High-performance computing

Drug development is routinely streamlined using computational approaches to improve hit identification and lead selection, enhance bioavailability, and reduce toxicity. New challenges arose because processing a large volume of data demands unprecedented computing resources. As part of a collaborative effort within LA-SiGMA, we ported several of our codes to heterogeneous computing platforms. For instance, GeauxDock can be deployed on multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). Further, a parallel version of eFindSite was implemented mainly for the Intel Xeon Phi platform. These parallel codes yield significant performance improvements considerably accelerating their large-scale applications.

For more information see:
1	Brylinski M, Feinstein WP. (2012) Setting up a meta-threading pipeline for high-throughput structural bioinformatics: eThread software distribution, walkthrough and resource profiling. J Comput Sci Syst Biol 6 (1): 001-010.
2	Ragothaman A, Boddu SC, Kim N, Feinstein WP, Brylinski M, Jha S, Kim J. (2014) Developing eThread pipeline using SAGA-Pilot abstraction for large-scale structural bioinformatics. Biomed Res Int 2014: 348725.
3	Feinstein WP, Brylinski M. (2015) Accelerated structural bioinformatics for drug discovery. High Performance Parallelism Pearls 2: 55-72.
4	Feinstein WP, Moreno J, Jarrell M, Brylinski M. (2015) Accelerating the pace of protein functional annotation with Intel Xeon Phi coprocessors. IEEE Trans Nanobioscience 14 (4): 429-39.
5	Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. (2016) GeauxDock: Accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE 11 (7): e0158898.
6	Feinstein WP, Brylinski M. (2016) Structure-based drug discovery accelerated by many-core devices. Curr Drug Targets 17 (14): 1595-609.

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