Bayesian ARTMAP prediction of biological activities for potential HIV-1 protease inhibitors using a small molecular dataset

Document Type

Conference Proceeding

Department or Administrative Unit

Chemistry

Publication Date

5-21-2014

Abstract

Several neural architectures were successfully used to predict properties of chemical compounds. Obtaining satisfactory results with neural networks depends on the availability of large data samples. However, most classical Quantitative Structure-Activity Relationship studies have been performed on small datasets. Neural models do generally infer with difficulty from such datasets. In our study, we analyze the performance of the Bayesian ARTMAP for the prediction of biological activities of HIV-1 protease inhibitors, when inferring from a small and structurally diverse dataset of molecules. The Bayesian ARTMAP is a neural model which uses both competitive learning and Bayesian prediction, and has both the universal approximation and best approximation properties. It is the first time when this model is used in a “real-world” function approximation application. We compare the performance of the Bayesian ARTMAP to several other models, each implementing a different learning mechanism. Experiments are performed within Weka's “Experimenter” standard environment. For our small and structurally diverse dataset of chemical compounds, the Bayesian ARTMAP is a good prediction tool, and the most accurate prediction models are the ones which perform local approximation.

Comments

This article was originally published in 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology. 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.

Journal

2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

Rights

Copyright © 2014, IEEE

Link to Full Text
Contact Author

Share

COinS