Abstract

In the past decades, the procedure to identify novel antibiotic compounds has been motivated by the heuristic discovery of the antibiotic penicillin by Fleming in 1929. Since then, researches have been isolating compounds from very wide range of living forms with the hope of repeating Fleming’s story. Yet, the rate of discovery of new pharmaceutical compounds has reached a plateau in the last decade and this has promoted the use of alternative approaches to identify antibiotic compounds. One of these approaches uses the accumulated information on pharmaceutical compounds to predict new ones using high-performance computers. Such approach brings up the possibility to screen for millions of compounds in computer simulations. The better predictors though use sophisticated algorithms that take up significant amount of computer time, reducing the number of compounds to analyze and the likelihood to identify potential antibiotic compounds. At the same time, the appearance of computer processors that may be tailored to perform specific tasks by the end of the past century provided a tool to accelerate high-performance computations. The current review focuses on the use of these dedicated processor devices, particularly Field Programmable Gate Arrays and Graphic Processing Units, to identify new antibacterial peptides. For that end, we review some of the common computational methods used to identify antibacterial peptides and highlight the difficulties and advantages these algorithms present to be coded into FPGA/GPU computational devices. We discuss the potential of reaching supercomputing performance on FPGA/GPU, and the approaches for parallelism on these platforms.