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Bradley Open Notebook Science ACSfall2012

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Jean-Claude Bradley presents at the American Chemical Society meeting on August 20, 2012. Examples are first presented to demonstrate how access to Open Notebooks can provide critical information not …

Jean-Claude Bradley presents at the American Chemical Society meeting on August 20, 2012. Examples are first presented to demonstrate how access to Open Notebooks can provide critical information not usually shared in the traditional publication process. The use of Google App Scripts to look up chemical properties allows for the use of Google Spreadsheets as a self-contained dashboard to plan and analyze chemical reactions. The concept of the Open Chemical Property Matrix (OCPM) is introduced and a smartphone app to suggest recrystallization solvents is then presented.

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  • 1. Shining a light on chemical properties with Open Notebook Science and open strategies Open Notebook Science/Open Chemistry/Electronic Lab Notebook ACS Symposium Jean-Claude Bradley Associate Professor of Chemistry Drexel University August 20, 2012
  • 2. Openness in Chemistry WHY?
  • 3. Dibenzalacetone derivatives docking against tubulin (paclitaxel site) (Andrew Lang)
  • 4. “Simple” aldol condensation synthesis Top Hit (no reports of synthesis) In top ten (a few reports of synthesis) (Andrew Lang)
  • 5. What is the current standard for “sufficient information” in communicating organic chemistry? By definition, all peer-reviewed published documentation has been approved as sufficient by authors, editors and reviewers.
  • 6. Searching for aldol condensations of acetone in the Reaction Attempts database (Andrew Lang)
  • 7. An example of a failed experiment in an Open Notebook with useful information
  • 8. A failed experiment reveals the importance of aldehyde solubility
  • 9. An example of a successful experiment in an Open Notebook
  • 10. Using a Google Spreadsheet for reaction planning and analysis
  • 11. Calling Google App Scripts
  • 12. Calling Google App Scripts (Andrew Lang and Rich Apodaca)
  • 13. Converting Google Apps Scripts Results to Values (Andrew Lang and Rich Apodaca)
  • 14. Never having to leave the Google Spreadsheet dashboard for access to key info (Andrew Lang and Rich Apodaca)
  • 15. A click away from an interactive NMR display (using JCAMP-DX format and ChemDoodle) (Andrew Lang)
  • 16. A click to all melting point sources contributing to the average
  • 17. Chemistry Google App Scripts Resource page (Andrew Lang and Rich Apodaca)
  • 18. Chemistry Google App Scripts description sheet (Andrew Lang and Rich Apodaca)
  • 19. Predicted solubilities (M) for reactant and product (Andrew Lang)
  • 20. Information from the literature on the target synthesis
  • 21. Information from the literature on the target synthesis
  • 22. Information from the literature on the target synthesis
  • 23. A successful synthesis by avoiding water, dramatically increasing NaOH and long reaction time
  • 24. Open Chemical Property Matrix (OCPM)Solubility (in 1-octanol Solubility (in watersaturated water @25C) saturated 1-octanol @25C) logP
  • 25. Open Chemical Property Matrix (OCPM)Solubility (in 1-octanol Solubility (in watersaturated water @25C) saturated 1-octanol @25C)Solubility (in water logPnear 25C) Solubility (in 1- octanol near 25C)
  • 26. Open Chemical Property Matrix (OCPM)Boiling point Vapor pressure Flash point Abraham Melting point descriptors logP Aqueous Octanol solubility solubility
  • 27. Types of Open Matrix Elements1. True measurements (from Open Datacollections e.g. Open melting pointdataset of 27,000)2. Calculatable descriptors (from OSSe.g. CDK, MOPAC7.1)3. Predicted properties (from Openmodels)
  • 28. What is the solubility of benzoic acid in boiling benzene?
  • 29. Lack of provenance details generates noise in the matrix What questions do these numbers answer?
  • 30. Examples of OCPM relationships
  • 31. Examples of OCPM flash point calculations
  • 32. Examples of OCPM solubility calculations
  • 33. Provenance of the experimental data points
  • 34. Practical applications of the OCPM1. The automatic open evaluation of models from the darkliterature to determine where they do and where theydon’t work in the chemical space2. The development of new open models built upon thepopulation of new measurements, descriptors andpredictions3. The identification of compounds with desired propertiesfrom virtual libraries
  • 35. Finding a good recrystallization solvent1. Estimate or look up the solubility at boiling2. Estimate or look up the solubility at a convenient lowertemperature (e.g. 25C or 0C)3. Determine the predicted recovery yield4. Lower the priority for solvents that boil too high (toohard to dry precipitate)5. Lower the priority where the solubility at boiling is toolow (wastes solvent and makes it harder to crystallize)
  • 36. Translate these requirements to desired properties1. Look up the solvent boiling point2. Look up the room temperature solubility or predict it viaAbraham descriptors predicted from a model using theCDK3. Look up the solute melting point or predict it via amodel using the CDK4. Use the melting point and the solubility at roomtemperature to predict the solubility at boiling5. Calculate the predicted recrystallization yield
  • 37. Implement as an App (Andrew Lang)
  • 38. What are good solvents to recrystallize benzoic acid? (Andrew Lang)
  • 39. Click on the solvent to see temp curve
  • 40. Deliver melting point data via App (Andrew Lang)
  • 41. Dibenzalacetone libraries are promising for connecting the OCPM with useful applications
  • 42. ConclusionsMore openness in chemistry can make science more efficientProvide interfaces that make sense to the end users:Open Data, Open Models and Open Source Software to modelersApps (smartphones, Google App Scripts, etc.) for chemists at the bench Acknowledgements Andrew Lang (code, modeling) Bill Acree (modeling, solubility data contribution) Antony Williams (ChemSpider services, mp data curation) Matthew McBride and Rida Atif (recrystallization and synthesis)

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