Automatic Compound Design by Matched Molecular Pairs
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Automatic Compound Design by Matched Molecular Pairs Willem van HoornSenior Solutions ConsultantProfessional Services
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• Matched Molecular Pairs (MMPs)• Implementation in PP• Reaction Fingerprints• Using MMPs as automatic learning machine
Contents
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Ceci n’est pas une MMP
Sildenafil Vardenafil
Similarity = 0.55 / 0.98 (ECFP_4 / MDL public keys)
MMP: - Single change- Typically: 1 or 2 bond cleavage; replace R-group or template
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Recent AZ review
http://pubs.acs.org/doi/abs/10.1021/jm200452d
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MMP as predictor of activity
Classic QSAR with full molecule descriptors QSAR using MMP
DpIC50(m-Br to m-Cl-p-F) = -0.19
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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
What have the MMPs done for us?
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“Learning Machine” using MMPs
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Example of MMP learning machine
1 2 transformation applied to compound 3 should yield more attractive compound 4
4
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MMP in Pipeline Pilot
Components
Protocols
PP 8.5 CU1
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PP MMP algorithm based on GSK publication
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Test set: EGFR from ChEMBL
Ed Griffen et alJ Med Chem. 2011, 54, 7739-50
- ChEMBL version 11
- 4609 IC50 values
- 3581 compounds
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Generate MMPs and transformations
>90k MMPs in
<1 minute
Slow!
MMP output
MMP transformation
Full transformation
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DpIC50 distribution of transformations
90,343 MMPs yield 180,684 transformations (AB / BA)
10fold 100fold 1000fold etc
bioisosters
activity cliffsactivity cliffs
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MMP transformations vs. full reactions
Not specific enough, seen >>1 in data set but large stddev(DpIC50)
Too specific, seen once in dataset, DpIC50 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 DpIC50)
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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
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Reaction mapping is necessary
Only features describing reaction site
Mapped
All features, no information whether atom is in product or reactant
Unmapped
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Reaction direction matters
Reaction fingerprints are not identical A→ B ≠ B → A
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MMP transformation as rules
“Rule” = MMP transformation Effect = DpIC50
Context of MMP
transformation
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Tanimoto seach of MMP transformations
DpIC50 = 1.9
A single observation…
DpIC50 = 1.8
DpIC50 = 1.5
DpIC50 = 1.3
… becomes more believable when looking at similars
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Express significance as Bayesian probability
Bayesian model “Good” molecules: DpIC50 ≥ 1
Rank test set by likelihood transformation will yield
≥10fold increase in potency
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Bayes can predict MMP 10 fold increase
• RCFP_6 > RCFP_4
• RCFP_4 >> RCFP_2
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Random Model
Perfect Model
dActivity_class_increase_RCFP_2 Model
dActivity_class_increase_RCFP_4 Model
dActivity_class_increase_RCFP_6 Model
% of Samples
% A
ctive
s Ca
ptur
ed
Enrichment plots of test set
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Confidence vs. DpIC50
Bayesian score = confidence
DpIC50
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Semi-quantitative Bayesian predictions
• Multi-category Bayesian• Class = DpIC50 bin• RCFP_6
Compare:• Normalised Probability (default)• #Enrichment• #EstPGood• Prediction
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#EstPGood score smallest prediction error
22.5%22.5%
30.0% 19%
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MMP vs. Full molecule transformations
vs.
Modelling with mapped reactions works better (it should)
22.5% 30.0%
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• 80% training set– Generate MMP transformations– Learn classic regression model (PLS)– Learn Bayesian model from reaction fingerprints
MMP Idea Generator: Training
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• ~5.6 predictions per test set molecule• MMP pIC50 := mean (pIC50reactant + DpIC50transformation)
• RCFP pIC50 := mean (pIC50reactant + DpIC50predicted by Bayes)
MMP Idea Generator: Test
Runtime ~ 30 min
~34k transformations >6.5M design ideas
Test set
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QSAR by MMP
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QSAR by Bayes / RCFP_6
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SAR by MMP vs. SAR by PLSECFP_6 / phys property descriptors
MMP PLS
• MMP predictions nearly as good as PLS predictions
• Not 100% like with like comparison: fewer predictions for MMP
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Consensus MMP & PLS predictions
Consensus: 26 / 62
Found by PLS: 10 / 56
Found by MMP: 11 / 56
Red: top 5% by pIC50 (59)
Solid: top 10% (118) by MMP or PLS. Total = 174
12 / 1006
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• 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
Conclusions
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Spares
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MMP vs. Bayes/RCFP predictions