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Assay Central: A New Approach to Compiling Big Data and Preparing Machine Learning Models for Drug Repurposing

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Oral presentation at 2017 ACS in DC - given by Kimberley Zorn

co-authors include Mary A. Lingerfelt, Alex M. Clark, Sean Ekins

for more details see www.collaborationspharma.com

Published in: Science
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Assay Central: A New Approach to Compiling Big Data and Preparing Machine Learning Models for Drug Repurposing

  1. 1. 1 Assay Central: A New Approach to Compiling Big Data and Preparing Machine Learning Models for Drug Repurposing Kimberley M. Zorn, Mary A. Lingerfelt, Alex M. Clark, Sean Ekins
  2. 2. Machine Learning for Drug Discovery ▶ Molecular pattern recognition of biological data ▶ Descriptors identify these patterns ▶ Define active and inactive features ▶ Used to generate predictions for drug activity at a certain target (organism, protein of interest) 2
  3. 3. What’s stopping us? ▶ Plenty of data available today… incorrectly formatted ▶ Vague details of experiments ▶ Minor & major errors in supplied SMILES/structures ▶ How do we know this structure is correct? ▶ How do we share results? ▶ How can the average scientist use this technology? 3
  4. 4. 4 Methods ▶ Share Java executable files over Google Drive or DropBox ▶ GitHub to share datasets and models in-house ▶ Private server for additional data backup in-house ▶ Bayesian algorithm using ECFP6 descriptors ▶ Molecule checking workflow outputs “Invalids” & merge duplicate molecule data ▶ First things first: Collect structure-activity data from public & private sources
  5. 5. 5 XMDS
  6. 6. Building Models FAST 6
  7. 7. Assay Central: Models & Statistics 7
  8. 8. 8
  9. 9. Assay Central: Predictions 9
  10. 10. 10
  11. 11. Assay Central: Honeycomb Viewer 11
  12. 12. 12
  13. 13. Assay Central: Prescriptions 13
  14. 14. 14
  15. 15. Drug Repurposing for Tuberculosis 15 ▶ Tuberculosis (https://www.cdc.gov/tb/statistics/default.htm) ▶ 1/3 of the population is infected ▶ 1.8 million deaths in 2015 ▶ Assay Central Models (~10) ▶ Public in vitro data & collaborator in vivo data ▶ Targeted models for PyrG & PanK ▶ Predicted compounds & sent for testing ▶ Vendor libraries + FDA approved drugs ▶ Two compounds active at either target, one at both
  16. 16. Repurposing Tilorone 16 ▶ Approved drug in Russia, not US ▶ Uses ("Registry of Medicinal Products (RLS). Tilorone: Prescribing Information") ▶ Antiviral (hepatitis, influenza) ▶ Interferon inducer ▶ Predicted active for Ebola & Dengue ▶ Active against Ebola (EC50 = 230 nM) ▶ Active against Dengue-2 (low µM - exact EC50 TBA)
  17. 17. Assay Central Today: 17 ▶ CPI database currently contains > 150 models ▶ Molecular Properties, Disease & ADME Targets ▶ Utilized for > 10 projects recently ▶ Predicting large vendor libraries & FDA approved drugs ▶ Parasites, Bacteria & Viruses ▶ One large consumer product company ▶ Sharing models with Java executable ▶ Training documentation www.assaycentral.org
  18. 18. Assay Central Tomorrow: 18 ▶ Compare algorithms, descriptors, and statistics ▶ Deep Learning ▶ Link a target to a disease ▶ Curation via metadata ▶ Feedback loop ▶ Propose and design new compounds
  19. 19. How would you care to collaborate? 19 ▶ Computationally inexpensive ▶ Requirements: Java & Google Chrome ▶ Fast & easy to share ▶ Customize your bundle ▶ Applicability/Honeycombs to establish validity ▶ Constantly improving… with user feedback! More information at: www.collaborationspharma.com
  20. 20. Thanks! 20 Collaborations Pharmaceuticals, Inc. Dr. Sean Ekins Dr. Maggie Hupcey Dr. Mary Lingerfelt [soon to be Dr.] Tom Lane [soon to be Dr.] Dan Russo Consultants Dr. Alex Clark (Assay Central) Valery Tkachenko (Deep Learning) Dr. Alex Korotcov (Deep Learning) Funded by R43GM122196 NIGMS
  21. 21. Prediction Scores 21 Clark, A.M., et al., J. Chem. Inf. Model. 2015, 55, 1231−1245.
  22. 22. 22 TB Subvalidations Work completed by Tom Lane

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