selectivity mining – multiple activities in activity miner

Multi-Dimensional Activity Cliff Analysis

Martin slater, Director Consulting

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Finding the critical points in your SAR

Activity Cliffs in 3D from Activity Miner

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Activity Cliffs

> Many names:> Disparity (Merck 1990s)> SALI (Guha/Drie 2008)> Activity Landscapes> Activity Cliffs

> For each pair of molecules 𝐴𝐴𝑐𝑐𝑐𝑐1 − 𝐴𝐴𝑐𝑐𝑐𝑐2𝐷𝐷𝐷𝐷𝐷𝐷𝑐𝑐𝑎𝑎𝑎𝑎𝑐𝑐𝑎𝑎12

> Usually distance = 1 – similarity> Similarity in 2D

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Where 2D similarity causes problems

> Bioisosteres – low 2D similarity but biologically similar

> Enantiomers and Chirality

> Locality issuesFingerprint locality (ECFP4 sim=1.0)

N

N

O

Cl

O

Cl N

N

NO

O

NO

O

O

ON

N

NN

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Using 3D molecular similarity

> 2D metrics are easy: 1:1 map to topology> 3D is defined for conformers, not for molecules

0.66

0.92

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Context required for 3D similarity

> Need context for 3D similarity – bound to the protein

> Align all molecules in the active site> Ligand view of alignment more informative than a

protein view?

N

N

S

N

OO

N

N

S

N

OO

N

N

S

N

OO

N

N

S

N

OO

N

N

S

N

OO

Protein ‘view’ of alignment

Ligand ‘view’ of alignment

Signal

N

O

N

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3D disparity in Forge’s Activity Miner

1. Generate conformers2. 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 (Distance cutoff of 0.05)5. Visualise

> Difficult – 100 molecules gives 4950 pairs!

𝐴𝐴𝑐𝑐𝑐𝑐1 − 𝐴𝐴𝑐𝑐𝑐𝑐2𝐷𝐷𝐷𝐷𝐷𝐷𝑐𝑐𝑎𝑎𝑎𝑎𝑐𝑐𝑎𝑎12

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Top Pairs

Highest Disparity Pairs Resort on any column

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Disparity MatrixClick to cell to send

to 3D window

Molecules

Molecules

Coloured by DisparityStrong colours SAR

Sortable table

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Activity View

Ten Nearest Compounds,

height = distance

Shade = DisparityStrong colours = Strong SAR

Current Focus Compound

Comparator compound

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Fields Electrostatic Environment

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Field Differences Inform Decisions

Difference plot – Regions where each molecule has stronger electrostatics

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Detailed Electrostatics decode SAR

Has an effect hereAlso has an effect here!

Difference plot – Regions where each molecule has stronger electrostatics

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Selectivity Cliffs

> Selectivity often as important as potency> Look at what structural changes caused large

changes in selectivity

> Now our visualisation problems are even worse!

𝜅𝜅 ≈∆ 𝑆𝑆𝑎𝑎𝑆𝑆𝑎𝑎𝑐𝑐𝑐𝑐𝐷𝐷𝑆𝑆𝐷𝐷𝑐𝑐𝑆𝑆∆ 𝑆𝑆𝑐𝑐𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐𝑆𝑆𝑆𝑆𝑎𝑎

=

𝐴𝐴𝑐𝑐𝑐𝑐𝐷𝐷𝑆𝑆𝐷𝐷𝑐𝑐𝑆𝑆𝛽𝛽𝐴𝐴𝑐𝑐𝑐𝑐𝐷𝐷𝑆𝑆𝐷𝐷𝑐𝑐𝑆𝑆𝑎𝑎 𝐴𝐴

−𝐴𝐴𝑐𝑐𝑐𝑐𝐷𝐷𝑆𝑆𝐷𝐷𝑐𝑐𝑆𝑆𝛽𝛽𝐴𝐴𝑐𝑐𝑐𝑐𝐷𝐷𝑆𝑆𝐷𝐷𝑐𝑐𝑆𝑆𝑎𝑎 𝐵𝐵

1 − 𝑆𝑆𝐷𝐷𝑆𝑆𝐷𝐷𝑆𝑆𝑎𝑎𝑆𝑆𝐷𝐷𝑐𝑐𝑆𝑆

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Visualising Selectivity Cliffs

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Selectivity matrices

Red = ↓ pKiβGreen = ↑ pKiα→ more α selective

GDC-0941pKiα = 9.06 pKiβ= 8.02

6pKiα = 9.06 pKiβ= 7.02

Sim 0.90

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More Electrostatic Differences

> Explain changes in selectivity (how does an indazole compare to a 2-aminopyrimidine?)

α/β=8.05/8.3 α/β=7.3/5.9

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Conclusions

> Activity Cliff/Disparity analysis provides quick insights into SAR> Focus on understanding the reason for a cliff> Drive design decisions

> Multiple ways to navigate the data> Compound focus > Most significant changes> Global overview > Cluster analysis

> 2D and 3D both useful> 2D provides insights into conformational changes> 3D provides insights into electrostatic effects

> Visualising multiple activities simultaneously allows selectivity analysis

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Acknowledgements

> Andy Vinter> Founder Cresset

> Tim Cheeseright> Director of products

> Rae Lawrence> North America sales and support

> Nigel Palmer> Developer

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> 3D Activity Cliffs available in Activity Miner module for Forge and Torch

3D Activity Cliffs

3D Design tool, SAR interpretation

SAR interpretation & Activity Cliffs, 3D Design, 3D QSAR, Pharmacophore modeling

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selectivity mining – multiple activities in activity miner

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