Generation of QSAR sets with a self-organizing map
Generation of QSAR sets with a self-organizing map
复制标题
DOI：
10.1016/j.jmgm.2004.03.003
复制
发表时间：
2004-09-01
影响因子：
2.9
通讯作者：
Jurs, PC
中科院分区：
文献类型：
--
作者：
Guha, R;Serra, JR;Jurs, PC
一个Kohonen自组织映射（SOM）是用来分类的数据集组成的二氢叶酸还原酶抑制剂的帮助下，外部设置的龙描述符。由此产生的分类是用来生成训练，交叉验证（CV）和预测集的QSAR建模使用ADAPT方法。的结果进行比较，使用活动分箱和球体排除法创建的集产生的QSAR模型。结果表明，SOM是能够产生的QSAR集的组成的整体数据集的相似性方面的代表。由此产生的QSAR模型的一半大小的出版，并具有可比的RMS误差。此外，QSAR集的RMS误差是一致的，表明良好的预测能力以及泛化能力。(C)2004爱思唯尔公司All rights reserved.
A Kohonen self-organizing map (SOM) is used to classify a data set consisting of dihydrofolate reductase inhibitors with the help of an external set of Dragon descriptors. The resultant classification is used to generate training, cross-validation (CV) and prediction sets for QSAR modeling using the ADAPT methodology. The results are compared to those of QSAR models generated using sets created by activity binning and a sphere exclusion method. The results indicate that the SOM is able to generate QSAR sets that are representative of the composition of the overall data set in terms of similarity. The resulting QSAR models are half the size of those published and have comparable RMS errors. Furthermore, the RMS errors of the QSAR sets are consistent, indicating good predictive capabilities as well as generalizability. (C) 2004 Elsevier Inc. All rights reserved.