1. Meet Molecular Architect Dr Mark Mackey Chief Scientifc Officer

2. Outline Fields, Field points and the good things you can do with them The alignment problem 3D-QSAR using Fields Examples SARS PLpro – small data set, known xtal structure NK3 – large data set, unknown xtal structure

3. Field Points Condensed representation of electrostatic, hydrophobic and shape properties (“protein’s view”) Molecular Field Extrema (“Field Points”) = Positive = Negative = Shape = Hydrophobic 3D Molecular Electrostatic Potential (MEP) Field Points 2D

4. +ve ionic H-bond acceptor Aromatic p cloud ‘H acceptor’ -ve ionic H-bond donor Hydrophobes Aromatic in-plane ‘H donor’ “Stickiest” surfaces (high vdW) Field points give you new insights into your molecule Explanatory Power of Fields = Positive = Negative = Shape = Hydrophobic Field point sizes show importance

5. Field Points have lots of applications Virtual screening Alignment Pharmacophore elucidation Bioisosteres etc

6. Field Points have lots of applications Virtual screening Alignment Pharmacophore elucidation Bioisosteres etc What about 3D QSAR?

7. The Alignment Problem Historically very difficult Early approaches template-based Issues with side chain orientations Some success with docked data sets Easy to fool yourself Correlation/causation

8. Alignment issues Ligand-centric view vs protein-centric Cramer, JCAMD, 2010, DOI 10.1007/s10822-010-9403-z

9. Which is better? “The superior statistical qualities of 3D-QSAR models based on poses that superimpose presumably critical ligand features, rather than docked conformations.” Clark R., JCAMD 2007, p587 Doweyko, J. Comp-Aided Mol. Des., 2004, p 587 Free alignment adds signal, but also noise. Worse statistics, better predictability?

10. N-methyl acetamide Imidazole Field Alignment

11. N-methyl acetamide Imidazole Field Scoring To score a particular alignment, we use the field points of molecule 1 to sample the actual field of molecule 2 Cheeseright et al, J. Chem Inf. Mod., 2006, 665

12. Field Scoring N-methyl acetamide Imidazole To score a particular alignment, we use the field points of molecule 1 to sample the actual field of molecule 2 and vice-versa Cheeseright et al, J. Chem Inf. Mod., 2006, 665

13. Field Sampling Field-point based QSAR descriptors

14. Field Sampling Field-point based QSAR descriptors

15. Advantages Many fewer sample points than grid-based methods E.g. Vegfr2 data set Du et al., J Mol Graph Model. 27 (2009) 642-652

16. Advantages Many fewer sample points than grid-based methods Sample points physically rather than statistically chosen Gauge invariant Consistent framework for alignment and QSAR

17. Initial validation Tested against literature CoMFA datasets 15 datasets with alignments available CoMFA average cross-validated RMSE is 0.72 Field QSAR using CoMFA alignments is 0.74 Simple model (volume indicator variable) is 0.83 Data sets re-aligned using field alignment RMSE 1.00

18. Interpretability Electrostatic Steric Variance

19. SARS PLpro

20. The target PLpro (Papain-like protease) is a DUB target which is critical for the replication of the coronavirus responsible for SARS Crystal structures available with bound ligands from 2 series of compounds: structurally related (PDB entries 3E9S and 3MJ5) Small number of analogues – challenge to see if we can use 3D-QSAR for small data sets

21. Alignment

22. Sampling points

23. Model PLS Components = 5 RMSE = 0.09 RMSEP = 0.38

24. Summary Able to build a predictive 3D-QSAR model based on small number of analogues Guided (by volume of Xtal structure) alignment worked best. Free alignment was OK, but noisier.

25. NK3 antagonists

26. NK3 example GPCR target (Tachykinin receptor 3) – selectively binds Neurokinin B – target for treatment of neurological disorders such as schizophrenia Three series of inhibitors from Euroscreen Scaffold-1 – 81 compounds with pIC50 (radioligand binding) in range 4.6-8.7 Scaffold-2 – 80 compounds with pIC50 in range 4.8-7.7 Errors in radioligand binding data c. ± 0.4

27. NK3 binding mode For a 3D method you need a 3D alignment FieldAlign can align to a reference FieldTemplater generates the reference FieldTemplater

28. NK3 binding mode prediction FieldTemplater Selection of 3 highly active scaffold-1 compounds plus 2 structurally dissimilar literature NK3 actives (Talnetant and SB-218795). Generated Templates filtered and candidate selected Conformation of most active scaff-1 structure then used as alignment target for other structures

29. 3D-QSAR details Alignment Free alignment to template conformation Field selection Generated Field points for both steric and electrostatic fields, with both sets at independent locations. 80/20 training/test split Most active and least active  training set 2nd most active, 2nd least active  test set Random distribution of remaining compounds

30. Initial models problematic When all else fails, talk to the chemists “Are you using the right tautomer?”

31. NK3 Series 1 RMSE 0.19, RMSEpred 0.64

32. NK3 Series 1 Sterics Electrostatics

33. Extend to scaff-2? Complete lack of predictivity Visual analysis suggests a shift in binding mode for scaff-2 Cross-series QSAR difficult Requires consistent binding modes!

34. NK1 Scaffold 2 RMSEpred 0.60

35. Summary Able to generate models based on alignment to predicted active conformation by templating Independent models within each of two series show reasonable predictivity and can be used to guide further work Cross-series analysis suggests different binding modes for the two series

36. Molecular Architect

37. Molecular Architect Initially FieldAlign + QSAR Align your molecules Build models Test models Fit new compounds to models Interactive feedback Add additional alignment options

38. Molecular Architect One tool for molecule designers Align QSAR Pharmacophore elucidation Bioisosteres What do I make next? Beta Q4 2011

39. Acknowledgements Cresset Andy Vinter Tim Cheeseright James Melville Chris Earnshaw Euroscreen Hamid Hoveyda JulienParcq

40. mark@cresset-group.com