Screening a Virtual Compound

Space

ChemAxon Ltd.

Máramaros köz 3/a1037 Budapest Hungary

www.chemaxon.com

Szabolcs CsepregiFerenc CsizmadiaSzilárd DórántNóra MátéGyörgy PirokZsuzsanna SzabóJenő VargaMiklós Vargyas

Drug researchFinding or making a needle in the hay stack?

virtual screening

JChem Screen

advantages

disadvantages

• fast• hits are readily available for

in vitro screening

• limited number of available compounds

advantages

disadvantages

• practically unlimited virtual compound space

• structural novelty

• synthetic accessibility of virtual hits is a problem

de novo design

JChem AnalogMaker

Drug researchFinding or making a needle in the hay stack?

virtual screening

JChem Screen

advantages

disadvantages

• fast• hits are readily available for

in vitro screening

• limited number of available compounds

advantages

disadvantages

• practically unlimited virtual compound space

• structural novelty

• synthetic accessibility of virtual hits is a problem

de novo design

JChem AnalogMaker

Virtual ScreeningFind something similar to a fistful of needles

corporate database known actives structures found

Molecular similarity

How to tackle it?

)&()()(

)&(),(

yxByBxB

yxByxT

n

iii yxyxE

1

2),(

Sequences/vectors of bits, or numeric values that can be compared by distance functions, similarity metrics.

Quantitative assessment of similarity/dissimilarity of structures• need a numerically tractable form• molecular descriptors, fingerprints, structural keys

000000010000110100000010101000000000011000001000010000100000100001000101100100100101100110100111001111010000001100000001100010000100010100011101010000110000101000010011000010100000000100100000000110111001110111111010000010001000011011011000000010011010000001000101001101000100000000100000000100100000001001000010001010000100011100011101000100001011101100110110010010001101001100001000010111010011010101011111100001000001111110001000010000100010100001000101001111010100001000100000000100100000101001000010001010000001000100010100010100100000000000001010000010000100000100000000010001010001001100000000000000000001010000001000000000000000000001000101000101000000000000001010000100100000000001000000000000000101010101111100111110100000000000011010100011100100001100101000010001010001100001000001100000000001000100000011000000000110000000000001000000000100001000000000000010101000000001000001001000000100010100010100000000100000000000010000000000000100001000011000000100010000110001001010000001010010101110001000010000100010100001000111000101000100001000010011100100100000100011000000001010000101010100010100010100100000000000010010000010010100100100010000

queries

targets

hypothesis fingerprint

metric

target fingerprints

Virtual screening using fingerprints

Multiple query structures010001010001110101000011000010100001001100001010000000010010000000011011100111011111101000001000100001101101100000001001101000000100010100110100010000000010000000010010000000100100001000101000010111010011010101011111100001000001111110001000010000100010100000010001000101000101001000000000000010100000100001000001000000000100010100010100000000000000101000010010000000000100000000000000010101010111110011111010000000000001101010001110010000110010100001000101000110000100000110000000000100010000001100000000011000000000000100000000010000100000000000001010100000000100000100100000

0101110100110101010111111000010000011111100010000100001000101000

hits

Optimized virtual screening

22, 1),(

iiii yx

iiiyx

iiiasymmetricweighted

Euclidean yxwyxwyxD

i iiii iiii ii iiii i

i iiiasymmetricscaledTanimoto

yxsyxsyyxsx

yxsyxD

),min(),min(1),min(

),min(1),(,

1,0 asymmetry factor

Nis scaling factor

1,0 asymmetry factor

1,0iw weights

Parameterized metrics

How good is optimized virtual screening?β2-adrenoceptor antagonist

1

10

100

1000

10000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Number of Active Hits

Num

ber

of H

its

Tanimoto Euclidean Optimized Ideal

Drug researchFinding or making a needle in the hay stack?

virtual screening

JChem Screen

advantages

disadvantages

• fast• hits are readily available for

in vitro screening

• limited number of available compounds

advantages

disadvantages

• practically unlimited virtual compound space

• structural novelty

• synthetic accessibility of virtual hits is a problem

de novo design

JChem AnalogMaker

JChem AnalogMakerWorkflow

LeadCandidates

FragmentationExamples

Fragmentation rules

Amide

Original molecule Generated fragments

Fragment 1 amide 2

Fragment 2

amide 1

ester 1Ester

Fragment 3ester 2

FragmentationRECAP rules

1 = amide 2 =ester 3 = amine 4 = urea

5 = ether 6 = olefin 7 = quaternary nirogen 8 = aromatic N carbon

9 = lactam N carbon 10 = aromatic carbon – aromatic carbon 11 = sulphonamide

Xiao Qing Lewell, Duncan B. Judd, Stephen P. Watson, Michael M. Hann; RECAP – retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J. Chem. Inf. Comput. Sci. 1998, 38, 511–522

create building block library

generate pharmacophore hypothesis of active compounds

create several starting compounds by random combination of some building blocks

select parent structure

generate variants of parent

start

stop

convergence or end of optimization

JChem AnalogMakerGeneral algorithm

Variant generationExample: TOPAS modifier

G. Schneider et al, J. Comput.-Aided Mol. Design, 14(2000): 487-494G. Schneider et al, Angew. Chem. Int. Ed., 39(2000): 4130-4133

Drug researchFinding or making a needle in the hay stack?

virtual screening

JChem Screen

de novo design

JChem AnalogMaker

advantages

disadvantages

• fast• hits are readily available for

in vitro screening

• limited number of available compounds

advantages

disadvantages

• practically unlimited virtual compound space

• structural novelty

• synthetic accessibility of virtual hits is a problem

Drug researchFinding or making a needle in the hay stack?

virtual screening

JChem Screen

de novo design

JChem AnalogMaker

advantages

disadvantages

• fast• hits are readily available for

in vitro screening

• limited number of available compounds

advantages

disadvantages

• practically unlimited virtual compound space

• structural novelty

• synthetic accessibility of virtual hits is a problem

?

?

advantages

disadvantages

• practically unlimited virtual compound space

• structural novelty

• synthetic accessibility of virtual hits is a problem

de novo design

JChem AnalogMaker

virtual screening

JChem Screen

advantages

disadvantages

• fast• hits are readily available for

in vitro screening

• limited number of available compounds

Drug researchScreening a virtual compound space

advantages

disadvantages

• fast• virtual molecules are likely

to be synthetically available• practically infinite virtual

compound space• structural novelty

random virtual synthesis

JChem Synthesizer

Screening a virtual compound spaceSmart reactions

Generic (simple)• the equation describes the transformation only• few hundred generic reactions can form the

basic armory of a preparative chemist

Specific (complex)• chemo-, recognizes reactive and inactive

functional groups• regio-, "knows" directing rules• stereo-, inversion/retention

Customizable• to improve reaction model quality

Smart reactionsChemoselectivity

REACTIVITY: !match(ratom(3), "[#6][N,O,S:1][N,O,S]", 1)

Smart reactionsRegioselectivity

SELECTIVITY: -charge(ratom(1))TOLERANCE: 0.0045

Smart reaction libraryExampleBaeyer-Villiger ketone oxidation

SELECTIVITY: charge(ratom(2), "sigma")

JChem SynthesizerWorkflow

Smart reaction

library

Synthesizer

Virtual compound space

Availablechemicals

Screen Hits

Active

set1

Screen Hits

Active

setn

JChem Synthesizer exampleDopamine D2 actives

Active sets were kindly provided by Aureus Pharma within a research collaboration between Aureus and ChemAxon.

Virtual hits

similarity: 2D pharmacophore fingerprint, weighted Euclidean metric optimized for 20 random d2 actives

JChem Synthesizer example

JChem Synthesizer exampleBest virtual hits

9.88 9.82

9.53 9.73

JChem Synthesizer exampleSynthesis path

step 1

Knoevenagel-Doebner condensation

step 2

Baylis-Hillman vinyl alkylation

JChem Synthesizer example

step 3

Lawesson thiacarbonylation

JChem Synthesizer example

step 4

Dess-Martin alcohol oxidization

JChem Synthesizer example

Software and performance data

• virtual reactions: 500-1000 reactions/s• random synthesis: 10-20 structures/s• pharmacophore fingerprint generation: 100

structure/s (includes pharmacophore point perception)

• metric optimization: 57 sec (13 parameterized metrics, 20 structures in training set, 50 spikes)

• virtual screening: 7500 structure/s• pure Java

client: P4 1.6GHz, RH Linux, java 1.4.2database server: P4 2.4GHz, Windows XP, MySQL

JChem Synthesizer example

Acknowledgements

Modest von Korff, Matthias Steger (Axovan is now part of Actelion.)

François Petitet

Alex Allardyce ChemAxon

Jean-Michael Drancourt

Contact

Miklós Vargyas

mvargyas@chemaxon.hu

office: +36 1 453 2661mobile: +36 70 381 3205