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

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Presentation on theme: "Shape and Color Clustering with SAESAR Norah E. MacCuish, John D. MacCuish, and Mitch Chapman Mesa Analytics & Computing, Inc."— Presentation transcript:

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

2 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.

3 SAESAR Features 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 Taylors (symmetric, asymmetric, non-disjoint, disjoint versions) Hierarchical (RNN implementations of Wards, Complete Link, Group Average) Conformer Generation OMEGA User supplied 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

4 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.

5 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

6 Datasets DatasetScreenStructuresConformers JNK3Primary Secondary FAKPrimary Secondary Rock-2Primary Secondary

7 Results Summary DatasetSecondary Screen Matches Expected if random Significant JNK3 Shape Color yes FAK Shape Color yes no Rock-2 Shape Color Marginal No

8 JNK3 Key Shapes

9 Jnk3 Color & shape 8 matches

10 JNK3 Color & Shape Common Hits Secondary screening hits which group both by shape and color

11 Xray Structures and Key Shapes Rock2 (2H9V) Matches 1st Key shape FAK (2ETM) Matches 1st Key shape JNK3 (2EXC) Sub-shape Match

12 Lead Hopping For SIRT1 Activators* SIRT1 Actives and Not Actives Input to SAESAR 3D Key Shape Query Potential leads are in a different 2D space, but similar 3D space as the active SIRT1 compounds Available Compounds *See, J. Bemis, Bioorganic Gordon Research Conference, June 2008.

13 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 > 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

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


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