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BIpredict: MOE/web S
BI di MOE/ b Server Enabled D li
                             E bl d Delivery
of In Silico Properties and Models
David C. Thompson, Ph.D
125 Years of Innovating for Patients and Their Families



                                           Founded 1885
                                           in Ingelheim, Germany


                       Family-owned                        Privately-held
                          g
                          global company
                                    p y                        for 125 years
                                                                       y


        Products marketed in                $17.7 billion                          41,500
        150+ countries                        2009 net sales                   employees worldwide


                                                                   Focus on
                        142 affiliates
                             ffili t                         Human
                                in
                                                         Pharmaceuticals
                        50 countries
                                                         & Animal Health

2
One Pill Makes You Larger [*]




My favourite papers from each period:
[1] J. Chem. Phys. 122, 124107 (2005)
[2] J. Chem. Phys. 128, 224103 (2008)
[3] J. Chem. Inf. Model. 49, 1889 (2009)
[4] J. Chem. Inf. Model. 51, 93 (2011)
[*] This slide title brought to you courtesy of Lewis Carroll
The Magic of Clip Art



       What are we trying to do in the pharmaceutical industry?
                                                                                 [*]

                                                           +                 =
       What are we trying to do as computational scientists working in the pharmaceutical
       industry?


                                                    +                    =
                                                        Chemical space


                                      We build models to try and expedite the drug
                                                  discovery process

[*] Side effects may include changing hair colour                                           4
Shameless slide reuse … [5]




                                                                                     “All models are wrong, but
                                                                                     some models are useful”
                                                                                     – G. E. P. Box

                                                                                    “…the validity of any given model is of limited
                                                                                   scope, as is the case with any mental construct
                                                                                 that we have about what our molecules are doing,
                                                                                 whether we used a software package or waved our
                                                                                        hands around in the air.” – D. Lowe




Simulation and its discontents, Sherry Turkle, Cambridge, MA: MIT Press (2009)
[5] D. C. Thompson et al. Schrödinger Regional User Meeting, New York, NY 2009
Taxonomy of Risk [6]


             Risk                                                                                             Uncertainty


      Randomness amenable to formal                                                      Randomness not amenable to formal
           statistical analysis                                                                statistical analysis

          “ … a given phenomenon may contain several levels of uncertainty at once, with some components being completely
          certain and others irreducibly uncertain”


          “In fact, we propose that the failure of quantitative models [in economics and finance] is almost always attributable
          to a mismatch between the level of uncertainty and the methods used to model it.”
                                                                                          it.




[6] “WARNING: Physics Envy May be Hazardous To Your Wealth!”, A. W. Lo, and M. T. Mueller arXiv:1003.2688v3 [q-fin.RM]           6
Okay, now what?


       • Focus on physicochemical properties [7, 8]

       • Enable scientists through light-weight clients
                                   light weight

       • Provide core scientific functionality through web services architecture




                                         +                        =
                                                 Chemical space




                          We build models to try and expedite the drug
                                      discovery process

[7] Nature Rev. Drug. Discov. 10, 197 (2011)
[8] Med. Chem. Comm., 2011 (DOI: 10.1039/c1md00017a)                               7
The Internet …




                 8
… is here to stay [*]




* Probably                 9
If it’s going to stay, we might as well use it


       A Web Service is a method of communication between two electronic devices over a
       network[9]

       Example*:



                                               Web Service




[9] http://en.wikipedia.org/wiki/Web_service
* Probably                                                                                10
BIpredict: An in silico molecular property prediction
framework

Initial requirement: Build a real-time physchem. property calculator engine that could be
used to address project concerns and issues at the medicinal chemistry desktop

Offer multiple interfaces to complement users preferred workflow

                                       Buy or Build?


Proposal: Rapid development of an in-house solution to allow us to focus on optimizing the
interaction between a web services layer and other BI systems and, most importantly, the
                                     y                 y         ,        p        y,
scientists
     • Leverage Molecular Operating Environment (MOE), and newly developed
     MOE/web SOAP application server technology
                      pp                      gy
          — Java-based web server
          — No Apache setup

                                                         M OE        M OE
                                                                             Pipeline
                                                                              Pilot®
                                                         ba tc h     SOA P


     • Focus on flexibility, ease of deployment, and extensibility
                                                                                             11
BIpredict architecture:
   How you consume, depends on what you see



 MOE             BIDATA          BIModel     Pipelining tools          Web Apps.   Command Line
    (G)                 (G)        (G + A)           (G + A)               (G)         (G +A)



Multiple front-ends
(data
(d t consumers)
              )

Single back-end
(data producer)
                                                 BIpredict
                                                (web services layer)
                              Synchronous


                                     General (G):
                                             ( )             Advanced (A):
                                                                        ( )
                                     Intended for               Abstract
                                        general              descriptors for
                                     consumption              comp. chem.
Development:
Oct. 2009 – Jan. 2010
Production:
Jan. 2010
                              Interface determines which descriptors are exposed
                                                                                                12
“One of the things about a real-time system is everything
    has to be timed out” [10]

   single back-end
   (data producer)                                            BIpredict
                                                             (web services layer)
                                                                             y

                  Asynchronous / Batched

                          Descriptor
                          classes
                           l
                                                             Scatter
                          User ID
                                                                                        Job ID
General:
42 descriptors
8 engines

Advanced:                                                               Y
3431 descriptors                                               N?                    Collect output
15 engines                Descriptor
                          names
Development:                                                 Gather
Jan.
Jan 2010 – June 2010      Job ID
Production:
June 2010                                                                           Packaged output

[10] Bernie Cosell, “Czar of the PDP-1 timesharing system”                                            13
What does this magic look like?




                                  With BIpredict panel
                                  open, workspace is
                                  ‘live’

                                  Physicochemical
                                  properties are updated
                                  as molecule is built
                                        l l i b il

                                  Atomistic descriptor
                                  values are appended
                                  directly to the molecule




                                                         14
Accessing Models in BIpredict




                                15
Reimplementation of Pfizer CNS Multi-Parameter
    Optimization design tool [11,12]

  • Driven by the scientists, turnaround of days

  • G h 119 literature compounds, visually inspect and triage
    Gather 9 li               d i ll i               d i

  • Identify physicochemical property Descriptors
           yp y              p p y          p
      • Sybyl clogP                 7


      • ACD logD (@ pH 7.4)         6

      • MOE MW                      5
                                               R² = 0.9834
                                                  -implementation
      • CADD TPSA                   4

      • MOE Lipinski HB donors      3

      • ACD M B i pKa
             Most Basic K
                                                Re-




                                    2


                                                                    1
  • Enable model through BIpredict
                      g    p                                        0
                                                                        0   1   2   3                4   5   6        7
                                                                                        Literature
[11] ACS Chem. Neurosci., 1, 435 (2010)
[12] Bioorg. Med. Chem. Lett, 18, 4872 (2008)                                                                    16
Begin at the beginning and go on till you come to the end:
     then stop [*]




     • All models are wrong
     • Expose those models that we think will expedite the
          p                                       p
       drug discovery process
     • Focus on extensible, light-weight, service delivery
                           , g       g ,                 y




[*] This slide title brought to you courtesy of Lewis Carroll
Cultural Highlight




                     18
Acknowledgements


Dr. Jörg Bentzien
Dr. Alex Clark
Cathy F
C th Farrellll
Dr. Sandy Farmer
Amy Gao
Dr.
Dr Scott Oloff
Dr. Miguel Teodoro

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BIpredict: MOE/web Server Enabled Delivery of In Silico Properties and Models

  • 1. BIpredict: MOE/web S BI di MOE/ b Server Enabled D li E bl d Delivery of In Silico Properties and Models David C. Thompson, Ph.D
  • 2. 125 Years of Innovating for Patients and Their Families Founded 1885 in Ingelheim, Germany Family-owned Privately-held g global company p y for 125 years y Products marketed in $17.7 billion 41,500 150+ countries 2009 net sales employees worldwide Focus on 142 affiliates ffili t Human in Pharmaceuticals 50 countries & Animal Health 2
  • 3. One Pill Makes You Larger [*] My favourite papers from each period: [1] J. Chem. Phys. 122, 124107 (2005) [2] J. Chem. Phys. 128, 224103 (2008) [3] J. Chem. Inf. Model. 49, 1889 (2009) [4] J. Chem. Inf. Model. 51, 93 (2011) [*] This slide title brought to you courtesy of Lewis Carroll
  • 4. The Magic of Clip Art What are we trying to do in the pharmaceutical industry? [*] + = What are we trying to do as computational scientists working in the pharmaceutical industry? + = Chemical space We build models to try and expedite the drug discovery process [*] Side effects may include changing hair colour 4
  • 5. Shameless slide reuse … [5] “All models are wrong, but some models are useful” – G. E. P. Box “…the validity of any given model is of limited scope, as is the case with any mental construct that we have about what our molecules are doing, whether we used a software package or waved our hands around in the air.” – D. Lowe Simulation and its discontents, Sherry Turkle, Cambridge, MA: MIT Press (2009) [5] D. C. Thompson et al. Schrödinger Regional User Meeting, New York, NY 2009
  • 6. Taxonomy of Risk [6] Risk Uncertainty Randomness amenable to formal Randomness not amenable to formal statistical analysis statistical analysis “ … a given phenomenon may contain several levels of uncertainty at once, with some components being completely certain and others irreducibly uncertain” “In fact, we propose that the failure of quantitative models [in economics and finance] is almost always attributable to a mismatch between the level of uncertainty and the methods used to model it.” it. [6] “WARNING: Physics Envy May be Hazardous To Your Wealth!”, A. W. Lo, and M. T. Mueller arXiv:1003.2688v3 [q-fin.RM] 6
  • 7. Okay, now what? • Focus on physicochemical properties [7, 8] • Enable scientists through light-weight clients light weight • Provide core scientific functionality through web services architecture + = Chemical space We build models to try and expedite the drug discovery process [7] Nature Rev. Drug. Discov. 10, 197 (2011) [8] Med. Chem. Comm., 2011 (DOI: 10.1039/c1md00017a) 7
  • 9. … is here to stay [*] * Probably 9
  • 10. If it’s going to stay, we might as well use it A Web Service is a method of communication between two electronic devices over a network[9] Example*: Web Service [9] http://en.wikipedia.org/wiki/Web_service * Probably 10
  • 11. BIpredict: An in silico molecular property prediction framework Initial requirement: Build a real-time physchem. property calculator engine that could be used to address project concerns and issues at the medicinal chemistry desktop Offer multiple interfaces to complement users preferred workflow Buy or Build? Proposal: Rapid development of an in-house solution to allow us to focus on optimizing the interaction between a web services layer and other BI systems and, most importantly, the y y , p y, scientists • Leverage Molecular Operating Environment (MOE), and newly developed MOE/web SOAP application server technology pp gy — Java-based web server — No Apache setup M OE M OE Pipeline Pilot® ba tc h SOA P • Focus on flexibility, ease of deployment, and extensibility 11
  • 12. BIpredict architecture: How you consume, depends on what you see MOE BIDATA BIModel Pipelining tools Web Apps. Command Line (G) (G) (G + A) (G + A) (G) (G +A) Multiple front-ends (data (d t consumers) ) Single back-end (data producer) BIpredict (web services layer) Synchronous General (G): ( ) Advanced (A): ( ) Intended for Abstract general descriptors for consumption comp. chem. Development: Oct. 2009 – Jan. 2010 Production: Jan. 2010 Interface determines which descriptors are exposed 12
  • 13. “One of the things about a real-time system is everything has to be timed out” [10] single back-end (data producer) BIpredict (web services layer) y Asynchronous / Batched Descriptor classes l Scatter User ID Job ID General: 42 descriptors 8 engines Advanced: Y 3431 descriptors N? Collect output 15 engines Descriptor names Development: Gather Jan. Jan 2010 – June 2010 Job ID Production: June 2010 Packaged output [10] Bernie Cosell, “Czar of the PDP-1 timesharing system” 13
  • 14. What does this magic look like? With BIpredict panel open, workspace is ‘live’ Physicochemical properties are updated as molecule is built l l i b il Atomistic descriptor values are appended directly to the molecule 14
  • 15. Accessing Models in BIpredict 15
  • 16. Reimplementation of Pfizer CNS Multi-Parameter Optimization design tool [11,12] • Driven by the scientists, turnaround of days • G h 119 literature compounds, visually inspect and triage Gather 9 li d i ll i d i • Identify physicochemical property Descriptors yp y p p y p • Sybyl clogP 7 • ACD logD (@ pH 7.4) 6 • MOE MW 5 R² = 0.9834 -implementation • CADD TPSA 4 • MOE Lipinski HB donors 3 • ACD M B i pKa Most Basic K Re- 2 1 • Enable model through BIpredict g p 0 0 1 2 3 4 5 6 7 Literature [11] ACS Chem. Neurosci., 1, 435 (2010) [12] Bioorg. Med. Chem. Lett, 18, 4872 (2008) 16
  • 17. Begin at the beginning and go on till you come to the end: then stop [*] • All models are wrong • Expose those models that we think will expedite the p p drug discovery process • Focus on extensible, light-weight, service delivery , g g , y [*] This slide title brought to you courtesy of Lewis Carroll
  • 19. Acknowledgements Dr. Jörg Bentzien Dr. Alex Clark Cathy F C th Farrellll Dr. Sandy Farmer Amy Gao Dr. Dr Scott Oloff Dr. Miguel Teodoro