dear RDKit experts,
i have a question about conformer generation, maybe best posed on an
example:
i'm working with macrocycles which have few amide groups. As in each amide
group H(-N) and O(=C) atoms can have two positions with respect to a rigid
part of a molecule (amide groups are direct neighbours of aromatic rings) -
"syn" and "anti" - one can think of few families of conformers: "syn syn
syn...", "syn syn anti...", "syn anti anti...", etc.
i would like to generate a set which would have representatives of all/most
of these families;
for that:
1. i started with constructing 3D structures of all representatives
manually (i.e. starting from the crystal structure, manipulating atoms in
all amide groups and pre-optimizing with MM in Avogadro just to have a set
of candidates representing all orientation families mentioned above)
2. then used AllChem.EmbedMultipleConfs with all the 3D structures
generated in step 1 as initial geometries, followed by MM optimization with
AllChem.UFFOptimizeMolecule
But as explained in
http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00654
the program starts with creating the "distance bound matrix" based on
empirical information including "ideal bond lengths, ideal bond angles, and
a few ideal torsion angles", therefore i'm wondering whether what i do
makes sense: does it matter with which conformer ("syn syn...", "syn
anti..." etc.) i start if anyway for all the conformers of the same
molecule the distance bound matrix would be the same? is that correct?
If that is indeed the case (= it does not matter with which 3D structure i
start), what would be your advice to assure that all orientation types
("syn syn...", "syn anti..." etc.) are in a final set?
Since there is a random distance matrix generation step, would you simply
repeat AllChem.EmbedMultipleConfs many times until you see all structures
you want to have in a set?
Or is there a smarter way to do it?
Thank you and best regards,
gosia
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