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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