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Kinome-wide activity classification of small molecules by deep learning

Bryce K. Allen, Nagi G. Ayad, Stephan C. Schürer
doi: https://doi.org/10.1101/512459

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doi 
https://doi.org/10.1101/512459
History 
  • January 4, 2019.
Copyright 
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.

Author Information

  1. Bryce K. Allen1,2,
  2. Nagi G. Ayad3,4,5 and
  3. Stephan C. Schürer1,2,5,*
  1. 1Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, US
  2. 2Center for Computational Science, University of Miami, Miami, FL, US
  3. 3Center for Therapeutic Innovation Miller School of Medicine, University of Miami, Miami, FL, US
  4. 4Miami Project to Cure Paralysis, Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, FL, US
  5. 5Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, FL, US
  1. ↵*To whom correspondence should be addressed: sschurer{at}miami.edu
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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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