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Pharmacophores in Chemoinformatics: 1. Pharmacophore Patterns & Topological Fingerprints. Dragos Horvath Laboratoire d’InfoChimie UMR 7177 CNRS – Université de Strasbourg horvath@chimie.u-strasbg.fr. The Pharmacophore Way of Life – A Medicinal Chemist’s Dream.

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pharmacophores in chemoinformatics 1 pharmacophore patterns topological fingerprints

Pharmacophores in Chemoinformatics:1. Pharmacophore Patterns & TopologicalFingerprints

Dragos Horvath

Laboratoire d’InfoChimie

UMR 7177 CNRS – Université de Strasbourg

horvath@chimie.u-strasbg.fr

the pharmacophore way of life a medicinal chemist s dream
The Pharmacophore Way of Life – A MedicinalChemist’sDream
  • (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 interactions
The Interaction Saga: (1) van der Waals Interactions
  • 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 solvation
The Interaction Saga: (2) Electrostatics & Solvation
  • Coulomb charge-charge interactions are easy to compute, once the partial charges Qk are assigned on the atoms…
    • ECoul=QiQj/4ped
  • … 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.
the interaction saga 2bis the hydrophobic effect
The Interaction Saga: (2bis) The HydrophobicEffect
  • 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 rules
Physical 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 groups
BioIsoSteres – 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 patterns
Pharmacophore 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 p harmacophore p atterns
Exploiting pharmacophore patterns…
  • 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!
tricentric pharmacophore fingerprints monitoring feature a rrangement
Tricentric Pharmacophore Fingerprints: monitoring feature arrangement

N

O

N

9

4

l

C

11

N

  • Topological: the distance between two features equals the (minimal) number of chemical bonds between them
  • Spatial: if stable conformers are known, use the distance in Ǻ between two features
example binary pharmacophore tri plets
Example: Binary Pharmacophore Triplets
  • Basis Triplets:
  • all possible feature combinations
  • at a given series of distances…

 ?

3

3

3

4

3

5

5

3

5

4

3

7

5

5

3

4

4

6

Ar4-Hp3-Hp4

Ar4-Hp3-Hp5

Hp7-Ar4-PC6

Hp3-Hp3-Hp3

Hp3-Hp3-Hp4

Hp3-Hp3-Hp5

Hp3-HA5-Ar5

Hp4-HA5-Ar5

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

first key improvement fuzzy mapping of atom triplets onto basis triplets in 2d fpt
First key improvement: Fuzzy mapping of atom triplets onto basis triplets in 2D-FPT

5

4

3

3

3

4

5

3

4

7

5

5

3

4

6

Ar4-Hp3-Hp4

Ar4-Hp3-Hp5

Hp7-Ar4-PC6

Hp3-Hp3-Hp3

Hp3-Hp3-Hp4

Hp3-Hp3-Hp5

Hp3-HA5-Ar5

Hp4-HA5-Ar5

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

combinatorial enumeration of basis triplets
Combinatorial enumeration of basistriplets

4

7

6

4

7

6

  • Example: there are 36796basis triplets,verifying triangle inequalities,when considering6 pharmacophore types and 11edge 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).

Hp7-Ar4-PC6

Ar4-Hp7-PC6 

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

Ar4-Hp7-Hp6

Ar5-Hp6-Hp7 

tri plet matching p rocedure
Tripletmatching procedure
  • 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:

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

second key improvement proteolytic equilibrium dependence of 2d fpt
Second key improvement: Proteolytic equilibrium dependence of 2D-FPT

Ar8-NC8-PC8

Ar5-NC5-PC8

12%

88%

?

some activity cliffs in rule based descriptor space are smoothed out in 2d fpt space
Some ‘activitycliffs’ in rule-baseddescriptorspace are smoothed out in 2D-FPT-space
  • Neutral
  • Cation
  • Neutral
  • 50%Cation
  • Neutral
  • 90%Cation
  • Neutral
  • Anion
  • Neutral
  • Anion
  • Neutral
  • Neutral
  • Neutral
  • 70%Cation
  • Neutral
  • 40%Cation
pharmacophore pattern based similarity queries lead hopping
Pharmacophore Pattern-Based Similarity Queries: Lead Hopping!

Pharmacophore

Hypothesis

Nearest Neighbors

Docking

?

Superposition-based Similarity Scoring

Best Matching Candidates

Reference

Fingerprint

Automated

Fingerprint

Matching...

Potential Pharmacophore

Fingerprint Library

successful qsar model construction with 2d fpt predicting c met tk activity
Successful QSAR model construction with 2D-FPT: predicting c-Met TK activity

25 variables entering nonlinear model

153 molecules for training: RMSE=0.4 (log units), R2=0.82

40 molecules for validation: RMSE=0.8 (log units), R2=0.53

8 validation molecules out of 40 mispredicted by more than 1 log

what more could be done
What more couldbedone?
  • 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…
pharmacophore hypotheses
Pharmacophore 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
ComPharmOverlay…

- chosen conformer of the reference

- chosen conformer of the candidate

- pair of matching atoms

- 3 Euler angles

- mirroring toggle

GA-controlled

overlay optimization

compharm pharmacophoric fields
ComPharm Pharmacophoric Fields

Pharmacophoric Features

Alk.

Aro.

HBA

HDB

(+)

(-)

1

X

X

X

X

X

X

11

12

13

14

15

16

2

X

X

X

X

X

X

21

22

23

24

25

26

3

X

X

X

X

X

X

Reference Atoms

31

32

33

34

35

36

4

X

X

X

X

X

X

41

42

43

44

45

46

5

X

X

X

X

X

X

51

52

53

54

55

56

  • 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...