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

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

    Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
    Title of host publicationProceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
    EditorsHerwig 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
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages357-359
    Number of pages3
    ISBN (Electronic)9781665421973
    DOIs
    Publication statusPublished - 2022
    Event2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 - Daegu, Korea, Republic of
    Duration: 2022 Jan 172022 Jan 20

    Publication series

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

    Conference

    Conference2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
    Country/TerritoryKorea, Republic of
    CityDaegu
    Period22/1/1722/1/20

    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

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