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BioMed Research International
Volume 2014 (2014), Article ID 624024, 7 pages
Research Article

Performance Studies on Distributed Virtual Screening

1Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
2Technische Universität Dresden, Zellescher Weg 12-14, 01069 Dresden, Germany
3Ludwig-Maximilians-Universität München, Butenandtstr aße 5-13, 81377 München, Germany
4Center for Research Computing, University of Notre Dame, P.O. Box 539, Notre Dame, IN 46556, USA

Received 6 March 2014; Revised 17 May 2014; Accepted 19 May 2014; Published 17 June 2014

Academic Editor: Ivan Merelli

Copyright © 2014 Jens Krüger et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly.