MDL Keys Revisited

Joseph L. Durant, Burton A. Leland, Douglas R. Henry and

James G. NourseMDL Information Systems

Overview

• What are MDL Keys?

• Constructing better keys– metrics– optimization by "educated guesswork"– optimization by Genetic Algorithms

• Conclusions

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

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

MDL Keys - Standard Implementation

• MDL’s SSKeys are encountered in 2 flavors:– a 960 keybitset– a 166 keybitset (Subset or User Keys)

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

The 166 Keybitset

• Originally created to embody an earlier MDL keybitset

• Largely correspond to “chemist-meaningful” features

166 Keybitset Definitions

1 - isotope

2 - 103<atomic number<256

.

84 - NH2

85 - CN(C)C

86 - CH2QCH2

.

165 - ring

166 - fragments

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.

Can We Construct Better Keys?

• Keybitsets optimized for substructure searching

• Keybitsets constructed to minimize memory/storage footprint

• But they work remarkably well already

But...

• bit-setting algorithm has untapped power

• algorithm defines ~3200 unique features

• algorithm allows keybit to be set for "N or more occurrences"

Find a Metric

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

Test Set

• 134 PAF antagonists

• 49 5-HT3 antagonists

• 49 TXA2 antagonists

• 40 ACE inhibitors

• 111 HMG-CoA reductase inhibitors

• 574 "random" MDDR compounds

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

Success Measure

Starting Points...

• 166 keybitset

• 960 keybitset

• 3234 keybitset

Modifying the 960 Keybitset

• all the "singly mapped" keybits – 726 keybitset

• all the 960 keybitset features, one feature per bit– 1387 keybitset

Initial Success Measures

Optimization?

Results of Random Pruning

Intelligent Selection(Educated Guesswork)

• Differentiating compounds– active from inactive– active from other actives

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 |

Surprisals for 166 Keybitset

Surprisal S/N for 166 Keybitset

Surprisal Pruning

Success Measure vs. Surprisal S/N

Success Measure vs. # of Keys

Success Measure vs. # of Keys

Success Measures

Success Measures

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

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

Success Measure

• Success Measure increases until "7 or more" occurrences

• 1283 keybits in final set

• Final success measure = 71.26%

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

Final Success Measures

Conclusions

• Key performance can be substantially improved by reoptimizing keybitsets

• Key performance is not substantially improved for MDL keybitsets longer than ~500 bits

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