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Kinome-wide activity classification of small molecules by deep learning

View ORCID ProfileBryce K. Allen, Nagi G. Ayad, Stephan C. Schürer
doi: https://doi.org/10.1101/512459
Bryce K. Allen
1Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, US
2Center for Computational Science, University of Miami, Miami, FL, US
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  • ORCID record for Bryce K. Allen
Nagi G. Ayad
3Center for Therapeutic Innovation Miller School of Medicine, University of Miami, Miami, FL, US
4Miami Project to Cure Paralysis, Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, FL, US
5Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, FL, US
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Stephan C. Schürer
1Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, US
2Center for Computational Science, University of Miami, Miami, FL, US
5Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, FL, US
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  • For correspondence: sschurer@miami.edu
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ABSTRACT

Deep learning is a machine learning technique that attempts to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. However, the application of deep learning to discriminating features of kinase inhibitors has not been well explored. Small molecule kinase inhibitors are an important class of anti-cancer agents and have demonstrated impressive clinical efficacy in several different diseases. However, resistance is often observed mediated by adaptive Kinome reprogramming or subpopulation diversity. Therefore, polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant disease. Their development would benefit from more comprehensive and dense knowledge of small-molecule inhibition across the human Kinome. Because such data is not publicly available, we evaluated multiple machine learning methods to predict small molecule inhibition of 342 kinases using over 650K aggregated bioactivity annotations for over 300K small molecules curated from ChEMBL and the Kinase Knowledge Base (KKB). Our results demonstrated that multi-task deep neural networks outperform classical single-task methods, offering potential towards predicting activity profiles and filling gaps in the available data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 04, 2019.
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Kinome-wide activity classification of small molecules by deep learning
Bryce K. Allen, Nagi G. Ayad, Stephan C. Schürer
bioRxiv 512459; doi: https://doi.org/10.1101/512459
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Kinome-wide activity classification of small molecules by deep learning
Bryce K. Allen, Nagi G. Ayad, Stephan C. Schürer
bioRxiv 512459; doi: https://doi.org/10.1101/512459

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