Prediction of drug–target binding affinity using similarity-based convolutional neural network

Abstract Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jooyong Shim, Zhen-Yu Hong, Insuk Sohn, Changha Hwang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/778c43bd612a4401b8e5e54df503addb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:778c43bd612a4401b8e5e54df503addb
record_format	dspace
spelling	oai:doaj.org-article:778c43bd612a4401b8e5e54df503addb2021-12-02T13:35:03ZPrediction of drug–target binding affinity using similarity-based convolutional neural network10.1038/s41598-021-83679-y2045-2322https://doaj.org/article/778c43bd612a4401b8e5e54df503addb2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83679-yhttps://doaj.org/toc/2045-2322Abstract Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in novel public affinity data available in the DT-related databases enables advanced deep learning techniques to be used to predict binding affinities. In this paper, we propose a similarity-based model that applies 2-dimensional (2D) convolutional neural network (CNN) to the outer products between column vectors of two similarity matrices for the drugs and targets to predict DT binding affinities. To our best knowledge, this is the first application of 2D CNN in similarity-based DT binding affinity prediction. The validation results on multiple public datasets show that the proposed model is an effective approach for DT binding affinity prediction and can be quite helpful in drug development process.Jooyong ShimZhen-Yu HongInsuk SohnChangha HwangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jooyong Shim Zhen-Yu Hong Insuk Sohn Changha Hwang Prediction of drug–target binding affinity using similarity-based convolutional neural network
description	Abstract Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in novel public affinity data available in the DT-related databases enables advanced deep learning techniques to be used to predict binding affinities. In this paper, we propose a similarity-based model that applies 2-dimensional (2D) convolutional neural network (CNN) to the outer products between column vectors of two similarity matrices for the drugs and targets to predict DT binding affinities. To our best knowledge, this is the first application of 2D CNN in similarity-based DT binding affinity prediction. The validation results on multiple public datasets show that the proposed model is an effective approach for DT binding affinity prediction and can be quite helpful in drug development process.
format	article
author	Jooyong Shim Zhen-Yu Hong Insuk Sohn Changha Hwang
author_facet	Jooyong Shim Zhen-Yu Hong Insuk Sohn Changha Hwang
author_sort	Jooyong Shim
title	Prediction of drug–target binding affinity using similarity-based convolutional neural network
title_short	Prediction of drug–target binding affinity using similarity-based convolutional neural network
title_full	Prediction of drug–target binding affinity using similarity-based convolutional neural network
title_fullStr	Prediction of drug–target binding affinity using similarity-based convolutional neural network
title_full_unstemmed	Prediction of drug–target binding affinity using similarity-based convolutional neural network
title_sort	prediction of drug–target binding affinity using similarity-based convolutional neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/778c43bd612a4401b8e5e54df503addb
work_keys_str_mv	AT jooyongshim predictionofdrugtargetbindingaffinityusingsimilaritybasedconvolutionalneuralnetwork AT zhenyuhong predictionofdrugtargetbindingaffinityusingsimilaritybasedconvolutionalneuralnetwork AT insuksohn predictionofdrugtargetbindingaffinityusingsimilaritybasedconvolutionalneuralnetwork AT changhahwang predictionofdrugtargetbindingaffinityusingsimilaritybasedconvolutionalneuralnetwork
_version_	1718392744722825216

Prediction of drug–target binding affinity using similarity-based convolutional neural network

Ejemplares similares