How to generate a graph from a SMILES molecule representation?
Question:
I have a dataset of molecules represented with SMILES strings. I was trying to represent this as graphs. Is there a way to do so? For instance, let’s say I have string CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1, is there a general way to convert this to a graph representation, meaning adjacency matrix and atom vector? I see questions addressing SMILES from graphs and I know rdkit has MolFromSmiles, but I can’t find something to get graph from SMILES string.
Answers:
You could try pysmiles.
Starting from the SMILES description you should be able to create a NetworkX graph and generate the desired objects with code along the lines of
from pysmiles import read_smiles
import networkx as nx
smiles = 'C12=C3C4=C5C6=C1C7=C8C9=C1C%10=C%11C(=C29)C3=C2C3=C4C4=C5C5=C9C6=C7C6=C7C8=C1C1=C8C%10=C%10C%11=C2C2=C3C3=C4C4=C5C5=C%11C%12=C(C6=C95)C7=C1C1=C%12C5=C%11C4=C3C3=C5C(=C81)C%10=C23'
mol = read_smiles(smiles)
# atom vector (C only)
print(mol.nodes(data='element'))
# adjacency matrix
print(nx.to_numpy_matrix(mol))
If you can accept a so-so visualization, you can also tentatively plot the molecule with
import matplotlib.pyplot as plt
elements = nx.get_node_attributes(mol, name = "element")
nx.draw(mol, with_labels=True, labels = elements, pos=nx.spring_layout(mol))
plt.gca().set_aspect('equal')
Fullerenes are fun to plot 🙂
To complete Davide’s answer https://stackoverflow.com/a/57063988/4240413, you can include the bond order to adjacency matrix using:
nx.to_numpy_matrix(mol, weight='order')
or according to networkx documentation using
nx.adjacency_matrix(mol, weight='order').todense()
networkx is a nice solution. If you are looking for something more custom, you can create the graph yourself. See example going in the other direction (SMILES from graph) in this post: SMILES from graph
You can use rdkit‘s Chem.GetAdjacencyMatrix():
from rdkit import Chem
import networkx as nx
smiles = 'CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1'
mol = Chem.MolFromSmiles(smiles)
# Get adjacency matrix
adjacency_matrix = Chem.GetAdjacencyMatrix(mol, useBO = True)
# Convert adjacency matrix to NetworkX graph
G = nx.from_numpy_array(adjacency_matrix)
I have a dataset of molecules represented with SMILES strings. I was trying to represent this as graphs. Is there a way to do so? For instance, let’s say I have string CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1, is there a general way to convert this to a graph representation, meaning adjacency matrix and atom vector? I see questions addressing SMILES from graphs and I know rdkit has MolFromSmiles, but I can’t find something to get graph from SMILES string.
You could try pysmiles.
Starting from the SMILES description you should be able to create a NetworkX graph and generate the desired objects with code along the lines of
from pysmiles import read_smiles
import networkx as nx
smiles = 'C12=C3C4=C5C6=C1C7=C8C9=C1C%10=C%11C(=C29)C3=C2C3=C4C4=C5C5=C9C6=C7C6=C7C8=C1C1=C8C%10=C%10C%11=C2C2=C3C3=C4C4=C5C5=C%11C%12=C(C6=C95)C7=C1C1=C%12C5=C%11C4=C3C3=C5C(=C81)C%10=C23'
mol = read_smiles(smiles)
# atom vector (C only)
print(mol.nodes(data='element'))
# adjacency matrix
print(nx.to_numpy_matrix(mol))
If you can accept a so-so visualization, you can also tentatively plot the molecule with
import matplotlib.pyplot as plt
elements = nx.get_node_attributes(mol, name = "element")
nx.draw(mol, with_labels=True, labels = elements, pos=nx.spring_layout(mol))
plt.gca().set_aspect('equal')
Fullerenes are fun to plot 🙂
To complete Davide’s answer https://stackoverflow.com/a/57063988/4240413, you can include the bond order to adjacency matrix using:
nx.to_numpy_matrix(mol, weight='order')
or according to networkx documentation using
nx.adjacency_matrix(mol, weight='order').todense()
networkx is a nice solution. If you are looking for something more custom, you can create the graph yourself. See example going in the other direction (SMILES from graph) in this post: SMILES from graph
You can use rdkit‘s Chem.GetAdjacencyMatrix():
from rdkit import Chem
import networkx as nx
smiles = 'CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1'
mol = Chem.MolFromSmiles(smiles)
# Get adjacency matrix
adjacency_matrix = Chem.GetAdjacencyMatrix(mol, useBO = True)
# Convert adjacency matrix to NetworkX graph
G = nx.from_numpy_array(adjacency_matrix)