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

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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.

abstract
ABSTRACT

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
slide10

JNK3 Color & Shape Common Hits

Secondary screening hits which group both by shape and color

slide11

Xray Structures and ‘Key Shapes’

JNK3 (2EXC)

Sub-shape

Match

FAK (2ETM)

Matches 1st

Key shape

Rock2 (2H9V)

Matches 1st

Key shape

slide12

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

Available

Compounds

*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
acknowledgements
Acknowledgements
  • Jean Bemis, Sirtris Pharmaceuticals, a GSK Company
  • Evan Bolton, PubChem, NIH
  • Software and Databases: CDK, R, PDB, ZINC, PubChem
  • OpenEye Scientific Software, Inc.