TY - JOUR A1 - Rajan, Kohulan A1 - Steinbeck, Christoph A1 - Zielesny, Achim T1 - Performance of chemical structure string representations for chemical image recognition using transformers T2 - Digital Discovery N2 - The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES perform in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were performed using publicly available datasets and the code used to train and evaluate the models has been made available to the public. Y1 - 2022 UR - https://whge.opus.hbz-nrw.de/frontdoor/index/index/docId/4116 SN - 2635-098X VL - 1.2022 IS - 1 SP - 84 EP - 90 PB - Royal Society of Chemistry CY - Cambridge ER -