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On Jul 31, 2008, at 3:38 AM, Egon Willighagen wrote:
> Hi all,
>
> I think we have been challenged by the Unilever Cambridge Center for
> Molecular Informatics, Dr Jonathan Goodman, Prof Robert Glen and Dr
> Antonio LlinĂ s [0].
>
> The challenge not just us, but any data modeler, by asking us to
> predict the solubility of 32 compounds, based on a computational model
> we can train against 100 knowns. Now, in my experience 100 is a bit on
> the low side, but at least higher than the bare limit to get low
> enough statistics to distinguish prediction models. Or, in other
> words, we barely can make a choice of a best model. But just enough to
> pick up the challenge I assume.
>
> Now, any one can make a reasonable model... the real challenge to me
> is to make a reproducable model, a model that anyone in the world can
> build themselves. So, with Blue Obelisk components (or other
> opensource tools).

Reproducible is definitely a worthy model. But equally important is  
quality.

You're right that given the small training set, it might a bit tricky  
to get a good quality model. But are you thinking of straight up  
QSAR? Recent papers [1,2] suggests that crystal lattice energies  
should be taken into account - but for that they used structures from  
the CCDC. At the very least, we'd need some sort of solvent enabled  
conformer generator. I don't know whether taking into account  
multiple confs (say via Boltzmann weighting) would be useful Also  
good partial charges would be useful (MOPAC would be a good option  
for that). It might also be useful to derive some sort of interaction  
descriptors from MD runs (described in [2])

[1] http://dx.doi.org/10.1021/mp7000878
[2] http://dx.doi.org/10.1021/mp070030+

- -------------------------------------------------------------------
Rajarshi Guha  <[EMAIL PROTECTED]>
GPG Fingerprint: D070 5427 CC5B 7938 929C  DD13 66A1 922C 51E7 9E84
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A beer delayed is a beer denied.


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