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Comprehensive analysis of applicability domains of QSPR models for chemical reactions

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dc.contributor.author Rakhimbekova A.
dc.contributor.author Madzhidov T.I.
dc.contributor.author Nugmanov R.I.
dc.contributor.author Gimadiev T.R.
dc.contributor.author Baskin I.I.
dc.contributor.author Varnek A.
dc.date.accessioned 2021-02-25T20:44:38Z
dc.date.available 2021-02-25T20:44:38Z
dc.date.issued 2020
dc.identifier.issn 1661-6596
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/162352
dc.description.abstract © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Nowadays, the problem of the model’s applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models’ performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several “best” AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem.
dc.relation.ispartofseries International Journal of Molecular Sciences
dc.subject Applicability domain
dc.subject Chemical reactions
dc.subject Chemoinformatics
dc.subject Machine learning
dc.subject QSAR/QSPR
dc.subject Quantitative Reaction–Property Relationship
dc.subject Reaction mining
dc.title Comprehensive analysis of applicability domains of QSPR models for chemical reactions
dc.type Article
dc.relation.ispartofseries-issue 15
dc.relation.ispartofseries-volume 21
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 1
dc.source.id SCOPUS16616596-2020-21-15-SID85089112683


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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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