The document discusses computational chemistry's applications in drug design, particularly focusing on the introduction of a novel 3D quantum mechanics-based structural descriptor and its potential utility. It covers challenges in new drug development, such as high costs and extensive structures needed for market approval, and presents methods for fragment-based de novo design to explore the vast chemical space efficiently. The document emphasizes the importance of understanding electron density for accurate calculations and the development of algorithms for linking and growing molecular fragments.

1. Computational Chemistry:
From Theory to Practice
6th December 2007
David C. Thompson

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

3. Some background, and some
theory

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/

15. Homebrew quantum mechanics
H1 Rz H2

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)]

17. And that might look like. . .

18. Summary
 Introduced a novel, 3D, quantum
mechanics based structural descriptor
– Its utility, if any, will be further examined
 Feedback is encouraged

19. Some background, and some
practice

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

26. Bridge Libraries
Bridge library derived from  Application of filters
corporate database
− Molecular Weight
≤ 3 rot. bonds ≤ 4 rot. bonds • <200 MW
− No. of rotatable bonds
Lib3 Lib4 • ≤3
• ≤4
OMEGA Expansion
 Conformational
expansion with OMEGA
Lib3E Lib4E
– 4 bridge libraries
• Lib3 → Lib3E
• Lib4 → Lib4E

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

33. CONFIRM: 1MTU example

34. CONFIRM: 1MTU example

35. CONFIRM: 1MTU example

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