In-silico screening without structural comparisons:

Peptides to non-peptidesin one step

Maybridge Workshop 23-24 Oct ‘03Bregenz Austria

Founded in November 2001Funding by The Wellcome Trust

Cresset Biomolecular Discovery

Virtual Screening

Virtual screening is the process of trying to find biologically-active molecules using a computer

Protein-based (X-ray, docking) Need a protein structure Problems with scoring functions

Ligand-based Structural similarity Not specific enough

The Science Problem

The Problem is that: There is no logical way to change Structural

Class and retain Biological Activity

Since we know that: Different structures can give the same

biological effect

Then the Answer is to: Define what it is that the target actually sees

if not structure

Fields, XEDs and FieldPrints

Fields A new method of describing molecular

properties

XEDs A new molecular modelling approach

FieldPrints A new virtual screening method

Fields

Chemically different, biologically similar molecules have a similar electron cloud.It is this that is seen by the target

Can we use a representation of that electron cloud to explore molecules’ biological properties?

Fields represent the key binding information contained in the electron cloud

COX-2 Inhibitor

N

SH2NO

O

N

F FF

Br

COX-2 Inhibitor

COX-2 Inhibitor

COX-2 Inhibitor

COX-2 Inhibitor

R. P. Apaya, B. Lucchese, S. L. Price and J. G. Vinter, (1995), J. Comp-Aid. Mol. Design, 9, 33-43.

The Field Template for a COX-2 Inhibitor

ACCs get Fields Wrong

Without a good description of atoms, the field points are incorrect!

R. P. Apaya, B. Lucchese, S. L. Price and J. G. Vinter, (1995), ’The matching of electrostatic extrema: A useful method in drug design? A study of phosphodiesterase III inhibitors’, J. Comp-Aid. Mol. Design, 9, 33-43.

Atom-centred charges

O

+0.16

-0.34

-0.05 -0.05

Fields from ACC’s

XEDs make Fields work

The Field Points from XED agree well with those obtained from Quantum Mechanics

Vinter & Trollope 1994 unpublished.

ACCs XEDs

C O

-0.5

-0.5

-0.5

-0.5

-0.5

-1.75

-1.75

+5

+1

eXtended Electron Distributions

H -0.1+0.1

H

-0.5

-0.5

+0.9+0.1

J. G. Vinter, (1994) ‘Extended electron distributions applied to the molecular mechanics of intermolecular interactions’, J Comp-Aid Mol Design, 8, 653-668.

The XED force field improves the description of electrostatics by extending electrons away from the nucleus

O

+0.16

-0.34

-0.05 -0.05

XEDsACCs

XEDs Model Life Better

X-ray structure of Benzene

Benzene docked onto Benzene using XEDs

Benzene docked onto Benzene using ACCs

Aromatic-Aromatic Interactions

GSK (SKF) “Azepanone-Based Inhibitors of Human and Rat Cathepsin K”, J. Med. Chem. 2001, Vol. 44, No. 9

Aromatic-Aromatic Interactions

XEDs - Summary

A much better treatment of electrostatics

o Simplified force field

o Hydrogen bonding

o Anomeric and gauche effects

o Aromatic-aromatic interactions

+

1rd7 + 1ra3Crystal Structures

=

=

+

Fields direct ligand binding mode

Dihydrofolate Reductase

Fields - Summary

Protein’s eye view

Represent “electron cloud” NOT structure

Distillate of important binding information

Peptide/Steroid/Organictreated identically

J. G. Vinter and K. I. Trollope, (1995). ‘Multi-conformational Composite Molecular Fields in the Analysis of Drug Design. Methodology and First Evaluation using 5HT and Histamine Action as examples’, J. Comp-Aid. Mol Design, 9, 297-307.

Virtual Screening with Fields

If field points are describing the ‘binding properties’ of molecules:

Can they be used for virtual screening?

Can we construct a fast & accurate way of searching a Field Database?

FieldPrint™ Search Method

SOO

N

NH2ClNHN

N

SHN

N

O

SO

O

NH

O NH2Cl

Cl O

N

NS NH2

Cl

NO

~125,000,000

101101100001111011001… 0010100100101…

The current database contains 2,500,000 commercially available compounds

50 conformations stored for each compound (125,000,000 conformations)

Results consist of similarity score for whole database

Hits can be filtered (e.g. supplier, MW, Lipinski etc.)

The Database

Refinement

The FieldPrint search ‘front-loads’ the database

We refine the FieldPrint results by performing true 3D field overlays

Overlays are usually performed on the top ~10-20% of the database (ranked by FieldPrint score)

Results are expressed as a field similarity

The 3D Field Overlay Principle

Fields – Examples

PPACK

D-Phe-Pro-Arg-CH2Cl

O

NN

O

NO

N

NN

NS

OO

Cl

O

N

N

PEPTIDE to NON-PEPTIDE

FieldPrint™ Performance

Thrombin (49 Spikes) PPACK (D-Phe-Pro-Arg-CH2Cl)

Retrieval of known inhibitors (spikes) from 600,000 compounds

0

20

40

60

80

100

0 20 40 60 80 100

% spikesfound

% ranked database screened

FieldPrint™ - Thrombin Spikes

NO N

HN

O

O

NS

HN

HN NH2NH2

N

H2N NH

3 From BMCL_8_3409

S

HO

X

RN

O

9 from JMC_43_649

NY

ONH

N

RN

SO O

9 From JCAMD_13_221

N

O

SNH

O O O

NHn

N

NH2

3 from BMCL_8_817

2 from BMCL_8_1697

O

N

NH2

Ph / H

H2NN

OO N

H

Cl

Cl

4 from JMC_41_1011

4 from JMC_40_830

N

OO N

H

NH2Ph / H

SN

O O

O

O

O

N

HN

NH2HN

NHHN

SOO

X

7 from JMC_42_4584

O

N

NHHN

SOO

X

N

S

7 From BMCL_10_1563

HIV NNRTI (52 Spikes)

FieldPrint™ Performance (2)

COX-2 Inhibitors (32 Spikes)

Retrieval of known inhibitors (spikes) from 600,000 compounds

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Validation

James Black Foundation (JBF) funded by Johnson&Johnson

GPCR target Exhausted Medicinal Chemistry of current series.

Molecule in clinical development Back-up series required Two active diverse molecules available for template

3 Month deadline Commission mid-August 2002. Generate and search database. Supply list of

compounds by mid-October 2002. Results returned early December 2002

FieldPrint™ Validation

A GPCR (43 Spikes) Distilled to 1000 Compounds

Visual inspection to 100

88 Purchased and tested

27 had pKb > 5 (better than M)

4 had pKb > 6 (better than 1M)

No structural similarity to any known actives.

MW range 350-600

Collaboration with the James Black Foundation

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Intelligent Lead Discovery

Change structural class [e.g. peptides to non-peptides, steroids to non-steroids]

As well as proteases, kinases (X-ray information)

we can;handle poorly defined targets [e.g. GPCRs,

Ion Channels]

because;no protein data is necessary

andminimal ligand 2D data is required

Where can Cresset be used?

Fast and flexible lead finding for new programs allowing multiple starting points for medicinal chemistry programs

Lead switching on existing programs

Patent busting

Moving away from ADMET problems

Finding back up series

Why should Cresset be used?

BO

NN

O

NO

N

NN

NS

OO

Cl

O

N

N

A

Diverse Structural Classes with Same Function

Peptide to non-peptide

Much more cost effective than HTSHTS 2,500,000 molecules @ £1 per molecule Cresset distils this to just a few hundred!

Significantly faster than conventional routesCresset could go from A to B in weeksMerck took 3 (?) years with 10 (?) Medicinal Chemists!

Cost in Time and Money

Acknowledgements Cresset

Dr J. G. Vinter Dr T. J. Cheeseright Dr M. D. Mackey Dr Sally Rose (consultant)

James Black Foundation (KCL, JnJ sponsored)

Prof. C. Hunter (Sheffield University)

The Wellcome Trust

Intelligent Lead Discovery