1. Future CAMD Workloads and their Implications for Computer System Design IEEE 6th Annual Workshop on Workload Characterization

2. What is CAMD?  Computer-Assisted Molecular Discovery used in … –drug discovery –agrochemical discovery (herbicides, insecticides, etc.) –“cosmeceutical” discovery  common objectives of all CAMD applications: –find a small molecule (“drug” or “ligand” or “active”) with the right chemical structure for optimal … 1.interaction with large biomolecule (“receptor” or “target” or “protein”) 2.ADMET properties (getting & keeping ligand near receptor in body) –decide which compounds (potential drugs) should be synthesized/purchased and tested (“screened”) next? –decide by using computer to first do “virtual screening”

3. Molecular discovery process Genomics, Proteomics Target ID/Validation & Structure Assay Development Hits Lead Identification Preclinical/ADMET Clinical Trials Sales & Marketing Lead Optimization Cheminformatics Modeling & Simulation Decision Support CAMD Bioinformatics

4. Three types of CAMD problems 1.Intensive computations on one structure or complex –getting 3D structure of target from genomic information “protein folding problem” – a classic CAMD problem area parallel/distributable algorithms exist but best done on a single processor huge number of possible conformations  short cuts taken –refining 3D structure of target from X-ray/NMR data –performing protein-ligand docking & scoring virtual receptor-ligand complexation  virtual screening flexibility of ligand is currently addressed flexibility of protein is rarely addressed due to  cpu time scoring functions are crude due to  cpu time –faster cpu’s and more memory (for protein folding) would enable better quality results

5. Three types of CAMD problems 2.Modest computations on MANY structures –millions of real compounds; billions of “virtual cmpds” –many subtasks associated with virtual screening; e.g.: convert 2D structure of ligand to 3D (Concord) generate multiple conformations of each ligand 3D structure perform various  cpu tests to identify which ligands merit further attention using  cpu methods (e.g., docking) –crude estimates of ADMET-related properties (e.g., solubility, membrane permeability, etc.) –crude shape-complementarity tests perform docking (at increasing levels of accuracy) –large input stream  ideally suited for distributed proc. –grid-computing using many thousands of nodes (and faster nodes) would enable better quality results

6. Three types of CAMD problems 3.Storing data for virtual compounds –millions of real compounds; billions+ of virtual cmpnds –why store data for virtual compounds? costs time & money to generate & regenerate data –science-related reasons data generated for one project is often useful for another must store data for each conformation of each structure must store data for each structure that a compound can adopt (Optive Research will introduce technology early next year) new technology will result in HUGE volumes of virtual data –IP-related, competition-related reasons pharma industry is already planning for offensive and defensive needs in the coming virtual-screening and virtual-IP “wars” –need means to store and access huge volumes of data

7. Closing comments  practitioners of CAMD are well aware that quality of current methods is limited by compute-resources  rate of discovery and quality of actives discovered would both improve if CAMD methods improved  given that the sales of many actives each exceed $1 billion per year, the market for improved compute-power (and improved CAMD software) is quite substantial  I sure hope that you computer architects can help!! ;-)

8. Contact Info  for questions about this short presentation, please feel free to contact me at: Dr. Robert S. Pearlman, Pres. & CSO Optive Research, Inc. 512-514-6222 bob.pearlman@optive.com  for questions about Optive Research, Inc. and/or about the Computer-Assisted Molecular Discovery software which we develop, contact me as indicated above or visit our web-site at: www.optive.com