Friedrich RippmannComputational Chemistry & Biology, Merck KGaA, Darmstadt, Germany

AI-PI, San Francisco, 27 February 2019

Turning methodological progress in operational benefit

Practical impact of AI on drug discovery

Merck KGaA, Darmstadt, Germany is the oldest pharmaceutical and chemical company worldwide

1668 Friedrich Jacob Merck (1621-1678)

buys the Angel Pharmacy (Engel-Apotheke)

1827 Emanuel Merck (1794-1855) starts

production on an industrial scale

North AmericaRest of the world

1914 WWI

friedrich.rippmann@merckgroup.com

33

Deep Learning & Machine Learning are at “Peak” Hype

See : Gartner Hype Cycle 2017, gartner.com

So: where isthe benefit???

Text Miningfor Competitive

Intelligence

HTS evaluation

Potential AI contributions to the Drug Discovery Process

HTS Image Analysis

Side effect prediction

HTS deconvolution

Cpd discovery & optimization without HTS

Cpd optimization

Pre-HTS supplementation byVirtual Screening

Drug/cpd repositioning/repurposing

Computer-aided synthesis

5

cryoEM and tomographyimage analysis

Patient selection

6

Main benefit, so far, is in Predictive Models, based on advanced Machine Learning

Applications are numerous (next slides)…

Where is the benefit of AI?????

Predictive modelsmust be easy togenerate

Deep Learning applied in Chemoinformatics

Collaboration is key

• Research collaboration with leader in the field of deep learning (Prof. Sepp Hochreiter, Uni Linz, winner of Tox21 challenge)

Neural Networkimage recognition Activity prediction

Deep learning technology

Machinery known from image recogniton

Applied to drug discovery

Generation of Predictive Models: example kinase models to predict selectivity

Achievements so far

• 277 novel kinase models generated• Data basis: 4,800 compounds measured in 277 kinase assays

high predictivity

goodpredictivity

reasonable predictivity

36 122 200

What’s in for the chemist?

• Prediction of kinase selectivity for newly designed molecules

Benefit: in silico profiling of compounds & cpd. ideas

Neural Networks & Hyperparameters

NN-Architectures

• Layer-Type

•Number of Layers

•Neurons per Layer

•Activation-Functions

Training-Parameters

•Optimiser

• Learning-Rate

•Weight-Decay

•Batch-Size

• Loss-Function

• …

Hyperparameters

Millions of unique

combinations possible

Genetic Algorithm for Hyperparameter Optimisation

5.1

5.2

4

1 2 3

Influence of Hyperparameters

1_activation (344)

First hidden

layer

Activation-function

of this layer

Number of

contributing pairs

Contributing pairs only differ by

the shown parameter

Boxplots are based on the

absolute difference of both inner-

kappa values of all contributing

pairs

User-Interface

Benefit: semi-automatic generation of high-quality Machine Learning models

Predictionsmust be easy to access

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MOCCA: Merck Online Computational Chemistry Analyzer

16

Available models

Global models PhysChem Kinase selectivity model

Project-specific models

Great variety, e.g.

- local hERG models

- AOX

- Biliary excretion (literature)

- Various target-specific activity models

- Nucleus permeability

- .. many more

Benefit: predicted bad compounds will NOT be made

17

Reduction of assay requests (2 months after discontinuation ofcomprehensive assaying of ALL newly synthesized compounds)

0

10

20

30

40

50

60

70

80

90

100

log P log D Ksol

-86% -30% -19%

There is potential for further reductions (especially for kinetic solubility)

• Biological assays have sizeable experimental variability

• Re-testing triggered automatically if reliable in silico prediction differs from experimental result

• Leads to better data quality, and subsequently to better models

• Virtuous circle: in silico models improve in vitro models and vice versa

Automatic re-testing improves data quality

Benefit: improvement of data quality & better models

Applied In Vitro ToxicologyBuilding In Silico Models to Trigger Retesting: A Strategy on Howto Use Predictive Models to Identify Potentially Incorrect In Vitro Intrinsic Clearance ResultsFabian P. Steinmetz, Carl Petersson, Ugo Zanelli, Paul CzodrowskiPublishedOnline:9Nov2018https://doi.org/10.1089/aivt.2018.0018

Supportingcomplexanalyses by AI=> achievingobjectiveness

Big Data waiting to be analyzed by AI

HTSEval: millions of molecules, thousands of actives, a lot of additional information

Current status: Standardized, half-automated analysis of HTS runs

Future status:Fully automated analysis of HTS runs (achieving OBJECTIVENESS) – but final selection remains with the chemist!

Benefit: objective series generation and prioritization

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Expert Systems supported by AI

Simulation of (human) Dose and Clearance

Assessment of PK parameters in early project phases(HD, HO, LO, ED)

automated data retrieval

standardized (mechanistic) estimation of missing data

(human) Clearance extrapolation with visual confidence check

easy comparison of compound profiles

interactive parameter modeling

“AI inside” in a complex Expert System

Hugecompoundlibraries basedon feasiblereactions

ELAB

REAXYS

70008 571423

21459

23%

Merck AcceSSible InVentory

BUILDING BLOCKS

CHEMICAL REACTIONS LOOK-UP

Tailored libraries

MASSIV space

look-up space(1020 per reference)

…

1020

in silico synthesis

0

200000

400000

600000

800000

1000000

1200000

1400000

BROAD MEDIUM NARROW BROAD MEDIUM NARROW BROAD MEDIUM NARROW

ELAB REAXYS

CLASSCODES

total

CLASSCODES

singular

CLASSCODES

unique

# o

fC

LA

SS

CO

DES

novel chemical matter

106

104

1020

Benefit: access to large novel compound spaces

AccuratePrediction ofbindingconstants

Technological advances allow for application of FEP in industry setting

GPU is 100x times faster than CPU

In-house cluster with 110 GPUs

GPU

FEP is based on molecular dynamics simulations with a detailed energy function, full flexibility and explicit solvent.

FEP prediction: Large-scale prospective benchmarking

Predictive performance evaluated over 10 validated projects

FEP validation prediction

RMSE [kcal/mol] 1.05 1.83

R2 0.78 0.35

% within 1 kcal 69 47

% within 2 kcal 93 7426

FEP+ in drug discovery at Merck

10 validated targets

>25,000 perturbations

5,000 final predictions

360 compounds synthesized

25 evaluated targets

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Broad application across multiple targets and series

2828

Integration of AI Tools for Compound Optimization

Merck Example

MASSIV: Enumeration of synthetically

accessible chemical space

MOCCA

FEP calculations

CHEMATICA/SYNTHIA

MOCCA: Application of predictive

models based on Deep Learning

FEP: Binding constant

prediction

12

3

Manual inspection

Virtual Screening as 1st filter

MASSIV

4 CHEMATICA: Retrosynthetic

evaluation & prioritization

Benefit: gaining speed in cpd. optimization

Mega trendSharing

activity data and corresponding models remain under respective owner control

assays from

partner 2

assays from

partner 3

assays from

partner 1

com

pounds

Federated and privacy-preserving learningmulti-task learning across partners

IMI2 Call 14

What‘s in forNBEs???

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Antibody Hit Discovery

From Diversity to Candidate Hits

Fu

nctio

nality

Affin

ity

Com

petitio

n/

MoA

Div

ersity

& F

un

ctio

nality

Bin

din

g &

Sele

ctiv

ity

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NCE Hit Optimization

Diversity and Potency

Higher Potency

Hit

Nearby Chemical Space Distant

Chemical Space

Theoretical Chemical

Space

High

Low

LowNon-binders

No pathwayToo unstable

High

Too large

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Antibody Affinity Maturation

Diversity and Affinity

Higher Affinity

Hit 1

Nearby Sequence

Space

High

Low

Distant Sequence

Space

High

LowNon-binders

Theoretical Sequence

SpaceToo large

Non-canonicalToo

unstable

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• Deep sequencing for heavy & light genes

• In silico pipeline for antibody V-gene annotation &

clustering by sequence

Capturing diversity to improve affinity

Is there a higher affinity variant in the same cluster as the reference hit?

IGHV3-30

Reference 1

Reference 2

IGHV4-31

Cluster TypeVH

variantVL

variant

VHi:VLjvariant pairs

XHybridoma

Fusion9 17 152

Y Spleen+LN 66 20 1320

Z Spleen+LN 156 37 5772

testable

test of patience

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Capturing diversity to improve affinity

The modular nature of antibodies results in additive effects on affinity

5x2x

5x2x

VH variant 5 fold better VL variant 2 fold better VH/VL variant

10 fold better

VH

VL

Vanita D. Sood - CONFIDENTIAL | 15.03.201837

The additivity model shows good correlation between calculated and measured affinities

• Experimental determination of kd of native

pairings

Cluster TypeVH

variantVL

variant

VHi:VLjvariant pairs

VHi:VLNVHN:VLjnative pairs

Y Spleen+LN 66 20 1320 85

Z Spleen+LN 156 37 5772 192 VHN:VLN

VHi :VLN

VHN:VLN

VLN : VLjPredicted Fold Affinity

Change of VHi:VLj

Anti-Y Anti-Z

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Capturing diversity to improve affinity

The additivity model applied to nearby sequence space

testable

testable

Cluster i

Cluster j

Cluster TypeVH

variantVL

variant

VHi:VLjvariant pairs

VHi:VLNVHN:VLjnative pairs

Y Spleen+LN 66 20 1320 85

Z Spleen+LN 156 37 5772 192

VH VL

Predictive Models for affinity using Machine Learning

Output prediction of binding affinity

DATA

TRAININGSET

FEATURE SELECTION

TRAIN MODEL

TESTMODEL

IMPROVEMODEL

MODEL

BINDING FOLDreference A K G V K A R L K E A S I K G Y YES 1

MUT 1 A P G V K A R L K E A R I K G I NO -1

MUT 2 M R G V K A R L K E A S I K G I NO -2

MUT 3 V P G V K A R L K E A S I K G Y YES 3

MUT 4 V R G V K A R L K E A L I K G I YES 2

MUT 5 V R G V K A R P K E A L I K G I YES(NO) 2(-1)

MUT 6 A R G V K A R L K E A L I K G Y YES 1

MUT 7 V P G V K A R P K E A S I K G I YES 2

MUT 8 M R G V K A R L K E A S R K G S NO -1

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Predictive Models for affinity using Machine Learning

Predict affinity class (better/worse) with accuracy & specificity

Binary Response:Anti-Y Binary Response:Anti-Z

BETTER WORSETOTAL COUNT

BETTER 94% 6% 250

WORSE 9% 91% 354

TRU

E C

LASS

PREDICTED CLASS

BETTER WORSETOTAL

COUNT

BETTER 12.5% 87.5% 88

WORSE 1% 99% 721

PREDICTED CLASS

Anti-Y Anti-Z

Sensitivity (TP/PV) 0.89 0.65

Specificity 0.96 0.90

Precision (TP/RP) 0.94 0.13

Accuracy 0.92 0.89

TRU

E C

LASS

But sensitivity & precision are not good with unbalanced training set

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Predictive Models for affinity using Machine Learning

Predict affinity classes (fold change bins) with accuracy & specificity

kd > 20x kd in [10x,20x] kd in [5x,10x] kd in [-5x,5x] kd < 5xTOTAL COUNT

RECALL

kd > 20x83.33% 0.00% 16.67% 0.00% 0.00% 6

83%

kd in [10x,20x]9.09% 81.82% 9.09% 0.00% 0.00% 11

82%

kd in [5x,10x]0.00% 0.00% 48.48% 51.52% 0.00% 33

48%

kd in [-5x,5x]0.00% 0.79% 1.06% 95.77% 2.38% 378 96%

kd <5x0.00% 0.00% 0.00% 20.45% 79.55% 176 76%

PRECISION 83% 75% 72% 87% 93%

TRU

E C

LASS

PREDICTED CLASS

Overall Accuracy:88% (anti-Y)

41Benefit: gaining speed in antibody optimization

Acknowledgements

The Team

An Qi

Yves FomekongNanfack

Qingyong Ji

David Nannemann

John Wesolowski

Jinyang Zhang

Youbin Wang

Shruti Pratapa

Jukka Konola

Xiubin Gu

Maria Soloviev

Xinyan Zhao

Christel Iffland

Mireille Krier

Tim Knehans

Vanita Sood

Fabian Steinmetz

Paul Czodrowski

Wolf-Guido Bolick

Christina Schindler

Thomas Clarke

Jim Yang

Alex Rolfe

Discovery Technologies Research Bioinformatics

1 2

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Questions?

Simulation done with NMSim/RCNMA; Gohlke, Rippmann et al.