### 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()