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PatWalters/depict_ppis.py

Created November 14, 2025 03:16
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Code generated by LLMs to depict proton pump inhibitors
Raw
depict_ppis.py
### Code from GPT-5
from rdkit import Chem
from rdkit.Chem import Draw
# 1. Names (for legends)
names = [
"Omeprazole",
"Esomeprazole",
"Lansoprazole",
"Dexlansoprazole",
"Pantoprazole",
"Rabeprazole",
]
# 2. SMILES
smiles_list = [
# Omeprazole (canonical SMILES)
"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC",
# Esomeprazole (S-enantiomer; isomeric SMILES with S@ on sulfur)
"COc1ccc2nc([nH]c2c1)[S@](=O)Cc1ncc(C)c(OC)c1C",
# Lansoprazole
"Cc1c(CS(=O)c2nc3ccccc3[nH]2)nccc1OCC(F)(F)F",
# Dexlansoprazole (R-enantiomer; S@ on sulfur in alternative orientation)
"n1c2ccccc2[nH]c1[S@](=O)Cc3nccc(c3C)OCC(F)(F)F",
# Pantoprazole
"FC(F)Oc1ccc2[nH]c(nc2c1)S(=O)Cc3nccc(OC)c3OC",
# Rabeprazole
"CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCCCOC",
]
# 3. Convert SMILES to RDKit Mol objects
mols = [Chem.MolFromSmiles(s) for s in smiles_list]
# Optional: quick sanity check
for n, m in zip(names, mols):
if m is None:
raise ValueError(f"SMILES failed to parse for: {n}")
# 4. Draw molecules: 3 per row with names under each structure
img = Draw.MolsToImage(
mols,
molsPerRow=3,
subImgSize=(300, 300),
legends=names
)
# 5. Save (and/or show) the image
img.save("ppis_omep_eso_lanso_dexlanso_panto_rabe.png")
# img.show() # uncomment to open in a window if your environment supports it
### Code from Claude 4.5 Sonnet
from rdkit import Chem
from rdkit.Chem import Draw
# SMILES dictionary
smiles_dict = {
'Omeprazole': 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=CC(=C3)OC',
'Esomeprazole': 'CC1=CN=C(C(=C1OC)C)C[S@](=O)C2=NC3=C(N2)C=CC(=C3)OC',
'Lansoprazole': 'CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F',
'Dexlansoprazole': 'CC1=C(C=CN=C1C[S@](=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F',
'Pantoprazole': 'COC1=C(C=C(C=C1)OC)CS(=O)C2=NC3=C(N2)C=C(C=C3)OCC(F)F',
'Rabeprazole': 'CCOC(=O)C1=C(C=CN=C1)CS(=O)C2=NC3=C(N2)C=CC(=C3)OC'
}
# Convert SMILES to molecules
mols = []
names = []
for name, smiles in smiles_dict.items():
mol = Chem.MolFromSmiles(smiles)
mols.append(mol)
names.append(name)
# Draw molecules with 3 per row
img = Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(400, 400),
legends=names)
img.save('ppi_structures.png')
img.show()
### Code from Perplexity
from rdkit import Chem
from rdkit.Chem import Draw
smiles_dict = {
'Omeprazole': 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC',
'Esomeprazole': 'COc1ccc2nc([nH]c2c1)[S@](=O)Cc1ncc(C)c(OC)c1C',
'Lansoprazole': 'CC1=C(OCC(F)(F)F)C=CN=C1CS(=O)C1=NC2=CC=CC=C2N1',
'Dexlansoprazole': 'CC1=C(OCC(F)(F)F)C=CN=C1C[S@@](=O)C1=NC2=CC=CC=C2N1',
'Pantoprazole': 'FC(F)Oc1ccc2[nH]c(nc2c1)S(=O)Cc3nccc(OC)c3OC',
'Rabeprazole': 'CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCCCOC',
}
names = list(smiles_dict.keys())
mols = [Chem.MolFromSmiles(smiles_dict[name]) for name in names]
img = Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(350,350), legends=names)
img.save("ppi_grid.png")
### Code from Gemini
import matplotlib.pyplot as plt
from rdkit import Chem
from rdkit.Chem import Draw
def draw_ppi_molecules():
"""
Generates an image of the six requested PPI molecules arranged in a 2x3 grid.
"""
# 1. Define Drug Names and Stereospecific SMILES
# Note: Stereochemistry is explicitly defined for Esomeprazole ([S@]) and Dexlansoprazole ([S@@])
ppi_data = {
'Omeprazole': 'CC1=C(C=C(N=C1)CS(=O)C2=NC3=CC=C(C)C=C3N2)OC', # Racemate (non-stereospecific SMILES)
'Esomeprazole': 'CC1=C(C=C(N1)C[S@](=O)C2=NC3=CC=C(C)C=C3N2)OC', # S-enantiomer
'Lansoprazole': 'Cc1nc(CS(=O)c2nc3ccc(F)c-3n2)c(C)n1C', # Racemate (non-stereospecific SMILES)
'Dexlansoprazole': 'Cc1nc(C[S@@](=O)c2nc3ccc(F)c-3n2)c(C)n1C', # R-enantiomer
'Pantoprazole': 'COC1=C(C)N(C)C=C1CS(=O)C2=NC3=CC=C(OCF2)C=C3N2',
'Rabeprazole': 'CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCCCOC'
}
# 2. Convert SMILES to RDKit molecules and get names
mol_list = [Chem.MolFromSmiles(smiles) for smiles in ppi_data.values()]
name_list = list(ppi_data.keys())
# 3. Define drawing parameters
mols_per_row = 3
num_mols = len(mol_list)
num_rows = (num_mols + mols_per_row - 1) // mols_per_row
# 4. Set up the figure and axes using matplotlib
fig, axes = plt.subplots(num_rows, mols_per_row, figsize=(15, 10))
axes = axes.flatten() # Flatten the array of axes for easy indexing
# 5. Draw each molecule
for i in range(num_mols):
ax = axes[i]
mol = mol_list[i]
name = name_list[i]
# Use RDKit's MolToImage to draw the molecule onto the Matplotlib axis
# Note: MolToImage requires a temporary PIL image conversion,
# but Draw.MolToMPL is often cleaner if available and configured.
# We'll use the standard Draw.MolToImage, which Matplotlib can display.
img = Draw.MolToImage(
mol,
size=(300, 300),
legend=name, # RDKit can add the name directly to the image
wedgeBonds=True, # Ensure stereochemistry is visible
fitImage=True
)
ax.imshow(img)
ax.axis('off') # Hide the axes lines and ticks
# Manually add the name below the structure if RDKit's legend is not prominent enough
ax.set_title(name, y=-0.15, fontsize=12)
# 6. Hide any unused axes (if fewer than 6 molecules)
for i in range(num_mols, len(axes)):
fig.delaxes(axes[i])
# 7. Adjust layout and display/save
plt.tight_layout()
# Save the figure to a file
file_name = 'ppi_structures.png'
plt.savefig(file_name, dpi=300)
print(f"Image successfully saved as: {file_name}")
# plt.show() # Uncomment this line to display the image immediately in a notebook or script
if __name__ == '__main__':
draw_ppi_molecules()
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