Pharmacophores in Chemoinformatics:Pharmacophores in Chemoinformatics:

1. Pharmacophore Patterns & Topological 1. Pharmacophore Patterns & Topological FingerprintsFingerprints

Dragos HorvathDragos Horvath

Laboratoire d’InfoChimieLaboratoire d’InfoChimie

UMR 7177 CNRS – Université de StrasbourgUMR 7177 CNRS – Université de Strasbourg

horvath@chimie.u-strasbg.frhorvath@chimie.u-strasbg.fr

The Pharmacophore Way of Life – A Medicinal The Pharmacophore Way of Life – A Medicinal Chemist’s DreamChemist’s Dream

• (Bio)Molecular Recognition is based on ligand-site interactions of extremely complicated nature– Understanding them requires a solid knowledge of statistical

physics and, therefore, of higher maths…

– But medicinal chemists hate maths… so they developed a simplified rule set to rationalize ligand binding.

• Functional groups of similar physicochemical behavior represent pharmacophore types: – Hydrophobic, Aromatic, Hydrogen Bond (HB) donors, Cations,

HB Acceptors, Anions.

– Now, we just need to know how each of the six types interacts with the site… welcome to the “pharmacophore” paradigm, farewell higher maths (for the moment, at least)

The Interaction Saga: (1) van der Waals The Interaction Saga: (1) van der Waals InteractionsInteractions

• Atoms are more or less hard spheres – squeezing them against each other causes a sharp rise in energy:– Erep=Aijd-12

• At distances larger than the sum of their « van der Waals spheres », an attractive term due to dipole-induced dipole interactions (London dispersion term) is predominant…– Eatt= - Bijd-6

The Interaction Saga: (2) Electrostatics & The Interaction Saga: (2) Electrostatics & SolvationSolvation

• Coulomb charge-charge interactions are easy to compute, once the partial charges Qk are assigned on the atoms…

– ECoul=QiQj/4d

• … and the solvent molecules are explicitly modeled – accountig for all the possible solvation shell structures, in order to estimate a solvation free energy.

• Alternatively, a continuum solvent model may be employed.

pi

ti

ui

vi

BEi;i

QiQk

BEk;k

pk

tk

npnt

np

neglected!

Eti

Epi

i

0 = Ep.np pi 1- ext

int

k

0 = Ep.np pk 1- ext

int

D. Horvath et al., J. Chem. Phys. 104, 6679 (1996)

The Interaction Saga: (2bis) The Hydrophobic The Interaction Saga: (2bis) The Hydrophobic EffectEffect

• The mysterious force that separates grease and water is not due to grease-grease van der Waals interactions being stronger than grease-water attraction!

• It is not of electrostatic nature either, because greasy alkyl chains have no charges!

• Actually, it’s not a force at all, but the consequence of the drift towards a more probable state of matter (?!)

• For practical purposes, however, it makes sense to believe that hydrophobes « attract » each other – for making hydrophobic contacts significantly improves binding affinity!

Physical Chemistry For Dummies: The RulesPhysical Chemistry For Dummies: The Rules

• Hydrophobes make favorable contacts with other hydrophobes (we do not want to know why!). Assume strenght proportional to the buried hydrophobic area.

• Hydrophobes in close contact to polar groups cause frustration, for they chase away the water molecules favorably solvating the latter and offer no substitute interactions

• Hydrogen bond donors seek to pair with acceptors, so that they may reestablish the water hydrogen bonds they lost

• Cations seek to pair with anions and avoid hydrophobes.• Shape is of paramount importance: groups of a same kind

may replace each other if they are shaped likely

BioIsoSteres – Equivalent Functional GroupsBioIsoSteres – Equivalent Functional Groups

• Wikipedia: bioisosteres are substituents or groups with similar physical or chemical properties that impart similar biological properties to a chemical compound

Pharmacophore PatternsPharmacophore Patterns

• The pharmacophore pattern of a molecule characterizes the relative arrangement of all its pharmacophore types– What pharmacophore types are represented?– How are they arranged (spatially, topologically) with

respect to each other ?– How can these aspects be captured numerically to yield

molecular descriptors of the pharmacophore pattern?

• Note: Pharmacophore patterns are essentially 3D. Since geometry is determined by connectivity, 2D “pharmacophore patterns” also make sense!

Exploiting Exploiting ppharmacophore harmacophore ppatternsatterns……

• N-dimensional vector D(M)=[D1(M), D2(M), …,DN(M)]; each Di encodes an element of the pharmacophore pattern– Allows meaningful quantitative definitions of molecular

similarity: • Neighborhood Behavior: Similar molecules - characterized by covariant

vectors - are likely to display similar biological properties

• As chemists do not easily perceive the pharmacophore pattern, such covariance may reveal hidden but real molecular relatedness…

– May serve as starting point for searching a binding pharmacophore – the subset of features that really participate in binding to a receptor

• Machine learning to select those elements Di that are systematically present in actives, but not in inactives of a molecular learning set!

Some Some eexamples of "xamples of "hhidden idden ssimilarity"imilarity"

0

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A1h

Alpha1

Alpha2

Beta1h

AT

1hB

ZD

cB

omb

B2h

CC

KA

hD

1hD

2hD

aUpt

ET

Ah

Galan

H1c

ML1

M1h

M3h

NK

1hN

PY

Muh

5HT

1Ah

5HT

1D5H

T2ch

5HT

3h5H

T6h

5HT

Upt

Sigm

a1V

1Ah

K-A

TP

Cl

CatB

Elast

PD

EII

PD

EIV

PK

CE

GF

-TK

PK

55fynH

IVP

NE

UP

Th

IL-8M

AP

kinC

GR

P

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NI N

N

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NON

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I

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O

NCl

O

H

Tricentric Pharmacophore Fingerprints: Tricentric Pharmacophore Fingerprints: monitoring feature amonitoring feature arrangementrrangement

• Topological: the distance between two features equals the (minimal) number of chemical bonds between them

N

N

O

N

Cl

99 4

11

• Spatial: if stable conformers are known, use the distance in Ǻ between two features

Example: Example: Binary Pharmacophore TriBinary Pharmacophore Tripletsplets

33 33

33

33

66

77

44

33 44

44

33 55

Hp3-H

p3-Hp3

Hp3-H

p3-Hp3

Hp3-H

p3-Hp4

Hp3-H

p3-Hp4

Hp3-H

p3-Hp5

Hp3-H

p3-Hp5

…… Ar4-H

p3-Hp4

Ar4-H

p3-Hp4

Ar4-H

p3-Hp5

Ar4-H

p3-Hp5

…… …… …… …… Hp7-A

r4-PC6

Hp7-A

r4-PC6

……Hp3-H

A5-A

r5

Hp3-H

A5-A

r555

55 33

0 0 0 … 0 0 … … 1 … … … 0 … … 0 …

Basis Basis TripletsTriplets::• all possible feature combinationsall possible feature combinations• at a given series of distances…at a given series of distances…

Hp4-H

A5-A

r5

Hp4-H

A5-A

r5

55

55 44

??

Pickett, Mason & McLay, J. Chem. Inf. Comp. Sci. 36:1214-1223 (1996)

………… ……

First key improvement: First key improvement: Fuzzy Fuzzy mapping of mapping of atom triplets onto basis triplets in 2D-FPTatom triplets onto basis triplets in 2D-FPT

33 33

33

44

66

77

44

33 44

55

55 33

0 0 0 … 0 0 … +6 … … +3 … … … … 0 …

55

55 44

Hp3-H

p3-Hp3

Hp3-H

p3-Hp3

Hp3-H

p3-Hp4

Hp3-H

p3-Hp4

Hp3-H

p3-Hp5

Hp3-H

p3-Hp5

…… Ar4-H

p3-Hp4

Ar4-H

p3-Hp4

Ar4-H

p3-Hp5

Ar4-H

p3-Hp5

…… ………… …… Hp7-A

r4-PC6

Hp7-A

r4-PC6

……Hp3-H

A5-A

r5

Hp3-H

A5-A

r5

Hp4-H

A5-A

r5

Hp4-H

A5-A

r5

………… ……

Di(m) = total occupancy of basis triplet i in molecule m.

Combinatorial enumeration of basisCombinatorial enumeration of basis tripletstriplets• Example: there are 36796 basis triplets, verifying triangle

inequalities, when considering 6 pharmacophore types and 11 edge lenghts between Emin=3 to Emax=13 with an increment of Estep=1: (3, 4, 5,…13)– Canonical representation: T1d23-T2d13-T3d12 with T3≥T2≥T1

(alphabetically).

44

66

77

Hp7-Ar4-PC6

Ar4-Hp7-PC6

– Out of two corners of a same type, priority is given to the one opposed to the shorter edge.

44

66

77

Ar4-Hp7-Hp6

Ar5-Hp6-Hp7

TriTripletplet matching pmatching procedurerocedure

• The triplet matching score represents the optimal degree of pharmacophore field overlap:– if corner k of the triplet is of pharmacophore type T, e.g. F(k,T)=1,

then it contributes to the total pharmacophore field of type T, observed at a point P of the plane:

)exp(),()(2

,

3

1Pk

kTTdTkFP

Horvath, D. ComPharm pp. 395-439; in "QSPR /QSAR Studies by Molecular Descriptors", Diudea, M., Editor, Nova Science Publishers, Inc., New York, 2001

Control parameters for tControl parameters for tririplet enumerationplet enumeration & & mmatchingatching in two 2D-FPT versions. in two 2D-FPT versions.

Parameter Description FPT-1 FPT-2

Emin Minimal Edge Length of basis triangles (number of bonds between two pharmacophore types)

2 4

Emax Maximal Triangle Edge Length of basis triangles 12 15

Estep Edge length increment for enumeration of basis triangles 2 2

e Edge length excess parameter: in a molecule, triplets with edge length > Emax+e are ignored

0 2

Maximal edge length discrepancy tolerated when attempting to overlay a molecular triplet atop of a basis triangle.

2 2

Hp = Ar

Gaussian fuzziness parameter for apolar (Hydrophobic and Aromatic) types

0.6 0.9

PC = NC

Gaussian fuzziness parameter for charged (Positive and Negative Charge) types

0.6 0.8

HA = HD

Gaussian fuzziness parameter for polar (Hydrogen bond Donor and Acceptor) types

0.6 0.7

l Aromatic-Hydrophobic interchangeability level 0.6 0.5

Number of basis triplets at given setup 4494 7155

Second key improvement: Second key improvement: Proteolytic Proteolytic equilibrium dependence of 2D-FPTequilibrium dependence of 2D-FPT

Ar5-N

C5-

PC8

Ar5-N

C5-

PC8

Ar8-N

C8-

PC8

Ar8-N

C8-

PC8

?12%

88%

Some ‘activity cliffs’ in Some ‘activity cliffs’ in rule-based descriptor rule-based descriptor spacespace are smoothed out in are smoothed out in 2D-FPT-space2D-FPT-space

•Neutral

•Cation

•Neutral

•Anion

•Neutral

• 90%C

ation

•Neutral

• 50%C

ation

•Neutral

•Anion •Neutral

•Neutral

•Neu

tral

• 40%

Cat

ion

•Neu

tral

• 70%

Cat

ion

Best Matching Candidates

Pharmacophore Pattern-Based Similarity Pharmacophore Pattern-Based Similarity Queries: Lead Hopping!Queries: Lead Hopping!

PharmacophoreHypothesis

AutomatedFingerprintMatching...

ReferenceFingerprint

Nearest Neighbors

Superposition-based Similarity Scoring

Potential Pharmacophore Fingerprint Library

?Docking

Some Some eexamples of "xamples of "hhidden idden ssimilarity"imilarity"

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A1h

Alpha1

Alpha2

Beta1h

AT

1hB

ZD

cB

omb

B2h

CC

KA

hD

1hD

2hD

aUpt

ET

Ah

Galan

H1c

ML1

M1h

M3h

NK

1hN

PY

Muh

5HT

1Ah

5HT

1D5H

T2ch

5HT

3h5H

T6h

5HT

Upt

Sigm

a1V

1Ah

K-A

TP

Cl

CatB

Elast

PD

EII

PD

EIV

PK

CE

GF

-TK

PK

55fynH

IVP

NE

UP

Th

IL-8M

AP

kinC

GR

P

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NI N

N

S

Br

H

N

NON

Cl

Cl

I

N

N

N

N

N

O

NCl

O

H

Successful Virtual Screening SimulationsSuccessful Virtual Screening Simulations

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Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (O PT 3) Confirm ed Inactives (O PT3)

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Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (O PT 3) Confirm ed Inactives (O PT3)Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (FPT -2) Confirm ed Inactives (FPT-2)

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Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (O PT 3) Confirm ed Inactives (O PT3)Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (FPT -2) Confirm ed Inactives (FPT-2)

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D2

TK

Successful QSAR model construction with 2D-Successful QSAR model construction with 2D-FPTFPT: predicting c-Met TK activity: predicting c-Met TK activity

4

4.5

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4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9

Calculated pIC50

Exp

erim

enta

l pI

C50

.

Learning Set Compounds Validation Set Compounds

25 variables entering nonlinear model153 molecules for training: RMSE=0.4 (log units), R2=0.8240 molecules for validation: RMSE=0.8 (log units), R2=0.538 validation molecules out of 40 mispredicted by more than 1 log

What more could be done?What more could be done?

• 3D FPT version under study

– does it pay off to generate conformers? How many would you need to get better results than with 2D-FPT? What’s the best conformational sampler to use?

• Accessibility-weighted fingerprints?

– class to return (topological and/or 3D) estimate of the solvent-accessible fraction of an atom?

• Tautomer-dependent fingerprints?

– if tautomers and their percentage were enumerated like any other microspecies…

THE END

Pharmacophore HypothesesPharmacophore Hypotheses

(A): From individual Active Leads: 2D/3D• ALL features in the Lead assumed relevant for binding

(B): Consensus hypotheses from set of Leads: 2D/3D• Ignore features that can be deleted without losing activity

(C): Site-Ligand interaction models: 3D*• Select Ligand features shown to interact with the site in the

3D X-ray structure of the site-ligand complex.

(D): Active Site filling models: 3D*• Design a pharmacophoric feature distribution complemen-

tary to the groups available in the active site* In these cases, docking may be performed starting from pharmacophore –based

overlays

ComPharm Overlay…ComPharm Overlay…

- chosen conformer of the reference

- chosen conformer of the candidate

- pair of matching atoms

- 3 Euler angles- mirroring toggle

GA-controlledoverlay optimization

ComPharm Pharmacophoric FieldsComPharm Pharmacophoric Fields

• A descriptor of the nature of the molecule’s pharmacophoric neigh-borhood “seen” by every reference atom, assuming an optimal overlay of the molecule on the reference...

Pharmacophoric FeaturesAlk. Aro. HBA HDB (+) (-)

1 X11 X12 X13 X14 X15 X16

2 X21 X22 X23 X24 X25 X26

3 X31 X32 X33 X34 X35 X36

4 X41 X42 X43 X44 X45 X46R

efer

ence

Ato

ms

5 X51 X52 X53 X54 X55 X56