Virtual Screening with Topomer CoMFA

1. Virtual Screening with Topomer CoMFA Dick Cramer “Brave New World of QSAR”, ACS August 19, 2002

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

3. 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 ACE Inhibitors H2 blockers

4. 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.

5. A B C D * Generating a Topomer attachment bond chiral atom free valence 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

6. 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)

7. 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

8. 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)

9. 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) (active structures are being followed up and so currently may be viewed only upon execution of a CDA) queries actives found

10. The Paradoxical Limitation of Similarity Selection Receptor • 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

11. CoMFA is a (3D-Q)SAR method.quickly, how does it work? Contour Maps Predictions PLS Bio QSAR equation QSAR Table = SYBYL MSS

12. 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

13. 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

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

15. 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)

16. Example Topomer Alignment:2 piece (5ht3) (61 structures: orthogonal views) X2 (.680 of model) X1 (.320 of model)

17. 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 methods bUsing “topomeric CoMFA fields”. #comp from xval SDEV min, not q2 max cOmission of one data set having suspect predictions

18. 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

19. 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

20. 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

21. Topomer CoMFA Searching Hits (5ht3) Best CoMFA Input Best Searching Hits Site 1: Potency effect (similarity) +1.2 (106) +0.8 Site 2: Potency effect (similarity) +1.8 +2.5 (112)

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

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

24. 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”

25. 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

26. Design / Implementation Katherine Andrews-Cramer Rob Jilek Use and Feedback Stefan Guessregen Mark Warne Katherine Andrews-Cramer Acknowledgments Dbtop (WDA queries) • Topomer CoMFA • Use and Feedback • Bernd Wendt • Mike Lawless

27. 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.