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Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization
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Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization

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  • Notes:The 2D drawing of a molecule gives limited information about its nature – in real life, molecules take on a 3D geometry whose nature can’t be truly represented by a flat cartoon.Consider the electrostatic potential surrounding a molecule and map that potential out to a surface as shown in the second figure. Field Points are points that are placed at the extrema of the MEP, with the point size governed by the size of the electrostatic contribution.Spatial points are also included at the van der Walls radii extrema.
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    • 1. Finding and using activity cliffs in 3D: Gaining more SAR information during lead optimization Tim Cheeseright, Rae Lawrence, Mark Mackey, Martin Slater
    • 2. © 2013 Cresset Group Ltd. 2 Agenda > Activity Cliffs > Why 3D > Generating Comparisons > Disparity > Visualisation > Case Studies
    • 3. © 2013 Cresset Group Ltd. 3 Similarity Hypothesis > Similar molecules have similar activities > Small changes lead to small changes > QSAR, virtual screening, LO O OH OH OH N F O NH O OH OH OH N O NH
    • 4. © 2013 Cresset Group Ltd. 4 Activity Cliffs > What about the bits where the similarity hypothesis breaks down? O N H N O N H N 6.8 4.4
    • 5. © 2013 Cresset Group Ltd. 5 The Interesting Bits
    • 6. © 2013 Cresset Group Ltd. 6 What “distance” > Usually distance = 1 – similarity > Similarity in 2D > Are we missing anything? OH O NH N N N H2N O OH H2N O OH N Cl N H N O N Cl N H N O Bioisosteres Chirality Fingerprint locality (ECFP4 sim=1.0)
    • 7. © 2013 Cresset Group Ltd. 7 3D molecular similarity > 2D metrics are easy: 1:1 map to topology > 3D is defined for conformers, not for molecules 0.93 0.94
    • 8. © 2013 Cresset Group Ltd. 8 3D molecular similarity > 2D metrics are easy: 1:1 map to topology > 3D is defined for conformers, not for molecules 0.58 0.94
    • 9. © 2013 Cresset Group Ltd. 9 Context is everything > Don’t need/want generic 3D similarity > Have activity context > Align all molecules to known bioactive reference conformer > Provides a conformation context to each molecule
    • 10. © 2013 Cresset Group Ltd. Cresset Technology > Condensed representation of electrostatic, hydrophobic and shape properties (“protein’s view”)  Molecular Field Extrema (“field points”) 3D Molecular Electrostatic Potential (MEP) Field Points = Positive = Negative = Shape = Hydrophobic 2D
    • 11. © 2013 Cresset Group Ltd. Non-Classical Comparisons Fields 0.66 Shape 0.98 Cheeseright et al, J. Chem Inf. Mod., 2006, 665
    • 12. © 2013 Cresset Group Ltd. Non-Classical Comparisons Fields 0.66 Shape 0.98 Cheeseright et al, J. Chem Inf. Mod., 2006, 665 Combined 0.82
    • 13. © 2013 Cresset Group Ltd. 3D disparity 1. Generate conformers 2. Align to reference(s) 3. Calculate similarity matrix on aligned conformations > Allow small movements 4. Calculate disparity matrix from similarity numbers > Similarity cutoff of 0.95 5. Visualise > Difficult – 100 molecules gives 4950 pairs!
    • 14. © 2013 Cresset Group Ltd. 14 Visualisation > Existing ways to visualise > Matrix views > Graph view (Guha/van Drie 2008) > Activity landscapes (Bajorath)
    • 15. © 2013 Cresset Group Ltd. 15 Snail plot
    • 16. © 2013 Cresset Group Ltd. 16 Flower plot
    • 17. © 2013 Cresset Group Ltd. 17 Fields
    • 18. © 2013 Cresset Group Ltd. 18 Field differences
    • 19. © 2013 Cresset Group Ltd. 19 AChE > Sutherland QSAR data set Magic methyl position 6.8 4.4 Disparity 49
    • 20. © 2013 Cresset Group Ltd. Phenyl to difluorophenyl 20 Has an effect here Also has an effect here!
    • 21. © 2013 Cresset Group Ltd. Example: PERK Kinase Inhibitors > When PERK is inhibited in cancer cells, the viability of those cells in nutrient or oxygen deprived conditions is reduced, which can lead to apoptosis and inhibition of tumor > Two related series: J. Med. Chem., 2012, 55, 7193.
    • 22. © 2013 Cresset Group Ltd. Activity View: Focus on Molecule #31 pIC50 = 8.8 > Short slices = high Similarity > Darker colour = higher Disparity > Green = Improved Activity > Areas of structural changes are “haloed”
    • 23. © 2013 Cresset Group Ltd. Investigating Molecule #31 (Activity View) pIC50 = 8.8 > Substituent position on the terminal ring pIC50 Similarity Substituent 25 9.8 0.897 meta-Cl 21 9.6 0.893 meta-F 24 9.7 0.862 meta-F meta-F ortho-F 22 9.7 0.859 ortho-CH3 19 9.4 0.857 meta-CF3 35 9.1 0.840 ortho-Cl 18 9.4 0.830 2-F(ortho), 5-F(meta) 23 9.7 0.810 meta-Cl > Small structural changes (high Sim) resulting in pIC50 improvement. > Removal of para substituent and replacement with meta and/or ortho groups improves activity. > Consistent with observations reported by GSK. (J. Med. Chem., 2012, 55, 7193) 3D Sim = 50% Fields, 50% Shape
    • 24. © 2013 Cresset Group Ltd. Activity View: Focus on Molecule #25 #25, pIC50 = 8.1 #18, pIC50 = 9.4 > Furanopyrimidine core replaced by a N-methyl pyrrolopyrimidine gives >1 log unit improvement. > Examine shape and electrostatics to understand
    • 25. © 2013 Cresset Group Ltd. #25 (pIC50=8.1) vs. #18 (pIC50=9.4)
    • 26. © 2013 Cresset Group Ltd. #25 (pIC50=8.1) vs. #18 (pIC50=9.4) > N-methyl substitution changes dipole across the ring, making NH2 less +ve (weaker H bond donor), BUT, adjacent N becomes more –ve (stronger H bond acceptor) > Activity increase in the N-methyl could be due to stronger aromatic-aromatic interaction through its larger π-cloud, rather than simply due to the substituent itself.
    • 27. © 2013 Cresset Group Ltd. 27 Pi-stacking probably part of the activity increase 4G31
    • 28. © 2013 Cresset Group Ltd. 28 Conclusions > Disparity/Activity Cliff analysis in 3D is a powerful tool > What > But also Why > Needs reliable 3D alignment and scoring > Data sets can be complicated, so clear visualisations are necessary > Activity Miner due for release very soon!
    • 29. © 2013 Cresset Group Ltd. 29 Acknowledgements > Tim Cheeseright > Rae Lawrence > Martin Slater > Nigel Palmer > Andy Vinter

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