支架嵌入：学习化学碎片，支架和化合物的结构
Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds
摘要：
分子似乎很自然地适合深度学习通过表示学习处理复杂结构的趋势，只要有足够的数据。然而，这种通常连续的表示对于将化学空间理解为一个域是不自然的，并且对于样品及其差异是特定的。我们专注于探索一种自然的结构，将化学空间表示为一个结构化的域：将药物样化学空间嵌入到一个基于通过包含算子链接的支架类的可重构超图中。本文展示了分子如何形成支架类，支架如何在超图中与每个支架相关，以及支架的这种结构如何自然地用于药物发现工作流程，如预测性质和优化分子结构。我们比较的假设和效用的各种嵌入的分子，如各自的诱导距离度量，其可扩展性表示化学空间作为一个结构化的域，和利用结构的学习任务的后果。
英文摘要：
Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data. However, this often continuous representation is not natural for understanding chemical space as a domain and is particular to samples and their differences. We focus on exploring a natural structure for representing chemical space as a structured domain: embedding drug-like chemical space into an enumerable hypergraph based on scaffold classes linked through an inclusion operator. This paper shows how molecules form classes of scaffolds, how scaffolds relate to each in a hypergraph, and how this structure of scaffolds is natural for drug discovery workflows such as predicting properties and optimizing molecular structures. We compare the assumptions and utility of various embeddings of molecules, such as their respective induced distance metrics, their extendibility to represent chemical space as a structured domain, and the consequences of utilizing the structure for learning tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
