Klopman G, Rosenkranz H S
Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106.
Mutat Res. 1992 Aug;272(1):59-71. doi: 10.1016/0165-1161(92)90008-a.
In order to develop methods for evaluating the predictive performance of computer-driven structure-activity methods (SAR) as well as to determine the limits of predictivity, we investigated the behavior of two Salmonella mutagenicity data bases: (a) a subset from the Genetox Program and (b) one from the U.S. National Toxicology Program (NTP). For molecules common to the two data bases, the experimental concordance was 76% when "marginals" were included and 81% when they were excluded. Three SAR methods were evaluated: CASE, MULTICASE and CASE/Graph Indices (CASE/GI). The programs "learned" the Genetox data base and used it to predict NTP molecules that were not present in the Genetox compilation. The concordances were 72, 80 and 47% respectively. Obviously, the MULTICASE version is superior and approaches the 85% interlaboratory variability observed for the Salmonella mutagenicity assays when the latter was carried out under carefully controlled conditions.
为了开发评估计算机驱动的构效关系方法（SAR）预测性能的方法，并确定预测性的限度，我们研究了两个沙门氏菌致突变性数据库的情况：（a）Genetox计划的一个子集，以及（b）美国国家毒理学计划（NTP）的一个数据库。对于两个数据库共有的分子，纳入“边缘数据”时实验一致性为76%，排除“边缘数据”时为81%。评估了三种SAR方法：CASE、MULTICASE和CASE/图形指数（CASE/GI）。这些程序“学习”了Genetox数据库，并用于预测Genetox汇编中不存在的NTP分子。一致性分别为72%、80%和47%。显然，MULTICASE版本更优，当沙门氏菌致突变性试验在严格控制的条件下进行时，它接近观察到的85%的实验室间变异性。
