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Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization

Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a certain limitation that results in tokens failing to reflect the true nature of molecules. T...

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Autores principales: Ucak, Umit V., Ashyrmamatov, Islambek, Lee, Juyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228139/
https://www.ncbi.nlm.nih.gov/pubmed/37248531
http://dx.doi.org/10.1186/s13321-023-00725-9
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author Ucak, Umit V.
Ashyrmamatov, Islambek
Lee, Juyong
author_facet Ucak, Umit V.
Ashyrmamatov, Islambek
Lee, Juyong
author_sort Ucak, Umit V.
collection PubMed
description Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a certain limitation that results in tokens failing to reflect the true nature of molecules. To address this issue, we developed the atom-in-SMILES tokenization scheme that eliminates ambiguities in the generic nature of SMILES tokens. Our results in multiple chemical translation and molecular property prediction tasks demonstrate that proper tokenization has a significant impact on prediction quality. In terms of prediction accuracy and token degeneration, atom-in-SMILES is more effective method in generating higher-quality SMILES sequences from AI-based chemical models compared to other tokenization and representation schemes. We investigated the degrees of token degeneration of various schemes and analyzed their adverse effects on prediction quality. Additionally, token-level repetitions were quantified, and generated examples were incorporated for qualitative examination. We believe that the atom-in-SMILES tokenization has a great potential to be adopted by broad related scientific communities, as it provides chemically accurate, tailor-made tokens for molecular property prediction, chemical translation, and molecular generative models.
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spelling pubmed-102281392023-05-31 Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization Ucak, Umit V. Ashyrmamatov, Islambek Lee, Juyong J Cheminform Research Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a certain limitation that results in tokens failing to reflect the true nature of molecules. To address this issue, we developed the atom-in-SMILES tokenization scheme that eliminates ambiguities in the generic nature of SMILES tokens. Our results in multiple chemical translation and molecular property prediction tasks demonstrate that proper tokenization has a significant impact on prediction quality. In terms of prediction accuracy and token degeneration, atom-in-SMILES is more effective method in generating higher-quality SMILES sequences from AI-based chemical models compared to other tokenization and representation schemes. We investigated the degrees of token degeneration of various schemes and analyzed their adverse effects on prediction quality. Additionally, token-level repetitions were quantified, and generated examples were incorporated for qualitative examination. We believe that the atom-in-SMILES tokenization has a great potential to be adopted by broad related scientific communities, as it provides chemically accurate, tailor-made tokens for molecular property prediction, chemical translation, and molecular generative models. Springer International Publishing 2023-05-29 /pmc/articles/PMC10228139/ /pubmed/37248531 http://dx.doi.org/10.1186/s13321-023-00725-9 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ucak, Umit V.
Ashyrmamatov, Islambek
Lee, Juyong
Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
title Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
title_full Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
title_fullStr Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
title_full_unstemmed Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
title_short Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization
title_sort improving the quality of chemical language model outcomes with atom-in-smiles tokenization
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228139/
https://www.ncbi.nlm.nih.gov/pubmed/37248531
http://dx.doi.org/10.1186/s13321-023-00725-9
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