Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)-Reference-Cited by-同舟云学术

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Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)

Published:2023-02-16 Issue:8 Volume:14 Page:2020-2033
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Yang Ziduo¹,Zhong Weihe¹,Lv Qiujie¹,Dong Tiejun¹^ORCID,Yu-Chian Chen Calvin¹²³^ORCID

Affiliation:

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

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

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

Funder

China Medical University Hospital

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.2c03906

Reference50 articles.

1. Proceedings of Machine Learning Research;Stärk H.,2022

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

3. InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions

4. Extended connectivity interaction features: improving binding affinity prediction through chemical description

5. Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

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