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Collaborative Mobile Apps Using Social Media and Appifying Data For Drug Discovery

Collaborative Mobile Apps Using Social Media and Appifying Data For Drug Discovery



Collaborative Mobile Apps Using Social Media and Appifying Data For Drug Discovery

Collaborative Mobile Apps Using Social Media and Appifying Data For Drug Discovery



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    Collaborative Mobile Apps Using Social Media and Appifying Data For Drug Discovery Collaborative Mobile Apps Using Social Media and Appifying Data For Drug Discovery Document Transcript

    • White PaperCollaborative 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. 1
    • Executive SummaryWe 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 toaccelerate 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 otherrelevant apps that can enable visualization and mining. Mobile apps that can pull in andintegrate public content from many sources relating to molecules and data are alsobeing developed. Apps for drug discovery are already evolving rapidly and are able tocommunicate with each other to create workflows, as well as perform more complexprocesses, enabling informatics aspects of drug discovery (i.e. accessing data,modeling and visualization) to be done anywhere by potentially anyone.AnalysisThe winds of change are blowing through the pharmaceutical industry creating a newecosystem, with pharmas becoming smaller nodes in a complex network in whichcollaborations (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 needto: 1. fundamentally revamp how drugs are developed, 2. determine methods by whichthey can be brought to market faster and 3. provide incentives that can facilitatetreatments for neglected and rare diseases. For these diseases specifically fundingcomes primarily from public sources, data is more open, and potential profits arethought to be non-existent. In both neglected and rare diseases, the partners are morelikely to share IP because the monetary value of the IP ceases to be a barrier. So whatcan we do that will address these needs?Technology development is moving faster but R&D organizations do not appear to bekeeping pace as they are still wedded to the desktop computer and internet of the late1990-2000s. The crowd is unwittingly providing us with valuable data (which we are notcapturing and saving) that can be readily extracted from the web and social networks.This can enable drug safety analysis, drug repurposing and marketing by sentimentanalysis 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 toolsand platforms to collect, analyze and deliver this knowledge is in its infancy, with manyof them disconnected as separate islands lacking integration. This, in many ways, isanalogous to what we are seeing with how mobile apps are being created and used forscience as individual components with little integration [3].Mobile Apps for Drug DiscoveryThe user community is demanding a new breed of chemical information software thatkeeps pace with the rapidly changing dynamics within the chemical industry (includingpharmaceuticals). Software for drug discovery scientists has to be affordable enough for 2
    • all to participate, have a sufficiently intuitive user interface that becoming an expert isnot mandatory, and be available anywhere, anytime.The pace at which mobile apps have claimed a prominent position within the workflowof so many professionals is impressive. Already the capabilities of mobile devices toaccess, search, manipulate and exchange chemistry-related data relevant to drugdiscovery almost parallel those capabilities which were available on desktop computersjust 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 complementeach other, as well as workstation-based and server-based software, will secure theirplace within chemical data workflows.The modular nature of first generation mobile apps means that it is often necessary touse more than one app to accomplish a particular workflow segment, e.g. using adatabase 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 discoverySecond generation cheminformatics apps will have the facility to perform many moresophisticated functions, and in order to make ever more powerful functionality practical,these apps will need to incorporate data sharing and collaboration features as anintegral part of their design e.g. QSAR data preparation and prediction, pharmacophoreanalysis, docking clients, 2D depiction tools for 3D data, to name but a few. Numerousadditional data sharing scenarios are possible, e.g. deeper integration with onlinechemical databases, direct integration with electronic lab notebooks and interfacing withlaboratory instrumentation via wireless communication methods. The combination of auser interface designed and optimized for the mobile form factor, cloud-based serverfunctionality for data warehousing and extra computational capacity, and collaborationfeatures for integration into an overall workflow, makes these projects not onlytechnologically feasible, but in many ways preferable to traditional software. 3
    • Mobile apps are currently much less well suited to managing big data collections thananalogous desktop software, due in large part to their limited computational and storageresources, but this will change in future. Currently apps function as components:frequent data sharing is therefore a necessary part of any workflow, which is effectivefor 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 aserver and fetching the best few results, are already well established. Activeparticipation in visualization and maintenance of large data collections will require newmethods for task subdivision and integration of apps within pipeline-based workflows[4].The increased availability of data and algorithms in the cloud, accessible via standardprogramming interfaces, enables the first generation of scientific apps to accesscapabilities that require more powerful processing power. In summary, perhaps themost crucial feature for making mobile devices a viable component of a drug discoveryworkflow is the ability to collaboratively share molecules and data. A second generationof mobile apps is already emerging, which takes advantage of the many differenttechnologies provided by mobile platforms that allow data to be passed back and forthbetween heterogeneous environments. This is potentially transformational.Finding AppsApps for science and drug discovery continue to expand in number, diversity andcapabilities. They may be categorized into scientific disciplines and further sub-categorized based on applications within a branch of science. As a service to thecommunity a wiki site called www.scimobileapps.com (Fig 2.) has been set up hostinga growing list of scientific apps for all available mobile platforms. This is a valuableresource which will continue to expand in content and may be useful for the creation offuture science-focused app stores. Fig 2. An example of a mobile app description on www.scimobileapps.com. 4
    • Appifying dataA myriad of data and multitude of datasets for drug discovery are already available to usonline but the challenge is to get them into a format that is useful. For example structureactivity tables in papers and supplemental data are rarely thought of as useful outside ofthe context of a single publication. What if this content was made available via a mobileapp 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 PharmaceuticalRoundtable solvent selection guide data (a PDF with molecule names and data) as astarting 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 PharmaceuticalRoundtable [5] solvent selection guide rates the listed solvents against 5 categories:safety, health, environment (air), environment (water), and environment (waste) [6]. Keyparameters and criteria were then chosen for each category (e.g. flammability is one ofthe safety criteria). The summary table assigns a score from 1 to 10 for each solventunder the respective categories, with a score of 10 being of most concern and a scoreof 1 suggesting few issues. This is further simplified by using color coding with scores inthe range 1 to 3 shown as green, 4 to 7 as yellow and 8 to 10 as red. This allows quickcomparison between various solvents. The app was built using the Objective-Cprogramming language, the API provided by Apple for native iOS development, and theMMDSLib library for cheminformatics functionality such as structure rendering [7, 8].The Green Solvents app uses solvent structures grouped by chemical class as theprimary point of entry. These solvents are also color coded with a brown backgroundsuggesting less desirable and a green background suggesting more desirable. The usercan scroll through all the solvents and click on a molecule of interest. This opens a boxwhich lists the molecule name, CAS registry number, scores for each category withcolor coding as well as links out to the ChemSpider website [9], the Mobile Reagentsapp [10] and the Mobile Molecular DataSheet.[11] Fig 3. Screenshots of the Green Solvents mobile App. 5
    • Another way to make data accessible is to tweet it into an app that enables you to mineit or perform other functions. One tool we have developed, Open Drug DiscoveryTeams, makes use of "tweeted" molecular data (Fig 4). Fig 4. An example of tweeting molecules into the ODDT appGetting Collaborations to Work – Open Drug Discovery TeamsThere is potentially an alternative approach that ignores the intellectual propertyassociated with early research in an effort to make drug discovery more open, in amanner more analogous to open source software [6]. Alongside the increasing mobilityof computers the shift to mobile apps presents an opportunity to impact drug discovery[12] and specifically create Open Drug Discovery Teams (ODDT) [13]. ODDT takesadvantage of the pharmaceutical data appearing in social media such as Twitter whichincludes experimental data, molecule structures, images and other information thatcould be used for drug discovery collaborations. This app can be used by scientists andthe public to follow a research topic by its hashtag, potentially publish data and sharetheir ideas in the open (Fig 5). Fig 5. Screenshots of the ODDT app on the iPhone and iPad and one of the topics 6
    • Initially we used the app to harvest Twitter feeds on the hashtags for the followingdiseases: malaria, tuberculosis, Huntington’s disease, HIV/AIDS, and Sanfilipposyndrome, as well as the research topic green chemistry [14] which is of interestbecause its community is highly receptive to open collaboration. We have since addedChagas Disease, Leishmaniasis, H5N1 bird flu and Giant Axonal Neuropathy. All ofthese subjects have high potential for positively impacting the research environmentusing computational approaches and dissemination of information via mobile apps [15,16]. We have used Twitter to feed content into these topics, by providing links tomolecules and links to structure-activity tables.We have also added the ability to endorse or reject documents by emitting a personaltweet with an encoded directive. We gather thumbnail images for each document, byparsing HTML files, and pre-analyzing molecular data such as molecular structures (2Dand 3D), reactions and collections of structures and data.More recently we have started to populate the app with documents summarized byGoogle Alerts,[17] and started a crowdfunding campaign using IndieGoGo [18] to assistin the integration of additional data sources.Future versions of the software could integrate with other cheminformatics and drugdiscovery 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 TeamsFor organisations that want to merge their proprietary data with public data one couldimagine using the ODDT app with a modified version of the server, designed to workwith non-public data sources. We will leverage the ongoing development of software foranalysis of documents and chemical data to provide informatics capabilities for contentdiscovery and extraction.ConclusionThe appification of drug discovery data and the potential for using social media forcollaboration lowers the barriers to participation and potentially enables anyone tobecome involved in drug discovery, anywhere.AcknowledgmentsThe photo for Sanfillipo Syndrome in the ODDT app is courtesy of Jill Woodwww.jonahsjustbegun.org 7
    • References1. 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 InformationPlease contact us for further details or suggestions at: aclark@molmatinf.com andekinssean@yahoo.comYou can learn more about the ODDT app at:http://www.scimobileapps.com/index.php?title=Open_Drug_Discovery_TeamsAnd frequent blogs at http://www.collabchem.com and http://cheminf20.org/ 8