdout",
          "text": [
            "Epoch - 8\n",
            "Training Results - Epoch: 8  Avg loss: 0.26\n",
            "Validation Results - Epoch: 8 Avg loss: 0.21\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1/7]  14%|#4         [00:00<?]"
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          "metadata": {}
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        {
          "output_type": "stream",
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          "text": [
            "Epoch - 9\n",
            "Training Results - Epoch: 9  Avg loss: 0.26\n",
            "Validation Results - Epoch: 9 Avg loss: 0.22\n"
          ]
        },
        {
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          "data": {
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          "metadata": {}
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          "name": "stdout",
          "text": [
            "Epoch - 10\n",
            "Training Results - Epoch: 10  Avg loss: 0.26\n",
            "Validation Results - Epoch: 10 Avg loss: 0.20\n"
          ]
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              "<table>\n",
              "<tbody>\n",
              "<tr><td>Num params             </td><td style=\"text-align: right;\">42100    </td></tr>\n",
              "<tr><td>Minimum train loss     </td><td style=\"text-align: right;\">    0.256</td></tr>\n",
              "<tr><td>Mean validation time   </td><td style=\"text-align: right;\">    0.248</td></tr>\n",
              "<tr><td>Minimum validation loss</td><td style=\"text-align: right;\">    0.203</td></tr>\n",
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      "cell_type": "code",
      "source": [
        "ss_utils.plot_train_and_val_using_mpl(out['train_loss_list'], out['val_lost_list'])\n",
        "ss_utils.plot_train_and_val_using_altair(out['train_loss_list'], out['val_lost_list'])"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/MolecularAI/pysmilesutils.git\n",
        "% cd pysmilesutils\n",
        "!pip install ."
      ],
      "metadata": {
        "id": "dD3JQVAW6Odb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e34dd87d-56ca-42b6-cfd3-a2ae70ce9ec4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'pysmilesutils'...\n",
            "remote: Enumerating objects: 88, done.\u001b[K\n",
            "remote: Counting objects: 100% (88/88), done.\u001b[K\n",
            "remote: Compressing objects: 100% (61/61), done.\u001b[K\n",
            "remote: Total 88 (delta 32), reused 76 (delta 24), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (88/88), done.\n",
            "/content/pysmilesutils\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Processing /content/pysmilesutils\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "Building wheels for collected packages: PySMILESutils\n",
            "  Building wheel for PySMILESutils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PySMILESutils: filename=PySMILESutils-1.1.0-py3-none-any.whl size=21206 sha256=9abd657c1700af8eaf1acc41f08e5a4f303311aa636d101f7b0130f5c1315ac9\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ggk28xr1/wheels/69/a5/c6/8edc84a45081dd5922bf23184dfcdc0f69e514de4d01a7ebf8\n",
            "Successfully built PySMILESutils\n",
            "Installing collected packages: PySMILESutils\n",
            "Successfully installed PySMILESutils-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "from pysmilesutils.tokenize import *\n",
        "from pysmilesutils.analyze import analyze_smiles_tokens"
      ],
      "metadata": {
        "id": "Xyq8GC22SUP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smiles_list_all = whole_df['Drug'].tolist()\n",
        "len(smiles_list_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lh8S-mauE1Xt",
        "outputId": "9462d83b-e469-4d65-8f0e-a6b4e83afc49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "640"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "atom_tokenizer = SMILESAtomTokenizer(smiles=smiles_list_all)"
      ],
      "metadata": {
        "id": "N7UxGs9TSZg5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "atom_tokenizer(smiles_list_all), len(atom_tokenizer(smiles_list_all)[0])   #len(test_1_s) = 115"
      ],
      "metadata": {
        "id": "ky7gtpzVSdhd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Vocabulary dictionary:\", atom_tokenizer.vocabulary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhcUIYEzSpjT",
        "outputId": "d1d979d8-572c-474f-c2fe-13963edd256d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocabulary dictionary: {' ': 0, '^': 1, '&': 2, '?': 3, 'O': 4, 'C': 5, '(': 6, 'S': 7, ')': 8, '[': 9, 'N': 10, '+': 11, ']': 12, 'c': 13, '1': 14, 'n': 15, '2': 16, '@': 17, 'H': 18, '=': 19, '#': 20, '3': 21, 's': 22, '/': 23, '\\\\': 24, '-': 25, '4': 26, '5': 27, '6': 28, '7': 29, '8': 30, '9': 31, 'P': 32, 'Cl': 33, 'o': 34, 'F': 35, 'Br': 36, 'Au': 37, 'I': 38, '.': 39}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_1 = 0\n",
        "for i in smiles_list_all:\n",
        "  if len(i) > max_len_1:\n",
        "    max_len_1 = len(i)\n",
        "print(max_len_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EhgxbIGD81y",
        "outputId": "f6d00207-a94a-41d2-c901-3a2a0c7a66a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing against DeepPurpose\n",
        "drug_encoding_name = 'MPNN' #'Transformer'   'MPNN'\n",
        "train_gnn, val_gnn, test_gnn = utils.data_process(X_drug = X,\n",
        "                                      y = y, \n",
        "                                      drug_encoding = drug_encoding_name,\n",
        "                                      # target_encoding = drug_encoding_name,\n",
        "                                      random_seed = 'TDC')"
      ],
      "metadata": {
        "id": "X7PCIel_K76h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d2beed6-a5d9-484c-ce44-621a6867db49"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drug Property Prediction Mode...\n",
            "in total: 578 drugs\n",
            "encoding drug...\n",
            "unique drugs: 578\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_gnn.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JYY88eJmEck2",
        "outputId": "e3411b5d-ab7b-4fa8-b5b4-99319efe2d42"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              SMILES  Label  \\\n",
              "0                                    CC(=O)Nc1ccccc1      1   \n",
              "1                         CC(=O)Nc1nnc(S(N)(=O)=O)s1      1   \n",
              "2  C=C1/C(=C\\C=C2/CCC[C@]3(C)[C@@H]2CC[C@H]3[C@@H...      1   \n",
              "3                    Cc1c(N(C)C)c(=N)n(-c2ccccc2)n1C      1   \n",
              "4  COc1ccc(CCN2CCC(Nc3nc4ccccc4n3Cc3ccc(F)cc3)CC2...      1   \n",
              "\n",
              "                                       drug_encoding  \n",
              "0  [[[tensor(1.), tensor(0.), tensor(0.), tensor(...  \n",
              "1  [[[tensor(1.), tensor(0.), tensor(0.), tensor(...  \n",
              "2  [[[tensor(1.), tensor(0.), tensor(0.), tensor(...  \n",
              "3  [[[tensor(1.), tensor(0.), tensor(0.), tensor(...  \n",
              "4  [[[tensor(1.), tensor(0.), tensor(0.), tensor(...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7511f51-09d6-4032-aa27-4924fccfa170\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SMILES</th>\n",
              "      <th>Label</th>\n",
              "      <th>drug_encoding</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CC(=O)Nc1ccccc1</td>\n",
              "      <td>1</td>\n",
              "      <td>[[[tensor(1.), tensor(0.), tensor(0.), tensor(...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CC(=O)Nc1nnc(S(N)(=O)=O)s1</td>\n",
              "      <td>1</td>\n",
              "      <td>[[[tensor(1.), tensor(0.), tensor(0.), tensor(...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C=C1/C(=C\\C=C2/CCC[C@]3(C)[C@@H]2CC[C@H]3[C@@H...</td>\n",
              "      <td>1</td>\n",
              "      <td>[[[tensor(1.), tensor(0.), tensor(0.), tensor(...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cc1c(N(C)C)c(=N)n(-c2ccccc2)n1C</td>\n",
              "      <td>1</td>\n",
              "      <td>[[[tensor(1.), tensor(0.), tensor(0.), tensor(...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>COc1ccc(CCN2CCC(Nc3nc4ccccc4n3Cc3ccc(F)cc3)CC2...</td>\n",
              "      <td>1</td>\n",
              "      <td>[[[tensor(1.), tensor(0.), tensor(0.), tensor(...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7511f51-09d6-4032-aa27-4924fccfa170')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a7511f51-09d6-4032-aa27-4924fccfa170 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7511f51-09d6-4032-aa27-4924fccfa170');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config_gnn = utils.generate_config(drug_encoding = drug_encoding_name, \n",
        "                         train_epoch = 3, \n",
        "                         LR = 0.001, \n",
        "                         batch_size = 128,\n",
        "                         mpnn_hidden_size = 32,\n",
        "                         mpnn_depth = 2\n",
        "                        )"
      ],
      "metadata": {
        "id": "l9QXTHrOEjMP"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_gnn = CompoundPred.model_initialize(**config_gnn)\n",
        "model_gnn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJEc4kq2En4I",
        "outputId": "569cca67-737c-4c21-e030-38ce06d6a903"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<DeepPurpose.CompoundPred.Property_Prediction at 0x7f17b483c3a0>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_gnn.train(train_gnn, val_gnn, test_gnn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nzLk7opME00f",
        "outputId": "0e93b413-c41a-462d-de3a-746a4d1d882b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Let's use 1 GPU!\n",
            "--- Data Preparation ---\n",
            "--- Go for Training ---\n",
            "Training at Epoch 1 iteration 0 with loss 0.69111. Total time 0.0 hours\n",
            "Validation at Epoch 1 , AUROC: 0.57422 , AUPRC: 0.92306 , F1: 0.93577\n",
            "Training at Epoch 2 iteration 0 with loss 0.36451. Total time 0.0 hours\n",
            "Validation at Epoch 2 , AUROC: 0.42296 , AUPRC: 0.84029 , F1: 0.93577\n",
            "Training at Epoch 3 iteration 0 with loss 0.43464. Total time 0.0 hours\n",
            "Validation at Epoch 3 , AUROC: 0.44257 , AUPRC: 0.86660 , F1: 0.93577\n",
            "--- Go for Testing ---\n",
            "Testing AUROC: 0.41320132013201316 , AUPRC: 0.851233385686426 , F1: 0.9308755760368664\n",
            "--- Training Finished ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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ijcU5VzjKy+Fb37Lh/dmS8TyNVKo6FZgaciyNRufOsPfeVofqssvijsY5VwhUYeJEu8vIZpmiTOdpPCUi3xeRZtkLpXFJJOCNN2Dz5rgjcc4VgspK+Pjj7DZNQeZ9Gvti8zQ+FZE/Bc1TrgESCVi7Fqb6/ZpzLgRR9GdAhklDVQ8Avg38DSgDJonIPBH5jYjsmc0AC1XyL9aH3jrnwpDsz8h2MdSMCxaq6hRVvQwbevs9rCTI1cA8EflXluIrWB072hobnjSccw2laknjmGOyv+xCnavcqupmVX1eVX8EnAZ8DBwZemSNQLJfY+PGuCNxzuWz+fOj6c+AeiQNEdlTRK4TkbnAC1gtqt+FHlkjkEjAV19BjlU6cc7lmaj6MyDz0VNtReQiEZkEzAN+AbyDVbztrKpXZTHGguX9Gs65MJSX23LS++6b/WtleqfxKXAP8BVWuLCDqg5W1Vc1k4qHLq327eHggz1pOOfqL8r+DMg8aQwHuqjqiar6sKquq/UIl5FEAiZNgm++iTsS51w+WrAAli6NpmkKMh9ye4eqfpztYBqj/v1h/XorZeycc3UVZX8G5OYa4Y3KsmX22K8flJTYwvDOOZep8nLYbTfYb79orudJI0Zjx8Lll299vmgRDB3qicM5l5lkf0a2602l8qQRo+HDYV2V3qF162x7oRg71u6gior8Tsq5sC1YAEuWWCd4VOpV5daF46OP6rY934wda3dOycSYvJOC8FcTc64xmjjRHqPqzwC/04hVly7pt7doAStXRhtLNlx7beHfSTkXp/JyKC62kkRRyXRy36kicl7K864i8raIfCEij4tI6+yFWLhuuglatdp2W7NmsGGDLc6UHBWRj957r/DvpJyLUxz9GZD5ncavgOKU53cCewCjgH7A9ZleUEQGiMgcEakUkWvSvH6uiKwQkWnBzwUpr50TVNedJyLnZHrNXDVoEIwaBV272l96164wZoyVFdlpJxuO+5vfwKZNcUeauQ0b4LrroHdv68dIp7o7LOdc5hYuhMWLo22aAkBVa/0BVgMDgt9bAuuB04PnFwDzMzxPE2A+sCfQHHgfOKDKPucC96Q5th2wIHhsG/zetqbr9erVS/PVF1+onnuuKqgedZTqokVxR1S7qVNVDznEYj77bNV771Vt1cqeJ39atVJ9+OG4I3Uu/91/v/2fmjEj/HMDFVrN52qmdxotgkQBVtG2KZBcF3wO0CnD8/QGKlV1gapuAMYDp2Z47HeAV1R1taquAV4BBmR4bN5p3druOsaOhfffh5494amn4o4qvY0bYcQI+Pa3bd7JP/4BDz1knd6jRm29s2jRwp57J7hzDTdxopUiOuCAaK+badL4EDgq+P1UYIqqrg2e7wasTXdQGrsDi1OeLwm2VfUDEZke9Jd0rsuxIjJURCpEpGLFihUZhpW7fvQjW91vr72grAwuvthmkOeKDz6Aww+3JqkzzoAZM2DgwK2vDxpko6auvBK2bIHvfje+WJ0rFHH1Z0DmSeNe4HoRqQD+F7g/5bU+wKwQY3oGKFHVHtjdxF/rcrCqjlLVUlUtLS4urv2APLDXXlaf6sor4U9/sg/p2bPjjWnTJuvI79XL6t48+aTdFe26a/r9y8qsv+P556ON07lC9OGHNqAk8v4MMq89dRfW1/A2MERVR6e8vBMwJsPrLQU6pzzfI9iWeq1Vqpos33cf0CvTYwtZ8+Zwxx32ofvpp/Zhfd999o0jajNmwBFHwK9+Zclg5kw47bSajzniCFuK8skno4nRuUKWHFkZ5aS+pLos9zpWVX+mqg9V2X6hqv4tw9NMBrqLSDcRaQ6cCUxI3UFEOqY8HQgkv1O/BJwYrO3RFjgx2NaonHSS9XEceST85Cdw1lmwNtPGwQbatAluucUS1qJF8Pe/w/jx1q5am6IiSyzPP59bzWvO5aO4+jMg83ka+4hI75TnLUXkFhF5RkQuyfRiqroJuAT7sJ8NPKaqM0VkhIgkW8IvFZGZIvI+cCl2h4OqrgZuwBLPZGBEsK3R6dgRXn4Zbr4ZHn/cOsnfeSe715w1yxLVtddan8XMmfA//1O3c5SV2eS+l1+ufV/nXPWS62dUN6w9q6obVqXbDnd9Gbg95fmdwAZgCrARuDiT80T9k89DbjP11luqXbuqNm2qeuutqps3h3v+TZtUb7tNdYcdVHfdVfXRR+t/rg0bVNu2VR08OLz4nGtsFi60obZ33529axDCkNtDgEkAIlIEDAauVtVewI3A0DATmctcnz4wbZo1/VxzDQwYYH0eYfjPf+Coo+Dqq+Hkk+3u4owz6n++Zs3sLmXCBBum65yru6jXz6gq06SxC7Aq+P1QbHLd48HzcmyynotJmzbw6KNw773wr39ZCZKXGtDbs3kz/O531uw1dy6MGwdPPGFrEDdUWRl89ll+l0hxLk7l5TZKMY7+DMg8aSwD9g5+PxGbAZ6cM9EayKNCF4VJxCbTVVRYAbMBA+Cqq2yYa13MnWsLQv3iF3aOmTOtsz2sseAnnAA77uijqJyrr4kTY+zPIPOkMQG4RUTuAK4E/p7y2sFYSQ+XAw48ECZPhosugt/+1pqXFmTwt7NlC/zhD3aXMns2/O1vNgP9W98KN76WLa2p66mn7I4mn/laIS5qH35oP3E1TUHmSeMa4FmslMcE4OaU1waytaSIywEtW8Kf/2wjq+bNs2amRx6pfv/KSvtHePnlcPzxNg/jxz/O3kzT006zciPZHvGVTcm1QhYtsrkyvuqii0Ic62dUJRrH7LCIlJaWakVFRdxhxGrRIitF8tZbMGQI9O1rdaI++gg6d7bb3Mcft8mDd90FgwdnvyzB559bE9oll1jfST4qKbE/26q6drVvgs5lw3nnwTPPwPLl2W2eEpEpqlqa9rW6JA0RaYeVDWmHVb59W3N4roQnDbNpE1x/vZX9ENl+FnmPHjbpbvd0VcCy5JRTbO7HggXR184JQ1FR+tn4ItbU51w27LmntRxku0+wpqSRca4SkRuxsh3PYPWgngGWisgNoUTpsqZpU7jxRthtt/QfdJ99Fm3CABtF9eGHNlw4H1W3JoivFeKyZdEiW0MjzqYpyHxG+GXAtcDDQALYP3h8GLhWRC7NWoQuNNUV/V28OP32bBo40L6t52q599rckOarUosWdjfnXDbkQn8GZH6ncRFwl6r+RFUnquqc4PEnwB+xyrcux+XSt+PiYhvam69Db3fZxR6Li7c2r/Xp42uFuOwpL4d27eCgg+KNI9OkUQI8V81rzwWvuxyXbk3yVq3i+3acrJA7Z04812+I0aOtBtjHH1sfxoUX2mCDZcvijswVqvJy+6IV1/yMpEwvvwqoLr8dyNbZ4i6HpVuTPM6V9L7/fXvMtyaqJUts4MB551l/EdhaJxs2wN13xxubK0wffZQb/RmQedJ4CrhBRM4WkaYAItJURM4CRgBPZCtAF65Bg6wDessWe4yzOaVzZ+jdO/+aqB580P78zj9/67bu3W3+yZ/+BF9+GVtorkDlSn8GZJ40fglMw0ZNrReRZdia4WOB97FOcufqrKzMZrB/9FHckWRmyxa4/3447jgb/phq2DBYs8Zedy5M5eXQti0cfHDckWS+ct8XQD9s9ved2KzwO4HvAseoqn+3cvWSXPHv6afjjSNT//yn3aH95Cfbv3bEEVa25fe/t7kxzoUl1vUzqqjLyn2qqs+q6tXBKKqrVfV5LeQp5S7r9tnHRoPkSxPV6NFWYTTZH1PVsGFbVzV0LgyLF9sk2DiWdk0nB/KWa+zKyqyk+/LlcUdSsxUr7I5o8GDYYYf0+3z3u7DfflYs0r9OuTDkUn8G1JA0RGSLiGzO8Mdvxl29lZVZX8GECbXvG6eHHrLFoy64oPp9ioqsrPzUqdaU5VxDJfszevSIOxJTbe0pEbkeyPi7kqr+X0gxhcZrT+UHVdh7b9h3XxvKmotUYf/9rWlq0qSa9/3mGyto2KNHwxbDcg7s/8ZBB0Xb71dT7amm1R2kqtdnLSLnUojY3cZdd8HatVtnW+eSSZNsEuKYMbXvu8MOcOmlcO218P77tkaJc/WxZAnMn28VoXOF92m4nHDaadb081x1dQdiNno07LwznH56ZvtfdBG0bm19G87VV7I/I1c6wSGGpCEiA0RkjohUisg1Nez3AxFRESkNnpeIyHoRmRb8/CW6qF22HXGErRKYi6OoPvvMRkP96Ee2VG0m2ra1Ybnjx+fPHBSXe8rLoU2b3OnPgIiThog0AUYCJwEHAGeJyHbLo4vITsDPgX9XeWm+qvYMfi7KesAuMkVFdrfxwguwfn3c0Wxr3DiLqaYO8HQuu8we//CH8GNyjUOy3lSTJnFHslXUdxq9gUpVXaCqG4DxwKlp9rsBuA34OsrgXLzKymDdOng5hxYPVrWmqUMPhV696nZsly5w1ll2/Jo12YnPFa6lS7cuxZxLok4auwOpqzcsCbb9l4gcBnRW1XSt291EZKqITBSRo7MYp4vBMcdYs04uNVG9954tFJVuBngmfvELq0X1F29MdXWUi/0ZkGMd4SJShJUnuTLNy58AXVT1UOAKYJyI7JzmHENFpEJEKlZUt+qQy0nNmtniTBMmWKd4Lhg9Glq2tP6M+jjkEDjxRPjjH20obr4YO9aGDRcV2ePYsXFH1PiUl9tIwlwbfRd10lgKdE55vkewLWknrAR7uYh8CBwBTBCRUlX9RlVXAajqFGA+sE/VC6jqKFUtVdXS4uLiLL0Nly1lZdbxXF4edyR2hzBuHJxxRsOGAQ8bBp9+Cg8/HF5s2TR2LAwdauVQVO1x6FBPHFHLxf4MiD5pTAa6i0g3EWkOnIkVPwRAVdeqantVLVHVEuAdYKCqVohIcdCRjojsCXQHFkQcv8uyE06wEUq50ET197/DF1/UvQO8quOOsz6RO+6wme+5bvhw61tKtW4dXH21l0aJytKlMG9e7vVnQMRJQ1U3AZcALwGzgcdUdaaIjBCRgbUc3g+YLiLTgMeBi1R1dXYjdlFr2RJOPtkWZtq8Od5YRo+2WeB9+zbsPCJ2t/Gf/8Czz4YTWzZVN0R46VLo1MmaEG+80QYsrPb/gVmRa/WmUlVbRqQQeBmR/DR+vI06evPNhn9g19fMmVa64Xe/gyuuaPj5Nm2ychCdO1txxlzWtWv6xNG2rRVkfPfdbZfo3XtvW0zr29+2x0MPteTv6u/CC+HRR2HVqniap2oqI5JTHeHOgd1pNG8ebxPVffdZx/zZZ4dzvqZN4fLLLRG+804458yW44/fflurVraU7UMP2R3TmjXw6qtw8822MNDEifb++vaFnXayxHHhhbYg1fTpvr5IXZWXw9FH515/BgCqWrA/vXr1UpefTjlFtaREdcuW6K+9fr1qu3aqZ5wR7nm/+EK1bVvVsrJwzxumGTNUW7RQPfBA1S5dVEVUu3ZVffjh2o9dulT16adVr71W9fjjVXfZRdV6QVRbtVI9+mjVK65QHT9edcGCbf9uH37YrlOX6xWqpUvtz+yOO+KLAajQaj5Xqy1Y6FycysqsDtW0afatNUpPP21t9Q3tAK+qdWv43/+1b+dz59oCVLlk/XprFtxpJ3jlFejYsW7Hd+oEp55qP2Cd/pWV1pw1ebI9jhwJd95pr7dvb01aLVva33VySHJytBbEu4Z9XHK5PwO8T8PlqJUroUMHqxR7ww3RXvu442DhQvvAC3t5zWXLrM/g3HNzb8LfJZfYh/pzz1kTYTZs2AAzZlgCSSaTGTPS79u1qy2t29hcdBE88oh9cYmrecr7NFzead/eZsJG3a8xfz689hqcf3521mPu0MFW/nvwwdxaqfAf/7CEcfnl2UsYYH1Vhx1mH4wPPAAffGCjy9JprIUec7o/A08aLoeddhrMmmUdr1G5/35LFueem71rXHmlfeO+557sXaMuliyBIUOsGfCWW6K/fpcu6ber2sTKuXOjjSdOn3xiI9NytWkKPGm4HPb979vjU09Fc72NG22RpVNOgd13r33/+tp3X2v3HzkSvvoqe9fJxObN8OMfW3/C+PHVr32eTTfdZKOzUrVsaV8ann8eDjjARmJ9/HH0sUUt1/szwJOGy2GdO9u4/6iSxvPPW7mPsDvA0xk2zNKyUgMAABQcSURBVNqsH3gg+9eqyS232AfVPffE1zE/aBCMGmV9GCL2OHq0NU3On2+DB8aMsfkg11xT2BWDy8ttsa+ePeOOpAbVDasqhB8fcpv/br3Vhh8uWpT9a51yimqnTqobN2b/WqqqRx5pw4qjul5Vb76p2qSJ6llnxTO0uS7mz1cdNMiG5LZpo3rbbarr1sUdVfj220/15JPjjqLmIbd+p+Fy2mmn2ePTT2f3OkuW2AJQ551nE/GicNVVNjroiSeiuV6qzz6zyr1dutgoruo6o3PFnntawcepU+HII60O1t572x1JoUwc/PRT67/L5aYp8OYpl+P22cfKeWR7FNWYMTavYMiQ7F4n1fe+Z/0bt98ebSFAVZsH8fHHNrRz5+0WGMhdhxxiQ4InTrRmrKFD4cAD4fHH87+YYj70Z4AnDZcHysqsXlO2hqhu2WKjpo4/3r7RRqWoyEZSvfcevP56dNe9/36r4HvjjXD44dFdN0z9+sGkSXYH2rQpnH669X/9859xR1Z/5eVbS7DkMk8aLueVldkH+4QJte9bH6++arOQo+gAr+rss23uxm9/G831Zs+GSy+1BDlsWDTXzBYRG4U2fbrdKS5bZu/rxBNhypS4o6u7iRNtfkZUzaP15UnD5bwePewOIFtNVKNHw667bh3iG6UWLexD/MUX7cMvm77+Gs4809Yreeih7ExejEOTJjavZu5cK1Hy3ntQWgo//KGtSZEPli2zhJ5rS7umUyD/bFwhE7G7jVdfhbVrwz338uU2G/qcc+KZowDw05/aB/kdd2T3OlddZYnpr3+te12pfNCihc1onz8ffv1r6/vYf3+bfZ7rczzypT8DPGm4PFFWZpPvnnsu3PM+9JCdN46mqaS2be36jzwCixdn5xrPPGOlzS+7LLtlQnLBLrvAiBGWPC66yPpw9t4bfvlLGzWWi8rLraDlYYfFHUntPGm4vHD44fbtOMwmKlVbN6NvX/tGGqfLL7d47ror/HMvXWpDiXv2hFtvDf/8uapDB5u0OGeODd2+9VZr5rz9dusDKSmxJrqSkvjXP0/Wm8r1/gzwpOHyRFGR/cd/4YXt16+urzfftA+UOO8ykrp2tTb4UaPC/TacLBOyfn18ZULitueelhSmToUjjrA5Huefb4MfVLeWYo8rcSxfnj/9GeBJw+WRsjJLGC+/HM75Ro+2OQqnnx7O+Rpq2DD44gu4997wznnrrfYt9p57bE5IY9azp5WK6dBh+zkd69bB8OHxxJVP/RngScPlkX79oF27cJqo1qyxuQqDBlkndC7o2dOGjN5119YFiRrirbfguutsxFQ2q/bmm+rm+8RVij2f+jPAk4bLI82a2SzqZ56xzuuGGDfOhqDmQtNUqquusvLY48Y17Dz5ViYkStWVYu/cOdo4ksrL4aij7N93PvCk4fJKWZl9IJaX1/8cqtY0ddhhufft7vjj7Y7jjjtsQmN9JMuELF1qI7J22SXcGPNdulLsYH0f9f0zr6/ly23NmHxpmoIYkoaIDBCROSJSKSLX1LDfD0RERaQ0Zdsvg+PmiMh3oonY5ZITTrDmpIY0UU2ZAu+/n3t3GWB3BL/4hX2QPP98/c7xwAPW9HbDDflbJiSbqpZi79LFZpaXl8PPfhZtDas33rDHfOkEB6ItjQ40AeYDewLNgfeBA9LstxPwBvAOUBpsOyDYfwegW3CeJjVdz0ujF6bTT1ft0EF106b6HT90qGqrVqqffRZuXGHZsEG1c2fVfv3qfuysWfbejjtOdfPm8GMrVFu2qA4bZmX4f/7z6ErFX3yx6o472t95LiGHSqP3BipVdYGqbgDGA6em2e8G4Dbg65RtpwLjVfUbVV0IVAbnc41MWZmVXXjnnbof++WX1l9wxhm522zTrBlccYV9C3333cyP+/prOOssa3oppDIhURCB226Dn//cBiJcfXU0dxz51p8B0TdP7Q6kznldEmz7LxE5DOisqlXn/tZ6bHD8UBGpEJGKFStWhBO1yyknnwzNm9evieqxxyxx5GLTVKoLLoA2bepWyPDqq63Z7cEHoVOnrIVWsETg97+3si6//S385jfZvd6KFTBzZn71Z0COdYSLSBFwJ3Blfc+hqqNUtVRVS4uLi8MLzuWMnXe2vo0nn6z7t8HRo23295FHZie2sLRubR9eTzwBlZW17//MM/DHP9o35VNOyX58hUrE5rScf76Vjr/hhuxdK9mf4UmjZkuB1IFtewTbknYCDgLKReRD4AhgQtAZXtuxrhEpK7NV76ZNy/yYGTOsSeuCC/JjCOrPfmbNFnfeWfN+H3+8tUzIbbdFE1shKyqyjvLBg+1uI1t/puXlNqijV6/snD9bok4ak4HuItJNRJoDZwL/XSVBVdeqantVLVHVEqwjfKCqVgT7nSkiO4hIN6A7UIcWX1dIBg60/9x1aaK67z5r1ho8OHtxhaljR4t1zBhrykgntUzII480zjIh2VBUZKPQzjoLrrnGmq3CVl5udc/yqT8DIk4aqroJuAR4CZgNPKaqM0VkhIgMrOXYmcBjwCzgReBiVd2c7Zhdbmrf3oYpZpo0vv4a/vY3q1/Vvn12YwvTlVda7Pfck/7122+3Vf/uvhv22y/a2ApdkyY2oOAHP7CBCSNHhnfulSvtzjffmqaAaIfcRv3jQ24L29132xDJ2bNr33fcONv3lVeyH1fYBg5U3XVX1a++2nb722+rNmmi+sMfRjdEtDH65hv7OwDVe+8N55xPPGHne+utcM4XNnJoyK1zoUmutPfUU7XvO3o0dOsG/ftnN6ZsGDYMVq2yZqqktWut6aRzZy8Tkm3Nm9uou5NOggsvtNFpDVVebkOjS0tr3TXneNJweWuPPWzGc21NVJWV1oRz/vn5OXehb1/o08c6xDdtshFjF15oCzaNG2dDc1127bCD/Ts74QQYMqThZdTztT8DPGm4PFdWBhUVNVcovf9+a58+77zo4gqTiN1tLFhgneNFRfDoo/be+/SJO7rGo0ULePpp60sbPNhKtdTHypXwwQd52p+BJw2X5047zR6ra6LauNGadU45Jb8nvH35pSWPlSu3bnvuufhXnGtsWrWyOTFHHmnNg5k0jVaVr/MzkjxpuLzWvTscdFD1TVTPPWclR3J9Bnhtfv3r3Fo4qDFr3dr+XZWW2mqLzz5bt+MnTszf/gzwpOEKQFmZLd2abnGd0aPtDuOkk6KPK0zVNb/FtXBQY7fzzvDii9Cjhw3JfemlzI8tL7c7lebNsxZeVnnScHmvrMzWQZgwYdvtixfbf+zzzoOmTeOJLSzVLRxU3XaXfW3a2NLD++9vI/n++c/aj1m1CqZPz9+mKfCk4QpAjx62gE7VJqoxYyyZnH9+PHGFKd3CQa1a2XYXn3bt4NVXYe+9rUpBsr+iOvnenwGeNFwBELG7jVdftfkLYOU17r/fhkh26xZvfGGounBQ1672fNCguCNz7dvbv70uXawC81tvVb/vxInQsiV8+9vRxRc2TxquIJSV2Uip54KC+q++au39+d4BnmrQICvSuGWLPXrCyB0dOljzVMeO1n9W3TooyfkZ+dqfAZ40XIE4/HD7D5tsoho92r4BnppuiS/nsqBTJ3jtNdh1V/jOd+C997Z9ffVq68/Iq6Vd0/Ck4QpCUZHN2XjhBfsW/o9/2AQsr/rqotS5syWO5Jov06dvfe2NN2zYdD73Z4AnDVdAdtnF5i5062blNr71rbgjco1RSYkljpYt4fjjYdYs215env/9GeBJwxWIsWNtbedU11/vM6ZdPPbayxJHkyZW6mX33e3fpyo8/njc0TWMJw1XEIYPt7uMVD5j2sVpn33g8svh889tdUWwtVGGDs3vLzOeNFxB8BnTLhf96U/bb8v3LzOeNFxB8BnTLhcV4pcZTxquIPiMaZeLCvHLjCcNVxB8xrTLRYX4ZSbPy7g5t9WgQZ4kXG5J/nscPtyapLp0sYSRz/9OPWk451wWFdqXmcibp0RkgIjMEZFKEbkmzesXicgHIjJNRN4UkQOC7SUisj7YPk1E/hJ17M4519hFeqchIk2AkcAJwBJgsohMUNVZKbuNU9W/BPsPBO4EBgSvzVfVnlHG7Jxzbquo7zR6A5WqukBVNwDjgW1Kyqnq5ylPdwSqLHLpnHMuLlEnjd2BxSnPlwTbtiEiF4vIfOB24NKUl7qJyFQRmSgiR6e7gIgMFZEKEalYsWJFmLE751yjl5NDblV1pKruBVwN/CrY/AnQRVUPBa4AxonIzmmOHaWqpapaWlxcHF3QzjnXCEQ9emop0Dnl+R7BtuqMB/4MoKrfAN8Ev08J7kT2ASqqO3jKlCkrRWRRQ4OOSHtgZdxBZFEhvz9/b/mrkN9fQ95b1+peiDppTAa6i0g3LFmcCfwodQcR6a6q84KnpwDzgu3FwGpV3SwiewLdgQU1XUxV8+ZWQ0QqVLU07jiypZDfn7+3/FXI7y9b7y3SpKGqm0TkEuAloAnwgKrOFJERQIWqTgAuEZHjgY3AGuCc4PB+wAgR2QhsAS5S1dVRxu+cc41d5JP7VPV54Pkq236T8vvPqznuCeCJ7EbnnHOuJjnZEd5IjYo7gCwr5Pfn7y1/FfL7y8p7E1WfBuGccy4zfqfhnHMuY540nHPOZcyTRsxEpLOIvC4is0RkpoikHQiQz0SkSTCT/9m4YwmbiLQRkcdF5D8iMltE+sQdU1hE5PLg3+QMEXlERFrEHVNDiMgDIrJcRGakbGsnIq+IyLzgsW2cMdZXNe/tt8G/y+ki8pSItAnjWp404rcJuFJVDwCOAC5OVvYtID8HZscdRJbcBbyoqvsBh1Ag71NEdsdK+JSq6kHYEPkz442qwR5ka/HTpGuAf6pqd+CfwfN89CDbv7dXgINUtQcwF/hlGBfypBEzVf1EVd8Lfv8C+9DZrh5XvhKRPbBJmvfFHUvYRGQXbP7Q/QCqukFVP4s3qlA1BVqKSFOgFfBxzPE0iKq+AVSd23Uq8Nfg978C3480qJCke2+q+rKqbgqevoNV4GgwTxo5RERKgEOBf8cbSaj+AFyFTcgsNN2AFcCYoPntPhHZMe6gwqCqS4E7gI+wum9rVfXleKPKig6q+knw+6dAhziDyaIhwAthnMiTRo4QkdbY5MXLqpSHz1si8l1guapOiTuWLGkKHAb8OSik+RX527yxjaBt/1QsMXYCdhSRH8cbVXapzT8ouDkIIjIcawYfG8b5PGnkABFphiWMsar6ZNzxhKgvMFBEPsSKT/YXkYfjDSlUS4Alqpq8M3wcSyKF4HhgoaquUNWNwJPAkTHHlA3LRKQjQPC4POZ4QiUi5wLfBQZpSJPyPGnETEQEaxOfrap3xh1PmFT1l6q6h6qWYJ2or6lqwXxbVdVPgcUism+w6ThgVg2H5JOPgCNEpFXwb/Q4CqSTv4oJbK1vdw7wjxhjCZWIDMCahgeq6rqwzutJI359gbOxb+HJ9c9Pjjsol7GfAWNFZDrQE7g55nhCEdw9PQ68B3yAfVbkdckNEXkEeBvYV0SWiMj5wK3ACSIyD7u7ujXOGOurmvd2D7AT8ErwufKXUK7lZUScc85lyu80nHPOZcyThnPOuYx50nDOOZcxTxrOOecy5knDOedcxjxpuEZJRK4XEQ1+bxM8j21inoj0DGJol+Y1FZHrYwjLue140nCN1X1Asox5G+A64p3N3TOIYbukgcVZcAUfXX5qGncAzsVBVZdgZUCyIphF3UxVNzT0XKr6TgghORcKv9NwjVKyeSqoLLww2Dw62KZBzZ7kvmUi8o6IrBORz0Tk7yLSpcr5PhSRh0VkiIj8B9iAlYRHRP5PRN4Tkc9FZKWIvCYiR6Qcey4wJng6LyWGkuD17ZqnRGSAiLwtIutFZK2IPJ1SziS5T7mIvCkixwfXXxcsqHRaA//4XCPmScM1dp8AZcHvt2BNQX2A5wBE5CKsmOQs4H+AC4GDgIkislOVcyWAK4D/wxbEmR5s3x34PVY19lysKN4bInJw8PpzwI3B76enxJAs2b2NoKbQc8CXwA+BnwYxvRksnpRqL2yhqDuD9/kJ8HcR2bvGPxXnquHNU65RU9VvRGRq8HRBalNQUK7+NmCMqg5J2f4uMAc4H1svJKkt0CsoZJh6jQtSjm0CvAjMBC4Afq6qK0RkfrDLNFWtrCXsG4EFwEnJRXZE5G1sdbYrscSV1B7op6rzgv3ewxLHGRRInSwXLb/TcK56fYCdsYKETZM/wGLgP9iqfaneqZowAILmoddFZBW2rsFGYB9g36r71iZY5Okw4NGUVdlQ1YXAJOCYKofMSyaMYL/l2J1OF5yrB7/TcK56uwWPr1bz+poqz7drTgqG8T4PvITdmXwCbMZGQ7WoR0xtAUl3LWzlua5VtlVd3hTgm3pe2zlPGs7VYFXweC7WnFTVF1WepysZ/QPs7qIsWMwI+O/KePVZT3xNcJ1vpXntW6RPEs6FxpOGc/bNG6Blle1vYYlhb1X9az3P3Qq7s/hvQhGR/ljz0MKU/aqLYRuq+pWITAFOF5HrVXVzcM6u2Mp6d9czTucy4knDOViG3VWcGSym9BW21OkqERkGjBSRYuAFYC02GuoYoFxVx9Vy7heBy4AHRWQM1pfxa2Bplf2SK/5dLCJ/xfo9plczz+PX2OipZ0XkT0BrbMTWWuB3dXjfztWZd4S7Rk9Vt2Ajmdpi/ReTge8Fr90LDMQ6rf+G9U9cj33hmpbBuV8CLsVWaHwWGAIMBiqr7Pd+cN7vAW8GMXSq5pwvYnNA2gCPAX/BlmI9SlU/zvBtO1cvvnKfc865jPmdhnPOuYx50nDOOZcxTxrOOecy5knDOedcxjxpOOecy5gnDeeccxnzpOGccy5jnjScc85l7P8BYRl2sRQgS4oAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "FE3M-k8sE5AD",
        "outputId": "b644c5c1-189c-4086-d766-e4b7695c00cc"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       SMILES  Label  \\\n",
              "0             CC(=O)Nc1ccccc1      1   \n",
              "1  CC(=O)Nc1nnc(S(N)(=O)=O)s1      1   \n",
              "\n",
              "                                       drug_encoding  \n",
              "0  ([184, 201, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...  \n",
              "1  ([184, 1000, 22, 763, 1049, 640, 0, 0, 0, 0, 0...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9cbb1121-d57d-4fc9-a015-8877d8d14208\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SMILES</th>\n",
              "      <th>Label</th>\n",
              "      <th>drug_encoding</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CC(=O)Nc1ccccc1</td>\n",
              "      <td>1</td>\n",
              "      <td>([184, 201, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CC(=O)Nc1nnc(S(N)(=O)=O)s1</td>\n",
              "      <td>1</td>\n",
              "      <td>([184, 1000, 22, 763, 1049, 640, 0, 0, 0, 0, 0...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9cbb1121-d57d-4fc9-a015-8877d8d14208')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9cbb1121-d57d-4fc9-a015-8877d8d14208 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9cbb1121-d57d-4fc9-a015-8877d8d14208');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training a Tree model\n",
        "import xgboost as xgb"
      ],
      "metadata": {
        "id": "gMjWN64OGy4L"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a variable for the train and test sets\n",
        "# label = pd.DataFrame(np.random.randint(2, size=4))\n",
        "# xgb_test = xgb.DMatrix(label, label)\n",
        "# xgb_train = xgb.DMatrix(np.array([train['drug_encoding'].tolist()]), np.array([train['Label'].tolist()]))\n",
        "xgb_test = xgb.DMatrix(test.iloc[:, 1:2], test.iloc[:, 1:2])"
      ],
      "metadata": {
        "id": "6tmx0fPoG23w"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(binary = True)"
      ],
      "metadata": {
        "id": "3BUW9sFwHho2"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv.fit(whole_df['Drug'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQCenLg0IMJe",
        "outputId": "127b60ee-ae5c-4a0e-a82a-a1c14fd497c1"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CountVectorizer(binary=True)"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_x = cv.transform(train['SMILES'])\n",
        "test_x = cv.transform(test['SMILES'])"
      ],
      "metadata": {
        "id": "-4EPuOFLIOX4"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_train = xgb.DMatrix(train_x, train.iloc[:, 1:2])\n",
        "xgb_test = xgb.DMatrix(test_x, test.iloc[:, 1:2])"
      ],
      "metadata": {
        "id": "pBJ6XFWuIga5"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param = {'eta': 0.75,\n",
        "         'max_depth': 50,\n",
        "         'objective': 'binary:logitraw'}\n",
        "xgb_model = xgb.train(param, xgb_train, num_boost_round = 30)"
      ],
      "metadata": {
        "id": "z8ldi8tiIhSf"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = xgb_model.predict(xgb_test)\n",
        "y_pred = np.where(np.array(y_pred) > 0.5, 1, 0)"
      ],
      "metadata": {
        "id": "pIm971kZLzga"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score\n"
      ],
      "metadata": {
        "id": "5Y3n_25-L3e7"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.sqrt(mean_squared_error(test['Label'], y_pred)))\n",
        "print(accuracy_score(test['Label'], y_pred))\n",
        "print(roc_auc_score(test['Label'], y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilrOerAnQQbi",
        "outputId": "2fc0433b-84ae-4abe-eb8e-d3fbe66dd752"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3474041668898256\n",
            "0.8793103448275862\n",
            "0.7036303630363037\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "njSomcQzQQ4o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}