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A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity
Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein–ligand binding affinity is not satisfactory so far. This pa...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070477/ https://www.ncbi.nlm.nih.gov/pubmed/33919681 http://dx.doi.org/10.3390/ijms22084023 |
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author | Shen, Huimin Zhang, Youzhi Zheng, Chunhou Wang, Bing Chen, Peng |
author_facet | Shen, Huimin Zhang, Youzhi Zheng, Chunhou Wang, Bing Chen, Peng |
author_sort | Shen, Huimin |
collection | PubMed |
description | Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein–ligand binding affinity is not satisfactory so far. This paper proposes a new cascade graph-based convolutional neural network architecture by dealing with non-Euclidean irregular data. We represent the molecule as a graph, and use a simple linear transformation to deal with the sparsity problem of the one-hot encoding of original data. The first stage adopts ARMA graph convolutional neural network to learn the characteristics of atomic space in the protein–ligand complex. In the second stage, one variant of the MPNN graph convolutional neural network is introduced with chemical bond information and interactive atomic features. Finally, the architecture passes through the global add pool and the fully connected layer, and outputs a constant value as the predicted binding affinity. Experiments on the PDBbind v2016 data set showed that our method is better than most of the current methods. Our method is also comparable to the state-of-the-art method on the data set, and is more intuitive and simple. |
format | Online Article Text |
id | pubmed-8070477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80704772021-04-26 A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity Shen, Huimin Zhang, Youzhi Zheng, Chunhou Wang, Bing Chen, Peng Int J Mol Sci Article Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein–ligand binding affinity is not satisfactory so far. This paper proposes a new cascade graph-based convolutional neural network architecture by dealing with non-Euclidean irregular data. We represent the molecule as a graph, and use a simple linear transformation to deal with the sparsity problem of the one-hot encoding of original data. The first stage adopts ARMA graph convolutional neural network to learn the characteristics of atomic space in the protein–ligand complex. In the second stage, one variant of the MPNN graph convolutional neural network is introduced with chemical bond information and interactive atomic features. Finally, the architecture passes through the global add pool and the fully connected layer, and outputs a constant value as the predicted binding affinity. Experiments on the PDBbind v2016 data set showed that our method is better than most of the current methods. Our method is also comparable to the state-of-the-art method on the data set, and is more intuitive and simple. MDPI 2021-04-14 /pmc/articles/PMC8070477/ /pubmed/33919681 http://dx.doi.org/10.3390/ijms22084023 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shen, Huimin Zhang, Youzhi Zheng, Chunhou Wang, Bing Chen, Peng A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity |
title | A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity |
title_full | A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity |
title_fullStr | A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity |
title_full_unstemmed | A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity |
title_short | A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity |
title_sort | cascade graph convolutional network for predicting protein–ligand binding affinity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070477/ https://www.ncbi.nlm.nih.gov/pubmed/33919681 http://dx.doi.org/10.3390/ijms22084023 |
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