Subject: Design of QSAR-training set

From: Christian Pilger <[email-masked]>
Date: Tue, 22 Aug 2000 14:02:21 +0200

Dear CCL'ers,

	I have a dataset comprised of 62 compounds which I would like to
use for QSAR. I got some promising results utilizing the GRID/Golpe
procedure on the whole bunch of compounds. Now I wish to split the dataset
into a training set and a prediction set in order to validate a model
obtained for the training set "quasi-externally".
My goal is to screen the descriptor space for compounds that represent the
dataset reasonably well. I've employed Factorial Designs (FD) in the
pricipal component space for this purpose with little success. Does anyone
have experience with this application of FD ?
Is anyone aware of other methods to perform this task ? I thought of
D-optimal design but have not found suitable tools (programs/source code)
for this purpose.
All comments are appreciated.

Cheers

Christian

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From: Elie Giraud <[email-masked]>
Date: Tue, 22 Aug 2000 17:54:27 +0200

Dear Christian,

In constrained situations, D-optimal design is the best choice. You can
do such a selection in your PP space by using MODDE (Umetri Sofwares):
WWW.umetrics.com. If needed, I can give you some useful references.
  
Tchao,

Elie.

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