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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.25168
|2 doi
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|a pubmed24n0935.xml
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|a (DE-627)NLM280597312
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Žuvela, Petar
|e verfasserin
|4 aut
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| 245 |
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|a Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 18.09.2019
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|a Date Revised 10.12.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2018 Wiley Periodicals, Inc.
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|a Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD2 , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD2 and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a ANN interpretation
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|a ANNs
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|a QSAR
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|a antioxidants
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|a flavonoids
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|a Antioxidants
|2 NLM
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| 650 |
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|a Flavonoids
|2 NLM
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1 |
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|a David, Jonathan
|e verfasserin
|4 aut
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| 700 |
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|a Wong, Ming Wah
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 39(2018), 16 vom: 15. Juni, Seite 953-963
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
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|g volume:39
|g year:2018
|g number:16
|g day:15
|g month:06
|g pages:953-963
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|u http://dx.doi.org/10.1002/jcc.25168
|3 Volltext
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