Centre d'Etudes et de Recherche sur le Médicament de Normandie , Normandie Univ, UNICAEN, CERMN , 14000 Caen , France.
Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen , Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC , 14000 Caen , France.
J Med Chem. 2018 Apr 26;61(8):3551-3564. doi: 10.1021/acs.jmedchem.7b01890. Epub 2018 Apr 18.
Historically, structure-activity relationship (SAR) analysis has focused on small sets of molecules, but in recent years, there has been increasing efforts to analyze the growing amount of data stored in public databases like ChEMBL. The pharmacophore network introduced herein is dedicated to the organization of a set of pharmacophores automatically discovered from a large data set of molecules. The network navigation allows to derive essential tasks of a drug discovery process, including the study of the relations between different chemical series, the analysis of the influence of additional chemical features on the compounds' activity, and the identification of diverse binding modes. This paper describes the method used to construct the pharmacophore network, and a case study dealing with BCR-ABL exemplifies its usage for large-scale SAR analysis. Thanks to a benchmarking study, we also demonstrate that the selection of a subset of representative pharmacophores can be used to conduct classification tasks.
从历史上看，构效关系（SAR）分析主要集中在小的分子集合上，但近年来，人们越来越努力地分析像 ChEMBL 这样的公共数据库中存储的大量数据。本文介绍的药效团网络致力于组织一组药效团，这些药效团是从大量分子数据集自动发现的。网络导航允许导出药物发现过程的基本任务，包括研究不同化学系列之间的关系、分析附加化学特征对化合物活性的影响，以及鉴定不同的结合模式。本文描述了构建药效团网络的方法，并通过一个涉及 BCR-ABL 的案例研究说明了其在大规模 SAR 分析中的应用。通过基准研究，我们还证明了选择一组代表性药效团子集可用于进行分类任务。
