Your SlideShare is downloading. ×
Mark Mackey, Cresset, 'Meet Molecular Architect, A new product for understanding SAR and gaining better activity predictions'
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

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

936

Published on

Published in: Technology, Business
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
936
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
9
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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

×