Thanks for letting people know about this. If we can get a consensus form
that people agree makes sense, this might be a nice addition to either the
RDKit/Scripts directory or the cookbook.
A couple of smallish comments after a quick skim:
- I would really strongly encourage you to use the ETKDG parameters (
http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00654) when doing the
embedding. This really helps a lot with the quality of the conformations
and lets you skip the UFF step.
- The built-in RMSD pruning has improved since JP's article, it may be
worth looking at that.
- If you want to make the embedding step itself robust, it wouldn't be a
bad idea to try switching to random coordinate generation if the initial
embedding fails.
Best,
-greg
On Wed, Jun 14, 2017 at 9:27 AM, Francois BERENGER <
beren...@bioreg.kyushu-u.ac.jp> wrote:
> Hello,
>
> I gave a try at reproducing the protocol described in:
>
> @article{DBLP:journals/jcisd/EbejerMD12,
> author = {Jean{-}Paul Ebejer and Garrett M. Morris and
> Charlotte M. Deane},
> title = {Freely Available Conformer Generation Methods:
> How Good Are They?},
> journal = {Journal of Chemical Information and Modeling},
> volume = {52},
> number = {5},
> pages = {1146--1158},
> year = {2012},
> url = {https://doi.org/10.1021/ci2004658},
> doi = {10.1021/ci2004658},
> }
>
> The resulting script is there:
>
> https://github.com/UnixJunkie/smi2sdf3d
>
> I hope I could reproduce their protocol exactly.
> Sorry, my python is so rusty these days.
>
> Comments and contributions are welcome.
>
> Even auditing the code for correctness is welcome since it is
> doing some scientific computation.
>
> It is a little bit too slow to my taste.
>
> You can use it like this to get a max of 10 conformers
> per molecule in your input.smi file:
>
> ./smi2sdf.py 10 input.smi output.sdf
>
> Best regards,
> Francois.
>
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