1. UGM 2006 Miklós Vargyas Scientific Workshop Maximum Common Substructure

2. UGM 2006 Slide 2 Workshop overview Introduction, concepts, theory Clustering, the role of MCS Applications Future plans

3. UGM 2006 Slide 3 Motivations Automated reaction mapping

4. UGM 2006 Slide 4 Mapping chemical reactions

5. UGM 2006 Slide 5 ChemAxons automapper Find parts common to both sides Map common parts

6. UGM 2006 Slide 6 ChemAxons automapper Map the rest –Score possible mappings –Find the one that scores the highest

7. UGM 2006 Slide 7 Concepts and theory MCS/MCES/MOS MCS complexity O(n m )

8. UGM 2006 Slide 8 MCS search methods / Clique Barrow and Burstall, 1976 Raymond and Willett, RASCAL, 2002 Details in brief –Construct the product graph of G 1 and G 2 Node count: |V 1 | |V 2 | –Find clique, it corresponds to largest matching Why is it good –Very elegant, pure graph theory –MCES can also be found –Disconnected MCS/MCES can be found –Node and edge coloring fits easily What are the drawbacks –Product graph is large and dense Recent advances in clique detection

9. UGM 2006 Slide 9 MCS search methods / Backtrack Crandell-Smith, 1983 Advantages –Flexible, easy to add constraints, incorporate chemical knowledge, heuristics –Dynamic programming –Various search strategies Recent algorithms –Jun Xu, GMA, 1995

10. UGM 2006 Slide 10 Comparison of methods Brint and Willett, 1986: Clique based substantially faster Recent publication, 2006: backtracking is superior We tested both approaches –Backtracking: 1.2 s (exhaustive search) –Clique based was stopped after 2 hours!!!

11. UGM 2006 Slide 11 ChemAxon MCS search approach Based on Wang and Zhou, EMCSS, 1996 Backtracking –Divide and conquer strategy –Create all spanning trees of the query graph 1 2 7 3 4 5 6 8 10 9 11 12 13 15 14

12. UGM 2006 Slide 12 ChemAxon MCS search approach –Use this as a route plan to traverse the target graph 1 2 3 4 5 1 2 7 3 4 5 6 8 10 9 11 12 13 15 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14

13. UGM 2006 Slide 13 An application of MCS Reaction automapping (live demonstration) Average mapping time: 320ms Complex structures cannot be mapped efficiently

14. UGM 2006 Slide 14 Product development philosophy Sophisticated technology High performance (speed, accuracy, features) Rounded, industry relevant functionality Customizable Extendable Long term relevance >300 active clients Client driven development Fast and reliable support Comprehensive API Platform independence (Java)

15. UGM 2006 Slide 15 LibMCS motivations However, finding MCS from a pair of molecules has limited usage for our study. When we get hits from HTS, we cluster them into groups and the chemists will eye browse each group to find the scaffolds that are potentially good templates for later expansion. One main use of MCS will be to process multiple compounds of similar structures and automate what chemists have been doing by eyes now. We expect to use MCS tools for two cases: 1) use to analyze hits from HTS screens. 2) use it as a sorting tool for data retrieval, i.e., whenever people export data from our database (compounds across assays), we run MCS so that structurally similar compounds are grouped together. Chemists like this very much (we currently do this by clustering based on overall Tanimoto similarity). The typical hits from screens range from 2000-10000 (in few cases). In lead optimization phase, the compound list is around 3000-5000 in a typical project. So if MCS tools can process 5000 compound under 5 seconds, it can be integrated with online web tools. Otherwise, if it takes several minutes, it will be only used to analyze hits off-line based on user requests. If it takes more than an hour, its usage will be very limited.

16. UGM 2006 Slide 16 Exact solution –Requires the pair-wise comparison of each structure n (n - 1) / 2 MCS computations Next problem is larger!! –All CS (above a given size) have to be found n (n - 1) / 2 CS computations Partitioning O(n 3 ) CS LibMCS is a hard problem to solve

17. UGM 2006 Slide 17 Pair-wise MCS table

18. UGM 2006 Slide 18 Pair-wise MCS computation Average MCS computation: 100ms First step: n (n - 1) / 2 MCS computations –100 structures: 50 99 100ms = 8 min –1000 structures: 14 hours Second step: larger problem has to be solved Practically not feasible approach

19. UGM 2006 Slide 19 Known approaches / Products Stahl and Mauser, 2004, 2005 –Cluster first (ES) –Find an MCS for each cluster Wilkens, Janes and Su, 2004 BioReason ClassPharmer ChemTK LeadScope Tripos ? Daylight ?

20. UGM 2006 Slide 20 ChemAxons approach Goal –Reduce the number of MCS pair computations Idea: guess which two structures give significant MCS –Similar compounds are likely to share large MCS –Similarity guided pair-wise MCS Not clustering by similarity and determine the MCS for the cluster Which molecular descriptor gives best correlation –ChemAxon fingerprint –BCUT (Burden matrix) Consequence –Approximate solution

21. UGM 2006 Slide 21 LibMCS algorithm Read input structures Generate fingerprint Calculate similarity matrix Make singletons Compute MCS MCS large Create new cluster Similarity above threshold Get two most similar More structures SSS Found Add to cluster n n n n y y y y

22. UGM 2006 Slide 22 Applications Screen analysis Data visualization and profiling Combinatorial library partitioning Buying new compounds ? Suggest more!!!!

23. UGM 2006 Slide 23 Application 1 / Screen analysis

24. UGM 2006 Slide 24 Activity filtering

25. UGM 2006 Slide 25 Live demonstration Partitioning mixed combinatorial library –Affect of parameters –Affect of modes –Benchmarks –Quality of clusters

26. UGM 2006 Slide 26 Combichem library scaffolds

27. UGM 2006 Slide 27 Combichem library scaffolds Turbo mode distorts clusters

28. UGM 2006 Slide 28 Combichem benchmark Influence of normal/fast/turbo mode Worth, distortion is not significant

29. UGM 2006 Slide 29 Development roadmap Soon –R-Group decomposition –Stereo care MCS –Preserving rings –Lower bound pre-filtering –Disconnected MCS –Multi cluster members Mid term –Integrate Ward/Jarvis-Patrick in the new GUI Long term –Integrate molecular descriptors, metrics –Integrate virtual screening

30. UGM 2006 Slide 30 Coming soon – R-Group decomposition

31. UGM 2006 Slide 31 Coming soon – R-Group decomposition

32. UGM 2006 Slide 32 Coming soon – Multi cluster

33. UGM 2006 Slide 33 Summary MCS developed for automatic reaction mapping MCS based hierarchical clustering Fast method Chemical adequacy must be improved Various uses, currently focusing on combinatorial library partitioning

34. UGM 2006 Slide 34 Acknowledgements Developers –Péter Vadász –Nóra Máté Ideas –Szabolcs Csepregi, Ferenc Csizmadia Special thanks to