Cargando…
Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction
Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representa...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070395/ https://www.ncbi.nlm.nih.gov/pubmed/37012352 http://dx.doi.org/10.1038/s42004-023-00857-x |
_version_ | 1785019009747386368 |
---|---|
author | Jiang, Yinghui Jin, Shuting Jin, Xurui Xiao, Xianglu Wu, Wenfan Liu, Xiangrong Zhang, Qiang Zeng, Xiangxiang Yang, Guang Niu, Zhangming |
author_facet | Jiang, Yinghui Jin, Shuting Jin, Xurui Xiao, Xianglu Wu, Wenfan Liu, Xiangrong Zhang, Qiang Zeng, Xiangxiang Yang, Guang Niu, Zhangming |
author_sort | Jiang, Yinghui |
collection | PubMed |
description | Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT). We design a pharmacophoric-constrained multi-views molecular representation graph, enabling PharmHGT to extract vital chemical information from functional substructures and chemical reactions. With a carefully designed pharmacophoric-constrained multi-view molecular representation graph, PharmHGT can learn more chemical information from molecular functional substructures and chemical reaction information. Extensive downstream experiments prove that PharmHGT achieves remarkably superior performance over the state-of-the-art models the performance of our model is up to 1.55% in ROC-AUC and 0.272 in RMSE higher than the best baseline model) on molecular properties prediction. The ablation study and case study show that our proposed molecular graph representation method and heterogeneous graph transformer model can better capture the pharmacophoric structure and chemical information features. Further visualization studies also indicated a better representation capacity achieved by our model. |
format | Online Article Text |
id | pubmed-10070395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100703952023-04-05 Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction Jiang, Yinghui Jin, Shuting Jin, Xurui Xiao, Xianglu Wu, Wenfan Liu, Xiangrong Zhang, Qiang Zeng, Xiangxiang Yang, Guang Niu, Zhangming Commun Chem Article Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT). We design a pharmacophoric-constrained multi-views molecular representation graph, enabling PharmHGT to extract vital chemical information from functional substructures and chemical reactions. With a carefully designed pharmacophoric-constrained multi-view molecular representation graph, PharmHGT can learn more chemical information from molecular functional substructures and chemical reaction information. Extensive downstream experiments prove that PharmHGT achieves remarkably superior performance over the state-of-the-art models the performance of our model is up to 1.55% in ROC-AUC and 0.272 in RMSE higher than the best baseline model) on molecular properties prediction. The ablation study and case study show that our proposed molecular graph representation method and heterogeneous graph transformer model can better capture the pharmacophoric structure and chemical information features. Further visualization studies also indicated a better representation capacity achieved by our model. Nature Publishing Group UK 2023-04-03 /pmc/articles/PMC10070395/ /pubmed/37012352 http://dx.doi.org/10.1038/s42004-023-00857-x Text en © The Author(s) 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jiang, Yinghui Jin, Shuting Jin, Xurui Xiao, Xianglu Wu, Wenfan Liu, Xiangrong Zhang, Qiang Zeng, Xiangxiang Yang, Guang Niu, Zhangming Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction |
title | Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction |
title_full | Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction |
title_fullStr | Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction |
title_full_unstemmed | Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction |
title_short | Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction |
title_sort | pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070395/ https://www.ncbi.nlm.nih.gov/pubmed/37012352 http://dx.doi.org/10.1038/s42004-023-00857-x |
work_keys_str_mv | AT jiangyinghui pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT jinshuting pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT jinxurui pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT xiaoxianglu pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT wuwenfan pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT liuxiangrong pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT zhangqiang pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT zengxiangxiang pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT yangguang pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction AT niuzhangming pharmacophoricconstrainedheterogeneousgraphtransformermodelformolecularpropertyprediction |