Virtual Screening with Topomer CoMFA

Dick Cramer

“Brave New World of QSAR”, ACS

August 19, 2002

Outline

• Topomers (similarity searching)• Method and strengths• Prospective “lead-hopping” results

• Topomer CoMFA• Methodology• Retrospective computational validation• Prospective results

Seeking and Developing “Islands of Activity” in “Chemistry Space”

• “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

B-lactams

H2 blockers

ACE Inhibitors

• 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)

Topomers are novel 3D models

© 1999 Tripos, Inc.

Generating a Topomer

A B C D

*

A => B: Attach “anchor group”;

generate 3D model;

overlap attachment bond

B => C: starting at attachment bond:

adjust chirality

select torsion end-points and adjust dihedral angles

attachmentbond

chiralatomfree

valence

Topomer Searching in Drug Discovery: Summary

• Many distinct advantages• Speed, to address the vastness of chemistry space* (1000’s 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)

Why topomer searching is so fast

R1: ordered by sorted topomer distance

R2: ordered by sorted topomer distance

R3: ordered by sorted topomer distance

VastVirtualLibrary

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)

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 @10 or 100 um

• Screening:, >50% 37 (14%). >30%: 56 (22%)• IC50’s: 25 cpds < 30 um, for 5 of 7 query structures• Active structures are clear “lead hops” (only 1

homologue)

queries actives found

(active structures are being followed up and socurrently may be viewed only upon execution of a CDA)

The Paradoxical Limitation of Similarity SelectionReceptor As similarity to an active

compound decreases:•activity usually decreases•but sometimes increases

Similarity selection => change is bad

BUT ...Successful lead optimization(um => nm potency)requires changes that help!

Such changes are discovered by (Q)SAR

CoMFA is a (3D-Q)SAR method.quickly, how does it work?

QSARequation

PLS

ContourMaps

Predictions

QSAR Table = SYBYL MSS

Bio

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

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

Class Description Example Top Align by

1 Common core CombinatorialLibrary

Side chains =>fragments

2 Similar topology,central bond(s)

Most JMC pubs. Split into two at acentral bond

3 Similar topology, nocentral bond(s)

Steroids Use othermethods

4 No similar topology Screeningdecks

Alwayschallenging

Implementing Topomeric CoMFA

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

Validating Topomeric CoMFA: Methodology

• How do topomeric alignments perform, compared to successful CoMFA alignments from literature?

• 10 recent CoMFA pubs => 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)

Example Topomer Alignment:2 piece (5ht3)

X1 (.320 of model)

X2 (.680 of model)

(61 structures:orthogonal views)

Lit. Top1a Top2b

Avg. # PLSComponents

4.2 5.5 3.6

Avg q2 (n=15) .636 .520 .502

Avg SdevPredictionc

(n=133)

.574 .623 .565

Validating Topomeric CoMFA:Remarkably 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 methodsbUsing “topomeric CoMFA fields”. #comp from xval SDEV min, not q2 maxcOmission of one data set having suspect predictions

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

What about Topomer CoMFA Searching?

• Topomer rules are structurally universal• Directly search VL’s (ChemSpace)• Directly search conventional DB’s (dbtop) for fragments

• Search objectives (to be “and’d” 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

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” == R’s in most active CoMFA input

• “best Searching Hits” == R‘s with highest predicted potency contribution (+ < 150 similarity + synthetically tractable)

• Shown for both “best R’s” 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 >1.0 log units

Topomer CoMFA Searching Hits (5ht3)

Best CoMFA Input Best Searching Hits

+0.8 +1.2 (106)

+1.8 +2.5 (112)

Potency effect(similarity)

Potency effect(similarity)

Site 1:

Site 2:

CoMFA Input vs. Best Hit in 3D (5ht3_1)

Best HitInput example

CoMFA Input vs. Best Hit in 3D (5ht3_2)

Input example

Best Hit

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”

Topomer CoMFA (Searching):Conclusions

• Automatic CoMFA alignments are a reality• lit. alignments => 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

Acknowledgments

• Design / Implementation• Katherine Andrews-

Cramer• Rob Jilek

• Use and Feedback• Stefan Guessregen• Mark Warne• Katherine Andrews-

Cramer

Dbtop (WDA queries) Topomer CoMFA

•Use and Feedback• Bernd Wendt• Mike Lawless

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.