Boosting discovery research productivity by generating novel digital ... - Merck Group · 2020. 7. 15. · Boosting discovery research productivity by generating novel digital components,

GLOBAL PHARMA R&D INFORMATICS CONGRESS

Lisbon, 30.11.2017

Friedrich Rippmann, Computational Chemistry & Biology

Boosting discovery research productivity by generatingnovel digital components, and integrating them into coherent workflows

Integration is key

(Digital) components to boost productivity

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4

Large virtual librariesonly the sky is the limit

Predictive modelspredict everything

Promote self servicebreak down hurdles

Integrate componentsand make them easy to use

Integration is key


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MASSIV – The creation of feasible novel chemical space

theoreticalfeasible

purchasabledrugs

103

1071015

1060

Type & number of moleculesAssuming 104 molecules are synthesized per year 100 billion years to get all feasible ones

We must find a smarter way to explore the chemical space

Virtual Compound spaces to feed Drug Discovery

Super MASSIV

MASSIV

BIG

SMALL

Title of Presentation | DD.MM.YYYY5

Exhaustive enumeration: 1.6 x 1011 molecules in GDB17 (Reymond et al. J. Chem. Inf. Model. 2012)

Virtual compound spaces

Merck AcceSSible InVentory

BUILDING BLOCKS

CHEMICAL REACTIONS LOOK-UP

Tailored libraries

MASSIV space

…

1020

in silico synthesis novel chemical matter

106

104 look-up space(105 per reference)

MASSIV applied to drug target XYZ

103

104

102

1.5 x 105

5.4 x1012MASSIV

(virtual space)

# o

fm

ole

cu

les

~200,000 eMoleculesBB118 encoded reactions

• MASSIV look-up

• cluster by reaction

• selection of results by reaction

• 3D overlay with reference structures

• MOCCA models (FUB) & expert selection

23 • synthesis & in vitro verification ongoing

Integration is key


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[email protected]





Pop Quiz:

Feedback: They all make use of “DeepLearning” aka “NeuralNetworks”

What do these techniques have in common? N

a. Self-driving car

b. Siri

c. Google Translator

d. Face recognition

Deep Learning: From Face Recognition to Drug Discovery

Hierarchical Feature Learning

Com

ple

tefa

ce

Edge

dete

cto

r

Facia

lfe

atu

res

Com

ple

tem

ole

cule

Substr

uctu

ral

ele

ment

Fin

gerp

rint

aka Deep Learning

Input: images Input: structures andbiological activity

Comprehensive Prediction of Kinase 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

Who contributed?

• Group of Prof. Hochreiter, Uni Linz (winner of the TOX21 Challenge)

Getting better drugs faster: Free Energy Perturbation @ Merck

Prerequisites

Protein structure(s)

Known binding mode

Assay

Target validation

Test on compoundswith known activity

Does FEP work for mytarget?

Production

Weekly ranking of newideas

Prioritize compoundsfor synthesis

Applied to 12 targetsso far

21 inhibitors used for validation

Integration is key


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Merck Online Computational Chemistry Analyzer

Components for boosting productivity

From virtual libraries to prediction of binding constants to synthesis ordering

422

449

673

# o

fm

ole

cu

les

• descriptor calculation

• removal of bad functional groups

• manual inspection & selection

• MOCCA Merck Online

Compchem Analyzer

• FEP calculations

synthesis request via online tool

70

30

6

Application of Predictive

models, based on Deep

Learning, and other Artificial

Intelligence methods

Accurate binding

constant prediction

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2

3

MASSIVMerck AcceSSible InVentory 1020

4


GPU computing is essential

Low-cost GPUs deliver

10 x 8 = 80 GPUs

Safer molecules, faster

Deep Learning

Molecular Dynamics


1. Seamless integration from idea to synthesized compound to assay result is crucial for productivity

2. All models (e.g. Regression-, Random Forest-, Deep Learning-based) generated in ONE coherent framework

3. Meaningful application of Deep Learning needs deep know-how

4. Free Energy Perturbation for binding constant prediction works, when it works (needs good X-ray complex structures)

5. GPU computing is essential

Conclusions

Integration is key


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Integrated & collaborative drug design

Integral prediction of all relevant

endpoints

Provide predictions in

integrated design environment

Levarageteamwork: make

it easy for all disciplines to

contribute theircomplementary

expertise

Integrated collaborative Compound Design

X-ray

CompChem


• Seamless integration from idea to synthesized compound to assay result is crucial for productivity

• All models (e.g. Regression-, Random Forest-, Deep Learning-based) generated in ONE coherent framework

• Meaningful application of Deep Learning needs deep know-how

• Free Energy Perturbation for binding constant prediction works, when it works (needs good X-ray complex structures)

• GPU computing is essential

• All Research data accessible to all scientists; all researchers independent of discipline on eye level

• External services easily accessible to all who need them (get rid of bureaucracy, but monitor what is done)

• Computational chemists are as responsible in ordering synthesis of compounds as are medicinal chemists

Conclusions

Documents

Boosting discovery research productivity by generating novel digital ... - Merck Group · 2020. 7. 15. · Boosting discovery research productivity by generating novel digital components,