Dimova Dilyana, Bajorath Jürgen
Department of Life Science Informatics, B-IT, LIMES, Program Unit Medicinal Chemistry and Chemical Biology, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, D-53113 Bonn, Germany.
Department of Life Science Informatics, B-IT, LIMES, Program Unit Medicinal Chemistry and Chemical Biology, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, D-53113 Bonn, Germany.
Eur J Med Chem. 2014 Nov 24;87:454-60. doi: 10.1016/j.ejmech.2014.09.087. Epub 2014 Sep 30.
The vast majority of activity cliffs that occur is sets of bioactive compounds are formed in a coordinated manner. This means that multiple and overlapping cliffs are formed by groups of structural analogs with varying activity. In network representations, coordinated activity cliffs emerge as clusters of varying size and topology. Activity cliff clusters are typically rich in structure-activity relationship (SAR) information but often difficult to analyze from a medicinal chemistry viewpoint. A key question is how to best access SAR information contained in activity cliff clusters without the need to evaluate many different clusters individually. Herein, we introduce a methodology for the systematic extraction of SAR information from activity cliff clusters that utilizes the concept of matching molecular series (MMS). Sequences of activity cliff-forming compounds are isolated from clusters that follow a activity gradient and series spanning large activity differences are preferentially selected. In addition to its systematic nature, an attractive feature of the approach is that SAR information associated with extracted series is readily interpretable. We show that MMS are abundant in activity cliff clusters from the current spectrum of bioactive compounds and that many MMS share compounds. The resulting pairs of connected MMS contain compounds with closely related structural cores and alternative substitution sites that reveal SAR determinants and preferred substituents.
绝大多数活性断崖出现在以协调方式形成的生物活性化合物集合中。这意味着多个且相互重叠的断崖是由具有不同活性的结构类似物组形成的。在网络表示中，协调的活性断崖表现为大小和拓扑结构各异的簇。活性断崖簇通常富含构效关系（SAR）信息，但从药物化学的角度来看往往难以分析。一个关键问题是如何在无需单独评估许多不同簇的情况下，最佳地获取活性断崖簇中包含的SAR信息。在此，我们介绍一种从活性断崖簇中系统提取SAR信息的方法，该方法利用了匹配分子系列（MMS）的概念。从遵循活性梯度的簇中分离出形成活性断崖的化合物序列，并优先选择跨越较大活性差异的系列。除了其系统性之外，该方法的一个吸引人的特点是与提取的系列相关的SAR信息易于解释。我们表明，MMS在当前生物活性化合物谱中的活性断崖簇中大量存在，并且许多MMS共享化合物。由此产生的成对连接的MMS包含具有密切相关结构核心和替代取代位点的化合物，这些位点揭示了SAR决定因素和优选取代基。
