shape and color clustering with saesar l.
Skip this Video
Loading SlideShow in 5 Seconds..
Shape and Color Clustering with SAESAR PowerPoint Presentation
Download Presentation
Shape and Color Clustering with SAESAR

Loading in 2 Seconds...

play fullscreen
1 / 14

Shape and Color Clustering with SAESAR - PowerPoint PPT Presentation

  • Uploaded on

Shape and Color Clustering with SAESAR Norah E. MacCuish, John D. MacCuish, and Mitch Chapman Mesa Analytics & Computing, Inc. ABSTRACT

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

Shape and Color Clustering with SAESAR

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
shape and color clustering with saesar

Shape and Color Clustering with SAESAR

Norah E. MacCuish, John D. MacCuish, and

Mitch Chapman

Mesa Analytics & Computing, Inc.


SAESAR identifies potentially interesting patterns in shape and color space for leads from HTS screening data. Analysis of a several public datasets will be described as well as a discussion of a successful analysis in an industrial setting.

saesar features
SAESAR Features
  • Modeling - Model Builder
    • Classification
      • Linear and Quadratic discrimination
      • KNN
  • Example Tasks
    • Find Key Shapes
    • Find Key Structures
    • Find Key Color Groups
    • Generate Predictive Model with Shape, Electrostatics, Color, 2D Structure, other variables
  • Data Exploration, Unsupervised and Supervised Learning with Shape, Electrostatics, and 2D Structure and Properties
  • Powerful OpenEye Scientific Software and Mesa Analytics & Computing tools with Visualization and 2D and 3D depictions.
  • Clustering
    • Taylor’s (symmetric, asymmetric, non-disjoint, disjoint versions)
    • Hierarchical (RNN implementations of Ward’s, Complete Link, Group Average)
  • Conformer Generation
    • OMEGA
    • User supplied
saesar 2d 3d clustering on shape and pharmacophore features
SAESAR - 2D & 3D Clustering on Shape and Pharmacophore Features
  • 2D Descriptors
    • MACCS ‘drug like’ keys and public keys from PubChem, 768 key fingerprints*
  • 3D Descriptors
    • OEShape - volume overlap
    • OEColor - hydrogen-bond donors, hydrogen-bond acceptors, hydrophobes, anions, cations, and rings, can be user defined

*New key-based molecular fingerprinter for visualization and data analysis in compound clustering, similarity searching,

and substructure commonality analysis,N. MacCuish, J.D.MacCuish, 233rd ACS, Chicago,March 25-29, 2007.

mining primary screening data
Mining Primary Screening Data
  • Three primary screens -JNK3,Rock2,FAK
  • Cluster hits in 3D shape (full, subshape)
  • Cluster in 3D color
  • Identify ‘Key shape’ clusters
  • Identify ‘Key color’ clusters
  • Validate with secondary screening data

JNK3 Color & Shape Common Hits

Secondary screening hits which group both by shape and color


Xray Structures and ‘Key Shapes’





Matches 1st

Key shape

Rock2 (2H9V)

Matches 1st

Key shape


Lead Hopping For SIRT1 Activators*

SIRT1 Actives and Not Actives

Input to SAESAR

Potential leads are in a

different2D space, but

similar3D space as the

active SIRT1 compounds

3D ‘Key Shape’ Query



*See, J. Bemis,Bioorganic Gordon Research Conference, June 2008.

lead hopping for sirt1 activators
Lead Hopping For SIRT1 Activators
  • SAESAR was used to identify key shapes which encapsulated 3D shape features of SIRT1 active compounds
  • Key shapes were queries in a virtual screened against 3D database of Available compounds
  • Sets of hits were identified:
    • 20 compounds had highest overall shape matching Tanimoto scores
    • 47 compounds had shape Tanimoto scores > 0.6
    • 172 compounds had Tversky score > 0.8
  • Compounds were ordered and screened in SIRT1 assay:
    • one novel scaffold was identified with low micromolar activity
    • optimization lowered SIRT1 activation potency
  • Jean Bemis, Sirtris Pharmaceuticals, a GSK Company
  • Evan Bolton, PubChem, NIH
  • Software and Databases: CDK, R, PDB, ZINC, PubChem
  • OpenEye Scientific Software, Inc.