Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular graph similarity based on distance between learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. Finally, we compare the applications of the embedding to standard practices, with a focus on known failure points and edge cases; concluding that our approach can be used in conjunction to existing methods.

中文翻译：

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深度度量学习在分子图相似性中的应用

基于图的方法在化学和药物发现中越来越重要，其应用范围从 QSAR 到分子生成。结合图神经网络和深度度量学习概念，我们公开了一个框架，用于根据与任何端点分离的学习嵌入之间的距离来量化分子图相似性。使用相似性的最小定义和来自公共化合物 ZINC 数据库的数据，这项工作展示了嵌入的特性及其对一系列应用的适用性，其中包括一种用于训练深度分子自动编码器的新型重建损失方法。最后，我们将嵌入的应用与标准实践进行了比较，重点是已知的故障点和边缘情况；得出结论，我们的方法可以与现有方法结合使用。