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Virtual screening is an approach for identifying promising leads for drugs and is used in the pharmaceutical industry. We present the parallelization of LIDAEUS (LIgand Discovery At Edinburgh UniverSity), creating a massively parallel high-throughput virtual-screening code. This program is being used to predict the binding modes involved in the docking of small ligands to proteins. Parallelization efforts have focused on achieving maximum parallel efficiency and developing a memory-efficient parallel sorting routine. Using an IBM Blue Gene/L™ supercomputer, runtimes have been reduced from 8 days on a modest seven-node cluster to 62 minutes on 1,024 processors using a standard dataset of 1.67 million small molecules and FKBP12, a protein target of interest in immunosuppressive therapies. Using more-complex datasets, the code scales upward to make use of the full processor set of 2,048. The code has been successfully used for the task of gathering data on approximately 1.67 million small molecules binding to approximately 400 high-quality crystallographically determined ligand-bound protein structures, generating data on more than 646 million protein-ligand complexes. A number of novel ligands have already been discovered and validated experimentally.
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