update

MIC/qsar_2D_predict.py

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+import joblib
+import numpy as np
+from rdkit import Chem
+from rdkit.Chem import AllChem
+from pathlib import Path
+from pprint import pprint
+# Function to calculate 2D-QSAR descriptors using Morgan Fingerprints
+def calculate_2dqsar_repr(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None:
+        return None
+    # Calculate Morgan fingerprint with radius 3 and 1024 bits
+    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=1024)
+    return np.array(fp)
+
+# Load the SDF file and convert to SMILES
+sdf_file_list = [i for i in Path('../predict_data').glob('*.sdf')]
+# sdf_file = '/mnt/c/project/qsar/predict_data/chem1.sdf'
+sdf_results = {}
+for sdf_file in sdf_file_list:
+    supplier = Chem.SDMolSupplier(sdf_file)
+    new_mol = [mol for mol in supplier][0]  # Assuming only one molecule in SDF
+    smiles = Chem.MolToSmiles(new_mol)
+
+    # Calculate the 2D-QSAR descriptors
+    descriptor = calculate_2dqsar_repr(smiles)
+    descriptor_array = np.array(descriptor).reshape(1, -1)
+
+    # Load the saved model (use the model that performed best in training)
+    model_file_list = [i for i in Path().cwd().glob('2d_qsar_*.pkl')]
+
+    results = {}
+
+    for model_file in model_file_list:
+        model = joblib.load(model_file)
+        # Predict the MIC value
+        predicted_mic = model.predict(descriptor_array)
+        # print(f"Predicted MIC value: {predicted_mic[0]}")
+        results[model_file.stem] = predicted_mic[0]
+
+    sdf_results[sdf_file.stem] = results
+    
+pprint(sdf_results)
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Filter out negative MIC values from sdf_results
+filtered_sdf_results = {}
+for sdf_name, model_results in sdf_results.items():
+    filtered_results = {model_name: mic_value for model_name, mic_value in model_results.items() if mic_value >= 0}
+    filtered_sdf_results[sdf_name] = filtered_results
+
+# Convert the filtered results to a DataFrame for easier plotting
+filtered_data = []
+for sdf_name, model_results in filtered_sdf_results.items():
+    for model_name, mic_value in model_results.items():
+        filtered_data.append({'SDF': sdf_name, 'Model': model_name, 'MIC': mic_value})
+
+df = pd.DataFrame(filtered_data)
+
+# Set up the matplotlib figure
+plt.figure(figsize=(12, 8))
+
+# Create a seaborn barplot with the filtered data
+sns.barplot(x='SDF', y='MIC', hue='Model', data=df, palette='tab20')
+
+# Customize the plot
+plt.title('Predicted MIC values by Model for Each SDF (Filtered)')
+plt.xlabel('SDF Files')
+plt.ylabel('Predicted MIC Values')
+plt.xticks(rotation=45)
+plt.legend(title='Model')
+
+# Show the plot
+plt.tight_layout()
+plt.show()