The impact of accelerator processors for high-throughput molecular modeling and simulation

Abstract

The recent introduction of cost-effective accelerator processors (APs), such as the IBM Cell processor and Nvidia's graphics processing units (GPUs), represents an important technological innovation which promises to unleash the full potential of atomistic molecular modeling and simulation for the biotechnology industry. Present accelerator processors can deliver over an order of magnitude more floatingpoint operations per second (flops) than standard processors, broadly equivalent to a decade of Moore's law growth, and significantly reduce the cost of current atombased molecular simulations. In conjunction with distributed and grid computing solutions, accelerated molecular simulations may finally be used to extend current in silico protocols by use of accurate thermodynamic calculations instead of approximate methods and simulate hundreds of protein-ligand complexes with full molecular specificity, a crucial requirement of in silico drug discovery workflows.Teaser phrase: New accelerated computing devices will unleash the predictive power of molecular modeling and simulation for biotechnology.Key words: Cell processor, graphics processing units (GPUs), accelerated computing, molecular modeling and simulation, drug discovery, distributed and grid computing Preprint submitted to Elsevier Science 5 August 2008Bringing a new drug to market is a long and expensive process[1] encompassing theoretical modeling, chemical synthesis and experimental and clinical trials. Despite the considerable growth of biotechnologies in the last ten years, the practical consequences of these techniques on the number of approved drugs has failed to meet expectation [2]. Nonetheless, the discovery process greatly benefits from the use of computational modeling [3], at least in the initial stages of compound discovery, screening and optimization [4].Among the techniques available, force-based molecular modeling methods (briefly, molecular modeling) such as molecular dynamics are particularly useful in studying molecular processes at the atomistic level, providing accurate information on macromolecular dynamics and thermodynamic properties. Bridging from the molecular-atomistic (femtosecond) to biological (micromillisecond) timescales is still an unaccomplished feat in computational biology: the complexity of the modeling impedes sufficient sampling of the evolution of the system, even on expensive high performance computing (HPC) resources. For instance, cost and sampling constraints have so far limited a routine molecular dynamics run over a PC cluster to a single protein system for tens of nanoseconds, barely sufficient to compute the binding free energy using an exact thermodynamic method [5]. This information is of great importance in the discovery process for virtual screening, lead optimization and in silico drug discovery [4], but needs to be computed for hundreds of protein-ligand systems efficiently and economically in order to open the way for molecular simulations to become a routine tool in the discovery workflows us...

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