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Issue 4, 2013
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Matched triplicate design sets in the optimisation of glucokinase activators – maximising medicinal chemistry information content

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Abstract

Successful lead optimisation requires the identification of the best compound within the chemical space explored during an optimisation campaign. This can be a costly and inefficient process leading to the synthesis of many sub-optimal compounds. In this paper, a method for carrying out this exercise more effectively is outlined. This relies on the generation of robust datasets on which to build predictive models in a paradigm termed “matched triplicate design sets”. The practical implementation of this approach is exemplified in the optimisation of a new series of glucokinase activators.

Graphical abstract: Matched triplicate design sets in the optimisation of glucokinase activators – maximising medicinal chemistry information content

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Article information


Submitted
13 Dec 2012
Accepted
30 Jan 2013
First published
31 Jan 2013

Med. Chem. Commun., 2013,4, 657-662
Article type
Concise Article

Matched triplicate design sets in the optimisation of glucokinase activators – maximising medicinal chemistry information content

M. J. Waring, S. N. L. Bennett, S. Boyd, L. Campbell, R. D. M. Davies, S. Gerhardt, D. Hargreaves, N. G. Martin, G. R. Robb and G. Wilkinson, Med. Chem. Commun., 2013, 4, 657
DOI: 10.1039/C3MD20367K

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