Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem.

Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance.

Methods: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used.

Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS.

Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

背景：图形编辑距离是一种用于解决容错图形匹配的方法。该方法通过确定将一个图转换为另一个图所需的最少修改次数来估计两个图之间的距离。这些修改称为编辑操作，具有关联的编辑成本，必须根据问题确定该成本。

目的：本研究专注于优化技术的使用，以了解在通过图形编辑距离比较图形时使用的编辑成本。

方法：图形表示使用药效基团类型节点描述来编码相关分子特性的简化的分子结构表示。这种归约技术被称为扩展归约图。使用了基于配体的虚拟筛选基准平台和RDKit上可用的筛选和统计工具。

结果：在实验中，使用学习成本的图形编辑距离比使用预定义成本的效果更好或相同。以六个公开可用的数据集为例：DUD-E，MUV，GLL＆GDD，CAPST，NRLiSt BDB和ULS-UDS。

结论：这项研究表明，图的编辑距离以及所学的编辑成本可用于识别结构多样的分子组中的生物活性相似性。此外，针对特定目标的编辑成本可能会为将来的药物设计工作提供有用的结构活性信息。