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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

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)

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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?

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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

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XMDS

Building Models FAST6

Assay Central: Models & Statistics

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Assay Central: Predictions

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Assay Central: Honeycomb Viewer

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Assay Central: Prescriptions

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Drug Repurposing for Tuberculosis

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▶ 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

Repurposing Tilorone

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▶ 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)

Assay Central Today:

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▶ 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

Assay Central Tomorrow:

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▶ Compare algorithms,

descriptors, and statistics

▶ Deep Learning

▶ Link a target to a disease

▶ Curation via metadata

▶ Feedback loop

▶ Propose and design new

compounds

How would you care to collaborate?

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▶ 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

Thanks!

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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

Prediction Scores

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Clark, A.M., et al., J. Chem. Inf. Model. 2015, 55, 1231−1245.

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TB Subvalidations

Work completed by Tom Lane