Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration

Junseok Choe; Keonwoo Kim; Minjae Ju; Sumin Lee; Jaewoo Kang

doi:10.1109/BigComp54360.2022.00079

Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration

Junseok Choe, Keonwoo Kim, Minjae Ju, Sumin Lee, Jaewoo Kang

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We introduce a novel approach for improving drug-target interaction (DTI) prediction. Our work addresses issues related to model interpretability, protein representation and structural changes in binding complexes in previous drug-target prediction models. We propose utilizing non-covalent residue-residue interactions in protein graphs, formulating an extended form of drug-target link prediction involving non-covalent atom-residue interactions and featuring a graph integration scheme that builds a stronger representation for binding complexes.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
Editors	Herwig Unger, Young-Kuk Kim, Eenjun Hwang, Sung-Bae Cho, Stephan Pareigis, Kyamakya Kyandoghere, Young-Guk Ha, Jinho Kim, Atsuyuki Morishima, Christian Wagner, Hyuk-Yoon Kwon, Yang-Sae Moon, Carson Leung
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	357-359
Number of pages	3
ISBN (Electronic)	9781665421973
DOIs	https://doi.org/10.1109/BigComp54360.2022.00079
Publication status	Published - 2022
Event	2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 - Daegu, Korea, Republic of Duration: 2022 Jan 17 → 2022 Jan 20

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022

Conference

Conference	2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
Country/Territory	Korea, Republic of
City	Daegu
Period	22/1/17 → 22/1/20

Keywords

binding affinity prediction
graph neural network
multi-dimensional bipartite link prediction
non-covalent interaction prediction

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems and Management
Health Informatics

Access to Document

10.1109/BigComp54360.2022.00079

Cite this

Choe, J., Kim, K., Ju, M., Lee, S., & Kang, J. (2022). Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration. In H. Unger, Y-K. Kim, E. Hwang, S-B. Cho, S. Pareigis, K. Kyandoghere, Y-G. Ha, J. Kim, A. Morishima, C. Wagner, H-Y. Kwon, Y-S. Moon, & C. Leung (Eds.), Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 (pp. 357-359). (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp54360.2022.00079

Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration. / Choe, Junseok; Kim, Keonwoo; Ju, Minjae et al.
Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. ed. / Herwig Unger; Young-Kuk Kim; Eenjun Hwang; Sung-Bae Cho; Stephan Pareigis; Kyamakya Kyandoghere; Young-Guk Ha; Jinho Kim; Atsuyuki Morishima; Christian Wagner; Hyuk-Yoon Kwon; Yang-Sae Moon; Carson Leung. Institute of Electrical and Electronics Engineers Inc., 2022. p. 357-359 (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choe, J, Kim, K, Ju, M, Lee, S & Kang, J 2022, Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration. in H Unger, Y-K Kim, E Hwang, S-B Cho, S Pareigis, K Kyandoghere, Y-G Ha, J Kim, A Morishima, C Wagner, H-Y Kwon, Y-S Moon & C Leung (eds), Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022, Institute of Electrical and Electronics Engineers Inc., pp. 357-359, 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022, Daegu, Korea, Republic of, 22/1/17. https://doi.org/10.1109/BigComp54360.2022.00079

Choe J, Kim K, Ju M, Lee S, Kang J. Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration. In Unger H, Kim Y-K, Hwang E, Cho S-B, Pareigis S, Kyandoghere K, Ha Y-G, Kim J, Morishima A, Wagner C, Kwon H-Y, Moon Y-S, Leung C, editors, Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 357-359. (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022). doi: 10.1109/BigComp54360.2022.00079

Choe, Junseok ; Kim, Keonwoo ; Ju, Minjae et al. / Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration. Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. editor / Herwig Unger ; Young-Kuk Kim ; Eenjun Hwang ; Sung-Bae Cho ; Stephan Pareigis ; Kyamakya Kyandoghere ; Young-Guk Ha ; Jinho Kim ; Atsuyuki Morishima ; Christian Wagner ; Hyuk-Yoon Kwon ; Yang-Sae Moon ; Carson Leung. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 357-359 (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022).

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abstract = "We introduce a novel approach for improving drug-target interaction (DTI) prediction. Our work addresses issues related to model interpretability, protein representation and structural changes in binding complexes in previous drug-target prediction models. We propose utilizing non-covalent residue-residue interactions in protein graphs, formulating an extended form of drug-target link prediction involving non-covalent atom-residue interactions and featuring a graph integration scheme that builds a stronger representation for binding complexes. ",

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author = "Junseok Choe and Keonwoo Kim and Minjae Ju and Sumin Lee and Jaewoo Kang",

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Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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