Schärfer Christin, Schulz-Gasch Tanja, Hert Jérôme, Heinzerling Lennart, Schulz Benjamin, Inhester Therese, Stahl Martin, Rarey Matthias
Center for Bioinformatics, University of Hamburg, Bundesstraße 46, 20146 Hamburg (Germany).
ChemMedChem. 2013 Oct;8(10):1690-700. doi: 10.1002/cmdc.201300242. Epub 2013 Aug 8.
The generation of sets of low-energy conformations for a given molecule is a central task in drug design. Herein we present a new conformation generator called CONFECT that builds on our previously published library of torsion patterns. Conformations are generated as well as ranked by means of normalized frequency distributions derived from the Cambridge Structural Database (CSD). Following an incremental construction approach, conformations are selected from a systematic enumeration within energetic boundaries. The new tool is benchmarked in several different ways, indicating that it allows the efficient generation of high-quality conformation ensembles. These ensembles are smaller than those produced by state-of-the-art tools, yet they effectively cover conformational space.
为给定分子生成低能量构象集是药物设计中的核心任务。在此，我们提出一种名为CONFECT的新构象生成器，它基于我们之前发表的扭转模式库构建。构象通过源自剑桥结构数据库（CSD）的归一化频率分布生成并排序。遵循增量构建方法，从能量边界内的系统枚举中选择构象。该新工具通过多种不同方式进行了基准测试，表明它能够高效生成高质量的构象集合。这些集合比现有工具生成的集合更小，但它们有效地覆盖了构象空间。
