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

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.