Shape and Color Clustering with SAESAR

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 • 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

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

7. Results Summary

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’ JNK3 (2EXC) Sub-shape Match FAK (2ETM) Matches 1st Key shape Rock2 (2H9V) Matches 1st Key shape

12. 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.

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 > 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

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.