Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

dc.creatorFernandez, M.
dc.creatorCaballero, J.
dc.creatorFernandez, L.
dc.creatorSarai, A.
dc.date2012-09-27T22:14:16Z
dc.date2012-09-27T22:14:16Z
dc.date2011-02
dc.date.accessioned2017-03-07T14:58:34Z
dc.date.available2017-03-07T14:58:34Z
dc.identifierMOLECULAR DIVERSITY Volume: 15 Issue: 1 Special Issue: SI Pages: 269-289 DOI: 10.1007/s11030-010-9234-9
dc.identifier1381-1991
dc.identifierhttp://dspace.utalca.cl/handle/1950/8912
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/375835
dc.descriptionCaballero, J (Caballero, Julio). Univ Talca, Ctr Bioinformat & Simulac Mol, Talca, Chile
dc.descriptionMany articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.
dc.languageen
dc.publisherSPRINGER, VAN GODEWIJCKSTRAAT
dc.subjectDrug design
dc.subjectEnzyme inhibition
dc.subjectFeature selection
dc.subjectIn silico modeling
dc.subjectQSAR
dc.subjectReview
dc.subjectSAR
dc.subjectStructure-activity relationships
dc.titleGenetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)
dc.typeArtículos de revistas


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