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 .

中文翻译：

---

“分子解剖学”：一种新的多维层次支架分析工具

支架表示被广泛用于根据共同的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的生物活性相关联。在本文中，我们提出了一种称为“分子解剖学”的新方法，作为一种灵活且无偏见的基于分子支架的指标来对大量化合物进行聚类。我们引入了一组不同抽象级别的九种分子表示，结合碎片规则，定义了分层互连分子框架的多维网络。我们证明，引入灵活的支架定义和多种修剪规则是识别相关化学部分的有效方法。这种方法允许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。我们还提出了一种导出网络可视化的程序，该网络可视化允许化合物数据集的完整图形表示，允许在支架空间中进行有效导航，并为执行高质量的 SAR 分析做出重大贡献。该协议可以通过 https://ma.exscalate.eu 的 Web 界面免费获取。