2. Overview
An introduction to computational chemistry
– Which method, where, and why?
A novel 3D QM-based descriptor (perhaps?)
Computational chemistry for drug design
– Fragment-based de novo design
4. The Problem
Motivation: Top 20 best-selling drugs
in America had sales of ~ $65bn in
2005[1]
New drug development costs are in
excess of $800M[2]
Roughly 10K structures are made and
tested for every new drug reaching the
market[3]
[1] The Best-Selling drugs in America, IMS health, 2006
[2] The Tufts Center for the study of drug development
[3] Boston Consulting Group, 2005
5. The Solution
Solve the Schrödinger equation:
H" = E"
Ψ determines all properties of the
system
!
6. Unfortunately…
“The underlying physical laws necessary
for the mathematical theory of a large part of
physics and the whole of chemistry are thus
completely known, and the difficulty is only
that the application of these laws leads to
equations much too complicated to be
soluble.” – P. A. M. Dirac (1929)
7. The Solution - DFT?
The electron density, ρ, can be derived from
Ψ
And, it turns out that all properties of a system
can be derived from ρ
– ρ is a function of 3 variables
– Ψ is a function of 4N variables
This is great, right?
– Sure, but didn’t I tell you? In getting this far, I made a functional which
contains all of the “confusion”, and I don’t rightly know what it looks like. . .
8. Accuracy vs. Speed
Accuracy
Speed
Ph.D
105-6 EMM 104 EHF 102 EDFT 101 E PD1
PD2
EDFT can be improved but we need to
understand the physics of how
“electrons get along”: Ec=E-EHF
9. Gas phase water: An example
A DFT calculation
takes ~9s
An “Exact”
calculation[4] took
150h, 250Gb of
memory, and 800Gb
of disk
[4] G. K.-L. Chan and M. Head-Gordon, J. Chem. Phys. 118, 8551 2003
10. Gas phase water: An example
Water has 10 electrons
The 1A4Q receptor has
~104 valence electrons
A full quantum
mechanical calculation
is just not practical
11. The Hospital that ate my Wife. . .
Information theoretic properties of a model system:
Sr = " $ # (r) ln[ #(r)]dr
S p = " $ % (p) ln[% (p)]dp
ST = Sr + S p
Doesn’t Sr look a little familiar?
!
12. A novel descriptor?
Continuous form of a measure used in molecular
similarity:
S = "# pi ln[ pi ]
i
Could we use Sr as a measure of similarity?
Moreover, could Sr be a 3D QM-based
!
structural descriptor?
– Literature search has shown that this has not been
considered before (I think)[5]
[5] M. Karelson, “Quantum-chemical descriptors in QSAR”, in Computational Medicinal
Chemistry for Drug Discovery, P. Bultnick et al, Eds., (New York, Dekker, 2003), pp 641-667
13. A novel descriptor?
We want to make this useful
– But we still have the problem of finding ρ in a
timely fashion
Why don’t we approximate ρ?
– We construct a pro-molecular density from a sum
of fitted s-Gaussians[6]
"(r) # " Mol (r) = % "$ (r) = % % c$i exp(&'$i (r & R$ ) 2 )
$ $ i
Turns out that this isn’t as bad as you might
think[7]
! P. Constans and R. Carbó, J. Chem. Inf. Sci. 35, 1046 1995
[6]
[7] J. I. Rodriguez, D. C. Thompson, and P. W. Ayers Unpublished data
14. Homebrew quantum mechanics
All of this has been done on my iMac at home
Molecular integrations performed using the
Becke/Lebedev grids in PyQuante[8]
Co-opted graduate students into doing
MathCad checks for me. . .
[8] Python Quantum Chemistry - http://pyquante.sourceforge.net/
16. Homebrew quantum mechanics
Molecule Sr
H2O -7.42
H2S 3.94
Benzene -27.09
Cyclohexane (chair) -35.94
Perhaps Sr isn’t that discriminatory?
Plan B - Sr (r) = " #(r)ln[ # (r)]
18. Summary
Introduced a novel, 3D, quantum
mechanics based structural descriptor
– Its utility, if any, will be further examined
Feedback is encouraged
20. Project involvement
Detailed analysis of in-house high-throughput virtual
screening protocol
− Detailed curation of large data set of protein-ligand
complexes
Late-stage discovery project support
− Lead optimization
− Lead generation
Fragment-based de novo design
21. Fragment-based de novo design:
The problem at hand
Search space of new molecular entities is essentially
infinite
– The number of chemically feasible, drug like molecules
~1060-10100
Such a large space cannot be searched exhaustively
De novo design offers a broad exploration of
chemical space
– The range of molecules generated is only limited by the
heuristics of the de novo design program
22. Ligand Efficiency
High ligand
efficiency area #G RT ln(IC50 )
LE = " $"
N N
Low ligand
efficiency area
!
R. Carr et al., Drug Discov. Today, 10, 987 2005
23. Project requirements
Exploit potential gaps in literature
If possible use in-house chemical equity
Modular design
Efficient deployment strategy
24. De novo design: Link or Grow?
LINK
GROW
G. Schneider et al., Nature Reviews Drug Discovery, 4, 649 2005
25. CONFIRM
O O- OH
d
A pre-prepared bridge library is
searched using the atom type of the
connection points, and the distance
d as a search query
Bridge library db
Bridges that match the search query d
are attached to the fragments O O- OH
+
N
N
Complete molecules are prepared
for docking – enumeration of …
O
tautomers, isomers, and ionization
states N
H
O O- OH
Prepared molecules are docked
into the target binding site
27. CONFIRM: Novelty
Bridges come from molecules within the Wyeth
CORP database:
– Bridges obtained “…from a given ring scaffold by removing
all of the atoms, except acyclic linker atoms, between pairs
of ring systems, and the anchor atoms on the ring system.”
[9]
Similar to CAVEAT[10], however:
– We do not use orientation of bonds, but location of atoms
(vector vs. scalar)
– CAVEAT searches 3D databases looking for suitable
molecular frameworks to satisfy the vector pairs
• We already have well defined positions of small molecule
binders
[9] R. Nilakantan et al., J. Chem. Inf. Mod. 46(3), 1069-1077 2006
[10] G. Lauri, and P. A. Bartlett, J. Comp.-Aided. Mol. Design 8(1), 51-66 1994
28. CONFIRM: Test Sets
Taken from the curated data set of protein-ligand
complexes
– High crystallographic resolution ≤ 2.2Å
– Two well resolved fragment moieties connected via a bridge
– Both fragments interact with spatially disparate regions of
the protein
PDB Ascension RMSD/Å
Resolution/Å
Code SP XP
1SRJ 1.80 1.19 0.95
1A4Q 1.90 0.27 0.29
1YDR 2.20 0.40 0.43
1FCZ 1.38 0.30 0.43
29. CONFIRM: 1SRJ example
-
O O OH
N
N
Bridge
3.7Å
Fragment 1 Fragment 2
1SRJ X-ray Structure (green carbons)
CONFIRM XP Pose (orange carbons)
30. CONFIRM: 1A4Q example
-O O
Bridge O
N
NH2
O HN O
Fragment 1
5.9Å
Fragment 2
No. of No. with 1A4Q X-ray Structure (green carbons)
Library Unique Fragment 1 and
Hits 2 RMSD < 2Å CONFIRM XP Pose (orange carbons)
Lib4 274 84
Lib4E 370 154
31. CONFIRM: 1MTU example
Important for binding – we wish to keep this fragment
Search bridge library for suggestions for bridging atoms
Use ROCS to search for alternative groups to go here
32. CONFIRM: 1MTU example
Search Lib4E with distance query of 5Å
– 2852 bridges
Search Lead-like database using ROCS and this
query:
X O
N
HN
Use Combo score, only keep top 100
Use CONFIRM to enumerate, prepare, and dock
36. Summary
Following comprehensive literature search, multiple
algorithms for linking/growing fragments developed
Final linking approach, dubbed ‘CONFIRM’, uses in-
house chemical equity
Modular design, allowed for rapid:
− Implementation
− Testing
− Analysis and modification
Publication completed, submitted to . . .
Currently exploring use on drug discovery projects
37. Acknowledgments
Computational Chemistry Group at Wyeth
Research Cambridge
Dr. Christine Humblet
Prof. K. D. Sen
Prof. P. W. Ayers
– J. S. M. Anderson
– J. I. Rodriguez