This document discusses using mobile apps for drug discovery workflows. It describes how mobile devices are revolutionizing computing through new user interfaces and availability anywhere. Several examples are given of simple app workflows for tasks like looking up structures, running searches, and sharing data. The document advocates for mobile and cloud-based approaches to replace desktop-based cheminformatics workflows. This could make specialized tasks more accessible and collaboration easier. The potential for mobile apps to transform existing software vendors is also noted. The majority of the document focuses on examples of developing mobile apps to enable drug discovery for tuberculosis.

1. Cheminformatics Workflows Using Mobile
Apps for Drug Discovery
Alex M. Clark1, Antony J. Williams2 and Sean Ekins3,4
1
Molecular Materials Informatics, 2 Royal Society of Chemistry, 3Collaborations in
Chemistry, 4 Collaborative Drug Discovery, Inc.,

2. The Computing Revolution #3
?
personal computers
portable laptops
mainframes
minicomputers
•
mobile tablets
smartphones
mobile is revolutionary: a clean break
 entirely new user interface
 no backward compatibility
 highly constrained resources
 applicable to entirely new situations

3. Fitting Mobile Apps into R&D Workflow
Williams et al DDT
16:928-939, 2011
Williams et al., In collaborative computational technologies for
biomedical research 2011
Arnold and Ekins, PharmacoEconomics 28: 1-5, 2010

4. Chemistry Apps
• Reference data
• Education
• Structure drawing
• Database searching
• 3D viewing
• Reactions & collections
• Property calculation
• Model building
• Graphical presentation
• Data sharing

5. Need for dedicated website / store for science Apps
– find out more at www.scimobileapps.com
http://goo.gl/Goa4e

6. Simple App Workflows
•
Look up structure in ChemSpider
•
Saving structure as molfile - open in MMDS
•
Run substructure search in ChEBI using MMDS webservice
•
Open molecule from MMDS and assign scaffolds in SAR Table
Generate substituents
•
Predict missing activities for compounds in SAR Table
•
Suggest compounds to make in SAR Table
•
Find a reaction in SPRESImobile
•
Use Yield101 to calculate synthesis yield
•
Share data with Dropbox using MolSync app
•
Tweet a reaction with MolSync
•
Read the data with ODDT mobile app
Clark AM, Williams AJ and Ekins S, Chem-Bio Informatics Journal, 13: 1-18 2013.

7. A Bigger Vision
• Mobile chemistry originally intended to support
desktop workflows
• Mobile+Cloud can be a total replacement
• Entirely new user expectations for apps:
- easy to learn
- delightful to use
- trivial to install
- inexpensive or FREE
• Extremely disruptive to existing software vendors!

8. APPIFYING DATA - From PDF to Mobile App
http://bit.ly/GzQ5ty
Lots of data
but how to
make it useful
for chemists?
Chemists see
structures
PDF not
accessible,
small text- too
much data

9. Green Solvents and Lab Solvents FREE Apps
Alex Clark made the App in 3 days
ACS Sustain Chem Eng 1: 8-13 (2013)
Android version – Lab Solvents
Includes GSK solvent data

10. 

TB Kills 1.6-1.7m/yr (~1 every 8 seconds)
1/3rd of worlds population infected!!!!

Multi drug resistance in 4.3% of cases
Extensively drug resistant increasing incidence
No new drugs in over 40 yrs until Bedaquiline
Drug-drug interactions and Co-morbidity with HIV

Increase in HTS phenotypic screening

1000’s of hits no

Use of computational methods with TB is rare



idea of target

11. IN VIVO INACTIVE IN VIVO ACTIVE
30MIND THE TB GAP in vivo data
years with little TB mouse

12. Predicting the target/s for small molecules
Pathway analysis
Binding site similarity to Mtb proteins
Docking
Bayesian Models - ligand similarity

13. TB molecules and target information database connects
molecule, gene, pathway and literature for >700 molecules

14. TB Mobile layout on iPhone and Android
iPhone
Android

15. TB Mobile Molecule Detail and Links
iPhone
Android

16. Molecules active against Mtb evaluated in TB Mobile app

17. Workflow from sketching molecules in MMDS mobile app
to exporting and opening with TB Mobile

18. TB Mobile – poster on
Jan 2013
http://goo.gl/UTTH0

19. TB Mobile – Is on iTunes and Google play
and it is FREE
http://goo.gl/vPOKS
http://goo.gl/iDJFR
http://goo.gl/7fGFW

20. What next ?





Update with more data
Add a weighting or scoring function to account
for heavily populated targets
Expand beyond the similarity measure
Add algorithms to predict activity
Could we appify data for other diseases/ targets

21. In vitro data
In vivo data
Target data
ADME/Tox data
& models
Connecting
data/tools
like a TB Spider
Drug-like scaffold creation
TB prediction tools
TB publications

22. TB Mobile in a TB Workflow
•
Preliminary work done with desktop software: com.mmi
•
Fragment TB Mobile structures, scaffold-like
•
Perform scaffold-substructure vs. 7000 in vitro
•
Derive R-groups, tidy, present graphically, browse...

23. Source Materials
•
Scaffold:
•
Scaffold origin: inhibitor of Glf target
•
87 molecules with in vitro activity (yes/no)
•
Scaffold seems to elicit an activity pattern
•
Next step: load it into the app ecosystem...
To see the rest
of the TB
workflow……
http://molmatinf.com/
venice.html

24. Open Drug Discovery Teams
•
•
Curation of open data, e.g. Twitter & RSS feeds
Rare & neglected diseases, precompetitive
areas

25. Harvested Tweet
•
•
Tweet got harvested into Tuberculosis topic
Inline preview browsed, with other
thumbnails

26. What we can do now…
Take HTS screening hits
Query public databases
Propose targets
Design / purchase analogs
Predict activity
All on a mobile device / anywhere

27. 27
Conclusion
•
Cheminformatics workflows historically the role of
specialists: expensive and/or complex
•
Mobile apps are much cheaper and much more
accessible to experimentalists
•
Mobile+cloud can:
- replace simple-to-medium tasks
- coexist with complex tasks run on desktop software
•
Other advantages:
- anywhere/anytime portability
- excellent collaboration and sharing
- non-existent installation or maintenance burden

28. Acknowledgments


Malabika Sarker, Carolyn Talcott, Joel Freundlich, Barry Bunin
2R42AI088893-02 “from the National Institute of Allergy And
Infectious Diseases. (PI: S. Ekins)
Poster 224
PAPER TITLE: “Dual Response and dataset Fusion for
Machine Learning Models for Hit to lead Optimization in
Mycobacterium Tuberculosis Drug Discovery”
Monday, January 20, 2014
Presentation Time: 1:00 PM – 3:00 PM
You can find me @. CDD Booth 653..