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Generative topographic mapping approach to chemical space analysis

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dc.contributor.author Gaspar H.
dc.contributor.author Sidorov P.
dc.contributor.author Horvath D.
dc.contributor.author Baskin I.
dc.contributor.author Marcou G.
dc.contributor.author Varnek A.
dc.date.accessioned 2018-09-19T21:52:49Z
dc.date.available 2018-09-19T21:52:49Z
dc.date.issued 2016
dc.identifier.issn 0097-6156
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/144442
dc.description.abstract © 2016 American Chemical Society.This chapter describes Generative Topographic Mapping (GTM) -A dimensionality reduction method which can be used both to data visualization, clustering and modeling. GTM is a probabilistic extension of Kohonen maps. Its probabilistic nature can be exploited in order to build regression or classification models, to define their applicability domain, to predict activity profiles of compounds, to compare large datasets, to screen for compounds of interest, and even to identify new molecules possessing desirable properties. Thus, GTM can be seen as a sort of a multi-purpose Swiss knife, each of its blades being able to shape an answer to a specific chemoinformatics question, based on a unique map.
dc.relation.ispartofseries ACS Symposium Series
dc.title Generative topographic mapping approach to chemical space analysis
dc.type Chapter
dc.relation.ispartofseries-volume 1222
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 211
dc.source.id SCOPUS00976156-2016-1222-SID84990849825


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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