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

• Authors: Zahra Garkani-Nejad , Naser Jalili-Jahani
• Languages of publication: EN

Abstracts

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. [...]

Keywords

EN

Quantitative structure-activity relationship; 2-phenylnaphthalenes; Genetic algorithm Regression analysis; Self-training artificial neural networks

• Publisher: De Gruyter Open
• Journal: Open Chemistry
• Year: 2010
• Volume: 8
• Issue: 4
• Pages: 877-885

Dates

published

1 - 8 - 2010

online

17 - 6 - 2010

Contributors

author

Zahra Garkani-Nejad

• Department of Chemistry, Faculty of Science, Vali-e-Asr University, 7718897111, Rafsanjan, Iran

author

Naser Jalili-Jahani

• Department of Chemistry, Faculty of Science, Vali-e-Asr University, 7718897111, Rafsanjan, Iran

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Identifiers

DOI

10.2478/s11532-010-0050-y