Message passing neural networks scoring functions for structure-based drug discovery

Dmitry S. Karlov, Petr Popov, Sergey Sosnin, Maxim V. Fedorov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    The scoring function for ranking protein-ligand complexes by their estimated binding affinity was developed based on the Message Passing Neural Network (MPNN). Behler-Parrinello Symmetric functions were utilized as descriptors of the atomic environment. The performance on the CASF 2016 benchmark reveals better results, as compared to the other methods.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2019
    Subtitle of host publicationWorkshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Proceedings
    EditorsVera Kurková, Igor V. Tetko, Pavel Karpov, Fabian Theis
    PublisherSpringer Verlag
    Pages845-847
    Number of pages3
    ISBN (Print)9783030304928
    Publication statusPublished - 2019
    Event28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany
    Duration: 17 Sep 201919 Sep 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11731 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference28th International Conference on Artificial Neural Networks, ICANN 2019
    Country/TerritoryGermany
    CityMunich
    Period17/09/1919/09/19

    Keywords

    • Affinity prediction
    • Graph convolutions
    • Neural networks
    • Scoring functions

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