Many pharmaceuticals act by selectively binding to a specific protein and inhibiting a process relevant to a disease. Therefore, the early stage of drug discovery consists of identifying potential compounds that bind to a protein of interest with a high affinity and specificity. Experimentally testing a large number of compounds is costly and time consuming. Virtual screening is an equivalent computational process that reduces the time and cost of discovering new drugs. Typically, high-throughput virtual screenings dock a large library of potential drugs into one specified protein. However, an inverse screening using a large number of proteins will help identify novel pharmaceuticals through the use of multiple conformational states of each protein as well as identify potential toxicity and side-effects of drugs interacting with important off-target proteins. This work aims to develop high-throughput multi-protein screening applications that can scale to exascale machines and develop data mining methods to extract meaningful results.

Acceleration of high-throughput molecular docking for novel drug discovery on supercomputers

245th National Meeting (2013)

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