Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related st...
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Autor*in:	Yiwei Wang [verfasserIn] Lei Huang [verfasserIn] Siwen Jiang [verfasserIn] Yifei Wang [verfasserIn] Jun Zou [verfasserIn] Hongguang Fu [verfasserIn] Shengyong Yang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	deep learning hERG classification model Capsule network convolution-capsule network restricted Boltzmann machine-capsule networks

Übergeordnetes Werk:	In: Frontiers in Pharmacology - Frontiers Media S.A., 2010, 10(2020)
Übergeordnetes Werk:	volume:10 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fphar.2019.01631

Katalog-ID:	DOAJ016825470

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520			\|a Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.
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allfields	10.3389/fphar.2019.01631 doi (DE-627)DOAJ016825470 (DE-599)DOAJ3f5472af89ba4ec6aa0d977fd1cd518a DE-627 ger DE-627 rakwb eng RM1-950 Yiwei Wang verfasserin aut Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies. deep learning hERG classification model Capsule network convolution-capsule network restricted Boltzmann machine-capsule networks Therapeutics. Pharmacology Yiwei Wang verfasserin aut Lei Huang verfasserin aut Lei Huang verfasserin aut Siwen Jiang verfasserin aut Yifei Wang verfasserin aut Jun Zou verfasserin aut Hongguang Fu verfasserin aut Shengyong Yang verfasserin aut In Frontiers in Pharmacology Frontiers Media S.A., 2010 10(2020) (DE-627)642889392 (DE-600)2587355-6 16639812 nnns volume:10 year:2020 https://doi.org/10.3389/fphar.2019.01631 kostenfrei https://doaj.org/article/3f5472af89ba4ec6aa0d977fd1cd518a kostenfrei https://www.frontiersin.org/article/10.3389/fphar.2019.01631/full kostenfrei https://doaj.org/toc/1663-9812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020
spelling	10.3389/fphar.2019.01631 doi (DE-627)DOAJ016825470 (DE-599)DOAJ3f5472af89ba4ec6aa0d977fd1cd518a DE-627 ger DE-627 rakwb eng RM1-950 Yiwei Wang verfasserin aut Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies. deep learning hERG classification model Capsule network convolution-capsule network restricted Boltzmann machine-capsule networks Therapeutics. Pharmacology Yiwei Wang verfasserin aut Lei Huang verfasserin aut Lei Huang verfasserin aut Siwen Jiang verfasserin aut Yifei Wang verfasserin aut Jun Zou verfasserin aut Hongguang Fu verfasserin aut Shengyong Yang verfasserin aut In Frontiers in Pharmacology Frontiers Media S.A., 2010 10(2020) (DE-627)642889392 (DE-600)2587355-6 16639812 nnns volume:10 year:2020 https://doi.org/10.3389/fphar.2019.01631 kostenfrei https://doaj.org/article/3f5472af89ba4ec6aa0d977fd1cd518a kostenfrei https://www.frontiersin.org/article/10.3389/fphar.2019.01631/full kostenfrei https://doaj.org/toc/1663-9812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020
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allfieldsGer	10.3389/fphar.2019.01631 doi (DE-627)DOAJ016825470 (DE-599)DOAJ3f5472af89ba4ec6aa0d977fd1cd518a DE-627 ger DE-627 rakwb eng RM1-950 Yiwei Wang verfasserin aut Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies. deep learning hERG classification model Capsule network convolution-capsule network restricted Boltzmann machine-capsule networks Therapeutics. Pharmacology Yiwei Wang verfasserin aut Lei Huang verfasserin aut Lei Huang verfasserin aut Siwen Jiang verfasserin aut Yifei Wang verfasserin aut Jun Zou verfasserin aut Hongguang Fu verfasserin aut Shengyong Yang verfasserin aut In Frontiers in Pharmacology Frontiers Media S.A., 2010 10(2020) (DE-627)642889392 (DE-600)2587355-6 16639812 nnns volume:10 year:2020 https://doi.org/10.3389/fphar.2019.01631 kostenfrei https://doaj.org/article/3f5472af89ba4ec6aa0d977fd1cd518a kostenfrei https://www.frontiersin.org/article/10.3389/fphar.2019.01631/full kostenfrei https://doaj.org/toc/1663-9812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020
allfieldsSound	10.3389/fphar.2019.01631 doi (DE-627)DOAJ016825470 (DE-599)DOAJ3f5472af89ba4ec6aa0d977fd1cd518a DE-627 ger DE-627 rakwb eng RM1-950 Yiwei Wang verfasserin aut Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies. deep learning hERG classification model Capsule network convolution-capsule network restricted Boltzmann machine-capsule networks Therapeutics. Pharmacology Yiwei Wang verfasserin aut Lei Huang verfasserin aut Lei Huang verfasserin aut Siwen Jiang verfasserin aut Yifei Wang verfasserin aut Jun Zou verfasserin aut Hongguang Fu verfasserin aut Shengyong Yang verfasserin aut In Frontiers in Pharmacology Frontiers Media S.A., 2010 10(2020) (DE-627)642889392 (DE-600)2587355-6 16639812 nnns volume:10 year:2020 https://doi.org/10.3389/fphar.2019.01631 kostenfrei https://doaj.org/article/3f5472af89ba4ec6aa0d977fd1cd518a kostenfrei https://www.frontiersin.org/article/10.3389/fphar.2019.01631/full kostenfrei https://doaj.org/toc/1663-9812 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020
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authorswithroles_txt_mv	Yiwei Wang @@aut@@ Lei Huang @@aut@@ Siwen Jiang @@aut@@ Yifei Wang @@aut@@ Jun Zou @@aut@@ Hongguang Fu @@aut@@ Shengyong Yang @@aut@@
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callnumber-first	R - Medicine
author	Yiwei Wang
spellingShingle	Yiwei Wang misc RM1-950 misc deep learning misc hERG misc classification model misc Capsule network misc convolution-capsule network misc restricted Boltzmann machine-capsule networks misc Therapeutics. Pharmacology Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers
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topic_title	RM1-950 Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers deep learning hERG classification model Capsule network convolution-capsule network restricted Boltzmann machine-capsule networks
topic	misc RM1-950 misc deep learning misc hERG misc classification model misc Capsule network misc convolution-capsule network misc restricted Boltzmann machine-capsule networks misc Therapeutics. Pharmacology
topic_unstemmed	misc RM1-950 misc deep learning misc hERG misc classification model misc Capsule network misc convolution-capsule network misc restricted Boltzmann machine-capsule networks misc Therapeutics. Pharmacology
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title	Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers
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title_full	Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers
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title_sort	capsule networks showed excellent performance in the classification of herg blockers/nonblockers
callnumber	RM1-950
title_auth	Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers
abstract	Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.
abstractGer	Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.
abstract_unstemmed	Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers
url	https://doi.org/10.3389/fphar.2019.01631 https://doaj.org/article/3f5472af89ba4ec6aa0d977fd1cd518a https://www.frontiersin.org/article/10.3389/fphar.2019.01631/full https://doaj.org/toc/1663-9812
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author2	Yiwei Wang Lei Huang Siwen Jiang Yifei Wang Jun Zou Hongguang Fu Shengyong Yang
author2Str	Yiwei Wang Lei Huang Siwen Jiang Yifei Wang Jun Zou Hongguang Fu Shengyong Yang
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doi_str	10.3389/fphar.2019.01631
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up_date	2024-07-03T23:08:53.943Z
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Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. 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