Collaboraive sharing of molecules and data in the mobile age

Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.
Collaborative Sharing of Molecules and Data
in the Mobile Age
Sean Ekins1,2*, Alex M. Clark3 and Joel S. Freundlich4,5
*
1 Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA.
2 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
3 Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, Quebec, Canada H3J 2S1
4 Department of Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University – New Jersey
Medical School, 185 South Orange Avenue, Newark, NJ 07103, USA.
5 Department of Pharmacology & Physiology, Rutgers University – New Jersey Medical School, 185 South Orange
Avenue, Newark, NJ 07103, USA.

Collaboration

“by provisioning the right amount of storage and compute resources, cost can be significantly
reduced with no significant impact on application performance”

CDD 2004 - present

CDD- a decade of drug discovery collaborations
2004 - present
SaaS
Easy to use
Used by
Academia
Industry,
Biotech
Private
Selective
collaboration
100’s of
published
datasets

• Online Zendesk
• CDD Models
• CDD Vision
• Integration of CDD Public,
ChemSpider, Zinc, and
PubChem.
Benefits of CDD Vault

Applications of CDD

• Three collaborations within Rutgers–NJMS
• Collaboration with Johns Hopkins, SRI, and CDD
• Collaboration with Johns Hopkins
• Collaboration with CDD
Supported by
7 Active NIH Grants
Chemical Probe
Evolution
Drug Discovery
Compound
Evolution
Target
Identification
& Validation
Freundlich Laboratory Collaborations Rely on CDD for Data Tracking!

Project overview
Phase I STTR – Proof of concept of mimic strategy
Phase II STTR – Expand mimic strategy and validation of phase I hits

Examples of vault used for STTR

Computationally searched >80,000 molecules – and used Bayesian
models for filter - narrowed to 842 hits -tested 23 compounds in vitro (3
picked as inactives), lead to 2 proposed as mimics of D-fructose 1,6
bisphosphate
Sarker et al., Pharm Res 2012, 29: 2115-2127
a.
b.
1R41AI088893-01

NIH Blueprint – CDD Partnership
Securely sharing data
New
Old

Another example of a big TB collaboration
7 Big Pharma and 4 academic institutes will open up targeted sections of their
compound libraries and share data with each other.
• Abbott
• AstraZeneca
• Bayer
• Eli Lilly
• GlaxoSmithKline
• Merck
• Sanofi
• Infectious Disease Research Institute
(IDRI)
• NIH National Institute of Allergy and
Infectious Diseases
• Texas A&M University
• Weill Cornell Medical College
TB Drug Accelerator

MM4TB
5 yr project – develop new medicines
for tuberculosis

Pharma with 24 Organizations in
Single, Secure MM4TB-CDD Vault®
• Text

MM4TB
• Provide CDD Vault
• Vault Support
• Cheminformatics
support to project
• Example using
CDD Vault to
share docking
data for Topo I
project
• Dock compounds
in homology
model of Mtb Topo
I then import data
in CDD
Godbole et al., Biochem Biophys Res Comm 446:916-20, 2014.

MM4TB – Topo I
• Mtb Topo I docking identified new inhibitors – collaboration With Nagaraja
group in India - Amsacrine
Godbole et al.,
Biochem Biophys
Res Comm
446:916-20,
2014.

Drug discovery is repetitive and there are 1000s of diseases
Drug discovery is high risk
Do we need robots or just smarter programs that discover the ideas we test?

MoDELS RESIDE IN PAPERS
NOT ACCESSIBLE…THIS IS
UNDESIRABLE
How do we share them?
How do we use Them?

Open ExtendedConnectivity Fingerprints
ECFP_6 FCFP_6
• Collected,
deduplicated,
hashed
• Sparse integers
• Invented for Pipeline Pilot: public method, proprietary details
• Often used with Bayesian models: many published papers
• Built a new implementation: open source, Java, CDK
– stable: fingerprints don't change with each new toolkit release
– well defined: easy to document precise steps
– easy to port: already migrated to iOS (Objective-C) for TB Mobile app
• Provides core basis feature for CDD open source model service

Data + One Click =
Uses Bayesian algorithm and FCFP_6 fingerprints

“Beautifully Simple” and equally fast to apply

Single point data > 300K molecules

The Mobile Age

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

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

Ekins et al., J Cheminform 5:13, 2013
Clark et al., J Cheminform 6:38 2014
Predict targets
Cluster molecules
http://goo.gl/vPOKS
http://goo.gl/iDJFR
Open fingerprints and bayesian method used in TB Mobile Vers.2

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

• 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
Simple App Workflows
Clark AM, Williams AJ and Ekins S, Chem-Bio Informatics Journal, 13: 1-18 2013.

• 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...
TB Mobile in a TB Workflow

• 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...
http://molmatinf.com/
venice.html
To see the rest
of the TB
workflow……
Source Materials

Open Drug Discovery Teams
• Curation of open data, e.g.
Twitter & RSS feeds
• Rare & neglected diseases,
precompetitive areas
• Tweet got harvested into
Tuberculosis topic
• Inline preview browsed, with
other thumbnails
Ekins et al., Mol Informatics, 31: 585-597, 2012

Rare diseases inspired an App that
may be a new kind of database
upload molecules by tweeting them-
1 tweet upload
Take our data with us anywhere
Bring data off the cloud into device
Advantages you get to analyze it in
the Cloud on a plane
Future: how
do we
deliver data

Conclusions
• 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
Desktop+Mobile+cloud = best case
• Other advantages:
- anywhere/anytime portability
- excellent collaboration and sharing
- non-existent installation or maintenance burden

PAPER ID: 22104 “Collaborative sharing of molecules and data in the mobile age” (final paper number: 43)
DIVISION: COMP; DAY & TIME OF PRESENTATION: August 10, 2014 from 4:45 pm to 5:15 pm
LOCATION: Moscone Center, West Bldg., Room: 2005
PAPER ID: 22094 “Expanding the metabolite mimic approach to identify hits for Mycobacterium tuberculosis ” (final paper
number: 78)
DIVISION: COMP: DAY & TIME OF PRESENTATION: August 11, 2014 from 9:00 am to 9:30 am
LOCATION: Moscone Center, West Bldg., Room: 2005
PAPER ID: 22120 “Why there needs to be open data for ultrarare and rare disease drug discovery” (final paper number: 48)
DIVISION: CINF:SESSION DAY & TIME OF PRESENTATION: August 11, 2014 from 10:50 am to 11:20 am
LOCATION: Palace Hotel, Room: Marina
PAPER ID: 22183 “Progress in computational toxicology” (final paper number: 125)
DIVISION: TOXI: DAY & TIME OF PRESENTATION: August 12, 2014 from 6:30 pm to 10:30 pm
LOCATION: Moscone Center, North Bldg. , Room: 134
PAPER ID: 22091 “Examples of how to inspire the next generation to pursue computational chemistry/cheminformatics” (final
paper number: 100)
DIVISION: CINF: Division of Chemical Information DAY & TIME OF PRESENTATION: August 13, 2014 from 8:25 am to 8:50 am
LOCATION: Palace Hotel, Room: Presidio
PAPER ID: 22176 “Applying computational models for transporters to predict toxicity” (final paper number: 132)
DIVISION: TOXI: DAY & TIME OF PRESENTATION: August 13, 2014 from 9:45 am to 10:05 am
LOCATION: InterContinental San Francisco, Room: Grand Ballroom A
PAPER ID: 22186 “New target prediction and visualization tools incorporating open source molecular fingerprints for TB mobile
version 2” (final paper number: 123)
DIVISION: CINF: DAY & TIME OF PRESENTATION: August 13, 2014 from 1:35 pm to 2:05 pm
LOCATION: Palace Hotel, Room: California Parlor
You can find me @...

All at CDD, SRI, MM4TB and many others …Funding: Bill and Melinda Gates Foundation
(Grant#49852) 1R41AI088893-01, 2R42AI088893-02, R43 LM011152-01,
9R44TR000942-02, 1R41AI108003-01, MM4TB, Software: Biovia

 Email: ekinssean@yahoo.com
 Slideshare: http://www.slideshare.net/ekinssean
 Twitter: collabchem
 Blog: http://www.collabchem.com/
 Website: http://www.collaborations.com/CHEMISTRY.HTM

Collaboraive sharing of molecules and data in the mobile age

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Collaboraive sharing of molecules and data in the mobile age