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Bradley Opal 2011
Bradley Opal 2011
Bradley Opal 2011
Bradley Opal 2011
Bradley Opal 2011
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Bradley Opal 2011

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Jean-Claude Bradley presents at the Opal Events 3rd Annual Drug Discovery Partnership: Filling the Pipeline on Pre-competitive Collaboration: Sharing Data to Increase Predictability

Jean-Claude Bradley presents at the Opal Events 3rd Annual Drug Discovery Partnership: Filling the Pipeline on Pre-competitive Collaboration: Sharing Data to Increase Predictability

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  • 1. Pre-competitive Collaboration: Sharing Data to Increase Predictability<br />3rd Annual Drug Discovery Partnership: Filling the Pipeline<br />Jean-Claude Bradley<br />Associate Professor of Chemistry<br />Drexel University<br />October 17, 2011<br />
  • 2. Opportunities for Competitive Collaboration<br />
  • 3. Industry is Sharing More <br />
  • 4. Solubility and <br />Melting Points <br />are critical properties in the drug discovery process<br />
  • 5. Data quality is essential for both measurements and predictions based on measurements<br />
  • 6. Openness is proving to be a powerful tool for assessing the reliability of data<br />
  • 7. Solubility prediction for Taxol using<br /> Abraham descriptors<br />Pred Exp<br />
  • 8. Predicted temperature dependent solubility of Taxol in water based on melting point (M) <br />
  • 9. The Trusted Source Model<br />Before online databases (early 90s) searching for properties like melting points using ONE “trusted source” was practical and acceptable as part of the chemistry culture.<br /><ul><li>CRC Handbook
  • 10. Merck Index
  • 11. Chemical Vendor Catalogs (e.g. Sigma-Aldrich)
  • 12. Peer-Reviewed Journals</li></ul>Single values don’t tend to be contradicted<br />
  • 13. Question Assumptions<br />Using technology, we can begin to replace the “trusted source” model with one based on transparency and provenance<br />
  • 14. The Chemical Information Validation Sheet <br />567 curated and referenced measurements from <br />Fall 2010 Chemical Information Retrieval course<br />
  • 15. Discovering outliers for melting points (stdev/average)<br />
  • 16. Investigating the m.p. inconsistencies of EGCG<br />
  • 17. Investigating the m.p. inconsistencies of cyclohexanone<br />
  • 18. Most popular data sources<br />
  • 19. Alfa Aesar donates melting points to the public<br />
  • 20. Open Melting Point Explorer<br />(Andrew Lang)<br />
  • 21. Outliers<br />MDPI <br />dataset<br />PhysProp (EPA donated all data to public also)<br />
  • 22. Outliers for ethanol: Alfa Aesar and Oxford MSDS<br />
  • 23. Inconsistencies and SMILES problems within MDPI dataset<br />
  • 24. MDPI Dataset labeled with High Trust Level<br />
  • 25. Open Melting Point Datasets<br />Currently 27,000 mps for 20,000 compounds<br />
  • 26. What is the melting point of 4-benzyltoluene?<br />American Petroleum Institute5 C<br />PHYSPROP-30 C<br />PHYSPROP 125 C<br />peer reviewed journal (2008)97.5 C<br />government database-30 C<br />government database4.58 C<br />
  • 27. The quest to resolve the melting point <br />of 4-benzyltoluene: liquid at room temp <br />and can be frozen <-30C (Evan Curtin)<br />
  • 28. Open Lab Notebook page measuring the melting point of 4-benzyltoluene<br />
  • 29. Motivation: Faster Science,Better Science<br />
  • 30. Ruling out all melting points above -15C?<br />
  • 31. Oops – 4-benzyltoluene freezes after 16 days at -15C!<br />
  • 32. Measuring the melting point by slowly heating from -15 C gives 5 C<br />
  • 33. There are NO FACTS, <br />only measurements embedded within assumptions<br />Open Notebook Science maintains the integrity of data provenance by making assumptions explicit<br />
  • 34. TRUST<br />PROOF<br />
  • 35. Common errors in datasets<br />multiple melting points for the same compound in the same database<br />stereochemistry issues<br />sign inversion<br />conversion errors (Kelvin/Celcius Fahrenheit/Celcius)<br />bad SMILES (non-rendering)<br />salts associated with SMILES for free base<br />using boiling point for melting point<br />
  • 36. Open Random Forest modeling of Open Melting Point data using CDK descriptors<br />(Andrew Lang)<br />R2 = 0.78, TPSA and nHdon most important<br />
  • 37. Melting point prediction service<br />
  • 38. Melting point predictions and measurements on iPhone/iPad (Andrew Lang and Alex Clark)<br />
  • 39. Publication of double+ validated melting point dataset to Nature Precedings and LuLu<br />
  • 40.
  • 41.
  • 42. Crowdsourcing Solubility Data<br />
  • 43. ONS Challenge Judges<br />
  • 44. ONS Challenge Award Winners<br />
  • 45. Web services for summary data<br />(Andrew Lang)<br />
  • 46. Reaction Attempts Book<br />
  • 47. Reaction Attempts Book: Reactants listed Alphabetically<br />
  • 48.
  • 49. Interactive NMR spectra using JSpecView or ChemDoodleand the Open JCAMP-DX format<br />
  • 50. Predicting Best Solvent for Imine Formation using solubility and melting point data <br />(Evan Curtin)<br />
  • 51. Predicting Yield of Imine Formation in Ethanol <br />(Evan Curtin)<br />
  • 52. Google Apps Scripts web services<br />
  • 53. Google Apps Scripts for conveniently exploring melting point data<br />
  • 54. Comparison of model with triple validated measurements<br />Straight chain carboxylic acids from 1 to 10 carbons<br />Straight chain alcohols from 1 to 10 carbons<br />
  • 55. Cyclic primary amines from 3 to 6 carbons (cyclobutylamine flagged for validation – only single source available) <br />
  • 56. Google Apps Scripts for planning reactions and creating schemes<br />
  • 57. Open Melting Points in Supplementary Data Pages of Wikipedia (Martin Walker)<br />
  • 58. All ONS web services<br />
  • 59. Some Initiatives Promoting More Openness in Drug Discovery<br />
  • 60. Open Primary Research in Drug Design using Web2.0 tools (malaria)(blogs, wikis, Second Life, mailing lists)<br />Rajarshi Guha<br />Indiana U<br />Tsu-Soo Tan<br />Nanyang Inst.<br />Docking<br />JC Bradley<br />Drexel U<br />Synthesis<br />Phil Rosenthal<br />UCSF<br />(malaria)<br />Dan Zaharevitz<br />NCI<br />(tumors)<br />Testing<br />
  • 61. Outcome of Guha-Bradley-Rosenthal collaboration<br />
  • 62. Conclusions<br /><ul><li>For science to progress quickly there is great benefit in moving away from a “trusted source” model to one based on transparency and data provenance
  • 63. Open Notebook Science can be a useful tool in this context</li>

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