Modern research increasingly relies on automated methods to assist
researchers. An example of this is Optical Chemical Structure Recognition
(OCSR), which aids chemists in retrieving information about chemicals from
large amounts of documents. Markush structures are chemical structures that
cannot be parsed correctly by OCSR and cause errors. The focus of this research
was to propose and test a novel method for classifying Markush structures.
Within this method, a comparison was made between fixed-feature extraction and
end-to-end learning (CNN). The end-to-end method performed significantly better
than the fixed-feature method, achieving 0.928 (0.035 SD) Macro F1 compared to
the fixed-feature method's 0.701 (0.052 SD). Because of the nature of the
experiment, these figures are a lower bound and can be improved further. These
results suggest that Markush structures can be filtered out effectively and
accurately using the proposed method. When implemented into OCSR pipelines,
this method can improve their performance and use to other researchers.

通过比较固定特征提取和端到端学习（CNN）方法，本研究提出并测试了一种用于对 Markush 结构进行分类的新方法。与固定特征方法相比，端到端方法表现显著更好，宏 F1 值达到 0.928（标准偏差 0.035），而固定特征方法为 0.701（标准偏差 0.052）。因实验性质，这些结果可进一步提高。结果表明，该提出的方法可以有效准确地过滤掉 Markush 结构，并能提高其应用于其他研究者的性能。