Title
A genetic algorithm optimized fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
3-15-2008
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.
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
Journal
Bioorganic & Medicinal Chemistry
Rights
Copyright © 2008 Elsevier Ltd. All rights reserved.
Comments
This article was originally published in Bioorganic & Medicinal Chemistry. The full-text article from the publisher can be found here.
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