Hessler Gerhard, Zimmermann Marc, Matter Hans, Evers Andreas, Naumann Thorsten, Lengauer Thomas, Rarey Matthias
Drug Design, Chemical Sciences, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany.
J Med Chem. 2005 Oct 20;48(21):6575-84. doi: 10.1021/jm050078w.
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 alpha1a 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.
我们提出了一种基于配体的虚拟筛选新方法，即将查询分子组合成一个名为MTree的多特征树模型。所有分子都由既定的特征树描述符来描述，该描述符源自拓扑分子图。一种新的成对比对算法基于相应官能团的合理化学匹配，实现一致的拓扑分子比对。这些多特征树模型可用于基于配体的虚拟筛选，以识别用于化学优化的新先导结构。在一个大型候选数据库上，对为血管紧张素转换酶和α1a受体生成的MTree模型进行回顾性虚拟筛选，对于筛选数据库的前1%，富集因子高达71。在命中率和质量方面，MTree模型优于使用单特征树的数据库搜索，并且还识别出了查询分子中未包含的替代分子支架。此外，这种新方法还识别出了已知对与靶点亲和力很重要的相关分子特征。
