So S S, Karplus M
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
J Med Chem. 1997 Dec 19;40(26):4347-59. doi: 10.1021/jm970487v.
The utility of genetic neural network (GNN) to obtain quantitative structure-activity relationships (QSAR) from molecular similarity matrices is described. In this application, the corticosteroid-binding globulin (CBG) binding affinity of the well-known steroid data set is examined. Excellent predictivity can be obtained through the use of either electrostatic or shape properties alone. Statistical validation using a standard randomization test indicates that the results are not due to chance correlations. Application of GNN on the combined electrostatic and shape matrix produces a six-descriptor model with a cross-validated r2 value of 0.94. The model is superior to those obtained from partial least-squares and genetic regressions, and it also compares favorably with the results for the same data set from other established 3D QSAR methods. The theoretical basis for the use of molecular similarity in QSAR is discussed.
描述了遗传神经网络（GNN）从分子相似性矩阵中获得定量构效关系（QSAR）的效用。在本应用中，研究了著名类固醇数据集的皮质类固醇结合球蛋白（CBG）结合亲和力。仅使用静电或形状属性即可获得出色的预测能力。使用标准随机化测试进行的统计验证表明，结果并非偶然相关。将GNN应用于组合的静电和形状矩阵可生成一个具有六个描述符的模型，其交叉验证的r2值为0.94。该模型优于从偏最小二乘法和遗传回归获得的模型，并且与其他已建立的3D QSAR方法对同一数据集的结果相比也具有优势。讨论了在QSAR中使用分子相似性的理论基础。
