skip to main content
10.1145/3543377.3543396acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicbbtConference Proceedingsconference-collections
research-article

Developing A Multi-task Edge-attention based Graph Deep Learning Algorithm for Kinase Polypharmacology Profiling

Published: 08 August 2022 Publication History

Abstract

The kinome-wide virtual profiling of molecules with structure data is a challenging task in drug discovery. Here, we present a virtual predicting model against a group of 118 kinases based on large-scale bioactivity data and the multitask graph deep neural network algorithm. The obtained model yields excellent prediction ability with auROC and F1-score of 0.8 on an internal testing dataset. Compared with conventional multi-tasks deep neural network models, the model consistently shows higher auROCs and F1-score on external datasets, despite the apparent deviation of chemical diversity distribution and the uncertainty in different data sources. Visualizing the weights of attention layers in this model can open the black box of this deep learning model and explain its prediction mechanism. Overall, this computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring new drug molecular structure.

References

[1]
Lander, E. S. (2001) ‘Initial sequencing and analysis of the human genome’, Nature, 4099 (6822), pp. 860-921.
[2]
Manning, G., (2002) ‘The Protein Kinase Complement of the Human Genome’, Science, 298 (5600), pp. 1912-1934.
[3]
Blume-Jense, P. & Hunter, T. (2001) ‘Oncogenic kinase signalling’, Nature, 411 (6835), pp. 355-365.
[4]
Klaeger, S., (2017) ‘The target landscape of clinical kinase drugs’, Science, 358 (6367).
[5]
Rodríguez-Peŕez,R.;Bajorath,J.r. (2019) ‘Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors’, ACS Omega, 4, pp. 4367−4375.
[6]
Niijima, S., Shiraishi, A. & Okuno, Y. (2012) ‘Dissecting kinase profiling data to predict activity and understand cross-reactivity of kinase inhibitors’, J Chem Inf Model, 52(4), pp. 901-912.
[7]
Bora, A., (2016) ‘Predictive Models for Fast and Effective Profiling of Kinase Inhibitors’, J Chem Inf Model, 56 (5), pp. 895-905.
[8]
Schürer, S. C. & Muskal, S. M. (2013) ‘Kinome-wide activity modeling from diverse public high-quality data sets’, J Chem Inf Model, 53 (1), pp. 27-38.
[9]
Manning, G., (2002) ‘The Protein Kinase Complement of the Human Genome’, Science, 298 (5600), pp. 1912-1934.
[10]
Li, X., (2019) ‘Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation’, J Med Chem, 31 July.
[11]
Rogers, D., Hahn, M. (2010) ‘Extended-connectivity fingerprints’, J Chem Inf Model, 50(5), pp.742‐754.
[12]
Lusci, A., Pollastri, G. & Baldi, P. (2013) ‘Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules’, J Chem Inf Model, 53(7), pp. 1563-1575.
[13]
Xu, Y., (2015) ‘Deep Learning for Drug-Induced Liver Injury’, J Chem Inf Model, 55 (10), pp. 2085-2093.
[14]
Goh, G. b., Hodas, N. O. & Vishnu, A. (2017) ‘Deep learning for computational chemistry’, J Comput Chem, 38 (16), pp. 1291-1307.
[15]
Shang, C., (2018) ‘Edge Attention-based Multi-Relational Graph Convolutional Networks’, arXiv Preprint.
[16]
Metz, J. T.; Johnson, E. F.; Soni, N. B.; Merta, P. J.; Lemma, K.; Hajduk, P. J. (2011) ‘Navigating the kinome’, Nat Chem Biol, 7(4), pp. 200-202.
[17]
Drewry, D. H.; Wells, C. I.; Andrews, D. M.; Angell, R.; Al-Ali, H.; Axtman, A. D.; Capuzzi, S. J.; Elkins, J. M.; Ettmayer, P.; Frederiksen, M. (2017) ‘Progress towards a public chemogenomic set for protein kinases and a call for contributions’, PLoS One, 12(8).
[18]
Xu, Y.; Ma, J.; Liaw, A.; Sheridan, R. P.; Svetnik, V. (2017) ‘Demystifying multitask deep neural networks for quantitative structure−activity relationships’. J. Chem. Inf. Model. 57, pp. 2490−2504.
[19]
Nair, V. & Hinton, G. E. (2010) ‘In Rectified linear units improve restricted Boltzmann machines’, Proceedings of the 27th International Conference on Machine Learning (ICML10), pp 807-814.
[20]
Kingma, D. P. & Ba, J. (2014) ‘Adam: A method for stochastic optimization’, Arxiv Preprint.
[21]
Seidel, T., Schuetz, D. A., Garon, A. & Langer, T. (2019) ‘The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design’, Progress in the Chemistry of Organic Natural Products, 110, pp. 99-141.
[22]
Goodfellow, I., Benjio, Y. & Courvile, A. (2016) Deep Learing. ISBN: 978-7-115-46147-6.
  1. Developing A Multi-task Edge-attention based Graph Deep Learning Algorithm for Kinase Polypharmacology Profiling

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICBBT '22: Proceedings of the 14th International Conference on Bioinformatics and Biomedical Technology
    May 2022
    190 pages
    ISBN:9781450396387
    DOI:10.1145/3543377
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 August 2022

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. deep learning
    2. edge-attention
    3. kinase
    4. multi-task

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICBBT 2022

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 65
      Total Downloads
    • Downloads (Last 12 months)20
    • Downloads (Last 6 weeks)4
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media