1. In-silico screening without structural comparisons: Peptides to non-peptides in one step Maybridge Workshop 23-24 Oct ‘03 Bregenz Austria

2. Founded in November 2001 Funding by The Wellcome Trust Cresset Biomolecular Discovery

3. 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

4. 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

5. Fields, XEDs and FieldPrints  Fields A new method of describing molecular properties  XEDs A new molecular modelling approach  FieldPrints A new virtual screening method

6. 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

7. COX-2 Inhibitor

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

12. The Field Template for a COX-2 Inhibitor

13. 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 Fields from ACC’s

14. XEDs make Fields work The Field Points from XED agree well with those obtained from Quantum Mechanics Vinter & Trollope 1994 unpublished. ACCsXEDs

15. eXtended Electron Distributions 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 XEDsACCs

16. XEDs Model Life Better X-ray structure of Benzene Benzene docked onto Benzene using XEDs Benzene docked onto Benzene using ACCs

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

18. Aromatic-Aromatic Interactions

19. XEDs - Summary A much better treatment of electrostatics o Simplified force field o Hydrogen bonding o Anomeric and gauche effects o Aromatic-aromatic interactions

20. + 1rd7 + 1ra3 Crystal Structures = = + Fields direct ligand binding mode Dihydrofolate Reductase

21. Fields - Summary  Protein’s eye view  Represent “electron cloud” NOT structure  Distillate of important binding information Peptide/Steroid/Organic treated 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.

22. 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?

23. FieldPrint™ Search Method ~125,000,000 101101100001111011001… 0010100100101…

24. FieldPrint TM Limitations  Fields are conformation dependent Need to populate database with conformations, not molecules Need to search with a specific conformation Throwing away some information (eg chirality)

25. Conformation Search  Pop 2D to 3D  Twist bond search  Minimisation of all found conformations  Filtered using 1.5Å RMSD  6 Kcal mol -1 cut-off  Keep 50 conformations  Rings not flexed & amides forced trans

26.  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

27. 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

28. The 3D Field Overlay Principle

29. Fields – Examples PPACK D-Phe-Pro-Arg-CH 2 Cl PEPTIDE to NON-PEPTIDE

30. FieldPrint™ Performance Thrombin (49 Spikes) PPACK (D-Phe-Pro-Arg-CH2Cl) Retrieval of known inhibitors (spikes) from 600,000 compounds % spikes found % ranked database screened

31. FieldPrint™ - Thrombin Spikes

32. HIV NNRTI (52 Spikes) FieldPrint™ Performance (2) COX-2 Inhibitors (32 Spikes) Retrieval of known inhibitors (spikes) from 600,000 compounds 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100

33. 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

34. FieldPrint™ Validation A GPCR (43 Spikes) Distilled to 1000 Compounds Visual inspection to 100 88 Purchased and tested 27 had pK b > 5 (better than  M) 4 had pK b > 6 (better than 1  M) No structural similarity to any known actives. MW range 350-600 Collaboration with the James Black Foundation

35. 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 and minimal ligand 2D data is required

36. 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

37. Why should Cresset be used? BA Diverse Structural Classes with Same Function Peptide to non-peptide Much more cost effective than HTS HTS 2,500,000 molecules @ £1 per molecule Cresset distils this to just a few hundred! Significantly faster than conventional routes Cresset could go from A to B in weeks Merck took 3 (?) years with 10 (?) Medicinal Chemists! Cost in Time and Money

38. 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

39. Intelligent Lead Discovery