The document discusses using mobile apps and social media to accelerate drug discovery through open collaboration and data sharing. It describes the Open Drug Discovery Teams (ODDT) app, which allows scientists to share data on topics through hashtags and provides tools to mine tweeted data. The document also discusses other apps being developed to make scientific data more accessible, such as one summarizing solvent safety data. It argues that appifying data and enabling collaboration through mobile apps can lower barriers to participation in drug discovery.

1. White Paper
Collaborative Mobile Apps Using Social Media
and Appifying Data For Drug Discovery
Sean Ekins1,2, Alex M. Clark3 and Antony J. Williams4
1
Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, U.S.A.
2
To whom correspondence should be addressed ekinssean@yahoo.com
3
Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, Quebec, Canada H3J 2S1.
4
Royal Society of Chemistry, 904 Tamaras Circle, Wake Forest, NC-27587, U.S.A.
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2. Executive Summary
We are at a watershed moment for drug discovery. Can we leverage social media,
collaborations and the masses of data that are in the public and private domains to
accelerate drug discovery? One possible way to do this may involve methods for
“appification” of data, whether in self-contained apps or those that push data to other
relevant apps that can enable visualization and mining. Mobile apps that can pull in and
integrate public content from many sources relating to molecules and data are also
being developed. Apps for drug discovery are already evolving rapidly and are able to
communicate with each other to create workflows, as well as perform more complex
processes, enabling informatics aspects of drug discovery (i.e. accessing data,
modeling and visualization) to be done anywhere by potentially anyone.
Analysis
The winds of change are blowing through the pharmaceutical industry creating a new
ecosystem, with pharmas becoming smaller nodes in a complex network in which
collaborations (with academics, CROs, public-private partnerships and not for profits)
are an important component of the business model [1]. Yet, still there is an urgent need
to: 1. fundamentally revamp how drugs are developed, 2. determine methods by which
they can be brought to market faster and 3. provide incentives that can facilitate
treatments for neglected and rare diseases. For these diseases specifically funding
comes primarily from public sources, data is more open, and potential profits are
thought to be non-existent. In both neglected and rare diseases, the partners are more
likely to share IP because the monetary value of the IP ceases to be a barrier. So what
can we do that will address these needs?
Technology development is moving faster but R&D organizations do not appear to be
keeping pace as they are still wedded to the desktop computer and internet of the late
1990-2000s. The crowd is unwittingly providing us with valuable data (which we are not
capturing and saving) that can be readily extracted from the web and social networks.
This can enable drug safety analysis, drug repurposing and marketing by sentiment
analysis using social media stream mining tools and real-time data from social networks
[2] (such as Teranode, Ceiba and Swarmology). However, the availability of such tools
and platforms to collect, analyze and deliver this knowledge is in its infancy, with many
of them disconnected as separate islands lacking integration. This, in many ways, is
analogous to what we are seeing with how mobile apps are being created and used for
science as individual components with little integration [3].
Mobile Apps for Drug Discovery
The user community is demanding a new breed of chemical information software that
keeps pace with the rapidly changing dynamics within the chemical industry (including
pharmaceuticals). Software for drug discovery scientists has to be affordable enough for
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3. all to participate, have a sufficiently intuitive user interface that becoming an expert is
not mandatory, and be available anywhere, anytime.
The pace at which mobile apps have claimed a prominent position within the workflow
of so many professionals is impressive. Already the capabilities of mobile devices to
access, search, manipulate and exchange chemistry-related data relevant to drug
discovery almost parallel those capabilities which were available on desktop computers
just a few years ago. We are confident that this budding ecosystem of chemistry apps
(Fig 1) will continue to grow rapidly, and that the ability of these apps to complement
each other, as well as workstation-based and server-based software, will secure their
place within chemical data workflows.
The modular nature of first generation mobile apps means that it is often necessary to
use more than one app to accomplish a particular workflow segment, e.g. using a
database searching app to locate data, and another to organize it into a collection.
Passing data back and forth between apps is therefore an integral and frequent activity.
Fig 1. Examples of mobile apps for drug
discovery
Second generation cheminformatics apps will have the facility to perform many more
sophisticated functions, and in order to make ever more powerful functionality practical,
these apps will need to incorporate data sharing and collaboration features as an
integral part of their design e.g. QSAR data preparation and prediction, pharmacophore
analysis, docking clients, 2D depiction tools for 3D data, to name but a few. Numerous
additional data sharing scenarios are possible, e.g. deeper integration with online
chemical databases, direct integration with electronic lab notebooks and interfacing with
laboratory instrumentation via wireless communication methods. The combination of a
user interface designed and optimized for the mobile form factor, cloud-based server
functionality for data warehousing and extra computational capacity, and collaboration
features for integration into an overall workflow, makes these projects not only
technologically feasible, but in many ways preferable to traditional software.
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4. Mobile apps are currently much less well suited to managing big data collections than
analogous desktop software, due in large part to their limited computational and storage
resources, but this will change in future. Currently apps function as components:
frequent data sharing is therefore a necessary part of any workflow, which is effective
for small collections, i.e. hundreds of rows of data, rather than thousands or millions.
Simple workflows involving big data collections, e.g. submitting a structure search to a
server and fetching the best few results, are already well established. Active
participation in visualization and maintenance of large data collections will require new
methods for task subdivision and integration of apps within pipeline-based workflows
[4].
The increased availability of data and algorithms in the cloud, accessible via standard
programming interfaces, enables the first generation of scientific apps to access
capabilities that require more powerful processing power. In summary, perhaps the
most crucial feature for making mobile devices a viable component of a drug discovery
workflow is the ability to collaboratively share molecules and data. A second generation
of mobile apps is already emerging, which takes advantage of the many different
technologies provided by mobile platforms that allow data to be passed back and forth
between heterogeneous environments. This is potentially transformational.
Finding Apps
Apps for science and drug discovery continue to expand in number, diversity and
capabilities. They may be categorized into scientific disciplines and further sub-
categorized based on applications within a branch of science. As a service to the
community a wiki site called www.scimobileapps.com (Fig 2.) has been set up hosting
a growing list of scientific apps for all available mobile platforms. This is a valuable
resource which will continue to expand in content and may be useful for the creation of
future science-focused app stores.
Fig 2. An example of a mobile app description on www.scimobileapps.com.
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5. Appifying data
A myriad of data and multitude of datasets for drug discovery are already available to us
online but the challenge is to get them into a format that is useful. For example structure
activity tables in papers and supplemental data are rarely thought of as useful outside of
the context of a single publication. What if this content was made available via a mobile
app or the data tweeted into an app that could mine the data and structures?
As one example of appifying data, we have used the ACS GCI Pharmaceutical
Roundtable solvent selection guide data (a PDF with molecule names and data) as a
starting point to develop the first mobile app for green chemistry called Green Solvents
(Fig. 3) that is freely available for iPhone, iPod and iPad. The ACS GCI Pharmaceutical
Roundtable [5] solvent selection guide rates the listed solvents against 5 categories:
safety, health, environment (air), environment (water), and environment (waste) [6]. Key
parameters and criteria were then chosen for each category (e.g. flammability is one of
the safety criteria). The summary table assigns a score from 1 to 10 for each solvent
under the respective categories, with a score of 10 being of most concern and a score
of 1 suggesting few issues. This is further simplified by using color coding with scores in
the range 1 to 3 shown as green, 4 to 7 as yellow and 8 to 10 as red. This allows quick
comparison between various solvents. The app was built using the Objective-C
programming language, the API provided by Apple for native iOS development, and the
MMDSLib library for cheminformatics functionality such as structure rendering [7, 8].
The Green Solvents app uses solvent structures grouped by chemical class as the
primary point of entry. These solvents are also color coded with a brown background
suggesting less desirable and a green background suggesting more desirable. The user
can scroll through all the solvents and click on a molecule of interest. This opens a box
which lists the molecule name, CAS registry number, scores for each category with
color coding as well as links out to the ChemSpider website [9], the Mobile Reagents
app [10] and the Mobile Molecular DataSheet.[11]
Fig 3. Screenshots of the Green Solvents mobile App.
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6. Another way to make data accessible is to tweet it into an app that enables you to mine
it or perform other functions. One tool we have developed, Open Drug Discovery
Teams, makes use of "tweeted" molecular data (Fig 4).
Fig 4. An example of tweeting molecules into the ODDT app
Getting Collaborations to Work – Open Drug Discovery Teams
There is potentially an alternative approach that ignores the intellectual property
associated with early research in an effort to make drug discovery more open, in a
manner more analogous to open source software [6]. Alongside the increasing mobility
of computers the shift to mobile apps presents an opportunity to impact drug discovery
[12] and specifically create Open Drug Discovery Teams (ODDT) [13]. ODDT takes
advantage of the pharmaceutical data appearing in social media such as Twitter which
includes experimental data, molecule structures, images and other information that
could be used for drug discovery collaborations. This app can be used by scientists and
the public to follow a research topic by its hashtag, potentially publish data and share
their ideas in the open (Fig 5).
Fig 5. Screenshots of the ODDT app on the iPhone and iPad and one of the topics
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7. Initially we used the app to harvest Twitter feeds on the hashtags for the following
diseases: malaria, tuberculosis, Huntington’s disease, HIV/AIDS, and Sanfilippo
syndrome, as well as the research topic green chemistry [14] which is of interest
because its community is highly receptive to open collaboration. We have since added
Chagas Disease, Leishmaniasis, H5N1 bird flu and Giant Axonal Neuropathy. All of
these subjects have high potential for positively impacting the research environment
using computational approaches and dissemination of information via mobile apps [15,
16]. We have used Twitter to feed content into these topics, by providing links to
molecules and links to structure-activity tables.
We have also added the ability to endorse or reject documents by emitting a personal
tweet with an encoded directive. We gather thumbnail images for each document, by
parsing HTML files, and pre-analyzing molecular data such as molecular structures (2D
and 3D), reactions and collections of structures and data.
More recently we have started to populate the app with documents summarized by
Google Alerts,[17] and started a crowdfunding campaign using IndieGoGo [18] to assist
in the integration of additional data sources.
Future versions of the software could integrate with other cheminformatics and drug
discovery related apps (e.g. structure searching, activity data extraction, structure-
activity series creation, automated model building, docking against known targets,
pharmacophore hypothesis generation etc.).
Drug Discovery Teams
For organisations that want to merge their proprietary data with public data one could
imagine using the ODDT app with a modified version of the server, designed to work
with non-public data sources. We will leverage the ongoing development of software for
analysis of documents and chemical data to provide informatics capabilities for content
discovery and extraction.
Conclusion
The appification of drug discovery data and the potential for using social media for
collaboration lowers the barriers to participation and potentially enables anyone to
become involved in drug discovery, anywhere.
Acknowledgments
The photo for Sanfillipo Syndrome in the ODDT app is courtesy of Jill Wood
www.jonahsjustbegun.org
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8. References
1. Ekins, S., et al., Three Disruptive Strategies for Removing Drug Discovery Bottlenecks
Submitted 2012.
2. Martens, D., B. Baesens, and T. Fawcett, Editorial survey: swarm intelligence for data
mining. Mach Learn, 2011. 82: p. 1-42.
3. Cooper, S., et al., Predicting protein structures with a multiplayer online game. Nature,
2010. 466(7307): p. 756-60.
4. Clark, A.M., S. Ekins, and A.J. Williams, Redefining cheminformatics with intuitive
collaborative mobile apps. submitted, 2012.
5. American Chemical Society Green Chemistry InstituteTM Pharmaceutical Roundtable
[cited; Available from: www.acs.org/gcipharmaroundtable.
6. Williams, A.J., et al., Current and future challenges for the collaborative computational
technologies for the life sciences, in Collaborative computational technologies for
biomedical research, S. Ekins, M.A.Z. Hupcey, and A.J. Williams, Editors. 2011, Wiley
and Sons: Hoboken, NJ. p. 491-517.
7. Molecular Materials Informatics. [cited; Available from:
http://molmatinf.com/mmdslib.html.
8. Clark, A.M., Basic primitives for molecular diagram sketching. J Cheminform, 2010. 2(1):
p. 8.
9. ChemSpider. [cited; Available from: www.chemspider.com.
10. Mobile Reagents. [cited; Available from: http://mobilereagents.com/.
11. MMDSLib. [cited; Available from: http://molmatinf.com/products.html#section14.
12. Williams, A.J., et al., Mobile apps for chemistry in the world of drug discovery. Drug Disc
Today, 2011. 16: p. 928-939.
13. Ekins, S., A.M. Clark, and A.J. Williams, Open Drug Discovery Teams: A Chemistry
Mobile App for Collaboration. Submitted, 2012.
14. Anastas, P.T. and J.C. Warner, Green Chemistry: Theory and Practice. 1998, New York:
Oxford University Press Inc.
15. Ekins, S., A.M. Clark, and A.J. Williams, Incorporating Green Chemistry Concepts into
Mobile Apps: Green Solvents. Submitted, 2012.
16. Ekins, S., A.M. Clark, and A.J. Williams. Communicating green chemistry by mobile
apps The Nexus Newsletter 2011 [cited; Available from:
http://portal.acs.org/portal/fileFetch/C/CNBP_027943/pdf/CNBP_027943.pdf.
17. Google Alerts [cited; Available from: http://www.google.com/alerts.
18. Ekins, S. and A.M. Clark. Open Drug Discovery Teams; Crowdfunding. 2012 [cited;
IndieGoGo]. Available from: http://www.indiegogo.com/projects/122117?a=701254.
Further Information
Please contact us for further details or suggestions at: aclark@molmatinf.com and
ekinssean@yahoo.com
You can learn more about the ODDT app at:
http://www.scimobileapps.com/index.php?title=Open_Drug_Discovery_Teams
And frequent blogs at http://www.collabchem.com and http://cheminf20.org/
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