Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

Abstract Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these te...

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Autores principales:	Sangmin Seo, Jonghwan Choi, Sanghyun Park, Jaegyoon Ahn
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Structure-based drug design Protein–ligand complex Binding affinity Attention mechanism Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f44ada125ed946ff99bc493a2989110c
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spelling	oai:doaj.org-article:f44ada125ed946ff99bc493a2989110c2021-11-14T12:13:07ZBinding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions10.1186/s12859-021-04466-01471-2105https://doaj.org/article/f44ada125ed946ff99bc493a2989110c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04466-0https://doaj.org/toc/1471-2105Abstract Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein–ligand complex is ongoing. Results In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein–ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein–ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. Conclusions We confirmed that an attention mechanism can capture the binding sites in a protein–ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA .Sangmin SeoJonghwan ChoiSanghyun ParkJaegyoon AhnBMCarticleStructure-based drug designProtein–ligand complexBinding affinityAttention mechanismComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Structure-based drug design Protein–ligand complex Binding affinity Attention mechanism Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Structure-based drug design Protein–ligand complex Binding affinity Attention mechanism Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Sangmin Seo Jonghwan Choi Sanghyun Park Jaegyoon Ahn Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
description	Abstract Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein–ligand complex is ongoing. Results In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein–ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein–ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. Conclusions We confirmed that an attention mechanism can capture the binding sites in a protein–ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA .
format	article
author	Sangmin Seo Jonghwan Choi Sanghyun Park Jaegyoon Ahn
author_facet	Sangmin Seo Jonghwan Choi Sanghyun Park Jaegyoon Ahn
author_sort	Sangmin Seo
title	Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
title_short	Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
title_full	Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
title_fullStr	Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
title_full_unstemmed	Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
title_sort	binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions
publisher	BMC
publishDate	2021
url	https://doaj.org/article/f44ada125ed946ff99bc493a2989110c
work_keys_str_mv	AT sangminseo bindingaffinitypredictionforproteinligandcomplexusingdeepattentionmechanismbasedonintermolecularinteractions AT jonghwanchoi bindingaffinitypredictionforproteinligandcomplexusingdeepattentionmechanismbasedonintermolecularinteractions AT sanghyunpark bindingaffinitypredictionforproteinligandcomplexusingdeepattentionmechanismbasedonintermolecularinteractions AT jaegyoonahn bindingaffinitypredictionforproteinligandcomplexusingdeepattentionmechanismbasedonintermolecularinteractions
_version_	1718429382175883264

Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

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