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fingerprints.py

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+import pandas as pd
+from rdkit import Chem
+from rdkit.Chem import AllChem
+
+def smiles_to_fingerprint(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None:
+        return None
+    fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)
+    return ",".join(map(str, list(fp)))
+
+# Load the original CSV
+df = pd.read_csv("EDC_data_sanitized.csv")
+
+# Use the correct column: 'standardized_smiles'
+df["fingerprint"] = df["standardized_smiles"].apply(smiles_to_fingerprint)
+
+# Drop rows where fingerprint generation failed
+df = df.dropna(subset=["fingerprint"])
+
+# Save to new file
+df.to_csv("edc_sanitized_with_fingerprints.csv", index=False)
+

sanitize_edc.py

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+import pandas as pd
+from molvs import Standardizer
+from rdkit import Chem
+
+def standardize_smiles(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None:
+        return None
+    std = Standardizer()
+    try:
+        std_mol = std.standardize(mol)
+        return Chem.MolToSmiles(std_mol)
+    except:
+        return None
+
+# Load and manually parse .txt file
+data = []
+with open("EDC_data.txt", "r") as f:
+    for line in f:
+        parts = line.strip().split()
+        if len(parts) < 6:
+            continue  # skip malformed lines
+        sample_id = parts[0]
+        smiles = parts[1]
+        name = parts[3]
+        label = parts[-2]  # sometimes it's merged, so this may need refining
+        source = parts[-1]
+        data.append((sample_id, smiles, name, label, source))
+
+df = pd.DataFrame(data, columns=["id", "smiles", "name", "label", "source"])
+
+# Sanitize
+df["standardized_smiles"] = df["smiles"].apply(standardize_smiles)
+df.dropna(subset=["standardized_smiles"], inplace=True)
+
+# Save
+df.to_csv("EDC_data_sanitized.csv", index=False)
+

train_classifier.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import (
+    classification_report,
+    confusion_matrix,
+    ConfusionMatrixDisplay,
+    roc_curve,
+    auc
+)
+
+# Load the dataset (adjust path if needed)
+df = pd.read_csv("edc_sanitized_with_fingerprints.csv")
+
+# Convert fingerprint column (stored as strings) back into lists
+df["fingerprint"] = df["fingerprint"].apply(eval)
+
+# Features and labels
+X = np.array(df["fingerprint"].tolist())
+y = df["label"].values
+
+# Split into training and testing sets (80/20 split with stratification)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, stratify=y, random_state=42
+)
+
+# Train the classifier
+clf = RandomForestClassifier(random_state=42)
+clf.fit(X_train, y_train)
+
+# Predictions and probabilities
+y_pred = clf.predict(X_test)
+y_proba = clf.predict_proba(X_test)[:, 1]  # Probabilities for class 1
+
+# Print classification report
+print(classification_report(y_test, y_pred))
+
+# --- Confusion Matrix ---
+cm = confusion_matrix(y_test, y_pred)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)
+disp.plot(cmap="Blues")
+plt.title("Confusion Matrix")
+plt.savefig("confusion_matrix.png")
+plt.show()
+
+# --- ROC Curve ---
+fpr, tpr, _ = roc_curve(y_test, y_proba)
+roc_auc = auc(fpr, tpr)
+
+plt.figure()
+plt.plot(fpr, tpr, color="darkorange", lw=2, label=f"ROC curve (AUC = {roc_auc:.2f})")
+plt.plot([0, 1], [0, 1], color="navy", lw=2, linestyle="--")
+plt.xlim([0.0, 1.0])
+plt.ylim([0.0, 1.05])
+plt.xlabel("False Positive Rate")
+plt.ylabel("True Positive Rate")
+plt.title("Receiver Operating Characteristic (ROC) Curve")
+plt.legend(loc="lower right")
+plt.savefig("roc_curve.png")
+plt.show()
+