Protein-ligand binding affinity prediction model based on graph attention network

Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to improve the performance of classical scoring functio...

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Autores principales:	Hong Yuan, Jing Huang, Jin Li
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	binding affinity structure-based drug design graph attention network scoring function machine learning Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/2810ec3d161f44c3a8bbf0616fa97335
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spelling	oai:doaj.org-article:2810ec3d161f44c3a8bbf0616fa973352021-11-29T05:44:23ZProtein-ligand binding affinity prediction model based on graph attention network10.3934/mbe.20214511551-0018https://doaj.org/article/2810ec3d161f44c3a8bbf0616fa973352021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021451?viewType=HTMLhttps://doaj.org/toc/1551-0018Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to improve the performance of classical scoring functions, has attracted many scientists' attention. In this paper, we have developed an affinity prediction model called GAT-Score based on graph attention network (GAT). The protein-ligand complex is represented by a graph structure, and the atoms of protein and ligand are treated in the same manner. Two improvements are made to the original graph attention network. Firstly, a dynamic feature mechanism is designed to enable the model to deal with bond features. Secondly, a virtual super node is introduced to aggregate node-level features into graph-level features, so that the model can be used in the graph-level regression problems. PDBbind database v.2018 is used to train the model. Finally, the performance of GAT-Score was tested by the scheme $C_s$ (Core set as the test set) and <italic>CV</italic> (Cross-Validation). It has been found that our results are better than most methods from machine learning models with traditional molecular descriptors.Hong YuanJing HuangJin LiAIMS Pressarticlebinding affinitystructure-based drug designgraph attention networkscoring functionmachine learningBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9148-9162 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	binding affinity structure-based drug design graph attention network scoring function machine learning Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	binding affinity structure-based drug design graph attention network scoring function machine learning Biotechnology TP248.13-248.65 Mathematics QA1-939 Hong Yuan Jing Huang Jin Li Protein-ligand binding affinity prediction model based on graph attention network
description	Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to improve the performance of classical scoring functions, has attracted many scientists' attention. In this paper, we have developed an affinity prediction model called GAT-Score based on graph attention network (GAT). The protein-ligand complex is represented by a graph structure, and the atoms of protein and ligand are treated in the same manner. Two improvements are made to the original graph attention network. Firstly, a dynamic feature mechanism is designed to enable the model to deal with bond features. Secondly, a virtual super node is introduced to aggregate node-level features into graph-level features, so that the model can be used in the graph-level regression problems. PDBbind database v.2018 is used to train the model. Finally, the performance of GAT-Score was tested by the scheme $C_s$ (Core set as the test set) and <italic>CV</italic> (Cross-Validation). It has been found that our results are better than most methods from machine learning models with traditional molecular descriptors.
format	article
author	Hong Yuan Jing Huang Jin Li
author_facet	Hong Yuan Jing Huang Jin Li
author_sort	Hong Yuan
title	Protein-ligand binding affinity prediction model based on graph attention network
title_short	Protein-ligand binding affinity prediction model based on graph attention network
title_full	Protein-ligand binding affinity prediction model based on graph attention network
title_fullStr	Protein-ligand binding affinity prediction model based on graph attention network
title_full_unstemmed	Protein-ligand binding affinity prediction model based on graph attention network
title_sort	protein-ligand binding affinity prediction model based on graph attention network
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/2810ec3d161f44c3a8bbf0616fa97335
work_keys_str_mv	AT hongyuan proteinligandbindingaffinitypredictionmodelbasedongraphattentionnetwork AT jinghuang proteinligandbindingaffinitypredictionmodelbasedongraphattentionnetwork AT jinli proteinligandbindingaffinitypredictionmodelbasedongraphattentionnetwork
_version_	1718407575377018880

Protein-ligand binding affinity prediction model based on graph attention network

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