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2010 | 8 | 4 | 877-885
Tytuł artykułu

Modeling the activity of 2-phenylnaphthalene inhibitors using self-training artificial neural networks

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The present study investigates the quantitative structure-activity relationship (QSAR) of 2-phenylnaphthalene ligands on an estrogen receptor (ERα). A data set comprising 70 derivatives of 2-phenylnaphthalene is used. The most suitable parameters, classified as topological, geometric and electronic are selected using a combination of genetic algorithm and multiple linear regression (GA-MLR) methods. Then, selected descriptors are used as inputs for a self-training artificial neural network (STANN). Analysis of the results suggests that the STANN model shows superior results compared to the multiple linear regressions (MLR) by accounting for 91.0% of the variances of the antiseptic potency of the 2-phenylnaphthalene derivatives. The accuracy of the 8-4-1 STANN model is illustrated using leave-multiple-out (LMO) cross-validation and Y-randomization techniques. [...]
Wydawca

Czasopismo
Rocznik
Tom
8
Numer
4
Strony
877-885
Opis fizyczny
Daty
wydano
2010-08-01
online
2010-06-17
Twórcy
  • Department of Chemistry, Faculty of Science, Vali-e-Asr University, 7718897111, Rafsanjan, Iran, garakani@mail.vru.ac.ir
  • Department of Chemistry, Faculty of Science, Vali-e-Asr University, 7718897111, Rafsanjan, Iran
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_s11532-010-0050-y
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