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Chemometrical treatment of electronic structures of 28 flavonoid derivatives
Author(s) -
Vračko Marjan,
Novič Marjana,
Perdih Marko
Publication year - 2000
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/(sici)1097-461x(2000)76:6<733::aid-qua6>3.0.co;2-a
Subject(s) - principal component analysis , artificial neural network , electronic structure , chemistry , computational chemistry , partial least squares regression , linear regression , biological system , spectrum (functional analysis) , molecular descriptor , quantitative structure–activity relationship , statistical physics , mathematics , stereochemistry , physics , computer science , artificial intelligence , quantum mechanics , statistics , biology
Electronic structures of 28 flavonoid derivatives were treated with the chemometrical methods, i.e., multiple linear regression, counterpropagation artificial neural networks, principal component analysis, and partial least‐squares method. Electronic structures given as density‐of‐states spectra were constructed from calculated molecular orbital energies. It was shown that all the methods recognize some structural features from electronic structures. The relationship between electronic structures and biological activity was also studied. The reported results, i.e., the predicted activities, are comparable to the results obtained by models built up with standard descriptors, i.e., topological indices, geometric parameters, and electrostatic indices or “spectrum‐like representations of three‐dimensional structures.” For linear models the correlation coefficients obtained by a cross validation leave‐one‐out method are close to 0.9, and for artificial neural network models they are about 0.8. © 2000 John Wiley & Sons, Inc. Int J Quant Chem 76: 733–743, 2000