MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction-Reference-Cited by-同舟云学术

MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

Published:2022 Issue:3 Volume:13 Page:816-833
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Yang Ziduo¹,Zhong Weihe¹,Zhao Lu¹²,Yu-Chian Chen Calvin¹³⁴^ORCID

Affiliation:

1. Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China

2. Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China

3. Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan

4. Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan

Abstract

MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drug–target affinity prediction and can provide explanations that are consistent with pharmacologists.

Funder

China Medical University Hospital

National Natural Science Foundation of China

Sun Yat-sen University

Guangzhou Municipal Science and Technology Project

Science, Technology and Innovation Commission of Shenzhen Municipality

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D1SC05180F

Reference78 articles.

1. Identifying drug–target interactions based on graph convolutional network and deep neural network

2. DeepDTA: deep drug–target binding affinity prediction

3. Target identification for biologically active small molecules using chemical biology approaches

4. Identifying compound efficacy targets in phenotypic drug discovery

5. An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction

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