Atom Mapping of two Molecules for finding a common core region#

This is a short tutorial trying to give a hand on how to work with Kartograf. First, the input is generated from two SMILES with RDKit. We will add all hydrogens and generate both molecules a 3D conformation with RDKit. Afterward, the molecules are transformed into SmallMoleculeComponent, which is the entrance into Kartograf and the OpenFE world:

from rdkit import Chem
from kartograf import SmallMoleculeComponent

# Preprocessing from Smiles - Here you can add your Input!
smiles = ["c1ccccc1", "c1ccccc1(CO)"]
rdmols = [Chem.MolFromSmiles(s) for s in smiles]
rdmols = [Chem.AddHs(m, addCoords=True) for m in rdmols]
[Chem.rdDistGeom.EmbedMolecule(m, useRandomCoords=False, randomSeed = 0) for m in rdmols]

# Build Small Molecule Components
molA, molB = [SmallMoleculeComponent.from_rdkit(m) for m in rdmols]

Next, we need to make sure, that the molecules are well aligned, as this is one of the core assumptions of Kartograf’s atom mapper in order to find a good mapping. Here we use a helper function from Kartograf`s atom aligner functions, that aligns the molecules based on their shape:

from kartograf.atom_aligner import align_mol_shape
# Align the mols first - this might not needed, depends on input.
a_molB = align_mol_shape(molB, ref_mol=molA)

Finally, we will generate the atom mapping. Here we decide to map hydrogens as well as heavy atoms (this is a default). As input, we give the aligned SmallMoleculeComponents and we will retrieve an AtomMapping from the mapper, that contains both components and the atom mapping from one to the other component:

from kartograf import KartografAtomMapper
# Build Kartograf Atom Mapper
mapper = KartografAtomMapper(atom_map_hydrogens=True)

# Get Mapping
kartograf_mapping = next(mapper.suggest_mappings(molA, a_molB))
print(kartograf_mapping.componentA_to_componentB) #print mapping
kartograf_mapping

If this code block is executed in a Jupyter Notebook, the cell will return a 2D visualization of the mapping:

../_images/alignment_benz_ol.png

Note that Kartograf does first solve the geometry problem, by finding atoms close to each other for the mapping. Next, it will apply a set of default rules, that will remove mappings, that cause a so-called ring-break, a ring-size change, and a ring-flexibility change. This default behavior can be turned off and/or customized to personal needs see the filtering tutorial.

Additionally, we could assess the quality of our mapping, there are several metrics in Kartograf that can be used to investigate the mapping. Here we are going to use the MappingRMSDScorer, which gives insight into how far the atoms need to travel from one state to the other, if they are mapped onto each other:

from kartograf.atom_mapping_scorer import MappingRMSDScorer

# Score Mapping
rmsd_scorer = MappingRMSDScorer()
score = rmsd_scorer(mapping=atom_mapping)
print(f"RMSD Score: {score}")

This will print the calculated score.