Matched Molecular Pairs (MMPs) are pairs of molecules that differ by a single structural transformation, which can be due to a chemical reaction but more often involves swapping one chemical group for another in a way that is not feasible in a single synthetic step. It is implicitly understood that in MMPs the static (common) part of the pair is significantly larger than the variable parts. MMPs are popular among medicinal chemists because the concept is closely related to how chemists think about a series of molecules: typically a series is defined as a static core with variable substitutions that each contribute to the overall properties of the molecule like potency, solubility, selectivity, etc. MMPs have been used to mine large sets of biological screening results to answer questions like “how much potency is gained by added a chloro atom in the para postion”. This analysis can be done at multiple levels, for instance all occurrences of the transformation, occurrences against a particular target or occurrences against a target family. For each transformation the average change in potency is recorded which can be used to make quantitative predictions. Suppose a pair of molecules were only the potency of one is known, but the other molecule is related to the first by a single transformation. The predicted potency of the second molecule is the potency of the first plus the average potency change associated with the transformation. Herein lies the power of MMPs compared to classic QSAR regression methods: not only can the potency of novel molecules be predicted but the transformations can be applied as idea generator to come up with reasonable ideas of what the novel molecule(s) should be. In the presentation it is shown how the above can be done using the new MMP algorithm in Pipeline Pilot 8.5 using publicly available datasets from ChEMBL. (Accelrys European Science Symposium, Brussels, June 2012)

1. Automatic Compound Design by
Matched Molecular Pairs
Willem van Hoorn
Senior Solutions Consultant
Professional Services

2. Contents
• Matched Molecular Pairs (MMPs)
• Implementation in PP
• Reaction Fingerprints
• Using MMPs as automatic learning machine

3. Ceci n’est pas une MMP
Similarity = 0.55 / 0.98 (ECFP_4 / MDL public keys)
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Sildenafil Vardenafil
MMP:
- Single change
- Typically: 1 or 2 bond cleavage; replace R-group or template

4. Recent AZ review
http://pubs.acs.org/doi/abs/10.1021/jm200452d

5. MMP as predictor of activity
Classic QSAR with full molecule descriptors QSAR using MMP
pIC50(m-Br to m-Cl-p-F) = -0.19

6. What have the MMPs done for us?
Classic QSAR / regression
• More generic, can predict >1 change
• Interpretability varies
MMPs
• Can only predict “one step away from known”
• Very interpretable
• Can answer “what to make next” challenge

7. “Learning Machine” using MMPs

8. Example of MMP learning machine
1  2 transformation applied to
4
compound 3 should yield more
attractive compound 4

9. MMP in Pipeline Pilot
Components
Protocols
PP 8.5 CU1

10. PP MMP algorithm based on GSK publication

11. Test set: EGFR from ChEMBL
- ChEMBL version 11
- 4609 IC50 values
- 3581 compounds
Ed Griffen et al
J Med Chem. 2011, 54, 7739-50

12. Generate MMPs and transformations
>90k Slow!
MMPs in
<1 minute
MMP output
Full transformation
MMP transformation

13. pIC50 distribution of transformations
90,343 MMPs yield 180,684 transformations (AB / BA)
bioisosters
activity cliffs activity cliffs
10fold 100fold 1000fold etc

14. MMP transformations vs. full reactions
Not specific enough, seen >>1 in Too specific, seen once in dataset,
data set but large stddev( pIC50) pIC50 statistics n=1
Would like to have something that describes
“reaction centre + nearby environment”
Would like increase confidence by looking at similar
MMP transformations (with similar pIC50)

15. PP reaction fingerprints: RCFP
• RCFP are similar to ECFP, atoms described by:
 Charge
 Hybridization
 Whether the atom is Reactant or Product
 Whether or not the atom is in the “Reaction Site”
• Need mapped reactions
PP 8.5

16. Reaction mapping is necessary
Mapped Unmapped
All features, no
Only features describing information whether
reaction site atom is in product or
reactant

17. Reaction direction matters
Reaction fingerprints are not identical A→ B ≠ B → A

18. MMP transformation as rules
Context of MMP
transformation
“Rule” = MMP transformation
Effect = pIC50

19. Tanimoto seach of MMP transformations
A single observation…
pIC50 = 1.3
pIC50 = 1.9
pIC50 = 1.5
… becomes more believable when looking at similars
pIC50 = 1.8

20. Express significance as Bayesian probability
Bayesian model
“Good” molecules:
pIC50 ≥ 1
Rank test set by likelihood transformation will yield
≥10fold increase in potency

21. Bayes can predict MMP 10 fold increase
Enrichment plots of test set
100%
90%
80%
70%
• RCFP_6 > RCFP_4
% Actives Captured
60%
• RCFP_4 >> RCFP_2
50%
40%
Random Model
30%
Perfect Model
20% dActivity_class_increase_RCFP_2 Model
dActivity_class_increase_RCFP_4 Model
10%
dActivity_class_increase_RCFP_6 Model
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% of Samples

22. Confidence vs. pIC50
pIC50
Bayesian score = confidence

23. Semi-quantitative Bayesian predictions
• Multi-category Bayesian
• Class = pIC50 bin
• RCFP_6
Compare:
• Normalised Probability (default)
• #Enrichment
• #EstPGood
• Prediction

24. #EstPGood score smallest prediction error
22.5%
22.5%
30.0% 19%

25. MMP vs. Full molecule transformations
vs.
22.5% 30.0%
Modelling with mapped reactions works better (it should)

26. MMP Idea Generator: Training
• 80% training set
– Generate MMP transformations
– Learn classic regression model (PLS)
– Learn Bayesian model from reaction fingerprints

27. MMP Idea Generator: Test
~34k transformations >6.5M design ideas
Test set
• ~5.6 predictions per test set molecule
• MMP pIC50 := mean (pIC50reactant + pIC50transformation)
• RCFP pIC50 := mean (pIC50reactant + pIC50predicted by Bayes)
Runtime ~ 30 min

28. QSAR by MMP

29. QSAR by Bayes / RCFP_6

30. SAR by MMP vs. SAR by PLS
ECFP_6 / phys property descriptors
MMP PLS
• MMP predictions nearly as good as PLS predictions
• Not 100% like with like comparison: fewer predictions for MMP

31. Consensus MMP & PLS predictions
Found by MMP: 11 / 56 Consensus: 26 / 62
Found by PLS: 10 / 56
12 / 1006
Red: top 5% by pIC50 (59)
Solid: top 10% (118) by MMP or PLS. Total = 174

32. Conclusions
• For one dataset it has been shown that
– MMP transformations can form basis of an
automatic “Learning Machine”
– Can select “significant rules”
– Consensus MMP/regresssion activity prediction
works better than individual predictions

34. Spares

35. MMP vs. Bayes/RCFP predictions