Recognition of drug-target interaction patterns using genetic algorithm-optimized Bayesian-regularized neural networks and support vector machines

Authors:
Michael Fernandez;Akinori Sarai;Shandar Ahmad
Affiliations:
Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu, Iizuka, Japan;Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu, Iizuka, Japan;National Institute of Biomedical Innovation, Ibaraki-shi, Osaka, Japan
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Genetic algorithm (GA) applied to feature selection and model optimization improved the performance of robust mathematical models such as Bayesian-regularized neural networks (BRANNs) and support vector machines (SVMs) on different drug design datasets. The selection of optimum input variables and the adjustment of network weights and biases to optimum values to yield generalizable predictors were optimized by combining Bayesian training and GA based-variable selection. Similarly, kernel and regularization parameters of SVMs were properly set by GA optimization. The predictors were more accurate and robust than previous published models on the same datasets. In addition, feature selection over large pools of molecular descriptors allowed determining the structural and atomic properties of the ligands that are ruling the biological interactions with the target.