A genetic algorithm optimized fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease

Authors

Levente Fabry-Asztalos, Central Washington UniversityFollow
Răzvan Andonie, Central Washington UniversityFollow
Catharine J. Collar, Central Washington University
Sarah Abdul-Wahid, Central Washington University
Nicholas Salim, Central Washington University

Department or Administrative Unit

Computer Science

Document Type

Article

Author Copyright

Copyright © 2008 Elsevier Ltd. All rights reserved.

Publication Date

3-15-2008

Journal

Bioorganic & Medicinal Chemistry

Abstract

A fuzzy neural network (FNN) was trained on a dataset of 177 HIV-1 protease ligands with experimentally measured IC₅₀ values. A set of descriptors was selected to build nonlinear quantitative structure–activity relationships. A genetic algorithm (GA) was implemented to optimize the architecture of the fuzzy neural network used to predict biological activity of HIV-1 protease inhibitors. Evolutionary methods were used to apply feature selection (FS) to this model. Results obtained on an external test set of 21 molecules, with and without feature selection, were compared. Applying feature selection to the GA-FNN resulted in a more accurate prediction of biological activity. Fuzzy IF/THEN rules were extracted from the optimized FNN. In the future the developed models are expected to be useful in the rational design of novel enzyme inhibitors for HIV-1 protease.

Comments

This article was originally published in Bioorganic & Medicinal Chemistry. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Recommended Citation

Fabry-Asztalos, L., Andonie, R., Collar, C. J., Abdul-Wahid, S., & Salim, N. (2008). A genetic algorithm optimized fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease. Bioorganic & Medicinal Chemistry, 16(6), 2903–2911. https://doi.org/10.1016/j.bmc.2007.12.055