To give some context, it's not that we're trying to sample a diverse
set of conformers (and find something close to the experimental). In
this case, we're generating initial geometries - to assess Naruki's
fragment-based builder in Open Babel.

But you raise an excellent point - by picking only one random
conformer (as we're doing), we'll absolutely going to have a higher
RMSD than sampling 50 conformers per compound and picking the best.

We'll try a quick test to be safe, but thanks for the suggestion.

-Geoff

On Mon, Feb 25, 2019 at 3:55 PM Greg Landrum <greg.land...@gmail.com> wrote:
>
> Hi Naruki,
>
> You're only generating a single conformer per molecule; I wouldn't expect 
> that to do particularly well. It's generally better to call 
> EmbedMultipleConfs().
>
> As an aside: I've looked at the platinum set too, it might be worth checking 
> out this RDKit blog post: 
> http://rdkit.blogspot.com/2017/05/looking-at-platinum-dataset.html
>
> -greg
>
>
> On Mon, Feb 25, 2019 at 11:53 AM Naruki Yoshikawa 
> <naruki.yoshik...@gmail.com> wrote:
>>
>> Dear all,
>>
>> I'm evaluating ETKDG method implemented in RDKit using the Platinum
>> Dataset introduced in a benchmark paper
>> https://pubs.acs.org/doi/abs/10.1021/acs.jcim.7b00505/
>> SMILES generated from the dataset is served as input and a 3D
>> conformer is generated.
>> We evaluate RMSD between generated structure and experimental structure.
>>
>> Although the author of the benchmark paper reported the mean RMSD to
>> be below 1.0 angstrom, my evaluation code reports around 1.5 angstrom.
>> I can't figure out why such a big difference occurs.
>>
>> My evaluation code is here:
>> https://gist.github.com/n-yoshikawa/0ba04a1b0c718c4cc8d83702f3759afa
>> There is a link to data in this gist.
>>
>> Thanks,
>> Naruki
>>
>>
>> _______________________________________________
>> Rdkit-discuss mailing list
>> Rdkit-discuss@lists.sourceforge.net
>> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss


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