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clean up
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CleanData.py

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Data-Preprocess.Py

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Diagrams/Graphs/Benchmark1k2101.png

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Diagrams/Graphs/CSARHiQ36.png

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Diagrams/Graphs/Test2016290.png

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Diagrams/Graphs/Train.png

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Diagrams/Graphs/Validation.png

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Diagrams/visualizations.ipynb

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"cells": [
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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" main_ax.set(xlim=(0, 12), ylim=(0, 12))\n",
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" main_ax.grid(True)\n",
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" main_ax.plot([0, 12], [0, 12], 'k--')\n",
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" main_ax.set_xlabel('Predicted pKa', fontsize=12)\n",
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" main_ax.set_ylabel('Experimental pKa', fontsize=12)\n",
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" main_ax.set_xlabel('Predicted pKd', fontsize=12)\n",
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" main_ax.set_ylabel('Experimental pKd', fontsize=12)\n",
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" main_ax.legend(loc='upper left') # Adding the legend\n",
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"\n",
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" x_hist_ax.hist(df.iloc[:, 0], bins=40, color=color, alpha=0.7)\n",
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aligned_binding_sites.csv

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benchmarking.ipynb

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" json.dump(experimental_pKd, file)\n",
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"plapt.clear_cache()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
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"from scipy.stats import pearsonr\n",
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"\n",
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"# Plotting the straight line test\n",
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"plt.scatter(experimental_pKd, predicted_pKd, color='blue')\n",
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"plt.plot([min(experimental_pKd), max(experimental_pKd)], [min(predicted_pKd), max(predicted_pKd)], color='red') # Line\n",
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"plt.xlabel('Experimental pKd')\n",
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"plt.ylabel('Predicted pKd')\n",
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"plt.title('Straight Line Test')\n",
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"plt.show()\n",
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"\n",
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"# Calculating Metrics\n",
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"mse = mean_squared_error(experimental_pKd, predicted_pKd)\n",
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"mae = mean_absolute_error(experimental_pKd, predicted_pKd)\n",
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"rmse = np.sqrt(mse)\n",
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"pearson_corr, _ = pearsonr(experimental_pKd, predicted_pKd)\n",
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"\n",
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"print(\"Mean Squared Error (MSE):\", mse)\n",
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"print(\"Mean Absolute Error (MAE):\", mae)\n",
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"print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
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"print(\"Pearson's Correlation Coefficient:\", pearson_corr)\n"
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]
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}
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],
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"metadata": {

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