Issue 2, 2022

Performance of chemical structure string representations for chemical image recognition using transformers

Abstract

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.

Graphical abstract: Performance of chemical structure string representations for chemical image recognition using transformers

Article information

Article type
Paper
Submitted
17 Sep 2021
Accepted
12 Jan 2022
First published
15 Jan 2022
This article is Open Access
Creative Commons BY license

Digital Discovery, 2022,1, 84-90

Performance of chemical structure string representations for chemical image recognition using transformers

K. Rajan, C. Steinbeck and A. Zielesny, Digital Discovery, 2022, 1, 84 DOI: 10.1039/D1DD00013F

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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