1. MDL Keys Revisited Joseph L. Durant, Burton A. Leland, Douglas R. Henry and James G. Nourse MDL Information Systems

2. Overview What are MDL Keys? Constructing better keys –metrics –optimization by "educated guesswork" –optimization by Genetic Algorithms Conclusions

3. What are MDL Keys a.k.a. SSKeys Originally designed to support sub-structure searching Bits encoding molecular features Most follow the structure of: –a property on atom A –a property on atom B –A and B are separated by N bonds (0<=N<=4) –this pattern is encountered M or more times

4. MDL Keys - What Are They? Some keys code for specific bonds (C-Cl, S-P) Other keys code for a property in an atomic neighborhood (C-CCO, Q-OO) Still others are custom properties –Sgroup properties –rings –atom types

5. MDL Keys - Standard Implementation MDL’s SSKeys are encountered in 2 flavors: –a 960 keybitset –a 166 keybitset (Subset or User Keys)

6. The 960 Keybitset Created to support substructure searching Encodes 1387 molecule features Encodes features with >0, >1, >2 and >4 occurrences Features can turn on 1, 2 or 3 keybits many of the keybits can be set by multiple features

7. The 166 Keybitset Originally created to embody an earlier MDL keybitset Largely correspond to “chemist- meaningful” features

8. 166 Keybitset Definitions 1 - isotope 2 - 103<atomic number<256. 84 - NH2 85 - CN(C)C 86 - CH2QCH2. 165 - ring 166 - fragments

9. Current Uses for MDL Keys Clustering/diversity –Brown & Martin, JCICS, 1996, 36, 572-584. –McGregor & Pallai, JCICS, 1997, 37, 443-448. Library generation/evaluation –Brown & Martin, J. Med. Chem., 1997, 40, 2304-2313. –Koehler, Dixon, & Villar, J. Med. Chem.,1999, 42, 4695-4704. –Ajay, Bemis, & Murcko, J. Med. Chem., 1999, 42, 4942-4951. –Koehler & Villar, J. Comp. Chem., 2000, 21, 1145-1152. Information content/comparison –Brown & Martin, JCICS, 1997, 37, 1-9. –Jamois, Hassan, & Waldman, JCICS, 2000, 40, 63-70. –Briem & Lessel, Perspect. Drug Disc. Des., 2000, 20, 231-244.

10. Can We Construct Better Keys? Keybitsets optimized for substructure searching Keybitsets constructed to minimize memory/storage footprint But they work remarkably well already

11. But... bit-setting algorithm has untapped power algorithm defines ~3200 unique features algorithm allows keybit to be set for "N or more occurrences"

12. Find a Metric

13. Success Measure Defined by Briem and Lessel, Perspect. Drug Disc. Des., 20, 231 (2000). Modified to account for ties Evaluates the ability to differentiate classes of activity

14. Test Set 134 PAF antagonists 49 5-HT3 antagonists 49 TXA2 antagonists 40 ACE inhibitors 111 HMG-CoA reductase inhibitors 574 "random" MDDR compounds

15. Success Measure - Evaluation Calculate the 10 nearest neighbors for each "active" molecule Calculate the fraction of nearest neighbors in the same activity class as the target Allow for ties; expand the number of neighbors until the tie is broken

16. Success Measure

17. Starting Points... 166 keybitset 960 keybitset 3234 keybitset

18. Modifying the 960 Keybitset all the "singly mapped" keybits –726 keybitset all the 960 keybitset features, one feature per bit –1387 keybitset

19. Initial Success Measures

20. Optimization?

21. Results of Random Pruning

22. Intelligent Selection (Educated Guesswork) Differentiating compounds –active from inactive –active from other actives

23. Surprisal Analysis Surprisal = log ( probability 1 / probability 2) probability 1 = "active" molecules probability 2 = "inactive" molecules assume Poisson-distributed errors | Surprisal S/N | = | Surprisal /  surprisal |

24. Surprisals for 166 Keybitset

25. Surprisal S/N for 166 Keybitset

26. Surprisal Pruning

27. Success Measure vs. Surprisal S/N

28. Success Measure vs. # of Keys

30. Success Measures

32. What About Multiple Occurrences? Keybits can be set for >0, >1, >2,... occurrences of features Inclusion of multiple occurrence keybits enhances performance for substructure searching

33. Assembling a Composite Keybitset Construct keybitsets for >0, >1, >2, >3... occurrences Surprisal prune to the 2-sigma level Concatenate the resulting keybitsets –only add keybits for new features

34. Success Measure Success Measure increases until "7 or more" occurrences 1283 keybits in final set Final success measure = 71.26%

35. Genetic Algorithm We used the SUGAL genetic algorithm package –written by Dr. Andrew Hunter at University of Sunderland, UK Identification of local minima is straightforward Small keybitsets with good performance can be identified The global minimum is elusive

36. Final Success Measures

37. Conclusions Key performance can be substantially improved by reoptimizing keybitsets Key performance is not substantially improved for MDL keybitsets longer than ~500 bits

38. Acknowledgements use of SUGAL Genetic Algorithm Package, written by Dr. Andrew Hunter at University of Sunderland, UK correspondence with and MDDR extregs from Dr. Hans Briem, Boehringer Ingelheim Pharma KG