Re: QSAR - How to statistically determine when variables are "wor king well together"

Dear Stefan Dove,

Please, have a look in my review article [Current Pharmaceutical Design,
2003, 9, 1575-1606] for an approach in which the pharmacophore is
revealed WITHOUT ARBITRARY DESCRIPTORS AND STATISTICS, while prediction
of activity quantitatively involves physically transparent parameters of
pharmacophore flexibility and anti-pharmacophore shielding.

Regards
Isaac

Stefan Dove wrote:

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>---
>
>Dear all,
>
>let me give the following comment to Hugo Kubinyi:
>
>How to derive models which are as simple as possible (and as complex as
>necessary)? The e x c l u s i v e selection of variables that explain
>as single variables is mostly not appropriate, but each variable
>selection must include these variables. Checking all three-variable
>combinations as recommended by Hugo will commonly retain such critical
>descriptors. The cited example, however, refers just to the case where
>the selection of only X-4 is at least not "nonsense". Trivially, if a
>single variable like X-4 explains m u c h of the data, the
>combination w i t h other variables will not improve the fit and the
>prediction, but in the case of multicollinearities the combination o f
> other variables may reproduce the effect of the single variable (Table
>7 in the cited article is a nice example of the influence of this rule
>in PLS).
>
>May be that I always overemphasize the goal to get more transparent
>results with interpretable models and therefore favor strict variable
>selection. As referee I often had to deal with manuscripts
>investigating the correlation of a huge number of topological
>descriptors with biological or chemical data, but in some cases a
>simple inspection of a table has shown that a single variable explained
>most of the SAR by discriminating between discrete structural features.
>Today, with Internet, fast computers and easily available software, it
>is much too simple also for unexperienced users to obtain large
>descriptor sets. Therefore we may not often enough call for chemical
>and pharmacological transparency of our results.
>
>Best regards,
>
>Stefan.
>
>
>Prof. Dr. Stefan Dove Tel. +49 941/943/4673
>Univ. Regensburg FAX +49 941/943/4820
>Inst. Pharmazie
>93040 Regensburg EMail: Stefan.Dove:+:chemie.uni-regensburg.de
>Germany
>
>
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-- 
Dr. Isaac B. Bersuker
Institute for Theoretical Chemistry
The University of Texas at Austin
Chem & Biochem Department
1 University Station A5300
Austin, TX 78712-0165
Phone: (512) 471-4671; Fax: (512) 471-8696
E-mail: bersuker!=!mail.cm.utexas.edu
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