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This is the talk I gave when I interviewed for my first role at Boehringer Ingelheim Pharmaceuticals, Inc.
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Computational Chemistry:From Theory to Practice
6th December 2007David C. Thompson
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
Some background, and sometheory
The Problem
Motivation: Top 20 best-selling drugsin America had sales of ~ $65bn in2005[1]
New drug development costs are inexcess of $800M[2]
Roughly 10K structures are made andtested for every new drug reaching themarket[3]
[1] The Best-Selling drugs in America, IMS health, 2006[2] The Tufts Center for the study of drug development[3] Boston Consulting Group, 2005
The Solution
Solve the Schrödinger equation:
Ψ determines all properties of thesystem
!
H" = E"
Unfortunately…
“The underlying physical laws necessaryfor the mathematical theory of a large part ofphysics and the whole of chemistry are thuscompletely known, and the difficulty is onlythat the application of these laws leads toequations much too complicated to besoluble.” – P. A. M. Dirac (1929)
The Solution - DFT?
The electron density, ρ, can be derived fromΨ
And, it turns out that all properties of a systemcan be derived from ρ– ρ is a function of 3 variables– Ψ is a function of 4N variables
This is great, right?– Sure, but didn’t I tell you? In getting this far, I made a functional which
contains all of the “confusion”, and I don’t rightly know what it looks like. . .
Accuracy vs. SpeedAccuracy
Speed
Ph.D
EEHF EDFTEMM PD1
PD2
EDFT can be improved but we need tounderstand the physics of how“electrons get along”: Ec=E-EHF
101102104105-6
Gas phase water: An example
[4] G. K.-L. Chan and M. Head-Gordon, J. Chem. Phys. 118, 8551 2003
A DFT calculationtakes ~9s
An “Exact”calculation[4] took150h, 250Gb ofmemory, and 800Gbof disk
Gas phase water: An example
Water has 10 electrons
The 1A4Q receptor has~104 valence electrons
A full quantummechanical calculationis just not practical
The Hospital that ate my Wife. . .
Information theoretic properties of a model system:
Doesn’t Sr look a little familiar?
!
Sr = " #(r)$ ln[#(r)]dr
Sp = " %(p)$ ln[%(p)]dp
ST = Sr + Sp
A novel descriptor?
Continuous form of a measure used in molecularsimilarity:
Could we use Sr as a measure of similarity?
Moreover, could Sr be a 3D QM-basedstructural descriptor?– Literature search has shown that this has not been
considered before (I think)[5]
!
S = " pii
# ln[pi]
[5] M. Karelson, “Quantum-chemical descriptors in QSAR”, in Computational MedicinalChemistry for Drug Discovery, P. Bultnick et al, Eds., (New York, Dekker, 2003), pp 641-667
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 sumof fitted s-Gaussians[6]
Turns out that this isn’t as bad as you mightthink[7]
[6] P. Constans and R. Carbó, J. Chem. Inf. Sci. 35, 1046 1995 [7] J. I. Rodriguez, D. C. Thompson, and P. W. Ayers Unpublished data!
"(r) # "Mol(r) = "$ (r)
$
% = c$ii
% exp(&'$i(r &R$ )2)
$
%
Homebrew quantum mechanics
All of this has been done on my iMac at home
Molecular integrations performed using theBecke/Lebedev grids in PyQuante[8]
Co-opted graduate students into doingMathCad checks for me. . .
[8] Python Quantum Chemistry - http://pyquante.sourceforge.net/
Homebrew quantum mechanics
RzH1 H2
Homebrew quantum mechanics
-35.94Cyclohexane (chair)
-27.09Benzene
3.94H2S
-7.42H2O
SrMolecule
Perhaps Sr isn’t that discriminatory?Plan B -
!
Sr(r) = "#(r)ln[#(r)]
And that might look like. . .
Summary
Introduced a novel, 3D, quantummechanics based structural descriptor– Its utility, if any, will be further examined
Feedback is encouraged
Some background, and somepractice
Project involvement
Detailed analysis of in-house high-throughput virtualscreening 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
Fragment-based de novo design:The problem at hand
Search space of new molecular entities is essentiallyinfinite– 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 ofchemical space– The range of molecules generated is only limited by the
heuristics of the de novo design program
Low ligandefficiency area
High ligandefficiency area
!
LE = "#G
N$ "
RT ln(IC50)
N
Ligand Efficiency
R. Carr et al., Drug Discov. Today, 10, 987 2005
Project requirements
Exploit potential gaps in literature
If possible use in-house chemical equity
Modular design
Efficient deployment strategy
De novo design: Link or Grow?
LINK
GROW
G. Schneider et al., Nature Reviews Drug Discovery, 4, 649 2005
CONFIRM
Bridge library db
O O-
OH
O O-
OH
d
N
N
NH
O
O O-
OH
+…
d
A pre-prepared bridge library issearched using the atom type of theconnection points, and the distanced as a search query
Bridges that match the search queryare attached to the fragments
Complete molecules are preparedfor docking – enumeration oftautomers, isomers, and ionizationstates
Prepared molecules are dockedinto the target binding site
Bridge Libraries Application of filters
− Molecular Weight• <200 MW
− No. of rotatable bonds• ≤3• ≤4
Conformationalexpansion with OMEGA– 4 bridge libraries
• Lib3 → Lib3E• Lib4 → Lib4E
Lib3
Bridge library derived fromcorporate database
Lib4
Lib3E Lib4E
OMEGA Expansion
≤ 3 rot. bonds ≤ 4 rot. bonds
CONFIRM: Novelty
Bridges come from molecules within the WyethCORP database:– Bridges obtained “…from a given ring scaffold by removing
all of the atoms, except acyclic linker atoms, between pairsof 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
CONFIRM: Test Sets
Taken from the curated data set of protein-ligandcomplexes– High crystallographic resolution ≤ 2.2Å– Two well resolved fragment moieties connected via a bridge– Both fragments interact with spatially disparate regions of
the protein
0.43
0.43
0.29
0.95
XPSP
0.301.381FCZ
0.402.201YDR
0.271.901A4Q
1.191.801SRJ
RMSD/ÅResolution/ÅPDB Ascension
Code
1SRJ X-ray Structure (green carbons) CONFIRM XP Pose (orange carbons)
3.7Å
-O O
N
N
OH
Fragment 1
Bridge
Fragment 2
CONFIRM: 1SRJ example
1A4Q X-ray Structure (green carbons) CONFIRM XP Pose (orange carbons)
O
O-O
NH2
HN OO
N
Fragment 1 Fragment 2
Bridge
5.9Å
154370Lib4E
84274Lib4
No. withFragment 1 and2 RMSD < 2Å
No. ofUnique
HitsLibrary
CONFIRM: 1A4Q example
CONFIRM: 1MTU exampleImportant 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
CONFIRM: 1MTU example
Search Lib4E with distance query of 5Å– 2852 bridges
Search Lead-like database using ROCS and thisquery:
Use Combo score, only keep top 100
Use CONFIRM to enumerate, prepare, and dock
X O
N
HN
CONFIRM: 1MTU example
CONFIRM: 1MTU example
CONFIRM: 1MTU example
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
Acknowledgments
Computational Chemistry Group at WyethResearch Cambridge
Dr. Christine Humblet Prof. K. D. Sen Prof. P. W. Ayers
– J. S. M. Anderson– J. I. Rodriguez