Part4_QSAR_Drug_Discovery.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Model using Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Loading datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('acetylcholinesterase_bioactivity_data_pIC50_pubchem_fp.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Features\n",
    "Acetlycholinesterase data set has 881 input features and 1 output variable (pIC50 values)\n",
    "Model uses these unqiue fingerprints to determine which parts (functional groups) are essential to design a potent drug\n",
    "### Input Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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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",
       "1              1           1           1           0           0           0   \n",
       "2              1           1           1           0           0           0   \n",
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       "4              1           1           0           0           0           0   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
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       "\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",
       "      PubchemFP872  PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  \\\n",
       "0                0             0             0             0             0   \n",
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       "...            ...           ...           ...           ...           ...   \n",
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       "\n",
       "      PubchemFP877  PubchemFP878  PubchemFP879  PubchemFP880  \n",
       "0                0             0             0             0  \n",
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       "\n",
       "[4695 rows x 881 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('pIC50', axis = 1)\n",
    "#X = X.drop('Name', axis = 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.pIC50\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4695, 881)\n",
      "(4695,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Low Variance Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4695, 137)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))    \n",
    "X = selection.fit_transform(X)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Split (80:20 Train:Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3756, 137) (3756,)\n",
      "(939, 137) (939,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n",
    "print(X_train.shape, Y_train.shape)\n",
    "print(X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Model with Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4858563481052658"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=100)\n",
    "model.fit(X_train, Y_train)\n",
    "r2 = model.score(X_test, Y_test)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot of Experimental vs Predicted pIC50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "ax = sns.regplot(Y_test, Y_pred, scatter_kws={'alpha':0.4})\n",
    "ax.set_xlabel('Experimental pIC50', fontsize='large', fontweight='bold')\n",
    "ax.set_ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
    "ax.set_xlim(0, 12)\n",
    "ax.set_ylim(0, 12)\n",
    "ax.figure.set_size_inches(5, 5)\n",
    "plt.show"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}