Update Processing Script.py

Processing Script.py

@@ -141,63 +141,6 @@ Long2023['X'] = [ \
         smiles))))
     for smiles in Long2023['SMILES']]
 
-Long2023['X'] = [Chem.MolFromSmiles(smiles) for smiles in Long2023['X']]
-for mol in Long2023['X']:
-    Chem.Kekulize(mol)
-
-Long2023['X'] = [Chem.MolToSmiles(mol, kekuleSmiles=True) for mol in Long2023['X']]
-
-problems = []
-for index, row in tqdm.tqdm(Long2023.iterrows()):
-    result = molvs.validate_smiles(row['X'])
-    if len(result) == 0:
-        continue
-    problems.append( (row['ID'], result) )
-
-#   Most are because it includes the salt form and/or it is not neutralized
-for id, alert in problems:
-    print(f"ID: {id}, problem: {alert[0]}")
-
-for index, row in tqdm.tqdm(Long2023.iterrows()):
-    mol = Chem.MolFromSmiles(row['X'])
-    Chem.Kekulize(mol)  # Attempt to kekulize the molecule
-    kekulized_smiles = Chem.MolToSmiles(mol, kekuleSmiles=True)  # Get the kekulized SMILES
-    if row['X'] != kekulized_smiles:
-        print(f"Molecule with ID {row['ID']} could not be kekulized.") 
-
-#5. Resolve kekulization error
-
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)C2=C3C(=CC=CC3=CC=C2)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=CC=CC3=CC=CC(=C23)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)NCCCl)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NCCCl)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC=O)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC=O)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)CCCCl)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)CCCCl)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)C(Cl)(Cl)Cl)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)C(Cl)(Cl)Cl)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc([N+](=O)[O-])cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC([N+](=O)[O-])=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(N)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(N)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)Nc4ccc(Cl)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NC4=CC=C(Cl)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)Nc4ccc(C#N)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NC4=CC=C(C#N)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=S)Nc4ccc(Cl)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=S)NC4=CC=C(Cl)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(/N=C/c4ccc(C#N)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(/N=C/C4=CC=C(C#N)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=S)Nc4ccc(C#N)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=S)NC4=CC=C(C#N)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NCc4ccc5c(c4)OCO5)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NCC4=CC=C5OCOC5=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)NC(=O)c4ccccc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NC(=O)C4=CC=CC=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'C[C@H]1Oc2cc(cnc2N)-c2c(nn(C)c2C#N)CN(C)C(=O)c2ccc(F)cc21', 'X'] = 'C[C@H]1OC2=C(N)N=CC(=C2)C2=C(C#N)N(C)N=C2CN(C)C(=O)C2=CC=C(F)C=C21'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)Nc4cc5c6c(cccc6c4)C(=O)N(CCN(C)C)C5=O)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NC4=CC5=C6C(=CC=CC6=C4)C(=O)N(CCN(C)C)C5=O)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NCc4ccc5c(c4)OCCO5)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NCC4=CC=C5OCCOC5=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)c4ccccc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)C4=CC=CC=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)NC(=O)CCl)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NC(=O)CCl)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)C(F)(F)F)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)C(F)(F)F)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)Nc4ccc(OC(F)(F)F)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NC4=CC=C(OC(F)(F)F)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(/N=C/c4cc(O)ccc4O)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(/N=C/C4=CC(O)=CC=C4O)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=S)Nc4ccc(OC(F)(F)F)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=S)NC4=CC=C(OC(F)(F)F)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NCc4cc5c(cc4[N+](=O)[O-])OCO5)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NCC4=CC5=C(C=C4[N+](=O)[O-])OCO5)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)Cc4ccc(Cl)cc4)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)CC4=CC=C(Cl)C=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(/N=C/c4cc5c(cc4[N+](=O)[O-])OCO5)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(/N=C/C4=CC5=C(C=C4[N+](=O)[O-])OCO5)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(/N=C/c4ccc5c(c4)OCO5)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(/N=C/C4=CC=C5OCOC5=C4)=CC3=CC=C2)C1=O'
-Long2023.loc[Long2023['X'] == 'CN(C)CCN1C(=O)c2cccc3cc(NC(=O)Nc4ccc5c(c4)OCO5)cc(c23)C1=O', 'X'] = 'CN(C)CCN1C(=O)C2=C3C(=CC(NC(=O)NC4=CC=C5OCOC5=C4)=CC3=CC=C2)C1=O'
-
-#6. Print problems again to check if the errors have been resolved
-
 problems = []
 for index, row in tqdm.tqdm(Long2023.iterrows()):
     result = molvs.validate_smiles(row['X'])
@@ -209,19 +152,19 @@ for index, row in tqdm.tqdm(Long2023.iterrows()):
 for id, alert in problems:
     print(f"ID: {id}, problem: {alert[0]}")
 
-#7. Select columns and rename the dataset
+#5. Select columns and rename the dataset
 
 Long2023.rename(columns={'X': 'new SMILES', 'Y': 'Label'}, inplace=True)
 Long2023[['new SMILES', 'Label']].to_csv('Long2023.csv', index=False)
 
-#8. Import modules to split the dataset
+#6. Import modules to split the dataset
 
 import sys
 from rdkit import DataStructs
 from rdkit.Chem import AllChem as Chem
 from rdkit.Chem import PandasTools   
 
-#9. Split the dataset into test and train
+#7. Split the dataset into test and train
 
 class MolecularFingerprint:
     def __init__(self, fingerprint):
@@ -403,7 +346,7 @@ smiles_index = 0  # Because smiles is in the first column
 realistic = realistic_split(HematoxLong2023.copy(), smiles_index, 0.75, split_for_exact_frac=True, cluster_method="Auto")
 realistic_train, realistic_test = split_df_into_train_and_test_sets(realistic)
 
-#10. Test and train datasets have been made
+#8. Test and train datasets have been made
 
 selected_columns = realistic_train[['new SMILES', 'Label']]
 selected_columns.to_csv("HematoxLong2023_train.csv", index=False)