Transcript MDL Keys Revisited Joseph L. Durant Douglas R. Henry and James G. Nourse

MDL Keys Revisited
Joseph L. Durant, Burton A. Leland,
Douglas R. Henry and
James G. Nourse
MDL 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 “chemistmeaningful” 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 / ssurprisal |
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