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

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Presentation at the Chemical Computing Group UGM in 2011. I describe my use of the MOE/web SOAP server and the model of delivering physico-chemical properties onto the medicinal chemists desktop.

Presentation at the Chemical Computing Group UGM in 2011. I describe my use of the MOE/web SOAP server and the model of delivering physico-chemical properties onto the medicinal chemists desktop.

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  • 1. BIpredict: MOE/web SBI di MOE/ b Server Enabled D li E bl d Deliveryof In Silico Properties and ModelsDavid 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 Health2
  • 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. LoweSimulation 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
  • 8. The Internet … 8
  • 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 predictionframeworkInitial requirement: Build a real-time physchem. property calculator engine that could beused to address project concerns and issues at the medicinal chemistry desktopOffer 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 theinteraction 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. 2010Production: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 IDGeneral:42 descriptors8 enginesAdvanced: Y3431 descriptors N? Collect output15 engines Descriptor namesDevelopment: GatherJan.Jan 2010 – June 2010 Job IDProduction: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
  • 18. Cultural Highlight 18
  • 19. AcknowledgementsDr. Jörg BentzienDr. Alex ClarkCathy FC th FarrellllDr. Sandy FarmerAmy GaoDr.Dr Scott OloffDr. Miguel Teodoro

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