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Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation
Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completel...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351086/ https://www.ncbi.nlm.nih.gov/pubmed/35927691 http://dx.doi.org/10.1186/s13321-022-00634-3 |
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author | Kong, Yue Zhao, Xiaoman Liu, Ruizi Yang, Zhenwu Yin, Hongyan Zhao, Bowen Wang, Jinling Qin, Bingjie Yan, Aixia |
author_facet | Kong, Yue Zhao, Xiaoman Liu, Ruizi Yang, Zhenwu Yin, Hongyan Zhao, Bowen Wang, Jinling Qin, Bingjie Yan, Aixia |
author_sort | Kong, Yue |
collection | PubMed |
description | Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00634-3. |
format | Online Article Text |
id | pubmed-9351086 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-93510862022-08-05 Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation Kong, Yue Zhao, Xiaoman Liu, Ruizi Yang, Zhenwu Yin, Hongyan Zhao, Bowen Wang, Jinling Qin, Bingjie Yan, Aixia J Cheminform Research Article Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00634-3. Springer International Publishing 2022-08-04 /pmc/articles/PMC9351086/ /pubmed/35927691 http://dx.doi.org/10.1186/s13321-022-00634-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Article Kong, Yue Zhao, Xiaoman Liu, Ruizi Yang, Zhenwu Yin, Hongyan Zhao, Bowen Wang, Jinling Qin, Bingjie Yan, Aixia Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation |
title | Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation |
title_full | Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation |
title_fullStr | Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation |
title_full_unstemmed | Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation |
title_short | Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation |
title_sort | integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351086/ https://www.ncbi.nlm.nih.gov/pubmed/35927691 http://dx.doi.org/10.1186/s13321-022-00634-3 |
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