Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph-Reference-Cited by-同舟云学术

Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph

Published:2023 Issue:35 Volume:25 Page:24110-24120
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Dong Lina¹,Shi Shuai²,Qu Xiaoyang¹,Luo Ding¹,Wang Binju¹³^ORCID

Affiliation:

1. State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China

2. Department of Algorithm, TuringQ Co., Ltd., Shanghai, 200240, China

3. Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen, 361005, China

Abstract

We proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein–ligand interactions from the 3D structures of protein–ligand complexes.

Funder

National Key Research and Development Program of China Stem Cell and Translational Research

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP03651K

Reference85 articles.

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5. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

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