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A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been...

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Autores principales: Ma, Mei, Lei, Xiujuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879511/
https://www.ncbi.nlm.nih.gov/pubmed/36701288
http://dx.doi.org/10.1371/journal.pcbi.1010812
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author Ma, Mei
Lei, Xiujuan
author_facet Ma, Mei
Lei, Xiujuan
author_sort Ma, Mei
collection PubMed
description Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug’s unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN–DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling.
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spelling pubmed-98795112023-01-27 A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions Ma, Mei Lei, Xiujuan PLoS Comput Biol Research Article Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug’s unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN–DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling. Public Library of Science 2023-01-26 /pmc/articles/PMC9879511/ /pubmed/36701288 http://dx.doi.org/10.1371/journal.pcbi.1010812 Text en © 2023 Ma, Lei https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ma, Mei
Lei, Xiujuan
A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
title A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
title_full A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
title_fullStr A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
title_full_unstemmed A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
title_short A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
title_sort dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879511/
https://www.ncbi.nlm.nih.gov/pubmed/36701288
http://dx.doi.org/10.1371/journal.pcbi.1010812
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