1. Establishing a Successful Virtual Screening Process Stephen Pickett Roche Discovery Welwyn

2. Introduction Challenges facing lead generation and lead optimisation Overview of computational methods in lead generation “Needle” screening Model Validation Conclusions

3. Challenges Facing Lead Generation and Lead Optimisation Reduce fall-out rate in development Nature of compounds, not just number of compounds is important Require leads not hits Fail fast

4. Challenges Facing Lead Generation and Lead Optimisation Increase robustness of candidates in humans Simultaneous optimisation of –Biological activity –Physicochemical properties –Pharmaceutic properties –Pharmacokinetic properties In vitro screens - synthesised compounds Computational screens - virtual compounds

5. Role for Computational Techniques Property Prediction Genern & Applicn of Predictive Models Compound Prioritisation PurchaseSynthesisScreening  Compound set comparisons  Compound filtering  Compound selection (virtual screening) Library Design Tasks Overview

6. Virtual screening Application of computational models to prioritise a set of compounds for screening Similarity to lead(s) –2D ›Substructural keys ›BCUTS, topological pharmacophores (CATS) –3D ›Pharmacophores ›Pharmacophore fingerprints ›Shape, surface properties, MFA Q/SAR models Fit to protein binding site

7. Process Targeted screening Enumeration Docking / Pharmacophore Scoring Property Filtering Compounds Reaction Ideas Reagents Prioritised Syntheses Prioritised Screening Library design Property Filtering Reagent Scoring

8. Process Requirements Robust and iterative –Flexibility –Reliability –Usability Substructural filters –acid anhydrides, reactive alkyl halides... –functional groups incompatible with chemistry Price, supplier, availability Reagent Scoring Rapid calculation of product properties Apply consistently across projects

9. Computational Methods in Lead Generation at RDW Biological Screening –Pharmacophore and/or docking for compound prioritisation. –Target families –Data analysis Needle Screening –Selection of diverse compound set for NMR screening library. –Designing a focussed needle set. Lead Generation libraries –Design of targeted libraries –Ligand-based design

10. Needle Screening: An application IMPDH –Inosine Monophosphate DeHydrogenase –Key enzyme in purine biosynthesis –Potential host target for halting viral replication. Known inhibitors VX-497 7nM MPA 20nM BMS 17nM “War-Head” 19  M

11. MPA “warhead” bound to IMPDH

12. Aim –Find novel replacements for phenyl oxazole “warhead”. ›Low molecular weight, chemically tractable “needles”. Methods –NMR screening –Structure-based virtual screening to select set of compounds for biological evaluation. Needle Screening: An application

13. Process Optimise virtual screening protocol (FlexX) Virtual screening of suitable small molecules –reagents available in-house Biological evaluation Develop chemistry around actives

14. Overview of FlexX Fragment based docking methodology –Break molecule into small fragments at rotatable single bonds –Dock multiple conformations of each fragment –Regenerate molecule from docked fragments Scoring Function –Trade-off between speed and accuracy –Focussed on identifying good intermolecular interactions –Takes no account of absent or poor interactions Post-processing of solutions required –Additional calculations –Visual inspection

15. Optimisation of Virtual Screening Protocol Dataset –47 t-butyl oxamides (40nm to >>40  M). 21 with IC50. Examine influence of Protein model –2 X-ray structures ›oxamide ›MPA analogue Crystal waters Scoring functions –Flex-X, ScreenScore and PLP

16. Binding site with four waters

17. Binding site with oxamide

18. Summary of Results Prediction of pKi values of actives –ScreenScore best in this case –Less dependence on X-ray structure –Best results when incorporating crystal waters Docked orientations good Identified most appropriate model set up –Good correlation with actives but... –Inactives cover range of scores 2 sub-classes of inactives poorly predicted –Intramolecular terms.

19. PCA analysis of docking scores

20. Correlation of Docking Score with pKi (N=21)

22. Virtual Screening Screening Sets –In-house available reagents: 3425 compounds after filtering Dock into best model from each X-ray structure Data analysis –Initial visual inspection of predicted binding mode –Clustering of structures –Further visual inspection and selection of 100 compounds 74 compounds available for biological evaluation

24. Screening results 8 compounds with % inhibition > 65% @250  M. 10% hit-rate with 50-fold reduction in compounds screened. Novel, patentable warheads Uncompetitive inhibition with respect to IMP

25. Thoughts on Model Validation Validate against known actives Efficiency (enrichment) –Ratio No. Actives found/No. Hits : No. Actives/DB size Effectiveness (coverage) –Ratio No. Actives found : No. Actives in DB Beware of over-fitting –Coverage across structural classes

26. Pharmacophore Hypothesis Validation

27. Docking Model Selection

28. Conclusions Effective virtual screening strategy established. Successfully applied to lead generation. Virtual needle screening powerful method for lead generation.

29. Acknowledgements Brad Sherborne, Ian Wall, John King-Underwood, Sami Raza Phil Jones, Mike Broadhurst, Ian Kilford, Murray McKinnell Neera Borkakoti