Statistical molecular design of balanced compound libraries for QSAR modeling

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Fältnamn	Indikatorer	Metadata
000		03235naa a2200409 4500
001		oai:DiVA.org:umu-35576
003		SwePub
008		100824s2010 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-355762 URI
024	7	a https://doi.org/10.2174/0929867107912336612 DOI
040		a (SwePub)umu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a for2 swepub-publicationtype
100	1	a Linusson, Annau Umeå universitet,Kemiska institutionen4 aut0 (Swepub:umu)analin99
245	1 0	a Statistical molecular design of balanced compound libraries for QSAR modeling
264	1	b Bentham Science Publishers Ltd.c 2010
338		a print2 rdacarrier
520		a A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.
650	7	a NATURVETENSKAPx Kemi0 (SwePub)1042 hsv//swe
650	7	a NATURAL SCIENCESx Chemical Sciences0 (SwePub)1042 hsv//eng
653		a statistical molecular design
653		a quantitative structure-activity relationship
653		a factorial design
653		a D-optimal design
653		a Principal Component Analysis
653		a library design
653		a drug design
653		a parallel synthesis
700	1	a Elofsson, Mikaelu Umeå universitet,Kemiska institutionen4 aut0 (Swepub:umu)miel0001
700	1	a Andersson, Ida Eu Umeå universitet,Kemiska institutionen4 aut0 (Swepub:umu)idaann02
700	1	a Dahlgren, Markus Ku Umeå universitet,Kemiska institutionen4 aut
710	2	a Umeå universitetb Kemiska institutionen4 org
773	0	t Current Medicinal Chemistryd : Bentham Science Publishers Ltd.g 17:19, s. 2001-2016q 17:19<2001-2016x 0929-8673x 1875-533X
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-35576
856	4 8	u https://doi.org/10.2174/092986710791233661

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Av författaren/redakt...: Linusson, Anna; Elofsson, Mikael; Andersson, Ida E; Dahlgren, Markus ...

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Av lärosätet: Umeå universitet

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