MDL Keys Revisited

1. MDL Keys Revisited Joseph L. Durant, Burton A. Leland, Douglas R. Henry and James G. NourseMDL 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 / ssurprisal |

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

29. Success Measure vs. # of Keys

30. Success Measures

31. 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