Naveja J Jesús, Medina-Franco José L
PECEM, School of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Front Chem. 2019 Jul 16;7:510. doi: 10.3389/fchem.2019.00510. eCollection 2019.
Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.
在此，我们引入星座图作为一种通用方法，该方法融合了不同且互补的分子表示形式，以增强化学空间的可视化表示和分析中所包含的信息。该方法基于基于子结构的化合物表示和分类与化学空间的“经典”基于坐标的表示相结合。该方法的一个显著成果是，将化合物按类似系列进行组织会导致分子组的形成，即在化学空间中形成“星座”。这种新方法具有通用性，可用于快速识别例如化学空间中易于解释的有洞察力的和“显著的”构效关系（StARs）。预计这种分析对于未注释分子的大型数据集中的先导物识别特别有用，例如通过高通量筛选获得的数据集。我们使用针对DNA甲基转移酶（DNMTs）和蛋白激酶B（AKT1）设计的两个聚焦抑制剂数据集来演示该方法的应用。
