bioactivity_prediction_app.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "9MdfbvFKXtXq"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "nerGP0fCXfgP",
    "outputId": "b639892c-8f7b-4b24-d02c-628c9f1a1460"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_url = 'https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv'\n",
    "dataset = pd.read_csv(dataset_url)\n",
    "dataset"
   ]
  },
  {
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       "      <th>4692</th>\n",
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       "    <tr>\n",
       "      <th>4694</th>\n",
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       "<p>4695 rows × 881 columns</p>\n",
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      ],
      "text/plain": [
       "      PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  PubchemFP5  \\\n",
       "0              1           1           1           0           0           0   \n",
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       "4              1           1           0           0           0           0   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "4690           1           1           1           1           0           0   \n",
       "4691           1           1           1           1           0           0   \n",
       "4692           1           1           1           1           0           0   \n",
       "4693           1           1           1           1           0           0   \n",
       "4694           1           1           1           1           0           0   \n",
       "\n",
       "      PubchemFP6  PubchemFP7  PubchemFP8  PubchemFP9  ...  PubchemFP871  \\\n",
       "0              0           0           0           1  ...             0   \n",
       "1              0           0           0           1  ...             0   \n",
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       "...          ...         ...         ...         ...  ...           ...   \n",
       "4690           0           0           0           1  ...             0   \n",
       "4691           0           0           0           1  ...             0   \n",
       "4692           0           0           0           1  ...             0   \n",
       "4693           0           0           0           1  ...             0   \n",
       "4694           0           0           0           1  ...             0   \n",
       "\n",
       "      PubchemFP872  PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  \\\n",
       "0                0             0             0             0             0   \n",
       "1                0             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "3                0             0             0             0             0   \n",
       "4                0             0             0             0             0   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "4690             0             0             0             0             0   \n",
       "4691             0             0             0             0             0   \n",
       "4692             0             0             0             0             0   \n",
       "4693             0             0             0             0             0   \n",
       "4694             0             0             0             0             0   \n",
       "\n",
       "      PubchemFP877  PubchemFP878  PubchemFP879  PubchemFP880  \n",
       "0                0             0             0             0  \n",
       "1                0             0             0             0  \n",
       "2                0             0             0             0  \n",
       "3                0             0             0             0  \n",
       "4                0             0             0             0  \n",
       "...            ...           ...           ...           ...  \n",
       "4690             0             0             0             0  \n",
       "4691             0             0             0             0  \n",
       "4692             0             0             0             0  \n",
       "4693             0             0             0             0  \n",
       "4694             0             0             0             0  \n",
       "\n",
       "[4695 rows x 881 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.drop(['pIC50'], axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JDwxgKHqYmD4",
    "outputId": "472cd19d-7dab-4f16-b03d-fab52d3fc782"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       6.124939\n",
       "1       7.000000\n",
       "2       4.301030\n",
       "3       6.522879\n",
       "4       6.096910\n",
       "          ...   \n",
       "4690    5.612610\n",
       "4691    5.595166\n",
       "4692    5.419075\n",
       "4693    5.460924\n",
       "4694    5.555955\n",
       "Name: pIC50, Length: 4695, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = dataset.iloc[:,-1]\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AQ9E0xUY_o_M"
   },
   "source": [
    "# Remove low variance features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qkgj-lsG_wOJ",
    "outputId": "d3d11b21-fdf3-4cfb-e0c8-0f2b5a1d779b"
   },
   "outputs": [
    {
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       "      <th>PubchemFP3</th>\n",
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      ],
      "text/plain": [
       "      PubchemFP3  PubchemFP12  PubchemFP13  PubchemFP15  PubchemFP16  \\\n",
       "0              0            1            0            1            1   \n",
       "1              0            1            0            1            0   \n",
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       "4              0            0            0            1            1   \n",
       "...          ...          ...          ...          ...          ...   \n",
       "4690           1            1            0            1            0   \n",
       "4691           1            1            0            1            0   \n",
       "4692           1            1            0            1            0   \n",
       "4693           1            1            0            1            0   \n",
       "4694           1            1            1            1            0   \n",
       "\n",
       "      PubchemFP18  PubchemFP19  PubchemFP20  PubchemFP37  PubchemFP143  ...  \\\n",
       "0               1            0            0            1             1  ...   \n",
       "1               1            1            1            0             1  ...   \n",
       "2               1            0            0            1             1  ...   \n",
       "3               1            0            0            1             1  ...   \n",
       "4               1            1            0            0             1  ...   \n",
       "...           ...          ...          ...          ...           ...  ...   \n",
       "4690            1            1            0            1             0  ...   \n",
       "4691            1            1            0            1             0  ...   \n",
       "4692            1            1            0            0             0  ...   \n",
       "4693            1            1            1            0             0  ...   \n",
       "4694            1            1            0            0             0  ...   \n",
       "\n",
       "      PubchemFP758  PubchemFP761  PubchemFP776  PubchemFP777  PubchemFP797  \\\n",
       "0                0             0             0             0             0   \n",
       "1                0             0             0             0             0   \n",
       "2                0             0             0             0             0   \n",
       "3                0             0             0             0             0   \n",
       "4                0             0             0             1             0   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "4690             0             0             0             1             1   \n",
       "4691             0             0             0             1             1   \n",
       "4692             0             0             0             1             1   \n",
       "4693             0             0             0             1             1   \n",
       "4694             0             0             1             1             1   \n",
       "\n",
       "      PubchemFP798  PubchemFP818  PubchemFP819  PubchemFP821  PubchemFP824  \n",
       "0                0             0             0             0             0  \n",
       "1                0             0             0             0             0  \n",
       "2                0             0             0             0             0  \n",
       "3                0             0             0             0             0  \n",
       "4                0             0             0             0             0  \n",
       "...            ...           ...           ...           ...           ...  \n",
       "4690             0             0             1             0             0  \n",
       "4691             0             0             1             0             0  \n",
       "4692             0             0             1             0             0  \n",
       "4693             0             0             1             0             0  \n",
       "4694             0             0             1             0             0  \n",
       "\n",
       "[4695 rows x 218 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "def remove_low_variance(input_data, threshold=0.1):\n",
    "    selection = VarianceThreshold(threshold)\n",
    "    selection.fit(input_data)\n",
    "    return input_data[input_data.columns[selection.get_support(indices=True)]]\n",
    "\n",
    "X = remove_low_variance(X, threshold=0.1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.to_csv('descriptor_list.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the app, use the following to get this same descriptor list\n",
    "# of 218 variables from the initial set of 881 variables\n",
    "# Xlist = list(pd.read_csv('descriptor_list.csv').columns)\n",
    "# X[Xlist]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LNohCdqQY5VZ"
   },
   "source": [
    "# Random Forest Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "EanoyG2eX9cV"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mLQJ2KLLY_9a",
    "outputId": "e0c22032-02af-40ca-c5c9-536982ec8627"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8606007951843838"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=500, random_state=42)\n",
    "model.fit(X, Y)\n",
    "r2 = model.score(X, Y)\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5f8KGWjZRSc"
   },
   "source": [
    "## Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MI3c8LB2ZCYW",
    "outputId": "1bc71664-c9f8-434f-a666-7f78a2d34da6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.97555142, 6.38304794, 4.94339087, ..., 5.802151  , 5.70372719,\n",
       "       5.62942524], shape=(4695,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = model.predict(X)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fXv7bcolZqa-"
   },
   "source": [
    "## Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6f13gYleZVKy",
    "outputId": "fd565d7f-26e4-45d1-89f7-55b37687746c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error (MSE): 0.34\n",
      "Coefficient of determination (R^2): 0.86\n"
     ]
    }
   ],
   "source": [
    "print('Mean squared error (MSE): %.2f'\n",
    "      % mean_squared_error(Y, Y_pred))\n",
    "print('Coefficient of determination (R^2): %.2f'\n",
    "      % r2_score(Y, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uWvxj1iSaL3n"
   },
   "source": [
    "# Data Visualization (Experimental vs Predicted pIC50 for Training Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "iPcFF0MjZlh8"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "QRNyIlGAaQQI",
    "outputId": "1cf12d14-4ba5-49d4-e627-902a049fba2e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Experimental pIC50')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(x=Y, y=Y_pred, c=\"#7CAE00\", alpha=0.3)\n",
    "\n",
    "# Add trendline\n",
    "# https://stackoverflow.com/questions/26447191/how-to-add-trendline-in-python-matplotlib-dot-scatter-graphs\n",
    "z = np.polyfit(Y, Y_pred, 1)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "plt.plot(Y,p(Y),\"#F8766D\")\n",
    "plt.ylabel('Predicted pIC50')\n",
    "plt.xlabel('Experimental pIC50')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YzKTmvZrbFVI"
   },
   "source": [
    "# Save Model as Pickle Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "DzjpPyVyb8XO"
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "b2K9ajBaaYUk"
   },
   "outputs": [],
   "source": [
    "pickle.dump(model, open('acetylcholinesterase_model.pkl', 'wb'))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "QSAR-web-app.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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