ML_7_1_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOJ4Gr0sAQQQxx2KaQL5Ttf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/francescopatane96/Bioactivity-prediction-with-ML/blob/main/ML_7_1_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download all public molecules in ZINC database (42271452 molecules), including around 1M compounds not in subset #6 that may not be available commercially (last update: 2014-11-24)"
      ],
      "metadata": {
        "id": "_fBqkCF_jz2O"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ThDpoxPZjD0P"
      },
      "outputs": [],
      "source": [
        "!wget http://zinc12.docking.org/db/bysubset/10/10_p0.smi.gz    #download all public molecules in ZINC, including around 1M compounds not in subset #6 that may not be available commercially"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from rdkit.Chem import MACCSkeys\n",
        "from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect"
      ],
      "metadata": {
        "id": "edGhl81c4owv"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://zinc12.docking.org/db/bysubset/17/17_p0.smi.gz   #or download a fraction of it"
      ],
      "metadata": {
        "id": "RUZpxt8yG0as",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f344713b-4aa5-4404-9355-4d0ccb6b65d4"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-04 15:04:46--  http://zinc12.docking.org/db/bysubset/17/17_p0.smi.gz\n",
            "Resolving zinc12.docking.org (zinc12.docking.org)... 169.230.26.169\n",
            "Connecting to zinc12.docking.org (zinc12.docking.org)|169.230.26.169|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2407726 (2.3M) [application/x-gzip]\n",
            "Saving to: ‘17_p0.smi.gz’\n",
            "\n",
            "17_p0.smi.gz        100%[===================>]   2.30M  4.46MB/s    in 0.5s    \n",
            "\n",
            "2022-09-04 15:04:47 (4.46 MB/s) - ‘17_p0.smi.gz’ saved [2407726/2407726]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gunzip 10_p0.smi.gz  #obtain .smi file\n"
      ],
      "metadata": {
        "id": "9p7eKHnAjwr1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc0bcb90-79f5-4020-900c-ef699b664ecd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gzip: 10_p0.smi.gz: No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gunzip 17_p0.smi.gz #''"
      ],
      "metadata": {
        "id": "LzSFGUgNG6OE"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
        "id": "nb6rggOKFM6u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1979e220-b870-424b-a4c1-0fe825b4104e"
      },
      "execution_count": 131,
      "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: rdkit in /usr/local/lib/python3.7/dist-packages (2022.3.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from rdkit) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from rdkit) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "Kyrk_DPOnpYW"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "objects = []                         #read pickle object file (pkl). It is our model\n",
        "with (open(\"IDH_model.pkl\", \"rb\")) as openfile:\n",
        "    while True:\n",
        "        try:\n",
        "            objects.append(pickle.load(openfile))\n",
        "        except EOFError:\n",
        "            break"
      ],
      "metadata": {
        "id": "emrTgGwZkuVX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4319e26-980b-466d-c929-1f6d7a3311fd"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator RandomForestClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  UserWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "\n",
        "from rdkit import rdBase\n",
        "\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit.Chem import Draw\n",
        "from IPython.display import display\n",
        "\n",
        "from rdkit.Chem import PandasTools\n",
        "import pandas as pd\n",
        "PandasTools.pd = pd\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "np.random.seed(69)\n",
        "\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#from bokeh import mpl\n",
        "#from bokeh.plotting import output_notebook, show\n",
        "\n",
        "print(\"RDKit Version: %s\" % rdBase.rdkitVersion)"
      ],
      "metadata": {
        "id": "fZLbFNUzFJQd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01c8ca16-3ef4-4bc2-d507-285c469785a8"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RDKit Version: 2022.03.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smiles_file = os.path.join(os.path.abspath(os.curdir) ,\"17_p0.smi\")\n",
        "\n",
        "mols_dict = dict()\n",
        "bad_mols = 0\n",
        "bad_smiles = list()\n",
        "dup_mols = 0\n",
        "with open(smiles_file, \"r\") as fd:\n",
        "    header = fd.readline()\n",
        "    for line in fd.readlines():\n",
        "        line = line.split()\n",
        "        #print line\n",
        "        # Add molecule information in a dictionary...\n",
        "        # SMILES: (Mol_Id, Mol_Obj)\n",
        "        smiles = line[0]\n",
        "        mol_id = line[1]\n",
        "        mol_obj = Chem.MolFromSmiles(smiles)\n",
        "        # Skip bad molecules...\n",
        "        if mol_obj is None:\n",
        "            bad_mols += 1\n",
        "            bad_smiles.append(smiles)\n",
        "            continue\n",
        "        if smiles in mols_dict:\n",
        "            dup_mols += 1\n",
        "            continue\n",
        "        mols_dict[smiles] = (mol_id, mol_obj)\n",
        "    # end for\n",
        "# end with\n",
        "\n",
        "print(\"Bad Mols: %s\" % bad_mols)\n",
        "print(\"Bad SMILES: %s\" % bad_smiles)\n",
        "print(\"Duplicate Mols: %s\" % dup_mols)\n",
        "print(len(mols_dict))"
      ],
      "metadata": {
        "id": "soby3joQEOAI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "210861c0-8f09-4277-e148-708e574cc1ce"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bad Mols: 5\n",
            "Bad SMILES: ['CC1=CC=N(=C(C#N)C#N)C=C1', 'c1nc(nn1CCC#N)N=N=N', 'c1ccc(cc1)C=N2=CC=CC=C2', 'C1=CC=N(=C(C#N)C#N)C=C1', 'c1cc(cc(c1)Cl(=O)(=O)=O)N']\n",
            "Duplicate Mols: 0\n",
            "325944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random"
      ],
      "metadata": {
        "id": "jfXBLOBMWpbW"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "selected_mols = random.choices(list(mols_dict.keys()), k=100)           #select 100 molecules of the dictionary, convert to list to output\n",
        "selected_mols"
      ],
      "metadata": {
        "id": "FtklT1URWnvf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0a4ef52-7daa-4db7-ec19-eec2e17e1d51"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O',\n",
              " 'C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2',\n",
              " 'Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C',\n",
              " 'Cc1ncc(s1)C(=O)[O-]',\n",
              " 'CCN(CC)c1ccc(nc1)Cl',\n",
              " 'CC1(CCC(=O)C[C@H]1C#N)C',\n",
              " 'C1CC2(CCC[NH+]2C1)C(=O)[O-]',\n",
              " 'C[C@H]1COc2cscc2O1',\n",
              " 'c1ccc(cc1)[C@@H](CO)[NH2+]C2CC2',\n",
              " 'CC1=[NH+][C@@H](CCN1C)C(=O)N',\n",
              " 'Cc1cc(c(o1)C)[C@H](CC2CC2)[NH3+]',\n",
              " 'c1c(nc([nH]c1=O)N)N',\n",
              " 'c1cncc2c1C[C@@H](S2)C#N',\n",
              " 'CC[C@@H](c1ccncn1)[C@@H](C)[NH3+]',\n",
              " 'Cn1cc(cn1)Cn2cc(cn2)N',\n",
              " 'Cc1c(c(cc([nH+]1)C(=O)OC)N)N',\n",
              " 'c1c(nc(o1)C2CCC2)CC(=O)[O-]',\n",
              " 'C[C@H](Cc1nc(on1)COC)[NH3+]',\n",
              " 'CCc1nccc(n1)C[C@@H](CC)[NH3+]',\n",
              " 'c1ccc(cc1)C2C(=O)CCC2=O',\n",
              " 'c1c[nH]c2c1cncc2C#N',\n",
              " 'CCC1CCC(CC1)[C@@H](C(=O)[O-])[NH3+]',\n",
              " 'C[C@H]([C@@H]1CCCO1)NC(=O)C2CC2',\n",
              " 'c1cc(cc(c1)OCCO)CO',\n",
              " 'c1c2c(n[nH]1)CC[C@@H]2[NH3+]',\n",
              " 'C[NH2+][C@@H]([C@H]1CCOC1)C(=O)N',\n",
              " 'C[C@H](CC[NH2+]C(C)(C)C)[NH3+]',\n",
              " 'C[C@H]1C(=[NH2+])N(C(=[NH+]1)CC(=O)OC)C',\n",
              " 'Cc1c(c[nH]n1)[C@@H]2C[C@@H](CC[NH2+]2)O',\n",
              " 'Cc1ccnc2c1[C@@H](CNC2)O',\n",
              " 'c1cc(=O)ccc2c1cco2',\n",
              " 'C1C[C@@H]([C@@H](C1)NCCO)C#N',\n",
              " 'CC(C)(c1cnoc1OC)[NH3+]',\n",
              " 'CC(C)(c1cc2c(o1)CCCC2)[NH3+]',\n",
              " 'Cn1cc([nH+]c1)CCC[NH3+]',\n",
              " 'COCCCCC[NH2+]C1CC1',\n",
              " 'C/C=C/CO[C@H]1CCC[C@@H]1C(=O)[O-]',\n",
              " 'C[C@@H]1C[C@H](CC[NH2+]1)[NH2+]C(C)(C)C',\n",
              " 'CN(C)c1ccc(cc1)F',\n",
              " 'C1CC(=[NH+]C1)N',\n",
              " 'CC[C@H](C(=O)[O-])[NH+](C)CCC(=O)[O-]',\n",
              " 'CCCC(=O)CC(C)C',\n",
              " 'CC[NH2+][C@@H]1CCC[C@@H]1C[NH+](C)C',\n",
              " 'Cc1c(c(nn1C)c2cc[nH]c2)N',\n",
              " 'c1c[nH]c(=O)c(c1[O-])Br',\n",
              " 'COc1ccc(c2c1CNCC2)F',\n",
              " 'c1ccc2c(c1)CC3([C@@H]2[NH3+])CC3',\n",
              " 'c1cc([nH]c(=O)c1C#N)C2CCCC2',\n",
              " 'CC[NH+](CC)C[C@@H]1CCC[C@@H]1[NH3+]',\n",
              " 'CCC[NH+](CCC)C[C@@H](C)C#N',\n",
              " 'c1ccc2c(c1)CNC(=[NH+]2)C3CC3',\n",
              " 'CC[C@@H](c1[nH]nnn1)[NH3+]',\n",
              " 'c1cc([nH+]cc1[O-])[C@H](CC(=O)[O-])O',\n",
              " 'CC1([C@@H]2[C@H]1[C@@H]([NH2+]C2)C(=O)[O-])C',\n",
              " 'CC(C)C[NH2+]C1CCNCC1',\n",
              " 'Cc1ccc(o1)C2(CC2)[NH3+]',\n",
              " 'C1CC[NH+](C1)[C@H]2CCOC[C@H]2C[NH3+]',\n",
              " 'Cc1ccc(cc1)[C@H](CC[NH2+]C)O',\n",
              " 'CC[C@@](C)(/C=C/C=C(C)C)O',\n",
              " 'c1ccc(cc1)c2nnnn2CC#N',\n",
              " 'CC(C)(C)[C@@H]1CN2CCC[C@@H]2C[NH2+]1',\n",
              " 'Cc1c(ccc2c1[C@@H](NCC2)C)F',\n",
              " 'CC(C)(C#N)[C@@H](c1ccccc1)[NH3+]',\n",
              " 'C[C@H]1C(=O)Nc2n1cc(c2)C#N',\n",
              " 'c1cc(ccc1NCCO)Cl',\n",
              " 'C[C@H](C[C@H]1C[NH2+]CCN1)[NH3+]',\n",
              " 'Cc1cc(no1)[C@H]2CCCC[NH2+]2',\n",
              " 'C1Cc2c(sc(n2)CC(=O)[O-])C1',\n",
              " 'c1c(nc(cn1)N2CCOCC2)N',\n",
              " 'Cc1ccn(n1)c2cc(cnc2)N',\n",
              " 'C[C@@H]1CO[C@H](N1)c2cccc(c2)F',\n",
              " 'C[C@H]1CCC[NH+](C1)[C@@H](C)CCCO',\n",
              " 'CC(C)[C@H](Cn1ccnc1)[NH3+]',\n",
              " 'CCc1cc(oc1)[C@H](CO)[NH3+]',\n",
              " 'c1cnnc(c1C#N)N2CCCCC2',\n",
              " 'Cc1ccc(cc1)[C@H]([C@H]2C[C@@H]2C)[NH3+]',\n",
              " 'c1cc(cc(c1)O)[C@H](CCO)[NH3+]',\n",
              " 'c1cc(cc(c1)[C@H]2C[C@@H]2C(=O)[O-])C#N',\n",
              " 'C[C@@H](C[NH2+]C)Sc1ccccn1',\n",
              " 'CC1([C@@H]([C@H](O1)C#N)C#N)C',\n",
              " 'CCC[NH2+][C@@H]1CC1(C)C',\n",
              " 'c1csc(n1)[C@@H]([C@@H]2CCOC2)[NH3+]',\n",
              " 'c1ccc2c(c1)NCc3cc[nH]c3O2',\n",
              " 'Cc1cccc2c1CC3(C2=O)CC3',\n",
              " 'C[C@H](C=C)C1(CCCCC1)O',\n",
              " 'Cn1c(cc(n1)N)OC(F)F',\n",
              " 'Cc1cnn(c1N)CC2CC2',\n",
              " 'c1cc(oc1)C[C@@H]2C(=O)NCC[NH2+]2',\n",
              " 'C[NH+]1CCC[C@H]1c2nnc(o2)C[NH3+]',\n",
              " 'c1c(n2cnnc2s1)CC[NH3+]',\n",
              " 'CC(C)Oc1ccc(c(c1)OC)O',\n",
              " 'C1CC[C@@H]([C@@H](CC1)O)[NH3+]',\n",
              " 'C1CCC(CC1)[C@H]2C[NH2+]CCCO2',\n",
              " 'CC(C)c1ccncc1Cl',\n",
              " 'CCC[NH+](CC)/C(=C/[O-])/C',\n",
              " 'Cc1cnnc(n1)[C@@H]([C@H](C)[NH3+])O',\n",
              " 'c1cnn(c1)c2ccnc(c2)C[NH3+]',\n",
              " 'CC(C)C[C@H](CO)[NH3+]',\n",
              " 'c1cc(cc(c1)Cl)C(C#N)(F)F',\n",
              " 'CC[C@H](c1cc(sc1F)C)[NH3+]']"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def listToDict(lst):\n",
        "    op = dict.fromkeys(lst, 5)\n",
        "    return op"
      ],
      "metadata": {
        "id": "3LkBVorCYlkF"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "selected_mols = listToDict(selected_mols)             #print the casual dictionary of 100 molecules\n",
        "selected_mols\n"
      ],
      "metadata": {
        "id": "2hmRtvFRZoPk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "094e3f82-45bc-4003-e526-4b38b48c354b"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O': 5,\n",
              " 'C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2': 5,\n",
              " 'Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C': 5,\n",
              " 'Cc1ncc(s1)C(=O)[O-]': 5,\n",
              " 'CCN(CC)c1ccc(nc1)Cl': 5,\n",
              " 'CC1(CCC(=O)C[C@H]1C#N)C': 5,\n",
              " 'C1CC2(CCC[NH+]2C1)C(=O)[O-]': 5,\n",
              " 'C[C@H]1COc2cscc2O1': 5,\n",
              " 'c1ccc(cc1)[C@@H](CO)[NH2+]C2CC2': 5,\n",
              " 'CC1=[NH+][C@@H](CCN1C)C(=O)N': 5,\n",
              " 'Cc1cc(c(o1)C)[C@H](CC2CC2)[NH3+]': 5,\n",
              " 'c1c(nc([nH]c1=O)N)N': 5,\n",
              " 'c1cncc2c1C[C@@H](S2)C#N': 5,\n",
              " 'CC[C@@H](c1ccncn1)[C@@H](C)[NH3+]': 5,\n",
              " 'Cn1cc(cn1)Cn2cc(cn2)N': 5,\n",
              " 'Cc1c(c(cc([nH+]1)C(=O)OC)N)N': 5,\n",
              " 'c1c(nc(o1)C2CCC2)CC(=O)[O-]': 5,\n",
              " 'C[C@H](Cc1nc(on1)COC)[NH3+]': 5,\n",
              " 'CCc1nccc(n1)C[C@@H](CC)[NH3+]': 5,\n",
              " 'c1ccc(cc1)C2C(=O)CCC2=O': 5,\n",
              " 'c1c[nH]c2c1cncc2C#N': 5,\n",
              " 'CCC1CCC(CC1)[C@@H](C(=O)[O-])[NH3+]': 5,\n",
              " 'C[C@H]([C@@H]1CCCO1)NC(=O)C2CC2': 5,\n",
              " 'c1cc(cc(c1)OCCO)CO': 5,\n",
              " 'c1c2c(n[nH]1)CC[C@@H]2[NH3+]': 5,\n",
              " 'C[NH2+][C@@H]([C@H]1CCOC1)C(=O)N': 5,\n",
              " 'C[C@H](CC[NH2+]C(C)(C)C)[NH3+]': 5,\n",
              " 'C[C@H]1C(=[NH2+])N(C(=[NH+]1)CC(=O)OC)C': 5,\n",
              " 'Cc1c(c[nH]n1)[C@@H]2C[C@@H](CC[NH2+]2)O': 5,\n",
              " 'Cc1ccnc2c1[C@@H](CNC2)O': 5,\n",
              " 'c1cc(=O)ccc2c1cco2': 5,\n",
              " 'C1C[C@@H]([C@@H](C1)NCCO)C#N': 5,\n",
              " 'CC(C)(c1cnoc1OC)[NH3+]': 5,\n",
              " 'CC(C)(c1cc2c(o1)CCCC2)[NH3+]': 5,\n",
              " 'Cn1cc([nH+]c1)CCC[NH3+]': 5,\n",
              " 'COCCCCC[NH2+]C1CC1': 5,\n",
              " 'C/C=C/CO[C@H]1CCC[C@@H]1C(=O)[O-]': 5,\n",
              " 'C[C@@H]1C[C@H](CC[NH2+]1)[NH2+]C(C)(C)C': 5,\n",
              " 'CN(C)c1ccc(cc1)F': 5,\n",
              " 'C1CC(=[NH+]C1)N': 5,\n",
              " 'CC[C@H](C(=O)[O-])[NH+](C)CCC(=O)[O-]': 5,\n",
              " 'CCCC(=O)CC(C)C': 5,\n",
              " 'CC[NH2+][C@@H]1CCC[C@@H]1C[NH+](C)C': 5,\n",
              " 'Cc1c(c(nn1C)c2cc[nH]c2)N': 5,\n",
              " 'c1c[nH]c(=O)c(c1[O-])Br': 5,\n",
              " 'COc1ccc(c2c1CNCC2)F': 5,\n",
              " 'c1ccc2c(c1)CC3([C@@H]2[NH3+])CC3': 5,\n",
              " 'c1cc([nH]c(=O)c1C#N)C2CCCC2': 5,\n",
              " 'CC[NH+](CC)C[C@@H]1CCC[C@@H]1[NH3+]': 5,\n",
              " 'CCC[NH+](CCC)C[C@@H](C)C#N': 5,\n",
              " 'c1ccc2c(c1)CNC(=[NH+]2)C3CC3': 5,\n",
              " 'CC[C@@H](c1[nH]nnn1)[NH3+]': 5,\n",
              " 'c1cc([nH+]cc1[O-])[C@H](CC(=O)[O-])O': 5,\n",
              " 'CC1([C@@H]2[C@H]1[C@@H]([NH2+]C2)C(=O)[O-])C': 5,\n",
              " 'CC(C)C[NH2+]C1CCNCC1': 5,\n",
              " 'Cc1ccc(o1)C2(CC2)[NH3+]': 5,\n",
              " 'C1CC[NH+](C1)[C@H]2CCOC[C@H]2C[NH3+]': 5,\n",
              " 'Cc1ccc(cc1)[C@H](CC[NH2+]C)O': 5,\n",
              " 'CC[C@@](C)(/C=C/C=C(C)C)O': 5,\n",
              " 'c1ccc(cc1)c2nnnn2CC#N': 5,\n",
              " 'CC(C)(C)[C@@H]1CN2CCC[C@@H]2C[NH2+]1': 5,\n",
              " 'Cc1c(ccc2c1[C@@H](NCC2)C)F': 5,\n",
              " 'CC(C)(C#N)[C@@H](c1ccccc1)[NH3+]': 5,\n",
              " 'C[C@H]1C(=O)Nc2n1cc(c2)C#N': 5,\n",
              " 'c1cc(ccc1NCCO)Cl': 5,\n",
              " 'C[C@H](C[C@H]1C[NH2+]CCN1)[NH3+]': 5,\n",
              " 'Cc1cc(no1)[C@H]2CCCC[NH2+]2': 5,\n",
              " 'C1Cc2c(sc(n2)CC(=O)[O-])C1': 5,\n",
              " 'c1c(nc(cn1)N2CCOCC2)N': 5,\n",
              " 'Cc1ccn(n1)c2cc(cnc2)N': 5,\n",
              " 'C[C@@H]1CO[C@H](N1)c2cccc(c2)F': 5,\n",
              " 'C[C@H]1CCC[NH+](C1)[C@@H](C)CCCO': 5,\n",
              " 'CC(C)[C@H](Cn1ccnc1)[NH3+]': 5,\n",
              " 'CCc1cc(oc1)[C@H](CO)[NH3+]': 5,\n",
              " 'c1cnnc(c1C#N)N2CCCCC2': 5,\n",
              " 'Cc1ccc(cc1)[C@H]([C@H]2C[C@@H]2C)[NH3+]': 5,\n",
              " 'c1cc(cc(c1)O)[C@H](CCO)[NH3+]': 5,\n",
              " 'c1cc(cc(c1)[C@H]2C[C@@H]2C(=O)[O-])C#N': 5,\n",
              " 'C[C@@H](C[NH2+]C)Sc1ccccn1': 5,\n",
              " 'CC1([C@@H]([C@H](O1)C#N)C#N)C': 5,\n",
              " 'CCC[NH2+][C@@H]1CC1(C)C': 5,\n",
              " 'c1csc(n1)[C@@H]([C@@H]2CCOC2)[NH3+]': 5,\n",
              " 'c1ccc2c(c1)NCc3cc[nH]c3O2': 5,\n",
              " 'Cc1cccc2c1CC3(C2=O)CC3': 5,\n",
              " 'C[C@H](C=C)C1(CCCCC1)O': 5,\n",
              " 'Cn1c(cc(n1)N)OC(F)F': 5,\n",
              " 'Cc1cnn(c1N)CC2CC2': 5,\n",
              " 'c1cc(oc1)C[C@@H]2C(=O)NCC[NH2+]2': 5,\n",
              " 'C[NH+]1CCC[C@H]1c2nnc(o2)C[NH3+]': 5,\n",
              " 'c1c(n2cnnc2s1)CC[NH3+]': 5,\n",
              " 'CC(C)Oc1ccc(c(c1)OC)O': 5,\n",
              " 'C1CC[C@@H]([C@@H](CC1)O)[NH3+]': 5,\n",
              " 'C1CCC(CC1)[C@H]2C[NH2+]CCCO2': 5,\n",
              " 'CC(C)c1ccncc1Cl': 5,\n",
              " 'CCC[NH+](CC)/C(=C/[O-])/C': 5,\n",
              " 'Cc1cnnc(n1)[C@@H]([C@H](C)[NH3+])O': 5,\n",
              " 'c1cnn(c1)c2ccnc(c2)C[NH3+]': 5,\n",
              " 'CC(C)C[C@H](CO)[NH3+]': 5,\n",
              " 'c1cc(cc(c1)Cl)C(C#N)(F)F': 5,\n",
              " 'CC[C@H](c1cc(sc1F)C)[NH3+]': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df = pd.Series(selected_mols).rename_axis('smiles').reset_index(name='keys')\n",
        "df"
      ],
      "metadata": {
        "id": "gsRyWjX7g8xW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "d864d0a3-80a9-4cc3-d5a6-66f29cdcf757"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                smiles  keys\n",
              "0   C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O     5\n",
              "1    C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2     5\n",
              "2     Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C     5\n",
              "3                  Cc1ncc(s1)C(=O)[O-]     5\n",
              "4                  CCN(CC)c1ccc(nc1)Cl     5\n",
              "..                                 ...   ...\n",
              "95  Cc1cnnc(n1)[C@@H]([C@H](C)[NH3+])O     5\n",
              "96          c1cnn(c1)c2ccnc(c2)C[NH3+]     5\n",
              "97               CC(C)C[C@H](CO)[NH3+]     5\n",
              "98            c1cc(cc(c1)Cl)C(C#N)(F)F     5\n",
              "99          CC[C@H](c1cc(sc1F)C)[NH3+]     5\n",
              "\n",
              "[100 rows x 2 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>smiles</th>\n",
              "      <th>keys</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cc1ncc(s1)C(=O)[O-]</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CCN(CC)c1ccc(nc1)Cl</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Cc1cnnc(n1)[C@@H]([C@H](C)[NH3+])O</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>c1cnn(c1)c2ccnc(c2)C[NH3+]</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>CC(C)C[C@H](CO)[NH3+]</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>c1cc(cc(c1)Cl)C(C#N)(F)F</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>CC[C@H](c1cc(sc1F)C)[NH3+]</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 columns</p>\n",
              "</div>\n",
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    {
      "cell_type": "code",
      "source": [
        "df2 = df.drop(columns=['keys'])\n",
        "df2\n"
      ],
      "metadata": {
        "id": "99C72FkxhQQK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "20a6d875-43d8-4065-8343-8a7f4126bd29"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                smiles\n",
              "0   C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O\n",
              "1    C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2\n",
              "2     Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C\n",
              "3                  Cc1ncc(s1)C(=O)[O-]\n",
              "4                  CCN(CC)c1ccc(nc1)Cl\n",
              "..                                 ...\n",
              "95  Cc1cnnc(n1)[C@@H]([C@H](C)[NH3+])O\n",
              "96          c1cnn(c1)c2ccnc(c2)C[NH3+]\n",
              "97               CC(C)C[C@H](CO)[NH3+]\n",
              "98            c1cc(cc(c1)Cl)C(C#N)(F)F\n",
              "99          CC[C@H](c1cc(sc1F)C)[NH3+]\n",
              "\n",
              "[100 rows x 1 columns]"
            ],
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              "      <th></th>\n",
              "      <th>smiles</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cc1ncc(s1)C(=O)[O-]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CCN(CC)c1ccc(nc1)Cl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Cc1cnnc(n1)[C@@H]([C@H](C)[NH3+])O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>c1cnn(c1)c2ccnc(c2)C[NH3+]</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>CC(C)C[C@H](CO)[NH3+]</td>\n",
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              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>c1cc(cc(c1)Cl)C(C#N)(F)F</td>\n",
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              "    <tr>\n",
              "      <th>99</th>\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-26c6a0fe-bdcf-4c15-a324-aad38de82062 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-26c6a0fe-bdcf-4c15-a324-aad38de82062');\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": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def smiles_to_fp(smiles, method=\"maccs\", n_bits=2048):\n",
        "    \"\"\"\n",
        "    Encode a molecule from a SMILES string into a fingerprint.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    smiles : str\n",
        "        The SMILES string defining the molecule.\n",
        "\n",
        "    method : str\n",
        "        The type of fingerprint to use. Default is MACCS keys.\n",
        "\n",
        "    n_bits : int\n",
        "        The length of the fingerprint.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    array\n",
        "        The fingerprint array.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    # convert smiles to RDKit mol object\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "\n",
        "    if method == \"maccs\":\n",
        "        return np.array(MACCSkeys.GenMACCSKeys(mol))\n",
        "    if method == \"morgan2\":\n",
        "        return np.array(GetMorganFingerprintAsBitVect(mol, 2, nBits=n_bits))\n",
        "    if method == \"morgan3\":\n",
        "        return np.array(GetMorganFingerprintAsBitVect(mol, 3, nBits=n_bits))\n",
        "    else:\n",
        "        # NBVAL_CHECK_OUTPUT\n",
        "        print(f\"Warning: Wrong method specified: {method}. Default will be used instead.\")\n",
        "        return np.array(MACCSkeys.GenMACCSKeys(mol))"
      ],
      "metadata": {
        "id": "9HAq9q6U4EkL"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add column for fingerprint\n",
        "df[\"fp\"] = df[\"smiles\"].apply(smiles_to_fp)\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "5e3sJhVR4GMT",
        "outputId": "ca4c574a-8cae-47fc-8200-c41e4ddc7109"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               smiles  keys  \\\n",
              "0  C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O     5   \n",
              "1   C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2     5   \n",
              "2    Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C     5   \n",
              "\n",
              "                                                  fp  \n",
              "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "
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              "      <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>keys</th>\n",
              "      <th>fp</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>C[C@H]([C@@](C)(C[NH3+])c1ccccc1)O</td>\n",
              "      <td>5</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C[C@H]1CCC[NH+]1[C@@H]2CC[NH2+]C2</td>\n",
              "      <td>5</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cc1cc2c([nH]1)C[NH2+][C@@H](C2)C</td>\n",
              "      <td>5</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.drop(columns=['keys', 'smiles'])\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "BOUKVHAN5XtN",
        "outputId": "f74dfbbe-12c1-4a56-8223-2c19a2bf5305"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   fp\n",
              "0   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "1   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "2   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "3   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "4   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "..                                                ...\n",
              "95  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "96  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "97  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "98  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "99  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
              "\n",
              "[100 rows x 1 columns]"
            ],
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              "\n",
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              "        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>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 1 columns</p>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-43ab4b61-db7b-4af8-9637-bd8b09dd5ccf');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[0:1]"
      ],
      "metadata": {
        "colab": {
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          "height": 81
        },
        "id": "RnhhXBybAGrk",
        "outputId": "6d4cd1be-a230-4aa0-f968-aa4f96eb73ad"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  fp\n",
              "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..."
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df2.to_csv('drugs.csv')\n",
        "\n"
      ],
      "metadata": {
        "id": "C2Xp4yYw6a0b"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.read_csv('drugs.csv')\n",
        "x"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "OpkvaZHcFVVG",
        "outputId": "8f20d825-6a9b-4c10-b5b4-def3bd90ecc7"
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      "execution_count": 210,
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              "    Unnamed: 0                                                 fp\n",
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              "      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-2af19708-1c67-4aa5-a6cf-81a01d3571a7 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-2af19708-1c67-4aa5-a6cf-81a01d3571a7');\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": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x2 = x1.replace(to_replace='\\]', value=\"\", regex=True)\n",
        "x2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "21ziCkhBKis1",
        "outputId": "5a4933af-11cd-4500-a19a-b2e1efc39c7f"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0                                                 fp\n",
              "0            0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n",
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              "..         ...                                                ...\n",
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              "\n",
              "[100 rows x 2 columns]"
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>fp</th>\n",
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              "      <td>0</td>\n",
              "      <td>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
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              "      <td>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
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              "      <td>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
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              "      <th>3</th>\n",
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              "      <td>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
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              "      <th>97</th>\n",
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              "</table>\n",
              "<p>100 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-542aebe7-29bc-46c4-8b49-c735582bb597')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </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-542aebe7-29bc-46c4-8b49-c735582bb597 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-542aebe7-29bc-46c4-8b49-c735582bb597');\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": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x2 = x1.replace(to_replace='\\]', value=\"\", regex=True)\n",
        "x2"
      ],
      "metadata": {
        "id": "fK8ePjVvLiet"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c = x2.to_csv('drugs.csv')"
      ],
      "metadata": {
        "id": "oHMBL80iLFkP"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x3 = x2.to_numpy()\n",
        "x3\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rCAbLiVqORk2",
        "outputId": "f671052a-38af-41c9-c803-923025a5994e"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        '0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\\n 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0\\n 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0\\n 0 1 1 1 0 0 0 1 1 0 1 0 1 1 1 0 0 1 0']],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('IDH_model.pkl', 'rb') as f:\n",
        "    loaded_classifier = pickle.load(f)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQnJqQu-DcmX",
        "outputId": "11a36871-a7e7-4883-c50d-e5c557c9a4ab"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator RandomForestClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  UserWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "objects = pd.read_pickle(\"IDH_model.pkl\")\n",
        "objects"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hk5biKTXP_F1",
        "outputId": "57725ec7-d93e-407f-8464-015b281ff245"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=200)"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.read_csv('foglio.csv')\n",
        "\n",
        "entry_pred = loaded_classifier.predict([x3])\n",
        "print(\"pIC50 prediction\", entry_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "NjNfyT2LCqCO",
        "outputId": "38d8cd07-2d5f-4fd0-8301-981c79b67d25"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-229-fa30b629a32d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'foglio.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mentry_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloaded_classifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"pIC50 prediction\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentry_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    806\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m         \"\"\"\n\u001b[0;32m--> 808\u001b[0;31m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    809\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    849\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 850\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    577\u001b[0m         Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n\u001b[1;32m    578\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    580\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No support for np.int64 index based sparse matrices\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    564\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    744\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 746\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    747\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m                 raise ValueError(\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\\n 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0\\n 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0\\n 0 1 0 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 0'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = loaded_classifier.predict(df3[0:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "4X48KcC23jKD",
        "outputId": "2d465c12-2708-474c-c7a6-e7187f3c699b"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:444: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
            "  f\"X has feature names, but {self.__class__.__name__} was fitted without\"\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-160-e7014082b1db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloaded_classifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    806\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m         \"\"\"\n\u001b[0;32m--> 808\u001b[0;31m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    809\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    849\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 850\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    577\u001b[0m         Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n\u001b[1;32m    578\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    580\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No support for np.int64 index based sparse matrices\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    564\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    744\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 746\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    747\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m                 raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   1991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1992\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mNpDtype\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1993\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1994\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1995\u001b[0m     def __array_wrap__(\n",
            "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(input_data):\n",
        "    # Reads in saved regression model\n",
        "    load_model = pickle.load(open('IDH_model.pkl', 'rb'))\n",
        "    # Apply model to make predictions\n",
        "    prediction = load_model.predict(input_data)\n",
        "    st.header('**Prediction output**')\n",
        "    prediction_output = pd.Series(prediction, name='pIC50')\n",
        "    molecule_name = pd.Series(load_data[1], name='molecule_name')\n",
        "    df = pd.concat([molecule_name, prediction_output], axis=1)\n",
        "    st.write(df)\n",
        "    st.markdown(filedownload(df), unsafe_allow_html=True)"
      ],
      "metadata": {
        "id": "bMnUHR0_8YFM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "load_data = pd.read_table('file.smi', sep=' ', header=None)\n",
        "load_data.to_csv('molecule.smi', sep = '\\t', header = False, index = False)"
      ],
      "metadata": {
        "id": "QjHhRU6O8uBb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.to_csv('drugs.csv')"
      ],
      "metadata": {
        "id": "Dsohp9Q7_GF9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Xlist = list(pd.read_csv('drugs.csv').columns)\n",
        "Xlist\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2Nos9iL96hs",
        "outputId": "e1a12cf4-7e41-4367-f996-8f28b07e8a29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Unnamed: 0', 'fp']"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "build_model(Xlist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "yLIPwEUi9xDS",
        "outputId": "b3a27fc7-f226-4a9f-adc2-6169a32e75e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:338: UserWarning: Trying to unpickle estimator RandomForestClassifier from version 0.23.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-117-f4beb6921118>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-89-a7a997ba667f>\u001b[0m in \u001b[0;36mbuild_model\u001b[0;34m(input_data)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mload_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'IDH_model.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m# Apply model to make predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'**Prediction output**'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mprediction_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pIC50'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    806\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m         \"\"\"\n\u001b[0;32m--> 808\u001b[0;31m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    809\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    849\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 850\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    577\u001b[0m         Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n\u001b[1;32m    578\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    580\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No support for np.int64 index based sparse matrices\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    564\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    744\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    745\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 746\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    747\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m                 raise ValueError(\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Unnamed: 0'"
          ]
        }
      ]
    }
  ]
}