Meet Molecular Architect<br />Dr Mark Mackey<br />Chief Scientifc Officer<br />
Outline<br />Fields, Field points and the good things you can do with them<br />The alignment problem<br />3D-QSAR using F...
Field Points<br />Condensed representation of electrostatic, hydrophobic and shape properties (“protein’s view”) <br />Mol...
+ve ionic<br />H-bond acceptor<br />Aromatic      p cloud ‘H acceptor’<br />-ve ionic<br />H-bond donor<br />Hydrophobes<b...
Field Points have lots of applications<br />Virtual screening<br />Alignment<br />Pharmacophore elucidation<br />Bioisoste...
Field Points have lots of applications<br />Virtual screening<br />Alignment<br />Pharmacophore elucidation<br />Bioisoste...
The Alignment Problem<br />Historically very difficult<br />Early approaches template-based<br />Issues with side chain or...
Alignment issues<br />Ligand-centric view vs protein-centric<br />Cramer, JCAMD, 2010, DOI 10.1007/s10822-010-9403-z<br />
Which is better?<br />“The superior statistical qualities of 3D-QSAR models based on poses that superimpose presumably cri...
N-methyl acetamide<br />Imidazole<br />Field Alignment<br />
N-methyl acetamide<br />Imidazole<br />Field Scoring<br />To score a particular alignment, we use the field points of mole...
Field Scoring<br />N-methyl acetamide<br />Imidazole<br />To score a particular alignment, we use the field points of mole...
Field Sampling<br />Field-point based QSAR descriptors<br />
Field Sampling<br />Field-point based QSAR descriptors<br />
Advantages<br />Many fewer sample points than grid-based methods<br />E.g. Vegfr2 data set <br />Du et al., J Mol Graph Mo...
Advantages<br />Many fewer sample points than grid-based methods<br />Sample points physically rather than statistically c...
Initial validation<br />Tested against literature CoMFA datasets<br />15 datasets with alignments available<br />CoMFA ave...
Interpretability<br />Electrostatic<br />Steric<br />Variance<br />
SARS PLpro<br />
The target<br />PLpro (Papain-like protease) is a DUB target which is critical for the replication of the coronavirus resp...
Alignment<br />
Sampling points<br />
Model<br />PLS Components = 5  RMSE = 0.09  RMSEP = 0.38<br />
Summary<br />Able to build a predictive 3D-QSAR model based on small number of analogues<br />Guided (by volume of Xtal st...
NK3 antagonists<br />
NK3 example<br />GPCR target (Tachykinin receptor 3) – selectively binds Neurokinin B – target for treatment of neurologic...
NK3 binding mode<br />For a 3D method you need a 3D alignment<br />FieldAlign can align to a reference<br />FieldTemplater...
NK3 binding mode prediction<br />FieldTemplater<br />Selection of 3 highly active scaffold-1 compounds plus 2 structurally...
3D-QSAR details<br />Alignment<br />Free alignment to template conformation<br />Field selection<br />Generated Field poin...
Initial models problematic<br />When all else fails, talk to the chemists<br />“Are you using the right tautomer?”<br />
NK3 Series 1<br />RMSE 0.19, RMSEpred 0.64<br />
NK3 Series 1<br />Sterics<br />Electrostatics<br />
Extend to scaff-2?<br />Complete lack of predictivity<br />Visual analysis suggests a shift in binding mode for scaff-2<br...
NK1 Scaffold 2<br />RMSEpred 0.60<br />
Summary<br />Able to generate models based on alignment to predicted active conformation by templating<br />Independent mo...
Molecular Architect<br />
Molecular Architect<br />Initially FieldAlign + QSAR<br />Align your molecules<br />Build models<br />Test models<br />Fit...
Molecular Architect<br />One tool for molecule designers<br />Align<br />QSAR<br />Pharmacophore elucidation<br />Bioisost...
Acknowledgements<br />Cresset<br />Andy Vinter<br />Tim Cheeseright<br />James Melville<br />Chris Earnshaw<br />Euroscree...
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Mark Mackey, Cresset, 'Meet Molecular Architect, A new product for understanding SAR and gaining better activity predictions'

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Mark Mackey, Cresset, 'Meet Molecular Architect, A new product for understanding SAR and gaining better activity predictions'

  1. 1. Meet Molecular Architect<br />Dr Mark Mackey<br />Chief Scientifc Officer<br />
  2. 2. Outline<br />Fields, Field points and the good things you can do with them<br />The alignment problem<br />3D-QSAR using Fields<br />Examples<br />SARS PLpro – small data set, known xtal structure<br />NK3 – large data set, unknown xtal structure<br />
  3. 3. Field Points<br />Condensed representation of electrostatic, hydrophobic and shape properties (“protein’s view”) <br />Molecular Field Extrema (“Field Points”)<br />= Positive <br />= Negative<br />= Shape<br />= Hydrophobic<br />3D Molecular Electrostatic Potential (MEP)<br />Field Points<br />2D<br />
  4. 4. +ve ionic<br />H-bond acceptor<br />Aromatic p cloud ‘H acceptor’<br />-ve ionic<br />H-bond donor<br />Hydrophobes<br />Aromatic in-plane ‘H donor’<br />“Stickiest” surfaces (high vdW)<br />Field points give you new insights into your molecule<br />Explanatory Power of Fields<br />= Positive <br />= Negative<br />= Shape<br />= Hydrophobic<br />Field point sizes show importance<br />
  5. 5. Field Points have lots of applications<br />Virtual screening<br />Alignment<br />Pharmacophore elucidation<br />Bioisosteres<br />etc<br />
  6. 6. Field Points have lots of applications<br />Virtual screening<br />Alignment<br />Pharmacophore elucidation<br />Bioisosteres<br />etc<br />What about 3D QSAR?<br />
  7. 7. The Alignment Problem<br />Historically very difficult<br />Early approaches template-based<br />Issues with side chain orientations<br />Some success with docked data sets<br />Easy to fool yourself<br />Correlation/causation<br />
  8. 8. Alignment issues<br />Ligand-centric view vs protein-centric<br />Cramer, JCAMD, 2010, DOI 10.1007/s10822-010-9403-z<br />
  9. 9. Which is better?<br />“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<br />Doweyko, J. Comp-Aided Mol. Des., 2004, p 587<br />Free alignment adds signal, but also noise. <br />Worse statistics, better predictability?<br />
  10. 10. N-methyl acetamide<br />Imidazole<br />Field Alignment<br />
  11. 11. N-methyl acetamide<br />Imidazole<br />Field Scoring<br />To score a particular alignment, we use the field points of molecule 1 to sample the actual field of molecule 2<br />Cheeseright et al, J. Chem Inf. Mod., 2006, 665<br />
  12. 12. Field Scoring<br />N-methyl acetamide<br />Imidazole<br />To score a particular alignment, we use the field points of molecule 1 to sample the actual field of molecule 2 and vice-versa<br />Cheeseright et al, J. Chem Inf. Mod., 2006, 665<br />
  13. 13. Field Sampling<br />Field-point based QSAR descriptors<br />
  14. 14. Field Sampling<br />Field-point based QSAR descriptors<br />
  15. 15. Advantages<br />Many fewer sample points than grid-based methods<br />E.g. Vegfr2 data set <br />Du et al., J Mol Graph Model. 27 (2009) 642-652<br />
  16. 16. Advantages<br />Many fewer sample points than grid-based methods<br />Sample points physically rather than statistically chosen<br />Gauge invariant<br />Consistent framework for alignment and QSAR<br />
  17. 17. Initial validation<br />Tested against literature CoMFA datasets<br />15 datasets with alignments available<br />CoMFA average cross-validated RMSE is 0.72<br />Field QSAR using CoMFA alignments is 0.74<br />Simple model (volume indicator variable) is 0.83<br />Data sets re-aligned using field alignment<br />RMSE 1.00<br />
  18. 18. Interpretability<br />Electrostatic<br />Steric<br />Variance<br />
  19. 19. SARS PLpro<br />
  20. 20. The target<br />PLpro (Papain-like protease) is a DUB target which is critical for the replication of the coronavirus responsible for SARS<br />Crystal structures available with bound ligands from 2 series of compounds: structurally related (PDB entries 3E9S and 3MJ5)<br />Small number of analogues – challenge to see if we can use 3D-QSAR for small data sets<br />
  21. 21. Alignment<br />
  22. 22. Sampling points<br />
  23. 23. Model<br />PLS Components = 5 RMSE = 0.09 RMSEP = 0.38<br />
  24. 24. Summary<br />Able to build a predictive 3D-QSAR model based on small number of analogues<br />Guided (by volume of Xtal structure) alignment worked best. Free alignment was OK, but noisier.<br />
  25. 25. NK3 antagonists<br />
  26. 26. NK3 example<br />GPCR target (Tachykinin receptor 3) – selectively binds Neurokinin B – target for treatment of neurological disorders such as schizophrenia<br />Three series of inhibitors from Euroscreen<br />Scaffold-1 – 81 compounds with pIC50 (radioligand binding) in range 4.6-8.7<br />Scaffold-2 – 80 compounds with pIC50 in range 4.8-7.7<br />Errors in radioligand binding data c. ± 0.4<br />
  27. 27. NK3 binding mode<br />For a 3D method you need a 3D alignment<br />FieldAlign can align to a reference<br />FieldTemplater generates the reference<br />FieldTemplater<br />
  28. 28. NK3 binding mode prediction<br />FieldTemplater<br />Selection of 3 highly active scaffold-1 compounds plus 2 structurally dissimilar literature NK3 actives (Talnetant and SB-218795).<br />Generated Templates filtered and candidate selected<br />Conformation of most active scaff-1 structure then used as alignment target for other structures<br />
  29. 29. 3D-QSAR details<br />Alignment<br />Free alignment to template conformation<br />Field selection<br />Generated Field points for both steric and electrostatic fields, with both sets at independent locations.<br />80/20 training/test split <br />Most active and least active  training set<br />2nd most active, 2nd least active  test set<br />Random distribution of remaining compounds<br />
  30. 30. Initial models problematic<br />When all else fails, talk to the chemists<br />“Are you using the right tautomer?”<br />
  31. 31. NK3 Series 1<br />RMSE 0.19, RMSEpred 0.64<br />
  32. 32. NK3 Series 1<br />Sterics<br />Electrostatics<br />
  33. 33. Extend to scaff-2?<br />Complete lack of predictivity<br />Visual analysis suggests a shift in binding mode for scaff-2<br />Cross-series QSAR difficult<br />Requires consistent binding modes!<br />
  34. 34. NK1 Scaffold 2<br />RMSEpred 0.60<br />
  35. 35. Summary<br />Able to generate models based on alignment to predicted active conformation by templating<br />Independent models within each of two series show reasonable predictivity and can be used to guide further work<br />Cross-series analysis suggests different binding modes for the two series<br />
  36. 36. Molecular Architect<br />
  37. 37. Molecular Architect<br />Initially FieldAlign + QSAR<br />Align your molecules<br />Build models<br />Test models<br />Fit new compounds to models<br />Interactive feedback<br />Add additional alignment options<br />
  38. 38. Molecular Architect<br />One tool for molecule designers<br />Align<br />QSAR<br />Pharmacophore elucidation<br />Bioisosteres<br />What do I make next?<br />Beta Q4 2011<br />
  39. 39. Acknowledgements<br />Cresset<br />Andy Vinter<br />Tim Cheeseright<br />James Melville<br />Chris Earnshaw<br />Euroscreen<br />Hamid Hoveyda<br />JulienParcq<br />
  40. 40. mark@cresset-group.com<br />

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