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A novel approach for screening discrete variations in organic synthesis
Author(s) -
Carlson Rolf,
Carlson Johan,
Grennberg Anders
Publication year - 2001
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.634
Subject(s) - singular value decomposition , principal component analysis , matrix (chemical analysis) , class (philosophy) , computer science , construct (python library) , algorithm , mathematics , artificial intelligence , chemistry , chromatography , programming language
In this paper we present a general strategy for screening discrete variations in organic synthesis. The strategy is based upon principal properties, i.e. principal component characterization of the constituents defining the reaction system. The first step is to select subsets of test items from each class of constituents defining the reaction space, i.e. substrates, reagents, solvents, catalysts, etc., so that the selected items from each class cover the properties considered. The second step is to construct a candidate matrix which contains all possible combinations of the items in the subsets. This matrix is a full multilevel factorial design. The third step is to assign a tentative model for the screening experiment and to construct the corresponding candidate model matrix. The fourth step is to select experiments to yield an experimental design that spans the variable space efficiently and that also gives good estimates of the model parameters. We present an algorithm that uses singular value decomposition to select experiments. The proposed strategy is then illustrated with an example of the Fischer indole synthesis. Copyright © 2001 John Wiley & Sons, Ltd.