Predicting scalar coupling constants by graph angle-attention neural network

Abstract Scalar coupling constant (SCC), directly measured by nuclear magnetic resonance (NMR) spectroscopy, is a key parameter for molecular structure analysis, and widely used to predict unknown molecular structure. Restricted by the high cost of NMR experiments, it is impossible to measure the SC...

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Autores principales:	Jia Fang, Linyuan Hu, Jianfeng Dong, Haowei Li, Hui Wang, Huafen Zhao, Yao Zhang, Min Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/26938c079f914428b59f6a95757082b4
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spelling	oai:doaj.org-article:26938c079f914428b59f6a95757082b42021-12-02T18:48:23ZPredicting scalar coupling constants by graph angle-attention neural network10.1038/s41598-021-97146-12045-2322https://doaj.org/article/26938c079f914428b59f6a95757082b42021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97146-1https://doaj.org/toc/2045-2322Abstract Scalar coupling constant (SCC), directly measured by nuclear magnetic resonance (NMR) spectroscopy, is a key parameter for molecular structure analysis, and widely used to predict unknown molecular structure. Restricted by the high cost of NMR experiments, it is impossible to measure the SCC of unknown molecules on a large scale. Using density functional theory (DFT) to theoretically calculate the SCC of molecules is incredibly challenging, due to the cost of substantial computational time and space. Graph neural networks (GNN) of artificial intelligence (AI) have great potential in constructing molecul ar-like topology models, which endows them the ability to rapidly predict SCC through data-driven machine learning methods, and avoiding time-consuming quantum chemical calculations. With a priori knowledge of angles, we propose a graph angle-attention neural network (GAANN) model to predict SCC by means of some easily accessible related information. GAANN, with a multilayer message-passing network and a self-attention mechanism, can accurately simulate the molecular-like topological structure and predict molecular properties. Our simulations show that the prediction accuracy by GAANN, with the log(MAE) = −2.52, is close to that by DFT calculations. Different from conventional AI methods, GAANN combining the AI method with quantum chemistry theory (Karplus equation) has a strong physicochemical interpretability about angles. From an AI perspective, we find that bond angle has the highest correlation with the SCC among all angle features (dihedral angle, bond angle, geometric angles) about multiple coupling types in the small molecule datasets.Jia FangLinyuan HuJianfeng DongHaowei LiHui WangHuafen ZhaoYao ZhangMin LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jia Fang Linyuan Hu Jianfeng Dong Haowei Li Hui Wang Huafen Zhao Yao Zhang Min Liu Predicting scalar coupling constants by graph angle-attention neural network
description	Abstract Scalar coupling constant (SCC), directly measured by nuclear magnetic resonance (NMR) spectroscopy, is a key parameter for molecular structure analysis, and widely used to predict unknown molecular structure. Restricted by the high cost of NMR experiments, it is impossible to measure the SCC of unknown molecules on a large scale. Using density functional theory (DFT) to theoretically calculate the SCC of molecules is incredibly challenging, due to the cost of substantial computational time and space. Graph neural networks (GNN) of artificial intelligence (AI) have great potential in constructing molecul ar-like topology models, which endows them the ability to rapidly predict SCC through data-driven machine learning methods, and avoiding time-consuming quantum chemical calculations. With a priori knowledge of angles, we propose a graph angle-attention neural network (GAANN) model to predict SCC by means of some easily accessible related information. GAANN, with a multilayer message-passing network and a self-attention mechanism, can accurately simulate the molecular-like topological structure and predict molecular properties. Our simulations show that the prediction accuracy by GAANN, with the log(MAE) = −2.52, is close to that by DFT calculations. Different from conventional AI methods, GAANN combining the AI method with quantum chemistry theory (Karplus equation) has a strong physicochemical interpretability about angles. From an AI perspective, we find that bond angle has the highest correlation with the SCC among all angle features (dihedral angle, bond angle, geometric angles) about multiple coupling types in the small molecule datasets.
format	article
author	Jia Fang Linyuan Hu Jianfeng Dong Haowei Li Hui Wang Huafen Zhao Yao Zhang Min Liu
author_facet	Jia Fang Linyuan Hu Jianfeng Dong Haowei Li Hui Wang Huafen Zhao Yao Zhang Min Liu
author_sort	Jia Fang
title	Predicting scalar coupling constants by graph angle-attention neural network
title_short	Predicting scalar coupling constants by graph angle-attention neural network
title_full	Predicting scalar coupling constants by graph angle-attention neural network
title_fullStr	Predicting scalar coupling constants by graph angle-attention neural network
title_full_unstemmed	Predicting scalar coupling constants by graph angle-attention neural network
title_sort	predicting scalar coupling constants by graph angle-attention neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/26938c079f914428b59f6a95757082b4
work_keys_str_mv	AT jiafang predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork AT linyuanhu predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork AT jianfengdong predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork AT haoweili predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork AT huiwang predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork AT huafenzhao predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork AT yaozhang predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork AT minliu predictingscalarcouplingconstantsbygraphangleattentionneuralnetwork
_version_	1718377610638000128

Predicting scalar coupling constants by graph angle-attention neural network

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