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A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we build and apply a graph-neural-network framework called self-attention-based message-passing ne...

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Autores principales: Tang, Bowen, Kramer, Skyler T., Fang, Meijuan, Qiu, Yingkun, Wu, Zhen, Xu, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035778/
https://www.ncbi.nlm.nih.gov/pubmed/33431047
http://dx.doi.org/10.1186/s13321-020-0414-z
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author Tang, Bowen
Kramer, Skyler T.
Fang, Meijuan
Qiu, Yingkun
Wu, Zhen
Xu, Dong
author_facet Tang, Bowen
Kramer, Skyler T.
Fang, Meijuan
Qiu, Yingkun
Wu, Zhen
Xu, Dong
author_sort Tang, Bowen
collection PubMed
description Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we build and apply a graph-neural-network framework called self-attention-based message-passing neural network (SAMPN) to study the relationship between chemical properties and structures in an interpretable way. The main advantages of SAMPN are that it directly uses chemical graphs and breaks the black-box mold of many machine/deep learning methods. Specifically, its attention mechanism indicates the degree to which each atom of the molecule contributes to the property of interest, and these results are easily visualized. Further, SAMPN outperforms random forests and the deep learning framework MPN from Deepchem. In addition, another formulation of SAMPN (Multi-SAMPN) can simultaneously predict multiple chemical properties with higher accuracy and efficiency than other models that predict one specific chemical property. Moreover, SAMPN can generate chemically visible and interpretable results, which can help researchers discover new pharmaceuticals and materials. The source code of the SAMPN prediction pipeline is freely available at Github (https://github.com/tbwxmu/SAMPN).
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spelling pubmed-70357782020-03-02 A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility Tang, Bowen Kramer, Skyler T. Fang, Meijuan Qiu, Yingkun Wu, Zhen Xu, Dong J Cheminform Research Article Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we build and apply a graph-neural-network framework called self-attention-based message-passing neural network (SAMPN) to study the relationship between chemical properties and structures in an interpretable way. The main advantages of SAMPN are that it directly uses chemical graphs and breaks the black-box mold of many machine/deep learning methods. Specifically, its attention mechanism indicates the degree to which each atom of the molecule contributes to the property of interest, and these results are easily visualized. Further, SAMPN outperforms random forests and the deep learning framework MPN from Deepchem. In addition, another formulation of SAMPN (Multi-SAMPN) can simultaneously predict multiple chemical properties with higher accuracy and efficiency than other models that predict one specific chemical property. Moreover, SAMPN can generate chemically visible and interpretable results, which can help researchers discover new pharmaceuticals and materials. The source code of the SAMPN prediction pipeline is freely available at Github (https://github.com/tbwxmu/SAMPN). Springer International Publishing 2020-02-21 /pmc/articles/PMC7035778/ /pubmed/33431047 http://dx.doi.org/10.1186/s13321-020-0414-z Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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
Tang, Bowen
Kramer, Skyler T.
Fang, Meijuan
Qiu, Yingkun
Wu, Zhen
Xu, Dong
A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
title A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
title_full A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
title_fullStr A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
title_full_unstemmed A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
title_short A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
title_sort self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035778/
https://www.ncbi.nlm.nih.gov/pubmed/33431047
http://dx.doi.org/10.1186/s13321-020-0414-z
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