Varuna: An Integrated Modeling Environment and Database for Quantum Chemical Simulations

1. CICC - Chemical Informatics And Cyberinfrastructure Collaboratory Department of Chemistry & School of Informatics Indiana University Bloomington Varuna: An Integrated Modeling Environment and Database for Quantum Chemical Simulations Chemical Prototype Projects October 21, 2005 Mu-Hyun Baik

2. State of Affairs in Computational Chemistry • High-level quantum simulations based on Density Functional Theory allow for very reliable simulations of chemical reactions for systems containing up to 500 atoms. • Combining Quantum Mechanics and Molecular Mechanics, we can construct highly realistic computer models of biologically relevant reactions. • Currently, chemical modeling studies are done in an isolated fashion and the computed data is typically collected in an unorganized manner (directory-jungle) and disregarded after completion of the study. • Modeling is currently done manually: vi, emacs and ssh are currently the most common interfaces of computational chemists.

3. Cyberinfrastructure Development • Depository for computational chemistry data. • Automated data collection and categorization • Chemical structure recognition • Mining of quantum chemical data • User independent domain expertise • Development of an integrated modeling environment • Automated execution of calculations • Automatic generation of input files, communication with number crunchers, recognition and correction of typical failures, automated import of main results, etc. • Computational resource management • Visualization

4. Data Structure • Currently Implemented: • Metadata: QM parameters, • Project data • Results: Energy components • Parser extracts all important • results • Visualizations • Future Work: • Structure recognition (2D and 3D fingerprints, SMILES, etc….) • Automatic generation of new structures based on computed results

5. Automated Computational Chemistry • Increase efficiency through automation => Make life easier- Allow high-throughput production=> Combinatorial Computational Chemistry- Increase depth of wavefunction analysis => Automated pattern-search- Simplify and visualize complicated data in intuitive graphical representations- Allow information recycling => Accumulation of group expertise (Data depository system, Web-Interface)

6. Chemical Prototype Projects

7. Pathogenesis of Alzheimer’s Disease Neuritic plaque with a core madeof Cu-b-Amyloid complex AD with cortical atrophy

9. Cisplatin: Profiling an Anticancer Drug

10. Computational Organic Chemistry

11. Diastereoselective [4+2+2] Carbocyclization • What is the mechanism of this transformation? • What is the source of the diastereoselectivity? • Can the scope of the reaction be extended? • Can we reverse the stereo-control using the same methodology? Evans, P. A. et al. Chem. Commun.2005, 63

12. Who cares ? Mehta, Singh. Chem. Rev.1999, 99, 881

13. Reaction Energy Profiles High CO Pressure Low CO Pressure High diastereoselectivity Low diastereoselectivity

14. Collaborative Network CICC Center for Catalysis (IU) Caulton Mindiola Evans Johnston Williams Newcomb (UI-Chicago) B12-Dependent Enzymes Baik-Group (IU) Computational Chemistry Molecular Modelling Lippard (MIT) Cisplatin, Methane Monooxygenase Szalai (UMBC) Alzheimer’s Disease Jacobsen (Harvard) Asymmetric Catalysis, Enzymatic Oxidations Sames (Columbia) Ir-, Rh-Catalyzed C-H activation

15. Center for Catalysis at IU-Bloomington Organic Synthesis Molecular Modeling Organometallic Catalyst Design Dan Mindiola Ken Caulton Mookie Baik Dave Williams Andy Evans Jeff Johnston Rational Design of Well-Defined, Efficient and mechanistically fully understood Catalysts for Natural Product Synthesis, Polymerization and C-C/C-H activation. Educational Goal: A new breed of chemists who can conduct high-level research in all three areas of Organic, Inorganic and Computational/Theoretical Research

17. General Research Philosophy Experiments New Chemistry Structures, Lifetimes, Rates, Isotope-Effects Activation Enthalpies, Redox-Potentials…. Prediction Model Chemistry Model Chemistry HOW? WHY? Theoretical Tools Analysis DFT, MP2, MM, QM/MM, etc.. Chemical Intuition MO-Diagram Energy-Decomposition What-If Game Handwaving

18. Inherent Problems of Organic Mechanism Discovery • Most of the time all you have is a reactant and a product, if you are lucky. • Intermediates, particularly the interesting reactive ones, can’t be observed directly. • “Classical Approach” of Constructing a New Mechanism: • Memorize as many as possible known mechanisms • Try to recognize similarities (mostly structural) and assume that what worked for one reaction may work for another • Mechanisms are often quite “arbitrary”.

19. “Classical” Approach to Proposing a Mechanism What we’ve seen before: Pauson-Khand-type Reaction Evans, P. A. et al. J. Am. Chem. Soc.2001, 123, 4609 Magnus, P. et al. Tetrahedron 1985, 41, 5861 Buchwald S. L. et al. J. Am. Chem. Soc.1996, 118, 11688.

20. “Classical” Approach to Proposing a Mechanism “Logical” mechanism for the [4+2+2]: Stereocontrol: Rh coordination is facially selective. The sterically bulky R1 group directs Rh to the correct side of the p-component. Evans, P. A. et al. Chem. Commun.2005, 63

21. Let’s think about this…. • Oxidative Addition involving the triple bond should be facile. • => (A) and (B) can’t be rate determining! • So, forming either bond (A) or (B) first is plausible, but: • Form (B) first => Stereochemistry at C2 is fixed !! • Stereocontrol at a reaction Step that is NOT rate determining??

22. New Proposal J. Am. Chem. Soc.2005, 127, 1603

23. Computational Model Chemistry • Density Functional Theory @ B3LYP/cc-pVTZ(-f) (Jaguar) • Numerically efficient up to 300 atoms => no compromises with respect to Model Size

24. Entropy

25. Continuum Solvation Model

26. Computed Reaction Energy Profiles J. Am. Chem. Soc.2005, 127, 1603

27. Computed Reaction Energy Profiles J. Am. Chem. Soc.2005, 127, 1603

28. Diastereoselectivity ?? J. Am. Chem. Soc.2005, 127, 1603

29. Reason for Diastereoselectivity J. Am. Chem. Soc.2005, 127, 1603

30. Understanding Pauson-Khand-Type Reactions: [2+2+1]

31. Mechanistic Alternatives High CO pressure Low CO pressure

32. What about Structural Alternatives?

33. Reaction Energy Profiles High CO Pressure Low CO Pressure High diastereoselectivity Low diastereoselectivity

34. Why is this reaction diastereoselective? Partial Charge Analysis Syn-Product forms by (+)-directed polarization. Anti-Product forms by (-)-directed polarization.

35. What is the physical basis of the new rule?

36. What is the physical basis of the new rule?

37. But, can we predict new chemistry? • Diastereoselectivity is CO-pressure dependent!

38. Precision in the Eyes of an Organic Chemist dppp: 1,3-bis(diphenylphosphino)propane

39. Hey – who said anything about phosphine?

40. So, WHY is this happening? High CO Pressure Low CO Pressure High diastereoselectivity Low diastereoselectivity

41. Does this make sense NOW? dppp: 1,3-bis(diphenylphosphino)propane

42. More Predictions Will Electron withdrawing groups on R1 reverse ds ?? Target: No! But: Can’t be made?

43. Conclusions • Theoretical “Characters” can actually predict new stuff if they try hard. • The diastereoselectivity of Rh-catalyzed Pauson-Khand reaction is a rare example of a purely electronically driven stereo-control (close to no steric influence!). • “Spectator Ligands” are actually not really just spectators at all. • Organic Chemistry does not necessarily have to be synonymous with: Alchemy or Mindless Memorizing

44. Center for Catalysis at IU-Bloomington Organic Synthesis Molecular Modeling Organometallic Catalyst Design Dan Mindiola Ken Caulton Mookie Baik Andy Evans Jeff Johnston Rational Design of Well-Defined, Efficient and mechanistically fully understood Catalysts for Natural Product Synthesis, Polymerization and C-C/C-H activation. Educational Goal: A new breed of chemists who can conduct high-level research in all three areas of Organic, Inorganic and Computational/Theoretical Research