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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 l.jpg

Shape and Color Clustering with SAESAR

Norah E. MacCuish, John D. MacCuish, and

Mitch Chapman

Mesa Analytics & Computing, Inc.

Abstract l.jpg

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 l.jpg
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 l.jpg
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 l.jpg
Mining Primary Screening Data Features

  • 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

Datasets l.jpg
Datasets Features

Results summary l.jpg
Results Summary Features

Slide10 l.jpg

JNK3 Color & Shape Common Hits Features

Secondary screening hits which group both by shape and color

Slide11 l.jpg

Xray Structures and ‘Key Shapes’ Features





Matches 1st

Key shape

Rock2 (2H9V)

Matches 1st

Key shape

Slide12 l.jpg

Lead Hopping For SIRT1 Activators* Features

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 l.jpg
Lead Hopping For SIRT1 Activators Features

  • 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 l.jpg
Acknowledgements Features

  • Jean Bemis, Sirtris Pharmaceuticals, a GSK Company

  • Evan Bolton, PubChem, NIH

  • Software and Databases: CDK, R, PDB, ZINC, PubChem

  • OpenEye Scientific Software, Inc.