Center for Bioinformatics (ZBH), University of Hamburg, Hamburg, Germany.
J Chem Inf Model. 2011 Sep 26;51(9):2156-63. doi: 10.1021/ci200014g. Epub 2011 Aug 18.
Reduced graph descriptors, like feature trees, are frequently applied in cases where the relative arrangement of functional groups is more important than exact substructure matches. Due to their ability to deal with fragmented molecules, they are well-suited for fragment space search and library design. We recently presented LoFT, a novel focused library design approach based on feature trees. During evaluation two drawbacks of the reduced graph descriptor were discovered: First, regioisomeric substructures cannot be distinguished in feature tree mappings which results in a large information loss especially when connecting R-groups to cores. Second, the automatic matching procedure might result in undesired alignments, since the knowledge on what is considered as core by the user is not taken into account. In the following, we will present two approaches to overcome those drawbacks. The generation of the feature trees is modified, so that different arene substitution patterns can be recognized and a customized matching is introduced, allowing the user to determine the parts of the query, where the reagents are allowed to match. Subsequently we investigate the improvements on library design by reviewing the design scenarios which were already used for the evaluation of LoFT.
简化的图描述符,如特征树,常用于相对功能基团排列比精确子结构匹配更重要的情况。由于它们能够处理碎片化的分子,因此非常适合片段空间搜索和库设计。我们最近提出了基于特征树的新型聚焦库设计方法 LoFT。在评估过程中,发现简化图描述符存在两个缺点:首先,在特征树映射中无法区分区域异构体亚结构,这会导致大量信息丢失,尤其是在将 R 基团连接到核心时。其次,自动匹配过程可能导致不理想的对齐,因为用户没有考虑到什么被认为是核心的知识。在接下来的内容中,我们将介绍两种克服这些缺点的方法。对特征树的生成进行了修改,以便识别不同的芳环取代模式,并引入了自定义匹配,允许用户确定试剂允许匹配的查询部分。然后,我们通过回顾已经用于 LoFT 评估的设计场景,研究了库设计的改进。