Artificial Neural Networks from MATLAB® in Medicinal Chemistry. Bayesian-Regularized Genetic Neural Networks (BRGNN): Application to the Prediction of the Antagonistic Activity Against Human Platelet Thrombin Receptor (PAR-1)

Title: Artificial Neural Networks from MATLAB® in Medicinal Chemistry. Bayesian-Regularized Genetic Neural Networks (BRGNN): Application to the Prediction of the Antagonistic Activity Against Human Platelet Thrombin Receptor (PAR-1)

Volume: 8 Issue: 18

Author(s): Julio Caballero and Michael Fernandez

Affiliation:

Keywords: Bayesian regularized-Genetic Neural Networks, MATLAB, Antagonists of the Human Platelet Thrombin Receptor (PAR-1), 5-amino-3-arylisoxazole derivatives, three-dimensional Quantitative structure-activity relationship

Abstract: Artificial neural networks (ANNs) have been widely used for medicinal chemistry modeling. In the last two decades, too many reports used MATLAB environment as an adequate platform for programming ANNs. Some of these reports comprise a variety of applications intended to quantitatively or qualitatively describe structure-activity relationships. A powerful tool is obtained when there are combined Bayesian-regularized neural networks (BRANNs) and genetic algorithm (GA): Bayesian-regularized genetic neural networks (BRGNNs). BRGNNs can model complicated relationships between explanatory variables and dependent variables. Thus, this methodology is regarded as useful tool for QSAR analysis. In order to demonstrate the use of BRGNNs, we developed a reliable method for predicting the antagonistic activity of 5-amino-3-arylisoxazole derivatives against Human Platelet Thrombin Receptor (PAR-1), using classical 3D-QSAR methodologies: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). In addition, 3D vectors generated from the molecular structures were correlated with antagonistic activities by multivariate linear regression (MLR) and Bayesian-regularized neural networks (BRGNNs). All models were trained with 34 compounds, after which they were evaluated for predictive ability with additional 6 compounds. CoMFA and CoMSIA were unable to describe this structure-activity relationship, while BRGNN methodology brings the best results according to validation statistics.

Cite this article as:

Caballero Julio and Fernandez Michael, Artificial Neural Networks from MATLAB® in Medicinal Chemistry. Bayesian-Regularized Genetic Neural Networks (BRGNN): Application to the Prediction of the Antagonistic Activity Against Human Platelet Thrombin Receptor (PAR-1), Current Topics in Medicinal Chemistry 2008; 8 (18) . https://dx.doi.org/10.2174/156802608786786570