Dear Alan
On Thu, Aug 4, 2011 at 8:38 PM, Alan Smith <[email protected]> wrote:
>
> The substructure matching method will certainly work but like you say it's
> not ideal for large data sets or highly symmetrical molecules.
> My plan was to code up the Morgan algorithm
> (http://ovir.icp.ac.ru/isostar/help/morgan.html) to find symmetry classes,
> which hopefully should not be too computationally expensive. However, if you
> think it's straightforward to expose the existing functionality to python,
> I'd rather wait for that (unless of course you think the Morgan algorithm
> would be much faster). Do you think this is a quick one that you'll be able
> to fit in the next release?
I did a bit more investigation this morning, and it turns out that it
is possible to get the required values already. Here's an example:
>>> m = Chem.MolFromSmiles('c1ncccc1c1cccnc1')
>>> Chem.AssignStereochemistry(m,cleanIt=True,force=True,flagPossibleStereoCenters=True)
>>> [atom.GetProp('_CIPRank') for atom in m.GetAtoms()]
['4', '5', '3', '1', '0', '2', '2', '0', '1', '3', '5', '4']
The key pieces is the "flagPossibleStereoCenters" argument, which
tells the stereochemistry assignment code to determine the CIP ranks
of atoms even if it doesn't find a potential stereocenter in the
molecule.
Note that this has an advantage over the substructure-matching version
of the approach in that it takes stereochemistry into account:
>>> m = Chem.MolFromSmiles('C[C@H]([C@H](F)Cl)[C@@H](F)Cl')
>>> Chem.AssignStereochemistry(m,cleanIt=True,force=True,flagPossibleStereoCenters=True)
>>> [atom.GetProp('_CIPRank') for atom in m.GetAtoms()]
['0', '1', '3', '5', '7', '2', '4', '6']
>>> m2 = Chem.MolFromSmiles('CC([C@H](F)Cl)[C@H](F)Cl')
>>> Chem.AssignStereochemistry(m2,cleanIt=True,force=True,flagPossibleStereoCenters=True)
>>> [atom.GetProp('_CIPRank') for atom in m2.GetAtoms()]
['0', '1', '2', '3', '4', '2', '3', '4']
I'm pretty sure this should give you the information you need to
assign symmetry classes.
Best Regards,
-greg
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