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
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.
2
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
3
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
4
Automated Computational Chemistry
Data
3D-Coordinates
Wave Functions
Modeling Software
FORTRAN Code
Queries
Recycling
Input File
Generation
Visualization
- Increase efficiency through automation
=> Make life easier
MO's (VRML),
Rxn Profiles
Researcher
Hardware
Varuna
- Allow high-throughput production
=> Combinatorial Computational Chemistry
SFTP, SSH
File Transfer
Job-Submission
Resource Management
External Data
- Increase depth of wavefunction analysis
=> Automated pattern-search
PubChem, CCDC
- Simplify and visualize complicated data in intuitive graphical representations
- Allow information recycling => Accumulation of group expertise
(Data depository system, Web-Interface)
5
Chemical Prototype Projects
6
Pathogenesis of Alzheimer’s Disease
Neuritic plaque with a core made
of Cu-b-Amyloid complex
AD with cortical atrophy
7
8
Cisplatin: Profiling an Anticancer Drug
9
Computational Organic Chemistry
10
Diastereoselective [4+2+2] Carbocyclization
R2
R2
R2 RhCl(IMes)(COD)
TsN
R3 AgOTf, PhMe, 
2
R1
1
TsN
2
R1 H
R3
2
vs
TsN
2
R1 H
R3
ds > 19:1
3
- 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
11
Who cares ?
Mehta, Singh. Chem. Rev. 1999, 99, 881
12
Reaction Energy Profiles
Low CO Pressure
Low diastereoselectivity
High CO Pressure
High diastereoselectivity
13
Collaborative Network
CICC
Center for Catalysis (IU)
Caulton
Mindiola
Evans
Johnston
Williams
Baik-Group (IU)
Computational Chemistry
Molecular Modelling
Lippard (MIT)
Cisplatin,
Methane Monooxygenase
Jacobsen (Harvard)
Asymmetric Catalysis,
Enzymatic Oxidations
Newcomb (UI-Chicago)
B12-Dependent Enzymes
Szalai (UMBC)
Alzheimer’s Disease
Sames (Columbia)
Ir-, Rh-Catalyzed
C-H activation
14
Center for Catalysis at IU-Bloomington
Organic
Synthesis
Molecular
Modeling
Organometallic
Catalyst Design
Andy Evans Jeff Johnston Dave Williams Mookie Baik Dan Mindiola
Ken Caulton
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
15
16
General Research Philosophy
Experiments
Structures, Lifetimes,
Rates, Isotope-Effects
Activation Enthalpies,
Redox-Potentials….
New Chemistry
Prediction
Model Chemistry
HOW?
Model Chemistry
WHY?
Theoretical Tools
Analysis
DFT, MP2, MM,
QM/MM, etc..
Chemical Intuition
MO-Diagram
Energy-Decomposition
What-If Game
Handwaving
17
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”.
18
“Classical” Approach to Proposing a Mechanism
What we’ve seen before: Pauson-Khand-type Reaction
X
LnM(n)
O
X
Reductive
Elimination
Migratory Insertion
O
X
X
M(n+2)Ln
Insertion
X
(n+2)
M
Ln
M(n)Ln
Oxidative Addition
CO
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.
19
“Classical” Approach to Proposing a Mechanism
“Logical” mechanism for the [4+2+2]:
X
R2
Stereocontrol:
Rh coordination is facially
selective. The sterically bulky
R1 group directs Rh to the
correct side of the p-component.
+
X
R1 i
Rh
R1
R2
ii
Ln
R2
+
X
+
A
Rh
R1 iii
LnRh(I)
Ln
R2
+
X
Rh
R2
R1
H
iv
X
R1 H
vi
Evans, P. A. et al. Chem. Commun. 2005, 63
20
Let’s think about this….
A
B
X
C
R1
- 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??
21
New Proposal
X
R2
+
X
R1 i
Rh
R1
R2
R2
+
X
Rh
R1
ii
ii
R2
Ln
Ln
R2
+
+
X
A
Rh
X
B
LnRh(I)
R
R iii
Ln
Ln
+
+
X
Rh
R2
R
H
iv
vii
R2
R2
X
Rh
R1
Rh
viii
X
R1 H
vi
J. Am. Chem. Soc. 2005, 127, 1603
22
Computational Model Chemistry
- Density Functional Theory @ B3LYP/cc-pVTZ(-f) (Jaguar)
- Numerically efficient up to 300 atoms => no compromises with respect to Model Size
G = G(GP) + G(Solv)
Compute from Continuum
Solvation Model.
G(GP) = H(GP) - TS(GP)
Compute from Vibrational
Frequency Calculation.
H(GP) = E(SCF) + ZPE + TCp
Electronic SCF Energy
Correction for Changes
in Zero-Point-Energy
Thermal Corrections of the
Enthalpy.
23
Entropy
24
Continuum Solvation Model
+
M
hˆi  hˆigas  
k
E ElSt .   
S

A
+
+
+
– –
+
–
+
–
–
+
+ –
–
+
–
+ –
+
–
–
O
–
+
+
–
H + –
+ H
–
+ –
+
+ +
+ +
+ +
–
–
– –
– –
–
qk
| rk  ri |
Z A  S (rS )
drS 
RA  rS

S
 (r )  S (rS )
r  rS

S S
 S (rS )  S (rS )
rA  rS
drS drS
drS dr
25
Computed Reaction Energy Profiles
X
R2
+
X
R1 i
Rh
R1
R2
ii
Ln
R2
+
X
A
Rh
R iii
LnRh(I)
Ln
R2
+
X
Rh
R2
R
H
iv
X
R1 H
vi
J. Am. Chem. Soc. 2005, 127, 1603
26
Computed Reaction Energy Profiles
X
R2
R1 i
R2
+
X
Rh
R1
ii
R2
Ln
+
X
B
LnRh(I)
R
Ln
Rh
vii
R2
+
X
R2
R1
Rh
viii
X
R1 H
vi
J. Am. Chem. Soc. 2005, 127, 1603
27
Diastereoselectivity ??
J. Am. Chem. Soc. 2005, 127, 1603
28
Reason for Diastereoselectivity
J. Am. Chem. Soc. 2005, 127, 1603
29
Understanding Pauson-Khand-Type Reactions: [2+2+1]
R2
O
2
R2
[RhCl(CO)L]x
CO
O + O
O
2
R1 H
R1
4
R2
5a
O
2
R1 H
5b
ds 5a:5b = > 19:1
R2
O
R2
O
O
Rh(I)Cl(CO)
R1 H
5a
R2
10a
4
Reductive
Elimination
Migratory Insertion
O
R1
R2
Cl
Rh CO
C
O
Cl
O
R1 H
R1
Insertion
CO
Rh
7
Oxidative
Addition
R2
Cl
O
Rh
CO
CO
R1 H
8a
30
Mechanistic Alternatives
R2
O
O
O
Rh(I)Cl(CO)
R1 H
5a
O
R1
Rh(I)Cl(CO)2
4
R2
Cl
R2
C
Rh CO
O
O
CO
Rh
O
R1 H
R1
Oxidative
Addition
R2
7
D
Rh
11
R1 H
Oxidative
Addition
R1 H
Low CO pressure
CO
O
13a
Cl
O
8a
Rh CO
Insertion
R2
CO
CO
O
CO
R1
Cl
O
Migratory Insertion
R2 Cl
Cl
Rh CO
O
Cl
Insertion
5a
R2
CO
O
R1 H
Reductive
Elimination
Reductive
Elimination
Migratory Insertion
10a
R2
R2
R1 H
Rh CO
CO
9a
CO
High CO pressure
31
What about Structural Alternatives?
R2
R2
O
Rh
Rh
CO O
Rh
R1
Rh
CO O
H
R1
Rh CO
Cl
R1
H
R2
R2
Rh CO
H
O
H
CO
CO
O
Rh
CO
R1
CO
Cl
Cl
CO
R1
Rh
R2
Cl
O
O
H
R2
CO
CO
CO
R1
R2
R2
Rh CO
CO
H
H
R1
Cl
Cl
Rh
CO
H
R1
R2
R2
O
O
Rh
Cl
H
R1
H
R1
O
CO
Cl
Cl
CO
Cl
CO
O
R2
R2
CO
O
Rh CO
Cl
Cl
R1
H
R1
H
32
Reaction Energy Profiles
Low CO Pressure
Low diastereoselectivity
High CO Pressure
High diastereoselectivity
33
Why is this reaction diastereoselective?
Partial Charge Analysis
-0.08
R2
Cl
O
Rh CO
O
Rh CO
CO
R1 H
0.11
R2 -0.30
Cl
O
R2 -0.38
Cl
Rh CO
CO
R1 H
0.34
-0.51
11-TSa
R2
Cl
O
CO
R1 H
-0.35
0.06
Rh CO
CO
R1 H
11-TSb
Syn-Product forms by (+)-directed polarization.
Anti-Product forms by (-)-directed polarization.
34
What is the physical basis of the new rule?
35
What is the physical basis of the new rule?
36
But, can we predict new chemistry?

Diastereoselectivity is CO-pressure dependent!
O
[RhCl(CO) 2]2
1atm CO
2
O + O
O
2
H
H3C
H3C
4
O
2
H
H3C
5a
5b
ds 5a:5b = > 19:1
[RhCl(CO) 2]2
O
reduced CO-pressure
2
O + O
O
2
H3C
H3C
4
O
2
H
5a
H3C
H
5b
?? ds 5a:5b = < 10:1 ??
37
Precision in the Eyes of an Organic Chemist
Cat.
O
O + O
O
2
2
H3C
H3C
4
H
H3C
5a
Cat.
[Rh(CO)Cl(dppp)]2
[Rh(CO)Cl(dppp)]2
[Rh(CO)Cl(dppp)]2
O
2
Ar:CO
0:100
90:10
95:5
H
5b
ds 5a:5b
> 19:1
> 19:1
--
dppp: 1,3-bis(diphenylphosphino)propane
38
Hey – who said anything about phosphine?
Cat.
O
O + O
O
2
[RhCl(CO) 2]2
2
H3C
H3C
4
O
2
H
H3C
5a
Ar:CO
0:100
90:10
95:5
H
5b
ds 5a:5b
> 19:1
11:1
6:1
39
So, WHY is this happening?
Low CO Pressure
Low diastereoselectivity
High CO Pressure
High diastereoselectivity
40
Does this make sense NOW?
Cat.
O
O + O
O
2
2
H3C
H3C
4
H
H3C
5a
Cat.
[Rh(CO)Cl(dppp)]2
[Rh(CO)Cl(dppp)]2
[Rh(CO)Cl(dppp)]2
O
2
Ar:CO
0:100
90:10
95:5
H
5b
ds 5a:5b
> 19:1
> 19:1
--
dppp: 1,3-bis(diphenylphosphino)propane
41
More Predictions
CH3
CF3
O
O
Et
H3C
Low CO High CO
26.57
27.59
33.56
anti 28.82
syn
Low CO High CO
26.53
27.63
28.83
anti 28.58
syn
O
O
O
H3C
H3C
H2N
Low CO High CO
22.21
21.58
23.72
anti 25.72
syn
Low CO High CO
25.48
25.14
29.41
anti 25.78
Low CO High CO
28.77
26.42
29.24
anti 27.79
syn
syn
Will Electron withdrawing groups on R1 reverse ds ??
CF3
O
O
No! But:
O
O
F3C
Low CO High CO
syn 27.79
24.75
29.38
anti 32.34
Target:
O
H3C
Low CO High CO
31.80
30.12
30.35
anti 26.38
syn
Can’t be made?
H3CO
C
O
Low CO High CO
27.28
25.83
31.42
anti 29.58
syn
O
O
F
Low CO High CO
syn 26.57
25.99
20.70
anti 22.28
Cl
Low CO High CO
26.54
30.95
25.33
anti 26.73
syn
42
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
43
Center for Catalysis at IU-Bloomington
Organic
Synthesis
Andy Evans
Jeff Johnston
Molecular
Modeling
Mookie Baik
Organometallic
Catalyst Design
Dan Mindiola
Ken Caulton
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
44
45