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preprocessing scripts/Firefly Luciferase Interference_ preprocessing script.py

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+# 1. Load Modules
+
+pip install rdkit
+pip install molvs
+import pandas as pd
+import numpy as np
+import rdkit
+import molvs
+from rdkit import Chem
+
+standardizer = molvs.Standardizer()
+fragment_remover = molvs.fragment.FragmentRemover()
+
+
+# 2. Convert the SDF file from the original paper into data frame
+# Before running the code, please download SDF files from the original paper
+# https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482
+
+from rdkit.Chem import PandasTools
+sdfFile = 'Firefly_Luciferase_counter_assay_training_set_curated.sdf'
+dataframe = PandasTools.LoadSDF(sdfFile)
+dataframe.to_csv('Firefly_Luciferase.csv', index=False)
+df = pd.read_csv('Firefly_Luciferase.csv')
+
+# 3. Resolve SMILES parse error
+# Some of the 'Raw_SMILES' rows contain TWO SMILES separated by ';'' and, they cause SMILES parse error (which means they cannot be read)
+# So we separated the SMILES and renamed the columns
+
+df.rename(columns = {'PUBCHEM_EXT_DATASOURCE_REGID': 'REGID_1'}, inplace = True)
+df.rename(columns = {'Other REGIDs': 'REGID_2'}, inplace = True)
+
+df.insert(2, 'REGID_3', np.NaN)
+
+df['REGID_3'] = df['REGID_2'].str.split(',').str[1]
+df['REGID_2'] = df['REGID_2'].str.split(',').str[0]
+
+df.insert(4, 'SMILES_2', np.NaN)
+df.insert(5, 'SMILES_3', np.NaN)
+
+df[['Raw_SMILES', 'SMILES_2', 'SMILES_3']] = df['Raw_SMILES'].str.split(';', expand=True)
+
+df.rename(columns= {'Raw_SMILES' : 'SMILES_1'}, inplace = True)
+
+# 4. Sanitize with MolVS and print problems
+
+df['X_1'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df['SMILES_1']]
+
+def process_smiles(smiles):
+    if pd.isna(smiles):
+        return None  
+    try:
+        return rdkit.Chem.MolToSmiles(
+            fragment_remover.remove(
+                standardizer.standardize(
+                    rdkit.Chem.MolFromSmiles(smiles))))
+    except Exception as e:
+        print(f"Error processing SMILES {smiles}: {e}")
+        return None  
+
+df['X_2'] = df['SMILES_2'].apply(process_smiles)
+
+def process_smiles(smiles):
+    if pd.isna(smiles):
+        return None  
+    try:
+        return rdkit.Chem.MolToSmiles(
+            fragment_remover.remove(
+                standardizer.standardize(
+                    rdkit.Chem.MolFromSmiles(smiles))))
+    except Exception as e:
+        print(f"Error processing SMILES {smiles}: {e}")
+        return None  
+
+df['X_3'] = df['SMILES_3'].apply(process_smiles)
+
+
+# 5. Rename the columns
+
+df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2', 'X_3' : 'newSMILES_3'}, inplace = True)
+
+# 6. Create a file with sanitized SMILES
+
+df[['REGID_1',
+ 'REGID_2',
+ 'REGID_3',
+ 'newSMILES_1',
+ 'newSMILES_2',
+ 'newSMILES_3',
+ 'log_AC50_M',
+ 'Efficacy',
+ 'CC-v2',
+ 'Outcome',
+ 'InChIKey',
+ 'ID',
+ 'ROMol']].to_csv('Firefly Luciferase_sanitized.csv', index = False)
+

preprocessing scripts/MSTI Thiol Interference_preprocessing script.py

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+# 1. Load Modules
+
+pip install rdkit
+pip install molvs
+import pandas as pd
+import numpy as np
+import rdkit
+import molvs
+from rdkit import Chem
+
+standardizer = molvs.Standardizer()
+fragment_remover = molvs.fragment.FragmentRemover()
+
+
+# 2. Convert the SDF file from the original paper into data frame
+# Before running the code, please download SDF files from the original paper
+# https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482
+
+from rdkit.Chem import PandasTools
+sdfFile = 'Thiol_training_set_curated.sdf'
+dataframe = PandasTools.LoadSDF(sdfFile)
+dataframe.to_csv('thiol.csv', index=False)
+df = pd.read_csv('thiol.csv')
+
+
+# 3. Resolve SMILES parse error
+# Some of the 'Raw_SMILES' rows contain TWO SMILES separated by ';'' and, they cause SMILES parse error (which means they cannot be read)
+# So we separated the SMILES and renamed the columns
+
+df.rename(columns = {'PUBCHEM_EXT_DATASOURCE_REGID': 'REGID_1'}, inplace = True)
+df.rename(columns = {'Other REGIDs': 'REGID_2'}, inplace = True)
+
+df.insert(2, 'REGID_3', np.NaN)
+
+df['REGID_3'] = df['REGID_2'].str.split(',').str[1]
+df['REGID_2'] = df['REGID_2'].str.split(',').str[0]
+
+df.insert(4, 'SMILES_2', np.NaN)
+df.insert(5, 'SMILES_3', np.NaN)
+
+df[['Raw_SMILES', 'SMILES_2', 'SMILES_3']] = df['Raw_SMILES'].str.split(';', expand=True)
+
+df.rename(columns= {'Raw_SMILES' : 'SMILES_1'}, inplace = True)
+
+
+# 4. Sanitize with MolVS and print problems
+
+df['X_1'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df['SMILES_1']]
+
+def process_smiles(smiles):
+    if pd.isna(smiles):
+        return None  
+    try:
+        return rdkit.Chem.MolToSmiles(
+            fragment_remover.remove(
+                standardizer.standardize(
+                    rdkit.Chem.MolFromSmiles(smiles))))
+    except Exception as e:
+        print(f"Error processing SMILES {smiles}: {e}")
+        return None  
+
+df['X_2'] = df['SMILES_2'].apply(process_smiles)
+
+def process_smiles(smiles):
+    if pd.isna(smiles):
+        return None  
+    try:
+        return rdkit.Chem.MolToSmiles(
+            fragment_remover.remove(
+                standardizer.standardize(
+                    rdkit.Chem.MolFromSmiles(smiles))))
+    except Exception as e:
+        print(f"Error processing SMILES {smiles}: {e}")
+        return None  
+
+df['X_3'] = df['SMILES_3'].apply(process_smiles)
+
+
+# 5. Rename the columns
+
+df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2', 'X_3' : 'newSMILES_3'}, inplace = True)
+
+
+# 6. Create a file with sanitized SMILES
+
+df[['REGID_1',
+ 'REGID_2',
+ 'REGID_3',
+ 'newSMILES_1',
+ 'newSMILES_2',
+ 'newSMILES_3',
+ 'log_AC50_M',
+ 'Efficacy',
+ 'CC-v2',
+ 'Outcome',
+ 'InChIKey',
+ 'ID',
+ 'ROMol']].to_csv('thiol_sanitized.csv', index = False)

preprocessing scripts/Nano Luciferase Interference_preprocessing script.py

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+# 1. Load Modules
+
+pip install rdkit
+pip install molvs
+import pandas as pd
+import numpy as np
+import rdkit
+import molvs
+from rdkit import Chem
+
+standardizer = molvs.Standardizer()
+fragment_remover = molvs.fragment.FragmentRemover()
+
+
+# 2. Convert the SDF file from the original paper into data frame
+# Before running the code, please download SDF files from the original paper
+# https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482
+
+from rdkit.Chem import PandasTools
+sdfFile = 'Nano_Luciferase_counter_assay_training_set_curated.sdf'
+dataframe = PandasTools.LoadSDF(sdfFile)
+dataframe.to_csv('Nano_Luciferase.csv', index=False)
+df = pd.read_csv('Nano_Luciferase.csv')
+
+# 3. Resolve SMILES parse error
+# Some of the 'Raw_SMILES' rows contain TWO SMILES separated by ';'' and, they cause SMILES parse error (which means they cannot be read)
+# So we separated the SMILES and renamed the columns
+
+df.rename(columns = {'PUBCHEM_EXT_DATASOURCE_REGID': 'REGID_1'}, inplace = True)
+df.rename(columns = {'Other REGIDs': 'REGID_2'}, inplace = True)
+
+df.insert(2, 'REGID_3', np.NaN)
+
+df['REGID_3'] = df['REGID_2'].str.split(',').str[1]
+df['REGID_2'] = df['REGID_2'].str.split(',').str[0]
+
+df.insert(4, 'SMILES_2', np.NaN)
+df.insert(5, 'SMILES_3', np.NaN)
+
+df[['Raw_SMILES', 'SMILES_2', 'SMILES_3']] = df['Raw_SMILES'].str.split(';', expand=True)
+
+df.rename(columns= {'Raw_SMILES' : 'SMILES_1'}, inplace = True)
+
+# 4. Sanitize with MolVS and print problems
+
+df['X_1'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df['SMILES_1']]
+
+def process_smiles(smiles):
+    if pd.isna(smiles):
+        return None  
+    try:
+        return rdkit.Chem.MolToSmiles(
+            fragment_remover.remove(
+                standardizer.standardize(
+                    rdkit.Chem.MolFromSmiles(smiles))))
+    except Exception as e:
+        print(f"Error processing SMILES {smiles}: {e}")
+        return None  
+
+df['X_2'] = df['SMILES_2'].apply(process_smiles)
+
+def process_smiles(smiles):
+    if pd.isna(smiles):
+        return None  
+    try:
+        return rdkit.Chem.MolToSmiles(
+            fragment_remover.remove(
+                standardizer.standardize(
+                    rdkit.Chem.MolFromSmiles(smiles))))
+    except Exception as e:
+        print(f"Error processing SMILES {smiles}: {e}")
+        return None  
+
+df['X_3'] = df['SMILES_3'].apply(process_smiles)
+
+
+# 5. Rename the columns
+
+df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2', 'X_3' : 'newSMILES_3'}, inplace = True)
+
+# 6. Create a file with sanitized SMILES
+
+df[['REGID_1',
+ 'REGID_2',
+ 'REGID_3',
+ 'newSMILES_1',
+ 'newSMILES_2',
+ 'newSMILES_3',
+ 'log_AC50_M',
+ 'Efficacy',
+ 'CC-v2',
+ 'Outcome',
+ 'InChIKey',
+ 'ID',
+ 'ROMol']].to_csv('Nano Luciferase_sanitized.csv', index = False)
+

preprocessing scripts/REDOX Interference_ preprocessing script.py

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+#1. Import modules
+
+pip install rdkit
+pip install molvs
+import pandas as pd
+import numpy as np
+import rdkit
+import molvs
+from rdkit import Chem
+
+standardizer = molvs.Standardizer()
+fragment_remover = molvs.fragment.FragmentRemover()
+
+# 2. Convert the SDF file from the original paper into data frame
+# Before running the code, please download SDF files from the original paper
+# https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482
+
+from rdkit.Chem import PandasTools
+sdfFile = 'Redox_training_set_curated.sdf'
+dataframe = PandasTools.LoadSDF(sdfFile)
+dataframe.to_csv('redox.csv', index=False)
+df = pd.read_csv('redox.csv')
+
+# 3. Resolve SMILES parse error
+# Some of the 'Raw_SMILES' rows contain TWO SMILES separated by ';'' and, they cause SMILES parse error (which means they cannot be read)
+# So we separated the SMILES and renamed the columns
+
+df.rename(columns = {'PUBCHEM_EXT_DATASOURCE_REGID': 'REGID_1'}, inplace = True)
+df.rename(columns = {'Other REGIDs': 'REGID_2'}, inplace = True)
+
+df.insert(3, 'SMILES_2', np.NaN)
+df['SMILES_2'] = df['Raw_SMILES'].str.split(';').str[1]
+df['Raw_SMILES'] = df['Raw_SMILES'].str.split(';').str[0]
+df.rename(columns= {'Raw_SMILES' : 'SMILES_1'}, inplace = True)
+
+df.insert(10, 'AC50_uM_2', np.NaN)
+df['AC50_uM_2'] = df['AC50_uM'].str.split(';').str[1]
+df['AC50_uM'] = df['AC50_uM'].str.split(';').str[0]
+df.rename(columns = {'AC50_uM': 'AC50_uM_1'}, inplace = True)
+
+# 4. Sanitize with MolVS and print problems
+
+df['X_1'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in df['SMILES_1']]
+
+def process_smiles(smiles):
+    if pd.isna(smiles):
+        return None  
+    try:
+        return rdkit.Chem.MolToSmiles(
+            fragment_remover.remove(
+                standardizer.standardize(
+                    rdkit.Chem.MolFromSmiles(smiles))))
+    except Exception as e:
+        print(f"Error processing SMILES {smiles}: {e}")
+        return None  
+
+df['X_2'] = df['SMILES_2'].apply(process_smiles)
+
+# 5. Rename the columns
+
+df.rename(columns={'X_1' : 'newSMILES_1', 'X_2' : 'newSMILES_2'}, inplace = True)
+
+# 6. Create a file with sanitized SMILES
+
+df[['REGID_1',
+ 'REGID_2',
+ 'newSMILES_1',
+ 'newSMILES_2',      
+ 'log_AC50_M',
+ 'Efficacy',
+ 'CC-v2',
+ 'Outcome',
+ 'InChIKey',
+ 'AC50_uM_1',
+ 'AC50_uM_2',
+ 'ID',
+ 'ROMol']].to_csv('redox_sanitized.csv', index = False)