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

3 Citations (Scopus)

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

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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 YK, Hwang E, Cho SB, Pareigis S, Kyandoghere K, Ha YG, Kim J, Morishima A, Wagner C, Kwon HY, Moon YS, 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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Improved Binding Affinity Prediction Using Non-Covalent Interactions and Graph Integration

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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