CHELMSFORD, Mass.—Mercury Computer Systems Inc. and Boston University have successfully migrated a fragment-based drug design (FBDD) application to the Cell Broadband Engine (BE) processor, achieving an order of magnitude improvement in processing time in a smaller system footprint over previous configurations.
FBDD is a promising new approach in the pharmaceutical discovery and design industry that depends heavily on computer simulation. FBDD simulates the chemistry and physics of molecular interactions in order to estimate how well potential drugs will bind to their target proteins. The joint goal of the Mercury-BU collaboration is to leverage the team's experience and know-how to develop innovative, commercially viable drug discovery products that, unlike similar methods used both in academia and industry, don't require X-ray crystallography, NMR or other supportive methodologies.
"This approach has the potential to revolutionize the cost and pace of new drug development," says Dr. Sandor Vajda, professor of biomedical engineering at BU. "With Mercury's hardware, software, and assistance in algorithm optimization, this method is more commercially viable."
The Structural Bioinformatics Lab of BU initially developed a highly regarded FBDD tool that creates a map of likely drug binding sites on the surface of proteins. However, this program took weeks to run on a departmental Linux cluster. Although later software iterations led to significant improvements on the Linux cluster, and even more dramatic improvements running on an IBM Blue Gene cluster, neither rivaled the processing speeds achieved on Mercury's Cell BE processor-based hardware.
The team successfully migrated the FBDD computer simulation in progressive steps from a shared departmental Linux cluster running for weeks to a single Cell BE processor running for less than three minutes. Mercury and BU report that the average computation time for the application running on the Cell BE processor is approximately 10 times faster than the same application running on a BlueGene processor, in a chip-to-chip comparison. This enhanced speed will enable biotech firms to use the newly created algorithms as a small molecule discovery and design platform.
"Moving away from a shared supercomputing infrastructure on a cluster to a dedicated supercomputer on a single Cell BE processor can make a tremendous difference in productivity for a development team," said Mirza Cifric, director of the Biotech group at Mercury Computer Systems.
Clusters are traditionally collections of otherwise independent computers, such as Pentium workstations or rack-mounted PCs. "Conversely, multi-core processors such as Cell BE are a collection of processing element that share the same silicon providing, in effect, multiple PCs on one chip," Cifric explains.
