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Genetic neural networks

SS So, M Karplus. Evolutionary optimization in quantitative structure-activity relationship An application of genetic neural networks. J Med Chem 39 1521-1530, 1996. [Pg.367]

Hemmateenejad, B., et al., Accurate prediction of the bloodbrain partitioning of a large set of solutes using ab intio calculations and genetic neural network modeling, /. Comp. Chem., 27,1125,2006. [Pg.171]

So, S.S. and Karplus, M. Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications. J. Med. Chem. 1997, 40, 4360-4371. [Pg.239]

So, S.S. Kaeplus, M. Genetic neural networks for quantitative structure-activity relationships improvements and application of benzodiazepine afBnity for benzodiazepine/GABAA receptors./. Med. Chem. 1996, 39, 5246-5256. [Pg.453]

So, S.-S. and Karplus, M. (1996a). Evolutionary Optimization in Quantitative Structure-Activity Relationship An Application of Genetic Neural Networks. J.Med.Chem.,39,1521-1530. [Pg.648]

Borowski, T., Krol, M., Broclawik, E., Baranowski, T, Strekowski, L. and Mokrosz, M.J. (2000) Application of similarity matrices and genetic neural networks in quantitative structure-activity relationships of 2- or 4-(4-methylpiperazino) pyrimidines 5-HT2A receptor antagonists./. Med. Chem., 43, 1901-1909. [Pg.996]

Hemmateenejad, B., Safarpour, M.A. andTaghavi, F. (2003) Application of ab initio theory for the prediction of acidity constants of some 1-hydroxy-9,10-anthraquinone derivatives using genetic neural network./. Mol Struct. (Theochem), 635, 183-190. [Pg.1065]

Wu Caifang, Zeng Yong. Study of gas content prediction based on Genetic Neural Network [J]. Earth Science Frontiers, 2003, 3. [Pg.97]

Quantitative Structure-Activity Relationship An Application of Genetic Neural Networks. [Pg.347]

Tang, G. and Bai, N. (1999) QSAR toxicity study of chlorated aliphatic hydrocarbons using genetic neural network GA-ANN approach. Zhongguo Huanjing Kexue 19, 539-543. [Pg.361]

Figure 14.20 Comparison between roving evenness predicted with neural network and with genetic neural network (adapted from Liu and Yu, 2008b)... Figure 14.20 Comparison between roving evenness predicted with neural network and with genetic neural network (adapted from Liu and Yu, 2008b)...

See other pages where Genetic neural networks is mentioned: [Pg.309]    [Pg.332]    [Pg.1010]    [Pg.331]    [Pg.357]    [Pg.362]    [Pg.348]    [Pg.368]    [Pg.8]    [Pg.422]    [Pg.168]    [Pg.168]    [Pg.173]    [Pg.221]   
See also in sourсe #XX -- [ Pg.221 ]




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