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TranGRU: focusing on both the local and global information of molecules for molecular property prediction
Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662124/ https://www.ncbi.nlm.nih.gov/pubmed/36405344 http://dx.doi.org/10.1007/s10489-022-04280-y |
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author | Jiang, Jing Zhang, Ruisheng Ma, Jun Liu, Yunwu Yang, Enjie Du, Shikang Zhao, Zhili Yuan, Yongna |
author_facet | Jiang, Jing Zhang, Ruisheng Ma, Jun Liu, Yunwu Yang, Enjie Du, Shikang Zhao, Zhili Yuan, Yongna |
author_sort | Jiang, Jing |
collection | PubMed |
description | Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information and global information of molecules are very important for molecular properties, we aim to integrate the bi-directional gated recurrent unit (BiGRU) into the original Transformer encoder, together with self-attention to better capture local and global molecular information simultaneously. To this end, we propose the TranGRU approach, which encodes the local and global information of molecules by using the BiGRU and self-attention, respectively. Then, we use a gate mechanism to reasonably fuse the two molecular representations. In this way, we enhance the ability of the proposed model to encode both local and global molecular information. Compared to the baselines and state-of-the-art methods when treating each task as a single-task classification on Tox21, the proposed approach outperforms the baselines on 9 out of 12 tasks and state-of-the-art methods on 5 out of 12 tasks. TranGRU also obtains the best ROC-AUC scores on BBBP, FDA, LogP, and Tox21 (multitask classification) and has a comparable performance on ToxCast, BACE, and ecoli. On the whole, TranGRU achieves better performance for molecular property prediction. The source code is available in GitHub: https://github.com/Jiangjing0122/TranGRU. |
format | Online Article Text |
id | pubmed-9662124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-96621242022-11-14 TranGRU: focusing on both the local and global information of molecules for molecular property prediction Jiang, Jing Zhang, Ruisheng Ma, Jun Liu, Yunwu Yang, Enjie Du, Shikang Zhao, Zhili Yuan, Yongna Appl Intell (Dordr) Article Molecular property prediction is an essential but challenging task in drug discovery. The recurrent neural network (RNN) and Transformer are the mainstream methods for sequence modeling, and both have been successfully applied independently for molecular property prediction. As the local information and global information of molecules are very important for molecular properties, we aim to integrate the bi-directional gated recurrent unit (BiGRU) into the original Transformer encoder, together with self-attention to better capture local and global molecular information simultaneously. To this end, we propose the TranGRU approach, which encodes the local and global information of molecules by using the BiGRU and self-attention, respectively. Then, we use a gate mechanism to reasonably fuse the two molecular representations. In this way, we enhance the ability of the proposed model to encode both local and global molecular information. Compared to the baselines and state-of-the-art methods when treating each task as a single-task classification on Tox21, the proposed approach outperforms the baselines on 9 out of 12 tasks and state-of-the-art methods on 5 out of 12 tasks. TranGRU also obtains the best ROC-AUC scores on BBBP, FDA, LogP, and Tox21 (multitask classification) and has a comparable performance on ToxCast, BACE, and ecoli. On the whole, TranGRU achieves better performance for molecular property prediction. The source code is available in GitHub: https://github.com/Jiangjing0122/TranGRU. Springer US 2022-11-14 2023 /pmc/articles/PMC9662124/ /pubmed/36405344 http://dx.doi.org/10.1007/s10489-022-04280-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Jiang, Jing Zhang, Ruisheng Ma, Jun Liu, Yunwu Yang, Enjie Du, Shikang Zhao, Zhili Yuan, Yongna TranGRU: focusing on both the local and global information of molecules for molecular property prediction |
title | TranGRU: focusing on both the local and global information of molecules for molecular property prediction |
title_full | TranGRU: focusing on both the local and global information of molecules for molecular property prediction |
title_fullStr | TranGRU: focusing on both the local and global information of molecules for molecular property prediction |
title_full_unstemmed | TranGRU: focusing on both the local and global information of molecules for molecular property prediction |
title_short | TranGRU: focusing on both the local and global information of molecules for molecular property prediction |
title_sort | trangru: focusing on both the local and global information of molecules for molecular property prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662124/ https://www.ncbi.nlm.nih.gov/pubmed/36405344 http://dx.doi.org/10.1007/s10489-022-04280-y |
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