Fernández Michael, Tundidor-Camba Alain, Caballero Julio
Molecular Modeling Group, Center for Biotechnological Studies, University of Matanzas, Matanzas, Cuba.
J Chem Inf Model. 2005 Nov-Dec;45(6):1884-95. doi: 10.1021/ci050263i.
Artificial neural network ensembles were used for modeling the cyclin-dependent kinase inhibition of 1H-pyrazolo[3,4-d]pyrimidine derivatives. The structural characteristics of these inhibitors were encoded in relevant 3D-spatial descriptors extracted by genetic algorithm feature selection. Bayesian-regularized multilayer neural networks, trained by the back-propagation algorithm, were developed using these variables as inputs. The predictive power of the model was tested by leave-one-out cross validation. In addition, for a more rigorous measure of the predictive capacity, multiple validation sets were randomly generated as members of neural network ensembles, which makes doing averaged predictions feasible. In this way, the predictive power was analyzed accounting for the averaged test set R values and test set mean-square errors. Otherwise, Kohonen self-organizing maps were used as an additional tool for the same modeling. The location of the inhibitors in a map facilitates the analysis of the connection between compounds and serves as a useful tool for qualitative predictions.
人工神经网络集成用于对1H-吡唑并[3,4-d]嘧啶衍生物的细胞周期蛋白依赖性激酶抑制作用进行建模。这些抑制剂的结构特征通过遗传算法特征选择提取的相关3D空间描述符进行编码。使用反向传播算法训练的贝叶斯正则化多层神经网络，以这些变量作为输入进行开发。通过留一法交叉验证测试模型的预测能力。此外，为了更严格地衡量预测能力，随机生成多个验证集作为神经网络集成的成员，这使得进行平均预测成为可能。通过这种方式，根据平均测试集R值和测试集均方误差分析预测能力。否则，使用Kohonen自组织映射作为同一建模的附加工具。抑制剂在映射中的位置便于分析化合物之间的联系，并作为定性预测的有用工具。
