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Practical cheminformatics
workflows with mobile
apps
                                                Dr. Alex M.
                                                   Clark
                                                 October 2012




 © 2012 Molecular Materials Informatics, Inc.
  http://molmatinf.com
                                1
2




                    Introduction
•   Mobile apps in chemistry happening real fast: 3 years ago nothing
    but the most trivial of apps...

•   ... now there's a whole ecosystem

•   For a detailed list of apps, see www.scimobileapps.com wiki

•   Complex workflows can be executed using just mobile apps &
    cloud-based infrastructure

•   Molecular Materials Informatics founded with the goal of making
    mobile + cloud solutions into a viable replacement for
    cheminformatics infrastructure




                                   info@molmatinf.com
3




              1. Information delivery
•   Many apps designed for mainly one-way transmission of information,
    i.e. content consumption

    -   Green Solvents

    -   Approved Drugs

    -   The Elements

    -   ACS Mobile, RSC Mobile

    -   Reagents, Organic Named Reactions,

•   Typically a very simple user interface: close to zero learning curve

•   Many for education, but some are relevant to professionals

•   Mobile devices very well suited for these types of apps: quite easy to
    produce, and can be very useful, despite their limited capacity
4




                   2. Catalog lookup
•   Apps designed to input a search query, submit the query to a server,
    then display and utilise the results

•   Examples include:

    -   ChemSpider Mobile

    -   SPRESImobile

    -   Mobile Reagents

    -   iKinase

    -   iProtein

•   These apps are moderately difficult to produce; most of the action
    takes place on remote servers, i.e. hosting the data and the search
    algorithms
5




                   3. Data visualisation
•   Apps designed to visualise existing chemical data, e.g. PDB entries,
    docking results, structure-activity relationships and collections of
    molecules

•   Examples include:

    -   Molecules

    -   PyMol

    -   iMolView

    -   iSpartan

    -   iMolecule Builder

    -   SAR Table
6




                      4. Data creation
•   Some of the most sophisticated apps provide the ability to assemble
    chemical data within the app:

    -   molecular structures

    -   reactions

    -   scalar data

    -   higher order markup (SAR)

•   Data can be created with the app UI, or pieces can be imported
    from other sources. Examples:

    -   Mobile Molecular DataSheet (MMDS)

    -   MolPrime

    -   SAR Table

    -   Chirys, ChemDoodle Mobile, ChemJuice
7




                   5. Lab notebooks
•   Apps designed for entering information about experiments

•   Ultimate objective is the paperless laboratory

•   Mobile laboratory notebooks are mostly generic rather than
    chemically aware, i.e. not specifically for synthesis

•   Prototypical example is Yield101 (education focused)

•   General purpose scientific lab notebook apps:

    -   LabGuru

    -   irisnote

•   Many web-based lab notebooks that are vendor-endorsed for iPad
    use
8




    6. Collaboration and sharing

•   Apps have many options for sharing and importing data, e.g.

    -   Email (personal, bidirectional)

    -   Remote hosting (private, collaborative)

    -   Web sharing and tweeting (global, open)

•   Examples:

    -   MMDS

    -   MolSync

    -   Reaction101

    -   ODDT
9




         Mobile app limitations
• Touchscreen is small, inaccurate: very different user interface
• Modern mobile processors are fast (Moore's law), but respecting
  battery life requires careful conservation of CPU resources

• Limited memory and constricted multi-tasking: an app has a tightly
  defined lifespan, and is expected to be lightweight, low-state

• Modular nature of apps makes large datasets hard to work with
• Functionality is often heavily network dependent
• Limited choice of development environments: native or web, stark
  choice of quality vs. portability




• When these limitations can be overcome, the mobile + cloud
  combination becomes a full replacement for desktop computing
10




                        Workflow

•   The following slides demonstrate a multipart workflow scenario
    using:

    -   iOS apps (Apple iPhone/iPod/iPad)

    -   cloud-hosted webservices

•   Each step is carried out using technology that is currently available...

•   ... and is representative of the state of the art

•   The sequential tasks are based on a medicinal chemistry
    exploration: investigating new tuberculosis drugs

•   Any workflow with a similar level of technology is probably either
    already possible, or could be made possible
11




                 Step 1: Lookup
•   Lookup a core scaffold for a series of known tuberculosis inhibitors
    using the ChemSpider Mobile app:
12




ChemSpider




  •   View ChemSpider record in mobile browser

  •   Structure can be downloaded as MDL Molfile

  •   Mofiles can be opened directly in apps

  •   Select Mobile Molecular DataSheet (MMDS)
13




Step 2: Lookup a datasheet

                  •   Imported molecular
                      structure into the scratch
                      sheet




                  •   Open the Run WebService
                      feature...
14




                  ChEBI Query




•   Substructure search for the core scaffold produces a new datasheet
15




               Step 3: Assign scaffolds




•   Open in SAR Table app...

•   ... which represents a table of
    compounds as scaffolds &
    substituents
16


1       3




2




5
    4
17




Scaffolds + Substituents


              •   Structure-activity relationship is
                  aided by classifying scaffold and
                  substituents

              •   Assignment process is partly
                  manual, partly automated

              •   Structure fragments can be drawn
                  within the app, copied from other
                  cells, or computed by a webservice
18




    Step 4: Merge

•   Have existing table, created from literature
    data:

    -   V. Makarov et al., Benzothiazinones kill
        Mycobacterium tuberculosis by
        blocking arabinan synthesis, Science 324,
        pp. 801-804 (2009)

•   Activity values colour-coded by defining a
    scheme

•   Append the query data to the end...
19



Appended rows
    •   Webservice builds a model based on existing
        activity values

    •   Applies predictions to missing values




     blank
    activity
20




Step 5: Examine SAR

          matrix
           view




      •    Predicted values suggest some promising R2 &
           R3 substituents, based on known compounds

      •    These are from ChEBI, so can be looked up
21




Step 6: Propose new lead

              •   Identify values for R2 and R3 by
                  examining current structure-
                  activity relationship

              •   Create a new row with the
                  proposed substituent moieties:



              Scaffold    R1     R2 R3     Molecule
22




              Step 7: Find a recipe




•   Open the core structure in SPRESImobile app

•   Search for similar structures, and search As Product to find a promising
    synthesis that can be adapted
23




Find reaction
     •   Found a promising synthesis

     •   Starting materials suitable for desired
         functionalisation

     •   Link to literature provided within the app:
         can be opened in Safari Mobile
24




         Step 8: Plan synthesis




•   Can open the reaction with Yield101 app: scheme is transferred

•   Need to customise the reaction components and add quantities

•   Have the literature reference on hand
25




                          Adapt synthesis




•   Edit structure reactant and product to include functional groups

•   Specify known quantities and expected yield: all implied quantities are
    calculated, as is the process mass intensity
26




Run the experiment


           • Can use the mobile device in the
             lab, and record the results directly

           • OR:
           • Create a PDF document, and print
             it directly from the device or send
             the file by email...

           • ... then enter the results later.
27



•           Step 9: Sharing data
    Many ways to share data: use Open-With to transfer to MMDS




•   From there, can:

    -   send data by email

    -   export presentation quality graphics

    -   share on web & tweet
28




Microsoft Office documents
              •   Outgoing emails can include many
                  attachments:

                  -   machine-readable data

                  -   graphics

              •   Graphics options include MS Word
                  and Excel documents with
                  embedded vector graphics
29




              Sharing with Dropbox




•   Can link datasheets to Dropbox
    using the MolSync app

•   Storing data on Dropbox opens up
    an entire landscape of
    collaboration and sharing options
30




Step 10: Open sharing
31




Open Drug Discovery Teams




•   Relevant tweets get picked up by the Open Drug Discovery Teams
    project, and made available for browsing with a free app

•   Chemical data is parsed and can be accessed with other apps
32




                      Conclusion

•   Gone from paper to idea to hypothesis to experiment to online
    publication... all using mobile apps

•   Multi-app workflows involve content creation, content consumption,
    networked services and remote computation

•   Realtime calculations are done by the apps, complex calculations are
    offloaded to cloud servers

•   Mobile apps are good for creating, visualising and sharing small
    datasets

•   Apps can access big datasets, but pipelining tools for manipulating
    big data are underdeveloped
33




                     Future work

•   Evolution from proof-of-concept phase to apps designed to solve
    specific workflows...

•   ... expect more customised apps

•   More integration with cloud services: apps cannot work with big
    data without back-end support

•   Pistoia Alliance is currently working on an AppStore, which provides

    -   an alternative storefront for mobile apps

    -   standardised service infrastructure, for big data & calculations

•   Gradual replacement of all mainstream chemical information
    software
34




                       References

•   Redefining Cheminformatics with Intuitive Collaborative Mobile
    Apps, A.M. Clark, S. Ekins, A.J. Williams, Molecular Informatics, 31, 569-
    584 (2012)

•   Mobile apps for chemistry in the world of drug discovery, A.J.
    Williams, S. Ekins, A.M. Clark, J.J. Jack, R.L. Apodaca, Drug Discovery
    Today, 16, 928-939 (2011)

•   Open Drug Discovery Teams: A Chemistry Mobile App for
    Collaboration, S. Ekins, A.M. Clark, A.J. Williams, Molecular Informatics,
    31, 585-597 (2012)

•   Basic primitives for molecular diagram sketching, A.M. Clark, Journal
    of Cheminformatics, 2:8 (2010)
Acknowledgments

• Antony Williams

• Sean Ekins

• Leah Rae McEwen, David Martinsen

• ACS CINF Division
                           http://molmatinf.com
                           http://molsync.com
• Inquiries to             http://cheminf20.org
  info@ molmatinf.co
  m                        @aclarkxyz

                      35

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Practical cheminformatics workflows with mobile apps

  • 1. Practical cheminformatics workflows with mobile apps Dr. Alex M. Clark October 2012 © 2012 Molecular Materials Informatics, Inc. http://molmatinf.com 1
  • 2. 2 Introduction • Mobile apps in chemistry happening real fast: 3 years ago nothing but the most trivial of apps... • ... now there's a whole ecosystem • For a detailed list of apps, see www.scimobileapps.com wiki • Complex workflows can be executed using just mobile apps & cloud-based infrastructure • Molecular Materials Informatics founded with the goal of making mobile + cloud solutions into a viable replacement for cheminformatics infrastructure info@molmatinf.com
  • 3. 3 1. Information delivery • Many apps designed for mainly one-way transmission of information, i.e. content consumption - Green Solvents - Approved Drugs - The Elements - ACS Mobile, RSC Mobile - Reagents, Organic Named Reactions, • Typically a very simple user interface: close to zero learning curve • Many for education, but some are relevant to professionals • Mobile devices very well suited for these types of apps: quite easy to produce, and can be very useful, despite their limited capacity
  • 4. 4 2. Catalog lookup • Apps designed to input a search query, submit the query to a server, then display and utilise the results • Examples include: - ChemSpider Mobile - SPRESImobile - Mobile Reagents - iKinase - iProtein • These apps are moderately difficult to produce; most of the action takes place on remote servers, i.e. hosting the data and the search algorithms
  • 5. 5 3. Data visualisation • Apps designed to visualise existing chemical data, e.g. PDB entries, docking results, structure-activity relationships and collections of molecules • Examples include: - Molecules - PyMol - iMolView - iSpartan - iMolecule Builder - SAR Table
  • 6. 6 4. Data creation • Some of the most sophisticated apps provide the ability to assemble chemical data within the app: - molecular structures - reactions - scalar data - higher order markup (SAR) • Data can be created with the app UI, or pieces can be imported from other sources. Examples: - Mobile Molecular DataSheet (MMDS) - MolPrime - SAR Table - Chirys, ChemDoodle Mobile, ChemJuice
  • 7. 7 5. Lab notebooks • Apps designed for entering information about experiments • Ultimate objective is the paperless laboratory • Mobile laboratory notebooks are mostly generic rather than chemically aware, i.e. not specifically for synthesis • Prototypical example is Yield101 (education focused) • General purpose scientific lab notebook apps: - LabGuru - irisnote • Many web-based lab notebooks that are vendor-endorsed for iPad use
  • 8. 8 6. Collaboration and sharing • Apps have many options for sharing and importing data, e.g. - Email (personal, bidirectional) - Remote hosting (private, collaborative) - Web sharing and tweeting (global, open) • Examples: - MMDS - MolSync - Reaction101 - ODDT
  • 9. 9 Mobile app limitations • Touchscreen is small, inaccurate: very different user interface • Modern mobile processors are fast (Moore's law), but respecting battery life requires careful conservation of CPU resources • Limited memory and constricted multi-tasking: an app has a tightly defined lifespan, and is expected to be lightweight, low-state • Modular nature of apps makes large datasets hard to work with • Functionality is often heavily network dependent • Limited choice of development environments: native or web, stark choice of quality vs. portability • When these limitations can be overcome, the mobile + cloud combination becomes a full replacement for desktop computing
  • 10. 10 Workflow • The following slides demonstrate a multipart workflow scenario using: - iOS apps (Apple iPhone/iPod/iPad) - cloud-hosted webservices • Each step is carried out using technology that is currently available... • ... and is representative of the state of the art • The sequential tasks are based on a medicinal chemistry exploration: investigating new tuberculosis drugs • Any workflow with a similar level of technology is probably either already possible, or could be made possible
  • 11. 11 Step 1: Lookup • Lookup a core scaffold for a series of known tuberculosis inhibitors using the ChemSpider Mobile app:
  • 12. 12 ChemSpider • View ChemSpider record in mobile browser • Structure can be downloaded as MDL Molfile • Mofiles can be opened directly in apps • Select Mobile Molecular DataSheet (MMDS)
  • 13. 13 Step 2: Lookup a datasheet • Imported molecular structure into the scratch sheet • Open the Run WebService feature...
  • 14. 14 ChEBI Query • Substructure search for the core scaffold produces a new datasheet
  • 15. 15 Step 3: Assign scaffolds • Open in SAR Table app... • ... which represents a table of compounds as scaffolds & substituents
  • 16. 16 1 3 2 5 4
  • 17. 17 Scaffolds + Substituents • Structure-activity relationship is aided by classifying scaffold and substituents • Assignment process is partly manual, partly automated • Structure fragments can be drawn within the app, copied from other cells, or computed by a webservice
  • 18. 18 Step 4: Merge • Have existing table, created from literature data: - V. Makarov et al., Benzothiazinones kill Mycobacterium tuberculosis by blocking arabinan synthesis, Science 324, pp. 801-804 (2009) • Activity values colour-coded by defining a scheme • Append the query data to the end...
  • 19. 19 Appended rows • Webservice builds a model based on existing activity values • Applies predictions to missing values blank activity
  • 20. 20 Step 5: Examine SAR matrix view • Predicted values suggest some promising R2 & R3 substituents, based on known compounds • These are from ChEBI, so can be looked up
  • 21. 21 Step 6: Propose new lead • Identify values for R2 and R3 by examining current structure- activity relationship • Create a new row with the proposed substituent moieties: Scaffold R1 R2 R3 Molecule
  • 22. 22 Step 7: Find a recipe • Open the core structure in SPRESImobile app • Search for similar structures, and search As Product to find a promising synthesis that can be adapted
  • 23. 23 Find reaction • Found a promising synthesis • Starting materials suitable for desired functionalisation • Link to literature provided within the app: can be opened in Safari Mobile
  • 24. 24 Step 8: Plan synthesis • Can open the reaction with Yield101 app: scheme is transferred • Need to customise the reaction components and add quantities • Have the literature reference on hand
  • 25. 25 Adapt synthesis • Edit structure reactant and product to include functional groups • Specify known quantities and expected yield: all implied quantities are calculated, as is the process mass intensity
  • 26. 26 Run the experiment • Can use the mobile device in the lab, and record the results directly • OR: • Create a PDF document, and print it directly from the device or send the file by email... • ... then enter the results later.
  • 27. 27 • Step 9: Sharing data Many ways to share data: use Open-With to transfer to MMDS • From there, can: - send data by email - export presentation quality graphics - share on web & tweet
  • 28. 28 Microsoft Office documents • Outgoing emails can include many attachments: - machine-readable data - graphics • Graphics options include MS Word and Excel documents with embedded vector graphics
  • 29. 29 Sharing with Dropbox • Can link datasheets to Dropbox using the MolSync app • Storing data on Dropbox opens up an entire landscape of collaboration and sharing options
  • 30. 30 Step 10: Open sharing
  • 31. 31 Open Drug Discovery Teams • Relevant tweets get picked up by the Open Drug Discovery Teams project, and made available for browsing with a free app • Chemical data is parsed and can be accessed with other apps
  • 32. 32 Conclusion • Gone from paper to idea to hypothesis to experiment to online publication... all using mobile apps • Multi-app workflows involve content creation, content consumption, networked services and remote computation • Realtime calculations are done by the apps, complex calculations are offloaded to cloud servers • Mobile apps are good for creating, visualising and sharing small datasets • Apps can access big datasets, but pipelining tools for manipulating big data are underdeveloped
  • 33. 33 Future work • Evolution from proof-of-concept phase to apps designed to solve specific workflows... • ... expect more customised apps • More integration with cloud services: apps cannot work with big data without back-end support • Pistoia Alliance is currently working on an AppStore, which provides - an alternative storefront for mobile apps - standardised service infrastructure, for big data & calculations • Gradual replacement of all mainstream chemical information software
  • 34. 34 References • Redefining Cheminformatics with Intuitive Collaborative Mobile Apps, A.M. Clark, S. Ekins, A.J. Williams, Molecular Informatics, 31, 569- 584 (2012) • Mobile apps for chemistry in the world of drug discovery, A.J. Williams, S. Ekins, A.M. Clark, J.J. Jack, R.L. Apodaca, Drug Discovery Today, 16, 928-939 (2011) • Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration, S. Ekins, A.M. Clark, A.J. Williams, Molecular Informatics, 31, 585-597 (2012) • Basic primitives for molecular diagram sketching, A.M. Clark, Journal of Cheminformatics, 2:8 (2010)
  • 35. Acknowledgments • Antony Williams • Sean Ekins • Leah Rae McEwen, David Martinsen • ACS CINF Division http://molmatinf.com http://molsync.com • Inquiries to http://cheminf20.org info@ molmatinf.co m @aclarkxyz 35