Self-organizing neural networks for modeling robust 3D and 4D QSAR: application to dihydrofolate reductase inhibitors

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DC pole	Wartość	Język
dc.contributor.author	Polański, Jarosław	-
dc.contributor.author	Gieleciak, Rafał	-
dc.contributor.author	Magdziarz, Tomasz	-
dc.contributor.author	Bąk, Andrzej	-
dc.date.accessioned	2017-12-02T16:12:11Z	-
dc.date.available	2017-12-02T16:12:11Z	-
dc.date.issued	2004	-
dc.identifier.citation	Molecules, (2004), iss. 12, p. 1148-1159	pl_PL
dc.identifier.issn	1420-3049	-
dc.identifier.uri	http://hdl.handle.net/20.500.12128/520	-
dc.description.abstract	We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.	pl_PL
dc.language.iso	en	pl_PL
dc.rights	Uznanie autorstwa 3.0 Polska	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/pl/	*
dc.subject	3D QSAR	pl_PL
dc.subject	4D QSAR	pl_PL
dc.subject	CoMSA	pl_PL
dc.subject	Self-organizing Neural Network	pl_PL
dc.subject	SOM-4D QSAR	pl_PL
dc.title	Self-organizing neural networks for modeling robust 3D and 4D QSAR: application to dihydrofolate reductase inhibitors	pl_PL
dc.type	info:eu-repo/semantics/article	pl_PL
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