Fernández Michael, Caballero Julio
Molecular Modeling Group, Center for Biotechnological Studies, University of Matanzas, Matanzas, C.P. 44740, Cuba.
Chem Biol Drug Des. 2006 Oct;68(4):201-12. doi: 10.1111/j.1747-0285.2006.00435.x.
Acetylcholinesterase inhibition was modeled for a set of huprines using ensembles of Bayesian-regularized Genetic Neural Networks. In the Bayesian-regularized Genetic Neural Network approach the Bayesian regularization avoids overfitted regressions and the genetic algorithm allows exploring a wide pool of three-dimensional descriptors. The predictive capacity of our selected model was evaluated by averaging multiple validation sets generated as members of neural network ensembles. When 60 members are assembled, the neural network ensemble provides a reliable measure of training and test set R(2)-values of 0.945 and 0.850 respectively. In other respects, the ability of the nonlinear selected genetic algorithm space for differentiate the data were evidenced when total data set was well distributed in a Kohonen self-organizing map. The analysis of the self-organizing map zones allows establishing the main structural features differentiated by our vectorial space.
使用贝叶斯正则化遗传神经网络集合对一组胡豆碱进行乙酰胆碱酯酶抑制建模。在贝叶斯正则化遗传神经网络方法中，贝叶斯正则化可避免过度拟合回归，遗传算法则允许探索大量的三维描述符。我们所选模型的预测能力通过对作为神经网络集合成员生成的多个验证集进行平均来评估。当组装60个成员时，神经网络集合分别提供了可靠的训练集和测试集R(2)值测量，分别为0.945和0.850。在其他方面，当总数据集在Kohonen自组织映射中分布良好时，非线性选择的遗传算法空间区分数据的能力得到了证明。对自组织映射区域的分析允许确定由我们的向量空间区分的主要结构特征。
