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使用生成地形映射进行特权结构基序检测与分析

Privileged Structural Motif Detection and Analysis Using Generative Topographic Maps.

作者信息

Kayastha Shilva, Horvath Dragos, Gilberg Erik, Gütschow Michael, Bajorath Jürgen, Varnek Alexandre

机构信息

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.

Laboratoire de Chemoinformatique, UMR 7140, Université de Strasbourg , 1 rue Blaise Pascal, Strasbourg 67000, France.

出版信息

J Chem Inf Model. 2017 May 22;57(5):1218-1232. doi: 10.1021/acs.jcim.7b00128. Epub 2017 Apr 21.

DOI:10.1021/acs.jcim.7b00128
PMID:28409625
Abstract

Identification of "privileged structural motifs" associated with specific target families is of particular importance for designing novel bioactive compounds. Here, we demonstrate that they can be extracted from a data distribution represented on a two-dimensional map obtained by Generative Topographic Mapping (GTM). In GTM, structurally related molecules are grouped together on the map. Zones of the map preferentially populated by target-specific compounds were delineated, which helped to capture common substructures on the basis of which these compounds were grouped together by GTM. Such privileged structural motifs were identified across three major target superfamilies including proteases, kinases, and G protein coupled receptors. Traditionally, the search for privileged structural motifs focused on scaffolds, whereas motifs were detected here without prior knowledge of compound classification in GTMs. This alternative way of navigating medicinal chemistry space further extends the classical, scaffold-centric approach. Importantly, detected motifs might also comprise fuzzy sets of similar scaffolds, pharmacophore-like patterns, or, by contrast, well-defined scaffolds with specific substituent patterns.

摘要

识别与特定靶标家族相关的“特权结构基序”对于设计新型生物活性化合物尤为重要。在此,我们证明这些基序可以从通过生成地形映射(GTM)获得的二维图谱所表示的数据分布中提取。在GTM中,结构相关的分子在图谱上聚集在一起。划定了图谱中优先被靶标特异性化合物占据的区域,这有助于捕捉共同的子结构,基于这些子结构,这些化合物在GTM中被聚集在一起。在包括蛋白酶、激酶和G蛋白偶联受体在内的三个主要靶标超家族中识别出了此类特权结构基序。传统上,对特权结构基序的搜索集中在支架上,而在此处检测基序时无需事先了解GTM中的化合物分类。这种探索药物化学空间的替代方法进一步扩展了经典的、以支架为中心的方法。重要的是,检测到的基序还可能包括相似支架的模糊集、类药效团模式,或者相反,具有特定取代模式的明确支架。

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