Collaboraive sharing of molecules and data in the mobile age

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2009 Collaborative Drug Discovery, Inc. Collaborative Sharing of Molecules and Data in the Mobile Age Sean Ekins 1 ,2* , Alex M. Clark 3 and Joel S. Freundlich 4,5 * 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.

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An overview of using collaborative software in small and large scale collaborations in drug discovery. A focus on Tuberculosis. Also analysis of collaboration and mobile apps for science

Transcript of Collaboraive sharing of molecules and data in the mobile age

Page 1: 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 2S14 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.

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Big DATA

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Collaboration

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

Cloud

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

CDD 2004 - present

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

CDD- a decade of drug discovery collaborations

2004 - present

SaaS

Easy to use

Used by AcademiaIndustry, Biotech

Private

Selective collaboration

100’s of published datasets

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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

Benefits of CDD Vault

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Applications of CDD

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

• 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 ProbeEvolution

Drug Discovery CompoundEvolution

TargetIdentification& Validation

Freundlich Laboratory Collaborations Rely on CDD for Data Tracking!

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Project overview

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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Examples of vault used for STTR

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

NIH Blueprint – CDD PartnershipSecurely sharing data

NewNew

OldOld

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Another example of a big TB collaboration7 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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

MM4TB

5 yr project – develop new medicines for tuberculosis

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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

• Text

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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.

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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.

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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?

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MoDELS RESIDE IN PAPERSNOT ACCESSIBLE…THIS IS UNDESIRABLE

How do we share them?How do we use Them?

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Open Extended Connectivity 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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Data + One Click =

Uses Bayesian algorithm and FCFP_6 fingerprints

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

“Beautifully Simple” and equally fast to apply

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Single point data > 300K molecules

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

The Mobile Age

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Examples of vault used for STTR

mainframesminicomputer

s

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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

• Reference data

• Education

• Structure drawing

• Database searching

• 3D viewing

• Reactions & collections

• Property calculation

• Model building

• Graphical presentation

• Data sharing

Chemistry Apps

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

Ekins et al., J Cheminform 5:13, 2013Clark et al., J Cheminform 6:38 2014

Predict targetsCluster molecules

http://goo.gl/vPOKS

http://goo.gl/iDJFR

Open fingerprints and bayesian method used in TB Mobile Vers.2

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

• 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.

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

• 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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

• 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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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

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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

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Archive, Mine, Collaborate© 2009 Collaborative Drug Discovery, Inc.

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

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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 pmLOCATION: 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 amLOCATION: 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 amLOCATION: 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 pmLOCATION: 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 amLOCATION: 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 amLOCATION: 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 pmLOCATION: Palace Hotel, Room: California Parlor  

You can find me @...

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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

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Email: [email protected]

- Slideshare: http://www.slideshare.net/ekinssean

- Twitter: collabchem

- Blog: http://www.collabchem.com/

- Website: http://www.collaborations.com/CHEMISTRY.HTM


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