论文标题
文学评论：化学信息学中的图表
Literature Review: Graph Kernels in Chemoinformatics
论文作者
论文摘要
这篇综述的目的是将读者介绍到图表和相应的文献中，并着重于直接应用化学信息学的人。图内核是允许推断分子和化合物特性的函数，可以帮助您完成诸如在药物设计中找到合适化合物等任务。内核方法的使用只是一种特殊的方式，两种方式量化了图之间的相似性。我们将讨论限制在这种方法上，尽管近年来出现了流行的替代方法，最著名的是图形神经网络。
The purpose of this review is to introduce the reader to graph kernels and the corresponding literature, with an emphasis on those with direct application to chemoinformatics. Graph kernels are functions that allow for the inference of properties of molecules and compounds, which can help with tasks such as finding suitable compounds in drug design. The use of kernel methods is but one particular way two quantify similarity between graphs. We restrict our discussion to this one method, although popular alternatives have emerged in recent years, most notably graph neural networks.
