Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany.
J Chem Inf Model. 2012 Sep 24;52(9):2354-65. doi: 10.1021/ci300306a. Epub 2012 Aug 23.
Activity cliffs are formed by pairs of structurally similar compounds that act against the same target but display a significant difference in potency. Such activity cliffs are the most prominent features of activity landscapes of compound data sets and a primary focal point of structure-activity relationship (SAR) analysis. The search for activity cliffs in various compound sets has been the topic of a number of previous investigations. So far, activity cliff analysis has concentrated on data mining for activity cliffs and on their graphical representation and has thus been descriptive in nature. By contrast, approaches for activity cliff prediction are currently not available. We have derived support vector machine (SVM) models to successfully predict activity cliffs. A key aspect of the approach has been the design of new kernels to enable SVM classification on the basis of molecule pairs, rather than individual compounds. In test calculations on different data sets, activity cliffs have been accurately predicted using specifically designed structural representations and kernel functions.
活性崖由作用于同一靶标但效力有显著差异的结构相似的化合物对形成。这种活性崖是化合物数据集活性景观中最显著的特征，也是构效关系（SAR）分析的主要焦点。在各种化合物集中寻找活性崖一直是许多先前研究的主题。到目前为止，活性崖分析主要集中在活性崖的数据挖掘及其图形表示上，因此本质上是描述性的。相比之下，目前还没有用于活性崖预测的方法。我们已经推导出支持向量机（SVM）模型来成功地预测活性崖。该方法的一个关键方面是设计新的核函数，以便能够基于分子对而不是单个化合物进行 SVM 分类。在对不同数据集的测试计算中，使用专门设计的结构表示和核函数可以准确地预测活性崖。
