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Friday, February 28, 2025

Predicted Toxicity: Curcumin vs Methylene Blue (MB)

CODE:


#CURCUMIN TOXICITY DATA

toxicity_predictions = {

    'Hepatotoxicity': 0.69,

    'Neurotoxicity': 0.87,

    'Nephrotoxicity': 0.90,

    'Respiratory toxicity': 0.98,

    'Cardiotoxicity': 0.77,

    'Immunotoxicity': 0.96,

    'Ecotoxicity': 0.73

}

toxicity_types = list(toxicity_predictions.keys())

toxicity_probabilities = list(toxicity_predictions.values())


mb_toxicity_predictions = {

    'Hepatotoxicity': 0.875, # Human Hepatotoxicity from ADMET

    'Neurotoxicity': 0.911, # Drug-induced


    'Nephrotoxicity': 0.878, # Drug-induced Nephrotoxicity from ADMET

    'Respiratory toxicity': 0.436, # Respiratory from ADMET

    'Cardiotoxicity': 0.77, # Assumed same as Curcumin

    'Immunotoxicity': 0.098, # R

'Ecotoxicity': 0.0 # Adding Ecotoxicity for Methylene Blue, assuming 0 if no data

}


# Ensure the keys are identical to the Curcumin toxicity data

toxicity_types_mb = list(mb_toxicity_predictions.keys())

toxicity_probabilities_mb = list(mb_toxicity_predictions.values())


# Set the width of the bars

bar_width = 0.35


# Set the positions of the bars on the x-axis

r1 = np.arange(len(toxicity_types))

r2 = [x + bar_width for x in r1]


# Plotting the Toxicity Prediction

plt.figure(figsize=(12, 8))


# Make the plot for the toxicity prediction

plt.bar(r1, toxicity_probabilities, color='blue', width=bar_width, edgecolor='grey',

label='Curcumin')

plt.bar(r2, toxicity_probabilities_mb, color='green', width=bar_width, edgecolor='grey', label='Methylene Blue (MB)')


# General layout

plt.xlabel('Toxicity Type', fontsize=12)

plt.ylabel('Probability', fontsize=12)

plt.title('Predicted Toxicity: Curcumin vs Methylene Blue (MB)', fontsize=14)

plt.xticks([r + bar_width/2 for r in range(len(toxicity_types))], toxicity_types, rotation=45, ha='right', fontsize=10)

plt.ylim(0, 1.1) # Set y-axis limit from 0 to 1

plt.legend()


plt.tight_layout()

plt.savefig('toxicity_comparison_plot.png') # Save the plot as a file

plt.show()






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