Utilize este identificador para referenciar este registo: http://hdl.handle.net/10451/21384
Título: Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized Genetic Neural Networks and ensemble averaging
Autor: Fernandez, Michael
Carreiras, M. Carmo
Marco, Jose L.
Caballero, Julio
Palavras-chave: Biochemistry & Molecular Biology
Chemistry, Medicinal
Data: 2006
Editora: TAYLOR & FRANCIS LTD
Citação: JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY. - Vol. 21, n. 6 (DEC 2006), p. 647-661
Resumo: Acetylcholinesterase inhibition was modeled for a set of 136 tacrine analogues using Bayesian-regularized Genetic Neural Networks (BRGNNs). In the BRGNN approach the Bayesian-regularization avoids overtraining/overfitting and the genetic algorithm (GA) allows exploring an ample pool of 3D-descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of diverse-training set neural network ensembles (NNEs). The ensemble averaging provides reliable statistics. When 40 members are assembled, the NNE provides a reliable measure of training and test set R values of 0.921 and 0.851 respectively. In other respects, the ability of the nonlinear selected GA space for differentiating the data was evidenced when the total data set was well distributed in a Kohonen Self-Organizing Map (SOM). The location of the inhibitors in the map facilitates the analysis of the connection between compounds and serves as a useful tool for qualitative predictions.
URI: http://hdl.handle.net/10451/21384
DOI: http://dx.doi.org/10.1080/14756360600862366
ISSN: 1475-6366
Aparece nas colecções:FF - Produção Científica 2000-2009

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