KFU. Card publication. Comprehensive analysis of applicability domains of QSPR models for chemical reactions

KAZAN
FEDERAL UNIVERSITY

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- 中文
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COMPREHENSIVE ANALYSIS OF APPLICABILITY DOMAINS OF QSPR MODELS FOR CHEMICAL REACTIONS

Form of presentation

Articles in international journals and collections

Year of publication

2020

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Authors, employees of KFU

Baskin Igor Iosifovich, author

Varnek Aleksandr , author

Gimadiev Timur Rustemovich, author

Madzhidov Timur Ismailovich, author

Nugmanov Ramil Irekovich, author

Authors, postgraduates KFU

Rakhimbekova Asima , postgraduate kfu

Bibliographic description in the original language

Rakhimbekova, A. Comprehensive analysis of applicability domains of QSPR models for chemical reactions / A. Rakhimbekova, T.I. Madzhidov, R.I. Nugmanov, T.R. Gimadiev, I.I. Baskin, A. Varnek // Int. J. Mol. Sci.-2020.-21(15)-pp.1-20. doi: 10.3390/ijms21155542.

Annotation

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 dat

Keywords

Applicability domain, Chemical reactions, Chemoinformatics, Machine learning, QSAR/QSPR, Quantitative Reaction?Property Relationship Reaction mining

The name of the journal

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=249811&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Baskin Igor Iosifovich	ru_RU
dc.contributor.author	Varnek Aleksandr	ru_RU
dc.contributor.author	Gimadiev Timur Rustemovich	ru_RU
dc.contributor.author	Madzhidov Timur Ismailovich	ru_RU
dc.contributor.author	Nugmanov Ramil Irekovich	ru_RU
dc.contributor.author	Rakhimbekova Asima	ru_RU
dc.date.accessioned	2020-01-01T00:00:00Z	ru_RU
dc.date.available	2020-01-01T00:00:00Z	ru_RU
dc.date.issued	2020	ru_RU
dc.identifier.citation	Rakhimbekova, A. Comprehensive analysis of applicability domains of QSPR models for chemical reactions / A. Rakhimbekova, T.I. Madzhidov, R.I. Nugmanov, T.R. Gimadiev, I.I. Baskin, A. Varnek // Int. J. Mol. Sci.-2020.-21(15)-pp.1-20. doi: 10.3390/ijms21155542.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=249811&p_lang=2	ru_RU
dc.description.abstract	INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES	ru_RU
dc.description.abstract	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 dat	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Applicability domain	ru_RU
dc.subject	Chemical reactions	ru_RU
dc.subject	Chemoinformatics	ru_RU
dc.subject	Machine learning	ru_RU
dc.subject	QSAR/QSPR	ru_RU
dc.subject	Quantitative Reaction?Property Relationship Reaction mining	ru_RU
dc.title	Comprehensive analysis of applicability domains of QSPR models for chemical reactions	ru_RU
dc.type	Articles in international journals and collections	ru_RU