Defining & Using Structure-Activity Landscapes Rajarshi Guha Background Visualization Utilization Predictive Models 3D models Chemical spaces Summary Numerical Characterization of Structure-Activity Relationships from a Medicinal Chemists Point of View Rajarshi Guha School of Informatics Indiana University National Medicinal Chemistry Symposium Pittsburgh, PA 15 th June, 2008
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Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Numerical Characterization ofStructure-Activity Relationships from a
Medicinal Chemists Point of View
Rajarshi Guha
School of InformaticsIndiana University
National Medicinal Chemistry SymposiumPittsburgh, PA15th June, 2008
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Structure Activity Relationships
Assumptions
I Similar molecules will have similar activities
I Small changes in structure will lead to small changes inactivity
I One implication is that SAR’s are additive
I This is the basis for QSAR modeling
Martin, Y.C. et al., J. Med. Chem., 2002, 45, 4350–4358
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Structure Activity Landscapes
Melanocortin-4 receptor inhibitors
N
N
O
F3C
NH2
NH2
Cl Cl
N
N
O
F3C
NH2
NH
Cl Cl
O
Ki = 39.0 nM Ki = 1.8 nM
N
N
O
NH2
NH
Cl Cl
O
F3C
NH2
N
N
O
F3C
NH2
NH
Cl Cl
O NH2
Ki = 10.0 nM Ki = 1.0 nM
Tran, J.A. et al., Bioorg. Med. Chem. Lett., 2007, 15, 5166–5176
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Structure Activity Landscapes
Rugged gorges or rolling hills?
I Small structural changes associated with large activitychanges represent steep slopes in the landscape
I Activity Cliffs
I But traditionally, QSAR assumes gentle slopes
I Machine learning is not very good for special cases
Maggiora, G.M., J. Chem. Inf. Model., 2006, 46, 1535–1535
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Characterizing the Landscape
Converting activity cliffs to numbers
I A cliff can be numerically characterized
I Structure-Activity Landscape Index (SALI)
SALIi ,j =|Ai − Aj |
1− sim(i , j)
I Cliffs are characterized by elements of the matrix withvery large values
Guha, R.; Van Drie, J.H., J. Chem. Inf. Model., 2008, 48, 646–658
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Visualizing the SALI Matrix
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Visualizing SALI Values
Alternatives?
I A heatmap is an easy to understand visualization
I Coupled with brushing, can be a handy tool
I A more flexible approach is to consider a network view ofthe matrix
The SALI graph
I Compounds are nodes
I Nodes i , j are connected if SALIi ,j > X
I Only display connected nodes
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Visualizing the SALI Graph
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I Nodes are ordered such that the tail node in an edge haslower activity than the head node
Defining & UsingStructure-Activity
Landscapes
Rajarshi Guha
Background
Visualization
Utilization
Predictive Models
3D models
Chemical spaces
Summary
Better Visualization
SALIViewer
I Java application for generating and visualizing SALIgraphs
I Create SALI graphs from SMILES and activity data,using the CDK fingerprints