chemged 0.1.0

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ChemGED

ChemGED is a Python package for enabling the appoximate graph edit distance (GED) computation between chemicals. Normally, GED is a NP-hard problem, but ChemGED uses heuristics to approximate the GED in a reasonable time.

Installation

You can install ChemGED using pip:

pip install chemged

Usage

To use ChemGED, you just create an ApproximateChemicalGED object, and then call the compute_ged method with two chemical structures. They can be SMILES or RDKit Mol objects.

from chemged import ApproximateChemicalGED
ged_calc = ApproximateChemicalGED()

chemical1 = "CCO"  # SMILES of the first chemical
chemical2 = "CCN"  # SMILES of the second chemical

# you can use SMILES strings directly
approx_ged = ged_calc.compute_ged(chemical1, chemical2)

# you can also use RDKit Mol objects
from rdkit.Chem import MolFromSmiles
approx_ged = ged_calc.compute_ged(MolFromSmiles(chemical1), MolFromSmiles(chemical2))

ChemGED also implements pdist and cdist functions to compute pairwise distances between sets of chemicals. These will return as Numpy arrays.

[!NOTE] pdist will return the vector-form distance vector, while cdist will return a square-form distance matrix. scipy.spatial.distance.squareform can be used to convert the vector-form distance vector to a square-form distance matrix.

from chemged import pdist, cdist

# Create a list of chemicals
chemicals = ["CCO", "CCN", "CC", "C"]

# Compute pairwise distances
distances_vector = pdist(chemicals)
print(distances_vector)  # Vector form

# Compute all-vs-all distances
distances_matrix = cdist(chemicals, chemicals)
print(distances_matrix)  # Square form

Documentation

You can read more detailed documentation in the docs folder.

Implementation

The approach used here uses bipartite graph matching[1], and most of its implementation in python is based off scripts from https://github.com/priba/aproximated_ged/tree/master. ChemGED uses RDKit to handle chemicals inside python.

References

[1] Riesen, Kaspar, and Horst Bunke. "Approximate graph edit distance computation by means of bipartite graph matching." Image and Vision computing 27.7 (2009): 950-959