Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1785050907916894208 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10228139 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ucakumitv improvingthequalityofchemicallanguagemodeloutcomeswithatominsmilestokenization AT ashyrmamatovislambek improvingthequalityofchemicallanguagemodeloutcomeswithatominsmilestokenization AT leejuyong improvingthequalityofchemicallanguagemodeloutcomeswithatominsmilestokenization |