Search-by-SMILES

Search-by-SMILES is a Python script leveraging RDKit and Pandas libraries to search for specific molecular patterns in a dataset. The workflow begins by importing an Excel sheet containing molecular data, which is then converted into a CSV format for easier manipulation. SMILES strings in the dataset are processed to create molecular objects using RDKit. Users can input a query SMILES string to search for specific substructures within the molecules. Matches are highlighted, visualized as a grid of molecule images, and saved to a specified directory. The tool is ideal for cheminformatics tasks requiring substructure searching and SMILES visualization.

1. Import Panda and RDKit libraries

import pandas as pd

from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem import AllChem
from rdkit.Chem import Descriptors
from rdkit.Chem import rdMolDescriptors

from io import StringIO

2. Input Excel sheet directory

input_xlsx = input("Enter the directory of the Excel sheet: ")
data_df = pd.read_excel(input_xlsx)
index_flag = input("Does the Excel sheet have an index? Type 'True' or 'False': ").lower() == 'false'

3. Convert it to CSV

csv_data = data_df.to_csv(index=index_flag)
csv_file = StringIO(csv_data)
df = pd.read_csv(csv_file)

3. Convert SMILES to MOLS by RDKit

df['mols'] = [Chem.MolFromSmiles(smiles) for smiles in df['SMILES']]

4. Search by SMILES and save it

cur_scaf_smiles = input('write SMILES of interest, ex: c2nn([C@H]3CCCO3)c3ncncc23')
mol_scaffold_of_interest = Chem.MolFromSmiles(cur_scaf_smiles)
mols_to_display = []
for mol_smi in df["SMILES"]:
    mol = Chem.MolFromSmiles(mol_smi)
    if mol is None:
        continue
    hit_ats = list(mol.GetSubstructMatch(mol_scaffold_of_interest))
    if hit_ats:
        mols_to_display.append((mol, hit_ats))

mols = [item[0] for item in mols_to_display]
hit_ats_list = [item[1] for item in mols_to_display]
img1 = Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(300, 300), 
                           legends=[smile for smile in df["Code"]], highlightAtomLists=hit_ats_list
                            ,returnPNG=False)

img1.save(input("/directory/../name.png"))
display(img)

5. Highlight the SMILES of interest

def get_atoms_to_highlight(mol_scaffold_of_interest, mol_compound_of_interest):
    atom_list = [i for i in range(mol_compound_of_interest.GetNumAtoms())]
    grouped_common_atoms = mol_compound_of_interest.GetSubstructMatches(mol_scaffold_of_interest)
    flattened_grouped_common_atoms = list(sum(grouped_common_atoms, ()))
    atoms_to_highlight = list(set(atom_list) - set(flattened_grouped_common_atoms))
    return atoms_to_highlight

cur_scaf_smiles = input('Write the SMILES of interest, e.g., c2nn([C@H]3CCCO3)c3ncncc23: ')

mol_scaffold_of_interest = Chem.MolFromSmiles(cur_scaf_smiles)
hl_atom_lists = []

for mol in df["mols"]:
    # Highlight atoms using the get_atoms_to_highlight function
    highlight = get_atoms_to_highlight(mol_scaffold_of_interest, mol)
    mol.__sssAtoms = highlight
    hl_atom_lists.append(highlight)

img = Draw.MolsToGridImage(df["mols"], 
                           legends=[name for name in df["Code"]], 
                           highlightAtomLists=hl_atom_lists, 
                           molsPerRow=10, maxMols= 1000,
                           subImgSize=(300, 300), useSVG=False,returnPNG=False)

# Display the image
img.save(input("/directory/.../name.png"))
display(img)