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Varuna: An Integrated Modeling Environment and Database for Quantum Chemical Simulations Chemical Prototype Projects CICC - Chemical Informatics And Cyberinfrastructure.

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Presentation on theme: "Varuna: An Integrated Modeling Environment and Database for Quantum Chemical Simulations Chemical Prototype Projects CICC - Chemical Informatics And Cyberinfrastructure."— Presentation transcript:

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

2 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 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  Services: 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 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 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 6 Chemical Prototype Projects

7 7 Pathogenesis of Alzheimer’s Disease AD with cortical atrophy Neuritic plaque with a core made of Cu-  -Amyloid complex

8 8

9 9 How does Varuna fit into all this? Force-field Database:  Currently, Cu-Ligand force-fields are being generated manually. We would like to develop a Service component that will do this automatically  These force-fields will be made available in the database. We already have ~400 plausible Cu-  -Amyloid high-resolution structures with QM energies: Data Mining Services are needed to compare structural similarities, reactivity indices, etc. The reactivity of the Cu-center in the peptide must be compared systematically against small molecule models.

10 10 Immediate Challenges A 3D structural representation is needed that can deal with:  Non-integer bond-orders, transition state structures with multi-center/multi-electron bonds  Many different quantum chemically derived property topologies The metadata is complex because of many technical parameters that make calculations difficult to compare

11 11 Cisplatin: Profiling an Anticancer Drug

12 12 Computational Organic Chemistry

13 13 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

14 14 Who cares ? Mehta, Singh. Chem. Rev. 1999, 99, 881

15 15 Reaction Energy Profiles Low CO Pressure High CO Pressure Low diastereoselectivity High diastereoselectivity

16 16 Collaborative Network Baik-Group (IU) Computational Chemistry Molecular Modelling Lippard (MIT) Cisplatin, Methane Monooxygenase Newcomb (UI-Chicago) B 12 -Dependent Enzymes Center for Catalysis (IU) Caulton Mindiola Evans Johnston Williams Sames (Columbia) Ir-, Rh-Catalyzed C-H activation Jacobsen (Harvard) Asymmetric Catalysis, Enzymatic Oxidations Szalai (UMBC) Alzheimer’s Disease CICC

17 17 Center for Catalysis at IU-Bloomington Organic Synthesis Andy EvansJeff Johnston Organometallic Catalyst Design Dan MindiolaKen Caulton Molecular Modeling Mookie Baik 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

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19 19 General Research Philosophy Theoretical Tools DFT, MP2, MM, QM/MM, etc.. Experiments Structures, Lifetimes, Rates, Isotope-Effects Activation Enthalpies, Redox-Potentials…. Model Chemistry HOW? Analysis Chemical Intuition MO-Diagram Energy-Decomposition What-If Game Handwaving Model Chemistry WHY? New Chemistry Prediction

20 20 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”.

21 21 “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.

22 22 “Classical” Approach to Proposing a Mechanism “Logical” mechanism for the [4+2+2]: Stereocontrol: Rh coordination is facially selective. The sterically bulky R 1 group directs Rh to the correct side of the  -component. Evans, P. A. et al. Chem. Commun. 2005, 63

23 23 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??

24 24 New Proposal J. Am. Chem. Soc. 2005, 127, 1603

25 25 Computational Model Chemistry - Density Functional Theory @ B3LYP/cc-pVTZ(-f) (Jaguar) - Numerically efficient up to 300 atoms => no compromises with respect to Model Size

26 26 Entropy

27 27 Continuum Solvation Model

28 28 Computed Reaction Energy Profiles J. Am. Chem. Soc. 2005, 127, 1603

29 29 Computed Reaction Energy Profiles J. Am. Chem. Soc. 2005, 127, 1603

30 30 Diastereoselectivity ?? J. Am. Chem. Soc. 2005, 127, 1603

31 31 Reason for Diastereoselectivity J. Am. Chem. Soc. 2005, 127, 1603

32 32 Understanding Pauson-Khand-Type Reactions: [2+2+1]

33 33 Mechanistic Alternatives Low CO pressure High CO pressure

34 34 What about Structural Alternatives?

35 35 Reaction Energy Profiles Low CO Pressure High CO Pressure Low diastereoselectivity High diastereoselectivity

36 36 Why is this reaction diastereoselective? Partial Charge Analysis Syn-Product forms by (+)-directed polarization. Anti-Product forms by (-)-directed polarization.

37 37 What is the physical basis of the new rule?

38 38 What is the physical basis of the new rule?

39 39 But, can we predict new chemistry? Diastereoselectivity is CO-pressure dependent!

40 40 Precision in the Eyes of an Organic Chemist dppp: 1,3-bis(diphenylphosphino)propane

41 41 Hey – who said anything about phosphine?

42 42 So, WHY is this happening? Low CO Pressure High CO Pressure Low diastereoselectivity High diastereoselectivity

43 43 Does this make sense NOW? dppp: 1,3-bis(diphenylphosphino)propane

44 44 More Predictions Will Electron withdrawing groups on R 1 reverse ds ?? No! But: Can’t be made? Target:

45 45 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

46 46 Center for Catalysis at IU-Bloomington Organic Synthesis Andy EvansJeff Johnston Organometallic Catalyst Design Dan MindiolaKen Caulton Molecular Modeling Mookie Baik 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

47 47


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