Hello,
Awhile back I had noticed that rdkit has issues with boron containing
compounds. One is below, and I admit it is a strange one. I read in an sdf
file and write it out after calculating a formal charge on the molecule.
It seems to be read into rdkit ok but writing errored out with "ValueEr
Well, I'm not really familiar with the Taylor-Butina clustering method, so
I'm proposing a methodology based on generalizing something that I found to
be useful in a somewhat different clustering context.
Presuming that what you are clustering is the fingerprints of structures,
and that you know w
On Sep 25, 2018, at 17:13, Peter S. Shenkin wrote:
> FWIW, in work on conformational clustering, I used the “most representative”
> molecule; that is, the real molecule closest to the mathematical centroid.
> This would probably be the best way of displaying a single molecule that
> typifies wh
Well yes I have this line indeed, I did not put the whole file for
clarity purpose. The thing is tools as MOE, Pymol read it without
problem but RDock for example can't read it properly and returns a
neutral N which is not the case. And if I open it with pymol and save it
back in mol format, th
Hi Colin,
The RDkit outputs charge information to mol blocks using the CHG line:
In [3]: m = Chem.MolFromSmiles('C[NH3+]')
In [4]: print(Chem.MolToMolBlock(m))
RDKit 2D
2 1 0 0 0 0 0 0 0 0999 V2000
0.0.0. C 0 0 0 0 0 0 0 0 0 0 0 0
(I see that I accidentally responded to Andrew, only, earlier; I'm copying
to the group this time.)
FWIW, in work on conformational clustering, I used the “most
representative” molecule; that is, the real molecule closest to the
mathematical centroid. This would probably be the best way of display
Hey everyone,
I have a question concerning the Chem.MolToMolFile() function.
When I open this file containing a N+ (here is the line corresponding in
the mol file) :
11.37003.4360 -11.8300 N 0 3 0 0 0 0 0 0 0 0 0 0
And I just save it back withotu any modification, the line
On Sep 21, 2018, at 14:53, Philipp Thiel
wrote:
> you probably read about the Tanimoto being a proper metric in case of having
> binary data
> in Leach and Gillet 'Introduction to Chemoinformatics' chapter 5.3.1 in the
> revised edition.
What we call Tanimoto is more broadly known as the Jacca
I was very happy to hear about the integration of MolVS into RDKit core
in the talk by Susan Leung at the recent UGM.
https://github.com/rdkit/UGM_2018/blob/master/Presentations/Leung_GSoC_RDKit-MolVS_Integration.pdf
This is going to be incredibly useful once it gets released.
To help with test
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