Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

Jeongtae Son; Dongsup Kim

doi:10.1371/journal.pone.0249404

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Open Access

Peer-reviewed

Research Article

Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

Jeongtae Son,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Writing – original draft

Affiliation Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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Dongsup Kim

Roles Funding acquisition, Resources, Supervision, Writing – review & editing

* E-mail: kds@kaist.ac.kr

Affiliation Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

https://orcid.org/0000-0002-5916-6799

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Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

Jeongtae Son,
Dongsup Kim

Published: April 8, 2021
https://doi.org/10.1371/journal.pone.0249404

About the Authors

Jeongtae Son

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Writing – original draft

Affiliation Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Dongsup Kim

Roles Funding acquisition, Resources, Supervision, Writing – review & editing

* E-mail: kds@kaist.ac.kr

Affiliation Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

https://orcid.org/0000-0002-5916-6799

Competing Interests

The authors have declared that no competing interests exist.

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Convolution
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Neural networks
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Protein structure
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Protein structure prediction
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Deep learning
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Drug discovery
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Molecular structure
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Protein interactions
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