Cargando…
Multi-order graph attention network for water solubility prediction and interpretation
The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reduc...
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/PMC9981901/ https://www.ncbi.nlm.nih.gov/pubmed/36864064 http://dx.doi.org/10.1038/s41598-022-25701-5 |
_version_ | 1784900206243872768 |
---|---|
author | Lee, Sangho Park, Hyunwoo Choi, Chihyeon Kim, Wonjoon Kim, Ki Kang Han, Young-Kyu Kang, Joohoon Kang, Chang-Jong Son, Youngdoo |
author_facet | Lee, Sangho Park, Hyunwoo Choi, Chihyeon Kim, Wonjoon Kim, Ki Kang Han, Young-Kyu Kang, Joohoon Kang, Chang-Jong Son, Youngdoo |
author_sort | Lee, Sangho |
collection | PubMed |
description | The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge. |
format | Online Article Text |
id | pubmed-9981901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99819012023-03-04 Multi-order graph attention network for water solubility prediction and interpretation Lee, Sangho Park, Hyunwoo Choi, Chihyeon Kim, Wonjoon Kim, Ki Kang Han, Young-Kyu Kang, Joohoon Kang, Chang-Jong Son, Youngdoo Sci Rep Article The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge. Nature Publishing Group UK 2023-03-02 /pmc/articles/PMC9981901/ /pubmed/36864064 http://dx.doi.org/10.1038/s41598-022-25701-5 Text en © The Author(s) 2023 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/) . |
spellingShingle | Article Lee, Sangho Park, Hyunwoo Choi, Chihyeon Kim, Wonjoon Kim, Ki Kang Han, Young-Kyu Kang, Joohoon Kang, Chang-Jong Son, Youngdoo Multi-order graph attention network for water solubility prediction and interpretation |
title | Multi-order graph attention network for water solubility prediction and interpretation |
title_full | Multi-order graph attention network for water solubility prediction and interpretation |
title_fullStr | Multi-order graph attention network for water solubility prediction and interpretation |
title_full_unstemmed | Multi-order graph attention network for water solubility prediction and interpretation |
title_short | Multi-order graph attention network for water solubility prediction and interpretation |
title_sort | multi-order graph attention network for water solubility prediction and interpretation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981901/ https://www.ncbi.nlm.nih.gov/pubmed/36864064 http://dx.doi.org/10.1038/s41598-022-25701-5 |
work_keys_str_mv | AT leesangho multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT parkhyunwoo multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT choichihyeon multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT kimwonjoon multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT kimkikang multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT hanyoungkyu multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT kangjoohoon multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT kangchangjong multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation AT sonyoungdoo multiordergraphattentionnetworkforwatersolubilitypredictionandinterpretation |