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Issue 35, 2020, Issue in Progress
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Drug–target affinity prediction using graph neural network and contact maps

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Abstract

Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area. Drug–target affinity (DTA) prediction is the most important step of computer-aided drug design, which could speed up drug development and reduce resource consumption. With the development of deep learning, the introduction of deep learning to DTA prediction and improving the accuracy have become a focus of research. In this paper, utilizing the structural information of molecules and proteins, two graphs of drug molecules and proteins are built up respectively. Graph neural networks are introduced to obtain their representations, and a method called DGraphDTA is proposed for DTA prediction. Specifically, the protein graph is constructed based on the contact map output from the prediction method, which could predict the structural characteristics of the protein according to its sequence. It can be seen from the test of various metrics on benchmark datasets that the method proposed in this paper has strong robustness and generalizability.

Graphical abstract: Drug–target affinity prediction using graph neural network and contact maps

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Article information


Submitted
11 Mar 2020
Accepted
07 May 2020
First published
01 Jun 2020

This article is Open Access

RSC Adv., 2020,10, 20701-20712
Article type
Paper

Drug–target affinity prediction using graph neural network and contact maps

M. Jiang, Z. Li, S. Zhang, S. Wang, X. Wang, Q. Yuan and Z. Wei, RSC Adv., 2020, 10, 20701
DOI: 10.1039/D0RA02297G

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

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    [Original citation] - Published by The Royal Society of Chemistry.

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