Celerino Abad-Zapatero, Ph.D., Associate Research Fellow, Protein Crystallography Laboratory, Abbott Laboratories

Richard A. Friesner, Ph.D., Professor of Chemistry, Columbia University

Klaus Mueller, Ph.D.,Head of Science & Technology Relations, Pharmaceutical Research, F. Hoffmann-La Roche AG

Mark Murcko, Ph.D., Vice President & Chief Technology Officer, Scientific Advisory Board, Vertex Pharmaceuticals Inc.

Tomi K. Sawyer, Ph.D., Senior Director, Chemical Sciences, Pfizer Research Technology Center

12:00 - 1:30 pm Pre-Conference Registration

1:30 -5:15 pm PRE-CONFERENCE SHORT COURSE*
*Separate registration is required.

Approaching difficult structural targets – discussed on two Ser/Thr kinases.

1:45 Design Strategies for Multiple Kinase Inhibitors
Sucha Sudarsanam, Chief Scientific Officer, Emilem Inc.
Recent clinical evidence indicates that drugs targeting multiple kinases have proven to be effective against treatment of cancers. Using crystal structural information of kinases and pharmacopeia of kinase inhibitors, computational techniques have provided starting points for designing inhibitors against individual targets. However, designing compounds that are effective against multiple kinases has remained a challenge. In this talk, we will outline strategies we have developed for designing inhibitors active against multiple kinases.

2:15 TBA

2:45 Refreshment Break

3:45 Kinase-Likeness as Viewed by Chemists and by Kinases
Anton Filikov, Ph.D., Principal Investigator, Informatics & Modeling, ArQule Inc.
The in silico rapid evaluation of molecules for promising biological activity is a rich area of research and a particularly challenging one. The Pharmaceutical industry has long been seeking new kinase inhibitors. So at ArQule we asked a simple question: Do kinase inhibitors carry any particular features that differentiate them from other molecules and in particular other drugs? Here we describe an approach to address this question that employs a method that uses a 2D Bayesian categorization model built on a training set obtained by docking a limited number of diverse drug-like compounds. Once these models are built on a small set of diverse kinase structures, they not only yield the chemical features that are particular to kinase inhibitors but also facilitate high-throughput screening of large chemical libraries for kinase-like compounds. This method will be compared with another technique, which involves the building of a Bayesian categorization model on an extensive database of known kinase inhibitors and other drug-like compounds.

4:15 Using Diverse Computational Approaches to Understand Protein/Ligand Binding Affinities in Structure-Based Drug Design: A Cross-Docking and Quantum Mechanical Study
Jose S. Duca, Ph.D., Senior Scientist, Computer Assisted Drug Design, Schering Plough Research Institute
Predicting protein/ligand binding affinity is one of the most challenging computational chemistry tasks. Numerous methods have been developed to address this challenge, but they all have limitations. Addressing protein flexibility has been a shortcoming of many methods. In this study, we used cross-docking of ~150 inhibitors into the full set of crystal structures for each inhibitor complexed with the kinase CDK2. In scoring relative binding potency based on multiple combinations of several target proteins, the dangers of over-fitting became apparent. Examples will be given of insights gained into ligand properties such as pKa values and relative tautomeric stabilities computed via ab initio quantum mechanical methods. The estimation of protein/ligand binding affinities from QM-MM calculations will also be discussed. Finally, various methods of computing binding affinities will be assessed vis-à-vis their speed and accuracy.

4:45 Discussion with Speakers

5:15 End of Short Course