Multiple-ligand-based virtual screening: Methods and applications of the MTree approach

被引:22
|
作者
Hessler, G
Zimmermann, M
Matter, H
Evers, A
Naumann, T
Lengauer, T
Rarey, M
机构
[1] Sanofi Aventis Deutschland GmbH, Chem Sci, Drug Design, Frankfurt, Germany
[2] Fraunhofer Inst Algorithms & Sci Comp, St Augustin, Germany
[3] Univ Hamburg, Ctr Bioinformat, Hamburg, Germany
[4] Max Planck Inst Informat, Saarbrucken, Germany
关键词
D O I
10.1021/jm050078w
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
We present a novel approach for ligand-based virtual screening by combining query molecules into a multiple feature tree model called MTree. All molecules are described by the established feature,tree descriptor, which is derived from a topological molecular graph. A new pairwise alignment algorithm leads to a consistent topological molecular alignment based on chemically reasonable matching of corresponding functional groups. These multiple feature tree models find application in ligand-based virtual screening to identify new lead structures for chemical optimization. Retrospective virtual screening with MTree models generated for angiotensin-converting enzyme and the ala receptor on a large candidate database yielded enrichment factors up to 71 for the first 1% of the screened database. MTree models outperformed database searches using single feature trees in terms of hit rates and quality and additionally identified alternative molecular scaffolds not included in any of the query molecules. Furthermore, relevant molecular features, which are known to be important for affinity to the target, are identified by this new methodology.
引用
收藏
页码:6575 / 6584
页数:10
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