背景:化学空间嵌入方法在各种研究环境中广泛用于降维,聚类和有效的可视化。嵌入过程生成的地图可以为药物化学家提供有价值的洞察力,化合物的物理化学和生物学性质。然而,众所周知,这些地图很难解释,当嵌入不同的化合物集时,地图上的\'\'景观\'\'容易发生\'\'重排\'\'。
结果:在这项研究中,我们提出了希尔伯特曲线辅助空间嵌入(HCASE)方法,该方法旨在通过根据药物化学家熟悉的逻辑组织结构来创建映射。首先,化学空间是在一组\'\'参考支架\'\'的帮助下创建的。这些支架根据现有技术中发现的受药物化学启发的支架-密钥算法进行分类。接下来,有序的支架被映射到折叠成更高维(这里:2D)空间的线。复杂折叠的线被称为伪希尔伯特曲线。化合物的嵌入是通过将其最相似的参考支架定位在伪希尔伯特曲线中并假定相应的位置来进行的。通过一系列的实验,我们演示了HCASE方法生成的地图的属性。嵌入的对象是DrugBank和CANVASS库的化合物,化学空间由ChEMBL数据库中提取的支架定义。
■HCASE方法的新颖性在于生成强大而直观的化学空间嵌入,这些嵌入反映了药物化学家的推理,并在该过程中优先使用空间填充(Hilbert)曲线。
背景:https://github.com/ncats/hcase。
BACKGROUND: Chemical space embedding methods are widely utilized in various research settings for dimensional reduction, clustering and effective visualization. The maps generated by the embedding process can provide valuable insight to medicinal chemists in terms of the relationships between structural, physicochemical and biological properties of compounds. However, these maps are known to be difficult to interpret, and the \'\'landscape\'\' on the map is prone to \'\'rearrangement\'\' when embedding different sets of compounds.
RESULTS: In this study we present the Hilbert-Curve Assisted Space Embedding (HCASE) method which was designed to create maps by organizing structures according to a logic familiar to medicinal chemists. First, a chemical space is created with the help of a set of \'\'reference scaffolds\'\'. These scaffolds are sorted according to the medicinal chemistry inspired Scaffold-Key algorithm found in prior art. Next, the ordered scaffolds are mapped to a line which is folded into a higher dimensional (here: 2D) space. The intricately folded line is referred to as a pseudo-Hilbert-Curve. The embedding of a compound happens by locating its most similar reference scaffold in the pseudo-Hilbert-Curve and assuming the respective position. Through a series of experiments, we demonstrate the properties of the maps generated by the HCASE method. Subjects of embeddings were compounds of the DrugBank and CANVASS libraries, and the chemical spaces were defined by scaffolds extracted from the ChEMBL database.
UNASSIGNED: The novelty of HCASE method lies in generating robust and intuitive chemical space embeddings that are reflective of a medicinal chemist\'s reasoning, and the precedential use of space filling (Hilbert) curve in the process.
BACKGROUND: https://github.com/ncats/hcase.