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Fast modeling of binding affinities by means of superposing significant interaction rules (SSIR) method

The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties

© International Journal of Molecular Sciences, 2016, vol. 17, núm. 6, p. 827

MDPI (Multidisciplinary Digital Publishing Institute)

Autor: Besalú i Llorà, Emili
Data: 1 juny 2016
Resum: The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties
Format: application/pdf
Cita: 025197
ISSN: 1661-6596
Accés al document: http://hdl.handle.net/10256/12968
Llenguatge: eng
Editor: MDPI (Multidisciplinary Digital Publishing Institute)
Col·lecció: AGAUR/2014-2016/2014 SGR-1202
Reproducció digital del document publicat a: http://dx.doi.org/10.3390/ijms17060827
Articles publicats (D-Q)
És part de: © International Journal of Molecular Sciences, 2016, vol. 17, núm. 6, p. 827
Drets: Attribution 4.0 Spain
URI Drets: http://creativecommons.org/licenses/by/4.0/es/
Matèria: Química combinatòria
Combinatorial chemistry
Títol: Fast modeling of binding affinities by means of superposing significant interaction rules (SSIR) method
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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