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Modern research increasingly relies on automated methods to assistresearchers. An example of this is Optical Chemical Structure Recognition(OCSR), which aids chemists in retrieving information about chemicals fromlarge amounts of documents. Markush structures are chemical structures thatcannot be parsed correctly by OCSR and cause errors. The focus of this researchwas to propose and test a novel method for classifying Markush structures.Within this method, a comparison was made between fixed-feature extraction andend-to-end learning (CNN). The end-to-end method performed significantly betterthan the fixed-feature method, achieving 0.928 (0.035 SD) Macro F1 compared tothe fixed-feature method's 0.701 (0.052 SD). Because of the nature of theexperiment, these figures are a lower bound and can be improved further. Theseresults suggest that Markush structures can be filtered out effectively andaccurately using the proposed method. When implemented into OCSR pipelines,this method can improve their performance and use to other researchers.

One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images