bioactivity_prediction_app.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQHZHevuXdEy"
   },
   "source": [
    "# **QSAR Model Building of Acetylcholinesterase Inhibitors**\n",
    "\n",
    "Chanin Nantasenamat\n",
    "\n",
    "*Data Professor YouTube channel, http://youtube.com/dataprofessor*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g1qtHa0zXfWM"
   },
   "source": [
    "# Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "Error",
     "evalue": "/usr/local/opt/python@3.11/bin/python3.11: No module named ipykernel\n",
     "output_type": "error",
     "traceback": [
      "Error: /usr/local/opt/python@3.11/bin/python3.11: No module named ipykernel",
      "at ChildProcess.<anonymous> (/Users/desmondgilmour/.vscode/extensions/ms-python.python-2020.2.64397/out/client/extension.js:1:455844)",
      "at Object.onceWrapper (events.js:288:20)",
      "at ChildProcess.emit (events.js:200:13)",
      "at maybeClose (internal/child_process.js:1021:16)",
      "at Process.ChildProcess._handle.onexit (internal/child_process.js:283:5)"
     ]
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "id": "nerGP0fCXfgP",
    "outputId": "b639892c-8f7b-4b24-d02c-628c9f1a1460"
   },
   "outputs": [
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dataset_url = '/PADI4_bioactivity_data__pIC50_pubchem_fp.csv'\n",
    "dataset = pd.read_csv(dataset_url)\n",
    "dataset"
   ]
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       "</table>\n",
       "<p>4695 rows × 881 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  PubchemFP5  \\\n",
       "0              1           1           1           0           0           0   \n",
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       "3              1           1           0           0           0           0   \n",
       "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",
       "2              0           0           0           1  ...             0   \n",
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       "...          ...         ...         ...         ...  ...           ...   \n",
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       "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",
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       "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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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4695 rows × 218 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PubchemFP3  PubchemFP12  PubchemFP13  PubchemFP15  PubchemFP16  \\\n",
       "0              0            1            0            1            1   \n",
       "1              0            1            0            1            0   \n",
       "2              0            1            0            1            1   \n",
       "3              0            1            0            1            1   \n",
       "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": 72,
   "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": 73,
   "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": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mLQJ2KLLY_9a",
    "outputId": "e0c22032-02af-40ca-c5c9-536982ec8627"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8605481717376796"
      ]
     },
     "execution_count": 74,
     "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": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MI3c8LB2ZCYW",
    "outputId": "1bc71664-c9f8-434f-a666-7f78a2d34da6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.96713845, 6.39396726, 4.93925173, ..., 5.802151  , 5.69163686,\n",
       "       5.62731398])"
      ]
     },
     "execution_count": 75,
     "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": 76,
   "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": 77,
   "metadata": {
    "id": "iPcFF0MjZlh8"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 79,
   "metadata": {
    "id": "DzjpPyVyb8XO"
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "b2K9ajBaaYUk"
   },
   "outputs": [],
   "source": [
    "pickle.dump(model, open('PADI4_model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ef4fyvrEb-NC"
   },
   "outputs": [],
   "source": []
  }
 ],
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