New QSAR methods applied to structure-activity mapping and combinatorial chemistry

被引：50
作者：
Burden, FR [1 ]
Winkler, DA
机构：
[1] Monash Univ, Dept Chem, Clayton, Vic 3168, Australia
[2] CSIRO, Div Mol Sci, Clayton, Vic 3169, Australia
来源：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES | 1999年 / 39卷 / 02期
关键词：
D O I：
10.1021/ci980070d
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
A comparison is made of a number of computationally efficient molecular indices with a view to the screening of very large virtual data sets of molecules. The use of Bayesian regularized neural networks is discussed, and their virtue in eliminating the need for validation sets, and potentially even test sets, is emphasized. The concept: of a virtual receptor is introduced, and this is illustrated by the results of screening a database of 40 000 molecules.
引用
收藏
页码：236 / 242
页数：7
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