Virtual Screening with Topomer CoMFA

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Virtual Screening with Topomer CoMFA

• Dick Cramer
• Brave New World of QSAR, ACS
• August 19, 2002

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Outline

• Topomers (similarity searching)
• Method and strengths
• Prospective lead-hopping results
• Topomer CoMFA
• Methodology
• Retrospective computational validation
• Prospective results

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Seeking and Developing Islands of Activity in
Chemistry Space
B-lactams
ACE Inhibitors

• Lead Discovery find land
• Lead Explosion define and claim island
• Lead Hopping find another island
• Lead Optimization find high enough peak
• But, instead of the two latitude / longitude
  dimensions of geographical exploration, there are
  an exponentially enormous number of ways to
  describe chemistry space.
• Poor descriptions destroy islands (MW)
• Topomers provide an excellent compass for drug
  discovery

H2 blockers
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Topomers are novel 3D models

• Only fragments have topomers!
• Whole similarity sum-of-fragment similarities
• How topomers handle 3D
• Structures oriented
• Overlay of open valences
• Single conformer
• CONCORD 3D structures
• Side-chain chiral via rules
• Topomer similarity is in
• Steric fields (as in CoMFA)
• Binned values
• Rot.bond-attenuated atomic fields
• Feature matching (as in conventional 3D
  searching)

• 1999 Tripos, Inc.

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Generating a Topomer
attachment bond
chiral atom
free valence
A gt B Attach anchor group generate 3D
model overlap attachment bond B gt C starting
at attachment bond adjust chirality select
torsion end-points and adjust dihedral angles
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Topomer Searching in Drug Discovery Summary

• Many distinct advantages
• Speed, to address the vastness of chemistry
  space (1000s of CAS units per second!)
• Has yet to fail in identifying promising and
  patentable biological activity
• Novelty of hits
• Accessibility of hits
• Physical interpretability of model
• Exists in either of two flavors
• ChemSpace (virtual libraries 1013)
• dbtop (conventional collections 106)
• No plans exist for distributing either flavor

when searching virtual libraries (ChemSpace)
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Why topomer searching is so fast
R3 ordered by sorted topomer distance
Vast Virtual Library
R2 ordered by sorted topomer distance
R1 ordered by sorted topomer distance
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Discovery projects using topomer-similarity-driven
Lead-Hopping

• Arena (structure originally found is still the
  lead)
• BMS (published validation, see references)
• Lipha (seven lead hop trials, five successes)
• LeadQuest screening (partially disclosable)

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Recent prospective topomer similarity results

• 7 query structures having different activities
  chosen from recent patents (WD Alert)
• 257 topomerically most (but not very) similar
  structures among 80K LeadQuest cpds (80K/(257/7)
  0.05) were selected (by dbtop) and tested _at_10
  or 100 um
• Screening, gt50 37 (14). gt30 56 (22)
• IC50s 25 cpds lt 30 um, for 5 of 7 query
  structures
• Active structures are clear lead hops (only 1
  homologue)

(active structures are being followed up and
so currently may be viewed only upon execution of
a CDA)
queries
actives found
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The Paradoxical Limitation of Similarity Selection
Receptor

• As similarity to an active
• compound decreases
• activity usually decreases
• but sometimes increases

Similarity selection gt change is bad BUT
... Successful lead optimization (um gt nm
potency) requires changes that help!
Such changes are discovered by (Q)SAR
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CoMFA is a (3D-Q)SAR method.quickly, how does it
work?
Contour Maps
Predictions
PLS
Bio
QSAR equation
QSAR Table SYBYL MSS
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Pros and Cons of CoMFA(a leading (3D-Q)SAR
method)

• Advantages of CoMFA
• very generally applicable
• robust, widely used and accepted
• models easy to understand, interpret
• excellent record for predicting potency
• Disadvantages of CoMFA
• Input alignment of 3D models is ill-defined
• Output does not select, only predicts

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Topomeric CoMFA a neat complementarity

• Can we perform successful CoMFAs based on
  (automatic / ignorant) topomer aligment rules?
• Yes! (surprisingly)
• the CoMFA input bottleneck is thereby broken
• Can we use the resulting CoMFA SARs to search for
  more active structures?
• the CoMFA (QSAR) output bottleneck disappears
• topomer searching becomes very useful in lead
  optimization

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Implementing Topomeric CoMFA

• Input molecules must be fragmented
• each fragment set gets its own CoMFA column
• data sets fall into four different classes

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Validating Topomeric CoMFA Methodology

• How do topomeric alignments perform, compared to
  successful CoMFA alignments from literature?
• 10 recent CoMFA pubs gt 14 end points (1
  alternative topomer fragmentation) 15 trials
• Literature alignments 8/15 used X-ray
• Data sets 6 Class 1 (3-piece), 9 Class 2
  (2-piece)

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Example Topomer Alignment2 piece (5ht3)
(61 structures orthogonal views)
X2 (.680 of model)
X1 (.320 of model)
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Validating Topomeric CoMFARemarkably Good
Results
Satisfactory results obtained in each of the 15
trials Average performance of automatic topomer
alignments almost identical to literature
alignments
aUsing standard CoMFA fields and methods bUsing
topomeric CoMFA fields. comp from xval SDEV
min, not q2 max cOmission of one data set having
suspect predictions
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Why do context-ignorant topomer alignments
perform so well?

• 15 successes in 15 trials is not just good luck
• Topomer alignments do align like with like
• Context-knowledgable (literature) alignment must
  be introducing as much noise as signal
• Example docking of combi (common core) libraries

Docking moves the core around, producing field
variation that is noise, because .. ..an
invariant core cannot cause changes in biological
activity
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What about Topomer CoMFA Searching?

• Topomer rules are structurally universal
• Directly search VLs (ChemSpace)
• Directly search conventional DBs (dbtop) for
  fragments
• Search objectives (to be andd together)
• Similarity to average of CoMFA input fields
• Predicted high potency
• Exploration of new regions (happens
  automatically)
• Required development of
• Binned electrostatic fields for all stored
  topomers
• Extracting features from CoMFA input structures

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Examples of Topomer CoMFA Searching results

• For each of the 15 validation data sets
• Searched 2-piece CS libraries in use (108
  structures)
• derived from commercially offered (readily
  accessible) reagents
• best CoMFA Inputs Rs in most active CoMFA
  input
• best Searching Hits Rs with highest
  predicted potency contribution ( lt 150
  similarity synthetically tractable)
• Shown for both best Rs are
• 2D structures with potency contributions
• 3D topomer structures overlaid on CoMFA grid
  (orthogonal views)
• In 13 of the 15 cases, best Searching Hits ..
• together exceed best experimental potency by gt1.0
  log units

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Topomer CoMFA Searching Hits (5ht3)
Best CoMFA Input
Best Searching Hits
Site 1
1.2 (106)
Potency effect (similarity)
0.8
Site 2
Potency effect (similarity)
1.8
2.5 (112)
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CoMFA Input vs. Best Hit in 3D (5ht3_1)
Input example
Best Hit
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CoMFA Input vs. Best Hit in 3D (5ht3_2)
Input example
Best Hit
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Three Prospective Applications of Topomer CoMFA

• Good topomer CoMFA models automatically obtained
  in 3/3 trials (two projects)
• Prediction of potencies satisfactory in 2/3
  trials (predicted/active r2 of .42 and .24)
• Difficulty with third unsatisfactory trial was
  little variation among potency predictions,
  because of
• Little structural variety in training set, /or
• Test set variation irrelevant to training set
  variation
• Errors of prediction are false positives much
  more often than false negatives

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Topomer CoMFA (Searching)Conclusions

• Automatic CoMFA alignments are a reality
• lit. alignments gt topomer alignments 15 / 15
  times
• 2D structures to finished CoMFA takes a few
  minutes
• Topomeric alignments enable topomer searching
• For improved potency as well as similarity within
  the vast search space accessible to topomers
• Novelty of hits seems self-evident
• New receptor space is being targeted
• Promises a uniquely powerful engine for lead
  optimization ...
• Initial applications confirm promise

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Acknowledgments
Dbtop (WDA queries)

• Topomer CoMFA
• Use and Feedback
• Bernd Wendt
• Mike Lawless

• Design / Implementation
• Katherine Andrews-Cramer
• Rob Jilek
• Use and Feedback
• Stefan Guessregen
• Mark Warne
• Katherine Andrews-Cramer

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References

• Cramer, R. D. Clark, R. D. Patterson, D. E.
  Ferguson, A. M. Bioisosterism as a molecular
  diversity descriptor steric fields of single
  topomeric conformers. J. Med. Chem. 1996, 39,
  3060-3069.
• Patterson, D. E. Cramer, R. D. Ferguson, A. M.
  Clark, R. D. Weinberger, L. E. Neighborhood
  behavior a useful concept for validation of
  molecular diversity descriptors. J. Med. Chem.
  1996, 39, 3049-30
• Cramer, R. D. Patterson, D. E. Clark, R. D.
  Soltanshahi, F. Lawless, M. S. Virtual
  libraries a new approach to decision making in
  molecular discovery research. J. Chem. Inf. Comp.
  Sci. 1998, 6, 1010-1023.
• Cramer, R. D. Poss, M. A. Hermsmeier, M. A.
  Caulfield, T. J. Kowala, M. C. Valentine, M. T.
  Prospective Identification of Biologically Active
  Structures by Topomer Shape Similarity Searching.
  J. Med. Chem. 1999, 42, 3919-3933.
• Andrews, K. M. Cramer, R. D. Toward General
  Methods of Targeted Library Design Topomer Shape
  Similarity Searching with Diverse Structures as
  Queries, J. Med. Chem, J. Med. Chem. 2000, 43,
  1723-1740.
• Cramer, R. D. Jilek, R. J. Andrews, K. M.
  dbtop Topomer Similarity Searching of
  Conventional Databases, J. Mol. Graph. Modeling
  2002, 20, 447-462.
• Cramer, R.D. Topomer CoMFA A Design Methodology
  for Rapid Lead Optimization, J. Med. Chem.,
  manuscript accepted.

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