Shape and color clustering with saesar l.jpg
Advertisement
This presentation is the property of its rightful owner.
1 / 14

Shape and Color Clustering with SAESAR PowerPoint PPT Presentation

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

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 l.jpg

Shape and Color Clustering with SAESAR

Norah E. MacCuish, John D. MacCuish, and

Mitch Chapman

Mesa Analytics & Computing, Inc.


Abstract l.jpg

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 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

  • 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


Results summary l.jpg

Results Summary


Jnk3 key shapes l.jpg

JNK3 ‘Key Shapes’


Jnk3 color shape 8 matches l.jpg

Jnk3 Color & shape 8 matches


Slide10 l.jpg

JNK3 Color & Shape Common Hits

Secondary screening hits which group both by shape and color


Slide11 l.jpg

Xray Structures and ‘Key Shapes’

JNK3 (2EXC)

Sub-shape

Match

FAK (2ETM)

Matches 1st

Key shape

Rock2 (2H9V)

Matches 1st

Key shape


Slide12 l.jpg

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 l.jpg

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 l.jpg

Acknowledgements

  • Jean Bemis, Sirtris Pharmaceuticals, a GSK Company

  • Evan Bolton, PubChem, NIH

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

  • OpenEye Scientific Software, Inc.


  • Login