Manelfi Candida, Gemei Marica, Talarico Carmine, Cerchia Carmen, Fava Anna, Lunghini Filippo, Beccari Andrea Rosario
Dompé Farmaceutici SpA, Via Campo di Pile, 67100, L'Aquila, Italy.
Department of Pharmacy, University of Naples "Federico II", 80131, Napoli, Italy.
J Cheminform. 2021 Jul 23;13:54. doi: 10.1186/s13321-021-00526-y. eCollection 2021.
The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities. In this paper, we present a novel approach called "Molecular Anatomy" as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks. We demonstrate that the introduction of a flexible scaffold definition and multiple pruning rules is an effective method to identify relevant chemical moieties. This approach allows to cluster together active molecules belonging to different molecular classes, capturing most of the structure activity information, in particular when libraries containing a huge number of singletons are analyzed. We also propose a procedure to derive a network visualization that allows a full graphical representation of compounds dataset, permitting an efficient navigation in the scaffold's space and significantly contributing to perform high quality SAR analysis. The protocol is freely available as a web interface at https://ma.exscalate.eu .
支架表示法被广泛用于基于共同核心结构对生物活性化合物进行分类，或将化合物类别与特定生物活性相关联。在本文中，我们提出了一种名为“分子解剖学”的新方法，作为一种灵活且无偏差的基于分子支架的度量标准，用于对大量化合物进行聚类。我们引入了一组九个处于不同抽象层次的分子表示，并结合碎片化规则，来定义一个层次互连的分子框架的多维网络。我们证明，引入灵活的支架定义和多个修剪规则是识别相关化学基团的有效方法。这种方法能够将属于不同分子类别的活性分子聚集在一起，捕获大部分结构活性信息，特别是在分析包含大量单例的库时。我们还提出了一种生成网络可视化的程序，该程序允许对化合物数据集进行完整的图形表示，从而在支架空间中进行高效导航，并显著有助于进行高质量的SAR分析。该协议可通过网络界面在https://ma.exscalate.eu上免费获取。
