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ProSAR: A New Methodology for Combinatorial Library Design Hongming Chen*† , Ulf Börjesson† , Ola Engkvist† , Thierry Kogej † , Mats A. Svensson‡ , Niklas Blomberg† , Dirk Weigelt‡ , Jeremy N. Burrows£‡ and Tim Lange‡§ DECS GCS Computational Chemistry, AstraZeneca R&D Mölndal, Pepparedsleden 1, SE-43183 Mölndal, Sweden, and Medicinal Chemistry, AstraZeneca R&D Södertälje, SE-151 85 Södertälje, Sweden
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J. Chem. Inf. Model., 2009, 49 (3), pp 603–614 DOI: 10.1021/ci800231d Publication Date (Web): February 16, 2009 Copyright © 2009 American Chemical Society
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[email protected]., † AstraZeneca R&D Mölndal., ‡ AstraZeneca R&D Södertälje., § Present address: European Patent Office, PatentLaan 2, 2288 EE Rijswijk, The Netherlands., £ Present address: Medicines for Malaria Venture, Route de Pré-Bois 20, 1215 Geneva, Switzerland.
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A method is introduced for performing reagent selection for chemical library design based on
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topological (2D) pharmacophore fingerprints. Optimal reagent selection is achieved by
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optimizing the Shannon entropy of the 2D pharmacophore distribution for the reagent set. The method, termed ProSAR, is therefore expected to enumerate compounds that could serve as a
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good starting point for deriving a structure activity relationship (SAR) in combinatorial library
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design. This methodology is exemplified by library design examples where the active
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compounds were already known. The results show that most of the pharmacophores on the substituents for the active compounds are covered by the designed library. This strategy is further expanded to include product property profiles for aqueous solubility, hERG risk assessment, etc. in the optimization process so that the reagent pharmacophore diversity and
Chen, Hongming
the product property profile are optimized simultaneously via a genetic algorithm. This strategy is applied to a two-dimensional library design example and compared with libraries designed by
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a diversity based strategy which minimizes the average ensemble Tanimoto similarity. Our results show that by using the PSAR methodology, libraries can be designed with simultaneously good pharmacophore coverage and product property profile.
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Received 9 July 2008 Published online 16 February 2009 Published in print 23 March 2009
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