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SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction

Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally pre...

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Autores principales: Wang, Shudong, Liu, Dayan, Ding, Mao, Du, Zhenzhen, Zhong, Yue, Song, Tao, Zhu, Jinfu, Zhao, Renteng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962986/
https://www.ncbi.nlm.nih.gov/pubmed/33737946
http://dx.doi.org/10.3389/fgene.2020.607824
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author Wang, Shudong
Liu, Dayan
Ding, Mao
Du, Zhenzhen
Zhong, Yue
Song, Tao
Zhu, Jinfu
Zhao, Renteng
author_facet Wang, Shudong
Liu, Dayan
Ding, Mao
Du, Zhenzhen
Zhong, Yue
Song, Tao
Zhu, Jinfu
Zhao, Renteng
author_sort Wang, Shudong
collection PubMed
description Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein–ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein–drug molecular complex. The SE module is added to the second and third convolutional layers to improve the non-linear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein–molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and AutoDock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID: 6cbg) to test the stability and robustness of the model. We found that the prediction results were not affected by the docking position, and thus, our model is of acceptable robustness.
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spelling pubmed-79629862021-03-17 SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction Wang, Shudong Liu, Dayan Ding, Mao Du, Zhenzhen Zhong, Yue Song, Tao Zhu, Jinfu Zhao, Renteng Front Genet Genetics Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein–ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein–drug molecular complex. The SE module is added to the second and third convolutional layers to improve the non-linear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein–molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and AutoDock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID: 6cbg) to test the stability and robustness of the model. We found that the prediction results were not affected by the docking position, and thus, our model is of acceptable robustness. Frontiers Media S.A. 2021-02-19 /pmc/articles/PMC7962986/ /pubmed/33737946 http://dx.doi.org/10.3389/fgene.2020.607824 Text en Copyright © 2021 Wang, Liu, Ding, Du, Zhong, Song, Zhu and Zhao. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Wang, Shudong
Liu, Dayan
Ding, Mao
Du, Zhenzhen
Zhong, Yue
Song, Tao
Zhu, Jinfu
Zhao, Renteng
SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction
title SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction
title_full SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction
title_fullStr SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction
title_full_unstemmed SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction
title_short SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction
title_sort se-onionnet: a convolution neural network for protein–ligand binding affinity prediction
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962986/
https://www.ncbi.nlm.nih.gov/pubmed/33737946
http://dx.doi.org/10.3389/fgene.2020.607824
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