Automatic Compound Design by Matched Molecular Pairs Willem van HoornSenior Solutions ConsultantProfessional Services

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

Contents

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

Recent AZ review

http://pubs.acs.org/doi/abs/10.1021/jm200452d

MMP as predictor of activity

Classic QSAR with full molecule descriptors QSAR using MMP

DpIC50(m-Br to m-Cl-p-F) = -0.19

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?

“Learning Machine” using MMPs

Example of MMP learning machine

1 2 transformation applied to compound 3 should yield more attractive compound 4

4

MMP in Pipeline Pilot

Components

Protocols

PP 8.5 CU1

PP MMP algorithm based on GSK publication

Test set: EGFR from ChEMBL

Ed Griffen et alJ Med Chem. 2011, 54, 7739-50

- ChEMBL version 11

- 4609 IC50 values

- 3581 compounds

Generate MMPs and transformations

>90k MMPs in

<1 minute

Slow!

MMP output

MMP transformation

Full transformation

DpIC50 distribution of transformations

90,343 MMPs yield 180,684 transformations (AB / BA)

10fold 100fold 1000fold etc

bioisosters

activity cliffsactivity cliffs

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)

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

Reaction mapping is necessary

Only features describing reaction site

Mapped

All features, no information whether atom is in product or reactant

Unmapped

Reaction direction matters

Reaction fingerprints are not identical A→ B ≠ B → A

MMP transformation as rules

“Rule” = MMP transformation Effect = DpIC50

Context of MMP

transformation

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

Express significance as Bayesian probability

Bayesian model “Good” molecules: DpIC50 ≥ 1

Rank test set by likelihood transformation will yield

≥10fold increase in potency

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

Confidence vs. DpIC50

Bayesian score = confidence

DpIC50

Semi-quantitative Bayesian predictions

• Multi-category Bayesian• Class = DpIC50 bin• RCFP_6

Compare:• Normalised Probability (default)• #Enrichment• #EstPGood• Prediction

#EstPGood score smallest prediction error

22.5%22.5%

30.0% 19%

MMP vs. Full molecule transformations

vs.

Modelling with mapped reactions works better (it should)

22.5% 30.0%

• 80% training set– Generate MMP transformations– Learn classic regression model (PLS)– Learn Bayesian model from reaction fingerprints

MMP Idea Generator: Training

• ~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

QSAR by MMP

QSAR by Bayes / RCFP_6

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

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

• 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

Spares

MMP vs. Bayes/RCFP predictions