caa a22 4500 445878363 CHVBK 20180317145534.0 cr unu---uuuuu 170323e20110201xx s 000 0 eng 10.1007/s11030-010-9234-9 doi (NATIONALLICENCE)springer-10.1007/s11030-010-9234-9 Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM) [Elektronische Daten] [Michael Fernandez, Julio Caballero, Leyden Fernandez, Akinori Sarai] Many 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. Springer Science+Business Media B.V., 2010 Drug design nationallicence Enzyme inhibition nationallicence Feature selection nationallicence In silico modeling nationallicence QSAR nationallicence Review nationallicence SAR nationallicence Structure-activity relationships nationallicence ADMET : Absorption, distribution, metabolism, excretion and toxicity nationallicence AD : Alzheimer's disease nationallicence log S : Aqueous solubility nationallicence ANNs : Artificial neural networks nationallicence BRANNs : Bayesian-regularized artificial neural networks nationallicence BRGNNs : Bayesian-regularized genetic neural networks nationallicence BBB : Blood-brain barrier nationallicence CoMFA : Comparative molecular field analysis nationallicence CG : Conjugated Gradient nationallicence GA : Genetic algorithm nationallicence GA-PLS : Genetic algorithm-based partial least squares nationallicence GA-SVM : Genetic algorithm-optimized support vector machines nationallicence GNN : Genetic neural networks nationallicence GSR : Genetic stochastic resonance nationallicence HIA : Human intestinal absorption nationallicence PPBR : Human plasma protein binding rate nationallicence Log P : Lipophilicity nationallicence LHRH : Luteinizing hormone-releasing hormone nationallicence MMP : Matrix metalloproteinase nationallicence MT : Mitochondrial toxicity nationallicence MLR : Multiple linear regression nationallicence MT- : Negative mitochondrial toxicity nationallicence NNEs : Neural network ensembles nationallicence EVA : Normal coordinate eigenvalue nationallicence BIO : Oral bioavailability nationallicence PLS : Partial least squares nationallicence P-gp : P-glycoprotein nationallicence PCC : Physicochemical composition nationallicence MT+ : Positive mitochondrial toxicity nationallicence PC-GA-ANN : Principal component-genetic algorithm-artificial neural network nationallicence PCs : Principal components nationallicence PPR : Projection pursuit regression nationallicence QSAR : Quantitative structure-activity relationship nationallicence QSPR : Quantitative structure-property relationship nationallicence RBF : Radial Basic Function nationallicence SOMs : Self-organized maps nationallicence SR : Stochastic resonance nationallicence SVMs : Support vector machines nationallicence Trb1 : Thyroid hormone receptor b1 nationallicence Tdp : Torsades de pointes nationallicence VKCs : Voltage-gated potassium channels nationallicence Fernandez Michael Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, 820-8502, Iizuka, Japan aut Caballero Julio Centro de Bioinformatica y Simulacion Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile aut Fernandez Leyden Barcelona Supercomputing Center—Centro Nacional de Supercomputación, Nexus II Building c/ Jordi Girona, 29, 08034, Barcelona, Spain aut Sarai Akinori Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, 820-8502, Iizuka, Japan aut Molecular Diversity Springer Netherlands 15/1(2011-02-01), 269-289 1381-1991 15:1<269 2011 15 11030 https://doi.org/10.1007/s11030-010-9234-9 text/html Onlinezugriff via DOI 1 review-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11030-010-9234-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fernandez Michael Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, 820-8502, Iizuka, Japan aut NATIONALLICENCE 700 1- Caballero Julio Centro de Bioinformatica y Simulacion Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile aut NATIONALLICENCE 700 1- Fernandez Leyden Barcelona Supercomputing Center—Centro Nacional de Supercomputación, Nexus II Building c/ Jordi Girona, 29, 08034, Barcelona, Spain aut NATIONALLICENCE 700 1- Sarai Akinori Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, 820-8502, Iizuka, Japan aut NATIONALLICENCE 773 0- Molecular Diversity Springer Netherlands 15/1(2011-02-01), 269-289 1381-1991 15:1<269 2011 15 11030 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer