Iyer Preeti, Stumpfe Dagmar, Vogt Martin, Bajorath J, Maggiora G M
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, D-53113 Bonn phone/fax: +49-228-2699-306/341.
College of Pharmacy & BIO5 Institute, University of Arizona, Translational Genomics Research Institute, 1295 North Martin, PO Box 210202, Tucson, AZ 85721, USA, 445 North Fifth Street, Phoenix, AZ 85004, USA.
Mol Inform. 2013 Jun;32(5-6):421-30. doi: 10.1002/minf.201200120. Epub 2013 Feb 27.
Activity landscapes provide a comprehensive description of structure-activity relationships (SARs). An information theoretic assessment of their features, namely, activity cliffs, similarity cliffs, smooth-SAR, and featureless regions, is presented based on the probability of occurrence of these features. It is shown that activity cliffs provide highly informative SARs compared to smooth-SAR regions, although the latter are the basis for most QSAR studies. This follows since small structural changes in the former are coupled with relatively large changes in activity, thus pinpointing specific structural features associated with the changes in activity. In contrast, Smooth-SAR regions are typically associated with relatively small changes in both structure and activity. Surprisingly, similarity cliffs, which occur when both compounds in a compound-pair have approximately equal activities but significantly different structures, are the most prevalent feature of activity landscapes. Hence, from an information theoretic point of view, they are the least informative landscape feature. Nevertheless, similarity cliffs do provide SAR information on potentially new active compound classes, and in that sense they are quite useful in drug discovery programs since they provide alternative possibilities should ADMET or other issues arise during the discovery and earlier preclinical development phases of drug research.
活性景观提供了结构-活性关系(SARs)的全面描述。基于这些特征出现的概率,对其特征,即活性悬崖、相似性悬崖、平滑SAR和无特征区域进行了信息论评估。结果表明,与平滑SAR区域相比,活性悬崖提供了信息丰富得多的SARs,尽管后者是大多数定量构效关系(QSAR)研究的基础。之所以如此,是因为前者的小结构变化与活性的相对大变化相关联,从而确定了与活性变化相关的特定结构特征。相比之下,平滑SAR区域通常与结构和活性的相对小变化相关联。令人惊讶的是,相似性悬崖,即在化合物对中的两种化合物具有大致相等的活性但结构显著不同时出现,是活性景观中最普遍的特征。因此,从信息论的角度来看,它们是信息最少的景观特征。然而,相似性悬崖确实提供了关于潜在新活性化合物类别的SAR信息,从这个意义上说,它们在药物发现计划中非常有用,因为如果在药物研究的发现和早期临床前开发阶段出现药物代谢动力学、药物安全性评价或其他问题,它们提供了替代可能性。