Polański J
Department of Organic Chemistry, Institute of Chemistry, University of Silesia, Katowice, Poland.
SAR QSAR Environ Res. 2000;11(3-4):245-61. doi: 10.1080/10629360008033234.
A novel method for modeling 3D QSAR has been developed. The method involves a multiple training of a series of self-organizing networks (SOM). The obtained networks have been used for processing the data of one reference molecule. A scheme for the analysis of such data with the PLS analysis has been proposed and tested using the steroids data with corticosteroid binding globulin (CBG) affinity. The predictivity of the CBG models measured with the SDEP parameter is among the best one reported. Although 3-D QSAR models for colchicinoid series is far less predictive, it allows for a discussion on the relative influence of the structural motifs of these compounds.
已开发出一种用于三维定量构效关系建模的新方法。该方法涉及对一系列自组织网络（SOM）进行多次训练。所获得的网络已用于处理一个参考分子的数据。已提出一种使用偏最小二乘法（PLS）分析此类数据的方案，并使用具有皮质类固醇结合球蛋白（CBG）亲和力的类固醇数据进行了测试。用SDEP参数衡量的CBG模型的预测能力在已报道的最佳模型之列。尽管秋水仙碱类系列的三维定量构效关系模型的预测能力要低得多，但它有助于讨论这些化合物结构基序的相对影响。
