def pains(filtered_df):
filteredData = filtered_df
params = FilterCatalogParams()
# Build a catalog from all PAINS (A, B and C)
params.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS)
catalog = FilterCatalog(params)
# Create empty dataframes for filtered data
rdkit_highLightFramePAINS = pd.DataFrame(columns=('CompID', 'CompMol', 'unwantedID'))
rdkit_noPAINS = pd.DataFrame(columns=('ChEMBL_ID', 'smiles','pIC50'))
rdkit_withPAINS = pd.DataFrame(columns=('ChEMBL_ID', 'smiles', 'pIC50','unwantedID'))
# For index and row in the filtered df
for i,row in filteredData.iterrows():
curMol = Chem.MolFromSmiles(row.smiles) # Current molecule
match = False # Set match to false
rdkit_PAINSList = []
# Get the first match
entry = catalog.GetFirstMatch(curMol)
if entry!=None:
# Add name of current unwanted subsftructure to list
rdkit_PAINSList.append(entry.GetDescription().capitalize())
# Add relevant matching information to dataframe
rdkit_highLightFramePAINS.loc[len(rdkit_highLightFramePAINS)] = [row.molecule_chembl_id, curMol,
entry.GetDescription().capitalize()]
match = True
if not match:
# Add to frame of PAINS free compounds
rdkit_noPAINS.loc[len(rdkit_noPAINS)] = [row.molecule_chembl_id, row.smiles, row.pIC50]
else:
# Add to frame of compounds that contain PAINS
# Put the relevant information in the dataframe with the unwanted substructures
rdkit_withPAINS.loc[len(rdkit_withPAINS)] = [row.molecule_chembl_id, row.smiles, row.pIC50, entry.GetDescription().capitalize()]
df = rdkit_noPAINS
# Drop unnecessary columns
## df_new = df.drop(['units', 'IC50'], axis=1)
df_new = df
# Create molecules from smiles and their fingerprints
create_mol(df_new, 2048)
# Add column for activity
df_new['active'] = np.zeros(len(df_new))
# Mark every molecule as active with an pIC50 of > 6.3
df_new.loc[df_new[df_new.pIC50 >= 6.3].index, 'active'] = 1.0
return df_new
def painspredict(thefile, theoutput):
os.remove('output.txt')
f1 = open(theoutput, 'w+')
mySMILESinput = pd.DataFrame(columns=['ID', 'my_smiles'])
params = FilterCatalogParams()
params.AddCatalog(FilterCatalogParams.FilterCatalogs.NIH)
catalog = FilterCatalog(params)
suppl = Chem.SmilesMolSupplier(thefile)
with open(thefile, 'r') as inf:
first_line = inf.readline()
inf.close()
with open(thefile, 'a') as inf:
inf.write(first_line)
inf.close()
inf = open(thefile, 'r')
sub_strct = [line.rstrip().split(" ") for line in inf]
ms = [x for x in suppl if x is not None]
i = 0
for mol in ms:
entry = catalog.GetFirstMatch(mol)
sphybrid = Chem.rdMolDescriptors.CalcFractionCSP3(mol)
if (entry is not None):
print(i,
sub_strct[i],
"PAINS",
entry.GetDescription(),
"Fsp3",
sphybrid,
file=f1)
else:
print(i, sub_strct[i], "PAINS OK", "Fsp3", sphybrid, file=f1)
i += 1
